Parental investment and the joint evolution of children’s cognitive and non cognitive skills evidences in vietnam

UNIVERSITY OF ECONOMICSHO CHI MINH CITY VIETNAM ERASMUS UNIVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

UNIVERSITY OF ECONOMICS

HO CHI MINH CITY

VIETNAM

ERASMUS UNIVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

PARENTAL INVESTMENT AND THE JOINT EVOLUTION OF CHILDREN’S COGNITIVE

AND NON-COGNITIVE SKILLS:

EVIDENCES IN VIETNAM

BY

TRAN KHANH HOA

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

UNIVERSITY OF ECONOMICS

HO CHI MINH CITY

VIETNAM

INSTITUTE OF SOCIAL STUDIES

THE HAGUE THE NETHERLANDS

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

PARENTAL INVESTMENT AND THE

JOINT EVOLUTION OF CHILDREN’S

COGNITIVE AND NON-COGNITIVE

SKILLS: EVIDENCES IN VIETNAM

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

TRAN KHANH HOA

A CKNOWLEDGEMENTS

Academically, I would like to express my sincere gratitude to Dr Pham KhanhNam, Dr Truong Dang Thuy and Mr Do Huu Luat for the valuable comments,instructions and support, without which my thesis would have not been completed

Personally, I would like to thank my big family, my loved one and friends for thegreat care, understanding, encouragement, tolerance and patience during my toughtime of writing the thesis

Tran Khanh Hoa

A BSTRACT

Given the importance of skill set as a predictor of life outcomes, this studyinvestigates the effect of parental investment on the development of cognitive and non-cognitive skills of 2,000 children, who were tracked from age 1 to age 12, usinglongitudinal data from Young Lives project in Vietnam A model of skill formation isestimated using ordinary least squares and full information maximum likelihood as well

as instrumental-variables to cope with missing values and potential endogeneity.Cognitive and non-cognitive skills are latent variables obtained from confirmatoryfactor analysis There are three important findings in this study Firstly, the resultssupport that parental investment plays a crucial role in the development of bothcognitive and non-cognitive skills in the first 12 years of child’s life Secondly, there iscompelling evidence for the presence of self-productivity of cognitive-skills as well ascross-productivity from cognitive skills to non-cognitive skills and vice versa Thirdly,the time span until the age of 12 is found to be critical period for the development ofcognitive skills while the age of 5 and the age of 8 are sensitive periods for cognitiveskills relative to the age of 12 These findings support policy towards an educationreform which help to promote not only cognitive skills but also non-cognitive skills;and policy towards an early childhood intervention programs, which aim at childrenliving under disadvantage circumstances

JEL Classification:

Keywords: parental investment, cognitive skill, non-cognitive skills, critical

and sensitive periods, children

T ABLE OF CONTENTS

ABSTRACT i

ABBREVIATION iv

LIST OF FIGURES v

LIST OF TABLES vi

Chapter 1 INTRODUCTION 1

1.1 Problem statement 1

1.2 Research objectives 3

1.3 Scope of the study 3

1.4 Structure of the thesis 4

Chapter 2 LITERATURE REVIEW 5

2.1 Human capital theory: skills matter 5

2.2 Cognitive skills and non-cognitive skills: measures and impacts 6

2.3 Determinants of skill development 8

2.3.1 Genetic endowment 8

2.3.2 Environment 9

2.4 Dynamics of skill development 11

2.4.1 Critical and sensitive periods: timing of parental investment 12

2.5 Analytical framework 13

Chapter 3 RESEARCH METHODOLOGY 15

3.1 Methodology 15

3.1.1 Technology of skill formation 15

3.1.2 Genetic endowment – Initial conditions 17

3.1.3 Method in cognitive, non-cognitive skills measurement: Latent variables 18

3.1.4 Instrument Variables 18

3.2 Data 19

3.2.1 Data source 19

3.2.2 Variables description 21

Chapter 4 RESEARCH RESULTS 25

4.2 Descriptive statistics 28

4.2.1 Summary statistics 28

4.2.2 Bivariate analysis 36

4.2.3 Empirical results 39

Chapter 5 CONCLUSIONS 57

5.1 Conclusion 57

5.2 Policy implication 59

5.3 Limitations and further research 59

REFERENCES 61

APPENDIX 67

A BBREVIATION

CDA Cognitive Developmental Assessment EGRA Early Grade Reading Assessment

FIML Full Information Maximum Likelihood

L IST OF FIGURES

Figure 1: A model of parent-child genetic and environment effects 8

Figure 2: A framework of skill development 12

Figure 3: Analytical framework 14

Figure 4: Expenditure on education from government and household, 2009-2013 27 Figure 5: Household average expenditure per student, all level of education, by income quantile, 2012 27 Figure 6: Household average expenditure per student, by level of education and location, 2012 28 Figure 7: Scatter plots - Cross productivity 81

