scholarly journals Social Participation as a Moderator for Caregivers’ Psychological Distress: a Dynamic Panel Data Model Analysis in Japan

2021 ◽  
Author(s):  
Takashi Oshio ◽  
Kemmyo Sugiyama
Keyword(s):  
Psychological Distress ◽  
Panel Data ◽  
Social Participation ◽  
Family Caregiving ◽  
Positive Impact ◽  
Nationwide Survey ◽  
Dynamic Panel Data ◽  
Moderating Effect ◽  
Dynamic Panel ◽  
Dynamic Framework

AbstractThe adverse impact of caregiving on caregivers’ mental health and the positive impact of social participation (SP) on health are both well understood. This study examined the moderating effect of SP on the association between family caregiving and caregivers’ psychological distress (PD). We used longitudinal data from 27,869 individuals born between 1946 and 1955 collected from a 14-wave nationwide survey, which was conducted from 2005 to 2018. We estimated dynamic panel data models, which could control for an individual’s time-invariant attributes in a dynamic framework, to examine how SP moderated the association between informal caregiving and a caregiver’s PD (defined by a Kessler score of 13 or higher). We observed that the onset of caregiving increased the probability of PD by 2.1% (95% confidence interval [CI]: 1.5%–2.6%) and 1.0% (95% CI: 0.5%–1.6%) for women and men, respectively, compared to 3.4% and 2.8% as the prevalence of PD for women and men, respectively. SP moderated the association between caregiving and a caregiver’s PD by 55.8% (95% CI: 31.9%–79.8%) and 73.5% (95% CI: 36.1%–110.9%) for women and men, respectively. In addition, the moderating effect of SP on a caregiver’s PD increased as the caregiver’s age advanced especially in women. These results suggest the need to keep family caregivers from being socially isolated, especially as they get older.

scholarly journals Social Capital as a Moderator for Caregivers’ Psychological Distress: A Dynamic Panel Data Model Analysis in Japan

2021 ◽  
Author(s):  
Takashi Oshio ◽  
Kemmyo Sugiyama
Keyword(s):  
Social Capital ◽  
Psychological Distress ◽  
Panel Data ◽  
Family Caregiving ◽  
Positive Impact ◽  
Psychological Impact ◽  
Dynamic Panel Data ◽  
Moderating Effect ◽  
Dynamic Panel ◽  
Dynamic Framework

Abstract Background: The adverse impact of caregiving on caregivers’ mental health and the positive impact of social capital (SC) on health are both well understood. This study examined the moderating effect of SC on the association between family caregiving and caregivers’ psychological distress (PD).Methods: We used longitudinal data from 27,869 individuals born between 1946 and 1955. The data were collected from a 14-wave nationwide longitudinal survey conducted from 2005 to 2018. We estimated dynamic panel data (DPD) models, which could control for an individual’s time-invariant attributes in a dynamic framework. We did this to examine how SC moderated the association between informal caregiving and a caregiver’s PD (defined by a Kessler score of 13 or higher). We also examined how the results varied over time, as the caregiver’s age advanced.Results: Of the respondents aged 50–73 years, 12.5% of women and 8.4% of men provided care to their older parents or spouses. The DPD model results showed that the onset of caregiving increased the probability of PD (M 3.4%) by 2.1% (95% confidence interval [CI]: 1.6%–2.7%) and 1.1% (95% CI: 0.5%–1.6%) for women and men, respectively. SC moderated the association between caregiving and a caregiver’s PD by 53.4% (95%: 30.4%–76.4%) and 84.9% (95% CI: 62.0%–107.8%) for women and men, respectively. We also observed that the moderating effect of SC on a caregiver’s PD increased as the caregiver’s age advanced in both women and men, preventing a deterioration in the psychological impact of caregiving. Conclusions: The results underscore the moderating effect of SC on the association between caregiving and PD. This suggests the need to keep family caregivers from being socially isolated, especially as they get older.

