INTRODUCTION
Problem Statements
Foreign language currently is a prominent issue in worldwide education and society
The rapid advancement of information and communication technologies has brought nations closer together, with English serving as the common intermediary language A significant portion of global knowledge, including books, academic papers, and documentation in various fields such as economy, science, and culture, is primarily published in English and widely shared online Bolton (2006) noted that English is experiencing the fastest expansion of any language worldwide This common language facilitates communication among speakers of different languages, promoting long-term cooperation As of 2003, English was recognized as the most widely used language globally, with over 1.2 billion speakers.
According to Crystal (2003), English is spoken in 75 countries, and Graddol (2008) predicts that the number of people learning English as a second language will approach two billion within the next decade In some Eastern European nations, English has replaced Russian as a primary school subject (Modiano, 2006) Recognizing the importance of English education, Nergis (2011) argues that government investment is essential In developing countries like Turkey, there is a belief that enhancing English proficiency is crucial for meeting modern communication needs, facilitated by the Turkish language reform.
In Vietnam, Thinh (2006) noted a remarkable increase in English language acquisition over the decade leading up to the country's entry into the WTO, with English learners comprising over 90% of all foreign language students This surge in English popularity contrasts with the relatively lower interest in other languages like Chinese, Japanese, and French.
The demand for English language skills has surged in Ho Chi Minh City, with over 900,000 learners flocking to language centers due to the need for job-seeking and overseas study opportunities This trend has prompted the introduction of English teaching programs in universities, high schools, and government agencies Following Vietnam's accession to the WTO and the implementation of open policies, a significant influx of foreign investors has entered the domestic market, necessitating a qualified labor force capable of effective communication with international partners As a result, mastering foreign languages has become a pressing issue in the context of today's global economic integration.
Recognizing the need for English language development, many parents are planning for their children a foundation of language, namely English, since they start school
This study aims to explore the significance parents attribute to English education by examining their spending on their children's English language learning at the primary school level in Ho Chi Minh City The findings will highlight the crucial role of English language education for both individual growth and national development, emphasizing the need for investment from both private individuals and the government in Vietnam.
Research Objectives
General objective: To investigate household’s expenditure on English language education for children at starting school age (primary school)
(1) To identify the importance of English language education in a household’s decision
(2) To identify the determinants of household’s expenditure on English education for primary school children in Ho Chi Minh City.
Research Questions
This study investigates the influence of various parental factors, including income, work environment, English proficiency, and demographic variables such as age, gender, marital status, location, home ownership, and employment status, on the financial investment in English education for their children We aim to determine the extent to which these factors contribute to educational expenditures.
Research methodology
This study bases on Pritchett and Filmer (1999)’s theory of education expenditure
This article employs a combination of descriptive statistics and regression analysis to assess the significance of English education and its expenditure determinants among households in Ho Chi Minh City Initially, descriptive statistics provide an overview of English education spending relative to total income and expenditure, alongside household characteristics Subsequently, ordinary least squares (OLS) regression identifies influential factors impacting English language expenditure for primary school-aged children Additionally, logit regression examines how these factors influence households' decisions to invest in English education Lastly, ordered logit regression addresses the scenario where English education expenditure is ranked on a scale from zero to six, rather than being continuous, highlighting the varying levels of expenditure among households.
The structure of the study
This study is structured into five chapters, beginning with an introduction in Chapter 1 Chapter 2 examines three key theories related to education expenditure: the household production function and the income elasticity of education spending, while also referencing recent empirical research on education expenditure in Vietnam and globally Chapter 3 outlines the research methodology, detailing data collection methods, variable explanations, measurements, and model specifications Chapter 4 provides an overview of education, with a specific focus on English education in Ho Chi Minh City, and analyzes influential factors such as parents' total income, expenditure, education levels, English proficiency, and various demographic characteristics that may impact English education expenses for primary school-aged children.
LITERATURE REVIEW
Theoretical literature
In order to comprehend the purpose of this research, we first discuss education expenditure theories
Theory of Education expenditure: Education production function
Pritchett and Filmer (1999) introduce a theory of education expenditure through the lens of the education production function, which illustrates how various inputs contribute to educational outcomes Their research focuses on the causal effects of both school inputs—such as teacher education levels, class sizes, teacher experience, and educational resources like textbooks—and non-school inputs, including family background, environmental factors, and children's innate abilities This comprehensive analysis highlights the multifaceted influences on children's academic achievements.
The specific function of education production is defined as below:
In the context of children's development, Cit represents outputs related to children, Sit signifies school inputs, Fit refers to non-school inputs such as family contributions, and Ii indicates children's innate abilities This framework utilizes a general term to approximate the fixed contributions of congenital variables, as there is a lack of data sets capable of measuring non-figurative variables like congenital ability.
Theory of household production function
Becker (1965) and Muth (1966) introduced a household production function model that illustrates how commodities purchased in the market serve as inputs for household production Specifically, they apply this model to analyze the production function related to child health.
Whereas, Yj are other good affect child’s health; Ik present for health input and à is the family-specific health endowments such as genetic characteristics or environmental factors
Later on, from this household production function, they developed household’s reduced-form demand function:
Zt = St ( p, F, à) Whereas, p is the price of goods or services; F is the exogenous income and à is the family specific endowment
To measure household expenditure on English language education, we consider education as a consumable good This analysis examines various factors influencing the household production function, including household income, family-specific endowments, and demographic variables Specifically, family-specific endowments include the English proficiency of parents, reflecting genetic characteristics, and the parents' working environment, representing environmental influences.
Theory of income elasticity of educational expenditure:
Benson (1961) highlights that household income significantly influences educational expenditure, as indicated by the income elasticity of education Households are categorized into low, middle, and high income, with low and high-income families exhibiting an income elasticity between zero and one In contrast, middle-income households show an income elasticity greater than one, demonstrating a stronger commitment to investing in their children's education This suggests that middle-income families allocate more resources towards education compared to other income groups, while low-income families tend to prioritize basic needs over educational quality, resulting in a slower growth of educational spending relative to their overall income.
Use the Lorenz curve to measure the distribution of income to the education expenditure The form of income elasticity of educational expenditure as follows:
Whereas E i is the elasticity of income v i (x) is the Engel function of the i expenditure items.
Empirical literature
This research aims to explore the factors influencing expenditure on English language education, viewing it as a crucial human capital investment, as highlighted by Espenshade (1997) The complexity of this expenditure necessitates a multifaceted analysis, as it cannot be adequately explained by a single independent variable While previous studies have primarily focused on general education expenses, there is a lack of detailed research specifically addressing English language education Key determinants of educational spending identified by Kanellopoulos (1997), Tansel (2006), and Donkoh (2011) include household income, expenditure, parental education, employment sector, employment status, age and gender of the household head, maternal employment, household size, and home ownership Drawing from these insights, we will utilize these independent variables to develop a new model aimed at understanding the determinants of spending on English language education, which will be further elaborated in the methodology section.
Household income significantly influences education expenditure, as wealthier families prioritize enhancing their living standards Investing in education is a key aspect of this improvement, particularly for the next generation, as highlighted by Donkoh (2011) Research by Glewwe (1999) and Tansel and Bircan further supports this connection.
(2006) and Donkoh and Amikuzuno (2011) found that when the household’s income rise they are more willing to pay for the private tutoring
The income educational expenditure elasticity of households serves as a key metric for assessing the relationship between income and education spending Research, particularly by Hashimoto and Heath (1995), indicates that middle-income households exhibit the highest income elasticity, exceeding one, suggesting that their education expenditure increases significantly with income growth In contrast, lower and higher income groups show an elasticity ranging from zero to one, indicating less sensitivity to income changes regarding education quality Studies by Kanellopoulos and Psacharopoulos (1997) in Greece and Hashimoto and Heath (1995) in Japan report income elasticity values of 3.18 and 2.35, respectively, both categorizing education expenditure as a luxury good However, Tansel and Bircan (2006) challenge this notion, finding a unitary elasticity of income on education demand, suggesting a more complex relationship between income and education spending.
