INTRODUCTION
Problem statement
The development of a child from early childhood to adolescence is a significant concern for both parents and society, as recent interdisciplinary studies indicate that cognitive and non-cognitive skills play a crucial role in determining future life outcomes Research shows that early childhood development influences various aspects such as education, health, labor market success, and social engagement (Francesconi & Heckman, 2016) For instance, Heckman et al (2006) demonstrated that cognitive and non-cognitive skills impact schooling choices, wages, and work experience in the US Similarly, Lindqvist and Vestman (2011) highlighted the importance of these abilities in predicting labor force participation and wages among both skilled and unskilled workers in Sweden.
Cognitive skills reflect a child's overall intelligence, while non-cognitive skills relate to their personality traits (Borghans et al., 2008) Both types of skills are influenced by genetic factors and environmental conditions While genetic endowment is essential and fixed, the expression of these genes can be affected by various environmental influences Among these, family environment factors—such as parental characteristics, parenting styles, socioeconomic status, and parental investment—play a crucial role in a child's development.
Understanding the optimal timing for parental investment in a child's skill development is crucial for effective policy-making Skill development is a dynamic process where cognitive and non-cognitive skills can enhance each other through mechanisms like self-productivity and cross-productivity, meaning that a higher skill level in one period can lead to increased skills in the future Additionally, recognizing critical and sensitive periods for skill acquisition is essential, as investments at various life stages yield different outcomes Sensitive periods are times when parental input is particularly effective, while critical periods are when investment must occur to be productive This knowledge is vital for policymakers aiming to subsidize investments for disadvantaged children, ensuring that support is provided at the most impactful times.
Evidence from developed countries highlights the timing of parental investment and its impact on cognitive and non-cognitive skill development across various life stages However, understanding this topic in emerging economies is crucial, as human development plays a vital role in driving economic growth.
Vietnam's economic modernization necessitates a skilled workforce, as highlighted by the World Bank's STEP survey, which reveals a significant rise in jobs requiring analytical and interpersonal skills since 1998 With the country's transition to nonagricultural sectors, there is an urgent need to focus on equipping the workforce with vital cognitive and non-cognitive skills.
This research highlights two key issues regarding skill development in Vietnam Firstly, the education system has historically prioritized cognitive skills over soft skills, such as self-esteem and self-control, despite the need for balanced attention to both Secondly, many parents, particularly those in disadvantaged situations, often overlook the significance of early childhood development due to a lack of awareness or insufficient support from government and community resources To address these concerns, it is essential to implement policies that raise parental awareness and provide necessary support from the early stages of child-rearing Moreover, recognizing critical periods for skill development may inform the timing and effectiveness of these investments.
Research objectives
Given the problem stated above, this research has three main objectives:
This study explores how parental investment influences the development of both cognitive and non-cognitive skills throughout various stages of childhood, while also considering additional factors such as household and community characteristics.
- Secondly, to find evidence for the presence of the reinforcement mechanism called self-productivity and cross-productivity.
Identifying critical and sensitive periods for investing in cognitive and non-cognitive skill development is essential for effective policymaking These findings can inform the design of targeted interventions and remediation programs specifically aimed at supporting disadvantaged children.
Scope of the study
To achieve the research objectives, the study investigates 2,000 children from
The Young Lives project tracked children born in 2001-2002 from five provinces in Vietnam—Lao Cai, Hung Yen, Da Nang, Phu Yen, and Ben Tre—over a span of 12 years Data was collected through four survey rounds conducted in 2002-2003, 2006-2007, 2009-2010, and 2013-2014, providing valuable insights into their development and well-being.
Structure of the thesis
The thesis comprises five chapters, starting with Chapter 1, which introduces the study by outlining the problem statement, research objectives, and scope Chapter 2 delves into a review of theories and existing empirical evidence related to skills formation In Chapter 3, the discussion focuses on the research methodology and the data utilized in the study.
4 provides the summary statistics, bivariate analysis and empirical results In sum, the conclusion along with policy implications and limitation of the research are presented in Chapter 5.
LITERATURE REVIEW
Human capital theory: skills matter
Human capital refers to the knowledge and characteristics that enhance a worker's productivity, encompassing both innate and acquired traits (Acemoglu, 1999) It can be classified through various perspectives, notably the Becker and Gardner views While both perspectives recognize the value of human capital in the job market and its impact on life outcomes, they differ in their conceptualization The Becker view treats human capital as a unidimensional entity, whereas the Gardner view, influenced by Howard Gardner's theory of multiple intelligences, advocates for a multi-dimensional understanding of human capital.
In 1983, Howard Gardner expanded the concept of intelligence beyond traditional linguistic and mathematical abilities by introducing seven additional intelligences, including interpersonal and intrapersonal skills This study aligns with Gardner's perspective by exploring both cognitive and non-cognitive skills as essential components of human capital.
In terms of the formation of human capital, genetic factors and investments can determine the differences in the level of human capital acquired by individuals.
Investments play a crucial role in enhancing human capital acquisition, while genetic factors contribute to the variability in human capital despite similar investment levels Extensive literature, including Becker (1994), highlights education and training as primary investments in human capital However, in less developed communities where formal education may be limited, non-schooling investments often surpass the magnitude of schooling investments, as noted by Behrman (1987).
Cognitive skills and non-cognitive skills: measures and impacts
Cognitive ability encompasses various dimensions of knowing and awareness, including perceiving, reasoning, and problem-solving Defined by the American Psychological Association, cognitive skills comprise language, memory, motor, and thinking skills Often equated with intelligence, cognitive ability is categorized into fluid intelligence, which pertains to the speed of learning, and crystallized intelligence, which reflects acquired knowledge Intelligence is typically assessed through standardized tests such as the Peabody Picture Vocabulary Test, Raven’s Progressive Matrices, Wechsler tests, and the Stanford-Binet test, with IQ (Intelligence Quotient) representing the scores from these assessments.
