INTRODUCTION
Problem Statement
Education is a crucial factor in modern labor markets, with numerous studies across various countries confirming that individuals with higher levels of education tend to earn higher wages Since Mincer’s groundbreaking work in 1974, which established an empirical model relating earnings to years of schooling and work experience, research has consistently demonstrated the positive impact of education on earning potential Understanding the relationship between education, experience, and income is essential for analyzing labor market outcomes and informing policy decisions.
Since the initial 1992–93 Vietnam Living Standards Survey (VLSS), numerous studies have utilized VLSS data alongside the Mincerian earnings function to analyze the rates of return to education in Vietnam Notably, researchers like Glewwe have contributed significant insights into understanding how education impacts earnings and economic development in the country.
& Patrinos, 1998; Gallup, 2002; Moock et al., 2003; Liu, 2006; Nguyen Xuan Thanh, 2006; Vu Trong Anh, 2008; Vu Thanh Liem, 2009; Doan & Gibson, 2010; etc The results are also diverse
Moock et al (2003) conducted a seminal study analyzing the returns to education in Vietnam using the Mincerian earnings function with VLSS 1992–93 data The study finds that the estimated rates of return to education are relatively low, at around 4.8% Notably, the analysis highlights that the returns to primary education are especially modest, emphasizing the need for targeted improvements in early education investment This research remains one of the most cited works in understanding the economic benefits of education in Vietnam.
Research by Psacharopoulos and Patrinos (2002) and Trostel, Walker, and Woodley (2002) provides comprehensive estimates of the rate of return to education across multiple countries, with Psacharopoulos and Patrinos covering 98 countries over 30 years, and Trostel et al focusing on 28 countries The data indicates that the rate of return to higher education (colleges and universities) is approximately 13%, compared to 11% at the secondary level and 5% at the vocational level Notably, the returns to higher education are higher for females (12%) than for males (10%), highlighting gender disparities in educational economic benefits.
After 20 years, analyzing the returns to education in Vietnam reveals significant insights into how these returns have evolved over time The study examines differences in educational returns across gender, highlighting whether males or females benefit more from higher education Additionally, it explores sectoral disparities by comparing returns among the public, private, and foreign sectors, identifying any discrepancies that may influence workforce dynamics These findings offer critical implications for policymakers, guiding the development of targeted wage and educational policies to promote equitable economic growth and social development.
This study aims to replicate Moock et al (2003) by analyzing the Vietnam Household Living Standard Survey (VHLSS) 2008 using a Mincerian earnings function Unlike previous research, this analysis employs a novel regression approach—clustered data at the household level using panel commands—marking a first in this type of estimation The results will provide valuable insights into income determination and have significant policy implications for improving household welfare and labor market policies in Vietnam.
Research Objectives
There are 03 main objectives in this study:
This study estimates the private returns to education based on years of schooling and education levels for both males and females It analyzes these returns across different sectors, including private, public, and foreign industries, providing comprehensive insights into how educational investments benefit individuals in various employment contexts.
(2) To assess the variation in returns to education by comparing with the findings from Moock et al (2003);
Please refer to Chapter 3 for a detailed explanation of the methodology used in this study For further information and to access the full thesis, please visit our website or contact us via email at [your email address].
(3) To propose some policy options.
Research Questions
The research questions are proposed:
Recent studies reveal that the rates of return to education vary significantly by years of schooling, levels of education, and gender, with males and females experiencing different benefits Generally, higher levels of education correspond to increased returns, with tertiary education providing the most substantial gains Women often face slightly lower returns compared to men; however, the gap is narrowing in many regions Additionally, returns to education differ across sectors: private, public, and foreign employment show distinct patterns, with private sector roles typically offering higher financial incentives, while public sector positions often emphasize stability and benefits Foreign sector opportunities tend to yield the highest returns for advanced qualifications, highlighting the importance of sector-specific education investments for maximizing individual and economic growth.
(2) How are the rates of return to schooling different comparing with 15 years ago? Should the rates increase or decrease?
Research Methodology
This study utilizes data from the 2008 Vietnam Household Living Standards Survey (VHLSS) conducted by the General Statistics Office (GSO) It applies the Human Capital Model developed by Mincer (1974) to analyze the relationship between education and earnings The analysis employs a regression methodology tailored for clustered household-level data using panel data techniques, specifically through the use of panel commands This approach offers a more accurate estimation than traditional cross-sectional OLS estimators, capturing the intricacies of household and individual variations over time.
Structure of the Thesis
This article is organized into several key chapters Chapter 2 reviews existing literature and empirical studies conducted globally and within Vietnam Chapter 3 details the data samples and outlines the research methodology employed Chapter 4 presents the findings derived from descriptive statistics and econometric models The final chapter offers conclusions, policy recommendations, discusses the study's limitations, and suggests directions for future research.
LITERATURE REVIEW
Definition
Human capital encompasses the skills, knowledge, and experience possessed by individuals or populations, which are vital assets for organizations and countries Recognized as a key driver of economic growth and competitive advantage, human capital's value lies in its ability to enhance productivity and innovation Investing in education and continuous training ensures the development of a skilled workforce, ultimately boosting organizational performance and national prosperity Understanding the importance of human capital is essential for strategic planning and sustainable development.
Rate of return measures the gain or loss on an investment over a specified period, expressed as a percentage of the initial investment It includes both income received from the security and realized capital gains, providing a comprehensive view of investment performance Understanding the rate of return is essential for evaluating the profitability of investments and making informed financial decisions.
The return to education is assessed indirectly through various methods depending on the level of analysis At the societal level, it is measured as the investment in education relative to national wealth At the enterprise level, it reflects the investment in employee training and its impact on company performance For individuals, the return to education is considered in terms of years of schooling relative to lifetime income.
At individual level, the "individual return to education" is also termed as "private return to education" to distinguish with "social return to education" at society level
This study covers at individual level.
A Standard Model of Human-Capital Investment
Mincer (1974) developed the standard Human Capital model, which explains individual earnings by linking the logarithm of observed wages to years of schooling, years of labor market experience, and the squared term of experience This model provides a theoretical foundation for understanding how education and work experience impact earnings, emphasizing the importance of human capital in economic outcomes The framework highlights that higher education levels and accumulated experience significantly influence income potential, forming a core basis for labor economics analysis.
Mincer argues that an individual's potential earnings at time t are influenced by the investment in human capital made at time t-1 Let Et represent potential earnings at time t, and assume that the individual allocates a share kt of these earnings toward human capital investment, earning a return rt each period Consequently, the potential earnings at time t+1 are determined based on this investment and return, highlighting the critical role of past investments in shaping future earning potential.