Hence, they conclude education is not a luxury good in the case of Turkey as it is discovery in some researches
This research examines the impact of informal educational activities, particularly private tutoring, on English language education Stevenson and Baker (1992) highlight that households are willing to invest significantly in these activities, driven by the desire for their children to achieve higher education and successful careers Consequently, the likelihood of increased spending on educational resources correlates with the overall expenditure levels of families Notably, families allocate approximately 11.2% of their total expenditure to tutor fees for extra educational support, with over 63% of this amount dedicated to foreign language education (Kanellopoulos and Psacharopoulos, 1997) This indicates a strong trend where parental investment in their children's education rises sharply in relation to their total household expenses.
To address issues related to variable selection and avoid simultaneous bias, some researchers have highlighted the potential bias arising from the simultaneous relationship between total and individual expenses Liviatan (1961) suggests using instrumental variables, such as household income, to mitigate this bias Tansel and Bircan (2006) conducted a test for the exogeneity of total household expenses using a Tobit model, as defined by Smith and Blundell (1986) Their methodology involved two steps: first, regressing total spending on household income, followed by incorporating the residuals from this regression into the Tobit model for additional education spending to assess significance Their findings indicated a significant result at the one percent level, leading them to use total household expenses as an explanatory variable rather than household income.
Numerous studies, including those by Knight and Shi (1996), Kanellopoulos and Psacharopoulos (1997), Tansel and Bircan (2006), Qian and Smyth (2011), and Donkoh and Amikuzuno (2011), have explored the relationship between parental education and educational expenditure These studies consistently highlight the positive influence of parental education on spending for children's education Notably, Knight and Shi (1996) emphasize that a parent's educational attainment is the most critical factor affecting education expenditure, with fathers' education being more influential than mothers' Similarly, Kanellopoulos and Psacharopoulos (1997) demonstrate that households led by individuals with higher educational qualifications are more likely to allocate additional income toward their children's education, with a reported 2.2% increase in the likelihood of educational spending for each additional year of education attained by the household head.
Research highlights the significant impact of a mother's education on decision-making regarding educational investments According to Qian and Smyth (2011), households where mothers possess senior secondary school or college degrees tend to allocate more resources towards education.
According to Donkoh and Amikuzuno (2011), individuals with higher education are more likely to provide their children with similar opportunities, fostering an expectation for them to achieve advanced educational levels.
Research by Kanellopoulos and Psacharopoulos (1997) and Andreou (2012) indicates that households in the private sector are more likely to invest in their children's education.
A study by Qian and Smyth (2011) revealed that households with fathers in professional occupations tend to allocate more funds towards education This finding highlights the significant role of the private sector as a key determinant in understanding the working environment of the household head.
According to Kanellopoulos and Psacharopoulos (1997), Donkoh and Amikuzuno
The occupation of parents significantly influences household investment in children's education, with two contrasting perspectives One viewpoint suggests that lower-class households are more inclined to invest in education to compensate for their own lack of educational attainment Conversely, upper-class parents may undervalue education, perceiving it as less important compared to their household wealth, which could lead to reduced spending on educational resources However, Andreou (2012) argues that parental occupation does not have a substantial impact on private educational expenditures.
Research indicates that the age of a household's head significantly influences educational investment decisions Studies by Kanellopoulos and Psacharopoulos (1997), Donkoh and Amikuzuno (2011), and Andreou (2012) consistently show a negative correlation between the age of the head and the household's educational spending choices.
Donkoh and Amikuzuno (2011) analyze the impact of age on household spending for education, using age and age squared as variables Their findings indicate that older household heads are less inclined to invest in education, whereas younger heads tend to recognize the long-term benefits of education and are more progressive in their approach This suggests that younger household heads are more likely to prioritize educational expenditures compared to their older counterparts.
Kanellopoulos and Psacharopoulos (1997) categorize age into six groups: under 25, 25-34, 35-44, 45-54, 55-64, and over 65 Their research reveals that the highest education spending occurs among individuals aged 35 to 44, with approximately 62.6% of heads in this age group willing to invest in education.
And the probability of expenses decline after this group
Tansel and Bircan (2006) presented a contrasting view regarding private tutoring expenditures in Turkey, revealing that older household heads are more inclined to invest in their children's education This study aims to examine the impact of the age of the household head on educational spending for children.
Conceptual framework
Overall of the above empirical literature, the study is going to test the relationship between the private expenditure for English education and the explanation variables as the following:
Figure 2.1 Analytical framework for the impact explanatory variable on English education expenditure
Age Gender (dummy V) Marital status Location (dummy V) Home ownership (dummy V) Employment status Number of children
PRIVATE EXPENDITURE for English language Education
RESEARCH METHODOLOGY
Sampling strategy and data collection
The study utilized primary data collected through face-to-face surveys, employing a questionnaire designed to address key topics such as household income, parents' education and English proficiency, parental careers, and various demographic factors including age, gender, and marital status The surveyed households consisted of families with children enrolled in pre-primary and primary education in Ho Chi Minh City Participants were randomly selected from different districts and sub-districts, with households chosen based on a random selection of houses within a designated ward of each district.
20 households were interviewed in each districts/sub-districts The internal districts in Ho Chi Minh City comprise district one to twelve, Tan Phu, Tan Binh, Binh Tan, Binh Thanh,
Go Vap, Phu Nhuan The sub-districts include Binh Chanh, Hoc Mon, Cu Chi, Nha Be whereas we excluded the Can Gio’s observation because of its low population density
In households without primary school-aged children, the next neighboring houses are approached for participation If the subsequent three homes also lack children in the target age group, the sample remains unfilled Some selected households opted out of responding due to personal circumstances, resulting in certain districts not achieving the desired 20 responses Ultimately, the survey yielded a total of 267 observations for this study, with the questionnaires detailed in the annex.
Variables’ measurement and explanation
3.2.1 Current expenditure on English education
The expenditure on English language education is a complex variable viewed as an investment in human capital (Espenshade and Fu, 1997) It encompasses both the costs associated with this investment and the expected returns, which can vary among spenders Due to the limited research on language education expenditure, this study builds on factors influencing general education spending to create a new model specific to English education Drawing on findings from Kanellopoulos and Psacharopoulos (1997), Tansel and Bircan (2006), and Donkoh and Amikuzuno (2011), we examine key factors such as total household income and expenditure, parental education, employment sector, age and gender of the household head, maternal employment status, household size, and homeownership This research aims to determine how these factors affect household spending on English education.
This study evaluates parental interest in children's English education by analyzing current expenditure levels, categorized into six tiers A ranking of zero indicates no spending on English education, while rank one corresponds to expenditures between 0 and 300,000 VND Rank two covers spending from 300,000 to 500,000 VND, rank three from 500,000 to 1,000,000 VND, rank four from 1,000,000 to 2,000,000 VND, and rank five signifies expenditures exceeding 2,000,000 VND These classifications are based on the average fees for extracurricular English classes at schools and English centers, with data collected from ten schools and eleven English centers catering to primary children at the beginner and pre-intermediate levels.
See the list of school and English center in the appendix
The impact of household income on education expenditure is significant, as wealthier households tend to invest more in education to enhance their living standards (Donkoh and Amikuzuno, 2011) Research by Glewwe and Desai (1999), Tansel and Bircan (2006), and Donkoh and Amikuzuno (2011) indicates that higher household income leads to increased willingness to pay for private tutoring This study posits that English education, as a form of private tutoring, is also influenced by total household income To assess this impact, two evaluation methods are utilized: direct evaluation, which employs OLS and Probit/Logit models to analyze the effects of all income levels on current English education expenditures, and indirect evaluation, which categorizes income into low, middle, and high brackets to calculate income elasticity for English education expenditure using the Tobit model.