Figure 8: Scatter plots - Self-productivity 81

Figure 9: Scatter plots - Cognitive skills vs Expenditure 82

Figure 10: Scatter plots - Non-cognitive skills versus expenditure 84

L IST OF TABLES

Table 1: Variable description 22

Table 2: Summary statistics - Round 1 (Age 1) 29

Table 3: Summary statistics - Round 2 (Age 5) 30

Table 4: Summary statistics - Round 3 (Age 8) 31

Table 5: Summary statistics - Round 4 (Age 12) 32

Table 6: Pairwise rank correlation matrix 35

Table 7: Reults - Round 1 - Age 1 40

Table 8: Results - Round 2 - Age 5 42

Table 9: Results on cognitive skills - Round 3 - Age 8 45

Table 10: Results on non-cognitive skills - Round 3 - Age 8 48

Table 11: Results on cognitive skills - Round 4 - Age 12 50

Table 12: Results on non-cognitive skills - Round 4 - Age 12 52

Table 13: Identification of sensitive periods 56

Table 14: Main characteristics of provinces selected 67

Table 15: Questions in measurement of non-cognitive skills 68

Table 16: Factor loading outcomes 70

Table 17: Results - Round 2 - Age 5 (Detailed expenditure) 71

Table 18: Results on cognitive skills - Round 3 - Age 8 (Detailed expenditure) 73

Table 20: Results on cognitive skills -Round 4 - Age 12 (Detail expenditure) 77Table 21: Results on non-cognitive skills - Round 4 - Age 12 (Detail expenditure) 79

“Human development is economic development.” James Heckman, Nobel Prize Laureate.

Chapter 1 INTRODUCTION

1.1 Problem statement

Development of a child from early childhood to adolescence has always been agreat concern of parents and the society Evidence from recent interdisciplinarystudies have showed that child development, particularly development of cognitiveand non-cognitive skills, accounts largely for future life outcomes of a child Inparticular, development during childhood affect education, health, labor marketoutcomes, and social engagement (Francesconi & Heckman, 2016) For example,Heckman et al (2006) found out in the US that schooling decisions, wages andwork experience are effected by cognitive and non-cognitive skills Anotherexample from Sweden, Lindqvist and Vestman (2011) pointed out the role ofcognitive and non-cognitive abilities in predicting labor force participation andwages among skilled and unskilled workers

Cognitive skills concern about child’s general intelligence while non-cognitiveskills reflect child’s personality traits (Borghans et al., 2008) Skills are shaped andinfluenced by both genetic endowment and factors from the environment Althoughgenetic endowment is critical and unchangeable, the expression of genes can varydepending on different environment factors Among the factors, factors from familyenvironment such as parental characteristics, parenting styles, family socioeconomiccondition, parental investment, etc., are the most important to child development

Knowing when parental investment is most effective for child’s skill development

is of policy importance Since skill development is a dynamic rather than a staticprocess, there are two important features to be noticed Firstly, cognitive

and non-cognitive skills can reinforce each other in a mechanism called productivity and cross-productivity (Cunha & Heckman, 2008) Self-productivity(or cross-productivity) means a higher stock of skills in one period can create higherstocks of skills (or higher stocks of another skill) in the next period Secondly, thereare critical and sensitive periods for skills acquisition because investment atdifferent stages of the life cycle may have different impacts on cognitive and non-cognitive skills “Sensitive period” is a period where parental inputs are moreproductive than in other stages And “critical period” is a period in whichinvestment is only productive in that period Understanding of sensitive and criticalperiods are useful, especially to policy makers For example, policies which aim tosubsidize investment to disadvantaged children could be more effective when it isprovided to the children at the right time.

self-So far, there is more supporting evidence from developed countries than fromless developed countries on the discussion above regarding the timing of parentalinvestment and the evolution of cognitive and non-cognitive skills throughout multiplestages of life Meanwhile, it is essential to learn about the topic in emerging economiesbecause human development is the key to economic development

In Vietnam, a skilled workforce is crucially needed for economicmodernization The STEP (Skills Towards Employment and Productivity) surveyconducted in Vietnam by World Bank showed that the proportion of jobs that usinganalytical and interpersonal skills has increased significantly since 1998 AsVietnam is shifting to more nonagricultural sectors, it is implied that more attentionshould be given to equip the workforce with essential skills such as cognitive andnon-cognitive skills