Non-cognitive skills, such as self-control, self-esteem, and persistence, are personal attributes that intelligence tests cannot measure These skills are often referred to by various terms, including personality traits, soft skills, character skills, and socio-emotional skills It's important to understand that non-cognitive skills are interconnected with cognitive skills, as many non-cognitive attributes can result from cognitive processes and vice versa (Borghans, 2008) To assess non-cognitive skills, two main measurement approaches are utilized: self-report questionnaires and conventional economic preference parameters, which include time preference, risk aversion, and leisure preferences Psychologists frequently employ self-report questionnaires to develop latent variables through factor analysis.
Cognitive and non-cognitive skills play a crucial role in predicting success in academics and careers, with earlier research primarily focusing on cognitive skills as the key determinant of school performance and wages For instance, Riding and Agrell (1997) demonstrated that cognitive skills significantly influenced the academic achievements of Canadian high school students Similarly, Murnane, Willett, and Levy (1995) highlighted the increasing importance of cognitive skills in determining wage levels among American high school seniors during the 1980s Furthermore, Ree, Earles, and Teachout (1994) identified cognitive ability as the strongest predictor of job performance within the US Army However, it wasn't until the 2000s that studies began to explore the impact of both cognitive and non-cognitive skills on academic performance and earnings, marking a shift in understanding the full spectrum of skills that contribute to success.
J J Heckman et al (2006) They found evidence in the US that both cognitive and non-cognitive skills affect schooling and work experience In fact, non-cognitive skills can raise wages thanks to their direct effect on productivity Similar evidence is found in other developed countries such as Germany (Heineck & Anger, 2010), Sweden (Lindqvist and Vestman (2011), Lundborg, Nystedt, and Rooth (2014)), the
UK (Carneiro, Crawford, & Goodman, 2007), Finland (Viinikainen, Kokko, Pulkkinen, & Pehkonen, 2010) Indeed, there are some evidence that non-cognitive skills outperform cognitive skills in predicting academic success (Duckworth & Seligman, 2005).
Cognitive and non-cognitive skills significantly influence various aspects beyond education and income Research by Hanushek and Woessmann (2008) demonstrated that cognitive skills strongly impact economic growth, utilizing robust data from the International Association for the Evaluation of Educational Achievement (IEA) and OECD countries Additionally, non-cognitive skills are crucial predictors of not only academic and career success but also overall life well-being, affecting areas such as health behaviors, marital stability, and criminal activity.
A study by Moffitt et al (2011) provides solid evidence on life-wellbeing The study is extraordinary since it followed cohort of 1000 children in Dunedin, NewZealand from birth to age
32 The results showed that children with low self-control are more likely to have health problems (27% vs 11%), commit crime (43% vs 13%) and be single-parent(58% vs.28%).
Determinants of skill development
Skill development is influenced by a variety of factors, which can be categorized into genetic influences and environmental inputs, including those from home, school, and community A model illustrates how parents play a crucial role in shaping these factors in their children, highlighting the genetic connections between parents and offspring.
A child's characteristics (PC) are shaped by the genetic traits inherited from their parents, while parental characteristics (PP) also influence children by contributing to the environmental factors (EC) in which they grow up.
Figure 1 : A model of parent-child genetic and environment effects
Genetic endowment plays a significant role in determining abilities, with studies indicating that 40% to 70% of the variations in cognitive abilities and personality traits can be attributed to genetic factors (Scarr, 1992) This is supported by evidence from adoption and twin studies, particularly the Minnesota adoption studies conducted by Scarr and Weinberg (1983), which demonstrated that genetic differences account for notable variations among individuals after controlling for selective placement.
Genetic factors are believed to account for 40% to 70% of the variance in IQ, while a study involving over 12,000 pairs of Swedish twins indicates that genetics can explain approximately 50% of the variance in personality traits.
The environment plays a vital role in shaping abilities, as various environmental conditions can influence the expression of genes This means that environmental factors can sometimes have a greater impact on an individual's traits than genetic inheritance alone.
Parents are crucial in developing a child's skills, particularly in their early years Parental investments encompass both financial and non-financial contributions, including expenditures on education, health, and clothing Family income often serves as a proxy for these investments, with research indicating that families with higher permanent income levels typically invest more in their children's development, resulting in children who possess greater skills.
Research indicates that children from families with varying income levels exhibit differences in cognitive and non-cognitive skills from an early age (Heckman & Carneiro, 2003; Cunha et al., 2006) However, using family permanent income as a measure of parental investment presents challenges, as income is closely linked to factors like parental education and skills This correlation necessitates careful interpretation of how permanent income influences skill development Additionally, parental investments extend beyond financial contributions to include significant time and effort, which have been recognized as crucial for child development in earlier studies (Hill & Stafford).
Despite limited empirical evidence on parental time investments due to data constraints, recent studies, such as those by Carneiro and Rodrigues (2009) and Hsin and Felfe (2014), highlight a positive correlation between parental time and child cognitive skills Notably, Bono et al (2016) found that increased time spent by mothers with their children, aged 3 to 7, significantly enhances both cognitive and non-cognitive skills Therefore, when assessing parental investment, it is essential to consider both time and financial contributions.
Besides parental investment, there are several other home factors which affect child development such as parental education, household size and number of child’s siblings.
As children mature, parental influence diminishes, allowing them greater freedom to choose their environments, with schools and neighborhoods significantly impacting their well-being, particularly during adolescence Sociological studies indicate various mechanisms by which neighborhoods influence child development, with mixed predictions regarding the effects of socioeconomic status on development (Mayer & Jencks, 1989) Research by Hertzman (2004) in Vancouver highlighted a strong correlation between children's development and the socioeconomic status of their neighborhoods While the neighborhood effects on child outcomes are often modest, it is crucial to control for these factors Furthermore, the longitudinal nature of the Young Lives survey may provide insights into whether neighborhood effects persist for both young children and adolescents, as suggested by Brook et al (1993).
Dynamics of skill development
Life cycle skill formation is a dynamic process, as highlighted by J Heckman and Carneiro (2003) This process involves self and cross productivity, meaning that skills developed at one stage can influence the acquisition of the same or different skills in later stages of life.
Early literature often overlooked non-cognitive abilities, focusing solely on cognitive skills as essential to human capital accumulation However, recent psychological research has prompted economists to acknowledge the significance of non-cognitive abilities in this process, leading to their integration into economic models (Borghans, 2008; Almlund, Duckworth, Heckman, and Kautz).