In this study, the household’s total income variable is also divided into five levels:
In evaluating income levels, the direct evaluation method categorizes individuals into four groups: those earning below 5,000,000 VND, those earning between 5,000,000 VND and 10,000,000 VND, those earning between 10,000,000 VND and 20,000,000 VND, and those earning over 20,000,000 VND Alternatively, the indirect evaluation method classifies income as low, middle, or high.
According to the World Bank's 2012 income group classification, low-income countries are defined as those with a Gross National Income (GNI) per capita of $1,035 or less.
Income classifications can be categorized into four main groups: lower income, which includes individuals earning 1,811,250 VND or less per month; lower middle income, ranging from 1,813,000 to 7,148,750 VND per month; upper middle income, from 7,148,750 to 22,076,250 VND per month; and high income, defined as earning 22,078,000 VND or more monthly For clarity, the low income group is considered level (1), earning below 5,000,000 VND, while the middle income group encompasses levels (2), (3), and (4), with level (5) representing the high income group.
Previous empirical studies indicate that middle-income households exhibit the highest income elasticity of education expenditure, exceeding a value of one In contrast, lower and higher income classes show elasticity values ranging between zero and one This suggests that an increase in income leads to a disproportionately higher rise in education spending among middle-income households compared to other income groups Furthermore, it appears that lower and higher income families may be less concerned about the quality of their children's education, as noted by Hashimoto and Heath.
This study aims to explore the impact of income increases on expenditure for English education, investigating whether income elasticity of English expenditure varies among low, middle, and high-income groups Despite some research indicating no significant differences in education demand across these income levels, this paper seeks to validate or challenge these findings through theoretical and empirical analysis.
H1: The household’s total income has positive relationship with the English education expenditure children
This research highlights the correlation between household total expenditure and spending on English education According to Stevenson and Baker (1992), households are likely to invest more in informal educational activities when they aspire for their children to achieve higher education and successful careers Consequently, the likelihood of increased educational spending rises with the overall expenditure of households Kanellopoulos and Psacharopoulos (1997) note that spending on foreign language education constitutes over sixty-three percent of total educational expenditure Therefore, it is evident that parents’ investment in their children’s education significantly escalates in relation to their overall household expenses.
This study utilizes the VHLSS 2010 dataset to categorize total household expenditure into six levels: (1) below 2,000,000 VND, (2) 2,000,000 to 3,500,000 VND, (3) 3,500,000 to 5,000,000 VND, (4) 5,000,000 to 10,000,000 VND, (5) over 10,000,000 VND The research aims to examine two key aspects: the potential positive relationship between total household expenditure and spending on English education, and whether the simultaneous consideration of total and individual expenses introduces bias when using income as an instrumental variable, as suggested by Liviatan (1961) To achieve this, Tansel and Bircan (2006) employ a Tobit model, as defined by Smith and Blundell (1986), consisting of two steps: first, regressing total spending on household income, and second, using the residuals from this regression in the Tobit model for additional education spending to test significance If the regression results indicate significance, the total household expenditure can be utilized as an explanatory variable in place of total household income.
In summary, the study is going to test the proposed hypothesis as following:
H2: The level of household’s total expenditure has positive relationship with the English education expenditure
H3: The current expenditure on extra education relates to the English education expenditure
Many studies found the positive impact of parental education to their spending for children’s education such as Knight and Shi (1996), Kanellopoulos and Psacharopoulos
(1997), Tansel and Bircan (2006), Qian and Smyth (2011) and Donkoh and Amikuzuno
A study by Knight and Shi (1996) emphasizes that a father's education significantly influences educational outcomes, more so than a mother's education Consequently, this research explores the relationship between parents' education levels and their investment in English education expenditures.
The education levels in the study, similar to the VHLSS 2010, are categorized from one to eight, reflecting the highest completed education of the mother or father, including Primary school, Junior high school, Senior high school, Primary vocational school, Colleges, University, Master's, and Doctorate The study aims to test the following hypothesis:
H4: Parents’ education level has a positive relationship with the expenditure on English education
H5: The level of English of Parents is positively related to their spending on English education
This study investigates the impact of employment sector on English education expenditure among households in Ho Chi Minh City Previous research by Kanellopoulos and Psacharopoulos (1997) and Andreou (2012) suggests that families working in the private sector are more inclined to invest in their children's education Specifically, we aim to determine whether parents employed in private sectors, particularly those associated with foreign companies or NGOs, allocate more funds for English education The employment sectors in Ho Chi Minh City are categorized into four groups: public sector, domestic private sector, foreign private sector, and other sectors Each employment sector is treated as a dummy variable for regression analysis, with the public sector serving as the control variable The study will ultimately test the hypothesis regarding the relationship between employment sector and education spending.
H6: Parents’ sector of employment could explain the current expenditure on English education
Research indicates that there are differences in how mothers and fathers allocate spending on their children To explore this further, we will examine whether the gender of the household head influences decisions regarding expenditures on English education Based on empirical studies, we will use a dummy variable for this analysis, where zero denotes a female head of household and one denotes a male head Subsequently, we will test the hypothesis outlined in this study.
H7: There is a difference between a mother household’s head and father household’s head in their way of expense on their children’s English education
Generally, the age of head appeared to be negative efficiency to their choice of spending according to Kanellopoulos and Psacharopoulos (1997), Donkoh and Amikuzuno
Research indicates differing perspectives on household dynamics regarding education Donkoh and Amikuzuno (2011) suggest that households led by younger heads are more progressive, recognizing the long-term benefits of education In contrast, Tansel and Bircan (2006) found that in Turkey, older heads of households are more inclined to invest in private tutoring for their children, indicating a correlation between age and willingness to spend on educational support.
Model specification
To investigate the impacts of all suggested independent variables to the English education expenditure, this study use five models to test the twelve proposed hypotheses
The study employs Ordinary Least Squares (OLS) to analyze the factors influencing English language expenditure among primary school children, highlighting the relationship between various explanatory variables and spending To assess the impact of these determinants on household decisions regarding English education investment, a logit regression is utilized, as the traditional OLS model is insufficient for this analysis Additionally, an ordered logit regression is implemented to address the issue of the dependent variable, English education expenditure, being ordinal rather than continuous In this research, expenditure levels for English education are categorized from zero to six, with unequal intervals between these levels.
In this study, E symbolizes the current expenditure on English education, while X represents income levels, Y indicates the English proficiency of parents, and Z refers to their working environment Additionally, T encompasses a range of demographic factors, including the age of parents, the gender of the household head, employment status of the mother, number of children, household location, and ownership status.
Various regression methods are utilized to achieve the research objectives, starting with Ordinary Least Squares (OLS) regression to examine the relationship between dependent and independent variables.
In this case, there are two specific models employed as following:
Model 3 and model 4: Logit models with income (model 3) and expenditure (model 4)
The logit regression is utilized to examine the impact of various determinants on households' decisions to invest in English language education Since the spending behavior is a binary dependent variable, a logit model is employed for this analysis Unlike ordinary least squares (OLS) regression, this model estimates the probability (P) that a household will allocate funds towards education based on different characteristics.
The probability of a household's spending on education is influenced by various independent variables, including income, the age and gender of the household head, and location, as outlined by Kanellopoulos and Psacharopoulos (1997) The analysis is conducted through two regression models: Model 3 focuses on income and additional explanatory variables, while Model 4 examines total expenditure alongside other explanatory factors for comparison The marginal effects of each explanatory variable are calculated following the logit model to assess their impact on English education expenditure.