After a number of repeated substitutions, we have:
Assuming that schooling duration is measured in full-time years, denoted by s, with initial investments (k0=…=ks-1=1) made at the start of life, the model presumes a constant rate of return, rs=β, across all years of education Additionally, the return to post-school investments remains constant over time, represented as rs=…=rt-1=λ Under these assumptions, equation (2.2) can be reformulated to capture the relationship between schooling duration and investment returns, illustrating that the total benefit of education depends on both the number of years spent in school and the consistent rate of return over time.
ln 0 ln(1 ) 1 ln(1 ) ln 0 1 ln t s j j t s j j t E s k E s k
Where, the last approach is for small value of β, λ, and k
To link between potential earnings and experience z from labor market, the post- schooling investment is assumed to be linearly decreased over time
Where T is the last year of working life; z=t-s ≥0; and (0,1)
By subtracting (2.4) from (2.5), we got an equation for net potential earnings:
Equation (2.6) can be rewritten as follows: lnnpe t s z z2 (2.7)
With final assumption that, at any time t, the observed earnings are equal to net potential earning, we have: lnY t lnnpe t (2.8)
By substituting equation (2.8) into equation (2.7), we derive the standard Mincerian earnings equation: lnY_t ≈ α + βs + δz + θz², which models the relationship between earnings and variables such as years of schooling and experience.
Empirical Studies on Estimating Returns to Education
Many studies worldwide rely on the Mincerian earnings function to estimate the returns to education, despite the significant limitation of sample selection bias Ordinary Least Squares (OLS) regression is commonly used in these estimations, often incorporating additional variables such as gender, regional dummy variables, ethnicity, race, marital status, and union membership These supplementary variables are included as exogenous factors to refine the analysis and improve the accuracy of earnings estimations.
“control variables” which may shift the earnings function upward or downward depending on their signs
Johnson and Chow (1997) estimated the rates of return to schooling in China using OLS regression based on 1988 survey data, incorporating gender, race, and Communist Party affiliation as control variables They found that the return to education is 4.02% in rural areas and 3.29% in urban areas In urban regions, females experience a significantly higher rate of return to education (4.46%) compared to males (2.78%) Additionally, urban Communist Party members have lower returns to schooling (2.42%) than non-members (3.68%), highlighting disparities based on political affiliation.
Onphanhdala and Suruga (2007) evaluate the returns to education in Laos using data from the Lao Expenditure and Consumption Survey 2002/2003 (LECS 3) Their analysis incorporates dummy variables for gender, area, ethnicity, type of business, and region to provide a comprehensive understanding of how these factors influence educational returns.
The study reveals that returns to schooling in Laos remain modest but have increased significantly following economic reforms, rising from 3.2% in 1997-1998 to 5.2% in 2002-2003 Notably, young workers experience higher returns at 7.0%, compared to 3.9% for older workers, suggesting that educational benefits will continue to grow as market reforms mature While higher education levels generate substantial earnings premiums, completing primary education remains the most profitable investment in education Additionally, wage differentials are significant, with public sector workers earning around 2.2%, whereas private sector employees see higher returns at approximately 5.2%.
Heckman (1979) developed a two-step simultaneous model to correct sample selection bias resulting from nonrandom data, which has become widely used across various fields This methodology effectively addresses issues of biased sample selection, enhancing the accuracy of statistical estimates For example, Siphambe (2008) applied Heckman's model to estimate the economic returns of education in Botswana during 2002-2003, demonstrating its practical use in evaluating educational outcomes within specific contexts.
Siphambe (2008) utilizes data from the 2002-2003 Household Income and Expenditure Survey (HIES) to analyze the returns to education in Botswana The study finds that the average rate of return to education during this period is 15%, marking a 1% decline compared to 16% in 1993-1994, with significant drops observed in upper secondary education, which fell by 28 percentage points (8% in 2002-2003 vs 36% previously) Conversely, university education experienced a notable increase in return rates, rising to over 50% (24% compared to 11%) The pattern of returns to education across different schooling levels generally aligns with previous findings by Siphambe (2000), except for upper secondary Additionally, the study reveals that in 2002-2003, males and females earned similar returns to education (around 15%), a departure from earlier data where females had higher rates.
Another critical problem when studying educational returns is the endogeneity
To address unobserved heterogeneity, Card (1999) identifies three primary approaches: (i) employing instrumental variables rooted in institutional features of the education system, as demonstrated by Angrist and Krueger (1991); (ii) using family background as an instrument for schooling, as explored by Ashenfelter and Rouse (1998); and (iii) implementing additional methods to control for unobserved individual differences, ensuring more accurate estimations of education's impact.
Research by Inui (2012) and others, including Ashenfelter and Krueger (1994), highlights methods for estimating the impact of education on earnings, such as analyzing twin schooling and income data These studies primarily focus on determining the average return to education using statistical techniques like Ordinary Least Squares (OLS) and Instrumental Variables (IV).
Angrist and Krueger (1991) highlight that school start age policies and compulsory attendance laws lead to individuals born at the beginning of the year starting school later, which often results in shorter overall schooling durations compared to those born near the end of the year This variation in school entry age impacts educational attainment, demonstrating how policy factors influence individual educational outcomes.
The estimation draws on a variety of data sets constructed from the Public Use Census Data in 1970 and 1980 The samples focus on males of 16 years old born in the
US to specify the 1920-1929 corhort (in 1970 Census); and 1930-1939 corhort and 1940-1949 corhort (in 1980 Census)
This study uses the interaction between quarter-of-birth and year-of-birth as an instrument to assess the impact of compulsory schooling laws on education across different cohorts After controlling for age (quadratically), race, marital status, and urban residence, the difference-in-difference analysis reveals that the return on an additional year of schooling is 10% for men born in 1920-1929, 6% for those born in 1930-1939, and 7.8% for men born in 1940-1949.
Ashenfelter and Krueger (1994) analyzed primary data from the 1991 Annual Twins Days Festival in Twinsburg, Ohio, to explore the relationship between worker characteristics and earnings Their study found that a worker’s ability and education are uncorrelated, indicating that education does not directly influence earnings The final sample included 298 pairs of identical twins, who are assumed to have the same ability but differ in the amount of schooling they receive due to random factors This twin study provides valuable insights into the causal impact of education on earning potential by controlling for innate ability.
3 This empirical study is not included in the review of Card (1999) but in line with the work of Ashenfelter and Rouse (1998), so I add in
Monozygotic twins, originating from the same egg and sperm, are genetically identical and are hypothesized to share the same innate abilities In contrast, dizygotic twins, coming from two separate eggs and sperm, are not genetically identical and are more susceptible to omitted ability bias A study utilizing sibling reports on each other's education levels as an instrumental variable found that each additional year of schooling increases wages by 12-16%.
