Leisure goods, education attainment and fertility choice

2011 ◽  
Vol 16 (2) ◽  
pp. 157-181 ◽  
Author(s):  
Ragchaasuren Galindev
2020 ◽  
Author(s):  
Ruoyan Sun ◽  
Henna Budhwani

BACKGROUND Though public health systems are responding rapidly to the COVID-19 pandemic, outcomes from publicly available, crowd-sourced big data may assist in helping to identify hot spots, prioritize equipment allocation and staffing, while also informing health policy related to “shelter in place” and social distancing recommendations. OBJECTIVE To assess if the rising state-level prevalence of COVID-19 related posts on Twitter (tweets) is predictive of state-level cumulative COVID-19 incidence after controlling for socio-economic characteristics. METHODS We identified extracted COVID-19 related tweets from January 21st to March 7th (2020) across all 50 states (N = 7,427,057). Tweets were combined with state-level characteristics and confirmed COVID-19 cases to determine the association between public commentary and cumulative incidence. RESULTS The cumulative incidence of COVID-19 cases varied significantly across states. Ratio of tweet increase (p=0.03), number of physicians per 1,000 population (p=0.01), education attainment (p=0.006), income per capita (p = 0.002), and percentage of adult population (p=0.003) were positively associated with cumulative incidence. Ratio of tweet increase was significantly associated with the logarithmic of cumulative incidence (p=0.06) with a coefficient of 0.26. CONCLUSIONS An increase in the prevalence of state-level tweets was predictive of an increase in COVID-19 diagnoses, providing evidence that Twitter can be a valuable surveillance tool for public health.


Author(s):  
Lilik Sugiharti ◽  
Martha Ranggi Primanthi

Objective - The objectives of the study were to analyze the general picture of poverty, and determinants of poverty in Indonesia. Understanding poverty characteristic is a main point for designing an effective poverty reduction strategy. During the last five years Indonesia has experienced a slowing down growth and the poverty rates has declined slightly. Some provinces or regions have managed to reduce the poverty well, while others have been slower, and also the distribution of the poor is uneven across both rural and urban, generally the rural is more than urban area. Methodology/Technique - Factors determining poverty of households were estimated and anayzed using a logit regression model, and it is found that such demographic factors as gender and age of households head, size of households, factors of production included accessibility to the technology and credit, working status, and education attainment, and also geographic characteristics significantly explain reasons for being poor. Moreover, increasing for accessibility of households to the technology and credit, reducing the size of households, and increasing an education attainment especially in rural area are important to do as a government priority intervention. Findings - The results of the determinants of poverty in Indonesia shows that poor households are those with large number of dependents and equipped with limited education access, and the majority of these households live in rural area. Novelty - Study suggests that increasing for accessibility of households to the technology and credit, reducing the size of households, and increasing an education attainment especially in rural area are important to do as a government priority intervention or policy implications. Type of Paper: Empirical Keywords: Logit Regression; Poverty Reduction, Indonesia. JEL Classification: I21, I22, I24.


2021 ◽  
pp. 000486742110512
Author(s):  
Francisco Tsz Tsun Lai ◽  
Vivien Kin Yi Chan ◽  
Tsz Wai Li ◽  
Xue Li ◽  
Stevan E Hobfoll ◽  
...  

Objective: There is a socioeconomic gradient to depression risks, with more pronounced inequality amid macroenvironmental potential traumatic events. Between mid-2019 and mid-2020, the Hong Kong population experienced drastic societal changes, including the escalating civil unrest and the COVID-19 pandemic. We examined the change of the socioeconomic gradient in depression and the potential intermediary role of daily routine disruptions. Method: We conducted repeated territory-wide telephone surveys in July 2019 and July 2020 with 1112 and 2034 population-representative Cantonese-speaking Hong Kong citizens above 15 years old, respectively. Stratified by year, we examined the association between socioeconomic indicators (education attainment, household income, employment status and marital status) and probable depression (nine-item Patient Health Questionnaire [PHQ-9] ⩾ 10) using logistic regression. Differences in the socioeconomic gradient between 2019 and 2020 were tested. Finally, we performed a path analysis to test for the mediating role of daily routine disruptions. Results: Logistic regression showed that higher education attainment in 2019 and being married in 2020 were protective against probable depression. Interaction analysis showed that the inverse association of higher education attainment with probable depression attenuated in 2020 but that of being married increased. Path analysis showed that the mediated effects through daily routine disruptions accounted for 95.9% of the socioeconomic gradient of probable depression in 2020, compared with 13.1% in 2019. Conclusion: From July 2019 to July 2020, the mediating role of daily routine disruptions in the socioeconomic gradient of depression in Hong Kong increased. It is thus implied that infection control measures should consider the relevant potential mental health impacts accordingly.


2018 ◽  
Author(s):  
Felix C Tropf

To what extent do genes influence the age at which you have your first child and the total number of children that you have? Does the (social) environment change genetic effects on fertility? Do genes lead to spurious associations between life outcomes such as education and age at first birth? The social sciences have been reticent to integrate a genetic approach to the study of fertility choice and behaviour, resulting in theories and findings that are largely socially deterministic. This dissertation investigates genetic and environmental influences on human fertility—aswell as their interplay—using both twin data as well as molecular genetic data of more than 31,000 genotyped individuals from 6 countries.


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