This research identifies two concerns about skill development in Vietnam First,the general education in Vietnam has long placed great emphasis on the acquisition ofknowledge or basic cognitive skills but not on soft skills or non-cognitive skills such asself-esteem or self-control Whereas, cognitive and non-cognitive skills should receive

parents, especially those with disadvantaged conditions, has neglected theimportance of early development of their children The children receive inadequateinvestment from their parents because the parents might not be aware of theimportance of investment on children or because the lack of support from thegovernment or social groups Hence, it is suggested that policies should help raiseparents’ awareness and provide parents with necessary support from very earlystages of raising their children The effectiveness of the intervention may lie on thetiming of the investment Evidence for sensitive and critical periods of skilldevelopment may provide an answer to whether more investment should be madeduring early childhood.

1.2 Research objectives

Given the problem stated above, this research has three main objectives:

cognitive and non-cognitive skills over multiple stages of childhood, along with otherdeterminants such as household and community characteristics

called self-productivity and cross-productivity

- Thirdly, to identify critical and sensitive periods for investing in thedevelopment of cognitive and non-cognitive skills The finding might be useful in policymaking process regarding designing interventions, remediation programs fordisadvantaged children

1.3 Scope of the study

To achieve the research objectives, the study investigates 2,000 children from

5 provinces in Vietnam (Lao Cai, Hung Yen, Da Nang, Phu Yen and Ben Tre).These children were born in 2001-2002 and were tracked from the age of 1 to theage of 12 The data is collected from four rounds survey of the Young Lives project

in 2002-3, 2006-7, 2009-10 and 2013-14

1.4 Structure of the thesis

The thesis is organized into five chapters Chapter 1 presents an introduction

to the study with problem statement, research objectives and scope of study.Chapter 2 reviews the theories and existing empirical evidence on skills formation.Following is the discussion on research methodology and data in Chapter 3 Chapter

4 provides the summary statistics, bivariate analysis and empirical results In sum,the conclusion along with policy implications and limitation of the research arepresented in Chapter 5

Chapter 2

The review captures literature on skill development and previous empirical

evidence of self-productivity, cross productivity, critical and sensitive periods The

terms “skill” and “ability” will be used interchangeably

2.1 Human capital theory: skills matter

Human capital is broadly defined as any stock of knowledge or characteristics theworker has (either innate or acquired) that increase his or her “productivity”(Acemoglu, 1999) There are several ways to view and classify human capital such asthe Becker view or the Gardener view Both views agree that human capital is valued inthe job market and helps explain life outcomes The difference between the two views

is that the Becker view regards human capital as a unidimensional object while theGardner view states that human capital should rather be represented by multi-dimensional ones This view comes after the development of multi-intelligence theorydeveloped by the social psychologist Howard Gardner in 1983 (Acemoglu, 1999) Inaddition to traditional linguistics intelligence and mathematical intelligence (cognitiveskills), Howard Gardner added seven more intelligences, including interpersonalintelligence and intrapersonal intelligence (non-cognitive skills) (Gardner, 2011).Accordingly, this study examines both cognitive and non-cognitive skills as humancapital, which is in line with the Gardner view

In terms of the formation of human capital, genetic factors and investments candetermine the differences in the level of human capital acquired by individuals On onehand, investments foster and enhance the acquisition of human capital On the otherhand, genetic factor helps explain the heterogeneity in human capital given the samelevel of investments So far, education and training have been deeply discussed

in many literature and considered as the most important investments in humancapital (Becker, 1994) In addition to education and training investments, other non-schooling investments are also important In fact, when schooling is limited in lessdeveloped communities, non-schooling investments have found to be greater inmagnitude than schooling investments (Behrman, 1987).

2.2 Cognitive skills and non-cognitive skills: measures and impacts

Cognitive ability has multiple facets Cognition or cognitive ability is defined inthe American Psychological Association Dictionary as “all forms of knowing andawareness such as perceiving, conceiving, remembering, reasoning, judging,imagining and problem solving” In specific, cognitive skills involve languageskills, memory skills, motor skills, thinking skills Moreover, cognitive ability isoften considered as intelligence, which is divided into fluid intelligence (the rate atwhich people learn) and crystallized intelligence (acquired knowledge) Due to thisfact, cognitive ability is commonly measured using scores on intelligence tests such

as Peabody Picture Vocabulary Test (a measure of children’s verbal intelligence),Raven’s Progressive Matrices (a nonverbal test), Wechsler tests or Stanford-Binettest (measures of general intelligence) (Neisser et al., 1996) The term “IQ”(Intelligence Quotient) indicates the scores of these intelligence tests