(2011) for a profound review of the impact of non-cognitive ability) Heckman and his co-authors proposed a model of technology which capture the interaction between cognitive and non- cognitive skills.
Empirical evidence supports self-productivity of skills through various studies, including Cunha and Heckman (2008) with the US Longitudinal dataset, Helmers and Patnam (2011) utilizing the Young Lives India Survey, and Coneus, Laucht, and Reuò (2012) examining the Germany Manheim study of children In terms of cross-productivity, Cunha and Heckman (2008) discovered that cognitive skills can be enhanced by the accumulation of non-cognitive skills from prior periods, although the reverse effect was not observed Conversely, Helmers and Patnam (2011) provided evidence for the cross-productivity of cognitive skills influencing non-cognitive skills, but found no support for the opposite relationship.
Figure 2: A framework of skill development
Family and external influences are crucial in developing skills, particularly in childhood, as they contribute significantly to the learning process The development of skills is dependent on the integration of these influences and the reinforcement of abilities over time A comprehensive framework for skill formation is depicted in Figure 2.
2.4.1 Critical and sensitive periods: timing of parental investment
Child development encompasses distinct phases, particularly early and late childhood, as highlighted by Carneiro, Cunha, and Heckman (2003) They emphasize that the influence of family and environmental factors varies across these stages Heckman further elaborates on this by introducing the concepts of critical and sensitive periods, underscoring the varying impact of parental investments during different developmental stages.
A sensitive period refers to a timeframe when parental investments yield greater benefits compared to other times, while a critical period is defined as the sole timeframe during which such investments are effective According to Phillips and Shonkoff (2000), various life cycle stages are crucial for developing different types of abilities.
Research indicates that early life stages are crucial for cognitive development, with Hopkins and Bracht (1975) identifying the period before age 10 as particularly sensitive for this growth Recent findings from Coneus et al further reinforce the significance of this early developmental window.
A study conducted in 2012 utilizing the Mannheim Study of Children in Germany highlighted that early childhood is a crucial phase for the development of cognitive skills, particularly emphasizing that the first four years of a child's life represent a critical period for cognitive development compared to later childhood.
Empirical research indicates that the sensitive periods for developing non-cognitive skills occur later than those for cognitive skills, as highlighted by studies from Cunha and Heckman (2007) and Borghans (2008) Specifically, Carneiro et al further emphasize this distinction in the timing of skill development.
Research indicates that noncognitive skills, which are more adaptable in later stages of development, can be significantly influenced by interventions during adolescence In contrast, cognitive skills tend to stabilize by the age of eight, suggesting a critical window for fostering noncognitive abilities.
Researchers recommend that intervention programs for disadvantaged children should be implemented during early childhood If interventions occur during adolescence or adulthood, the focus should shift towards enhancing noncognitive skills rather than cognitive skills.
Analytical framework
The analytical framework illustrated in Figure 3, based on the skill development framework from the previous chapter, enables an investigation into the impact of investments on skills, as well as the cross-productivity and self-productivity effects, and identifies critical and sensitive periods for skill formation Chapter 2 provides empirical evidence supporting the relationships shown in the diagram, drawing on research by Cunha and Heckman.
(2008), Coneus et al (2012), Helmers and Patnam (2011) and Hernández-Alava and Popli (2017) provide evidence for self-productivity, cross productivity and the impacts of parental investments on
Child characteristics Household characteristics Community characteristics
Investment Cognitive Non-cognitive Period t
Self-productivity and cross-productivity are critical in understanding skill development, particularly when considering the differences between developed and developing countries Research by Scarr and Weinberg (1983) and Floderus-Myrhed et al (1980) highlights the significant impact of genetic factors on a child's IQ and personality Additionally, parental investment and various household factors—such as size, economic status, and social networks—play a vital role in skill development, as noted by Duncan and Brooks‐Gunn (2000), Pelto et al (1991), and Fafchamps and Gubert (2007) Furthermore, community characteristics, including neighborhood socioeconomic status and social issues, are essential for analyzing skill formation, supported by findings from Mayer & Jencks (1989) and Brooks-Gunn et al (1993).
RESEARCH METHODOLOGY
Methodology
Cunha and Heckman (2007, 2008) present a model that explores the dynamics of skill formation, highlighting that a child's current skill level is influenced by their past skills, parental investment, and various contemporaneous factors, including caregiver, household, and community characteristics Given the 3-4 year intervals in the sample, this thesis posits that parental investments have an immediate effect on skill development, consistent with the findings of Cunha et al (2006).
� � denotes a child’s level of skill k for age t, � 𝑡 denotes parental investment at age t (� ∈ {1, … 𝑇}), and 𝑋 𝑡 denotes a vector of child, caregiver, household andcommunity characteristics.
In accordance to Cunha and Heckman (2007), the function in (1) is specified as a linear specification as below:
This model facilitates the analysis of the interplay between cognitive and non-cognitive skills, as well as the relationship between current and past skill levels, effectively exploring both self-productivity and cross-productivity.
In terms of critical and sensitive periods, I follow the mathematical definition
� of Cunha et al (2006) Period t is defined as a critical period for skill � 𝑡 if
Period t is a sensitive period for � � if
Equation (3) indicates that time period t is essential for the development of skill k, as parental investment is effective only during this specific period and not in any other In contrast, equation (4) highlights that period t is a sensitive phase compared to period t+j, as parental investment yields greater productivity in period t at the same level of skill and inputs than in any other period.
Empirically, to identify critical periods, we look at the periods when coefficients for investments are significant different from zero To identify sensitive periods, we
— � � Bootstrap is used for the difference to check the hypothesis if
— � � > 0 t is a sensitive period if the hypothesis cannot be
Due to the lack of data on cognitive and non-cognitive skills at age 1, child health is assessed in relation to psychosocial risk factors (PRF) in round 1 PRF includes parenting factors, such as caregiver sensitivity, and contextual factors like maternal depression and exposure to violence, as outlined by Walker et al (2007) High levels of PRF are anticipated to negatively impact child health, contributing to stunted growth Subsequently, child health is considered an independent variable influencing skill development by age 5.