In this study, the English education expenditure is categorized into six ordinal levels, ranging from zero to five, making it an ordinal dependent variable Therefore, the ordered logit model is appropriate for analyzing the relationship between this ordered dependent variable and a set of independent variables This model estimates the ordered outcomes as a linear function of the explanatory variables and the cutpoints According to Fu (1998), the probability of observing a specific outcome is determined by the estimated independent variables and the error term, relative to the defined cutpoints.
In this study, current expenditure for English education is categorized into five levels based on the amount spent: level 0 indicates no expenditure, level 1 represents expenses below 300,000 VND, level 2 covers expenses from 300,000 VND to 500,000 VND, level 3 includes expenses from 500,000 VND to 1,000,000 VND, level 4 accounts for expenses from 1,000,000 VND to 2,000,000 VND, and level 5 signifies expenses exceeding 2,000,000 VND The independent variables analyzed include the household's total income, total expenditure, payments for extra classes, parents' education and English proficiency levels, and various demographic factors such as the parent's age, the gender of the household head, whether the wife is employed, the number of children, the household's location, and ownership status The coefficients β1, β2, …, βk represent the explanatory power of these variables.
K 1 , K 2 ,… K k-1 , are the cutpoints u j is the logistically distributed of error term
Table 3.1: Variable description and expected sign for Model 1
Variable name Description Units Expected sign
CPE Current payment for English education VND dIncome2 Dummy household’s total income variable Equal to income rank from
5,000,000 to 10,000,000; otherwise equal to income below 5,000,000 VND Positive (+) dIncome3 Dummy household’s total income variable Equal to income rank from
10,000,000 to 20,000,000; otherwise equal to income below 5,000,000 VND Positive (+) dIncome4 Dummy household’s total income variable Equal to income rank over
20,000,000; otherwise equal to income below 5,000,000 VND Positive (+) dtotalexp2 Dummy total expenditure variable Equal to rank from 2,000,000 to
3,500,000; otherwise equal to income below 2,000,000 VND Positive (+) dtotalexp3 Dummy total expenditure variable Equal to rank from 3,500,000 to
5,000,000; otherwise equal to income below 2,000,000 VND Positive (+) dtotalexp4 Dummy total expenditure variable Equal to rank from 5,000,000 to
The analysis reveals that a total expenditure variable, represented as dtotalexp5, is positively correlated with income levels above 10,000,000 VND, while those earning below 2,000,000 VND show a similar trend Additionally, current payments for extra education (cpec) exhibit a positive relationship, suggesting that higher investment in education correlates with increased financial outcomes The education levels of both spouses, ranked from 1 to 8, also show a positive association, indicating that higher education contributes to better financial status Furthermore, the husband's proficiency in English (englishh) and the wife's English knowledge (englishw), both coded as dummy variables, positively influence the overall financial performance, with a value of 1 indicating proficiency and 0 indicating a lack thereof.
The article discusses various variables affecting household dynamics, highlighting the dummy employment status of the wife (dwifeworking), which is positive when she stays at home It also examines the household's location (dlocal), indicating a positive correlation if situated in an urban area Additionally, the ages of both the husband (ageh) and wife (agew) are considered, showing a negative relationship with household outcomes Lastly, the ownership of a house (ownh) is addressed, with a positive impact when households own their residence.
Nchildren Number of children Child (+)/(-)
EMPIRICAL ANALYSIS RESULTS
General information of the Household’s characteristic in HCM City
A recent survey reveals that in Ho Chi Minh City, most parents of primary school-aged children have attained education levels primarily at the senior high school and university levels The education distribution between husbands and wives is nearly equal, although husbands generally possess higher educational qualifications Specifically, 29.96% of husbands have completed university, compared to only 25.09% of wives Additionally, 7.87% of households have husbands with a Doctorate in Physical Education, while no wives in the surveyed households have reached this level of education.
A comparison of the Vietnam Household Living Standard Survey (VHLSS) 2010 reveals key insights into family education levels: (1) husbands generally possess higher education than their wives; (2) in Ho Chi Minh City, parents' education levels are predominantly concentrated in no education, primary, junior high, and senior high school, with university-educated parents comprising a notable 11.69% of the total.
Figure 4.1 The education level of the parents in Ho Chi Minh City – Author’s survey 2013
Figure 4.2 The education level of the parents in Ho Chi Minh City – VHLSS 2010
General information of English education in Vietnam
In Vietnam, English has been a compulsory subject in junior and senior high schools since 1982, with a structured curriculum that initially included a three-year program for grades 10-12 and a seven-year program for grades 6-12 Following 2002, English became mandatory in junior and senior high schools and an elective subject in primary schools, where it is taught from grades 3 to 5 with two periods per week, increasing to three periods per week in higher grades This comprehensive English language teaching program aims to equip students with essential skills in listening, speaking, reading, and writing, ensuring they graduate with a foundational proficiency in English and the ability to comprehend textbooks with the aid of a dictionary.
Between 2008 and 2013, the number of foreign language centers in Vietnam surged from 843 to 1,935, reflecting a significant expansion in English education as reported by the Ministry of Education and Training (MOET) This growth is supported by the National Foreign Languages 2020 Project, established under Decision No 1400/QD-TTg on September 30, 2008, aimed at integrating foreign language instruction into the national education system The project aspires to create a comprehensive qualifications framework with six levels, aligning with international language proficiency standards, thereby enhancing the potential for successful language education in the country.
To enhance foreign language proficiency, it is essential to establish and enforce mandatory language training programs in basic schools These programs should align with the foreign language qualifications framework, ensuring that students achieve level 1 qualifications by primary school graduation, level 2 by junior high school, and level 3 by senior high school.
Thirdly, implement training according to new foreign language program for the university
Establish and implement teaching by English in some basic subjects and major subjects for the last year student of university
The current state of English teaching and learning in Vietnam is still inadequate, prompting many parents to supplement their children's education by enrolling them in English language centers Numerous domestic and international centers, such as the British Council, VUS, VASS, Apollo, and ILA, are thriving in Ho Chi Minh City and Hanoi, with plans for significant investments and nationwide expansion Additionally, many smaller centers and various educational institutions, both private and public, have emerged to address the growing demand for English education across the country.
Table 4.1 The development of number of foreign language center through the years
Source: Ministry of Education and Training
Importance of English expenditure in household’s decision
Figure 4.3 illustrates that households in Ho Chi Minh City allocate a significant portion of their total education expenditure to English education.
Basic education accounts for an average of 61.4% of total educational expenses in households In relation to household finances, it represents 4.7% of total income and 6.7% of total expenditures The study indicates that spending on English education is viewed similarly to private tutoring, with 84.7% of households willing to invest in English classes and 84.3% for other extracurricular activities Most families spend between 300,000 VND to 500,000 VND per month on these educational services For further details, please refer to the table in the appendix.
Figure 4.3 The distribution of current English education expenditure in basic education, total income and total expenditure
Empirical analysis results
This section presents the empirical analysis results and discussions, beginning with descriptive statistics for both the dependent and explanatory variables, alongside their correlation matrix It then summarizes the estimated outcomes from three regression models, including the marginal effects derived from these regressions and a detailed discussion of the findings.
This section outlines the descriptive statistics derived from the survey data, offering essential insights for subsequent model discussions The primary dependent variable is illustrated through histogram graphics, while explanatory variables are statistically summarized in Table 4.1, which includes metrics such as mean, frequency, maximum, minimum, and standard deviation Notably, 84.7% of households are willing to invest in their children's English education, with the majority spending 300,000 VND or less per month, accounting for approximately 41% of all households.