Other personal attributes which cannot be measured using intelligence tests, areconsidered non-cognitive skills, i.e self-control, self-esteem, persistence, etc In theliterature, non-cognitive skills are expressed under different names such aspersonality traits, soft skills, character skills, and socio-emotional skills One thing

to be noted is that “non-cognitive” is not just juxtaposed with “cognitive” sincemany aspects of non-cognitive skills are the outcome of cognitive skills and viceversa (Borghans, 2008) Regarding the measures of non-cognitive skills, there aretwo measurement approaches which are self-report questionnaires and conventionaleconomic preference parameters (such as time preference, risk aversion andpreference for leisure) Self-report questionnaire is commonly used among

The role of cognitive and non-cognitive skills in predicting success in academicand career is apparent Previously, only cognitive skills were examined as adeterminant of school attainments and wages For example, school performance wasshowed to be affected significantly by cognitive skills among Canadian High schoolstudents in a study of Riding and Agrell (1997) In addition, Murnane, Willett, andLevy (1995) found out an evidence of a growing importance of cognitive skills indetermining wage level among American high school seniors in the 1980s Ree,Earles, and Teachout (1994) also showed that cognitive ability is the best predictorfor job performance criteria in US Army Not until 2000s are there more studiesinvestigating in the role of both cognitive and non-cognitive skills on the schoolperformance and earning One of the most well-known work is an intensive study of

J J Heckman et al (2006) They found evidence in the US that both cognitive andnon-cognitive skills affect schooling and work experience In fact, non-cognitiveskills 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-cognitiveskills outperform cognitive skills in predicting academic success (Duckworth &Seligman, 2005)

Besides schooling and earnings, cognitive and non-cognitive skills also bringother benefits For instance, in the work of Hanushek and Woessmann (2008),cognitive skills were proved to have a powerful impact on economic growth They useddata from International Association for the Evaluation of Educational Achievement(IEA) economic indicators from the OECD countries for investigation, and the resultswere remarkably robust In terms of non-cognitive skills, they are important predictornot only for academic and career success but also for life well-being such as healthbehaviors, marriage, and criminal offending A study by Moffitt et al (2011) providessolid evidence on life-wellbeing The study is extraordinary since it followed cohort of

1000 children in Dunedin, New Zealand from birth to age

32 The results showed that children with low self-control are more likely to have healthproblems (27% vs 11%), commit crime (43% vs 13%) and be single-parent (58%vs.28%).

2.3 Determinants of skill development

There are several factors affecting the skill development process The factorscan be put into two groups: genetic factors and the inputs from the environment(such as home, school and community) A model in Figure 1 illustrates the role ofparents in setting these factors in their children GP, GC denotes genetic factors ofparents and children respectively

from parents At the same time, parental characteristics (PP) affect the childrenthrough providing inputs into the living environment of the children (EC)

Figure 1: A model of parent-child genetic and environment effects

Source: Scarr (1992)

2.3.1 Genetic endowment

Firstly, abilities can be largely explained by genetic endowment Geneticstudies in North American and European populations show that 40% to 70% ofvariations in cognitive abilities and personalities were due to genetic variation(Scarr, 1992) Evidence for this argument is found in the adoption and twin studies.Specifically, in the Minnesota adoption studies, after corrected for selectiveplacement, Scarr and Weinberg (1983), showed that differences in genes explained

40% to 70% of IQ variance Likewise, results from a study on over 12,000 Swedishtwin pairs suggest that genetic factors can account for half of the variance inpersonality (Floderus-Myrhed, Pedersen, & Rasmuson, 1980).

2.3.2 Environment

Secondly, environment is crucial in determining abilities In facts, heritabilitydiscussed in the previous section can be diluted by different environmentalcircumstances Since inputs from the environment can causes genes to expressthemselves differently, environment factors may outweigh genetics factors

2.3.2.1 Home environment:

Parental investment

Parents play a vital role in skills development of a child, especially during theearly stages of life Inputs from parents or so-called parental investments involve bothfinancial and non-financial elements On one hand, parental investment is commonlyregarded as expenditure being spent on child’s well-being such as education, health andclothing Otherwise, in many of the cases, family income may be taken intoconsideration as a proxy for parental investment The empirical literature supports thatfamilies with higher level of permanent income on average invest more in their childrenand have children with greater skills (Carneiro & Ginja, 2016; Francesconi

& Heckman, 2016) Moreover, the divergence in cognitive and non-cognitive skillsamong children growing up in families with different level of income is observed at earlyages of childhood (J Heckman and Carneiro (2003); Cunha, Heckman, Lochner, andMasterov (2006)) However, the empirical challenge in taking family permanent income as aproxy for parental investments is that the level of income is highly correlated with familybackground factors such as parental education and skills Therefore, caution needs to betaken when interpreting the effect of permanent income on skill development On the otherhand, parents invest not only money in their children but also time and effort Theimportance of parental time investments to child development has been mentioned in earlyliterature such as Hill and Stafford