� non-cognitive skills are not available for child at age 5, only cognitive skills are measured The following set of linear equations is proposed for empirical estimation:
The equations presented are estimated using Ordinary Least Squares (OLS) and Full Maximum Information Likelihood (FMIL), based on the assumption that latent cognitive and non-cognitive skills are independent of the error term for the specified range.
� �(�𝑜 𝑡 , � 𝑡+1 ) = 0 Secondly, the errors are serially � � uncorrelated, i.e
� �(�𝑜 𝑡 , � 𝑡+1 ) = 0 Thirdly, � �(�𝑜 𝑡 , � 𝑡 ) = 0 and � �(�𝑜 𝑡 , � 𝑡+1 ) 3.1.2 Genetic endowment – Initial conditions0.
As previously mentioned in section 2.2.1, genetic endowment is a vital determinant of skill development The genetic endowment is reflected through an
This study examines the initial skill set of children, represented as C0 = (o, 0), acknowledging that this genetic endowment is both unobserved and varies among individuals Rather than employing the first differences approach recommended by Todd and Wolpin (2007) to account for these unobserved genetic traits, we adopt the methodology of Helmers and Patnam (2011) by incorporating control variables such as child height, weight, maternal health during pregnancy, and the location of childbirth Notably, height is included as a control variable due to research indicating that genetic factors may account for up to 90% of the variation in height (Weedon et al., 2007).
3.1.3 Method in cognitive, non-cognitive skills measurement: Latent variables
Cognitive and non-cognitive skills can be assessed through multiple measures, leading to the use of latent variables instead of relying on a single metric Latent variables are inferred from observable indicators, and their estimation is achieved through confirmatory factor analysis To estimate these latent variables, a one-factor model is utilized.
The variable \( Z_i \) represents various observed indicators of a latent variable, while \( \alpha_t \) and \( \lambda_{t} \) signify the measure-specific intercept and factor loading, respectively Additionally, \( \eta \) and \( \epsilon \) are unobserved components The model is based on key assumptions, including the mutual independence of error terms \( \epsilon \), which are independently distributed over time and among children.
(2) the factor � and error term 𝜀 are uncorrelated and have an expected value of zero, (3) the factor � and observed variables � are assumed to be linearly related.
Endogeneity due to measurement error can be reduced through the estimation of latent variables; however, simultaneity between parental investment and a child's skill level can still create challenges Parental investment and skill development are often codetermined, where increased parental investment can enhance a child's skills, while a child's higher skill level may influence parents to adjust their investment To address potential simultaneity bias, an instrumental variable (IV) strategy is utilized.
Informed by Helmers and Patnam (2011), I utilize household-specific shocks and the order of childbirth as instrumental variables The rationale behind these instruments' validity lies in the fact that household shocks and birth order influence skill levels solely through parental investment.
Household-specific shocks significantly influence parental investment in children by affecting household wealth Research by Carneiro and Ginja (2016) utilizing the US Longitudinal Survey indicates that income shocks lead to changes in parental investment, with both time and material resources linked to child development outcomes Additionally, Del Boca, Flinn, and Wiswall (2016) highlight that parental labor market shocks can also impact the time parents invest in their children, further supporting the notion that household shocks credibly affect parental investment strategies.
Parental investment in children is significantly influenced by birth order, impacting child development indirectly Research by Lehmann, Nuevo-Chiquero, and Vidal-Fernandez (2016) indicates that parents modify their investment strategies based on whether a child is the firstborn or a later-born sibling This variation in parental investment serves as a plausible explanation for the differences observed in cognitive achievements among children of different birth orders.
Data
The panel data analyzed in this study is sourced from the Young Lives project in Vietnam, encompassing four survey rounds conducted in 2002, 2006, 2009, and 2013 This longitudinal survey tracks two cohorts of children, with the older cohort consisting of 1,000 individuals born in 1994.
1995 and the younger cohort comprises 2,000 children who were born in 2001-
2002 Young Lives surveys were conducted in five provinces (Lao Cai, Hung Yen,
Da Nang, Phu Yen, Ben Tre) which represent five out of nine socio-economic regions of Vietnam
The article highlights five key regions in Vietnam: the North-East Region, Red River Delta, Cities, South Central Coast, and Mekong River Delta, with a detailed summary of their characteristics provided in Table 14 of the appendix Each of these provinces features four sentinel sites, selected to include two from poorer households, one from an average household, and one from an above-average household This targeted selection process results in the Young Lives survey oversampling poorer households compared to other national surveys like the Demographic and Health Survey (DHS) and the Vietnam Household Living Standards Survey (VHLSS) Nonetheless, the Young Lives dataset remains child-focused and strives to reflect the diversity of children across the country.
In a study conducted in 2014, researchers selected 50 children born in 1994-95 and 100 children born in 2001-02 from each sentinel site using a random sampling technique The children are monitored throughout all survey rounds, with attrition rates of 3.6% for the younger cohort and 11.3% for the older cohort This research focuses exclusively on the younger cohort, and the survey questionnaire designed for this group will be discussed in detail below.
The survey consists of three main components: a core child questionnaire, a household questionnaire, and a community questionnaire The child questionnaire is tailored for different developmental stages and includes sections on education, work, health, emotional well-being, social skills, and cognitive assessments, applicable to children aged 8 and older, while caregivers respond for younger children The household questionnaire focuses on family dynamics, including parental background, education, economic status, food consumption, and child health, with additional details on pregnancy and breastfeeding for infants Lastly, the community questionnaire gathers insights from local representatives about economic conditions and factors influencing child well-being in the community.
Table 1 outlines the variables used in the study, including cognitive skills, non-cognitive skills, and child health as the dependent variables, which are assessed through confirmatory factor analysis (CFA) CFA is advantageous for estimating a one-factor model and validating its effectiveness The main independent variables consist of expenditures, the quality of the caregiver-child relationship, and parental responsiveness (PRF) for round 1 exclusively Additionally, control variables encompass various characteristics of children, caregivers, households, and neighborhoods.
