Parental risk attitudes and child cognitive outcome in Indonesia

Hartarto, Romi Bhakti; Aravena, Claudia; Bhattacharjee, Arnab

doi:10.1080/09645292.2023.2184316

Cited by 5 publications

(4 citation statements)

References 57 publications

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“…This suggests that the endogeneity issue biased the coefficients downward. Moreso, the findings further strengthens the argument that risk preference among parents is a key determinant of child education in Ghana, which is consistent with Bezil Results from Table 3 shows the willingness of parents to take risk is associated with a positive influence on the overall learning outcome from child education at 99% confidence level, similar to the findings of Hartarto et al, (2023). Consequently, children from risk loving households have 0.311 units higher overall learning outcome than children from risk averse households.…”

Section: The Effect Of Risk Preference On Child Learning Outcomes (Iv...supporting

confidence: 85%

“…Therefore, the study operationalized risk preference as the quality of a household head being risk averse or risk loving. As indicated by Hartarto, Aravena, and Bhattacharjee, (2023); Sovero, (2018), we acknowledge that the hypothetical investment question used to measure RP may not be reflective of the actual behaviour of the respondents in real life. Nonetheless, empirical studies have established that a consistent and strong association exist between incentivized RP experiments and hypothetical RP responses (Binswanger, 1981;Alan, Baydar, Boneva, Crossley, & Ertac, 2017;Hartarto et al, 2023).…”

Section: Risk Preference (Rp)mentioning

confidence: 97%

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Risk Preference and Child Learning outcomes: A Study on Ghana

Mefful,

Nunoo,

Kofinti

2024

Preprint

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Human capital investment is noted to increase labour market success and future welfare security. But empirical studies have established that investment in human capital formation is a risky venture. Consequently, the risk preference of funders is pivotal in influencing the optimal allocation of household’s scarce resources in educational investment. Against this backdrop, we leveraged the seventh round of Ghana Living Standards Survey data to explore the relationship between parental risk preference and child learning outcomes. The instrumental variable estimation technique was employed to solve the endogeneity problem associated with the two variables. We found that parental risk preference is a significant determinant of children’s learning outcomes in Ghana given that children from risk loving homes have better learning outcomes. In addition, risk loving homes have better learning outcomes for girls and public-school children compared to boys and private school attendees. Further, children from risk-loving female headed households have better learning outcomes compared to children from risk-loving male headed households. Finally, risk loving households spend more on the education of their children than risk averse households. Our empirical test also showed that household expenditure on child education is the channel through which risk preference influence the learning outcome of children. We therefore recommend that policy makers implement programs that induce risk loving attitudes in parents. JEL Classification: JELI22, JEL I20, JEL I21, JEL J24, JEL D81

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Section: The Effect Of Risk Preference On Child Learning Outcomes (Iv...supporting

confidence: 85%

Section: Risk Preference (Rp)mentioning

confidence: 97%

Risk Preference and Child Learning outcomes: A Study on Ghana

Mefful,

Nunoo,

Kofinti

2024

Preprint

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“…This special attention is evidenced by the existence of a roadmap called "Making Indonesia 4.0" which was declared in 2018 (Setiono, 2019). This roadmap is needed as guidelines because of changes in business processes that depend on the intensive use of new digital technologies that require education updates for new workforces and retraining for existing workers (Hartarto, 2018).…”

Section: Analysis and Discussionmentioning

confidence: 99%

Intellectual Capital Measurement: Extended-Vaic Vs Vaic Which One Is Better?

Pradono

Bertuah

2022

JAB

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This study aims to make comparison between two measurement model for Intellectual Capital and explore its impact on financial and stock performance. As many as 19 non-financial companies with 183 observations for the period of 2010-2020 (unbalanced panel) was used for regression model. Fixed Effect Model with robust is the most suitable model for panel data regression in this research. According to the results both models are fit to predict internal financial performance with ROA and ROE as proxy but not fit to predict stock valuation in the market with MBV as proxy. VAIC model is better for IC measurement than E-VAIC model. Human Capital is the most significant component both for VAIC and E-VAIC model. The result also indicated that Relationship capital efficiency has pure moderating role.

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“…This investigation's findings can act as a guide for educators and other researchers in creating further studies, such as determining learning approaches or developing relevant learning materials. Computational thinking is undeniably intertwined with divergent thinking, innovativeness, resolving problems, conceptual thinking, repetition, collaboration, patterns, synthesis, and self-reflection (Hartarto, 2018). The students' capability for self-reflection is a crucial emotional factor that contributes to the process of resolving mathematical issues.…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness Analysis of Computational Thinking Patterns and Levels of Students’ Meta-Cognitive Awareness in Solving Learning Problems

Sudadi,

Lolang,

Ariawan

et al. 2023

joe

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The goal of this study is to characterize students' computational thinking skills in problem-solving in terms of their degree of metacognition awareness. The study is descriptive and qualitative in nature. The participants were selected using purposive sampling, comprising of two students with reflective metacognitive awareness, two students with strategic metacognitive awareness, two students with aware metacognitive awareness, and two students with tacit metacognitive awareness. The data was collected through written tests and interviews and analyzed based on the computational thinking indicators. The findings reveal that the computational thinking abilities of students who use metacognitive awareness in problem-solving are abstraction, pattern recognition, and decomposition. Furthermore, students with strategic metacognitive awareness exhibit proficiency in abstraction and pattern recognition. Additionally, students who use metacognition awareness through abstraction or decomposition exhibit computational thinking abilities when solving problems. However, students with tacit metacognitive awareness do not meet the computational thinking indicators while solving problems.

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Parental risk attitudes and child cognitive outcome in Indonesia

Cited by 5 publications

References 57 publications

Risk Preference and Child Learning outcomes: A Study on Ghana

Risk Preference and Child Learning outcomes: A Study on Ghana

Intellectual Capital Measurement: Extended-Vaic Vs Vaic Which One Is Better?

The Effectiveness Analysis of Computational Thinking Patterns and Levels of Students’ Meta-Cognitive Awareness in Solving Learning Problems

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