(1974), Leibowitz (1974) and Leibowitz (1977) Despite that fact, empiricalevidence on parental time investments is still limited due to data constraint Somerecent research demonstrating a positive relationship between parental time andchild cognitive skills are studies of Carneiro and Rodrigues (2009) and Hsin andFelfe (2014) A few studies look at the impact of parental time on non-cognitivestudies The work of Bono et al (2016) is one of the most recent study which showsthat the more time mothers spend with their children the higher level of cognitiveand non-cognitive skills children (aged 3 to age 7) attain In general, both moneyand time should be taken into consideration when measuring parental investment.

Besides parental investment, there are several other home factors whichaffect child development such as parental education, household size and number ofchild’s siblings

2.3.2.2. School and neighborhood

As children grow up, the influences of parents on them are diluted because theolder the children get, the freer they are to select their own environment.Apparently, school and neighborhood play a vital role in determining the well-being

of children, especially among older children or adolescence Based on sociologyliterature, there are several mechanisms in which neighborhood may affect childdevelopment Empirically, some models predict that higher socioeconomic statusneighbors have a negative effect on children development, while other modelspredict the opposite (Mayer & Jencks, 1989) A research on Vancouverneighborhoods by Hertzman (2004) revealed strong relationship between children’sdevelopment and the socio-economic status and other features of the neighborhoods

in which they lived Thus, socioeconomic status of the neighborhood is taken intoconsideration when estimating child development Although reported neighborhoodeffect on child outcome are often modest, controlling for the effect is essential Inaddition, the longitudinal property of Young Lives survey may allow us to seewhether neighborhood effects hold for both young children and adolescents as is

2.4 Dynamics of skill development

According to J Heckman and Carneiro (2003), life cycle skill formation is adynamic rather than static process The underlying mechanism of the process areself and cross productivity In order words, a skill acquired at one stage can affectthe acquisition of that skill (or another skill) at a later stage of life

In the early literature, the role of non-cognitive ability is neglected, and onlycognitive ability is considered as a part of human capital accumulation process.With a growing body of psychology literature, economists have recognized theimportance of non-cognitive ability in human capital and integrated it into economicmodel (see Borghans (2008); Almlund, Duckworth, Heckman, and Kautz (2011) for

a profound review of the impact of non-cognitive ability) Heckman and his authors proposed a model of technology which capture the interaction betweencognitive and non-cognitive skills

co-Empirical evidence for self-productivity is straightforward while that forcross-productivity is a mix Applying the formulation of technology of skillformation, self-productivity of skills is found in various studies such as that ofCunha and Heckman (2008) using US Longitudinal dataset ; that of Helmers andPatnam (2011) using Young Lives India Survey; or that of Coneus, Laucht, andReuß (2012) using Germany Manheim study of children Regarding cross-productivity, Cunha and Heckman (2008) found that cognitive skills in one periodcan be reinforced by the accumulation of non-cognitive skills in the previous period;meanwhile, evidence for the reverse case was not found In contrast, Helmers andPatnam (2011) found the evidence in favor of cross-productivity of cognitive onnon-cognitive skills but not vice versa

Prenatal Inherited Prenatal

Figure 2: A framework of skill development

Source: Kautz et al (2014)

As discussed in the previous sections, family together with extrafamilial inputs

play a vital role in shaping skills, especially during childhood Indeed, development

process relies on the accumulation of these inputs and the reinforcement of skills A

general framework of skill formation is illustrated in figure 2

2.4.1 Critical and sensitive periods: timing of parental investment

In Figure 2, child development is treated as a single period However,

Carneiro, Cunha, and Heckman (2003) argued that there should be a distinction

between early and late childhood since the effects of family and environment inputs

on children during different periods might be different Indeed, based on the

strength of the effect of parental investments, Heckman introduced the concept of

A sensitive period is a period in which the parental investments are moreproductive than in other periods A critical period is the only period in which parentalinvestments are productive Phillips and Shonkoff (2000) emphasize that differentstages of the life cycle are critical to the formation of different types of abilities.