Cognitive abilities are assessed through validated cognitive tests, including the Peabody Picture Vocabulary Test (PPVT), Cognitive Developmental Assessment (CDA), Early Grade Reading Assessment (EGRA), Mathematics test, and Reading comprehension test The PPVT specifically evaluates vocabulary acquisition in individuals aged 2.5 years to adulthood, ensuring a comprehensive understanding of cognitive development.
1 CDA is a cognitive test developed specifically to assess cognitive achievement of pre-primary children CDA has several subtests but only quantity subtest is used in
YL 2 EGRA is designed to measure the literacy acquisition of children in their early grades 3 The math test includes two sections which aim to measure basic quantitative and number
1 PPVT test is un-timed and orally administered The examinee selects the picture that best represents the meaning of a stimulus word presented orally by the examiner (Cueto et al., 2009).
The quantity subtest requires children to select an image from a group of three or four options that best represents the verbally presented concept, such as "few," "most," or "nothing."
The EGRA adapted for young learners includes three subtests: familiar-word identification, passage reading, and listening comprehension The overall score is determined by the number of correct words read per minute in familiar-word identification and passage reading, as well as the correct responses to reading comprehension items Cognitive tests are not administered in round 1 due to the child's young age At age 5 in round 2, the PPVT and CDA tests are utilized, with their reliability and validity confirmed by Cueto et al (2009) In round 3, further assessments continue as the child develops.
8, the tests for cognitive skills are PPVT, EGRA and a math test In round 4, at the age of 12, the children take part in PPVT, a math test and a reading test.
Non-cognitive skills are assessed using a self-administered Likert-scale questionnaire, designed for children aged 1 and 8 who can respond independently This assessment focuses on key indicators such as self-esteem, based on Rosenberg's 1965 framework, and self-efficacy, drawing from the theories of Rotter (1966) and Bandura.
In 1993, research highlighted the significance of friendliness and social trust, with questions specifically tailored for children Each indicator features a comprehensive set of 4-8 relevant questions, as detailed in the appendix (Table 15) The validity of these measures has been validated by Dercon and Krishnan (2009).
Variables Definition Notation Measurement scale
Estimated latent variables for measures of ability to read, to write, to think critically, to solve problems, etc.
Estimated latent variables for measures of personality traits based on a set of self-administered questionnaire using Likert scale.
Estimated latent variables for measures child health at age 1, using height and weight for age z- score
Variables Definition Notation Measurement scale
No of document at birth
Estimated latent variables for measures of psychosocial risk to children at age 1 Higher risk leads to child stunted and underweight.
Clothing expenditure Spending on clothing and footwear for index child, based on the assumption that all children in the household receive an equal share of spending
Quality of CG-HH relationship
Over the past year, total spending on the index child includes education, clothing, and presents, assuming that all children in the household share these expenses equally This comprehensive financial overview reflects the household's investment in the index child's development and well-being.
An index base of caregiver’s response to questions regarding CH’s friends, teachers and activities after school (Only available in Round 3 and 4)
Child male 1 = Male, 0 = Female male Dummy
Child siblings Number of siblings of the child nsiblings Count Preschool 1= Attend preschool 0 = Do not attend preschool preschool Dummy
Child years of schooling Number of schooling year yrsch Count
Parental altruism Normalized on a scale from 0-1 A combination of the questions on Paltruism Index
Variables Definition Notation Measurement scale why parents feel it is important to have children (Only available for Round 2)
Caregiver education Education level of child’s primary caregiver (category: none, primary, secondary, highschool or vocational, university or higher)
Household social connectedness refers to the strength of kinship support within a community, reflecting how actively families engage with one another and the level of trust that exists among community members The HHSC index serves as a measure of these dynamics, highlighting the importance of social ties in fostering a supportive environment.
Household size Number of household member hhsize Count Poor Household is classified as poor using monthly per capita expenditure based on poverty line Poor Dummy
Urban 1= household is in urban areas Urban Dummy
Fraction of households in the commune which were classified as poor
An index showing the level of social problems in the community (i.e theft, gang, drugs, alcoholism )
Over the past four years, households have faced significant economic shocks that led to substantial wealth loss In the first round of analysis, a dummy variable was utilized to assess these impacts In subsequent rounds, specifically rounds two, three, and four, the concept of household shock was measured by evaluating changes in household wealth across two distinct periods.
Child birth order The order of the child birth in the hhshock Dummy/
Continuous household relative to his siblings birthorder Nominal
RESEARCH RESULTS
Overview
Child’s skill development in Vietnam
Vietnam has experienced remarkable economic growth over the past two decades, transitioning from a low-income nation in the 1980s to a middle-income country by 2015 To sustain this growth, it is crucial for Vietnam to cultivate a skilled workforce Employers are increasingly seeking workers with not only technical abilities but also critical thinking, problem-solving skills, and positive interpersonal attitudes Research indicates that both cognitive and non-cognitive skills develop early in childhood and can mutually reinforce each other Therefore, Vietnam should focus on transforming its education system to enhance teaching methods and curricula, enabling students to become effective problem-solvers, critical thinkers, and strong communicators This educational shift will encourage parents to prioritize the development of both cognitive and non-cognitive skills in their children, addressing the long-standing neglect of non-cognitive skill development that has hindered employers from finding adequately skilled workers.
Children aged 0-14 make up over 25% of Vietnam's population and represent the future workforce of the economy However, a significant number of these children live in poverty, with a reported child poverty rate of 37% according to Roelen, Gassmann, and de Neubourg (2010) Given that Young Lives focuses on the impoverished population, we aim to explore how investment influences skill development in children, particularly those from disadvantaged backgrounds.
Investment in children: education expenditure
Education expenditure represents the most substantial parental investment in children, with clear data available on its impact This investment is a collaborative effort between government support and household contributions.
Between 2009 and 2013, education expenditure in Vietnam saw a steady increase, reflecting significant government investment aimed at meeting the Millennium Development Goals and Sustainable Development Goals In 2013, government spending on education represented 5.7% of GDP and 18.5% of total government expenditure, figures that, while relatively high for the region, remain modest due to Vietnam's smaller GDP Consequently, households have been compelled to contribute significantly to education costs, leading to equity concerns as wealthier families can invest substantially more in their children's education compared to poorer households On average, households allocate approximately 6% of their total expenditure, or 13% of their non-essential spending, to education, with the wealthiest households spending about seven times more than the poorest This disparity is particularly pronounced in non-public schools compared to public institutions.