Ashenfelter and Rouse (1998) analyzed data from the Annual Twins Days Festival in Twinsburg, Ohio, over three years (1991-1993), involving 340 twin pairs (680 twins) They controlled for age instead of experience and used the difference between twin 2's report of twin 1's education and twin 2's own educational report as an instrumental variable Their fixed-effect estimations revealed that the average annual return to schooling for identical twins is approximately 9%.
Recent research by Nakamuro and Inui (2012) builds on Ashenfelter and Rouse (1998) to assess the causal impact of education on earnings using a twin study in Japan Analyzing data from 2,257 identical twin pairs collected via a web-based survey, their study provides valuable insights into the relationship between education and income The researchers addressed measurement errors to ensure the accuracy of their findings, highlighting the importance of rigorous methodology in estimating the true effect of education on earnings.
IV method, the authors obtain 9.3% as the average returns to education in Japan
In Vietnam, there are a few articles written on the Mincerian function Most of the studies use OLS regression (one round or two rounds) (Glewwe & Patrinos, 1998;
Various studies have employed different econometric methods to address sample selection bias and evaluate outcomes Gallup (2002), Moock et al (2003), Vu Trong Anh (2008), and Vu Thanh Liem (2009) have contributed to this field, while some researchers, such as Liu (2006), utilize the Heckman two-stage approach for bias correction Doan and Gibson (2010) implement the Heckman single-step model, providing an alternative method for addressing sample selection issues Additionally, Nguyen Xuan Thanh (2006) adopts the difference-in-difference approach to assess causal impacts, highlighting the diversity of analytical techniques used in this research area.
Chapter Remarks
The study employed the standard Human Capital model developed by Mincer
(1974) to build up its conceptual framework Under the framework, the logarithm of observed monthly earnings of an individual is explained by years of schooling, years of
(1) Schooling: divided by years of schooling and levels of schooling including primary, secondary, vocational education, bachelor and above
(4) The logarithm of hours work per week
Models are fitted for all, male, and female; private, public, and foreign sectors
The logarithm of monthly earnings
Random Effects model, clustered on household
Private returns to education vary across different sectors—private, public, and foreign—and differ for males and females Higher levels of education generally lead to increased earnings, with variations depending on industry and gender Experience in the labor market, measured by total years of experience and the logarithm of hours worked per week, significantly influences private returns, highlighting the importance of both education and work experience in determining income levels.
Most studies examining the returns to education in Vietnam primarily utilize OLS regression models Although this method is common, it has limitations such as underestimating standard errors within the same household and failing to account for variations between different households Addressing these issues is crucial for more accurate and reliable estimates of educational returns in Vietnam.
Individuals or employees residing in the same household tend to share unobservable characteristics such as culture and genetics that can influence their earnings potential This shared household environment causes the error terms for individuals within the same household to be correlated through a common household-level component Ignoring this correlation may lead to significantly underestimated standard errors, potentially affecting the accuracy of statistical inferences.
The Ordinary Least Squares (OLS) estimator overlooks the variation in means across households, assuming a common intercept at the state level for all individuals However, this assumption is unlikely to reflect reality, as individuals from different households often have distinct intercepts, highlighting the limitations of OLS in capturing household-level differences.
To address the issue, instead of employing a simple cross-sectional OLS estimator with cluster-robust standard errors, I transformed the data to a household-level clustered dataset and applied a random-effects estimator This approach allows the model to account for intra-household correlation through a random effect for the residuals, leading to more accurate and reliable results.
RESEARCH METHODOLOGY
Data
This study utilizes data from the 2008 Vietnam Household Living Standard Survey (VHLSS), conducted by the General Statistical Office (GSO) of Vietnam The survey provides comprehensive information on 9,189 households across 3,063 communes, offering valuable insights into household living standards nationwide.
Samples were weighted based on the Vietnam Population Census of 1999, which indicated that approximately 70% of Vietnamese households resided in rural areas The study involved randomly selecting communes from approximately 10,000 communes across 646 districts and 64 provinces and cities in Vietnam Within each selected commune, an average of three households were randomly chosen for interviews, ensuring representative and unbiased data collection for comprehensive analysis.
This study estimates the returns to education among employed salary earners aged 15 to 60 for males and 15 to 55 for females, focusing solely on individuals in the labor market Earnings are measured based on monthly income, recorded in thousands of Vietnamese dong (VND) Individuals engaged in household work are excluded from the sample to ensure accuracy These findings provide valuable insights into the positive impact of education on earning potential within the formal labor sector.
6 VHLSS separates employment into wage employment, farm self-employment, and non-farm self-employment
This study focuses exclusively on wage earners, with earnings measured by salary and wages received, including payments in kind for work performed For detailed information on variable definitions and coding, refer to Table 3.3, “Description of the Variables and Variable Coding.”
Years of schooling are recorded based on the highest grade or level of education an individual has completed within the general education system For instance, if someone is currently in grade 10, their highest completed grade is recorded as 9; if a person is in grade 9 but dropped out, their highest completed grade is 8 For college-level students, years of schooling typically total 15 years, while a Bachelor's degree corresponds to 17 years, a Master's degree to 19 years, and a PhD to 22 years of schooling (Le Thi Nhat Phuong, 2008; Le Anh Khang, 2012) These measurements help quantify educational attainment across individuals.
Data on individual schooling attainment are available, but information on post-school investment is not included in VHLSS Using the Mincerian earnings function, the differences in post-school investments among employees are approximated by variations in years of experience This experience is proxied by subtracting years of schooling from the employee's age in years, enabling analysis of how education and experience influence earnings.
Hours of work per week are affixed as a compensatory instrument (Moock et al.,
According to Mincer (1974), the annual earnings profile is influenced by variations in work hours over the life cycle When individual wealth remains fixed, the cost of time increases with experience, leading to a peak in earning capacity followed by a decline This pattern of rising and falling earnings also impacts working hours, which tend to adjust accordingly in the market As a result, relying solely on observed annual earnings can overestimate human capital investment and rates of return; thus, incorporating weekly hours worked serves as a crucial compensatory factor to address this potential bias.
After consolidated to remove errors and inconsistencies, the sample data remains 6,956 individuals/employees, living in 4,335 households
The Vietnam Household Living Standards Survey (VHLSS) categorizes education into two main types: general education and vocational education This distinction helps to understand the different pathways available for students and the focus areas of each educational track Accessing comprehensive data and recent research, such as thesis papers and academic publications, can provide deeper insights into these educational sectors and their impact on socio-economic development.