The histogram in Figure 4.4 illustrates the current spending on English education in Ho Chi Minh City, categorized into distinct expenditure ranges The categories are as follows: 0 indicates no expenditure, 1 represents spending between 0 and 300,000 VND, 2 covers the range from 300,000 to 500,000 VND, 3 includes expenditures from 500,000 to 1,000,000 VND, 4 spans from 1,000,000 to 2,000,000 VND, and 5 signifies spending exceeding 2,000,000 VND.
Figure 4.5 Histogram graphic of the dummy variable of current payment for English education in Ho Chi Minh City (0 – no expenditure on English education, 1 – present expenditure on English education)
Dummy variable of current payment for English education
The survey shows that the groups of household which gain total income from 5,000,000 VND to 10,000,000 VND per month mostly predominate over the households in
In Ho Chi Minh City, the distribution of household income reveals that the middle-income group constitutes nearly 70% of the total, making it the largest segment The low-income group follows at 23.60%, while the high-income group represents only 6.74% Among the five income categories, households earning between 5 to 10 million VND account for 40.07% of those surveyed Additionally, 23.60% of households earn below 5 million VND, and 20.60% earn between 10 to 20 million VND Conversely, the highest income bracket, with earnings exceeding 30 million VND, has the lowest representation at just 6.75% This data underscores the dominance of the middle-income group in Ho Chi Minh City's household income distribution.
In Ho Chi Minh City, household income distribution is categorized into two main frameworks: one divides households into five groups based on income levels—below 5 million VND, 5 to 10 million VND, 10 to 20 million VND, 20 to 30 million VND, and over 30 million VND; the other simplifies this distribution into three broader categories: low income, middle income, and high income.
The majority of households surveyed, accounting for 74.15%, report monthly expenditures between 3,500,000 VND and 10,000,000 VND, with 40.82% specifically spending between 3,500,000 VND and 5,000,000 VND This spending pattern aligns with the income distribution of the middle-income group, whose earnings primarily cover essential needs such as food, education, and health Additionally, there is a strong correlation between total income and total expenditure, indicating that as income increases, so does the ability to spend Notably, most households invest in private tutoring for their children, with only 15.67% opting out of extra classes.
This number reflects the interest of the parents to their children’s education nowadays
The study explores the demographic characteristics of households in Ho Chi Minh City, revealing that over 35% of parents are proficient in English, with a higher percentage among husbands compared to wives Approximately 29.96% of husbands have completed university education, while the percentage for wives is lower Employment data indicates that 25.09% of husbands and 28.46% of wives work in the public sector, with 34.08% of husbands and 25.47% of wives employed by domestic companies; however, employment in foreign companies and NGOs is limited, and about 25% of individuals work informally Additionally, 18.66% of wives are homemakers The survey shows that 77.9% of household heads are male, with husbands aged between 26 and 58 and wives between 25 and 55 Most households, 75%, are located in urban areas, and over 64% own their homes The average number of children per household is 1.65, reflecting a trend towards smaller families with one to two children at primary school age.
Table 4.2 Summary statistic of variables (a)
Cpe cpe0 cpe1 cpe2 cpe3 cpe4 cpe5
Income dincome1 dincome2 dincome3 dincome4 dincome5
Total expenditure dtotalexp1 dtotalexp2 dtotalexp3 dtotalexp4 dtotalexp5 dtotalexp6
1.50 8.99 40.82 33.33 11.99 3.37 cpec dcpec1 dcpec2 dcpec3 dcpec4 dcpec5
15.67 44.78 26.49 8.58 4.48 eduh Primary Senior HS Junior HS Primary VS College University Master PhD
6.74 15.36 28.84 8.99 0 29.96 2.25 7.87 eduw Primary Senior HS Junior HS Primary VS College University Master PhD
Englishh_0 38.20% husband know English 61.80% don’t know English
Englishw_0 35.96% wife know English 64.04% don’t know English
Private (Foreign.) NGOs Stay at home
Private (Foreign.) NGOs Stay at home
Wifeworking 81.34% wife are working 18.66% wife stays at home
Genderhh 77.9% HH are male 22.1% are female
Local 75% HH in urban 25% HH in Suburban
Ownh 64.18% own a house 35.82% not own a house
Table 4.2 Summary statistic of variables (b)
Variable Mean Std Dev Min Max
Table 13 in the Appendix illustrates the correlation among explanatory variables, revealing no significant correlations except for the education levels and ages of both spouses This aligns with the social characteristics of Vietnam Additionally, the data indicates that the high-income group five tends to spend more, resulting in a notable correlation between income group five and total expenditures in groups five and six This correlation is further supported by the relationship between total expenditure and current payments for English education, reinforcing Benson's (1961) theory on the income elasticity of education expenditure.
The article examines the correlation between household income, total expenditure, and spending on English education Table 4.2 clearly indicates that households with higher incomes allocate more funds towards their children's English education Specifically, households earning below 5 million VND per month predominantly invest in low-level English education, with 37% spending under 300,000 VND, 15% spending between 300,000 and 500,000 VND, and 27% spending nothing at all In contrast, households earning over 30 million VND show a significant increase in spending, primarily in the ranges of 1,000,000 to 2,000,000 VND and above 2,000,000 VND per month Similarly, total household expenditure influences educational spending, with those having higher expenditures investing more in English education, particularly in the 1,000,000 to 2,000,000 VND and above 2,000,000 VND categories Conversely, households with lower total expenditures prioritize basic living needs and spend less on English education.
Table 4.3 The relationship of total income and current payment for English education
Current payment for English education (VND)
20 - 30 millions VND 2% 8% 6% 23% 12% 0% over 30 millions VND 0% 3% 6% 3% 40% 25%
Table 4.4 The relationship of household’s total expenditure and current payment for
Current payment for English education (VND)
10 - 15 millions VND 2% 5% 13% 23% 36% 25% over 15 millions VND 0% 2% 4% 3% 12% 25%
This chapter addresses the research questions posed in Chapter One and is grounded in the hypotheses outlined in Chapter Three The focus of the study is to highlight the significance of English education in Vietnam by examining how factors such as parents' income, work environment, English proficiency, and other demographic variables influence their expenditure on English education for their children Table 4.5 presents the results from two regression methods, as defined in the methodology section, analyzing the relationship between these explanatory variables and the current spending on English education for children in Ho Chi Minh City.
Table 4.5 The results of the models
Current payment for English education
Coefficient Coefficient Coefficient Marginal effect (dy/dx)
Coefficient Marginal effect (dy/dx)
Current payment for extra class
0.327 *** 0.309 *** 0.368 0.020 0.188 0.011 (0.00) (0.00) (0.16) (0.18) (0.47) (0.48) Dummy English var of husband
0.009 0.049 -1.121 -0.069 -0.971 -0.063 (0.96) (0.80) (0.08) (0.14) (0.13) (0.18) Dummy English var of wife
-0.393 ** -0.356 * -0.489 -0.028 -0.267 -0.016 (0.04) (0.06) (0.42) (0.47) (0.68) (0.70) Dummy Public sector of husband
0.096 0.082 -0.365 -0.021 -0.300 -0.018 (0.60) (0.66) (0.59) (0.60) (0.64) (0.66) Dummy Public sector of wife
( * ): Statistic significance at 10% level, ( ** ): Statistic significance at 5% level, ( *** ): Statistic significance at 1 % level
Models 1 and 3 examine the relationship between total income and various explanatory variables, while models 2 and 4 focus on the connection between total expenditure and other explanatory factors, particularly concerning current spending on English education The findings from model 1 indicate a positive correlation between total income, current payments for extra education, and the education level of the wife with expenditures on English education Notably, households in the highest income bracket, exceeding 20 million VND, show a statistically significant influence on English education spending at a 1% significance level This supports the assertions of Glewwe and Desai (1999), Tansel and Bircan (2006), and Donkoh and Amikuzuno (2011) that higher income levels lead to a greater willingness to invest in additional education.