Bi lli on Th ou sa nd V ND (B as e on 2 01 3
Central government Household Total in 2013 prices
A comparison between public and non-public schools reveals that families in rural areas invest significantly less in their children's education, spending twice as much as urban households from pre-primary to upper secondary high school.
Figure 4: Expenditure on education from government and household, 2009-2013
Figure 5: Household average expenditure per student, all level of education, by income quantile, 2012
Figure 6: Household average expenditure per student, by level of education and location, 2012
Descriptive statistics
This study focuses on the skill formation process in early childhood, utilizing data from a younger cohort born between 2001 and 2002 We analyze four survey rounds conducted in 2002, 2006-2007, 2009-2010, and 2013-2014, tracking 2,000 children from ages 1 to 12 Notably, 80% of the children reside in rural areas, while 20% live in urban settings, with a nearly equal distribution of genders (52% male and 48% female).
Due to the varying nature of child development across different stages, each survey round is treated independently rather than pooled together The questionnaires are tailored to capture age-specific information relevant to child development Round 1 provides foundational data for children at the age of 1, focusing on essential health information from birth, despite not measuring cognitive and non-cognitive skills at this stage This health data is vital for understanding skill development in later stages Both parental risk factors (PRF) and child health are assessed as latent variables through confirmatory factor analysis, with higher values indicating greater risk Summary statistics for Round 1 variables are presented in Table 2.
Table 2: Summary statistics - Round 1 (Age 1)
Variable Obs Mean Std dev Min Max
No doctor present at birth 1824 0.603 0.489 0.000 1.000
CH: Child; CG: caregiver; HH: Household; CM: Community b 0 indicates no ante-natal care, 3 indicates high level of ante-natal care a High value indicates low level of caregiver education
Table 3 summarizes the variables used in round 2, which includes cognitive skill tests for children at age five The Peabody Picture Vocabulary Test (PPVT) and the Cognitive Development Assessment (CDA) are utilized to evaluate verbal and quantitative abilities, respectively Instead of raw scores, Rasch scores derived from Item Response Theory (IRT) are employed to reduce measurement error in estimating latent cognitive abilities However, non-cognitive skill measures are not applicable for five-year-olds, resulting in a lack of data in this area Regarding parental investment, education expenses represent the largest portion of total child expenditure, with average spending on clothing and gifts being nearly equal.
Table 3: Summary statistics - Round 2 (Age 5)
Variable Obs Mean Std Dev Min Max
Variable Obs Mean Std Dev Min Max
CH: Child; CG: caregiver; HH: Household; CM: Community
Table 4 presents the summary statistics for round 3, highlighting the replacement of the CDA test with the Early Grade Reading Assessment (EGRA) for cognitive skills evaluation The PPVT and EGRA scores are reported as Rasch scores, and new writing and reading items have been introduced On average, children demonstrate the ability to read words and write, albeit with some errors Additionally, round 3 introduces data on children's non-cognitive skills, measured through a set of questions outlined in the appendix, which assess self-efficacy, self-esteem, and social scores The first two concepts utilize a Likert scale, while social scores are determined via yes/no questions Factor loadings for latent variable estimates can also be found in the appendix Notably, education expenditure emerges as the largest average cost, accompanied by the highest standard deviation.
Table 4: Summary statistics - Round 3 (Age 8)
Quality of CH-CG relationship 1,943 0.000 0.265 -0.782 0.116
CH: Child; CG: caregiver; HH: Household; CM: Community a Estimated latent variables b Being estimated based on CG's response to questions regarding their trust in the community
In Round 4, the variables are outlined in Table 5, featuring cognitive skill indicators such as the PPVT, a math test, and a reading test Measurements for self-efficacy and self-esteem persist, while the social score is now represented by a friendliness score Additionally, all non-cognitive skill questions in this round utilize a Likert scale for assessment.
At this stage, spending on gifts and treats represents a minor fraction of overall expenses, while educational costs exceed clothing and footwear expenditures by more than four times.
Table 5: Summary statistics - Round 4 (Age 12)
Variable Obs Mean Std Dev Min Max
Variable Obs Mean Std Dev Min Max
Quality of CH-CG relationship 1,928 0.001 0.322 -0.672 0.228
CH: Child; CG: caregiver; HH: Household; CM: Community a Estimated latent variables
Table 6 presents the Pairwise rank correlation matrix for our primary variables of interest, revealing initial insights into the predicted relationships Notably, the analysis indicates a significant negative correlation between parental risk factors (PRF) and child health at age 1, as well as with various skills in later stages of development, while controlling for other variables Additionally, a strong positive correlation is observed among cognitive skills across different developmental stages However, the correlation between non-cognitive skills at ages 8 and 12 is not significant Furthermore, our findings demonstrate a significant positive relationship between investment in children and their skills, both concurrently and over time, with the exception of non-cognitive skills at age 12.
Table 6: Pairwise rank correlation matrix
CS: Cognitive skills; NCS: Non-cognitive skills; CH: Child's health;
LTE: Log of total exp for child; PRF: Psychosocial Risk Factor
* indicates significant at 10%; ** at 5%; *** at 1%
4.2.2.1 Preliminary evidence of self-productivity and cross productivity
Figure 7 demonstrates three potential cases of cross productivity in the study.
The diagram illustrates a positive correlation between children's cognitive skills at age 5 and their non-cognitive skills by age 8 Additionally, higher non-cognitive skills at age 8 are linked to elevated cognitive skills at age 12 However, the relationship between cognitive skills at age 8 and non-cognitive skills at age 12 is less definitive.
Preliminary evidence suggests a strong self-productivity of cognitive skills, as indicated by data showing that children with higher cognitive abilities at an early age are more likely to achieve advanced skills later in life Specifically, cognitive skills acquired at age 5 correlate positively with those obtained at age 8, and a scatter plot for age 12 reveals a significant relationship between current and past cognitive skills In contrast, non-cognitive skills exhibit weak evidence of self-productivity, with a nearly flat predicted line suggesting a lack of clear correlation.