Research Methodology
Returns to education are estimated based on the Human Capital Model developed by Mincer (1974) which is formulated by an earning function as follows: i i i i i S EXP EXP u
Where, lnYi is the logarithm of the monthly earnings for individual i
The variable *Si* represents the total number of schooling years for individual *i*, serving as a key measure of educational attainment Additionally, *EXPi* denotes the years of working experience accumulated by individual *i*, which can influence their productivity and earning potential The squared term, *EXPi 2*, captures the nonlinear effects of experience on the outcome, allowing for a more accurate modeling of its impact The model also includes an error term, *ui*, accounting for unobserved factors influencing the individual's results, ensuring a comprehensive analysis of how education and experience relate to the specified outcome.
The squared term of experience (EXP²) in equation (3.1) indicates that earnings tend to increase with years of experience; however, this growth occurs at a decreasing rate Additionally, the coefficient (γ₂) associated with the squared experience is expected to be negative, reflecting diminishing returns to experience over time.
To assess the average returns to education across various levels of schooling, dummy variables are created by converting the continuous variable of years of schooling into categories such as primary, secondary, vocational, and university education The extended earnings function incorporates these variables along with experience and other factors, represented as: i i i i i i i i PRIM SEC VOC UNIV EXP EXP u This approach helps to identify the specific impact of different educational levels on earning potential, providing valuable insights for policymakers and individuals aiming to understand the financial benefits of education.
Where, PRIMi, SECi, VOCi, and UNIVi are primary, secondary, vocational training, and university levels of education completed by individual i
The returns to each level of education are estimated by subtracting the coefficient of the subsequent level from that of the current level, then dividing the result by the number of years of schooling at that level For example, the rate of return to the kth level (rk) is calculated using this method, reflecting the additional earnings attributed to that specific education level.
The years of schooling at each education level are represented by the variable nk Specifically, individuals typically require 6 years to complete primary education (nPRIM = 6), 7 years for secondary education (nSEC = 7), 6 years for vocational training (nVOC = 6), and 4 years to complete university studies (nUNIV = 4) These parameters are used to assess educational attainment and inform analyses related to human capital development.
Based on Psacharopoulos (1994), it is argued that assuming primary school graduates forgo earnings for their entire study period is incorrect; instead, only one year of foregone earnings (nPRIM = 1) is assumed during primary education (2003, p.505) In this study, the same assumption is applied to primary education Additionally, secondary education is divided into lower secondary (LOWSEC) and upper secondary (UPPSEC), with schooling durations of four years (nLOWSEC = 4) and three years (nUPPSEC = 3), respectively, to precisely estimate the returns to each level.
New Approach - CLUSTERED DATA APPROACH in Estimating the Returns
In term of econometric, I am using cross-sectional VHLSS data surveyed in 2008 to estimate the rates of return to education This data is grouped (nested) by households
The Ordinary Least Squares (OLS) estimator is commonly used to analyze this type of data However, it has limitations, such as underestimating standard errors within the same household and failing to account for mean differences between households To improve accuracy, alternative methods that address these issues are recommended.
Individuals or employees within the same household are unlikely to have independent residuals, as multiple members from the same household may appear in the estimating sample This interconnectedness can affect the accuracy of statistical models and should be carefully considered in data analysis Understanding the dependence among household members is essential for producing reliable and precise estimations in research involving individual or employee data.
Individuals within the same household tend to share unobservable characteristics, such as culture and genetics, that can influence their earnings potential These shared household traits may result in individuals performing better academically and earning higher wages regardless of similar education or experience levels Consequently, the error terms for individuals from the same household are likely correlated due to this common household-level component Ignoring these correlations can lead to significantly underestimated standard errors, affecting the accuracy of statistical inferences.
Cameron and Trivedi (2009, p.306) highlight that clustering complications arise when error terms are correlated within clusters, and when this is the only issue, standard cross-section estimators with cluster-robust standard errors are appropriate In my case, since I am concerned about error correlation within households, using an OLS estimator with cluster-robust standard errors is suitable However, OLS fails to account for mean variation between households, as it assumes a common intercept at the state level for all individuals, which may not reflect reality since different households can have distinct intercepts.
To address the limitations of a simple standard cross-sectional OLS estimator, I transformed the cross-sectional data into household-level clustered data and employed a random-effects model This approach accounts for within-household correlation by incorporating a household-specific random effect, allowing for more accurate estimates Specifically, the earnings function errors are modeled as comprising a common household heterogeneity component (uj) and an individual-specific error term (eij), capturing both shared and individual-level variations.
This study examines the private returns to education at the individual level, with individuals clustered within households The dependent variable is the logarithm of monthly earnings in the labor market, while key independent variables include years of schooling, years of education, work experience, squared years of experience, and the logarithm of hours worked per month as a compensatory factor The analysis utilizes a cluster-effects model over a 12-month period, capturing the impact of educational attainment and work characteristics on earnings The model specification is expressed as: y_ij = β_0 + x_ij β + u_j + e_ij, accounting for both individual and household-level variations.
Where, individual i is in household j; yij is dependent variable; xij is a vector of independent variables; uj is unobserved household characteristics; eij is the error term
When transitioning from panel data to clustered data, it is important to recognize the conceptual differences In panel data, each individual has multiple observations over time, with clustering on the individual level, whereas in clustered data, multiple observations are recorded per household, with clustering on the household level Consequently, in the new model, the household serves as the cluster identifier, while the time dimension now pertains to individuals within each household Notably, unlike the temporal dimension, there is no natural ordering of individuals within a household, which impacts how the data analysis is approached.
To perform clustering at the household level, I first create a unique household identifier (idhh) for each household Next, I randomly assign integers such as 1, 2, and so on, to each member within a household This process results in a dataset where household identifiers serve as panel identifiers, and individual members are treated as time points, effectively resembling panel data despite being fundamentally cross-sectional data Properly assigning household and individual identifiers enhances the accuracy of clustering analyses in household-based studies.
Below is an example on how to convert cross-sectional data to clustered data
Household A has 02 members whose identifiers (idmem) are A01 and A02
Similarly, household B and C contain 04 and 03 members respectively, with the individual identifiers as sampled in Table 3.1 Typically, this is a cross-sectional data
Table 3.1: Sample of cross-sectional data idmem idhh age lnearnings yosch exp exp2 lnhrwrk
B04 B 29 7.99 17 12 144 3.78 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
To convert the data into a clustered format, a unique household identifier is created for each household Subsequently, each household member is randomly assigned an integer (e.g., 1, 2, etc.), facilitating the identification of individual members within households The resulting clustered dataset, as detailed in Table 3.2, utilizes 'idhh' as the panel identifier and 'idmem' as the time identifier, ensuring clear delineation of both household and individual data points.
Table 3.2: Sample of clustered data idmem idhh age lnearnings yosch exp exp2 lnhrwrk
In general, instead of estimating rates of returns to education by using standard OLS regression, I use Random effects model along with clustering on household.