Model 1 indicates that spending on extra classes positively influences expenditure on English education at a 1% significance level Conversely, a wife's English proficiency negatively impacts English education spending at a 5% significance level This phenomenon can be attributed to the tendency of Vietnamese women to dedicate time to teaching their children at home Consequently, wives with higher English proficiency are able to instruct their children themselves, thereby reducing the need for additional English education expenses, aligning with findings from Huy's research.
In 2012, a significant correlation was observed between a wife's education level and her willingness to invest in her children's English education, with results showing a strong distribution at the 1% significance level.
The analysis reveals a positive correlation between the wife's age and the expenditure on English education, indicating that older wives are more inclined to invest in this form of education, with results significant at the 5% level Additionally, households that own a home tend to allocate more funds towards English education This trend can be attributed to the financial stability that homeownership provides, as it eliminates rental costs and reduces concerns about saving for housing, thereby increasing the available budget for other expenses, such as children's education.
In this study, various factors such as total expenditure, parents' education, employment sector, wife’s employment status, gender of the household head, husband's age, household location, and number of children were found to have no statistically significant impact on the research subject Model 2 specifically examines the correlation between total expenditure and English education spending, revealing that households with expenditures exceeding 10 million have a positive influence on English education expenses at a low significance level of 10% However, when total income and total expenditure are analyzed together, as shown in Table 11 in the appendix, both variables demonstrate a positive effect on household spending for English education at a 5% significance level Other controlled variables remain consistent with Model 1, except for home ownership, which does not significantly affect spending on English education in this model.
CONCLUSIONS, RECOMMENDATIONS AND LIMITATIONS
Conclusions and recommendations
This study examines the significance of English education in Ho Chi Minh City and the factors influencing household decisions regarding English education expenditures for primary school-aged children The findings reveal that English education is increasingly vital, with 84.7% of households investing in English education from the start of their children’s schooling The research identifies key household characteristics, such as the income and education level of the wife, as influential on English education spending, while other factors like the husband’s education level, English proficiency, employment status, age, location, and number of children show no significant impact Notably, the study highlights the critical role of the wife in making educational expenditure decisions, underscoring her importance within the family structure.
The study highlights the crucial role of English education in Ho Chi Minh City and across Vietnam, urging the government to take action It reveals that English education spending is influenced by household income, the mother's education and English proficiency, and home ownership However, there is a significant disparity in spending among different income groups, with only children from households earning over 20 million VND being able to access high-quality English education.
Low-income households often struggle to afford quality education, leading to inequities in children's learning opportunities To address this issue, government intervention is necessary to promote educational equity across different income levels Raising awareness among mothers about the importance of English education can encourage them to prioritize their children's learning Many families recognize the value of English and enroll their children in English classes from a young age Therefore, the government should consider implementing English as a foreign language in primary schools, following the successful bilingual education models seen in several European countries.
Limitations
The study acknowledges several limitations in its data collection and methodology Firstly, due to privacy concerns, households did not disclose their total income and expenditure, providing only approximate figures, which necessitated the use of ranked values and dummy variables for analysis This approach introduces potential bias in understanding income and expenditure Secondly, calculating expenditure on English education is complicated for households with children in international schools, as costs for English and general education cannot be separated Consequently, the research focuses on households in standard schools Thirdly, the study is limited to analyzing English education in Ho Chi Minh City and does not provide a comprehensive overview of the situation across Vietnam, particularly in rural areas, suggesting future research should broaden its scope and incorporate the Government's VHLSS survey Additionally, the study should explore the reverse causality effect of English education expenditure on future income Lastly, the inability to evaluate the income elasticity of English expenditure due to the dependent variable not being a string variable highlights the need for future research to calculate this elasticity for a more in-depth understanding of household spending on English education.
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Table 01 – OLS regression result – Model 01
_cons -1.363547 5194084 -2.63 0.009 -2.386623 -.3404717 Nrofchildren -.0440684 0860152 -0.51 0.609 -.2134919 1253552 ownh 2855928 1319733 2.16 0.031 0256458 5455399 local 031588 1384509 0.23 0.820 -.2411179 304294 agew 0436632 0186145 2.35 0.020 0069983 0803282 ageh -.004596 0168757 -0.27 0.786 -.0378359 0286438 genderhh 0470405 1475008 0.32 0.750 -.2434909 3375719 wifeworking -.2590881 173704 -1.49 0.137 -.6012319 0830556 soew_0 -.2628781 1896199 -1.39 0.167 -.6363711 110615 soeh_0 0964413 1813795 0.53 0.595 -.2608209 4537035 englishw_0 -.3926812 1897022 -2.07 0.040 -.7663365 -.0190259 englishh_0 0091718 19381 0.05 0.962 -.3725746 3909181 eduw 3274595 0780133 4.20 0.000 1737971 4811218 eduh -.0630879 0575058 -1.10 0.274 -.1763568 0501809 cpec 5003437 0665682 7.52 0.000 3692248 6314627
2 -.0009339 1591595 -0.01 0.995 -.3144295 3125616 incomet cpe Coef Std Err t P>|t| [95% Conf Interval]
Root MSE = 94909 R-squared = 0.4592 Prob > F = 0.0000 F( 17, 245) = 15.75Linear regression Number of obs = 263
Table 02 – Multicolinearity test of OLS – Model
Nrofchildren 1.15 0.869824 ownh 1.24 0.807576 local 1.18 0.846103 agew 3.40 0.293784 ageh 3.49 0.286877 genderhh 1.06 0.947707 wifeworking 1.22 0.819173 soew_0 1.64 0.610243 soeh_0 1.59 0.627896 englishw_0 2.37 0.421661 englishh_0 2.26 0.441967 eduw 3.38 0.295742 eduh 2.96 0.337550 cpec 1.19 0.841647
Table 03 – Logit regression result 01 – Model 03 (a)
_cons -3.657033 1.82147 -2.01 0.045 -7.227048 -.0870184 Nrofchildren -.3496952 3001152 -1.17 0.244 -.9379102 2385198 ownh 1.009829 4438265 2.28 0.023 1399455 1.879713 local 3828772 4142548 0.92 0.355 -.4290471 1.194802 agew 1760758 0688444 2.56 0.011 0411433 3110083 ageh -.0614185 0621596 -0.99 0.323 -.1832491 0604121 genderhh -.2093613 5090875 -0.41 0.681 -1.207154 7884318 wifeworking -.2818758 5803707 -0.49 0.627 -1.419381 8556299 soew_0 -.6166213 6564968 -0.94 0.348 -1.903331 6700889 soeh_0 -.3652005 6732687 -0.54 0.588 -1.684783 954382 englishw_0 -.4890562 6118155 -0.80 0.424 -1.688192 7100802 englishh_0 -1.120923 6422311 -1.75 0.081 -2.379673 1378264 eduw 3677952 2614649 1.41 0.160 -.1446667 880257 eduh -.1257198 1681974 -0.75 0.455 -.4553806 2039411 cpec 1.744994 4311084 4.05 0.000 9000376 2.589951
2 -.351869 6083459 -0.58 0.563 -1.544205 840467 incomet dcpe Coef Std Err z P>|z| [95% Conf Interval]
Log pseudolikelihood = -80.705588 Pseudo R2 = 0.2910 Prob > chi2 = 0.0598 Wald chi2(17) = 26.89Logistic regression Number of obs = 263
Table 04 – Marginal effect after Logit regression – Model 03 (b)
The analysis of the marginal effects using the logit model reveals that the change in the dummy variable from 0 to 1 yields specific outcomes for various predictors Notably, the variable "Nrofch" shows a marginal effect of -0.0187 with a p-value of 0.268, indicating no significant impact Conversely, "ownh" presents a marginal effect of 0.0628, approaching significance with a p-value of 0.080 The variable "agew" is significant with a positive marginal effect of 0.0094 (p = 0.026), while "cpec" demonstrates a strong positive effect of 0.0932 with a p-value of 0.000 Other variables such as "dinco~t4" also show significance (0.0625, p = 0.039), whereas "genderhh," "wifewo~g," and "eduh" do not indicate significant effects The overall prediction for the probability of "dcpe" stands at 0.9434, highlighting the model's effectiveness in estimating outcomes.