For parental investment on children, we examine each component of expenditure: clothing, education, presents (or treats) as well as the total expenditure.
At age 5, scatter plots indicate that higher investment levels correlate with enhanced cognitive skills, particularly highlighting a positive relationship between clothing expenditure and cognitive abilities However, two potential outliers in clothing expenditure will be excluded from the analysis Additionally, both education and gift expenditures also show a positive association with cognitive skills.
At age 8, a correlation is evident: increased spending correlates with enhanced skill levels Scatter plots reveal potential outliers in clothing expenditures exceeding 10 million dong, education expenditures surpassing 60 million dong, and presents expenditures over 4 million dong.
At age 12, a significant positive correlation emerges between cognitive skills and parental expenditure, indicating a stronger impact of investment compared to ages 5 and 8 The analysis reveals that this relationship remains positive across various types of expenditure However, an outlier is identified in education spending exceeding 60 million VND, prompting the decision to eliminate all potential outliers before estimating the model.
4.2.2.3 Non-cognitive skills versus expenditure
At age 8, scatter plots indicate a positive correlation between parental investment and children's non-cognitive achievements, with all forms of expenditure showing a beneficial link to these skills However, the clarity of this relationship may be affected by outliers, particularly clothing expenditures exceeding 10 million dong and education expenses surpassing 60 million dong.
At age 12, the analysis reveals no significant correlation between parental investment and non-cognitive achievement, potentially influenced by outliers in the sample To enhance the accuracy of our findings, we will exclude these outliers in the subsequent analysis.
The scatter plots indicate a clear trend of cross productivity over time Notably, self-productivity shows a strong correlation with cognitive skills, while the link to non-cognitive skills is less pronounced Additionally, the relationship between investment and skill level is more significant for cognitive skills compared to non-cognitive skills.
CONCLUSIONS
Conclusion
Over the past two decades, Vietnam has transformed from a poor nation to a middle-income country, showcasing remarkable economic growth The future trajectory of this growth hinges on effective economic restructuring, with workforce skills playing a vital role According to the World Bank's STEP (Skills Towards Employment and Productivity) survey, there has been a significant rise in jobs requiring analytical and interpersonal skills in Vietnam.
In 1998, as Vietnam transitions towards nonagricultural sectors, it becomes crucial to equip the workforce with essential cognitive and non-cognitive skills, which begin to develop in early childhood The development of these skills is influenced by parental investment, household characteristics, and community environments This process is dynamic, as skills can be enhanced through both self-productivity and cross-productivity mechanisms While most research on childhood skill development has focused on developed countries, this study aims to explore the unique context of Vietnam, examining the effects of self-productivity and cross-productivity, as well as the impact of parental investments on child development, to identify sensitive and critical periods for skill acquisition.
The study utilizes a longitudinal dataset from the Young Lives Project in Vietnam, tracking 2,000 children from ages 1 to 12 across four survey rounds conducted in 2002, 2006, 2009, and 2013 It estimates cognitive and non-cognitive skills as latent variables through confirmatory factor analysis The primary model, based on the skill formation framework by Cunha and Heckman (2007, 2008), is analyzed using Ordinary Least Squares (OLS) and Full Information Maximum Likelihood (FIML) methods.
IV The main findings are as below:
Parental investment is vital for a child's skill development, significantly enhancing both cognitive and non-cognitive skills, with the exception of non-cognitive skills at age 12 Specifically, expenditures on clothing and presents are influential for children at age 5, while educational spending does not show a significant impact until later ages.
8 Besides monetary investment, quality of relationship between caregiver and the child, a proxy for time investment, is also positively affect the level of non- cognitive skills at age 8 and cognitive skills at age 12 Interestingly, on average, the return on parental investment for skills are higher for children who lives in rural than those who lives in urban areas.
The results indicate a self-productivity effect for cognitive skills between the ages of 5 and 12, with skills acquired at age 5 significantly enhancing those at age 8, which in turn leads to improved cognitive skills by age 12 Additionally, there is empirical evidence of cross-productivity between cognitive and non-cognitive skills; however, this relationship does not hold at age 12 Consistent with Cunha and Heckman (2008), the findings reveal that non-cognitive skills developed at age 8 positively influence cognitive skills at age 12, demonstrating a significant and robust effect across all models and specifications.
The period up to age 12 is crucial for cognitive skill development, with ages 5 and 8 identified as sensitive stages This indicates that early childhood significantly influences cognitive growth, emphasizing the importance of nurturing these skills during these formative years.
6 return of parental investments on child’s cognitive skills This finding is consistent with findings from Coneus et al (2012) in Germany.
Policy implication
The study provides valuable empirical evidence that enhances current literature on child development, offering important implications for policymakers focused on interventions for disadvantaged children.
The study highlights the interconnection between cognitive and non-cognitive skills from an early age, supporting the need for a transformation in education This transformation involves updating teaching methods and curricula to foster effective problem-solving, critical thinking, and enhanced communication Additionally, it aims to develop teamwork abilities, self-confidence, and determination among students.
Early intervention programs targeting disadvantaged children are crucial, as parental investment significantly impacts cognitive development during early childhood These formative years represent sensitive periods for skill acquisition, making early childhood initiatives potentially more effective than remediation efforts implemented during late adolescence or adulthood.
Limitations and further research
The study has several limitations that future research could address Firstly, the data is restricted to children aged 12, which prevents a clear connection between early skills and adult success Cunha and Heckman (2008) successfully linked childhood skills to adult earnings, providing a valuable framework Secondly, the IV specification in round 4 for cognitive skills does not pass the overidentification test, indicating the need for alternative instrumental variables more suitable for the Vietnamese context Lastly, the analysis does not consider the influence of peer groups on a child's skill development, particularly in later stages.
As a child grows older, the impacts of peers on the skill formation is significant.
Future research should explore how cognitive and non-cognitive skills influence children's long-term outcomes, while also accounting for the effects of school and peer groups.
