Studying informal care during the pandemic: mental health, gender and job status

Unexpected negative health shocks such as COVID-19 put pressure on households to provide more care to relatives and friends. This study uses data from the UK Household Longitudinal Study to investigate the impact of informal caregiving on mental health during the COVID-19 pandemic. Using a difference-in-differences analysis, we find that individuals who started providing care after the pandemic began reported more mental health issues than those who never provided care. Additionally, the gender gap in mental health widened during the pandemic, with women more likely to report mental health issues. We also find that those who began providing care during the pandemic reduced their work hours compared to those who never provided care. Our results suggest that the COVID-19 pandemic has had a negative impact on the mental health of informal caregivers, particularly for women

Health Care Expenditure and Income: a Global Perspective

Abstrac

Studying informal care during the pandemic: mental health, gender and job status

Unexpected negative health shocks such as COVID-19 put pressure on households to provide more care to relatives and friends. This study uses data from the UK Household Longitudinal Study to investigate the impact of informal caregiving on mental health during the COVID-19 pandemic. Using a difference-in-differences analysis, we find that individuals who started providing care after the pandemic began reported more mental health issues than those who never provided care. Additionally, the gender gap in mental health widened during the pandemic, with women more likely to report mental health issues. We also find that those who began providing care during the pandemic reduced their work hours compared to those who never provided care. Our results suggest that the COVID-19 pandemic has had a negative impact on the mental health of informal caregivers, particularly for women

The role of economic news in predicting suicides

JEL classification: I14; I15.Data availability: The authors do not have permission to share data.Appendix. Brief overview of the WordNet-Affect: A synset is a group of data elements that are considered semantically equivalent for the purposes of information retrieval. WordNet-Affect is an extension of WordNet Domains (see Magnini and Cavaglià, 2000), that includes a set of synsets suitable to represent affective concepts representing moods, situations eliciting emotions, or emotional responses. The authors specifically initially identified a set of words that directly refer to emotional states (e.g. fear, cheerful, sad). Then, they expanded this initial set by implementing an unsupervised algorithm that exploited a mechanism of semantic similarity to automatically acquire from a large corpus of texts (100 millions of words). The final data set includes 1641 terms characterising 28 different emotions. Further information on the approach are available at the web link https://wndomains.fbk.eu/wnaffect.html .In this paper we explore the role of media and language used to comment on economic news in nowcasting and forecasting suicides in England and Wales. This is an interesting question, given the large delay in the release of official statistics on suicides. We use a large data set of over 200,000 news articles published in six major UK newspapers from 2001 to 2015 and carry sentiment analysis of the language used to comment on economic news. We extract daily indicators measuring a set of negative emotions that are often associated with poor mental health and use them to explain and forecast national daily suicide figures. We find that highly negative comments on the economic situation in newspaper articles are predictors of higher suicide numbers, especially when using words conveying stronger emotions of fear and despair. Our results suggest that media language carrying very strong, negative feelings is an early signal of a deterioration in a population’s mental health

The impact of air pollution on hospital admissions: Evidence from Italy

In this paper we study the impact of air pollution on hospital admissions for chronic obstructive pulmonary disease for 103 Italian provinces, over the period from 2004 to 2009. We use information on annual mean concentrations of carbon monoxide, nitrogen dioxide, particulate matter, and ozone measured at monitoring station level to build province-level indicators of pollution. Hence, we estimate a regression model for hospital admissions, where we allow our aggregate measures of pollution to be subject to measurement error and correlated with the error term. We also adopt standard errors for estimates that are robust to serial and spatial correlation in the error term, to allow for temporal persistence and geographical concentration of unobservable risk factors.We find that higher levels of particulate matter are associated with higher levels of hospitalisation for children, while ozone plays an important role in explaining hospital admissions of the elderly. Other factors that appear to have an effect on hospital admissions for chronic obstructive pulmonary disease are precipitation and provincial unemployment rate