Table 05 – OLS regression result – Model 02
_cons -1.209754 5903896 -2.05 0.042 -2.372664 -.0468432 Nrofchildren -.0535274 0896321 -0.60 0.551 -.2300789 123024 ownh 2427965 1341195 1.81 0.071 -.0213831 5069762 local 0542165 1396164 0.39 0.698 -.2207907 3292237 agew 0433232 0181667 2.38 0.018 0075395 0791068 ageh -.0051432 0165262 -0.31 0.756 -.0376953 027409 genderhh -.0242883 1474851 -0.16 0.869 -.3147946 2662181 wifeworking -.2760428 1772245 -1.56 0.121 -.625128 0730423 soew_0 -.2352022 1909597 -1.23 0.219 -.6113421 1409377 soeh_0 0817378 185508 0.44 0.660 -.2836637 4471393 englishw_0 -.3558126 1872655 -1.90 0.059 -.7246759 0130506 englishh_0 0488498 1904441 0.26 0.798 -.3262744 4239741 eduw 3086596 0809731 3.81 0.000 1491642 468155 eduh -.0539666 0577517 -0.93 0.351 -.167722 0597888 cpec 4552241 073478 6.20 0.000 3104921 5999561 dtotalexpt5 6874581 3585757 1.92 0.056 -.0188406 1.393757 dtotalexpt4 2198905 3040206 0.72 0.470 -.3789491 8187302 dtotalexpt3 0035058 2393392 0.01 0.988 -.4679287 4749402 dtotalexpt2 -.0409011 251212 -0.16 0.871 -.535722 4539198 cpe Coef Std Err t P>|t| [95% Conf Interval]
Root MSE = 95694 R-squared = 0.4525 Prob > F = 0.0000 F( 18, 244) = 18.02Linear regression Number of obs = 263
Table 06 – Multicolinearity test of OLS – Model 02
Mean VIF 4.81 genderhh 1.08 0.928898 Nrofchildren 1.19 0.840553 local 1.21 0.826600 wifeworking 1.24 0.808125 ownh 1.25 0.800912 cpec 1.29 0.775292 soeh_0 1.59 0.630849 soew_0 1.67 0.597484 englishh_0 2.26 0.443037 englishw_0 2.34 0.426472 eduh 2.85 0.351058 agew 3.42 0.292610 ageh 3.49 0.286735 eduw 3.52 0.283793 dtotalexpt2 7.02 0.142376 dtotalexpt5 12.43 0.080422 dtotalexpt4 19.33 0.051727 dtotalexpt3 19.45 0.051414 Variable VIF 1/VIF vif
Table 07 – Logit regression result – Model 04 (a)
_cons -6.003235 2.556035 -2.35 0.019 -11.01297 -.9934975 Nrofchildren -.1674733 3214882 -0.52 0.602 -.7975786 462632 ownh 736127 4496512 1.64 0.102 -.1451731 1.617427 local 39491 4160752 0.95 0.343 -.4205823 1.210402 agew 1924707 0741705 2.59 0.009 0470992 3378422 ageh -.0646416 065009 -0.99 0.320 -.1920569 0627737 genderhh -.4665157 5253627 -0.89 0.375 -1.496208 5631762 wifeworking -.3144039 6618085 -0.48 0.635 -1.611525 9827169 soew_0 -.5504442 6030762 -0.91 0.361 -1.732452 6315634 soeh_0 -.2999803 6442077 -0.47 0.641 -1.562604 9626436 englishw_0 -.2666764 6403972 -0.42 0.677 -1.521832 988479 englishh_0 -.9710477 6419314 -1.51 0.130 -2.22921 2871147 eduw 1883084 2600612 0.72 0.469 -.3214021 698019 eduh -.0994752 1741595 -0.57 0.568 -.4408215 2418711 cpec 1.64758 4723387 3.49 0.000 7218131 2.573347 dtotalexpt5 3.888525 1.759674 2.21 0.027 4396263 7.337423 dtotalexpt4 2.419262 1.435419 1.69 0.092 -.3941084 5.232632 dtotalexpt3 2.263512 1.261546 1.79 0.073 -.2090731 4.736098 dtotalexpt2 2.143534 1.294358 1.66 0.098 -.3933617 4.680429 dcpe Coef Std Err z P>|z| [95% Conf Interval]
Log pseudolikelihood = -81.443602 Pseudo R2 = 0.2845 Prob > chi2 = 0.0023 Wald chi2(18) = 39.67Logistic regression Number of obs = 263
Table 08 – Marginal effects after Logit regression – Model 04 (b)
The analysis of marginal effects using the logit model reveals significant insights into the relationship between various independent variables and the probability of discrete changes in the dummy variable Notably, the variable "cpec" exhibits a strong positive effect with a coefficient of 0.094669, indicating a statistically significant relationship (p < 0.001) Other variables such as "dtota~t5" and "dtota~t2" also demonstrate positive effects with coefficients of 0.104329 and 0.063770, respectively, both significant at the 0.025 and 0.037 levels Conversely, variables like "genderhh" and "wifewo~g" show negative coefficients, but they are not statistically significant The analysis highlights the importance of age, education, and household characteristics in influencing the probability of the outcome variable, providing a comprehensive understanding of the factors at play.
Table 09 – Statistic summary of variable cpe/cpbe, cpe/income, cpe/totalexp cpetotalexp 267 06704 0515155 0 4705882 cpeincome 267 0470075 0435199 0 3 cpecpbe 267 6139513 7110703 0 4 Variable Obs Mean Std Dev Min Max sum cpecpbe cpeincome cpetotalexp
Table 10 – The description of cpe and cpec Total 268 100.00 over 1,000,000 12 4.48 100.00 500,000 - 1,000,000 23 8.58 95.52 300,000 - 500,000 71 26.49 86.94 below 300,000 120 44.78 60.45
0 42 15.67 15.67 extra class Freq Percent Cum.
Current payment for tab cpec
Current payment for tab cpe
Table 11 – The relationship between total income and total expenditure to cpe
_cons 25 5538639 0.45 0.652 -.8406895 1.340689 dtotalexp6 1.727824 7812856 2.21 0.028 1892874 3.266361 dtotalexp5 1.950373 6677354 2.92 0.004 6354436 3.265303 dtotalexp4 1.484905 6184192 2.40 0.017 267091 2.70272 dtotalexp3 1.022503 5846873 1.75 0.082 -.1288853 2.173891 dtotalexp2 6666667 5982413 1.11 0.266 -.5114126 1.844746 dincome5 1.104559 4581295 2.41 0.017 2023931 2.006725 dincome4 2338345 363168 0.64 0.520 -.4813296 9489985 dincome3 1816083 3129473 0.58 0.562 -.4346592 7978759 dincome2 -.0075167 2249593 -0.03 0.973 -.450515 4354815 cpe Coef Std Err t P>|t| [95% Conf Interval]
Total 411.198502 266 1.54585903 Root MSE = 1.1077 Adj R-squared = 0.2062 Residual 315.354599 257 1.2270607 R-squared = 0.2331 Model 95.8439027 9 10.6493225 Prob > F = 0.0000 F( 9, 257) = 8.68 Source SS df MS Number of obs = 267
The correlation matrix reveals significant relationships among various explanatory variables, highlighting the interconnectedness of income and educational factors Notably, dincome2 shows a strong negative correlation with dincome3 (-0.42), while dtotalexp3 exhibits a positive correlation with dincome5 (0.31) The educational variables, eduh and eduw, demonstrate moderate positive correlations with several income categories, suggesting a link between education and income levels Additionally, the variables related to household demographics, such as genderhh and ageh, show minimal correlations with income, indicating other factors may influence these relationships Overall, this analysis underscores the complexity of socioeconomic factors affecting income and education.