Most available evidence support that early period of life is important tocognitive development Hopkins and Bracht (1975) suggested that before age 10 issensitive period for cognitive development Recent evidence from Coneus et al.(2012), using Mannheim Study of Children in Germany, also demonstrated thatearly childhood is a sensitive period for cognitive skill relative to late childhood andthat critical period to cognitive skills is the first 4 years of a child

Regarding non-cognitive skills, empirical evidence showed that the sensitiveperiods for noncognitive skills occur later than do sensitive periods for cognitiveskills (Cunha and Heckman (2007); Borghans (2008)) In particular, Carneiro et al.(2003) found that noncognitive skills are more malleable at later ages whilecognitive skills are quiet stable by the age of eight In addition, evidence fromAlmlund et al (2011) showed that noncognitive skills are more responsive tointerventions during adolescent than in early childhood

As a result, researchers suggest that intervention program to disadvantagedchildren should be carried out in child early childhood Otherwise, interventionduring adolescents and adulthood should focus more on noncognitive skills instead

of cognitive skills

2.5 Analytical framework

Based on the framework of skill development presented in the previous chapter,figure 3 illustrates the analytical framework being used in this study The frameworkallows us to investigate the effect of investments on skills; the cross-productivity andself-productivity effects; and the critical and sensitive periods of skill formation.Empirical evidence for the relationships depicted in the diagram are discussed inchapter 2 The works from Cunha and Heckman (2008), Coneus et al (2012), Helmersand Patnam (2011) and Hernández-Alava and Popli (2017) provide evidence for self-productivity, cross productivity and the impacts of parental investments on

skill development The first two papers examine the context of developed countries

while the latter examines a case of developing country In terms of

child’s characteristics, genetic factors are found to account for a large part of the

variance in IQ and personality in the study of Scarr and Weinberg (1983) and

Floderus-Myrhed et al (1980) Besides, parental investment, other inputs from the

household such as household size, household economic status, household social

networks are worth to take into account when investigating the skill development,

as suggested by Duncan and Brooks‐Gunn (2000); Pelto et al (1991) and

Fafchamps and Gubert (2007) In terms of community characteristic, the study

includes neighborhood social economic status and social problem into the analysis

of skill formation given the literature from (Mayer & Jencks, 1989), (Brooks-Gunn

et al., 1993)

characteristics characteristics characteristics

Chapter 3

3.1 Methodology

3.1.1 Technology of skill formation

Cunha and Heckman (2007, 2008) propose a model to investigate thedynamics of skill formation where a child’s current level of skills is explained byhis past level of skills, parental investment and other contemporaneous variablessuch as caregiver, household and community characteristics Since the time spanbetween each period in the sample is 3-4 years, this thesis assume that parentalinvestments have an immediate impact on skills ‘development, which is in line withCunha et al (2006) The general technology of skill formation is:

= ( −1 , , )

(1)

at age t ( ∈ {1, … }), and denotes a vector of child, caregiver, household and community characteristics.

In accordance to Cunha and Heckman (2007), the function in (1) is specified

as a linear specification as below:

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 if

Equation (3) demonstrates that t is critical period for the development of skill k if

parental investment is only productive in period t but not in any other period t+j≠0

Meanwhile, equation (4) shows that t is a sensitive period relative to period t+j if, at

period t than in another period t+j

Empirically, to identify critical periods, we look at the periods when coefficients for

investments are significant different from zero To identify sensitive periods, we estimate the

0 t is a sensitive period if the hypothesis cannot be rejected.

Due to the unavailability of data on cognitive and non-cognitive skills at the age

of 1, in round 1, child health is estimated against psychosocial risk factors (PRF)

According to Walker et al (2007), PRF comprise of parenting factors (caregiver

sensitivity) as well as contextual factors (maternal depression and exposure to

violence) It is expected that high PRF adversely affects child health, leading to child

stuntedness Then, child health enters the next period as an independent variable,

explaining the level of skills at the age of 5 Since, the instruments used to measure

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 above are estimated using OLS and Full Maximum Information Likelihood (FMIL) with the following

assumptions Firstly, latent cognitive and non-cognitive skills are independent of the error term for ∈ ( , ) Specifically, ( ,

+1 ) = 0 Secondly, the errors are serially uncorrelated, i.e ( , +1 ) = 0 Thirdly, ( , ) = 0 and ( , +1 ) = 0.

3.1.2 Genetic endowment – Initial conditions

As previously mentioned in section 2.2.1, genetic endowment is a vital

determinant of skill development The genetic endowment is reflected through an

individual initial skill set denoted as 0 = ( , 0 ) However, this endowment is

unobserved and heterogeneous across children Instead of using first differences

suggested by Todd and Wolpin (2007) to capture the unobserved characteristics of

genetic endowment, this study follows Helmers and Patnam (2011) to use several

control variables such as child height and weight, maternal health during pregnancy and

child birth’s location Height is one of the control variables since the researchers show

that genes may explain up to 90% of variation in height (Weedon et al., 2007)

3.1.3 Method in cognitive, non-cognitive skills measurement: Latent variables

There are several measures for cognitive and non-cognitive skills Thus, latent

variables are used instead of a single measure Latent variables are variables that are

not directly observed but are inferred from other indicators that are observed Given

the observed indicators, latent variables are estimated using confirmatory factor

analysis A one-factor model is employed to estimate the latent variables The

model is specified as below:

, , , ,

, , , ,

denotes different observed indicators of the latent variable , and , represents a measure-specific intercept and factor loading respectively and are unobserved.