scholarly journals Real exchange rate undervaluation and sectoral growth in South Africa

2018 ◽  
Vol 9 (4) ◽  
pp. 462-476
Author(s):  
Brian Tavonga Mazorodze ◽  
Dev D. Tewari
Keyword(s):  
South Africa ◽  
Exchange Rate ◽  
Panel Data ◽  
Real Exchange Rate ◽  
Positive Impact ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Content Type ◽  
Sectoral Growth ◽  
The Impact

PurposeThe purpose of this paper is to establish the empirical link between real exchange rate (RER) undervaluation and sectoral growth in South Africa between 1984 and 2014.Design/methodology/approachThe study employs a dynamic panel data approach estimated by the system generalised method of moments technique in a bid to control for endogeneity.FindingsThe authors find a significant positive impact of undervaluation on sectoral growth which increases with capital accumulation. Also, the authors confirm that undervaluation promotes sectoral growth up to a point where further increases in undervaluation retards growth.Practical implicationsThe results confirm the importance of policies that keep the domestic currency weaker to foster sectoral growth.Originality/valueThe originality of this paper lies in establishing the impact of exchange rate undervaluation on growth at a sector level in the context of South Africa using a dynamic panel data approach.

scholarly journals Impact of Population Growth on Economic Growth in Africa: A Dynamic Panel Data Approach (1980 -2015)

2018 ◽  
Vol 6 (4) ◽  
Author(s):  
Amade Peter ◽  
Ibrahim H. Bakari
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Population Growth ◽  
Negative Impact ◽  
Positive Impact ◽  
Dynamic Panel Data ◽  
African Countries ◽  
Dynamic Panel ◽  
System Gmm ◽  
The Impact

This study examines the impact of population growth on the economic growth of African countries using panel data approach from 1980 -2015. The impact of population growth on economic growth is still largely controversial at national and regional levels. The study used annual secondary data of fifty three (53) African countries sourced from the World Development Indicators database. Data were collected for economic growth, proxied by GDP, population growth, fertility rate, crude death rate and inflation rate. The data were analyzed using descriptive statistics, as well as dynamic panel models of difference and system GMM. The results of the difference and system GMM suggest that population growth exerts a positive impact on economic growth of Africa while fertility has a negative impact on economic growth of Africa. The paper concludes and recommends that population growth impacts positively on economic growth and thus African countries should adopt and implement pragmatic policy measures that will enhance the productivity of its population so as to reap more demographic dividends.

Import, Export and Economic Growth: A GMM Analysis Based on Dynamic Panel Data from Chinese Prefecture City

2013 ◽  
Vol 869-870 ◽  
pp. 676-682
Author(s):  
Li Ming Hong
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Development Strategy ◽  
Positive Impact ◽  
Dynamic Panel Data ◽  
Environmental Damage ◽  
Dynamic Panel ◽  
The Development Strategy ◽  
First Time ◽  
Growth Hypothesis

This paper employed GMM for the first time to estimate the dynamic relevance between foreign trade and the economic growth based on the panel data of 284 Chinese Prefecture cities from 1994 to 2010. We find that both export and import improve the economic in general and the export-led growth hypothesis holds in China. Especially, the positive impact of import maybe even outnumber export. This finding is important to reconsider the weight of the export and import and correct the development strategy to sustainable development strategy.That would help reduce the pollution from inefficiency production to export and the environmental damage. Furthermore there is no significant evidence of investment and human capital in promoting economic development .

Bayesian estimation of dynamic panel data gravity model

2021 ◽  
Vol 40 (7) ◽  
pp. 607-634
Author(s):  
Moonhee Cho ◽  
Xiaoyong Zheng
Keyword(s):  
Panel Data ◽  
Bayesian Estimation ◽  
Gravity Model ◽  
Dynamic Panel Data ◽  
Dynamic Panel
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