The analysis reveals various correlations among factors such as the number of children (Nrofchildren), income levels (dincome2 to dincome5), and total expenses (dtotalexp2 to dtotalexp6) Notably, the presence of children shows a negative correlation with several variables, indicating potential financial impacts The data also highlights relationships between educational attainment (eduh, eduw) and language proficiency (englishh_0, englishw_0), with significant connections observed among household dynamics, including the roles of spouses (soeh, soew) and employment status (wifeworking) Age factors (ageh, agew) also demonstrate relevant associations, suggesting demographic influences on these variables Understanding these correlations can provide insights into family financial planning and resource allocation.
Table 14 - The results of the models
Model 1 Model 2 Model 3 Model 4 Model 5
OLS 1 OLS 2 Logit Ordered Logit Tobit dincome2 -0.1141 -0.0149 -0.8824 -0.3152 -0.1141 dincome3 -0.1302 0.1100 -1.1158 -0.2819 -0.1302 dincome4 0.0631 0.2830 0.7016 0.2856 0.0631 dincome5 1.1466 *** 1.2753 *** (omitted) 2.5108 *** 1.1466 *** dtotalexp2 -0.0173 1.9596 0.7523 -0.0173 dtotalexp3 0.0754 2.6390 1.1614 0.0754 dtotalexp4 0.2648 2.9232 * 1.5204 0.2648 dtotalexp5 0.3637 3.5090 1.5906 0.3637 dtotalexp6 -0.0792 (omitted) 0.2832 -0.0792 cpec 0.4903 *** 0.5083 *** 1.5987 *** 1.0854 *** 0.4903 *** eduh -0.0455 -0.0457 -0.1096 -0.1371 -0.0455 eduw 0.2839 *** 0.2909 *** 0.2883 0.6514 *** 0.2839 *** englishh_0 0.1231 0.0964 -0.9516 0.1391 0.1231 englishw_0 -0.4324 ** -0.4309 ** -0.5446 -0.9070 ** -0.4324 ** soeh_0 0.0452 0.0410 -0.2945 0.1520 0.0452 soew_0 -0.1284 -0.1321 -0.6157 -0.4450 -0.1284 wifeworking -0.2180 -0.2419 -0.2230 -0.4132 -0.2180 genderhh -0.0295 0.0203 -0.4308 0.0124 -0.0295 ageh -0.0034 -0.0019 -0.0545 -0.0308 -0.0034 agew 0.0428 ** 0.0410 ** 0.1826 *** 0.1140 *** 0.0428 ** local 0.0098 -0.0118 0.4860 0.1003 0.0098 ownh 0.2786 ** 0.2777 ** 0.8505 * 0.5253 * 0.2786 **
( * ): Statistic significance at 10% level, ( ** ): Statistic significance at 5% level, ( *** ): Statistic significance at 1 % level
Table 15 – Correlation between household’s total income and total expenditure and current payment for English education cpe 0.4299 0.4521 1.0000 totalexp 0.8064 1.0000 income 1.0000 income totalexp cpe
(obs&7) cor income totalexp cpe
Table 16 – OLS regression result of all explanatory variables
The analysis reveals several key findings regarding various factors and their statistical significance The variable "ownh" shows a positive correlation with a coefficient of 0.2786, significant at p=0.042, indicating that ownership may influence outcomes Age-related variables, specifically "agew," are positively significant (p=0.023), while "ageh" shows no significant effect Gender and employment status of the household head do not exhibit significant impacts, as seen with "genderhh" (p=0.842) and "wifeworking" (p=0.191) English proficiency ("englishw_0") is negatively significant (p=0.021), suggesting a potential barrier in communication Education levels, particularly "eduw," are positively significant (p=0.000), highlighting the importance of education The variable "cpec" also shows a strong positive correlation (p=0.000), reinforcing its relevance Income variables present mixed results, with "dincome5" being significantly positive (p=0.008), while other income categories do not show significant correlations Overall, these results underscore the complexity of the relationships between these factors and their impact on the studied outcomes.
Total 408.114068 262 1.55768728 Root MSE = 93625 Adj R-squared = 0.4373 Residual 209.49897 239 876564727 R-squared = 0.4867 Model 198.615099 23 8.63543908 Prob > F = 0.0000 F( 23, 239) = 9.85 Source SS df MS Number of obs = 263
Table 17 - The results of the models (OLS, Logit, Tobit)
Current payment for English education
Coefficient Coefficient Marginal effect (dy/dx)
Over 30 millions VND 1.275 *** (omitted) (omitted) 6.654 *** 6.654 ***
Current payment for extra class
Dummy English var of husband 0.096 -1.078 -0.080 -0.729 * -0.729 *
Dummy English var of wife -0.431 ** -0.486 -0.034 -0.242 -0.242
Dummy Public sector of husband
Dummy Public sector of wife
( * ): Statistic significance at 10% level, ( ** ): Statistic significance at 5% level, ( *** ): Statistic significance at 1 % level
Table 18 - The results of the models (OLS, Logit, Tobit)
Current payment for English education
Coefficient Coefficient Marginal effect (dy/dx)
Coefficient Marginal effect (dy/dx)
Current payment for extra class
(0.00) *** (0.46) (0.47) (0.69) (0.69) Dummy English var of husband
Dummy English var of wife -0.364 -0.307 -0.021 -0.165 -0.165
(0.06) * (0.66) (0.68) (0.71) (0.71) Dummy Public sector of husband
-0.023 -0.300 -0.020 -0.055 -0.055 (0.54) (0.62) (0.63) (0.47) (0.47) Dummy Public sector of wife
( * ): Statistic significance at 10% level, ( ** ): Statistic significance at 5% level, ( *** ): Statistic significance at 1 % level
M Please tell us the full name of your family's members, start from the household's lead
Gender Relationship to the household's head Year of
How long has [name] been in HCMc?
(Write the name of district)
B The family's member is the one who live and eat together with your family at least 6/12 months and shares the expenses
E Mark * on the right of household's head name
Grandchildrens…… 6 Divorced…… 4 Nr Of Nr Of
Others……… 7 year Legal separation 5 Years Months
Please let us know the education information of your household's members
The highest education level of [name] attained
What is the type of the school?
Certificate / Grade Self-assessment the
E No certificate 0 Public school… 1 [name] Does the [name] take part in any English class within last 12 months?
M Primary 1 Charter school…2 attain (if any)
B Junior high school 2 Private school… 3 Don't know………… 0
E Senior high school 3 International school………… 4 Pre-intermediate (basic communication)…… 1
O Vocational colleges 7 Advance (fluent in 4 skills (Listen, speak, read, write………… 3
PhD……… 11 At school At English center Other (specific )… 12
[name]… Expense for education of …[name] per month (1000 VND)
Expenditure for food Expenditure for Health's care a
Extra English class at school c
Extra learning English at English center d
Table 20 – Price of English class of beginner and pre-intermediate of English centers and school in Ho Chi Minh City
Class name Price Divided Rank of CPE
ILA Jumpstart (below 6 years old) 2,752,000
VND/month Beginnner and Pre-intermediate level (from 6-11 years old)
For preschool children (below 6 years old)
Superkids For primary school children (from 6-11 years old)
Apollo Kindy (below 6 years old) 1,204,000
VND/month HCM University of
VND/month English at primary school