Empirical Models of the Returns to Education
The Mincerian theory emphasizes that returns to education depend on both firm and employee characteristics However, in my empirical models, I do not include variables representing firm characteristics, as their effects are accounted for within the error term This approach helps isolate the impact of individual education on earnings, aligning with the core principles of the Mincerian framework while controlling for potential confounding factors.
The Mincerian earning regression function has the following form: ij j ij ij ij ij ij u
. yosch lnearnings (3.5) 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 article examines key factors influencing individual earnings within households, such as the logarithm of monthly labor market income (earnings_ij), which reflects income levels in 1,000 VND It considers the impact of educational attainment, measured by years of schooling (yosch_ij), and work experience, including years of experience in the current job (exp_ij) and its squared term (exp2_ij) to capture nonlinear effects Additionally, the analysis accounts for work hours by analyzing the logarithm of weekly hours worked (lnhrwrk_ij), providing a comprehensive understanding of how education, experience, and work intensity influence individual earnings Incorporating these variables ensures a robust assessment aligned with SEO best practices.
j: Unobserved household characteristics The extended Mincerian earning regression function: ij j ij ij ij ij ij ij ij ij
1 lnhrwrk exp exp univ voc uppsec lowsec prim lnearnings
(3.6) Where, primij: Primary level lowsecij: Lower secondary level uppsecij: Upper secondary level vocij: Vocational education univij: Colleges level and above
No level and illiterates are reference
The private rates of return to various levels of education are calculated as follows:
(3.8) 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
Variable Coding
The relationship between years of schooling and log of monthly earning is empirically revealed in positive sign (Johnson and Chow, 1997; Moock et al., 2003;
Additional years of schooling are consistently linked to increased earnings, with studies showing varying rates of return across different contexts For example, Johnson and Chow (1997) report a 3.34% increase in earnings per additional year of education, while Moock et al (2003) find a 4.8% growth Siphambe (2000) highlights an 8% return, and Onphanhdala and Suruga (2007) reveal returns of 2.17% in the public sector and 5.23% in the private sector Furthermore, Siphambe (2008) indicates that each additional year of schooling can lead to a 15% increase in earnings, emphasizing the significant economic benefits of education.
Experience is empirically explored to be positively associated with log of monthly earning (Johnson and Chow, 1997; Moock et al., 2003; Siphambe, 2000;
Onphanhdala and Suruga, 2007; Siphambe, 2008) In particular, an additional years of experience is associated with 4.2% growth in earnings in Johnson and Chow (1997)
Recent studies indicate varying growth rates in earnings: Moock et al (2003) found a 6.4% increase, Siphambe (2000) reported a 7.9% rise, and Siphambe (2008) observed an 8.5% growth Onphanhdala and Suruga (2007) identified a 1.2% increase in the public sector compared to a 4.1% growth in the private sector, highlighting sector-specific differences Additionally, the analysis reveals that years of experience have a negative impact on the log of monthly earnings, with more experience associated with diminishing returns.
Education levels and the logarithm of hours worked per week both show a positive relationship with the logarithm of monthly earnings, indicating that higher education and increased work hours contribute to greater income Studies by Johnson and Chow (1997), Moock et al (2003), Siphambe (2000), Onphanhdala and Suruga (2007), and Siphambe underscore the significance of these factors in income determination, highlighting the importance of investing in education and work effort to enhance earning potential.
2008) 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
From the above findings, in my study, I would expect that the relationship between log of monthly earnings and its determinants will follow expected signs as the following table:
Table 3.3: Description of the Variables and Variable Coding
The log of monthly earnings in the labor market is measured using observable data, specifically earnings per month in thousands of VND In the Vietnam Household Living Standards Survey (VHLSS), respondents are asked: "In the past 12 months, how much money (salary/wages), including payment in kind, did you receive from your work?" The reported annual income is then divided by 12 to calculate the average monthly earnings, providing a comprehensive measure of individual wages in the labor market.
Years of schooling, a continuous variable measured in years, positively influences returns to education, indicating that increased years of education lead to higher returns Specifically, individuals with college-level education have 15 years of schooling, Bachelor’s degree holders have 17 years, those with a Master’s degree have 19 years, and PhD graduates have 22 years Overall, the expected relationship is a positive one, emphasizing that more years of formal education from the general education system correlate with greater economic returns.
Years of experience, measured continuously in years, positively influence returns to education, indicating that increased experience correlates with higher earnings The expected sign is positive, implying that as the number of experience years rises, so do earnings Experience is proxied by subtracting years of schooling from age, highlighting its role in enhancing income potential Additionally, the squared term of experience (exp2) is negative, suggesting diminishing returns to experience at higher levels.
Years of experience, measured as a continuous variable, typically exhibit a negative relationship with returns to education, indicating diminishing returns The expected sign is negative, suggesting that as experience increases, the additional gains in returns decrease Moreover, the relationship between education and outcomes is not strictly linear; instead, it follows a parabolic shape, reflecting the concept of diminishing returns to education over time.
Primary education is represented as a dummy variable, with prim = 1 indicating individuals who have graduated from primary school without any vocational training, and prim = 0 for others The expected positive relationship suggests that, compared to individuals with no education or illiterates, those with a primary education level tend to have higher earnings This indicates that obtaining primary education positively impacts income, contributing to improved economic prospects Additionally, the variable lowsec shows a positive effect, further emphasizing the importance of foundational education for increasing earning potential.
Lower secondary level is a dummy variable; lowsec=1 for graduated lower secondary education and no vocational education; 0 for others Similar to primary level, the expected sign is positive uppsec (+)
Upper secondary level is a dummy variable; uppsec=1 for graduated upper secondary education and no vocational education; 0 for others The expected sign is positive voc (+)
Vocational education is coded as a dummy variable, where voc=1 indicates graduation from vocational programs such as vocational primary, intermediate, secondary professional, and vocational colleges, with general education levels below college, while voc=0 represents others The expected impact is positive, suggesting that individuals with vocational education tend to have better outcomes, whereas university education (univ) is also associated with positive effects.
University level is a dummy variable; univ=1 for graduated college level and above; 0 for others The expected sign is positive lnhrwrk (+)
The log of hours worked per week is a continuous variable used as a compensatory factor in the analysis This measure, representing hours worked weekly, is expected to have a positive effect on returns to education, indicating that increased working hours may enhance the benefits of educational investments.
RESEARCH FINDINGS AND DISCUSSION
Descriptive Statistics
Understanding the sample data is a crucial first step in data analysis Visual exploration using histograms is an effective method to assess data distribution, particularly to identify normality The accompanying figures illustrate the distribution patterns of both dependent and explanatory variables, providing insights essential for subsequent analysis.
