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2024 | OriginalPaper | Chapter

Household Socio-Economic Characteristics of NEETs in Thailand

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Abstract

Due to the heterogeneous socio-economic backgrounds and multifaceted causes contributing to the phenomenon of NEETs (youths not in education, employment, or training), this study classifies youths into six groups by their status including in-school, employed, NEET due to job seeking, NEET due to family responsibility, NEET due to disability, and other NEETs. Subsequently, we analyze a comprehensive list of socio-economic factors associated with different groups of youths in Thailand. The empirical analysis employs the Generalized Maximum Entropy (GME) model for the multinomial choice to analyze Thailand’s socio-economic survey data. Consistent with prior research, the findings reveal that youths living in poverty or lower consumption expenditure households are more inclined to leave formal education to work or become NEETs, except in the case of NEETs due to disability. This highlights the necessity to extend support beyond education and employment opportunities to address family responsibilities, particularly for youths in households with high dependency ratios or infants. Moreover, the study underscores the diversity of NEET situations among youths from other different socio-economic backgrounds. Notably, there is evidence linking NEET status due to job-seeking with youths from higher socio-economic backgrounds, suggesting potential labor market challenges. Additionally, youths with disabilities encounter barriers not only due to their condition but also in job-seeking. While common policy measures exist, tailored interventions are crucial to facilitate their reintegration into formal education or the labor market.

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Footnotes
1
The 2019 SES data comprises of 11,801 individuals aged between 15 and 24. However, 14 observations were excluded because they resided in the households as housemaids and, thus, the household characteristics did not accurately reflect their socio-economic status. Additionally, 47 observations were excluded due to inconsistencies in their reported educational status. As a result, the analysis in this study was conducted on a sample of 11,740 youths.
 
2
Due to the restrictions of the SES data, this study is unable to categorize NEETs into the seven categories outlined by Eurofound [5] and can only classify NEETs into four categories.
 
3
As per the definition of unemployment, which encompasses both (1) individuals actively seeking employment, and (2) those not actively seeking employment but willing and able to work if an opportunity arises, the seeking group represents only a subset of the overall unemployed NEET population, and not the entire group.
 
4
For the result comparison purpose, all \(x_{i k}=\frac{X_{i k}-\bar{X}_{i k}}{S D\left( X_{i k}\right) }\) are the standardized value of the corresponding explanatory variable \(X_{i k}\).
 
5
To calculate the poverty dummy variable, this study compares each household’s per-capita consumption with the regional poverty line reported by NESDC (2019). All youths residing in a household that falls below the poverty line is considered to be in a state of poverty.
 
6
See Benjamin and Berger [2] for more explanation on the Bayes factor upper bound (BFUB).
 
7
There is no specific cut-off point for a strong evidence. However, for a reference point, the p-value of 0.01 corresponds to a BFUB of 8.13 [2].
 
8
socioclass15 is selected as the base group for the analysis because it is the largest group (40.2% of the sample).
 
9
The dependency ratios considered in this study include the ratio of infants (aged 3 and under), children (aged 4–15), and seniors (aged 65 and over), which are calculated from the numbers of infants, children, and seniors in the households divided by household size.
 
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Metadata
Title
Household Socio-Economic Characteristics of NEETs in Thailand
Author
Supanika Leurcharusmee
Copyright Year
2024
DOI
https://doi.org/10.1007/978-3-031-67770-0_29