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Table 14: Main characteristics of provinces selected
- typical rural; representative of the Red River Delta region
- main source of income: rice farming
- near big cities (between Hanoi and Hai Phong)
- will be influenced by urbanization
- absolute number of poor people is high, although percentage of population living under poverty line is not high
- typical province for the North-East and North-West
- high percentage of minority ethnic groups
- underdeveloped infrastructure; far from big cities
- border with China, subjected to commercial and trading activities
- ranking in the middle of the five-city group
- attractive to rural people in central provinces for migration, including child labour and drug problems.
- high potential for environmental pollution
- receiving big investment from international sources and government
- poverty rate is low Phu Yen - typical province for central coast of Vietnam
- mix of coastal, midland and highland
- main sources of income from agriculture and seafood
- very high percentage of population is living under poverty line
- high child migration rate to cities Ben Tre - typical province for Mekong River Delta
- difficult transportation within and between communes
- high percentage of landless families in rural area
- low educational level providing agricultural raw materials to industrial zones high percentage of population living under poverty line.
Source: Tran Tuan, Pham Thi Lan, Trudy Harpham et al., (2003)
Table 15: Questions in measurement of non-cognitive skills
If I try hard, I can improve my situation in life
I like to make plans for my future studies and work
If I study hard at school I will be rewarded by a better job in future
I am proud of my clothes
I am proud of my shoes or having shoes
I am never embarrassed because I do not have the right books, pencils and other equipment for school
I am proud that I have the correct uniform
I am proud by/ of the work I have to do
Number of friends child spoke to in the past week *
Do your friends look up to you as a leader?
Do other children include you in their games?
Do you help other children who have a problem?
I am confident that I can deal with unexpected.
I can remain calm when facing difficulties because I can rely on my coping abilities.
I can usually handle whatever comes my way.
Thanks to my resourcefulness, I know how to handle unforeseen situations.
Self-esteem I am proud of my clothes.
I am as good as most other people
Overall, I have a lot to be proud of.
A lot of things about me are good When I do something, I do it well
In general, I like to be the way I
I am popular with kids of my own age Most other kids like me
Other kids want me to be their friend
I have more friends than most other kids
I get along with other kids easily
Latent variable: Coefficient Standard errors
No doctor present at birth 0.387 0.027
Table 17: Results - Round 2 - Age 5 (Detailed expenditure)
Dependent variable: Cognitive skills – Age 5
Dependent variable: Cognitive skills – Age 5
Table 18: Results on cognitive skills - Round 3 - Age 8 (Detailed expenditure)
Dependent Variable: Cognitive skills - age 8
Quality of CG-CH 0.436 0.596 0.277 0.530 relationship (0.504) (0.503) (0.449) (0.426)
Dependent Variable: Cognitive skills - age 8
Robust standard errors in parentheses
Table 19: Results on non-cognitive skills - Round 3 - Age 8 (Detail expenditure)
Dependent Variable: Non-cognitive skills - age 8
Quality of CG-CH 0.032 0.045** 0.028* 0.028* relationship (0.022) (0.022) (0.017) (0.017)
Dependent Variable: Non-cognitive skills - age 8
Robust standard errors in parentheses
Table 20: Results on cognitive skills -Round 4 - Age 12 (Detail expenditure)
Dependent Variable: Cognitive skills - age 12
Quality of CG-CH 0.931*** 0.881*** 0.833*** 0.835*** relationship (0.242) (0.246) (0.209) (0.204)
Dependent Variable: Cognitive skills - age 12
Robust standard errors in parentheses
Table 21: Results on non-cognitive skills - Round 4 - Age 12
Dependent Variable: Non-cognitive skills - age 12
Quality of CG-CH -0.016 -0.024 -0.013 -0.018 relationship (0.039) (0.041) (0.035) (0.035)
Dependent Variable: Non-cognitive skills - age 12
Robust standard errors in parentheses
- - - - 1 - 0 0 gn iti ve s ki lls - - - 1 - 1 0 5 0 5 N on -c og ni tiv e sk ill s (a ge N on -c og ni tiv e sk ill s (a ge - - - - 0 1 2 0 1 2
Cognitive skills (age 5) 100 200 -1 -.5 Non-cognitive skills (age 8) 0 5 -30 -20 -10 Cognitive skills (age 8) 0 10
Figure 7: Scatter plots - Cross productivity
Figure 8: Scatter plots - Self-productivity
Cognitive skills are crucial for development, particularly during early childhood These skills encompass various mental processes, including memory, attention, and problem-solving abilities, which are essential for learning and everyday functioning As children grow, their cognitive skills evolve, significantly impacting their academic performance and social interactions Understanding the stages of cognitive skill development can help parents and educators support children in reaching their full potential.
Log expenditure on children(per child) 0 Exp on children clothing and footware (per child) in '000 VND 1000 2000 3000 4000 0 Expenditure on children's education (per child) in '000 VND 2000 4000 6000
Expenditure on children's present and treats (per child) in '000 VND
Log expenditure on children(per child) Expenditure on children clothing and footware (per child) in '000 VND 0 5000 10000
Figure 9: Scatter plots - Cognitive skills vs Expenditure
Cognitive skills are essential for various age groups, influencing learning and problem-solving abilities Understanding these skills at different developmental stages can enhance educational strategies and support cognitive growth By focusing on cognitive skills across ages, we can better tailor interventions and resources to foster intellectual development and improve overall learning outcomes.
Expenditure on children's education (per child) in '000 VND
Expenditure on children's present and treats (per child) in '000 VND 4 6 8 10 12 14
Log expenditure on children(per child)
Expenditure on children clothing and footware (per child) in '000 VND
Exp on children's present and treats (per child) in '000 VND
Expenditure on children's education (per child) in '000 VND
Figure 9: Scatter plot – Cognitive skills vs Expenditure (cont.)
N on -c og ni tiv e sk ill s (a ge - - 0 gn iti ve - - 0 1 2 co gn iti ve s ki lls (a ge - - 0 1 N on -c og ni tiv e sk ill s (a ge - - 0 - - 0
Exp on children clothing and footware (per child) in '000 VND
Expenditure on children's education (per child) in '000 VND
Exp on children's present and treats (per child) in '000 VND
Log expenditure on children(per child) Log expenditure on children(per child)
Figure 10: Scatter plots - Non-cognitive skills versus expenditure