The assumptions behind the model are: (1) the error terms are mutually independent

and independently distributed over time and across children, (2) the factor and error

term are uncorrelated and have an expected value of zero, (3) the factor and

observed variables are assumed to be linearly related

3.1.4 Instrument Variables

Although endogeneity due to measurement error can be mitigated by the

estimation of latent variables, endogeneity may still arise due to the potential

simultaneity between parental investment and child’s skill level Parental

investment and skill level may be codetermined On one hand, higher level of

parent al investment may result in higher level of skill On the other hand, a child

with a higher level of skill may lead parents to reduce or increase the investment on

that child To avoid the simultaneity bias, IV strategy is employed

Learning from Helmers and Patnam (2011), I choose household specific

shocks and a child birth’s order as instruments The argument for the validity of the

instruments is that household shocks and birth order affect skill level only throughparental investment.

In terms of household specific shocks, the shocks are likely to affect householdwealth and thereby making parents adjust their investment in the children Carneiro andGinja (2016), using the US Longitudinal Survey, find that income shocks areresponsible for the change in parental investment and that parental inputs (both timeand goods) are associated with changes in child development In other words, theyshow that the impact of shocks works through the impact on parental investment.Another evidence from Del Boca, Flinn, and Wiswall (2016) emphasize that therelationship between time investment of parents and child development is possiblygenerated by the impact of parental labor market shocks In short, the assumption forthe validity of household shocks seems credible

Likewise, parental investment in children is likely to be affected by child birthorder Parental investment is believed to act as a channel for birth order to pose animpact on child development instead of a direct impact Parents tend to adjustinvestment towards first child and later-born children The empirical evidence fromLehmann, Nuevo-Chiquero, and Vidal-Fernandez (2016) suggest that changes inparental investment can be explained using birth order and that parental investmentare plausible explanation for differences in cognitive achievement among differentbirth orders

3.2 Data

3.2.1 Data source

The data is a panel data retrieved from Young Lives study in Vietnam throughfour survey rounds in 2002, 2006, 2009 and 2013 The survey follows two cohorts ofchildren The older cohort comprises 1,000 children who were born in 1994-1995 andthe younger cohort comprises 2,000 children who were born in 2001-2002 YoungLives surveys were conducted in five provinces (Lao Cai, Hung Yen, Da Nang, PhuYen, Ben Tre) which represent five out of nine socio-economic regions of Vietnam

(North-East Region, Red River Delta, Cities, South Central Coast and Mekong RiverDelta) The main characteristics of the five selected provinces are summarized in theTable 14 in the appendix In each province, four sentinel sites are selected Amongwhich, two are from the poor group, one from the average and one from the above-average group This selection of sentinel sites leads Young Lives survey to oversamplethe poorer household as compared to other national surveys such as Demographic andHealth Survey (DHS) or Vietnam Household Living Standards Survey (VHLSS).Despite this fact, Young Lives dataset is child-focused and aims to cover the diversity

of children in the country (Young Lives, 2014) Then, within each sentinel site, 50children born in 1994-95 and 100 children born in 2001-02 were selected using randomsampling technique The children are tracked throughout all survey rounds Theattrition rates (drop-out rates) is 3.6% and 11.3% for the younger cohort and oldercohort respectively This study employs only the younger cohort The surveyquestionnaire for the younger cohort will be discussed below

The survey comprises of a core child questionnaire, a household questionnaireand a community questionnaire First, given the differences in child characteristics overvarious stages of life, the child questionnaire is adjusted in every round Overall, thequestionnaire for each child includes sections on school and work; time-use; health;feeling, attitudes and perceptions; social networks, social skills and social support; andcognitive tests The child questionnaire is only available to children from age 8onwards For children age 1 and age 5, the questions were answered by the caregivers.Second, the household questionnaire investigates household circumstances andincludes sections on parental background; household and child education; livelihoodsand asset framework; household food and nonfood consumption and expenditure;social capital; economic changes and recent life history; socio-economic status; Childhealth; anthropometry; caregiver perceptions and attitudes At age 1, householdquestionnaire also includes information on the pregnancy, delivery and breast-feeding

on the mother Finally, community questionnaire is answered by communityrepresentatives to collect information about

the characteristics of the locality such as the economy and issues affecting being of the children living in the community.