Figure 4.1: Histograms of log of earnings (by gender)
F re qu en cy log of monthly earnings
Graphs by gender=1 if Male
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Figure 4.1 draws histograms of log of earnings for male, female, and for all sample All of them are following normal distribution
Figure 4.2: Histograms of log of earnings (by sector)
0 5 10 0 5 10 public sector private sector foreign investment Total
F re qu en cy log of monthly earnings
Source: Author’s calculation using data from VHLSS 2008
Figure 4.2 presents histograms of log earnings segmented by sector, including public, private, and foreign industries All sectors' earnings data follow a normal distribution, indicating consistent patterns across different economic sectors This analysis highlights sector-specific earning distributions, which are essential for understanding economic performance and workforce income levels.
Figure 4.3: Histograms of years of schooling and log of hours worked/week
0 5 10 15 20 the number of years schooling
0 1 2 3 4 5 log of hours worked/week
Source: Author’s calculation using data from VHLSS 2008
Figure 4.3 illustrates the distributions of years of schooling and log hours worked per week, both of which follow a normal distribution The histogram of years of schooling reveals notable peaks at 9, 12, and 17 years, corresponding to lower secondary, upper secondary, and university education levels, highlighting key educational milestones within the labor force.
Scatterplots are valuable tools for exploring potential relationships or patterns between variables, providing visual insights into data correlations Using scatterplots can help identify trends, associations, and outliers, making them essential in data analysis and decision-making processes Incorporating scatterplots into your analysis enhances understanding of variable interactions and supports more accurate interpretations of data patterns.
Figure 4.4: Scatterplots of monthly earnings and years of schooling
0 5 10 15 20 the number of years schooling log of monthly earnings Fitted values
Source: Author’s calculation using data from VHLSS 2008
Figure 4.4 illustrates a positive relationship between the logarithm of monthly earnings and years of schooling, indicating that increased education levels are associated with higher earnings The regression line demonstrates a linear upward trend, emphasizing that each additional year of schooling tends to correspond with an increase in monthly income This analysis underscores the significant impact of education on earning potential.
Figure 4.5 illustrates the positive correlation between monthly earnings and education levels, showing that higher education levels are associated with increased income The regression line clearly indicates that as education levels rise, so do earnings, highlighting the significant economic returns to higher education.
Figure 4.5: Scatterplots of monthly earnings and education levels
No level PRIM LOWSEC UPPSEC VOC UNIV education levels log of monthly earnings Fitted values
Source: Author’s calculation using data from VHLSS 2008
Figure 4.6: Scatterplots of monthly earnings and years of experience
0 20 40 60 years of experience log of monthly earnings Fitted values
Based on calculations utilizing data from VHLSS 2008, the study provides valuable insights into key socioeconomic indicators The analysis highlights trends in income, education, and living standards, offering a comprehensive overview of household dynamics These findings are essential for policymakers aiming to address disparities and promote sustainable development The report emphasizes the importance of continuous data collection and rigorous analysis to inform effective decision-making Overall, this research contributes to a deeper understanding of social and economic progress in the region.
Monthly earnings do not increase linearly with years of experience, as shown in Figure 4.6 Instead, the fitted line indicates a quadratic relationship, suggesting that earnings tend to grow with experience but at a decreasing rate This insight highlights the importance of including squared years of experience in regression models to accurately capture the nonlinear pattern of earnings growth over time.
4.1.2 Descriptive Statistics of the Dataset
Table 4.1 provides the descriptive statistics of the dataset The mean age of the sample is 34 years, higher than in 1992/1993 sample of Moock et al (2003) which is
31 years The mean age of males is 34.6 years, slightly higher than of females 33 years
According to recent data, the average length of schooling is approximately 9.5 years, reflecting improvements in educational attainment In comparison, Moock et al (2003) reported an average of 7.9 years of schooling Educational attainment is relatively balanced between males and females, with males averaging 9.34 years and females slightly higher at 9.77 years These figures highlight progress in achieving gender equality in education and emphasize the importance of continued efforts to improve access to quality schooling.
Approximately 13% of the labor force is illiterate or has no formal education, while 21% have only attained primary education An additional 25% have completed lower secondary schooling, and 14% have achieved upper secondary education, highlighting significant educational disparities within the workforce Notably, Moock et al (2003) report higher illiteracy and primary education levels at 22% and 50%, respectively, emphasizing the critical need for improved access to quality education to boost workforce skills and economic growth.
(2003)), 13% vocational (12% in Moock et al (2003)), and 14% accomplish colleges and above (only 7% in Moock et al (2003))
Vietnam has seen a significant change in sectoral ownership after 15 years when more labor forces are employed in private sector and foreign sector appears
The private sector accounts for approximately 61% of the labor force, compared to 33% in the public sector, reflecting a significant dominance in employment distribution Additionally, about 6% of the labor force is employed in the foreign sector, highlighting the sector's role in the overall employment landscape These figures indicate shifts from previous data, such as Moock et al (2003), where the private sector comprised 58%, the public sector 42%, and the foreign sector employment was negligible or zero Understanding these employment patterns is essential for analyzing economic development and sectoral contributions within the economy.
The Law on Foreign Investment in Vietnam was first adopted on November 8, 1996, marking a significant milestone in opening up the Vietnamese market to foreign investors This legislation aimed to create a more favorable environment for foreign investment, fostering economic growth and international cooperation Since its enactment, the law has played a crucial role in attracting foreign capital and enhancing Vietnam's integration into the global economy.
The mean monthly earnings in the current study are approximately VND 1,428,000 (USD 68.84), showing an increase from VND 152,000 (USD 14) reported in Moock et al (2003) Men’s earnings are about 17% higher than women’s (VND 1,513,460 vs VND 1,293,880), indicating a significant reduction from the 40% gender earnings gap observed two decades ago, which reflects positive progress in gender equality and Vietnam’s economic development The average hours worked per week is 47, slightly higher than the 46 hours reported in Moock et al (2003).
Regression Results
Table 4.2 presents the estimated results of simple Mincerian earnings functions based on years of schooling Overall, the analysis indicates that each additional year of schooling yields an approximate private rate of return of 9%, highlighting significant returns to education In contrast, Moock et al reported a lower return of only 5%, suggesting variations in the valuation of educational investments across studies These findings underline the positive impact of increased education on individual earnings and its importance for economic growth.
9 Exchange rate year 2008: 1USD = 16,977 VND (source: ADB)
In 1993, the exchange rate was 10,857 VND per 1 USD, according to the Asian Development Bank (ADB).
(2003), nearly double during the last 15 years Females enjoy higher returns to school than males (11.47% vs 8.33%) This pattern unchanged when comparing with Moock et al (2003), whereas 6.8% vs 3.4%
Table 4.2: Earnings function by years of schooling
Log of hours worked/week 0.6480 **** 0.6784 **** 0.6043 ****
Number of groups 4,335 3,510 2,337 sigma_u 0.41 0.39 0.37 sigma_e 0.55 0.53 0.57 rho 0.36 0.35 0.29
Note: * significant at 10% level , **significant at 5% level, *** significant at 1% level, **** significant at 0.1% level
Source: Author’s calculation using data from VHLSS 2008
Post-schooling investment yields an average annual return of 7.31% per additional year of experience, surpassing the 6.4% rate reported by Moock et al (2003) Both men and women benefit similarly from post-schooling investment, with estimated returns of 7.25% for men and 7.01% for women These findings highlight the significant economic value of further education and experience, underscoring its role in enhancing individual earnings across genders.
The intraclass correlation coefficient (rho) of 0.36 indicates a moderate correlation of individuals within households When the intraclass correlation approaches zero, household clustering becomes insignificant, allowing for the use of simple regression models without the need for random effects In such cases, there is no distinction between Ordinary Least Squares (OLS) and Random Effects (RE) estimators, simplifying the analysis process.
An intraclass correlation close to 1 indicates minimal variation between individuals, meaning everyone is quite similar In this case, the reported rho value of 0.36 suggests a moderate correlation among individuals within households This indicates that individuals within the same household tend to be similar, making the random effects (RE) estimator the more appropriate choice for analysis Using the RE estimator accounts for the within-household correlation and provides more accurate results.
The standard deviation of unobserved household characteristics, sigma_u = 0.41, represents the variability of the intercept at the household level (Table 4.2) The residual standard deviation at the individual level, sigma_e = 0.55, indicates the variability of individual-specific residuals (Table 4.2) The intraclass correlation coefficient (rho), which measures the proportion of total variance attributable to household-level differences, can be calculated using these two standard deviations.
e sigma u sigma u sigma n correlatio Intraclass
The standard deviation (sigma_u) of 0.41 indicates significant variation in intercepts at the household level Alongside the common intercept of 2.6296 (as shown in Table 4.2), this suggests that individual deviations are normally distributed with a mean of 2.6296 and a standard deviation of 0.41 These findings demonstrate that individuals from different households have varying intercepts, highlighting the influence of household-level factors on the overall model.
Table 4.3: Earnings function by sector of employment
Log of hours worked/week 0.5891 **** 0.7117 **** 0.5898 ****
Number of groups 1,700 2,816 337 sigma_u 0.47 0.42 0.43 sigma_e 0.47 0.55 0.43 rho 0.50 0.37 0.50
Note: * significant at 10% level , **significant at 5% level, *** significant at 1% level, **** significant at 0.1% level
Source: Author’s calculation using data from VHLSS 2008
Employees in the public sector experience higher returns to schooling at 9.95% compared to 5.59% in the private sector, indicating a greater economic benefit from further education in public employment The foreign sector shows the highest rates of return among the three, at 11.9%, emphasizing the significant value of additional schooling in international employment contexts According to Moock et al (2003), the rates were 6.2% for public sector workers and 3.9% for private sector employees, with the foreign sector not considered due to its smaller share at that time These findings highlight sectoral differences in the economic returns to education, underscoring the importance of sector-specific analysis for policy development.
Post-schooling investment yields the highest rates of return in the foreign sector at 8.15%, followed by the public sector at 8%, and the private sector at 6.38% Compared to 15 years ago, when returns were 4.6% for the public sector and 7.2% for the private sector (Moock et al., 2003), these rates have significantly changed, with the foreign sector now offering the highest returns The shifts indicate a notable transformation in the relative benefits of post-schooling investments across sectors over time.
Table 4.4: Earnings function with schooling levels (for all, males, and females)
Log of hours worked/week 0.6856 **** 0.7178 **** 0.6540 ****
Number of groups 4,335 3,510 2,337 sigma_u 0.39 0.36 0.35 sigma_e 0.55 0.53 0.57 rho 0.33 0.31 0.28
Note: * significant at 10% level , **significant at 5% level, *** significant at 1% level, **** significant at 0.1% level
Source: Author’s calculation using data from VHLSS 2008
Dummy variables representing years of schooling reveal that higher education levels significantly increase earnings premiums, with university graduates experiencing the highest returns to education Specifically, university-level workers earn approximately 126% more than those with no formal education, compared to a 43.7% increase reported in Moock et al (2003) Vocational education yields about a 77% higher income, closely aligned with the 20.7% in prior research Additionally, workers with upper and lower secondary education see earnings increases of 50% and 26%, respectively, compared to a 32.5% return at the secondary level in earlier studies Primary-level laborers generally earn around 16.21% more than uneducated workers, consistent with the 13.4% reported in Moock et al (2003).
The earnings function outcomes by education levels, presented in Table 4.4, highlight the earnings premiums associated with each educational level compared to having no formal education Table 4.5 further illustrates the private rates of return to schooling at various education levels, using a flexible level as the benchmark for comparison Notably, higher earnings premiums are linked to substantial private returns to education, indicating that increased investment in schooling yields significant financial benefits for individuals (Moock et al.).
2003, p.507) The outcomes in Table 4.5 obtains from equation (3.3) and the earning function results disclosed in Table 4.4
Table 4.5: Private rates of return to schooling by level of education (%)
Educational level All Males Females
Primary (vs less than primary) 16 15 16
Upper secondary (vs lower secondary) 8 8 10
Source: Author’s calculation using data from VHLSS 2008
According to Table 4.5, investing in university education yields the highest returns, with a 19% higher rate of return compared to upper secondary education after four years Both males and females benefit equally from university-level education, receiving a 20% return These findings align with Moock et al (2003), which also identified university education as the most advantageous investment in human capital.
This article compares educational partition levels across different reference systems, highlighting the importance of standardization in assessment methods Where appropriate, comparisons are made with Moock et al (2003) to ensure accuracy and relevance Clarifying these differences enhances understanding of educational progress and supports the development of more effective learning strategies.
Primary education offers a strong investment with a 16% rate of return, slightly lower at 13% according to Moock et al (2003) Both vocational and upper secondary education yield similar returns of around 8%, despite vocational training requiring six years to complete compared to just three years for upper secondary schooling Given the comparable rates of return and shorter completion time, investing in upper secondary education appears to be a more efficient and preferable alternative over vocational training.
Lower secondary education offers a poor return on investment, with an overall rate of just 2% Extending primary education by four years to reach the lower secondary level yields only a 2% return, indicating limited financial benefit Conversely, investing at least three additional years to attain upper secondary education can provide an additional 8% return, highlighting the higher value of progressing beyond the lower secondary stage.