Table 1 presents the description of variables The dependent variables arecognitive skills, non-cognitive skills and child health (in round 1) These variablesare latent variables being estimated using confirmatory factor analysis (CFA) CFA

is favorable since it allows us to estimate one-factor model and test the validity ofthe model The primary independent variables are expenditures, quality ofrelationship between caregivers and the child, and PRF (for round 1 only) In terms

of control variables, children, caregivers, household and neighborhoodcharacteristics are included

The estimation of cognitive abilities is based on the results from cognitive testswhich had gone through pilot testing to ensure their suitability and validity Thecognitive tests being used are Peabody Picture Vocabulary Test (PPVT), CognitiveDevelopmental Assessment (CDA), Early Grade Reading Assessment (EGRA),Mathematics test and Reading comprehensive test The PPVT test assesses thevocabulary acquisition of a person from 2.5 years old to adulthood 1 CDA is acognitive test developed specifically to assess cognitive achievement of pre-primarychildren CDA has several subtests but only quantity subtest is used in YL2 EGRA isdesigned to measure the literacy acquisition of children in their early grades 3 Themath 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).

2 The quantity subtest asks the children to pick an image from a selection of three or four that best reflected the concept verbally presented by the examiner (i.e few, most, nothing, etc.)

3 The EGRA adapted for YL has three subtests: familiar-word identification, passage reading and listening comprehension The final score comprises of number of correct words read in a minute in familiar- word identification; number of correct words read in a minute in passage reading; number of correct

responses to reading comprehension items in passage reading; and number of correct responses to reading comprehension and listening comprehension (Cueto & Leon, 2012).

notion; and ability to work on basic mathematics operations with number Thesecognitive tests are not available in round 1 since the child is too young In round 2,

at the age of 5, PPVT and CDA are used The reliability and validity of the two tests

in round 2 are confirmed in a paper by Cueto et al (2009) In round 3, at the age of

8, the tests for cognitive skills are PPVT, EGRA and a math test In round 4, at theage of 12, the children take part in PPVT, a math test and a reading test

Regarding the measurements of non-cognitive skills, they are estimated based

on a Likert-scale self-administered questionnaire This questionnaire is onlyavailable to 1-year old and 8-year old children, who are able to answer thequestionnaire by themselves The indicators being measured are self-esteem (based

on Rosenburg 1965), self-efficacy (based on Rotter 1966 and Bandura 1993),friendliness and social trust The questions are adjusted to be relevant to children Afull set of questions for each indicator is presented in the appendix (Table 15) Foreach indicator, there are 4-8 questions The validity of these measures is confirmed

by Dercon and Krishnan (2009)

Table 1: Variable description

Estimated latent variables formeasures of personality traitsbased on a set of self-administeredquestionnaire using Likert scale

score

to child stunted and underweight.

Independent variables

Clothing expenditure Spending on clothing and footwear

for index child, based on theassumption that all children in the ‘000 VNDhousehold receive an equal share

of spendingEducation Spending over the last 12 months

expenditure on education for index child, based

on the assumption that all children ‘000 VND

in the household receive an equalshare of spending

Presents or treats Spending over the last 12 months

expenditure on present or treats for index child,

based on the assumption that all ‘000 VNDchildren in the household receive

an equal share of spendingLog of total Log of total spending on index

expenditure child (clothing, education, presents Continuous

or treat)Quality of CG-HH An index base of caregiver’s

relationship response to questions regarding

CH’s friends, teachers and Indexactivities after school (Only

available in Round 3 and 4)

Other Control variables

Child male 1 = Male, 0 = Female

Child siblings Number of siblings of the child

Preschool 1= Attend preschool 0 = Do not

attend preschoolChild years of Number of schooling year

23

why parents feel it is important tohave children (Only available forRound 2)

Caregiver education Education level of child’s primary

caregiver (category: none,primary, secondary, highschool orvocational, university or higher)Household social An index of the household

connectedness responses to the questions of its

kinship support base in community, how active it is andlevel of trust within the

community

Careed1

Careed3 Careed4

Household size Number of household member

Poor Household is classified as poor

using monthly per capita expenditure based on poverty line

Urban 1= household is in urban areas

Community social Fraction of households in the

economic status commune which were classified

as poor

Community social An index showing the level of

problems social problems in the community

(i.e theft, gang, drugs,alcoholism )

Instruments

Household shocks The household experience any

economic shocks that resulted inserious loss of wealth over the lastfour years In round 1, a dummy