Chapter Remarks
In general, the sample profile contains variables which follow normal distribution
Bivariate analysis indicates a positive correlation between the logarithm of monthly earnings and both years of schooling and education levels, highlighting that higher education is associated with increased earnings Additionally, the relationship between log monthly earnings and years of experience follows a quadratic pattern, suggesting that earnings tend to rise with experience up to a certain point before plateauing or declining These findings underscore the significant impact of education and experience on monthly income, emphasizing the importance of investing in education and long-term experience for better earnings potential.
The average age of the sample population is 34 years, with an average of 9.5 years of schooling Approximately 13% of the labor force are illiterate or have no formal education, while 21% have primary education, 25% have completed lower secondary, and 14% have attained upper secondary education Additionally, 14% of workers have graduated from college or higher education The public sector employs about 33% of the labor force, compared to 61% in the private sector and only 6% in the foreign sector The average monthly income is approximately VND 1,428,000 (USD 84), reflecting the employment distribution and educational attainment within the workforce.
Multivariate analysis reveals that each additional year of schooling is associated with an 8.95% increase in the average rate of return to education, with females experiencing higher returns (11.47%) compared to males (8.33%) Workers in the foreign sector enjoy the highest returns at 11.9%, followed by the public sector at 9.95%, and the private sector at 5.59% Investing in university and primary education is highly beneficial, whereas lower secondary education offers a relatively poor return Upper secondary and vocational educations are considered good medium-level investments, making them viable options for career advancement.
CONCLUSION AND POLICY RECOMMENDATION
Conclusion of the Study
Using household-level clustered data from the 2008 Vietnam Household Living Standards Survey (VHLSS 2008), along with a Random-effects model that accounts for individual household variations, and applying a Mincerian earnings function to estimate the rates of return to education in Vietnam, this study reveals significant insights into the impact of educational attainment on household income The analysis indicates that each additional year of education substantially increases household earnings, highlighting the positive correlation between education and income growth in Vietnam These findings provide valuable evidence for policymakers aiming to enhance human capital through targeted education investments to foster economic development.
An additional year of schooling is associated with an 8.95% increase in the average rate of return to education, nearly doubling the 5% rate reported 15 years ago in Moock et al (2003) Females experience higher returns to schooling than males, with rates of 11.47% versus 8.33%, a gender gap that has remained consistent over the past 15 years (6.8% for females vs 3.4% for males).
Workers in the public sector currently experience higher rates of return to education (9.95%) compared to those in the private sector (5.59%) However, the foreign sector offers the highest returns, at 11.9%, among the three sectors Over the past 15 years, these rates have significantly increased—from 6.2% in the public sector and 3.9% in the private sector—indicating a notable expansion in educational returns while maintaining the same overall pattern.
Higher levels of education consistently yield greater rates of return, with university graduates experiencing the highest benefit at 126% higher than those with no education Vocational-level workers see a 77% higher return, upper secondary workers 50%, lower secondary workers 26%, and primary-level laborers 16.21% Compared to 15 years ago, these returns have increased significantly: university-level returns rose from 43.7% to 126%, vocational from 20.7% to 77%, secondary from 32.5% to higher levels, and primary from 13.4% to 16.21%, highlighting the growing economic value of higher education.
Investing in university education offers the highest returns, with a 19% higher rate of return compared to upper secondary level after four years Primary level investment yields a 16% return, significantly higher than no education, which stood at 13% in 1992 The rates for upper secondary and vocational education are both 8%, compared to lower secondary, while the return for lower secondary versus primary is only 2%.
Policy Recommendation
The disparity in return rates between the public and foreign sectors makes it difficult for the public sector to retain and attract skilled workers once a wage gap exists To address this, the government should consider increasing public sector wages to at least match those in the foreign sector Equalizing wages can help retain and attract top talent, improve workforce efficiency by reducing external comparison incentives, and ensure employees can fully focus on their current roles without seeking higher returns elsewhere.
Higher education levels, such as university and upper secondary education, offer higher rates of return, indicating significant potential for private financing in these sectors Shifting some of the cost burden from the government to individuals and their families is unlikely to reduce incentives for investment in these levels due to their high returns To promote access to higher education, the government should encourage private financial institutions to provide education loans and establish favorable conditions for individuals seeking preferential loan options.
The rates of return on vocational and upper secondary education are both approximately 8%, highlighting alternative pathways for students after completing lower secondary school This finding provides valuable insights for policymakers aiming to harmonize employment opportunities across different education levels and for career advisors guiding lower-secondary graduates based on their skills, abilities, and labor market demand.
Limitations of the Study
A key limitation of this study is the potential endogeneity of the empirical model's regressors, particularly years of schooling The error term may capture unobserved factors like innate ability, which directly influence earnings, making it difficult to distinguish the effects of education from inherent personal traits Individuals with higher innate ability tend to achieve more years of schooling and higher earnings, leading to an endogeneity problem To address this, including ability measures such as IQ scores would be ideal; however, these variables are absent from the available dataset Consequently, the analysis may overestimate or underestimate the true impact of years of schooling on earnings due to this unaddressed endogeneity.
An alternative solution to address endogeneity is the Instrumental Variable (IV) approach; however, this research does not utilize IV due to the difficulty of identifying suitable instruments from VHLSS data Instead, the study focuses on capturing household-level effects—such as ability—by using a random-effects estimator that assigns these effects randomly across individuals within households This method allows for accounting for unobserved household-level factors without relying on instrumental variables.
The coefficient for years of experience may be overestimated, particularly for women who often temporarily leave the workforce due to pregnancy or caregiving responsibilities These periods out of labor force are not accurately captured in the VHLSS survey, potentially biasing the evaluation of women's actual work experience.
The current sample focuses solely on wage earners, neglecting over 80% of the Vietnamese labor force engaged in farm and non-farm self-employment Expanding the study to include this significant portion could significantly alter the results, providing a more comprehensive understanding of the labor market dynamics in Vietnam.
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To address the limitations of the current study, future research should consider employing an IV (Instrumental Variable) approach This method helps to mitigate unobserved heterogeneity by identifying suitable instrument variables that are correlated with years of schooling but uncorrelated with the error term Utilizing valid instruments ensures that the estimated impact of education on earnings is more accurate and free from biases caused by unmeasured factors Incorporating the IV approach can significantly enhance the robustness and validity of findings related to the returns to education.
For future research, incorporating multi-level mixed-effect models is essential, as they effectively handle VHLSS data structured by multiple hierarchical categories such as households, villages, communes, and provinces These models enable the examination of effects that vary across different groups by allowing intercepts and slopes to vary randomly, capturing the inherent heterogeneity between groups This approach enhances the accuracy and depth of analysis when exploring complex, nested data structures.
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