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Prior research shows that economic downturns are associated with increases in mental illness. However, we know little about whose mental health is most negatively affected. Is it the young or old, men or women, employed or non-employed, rich or poor? Using an 18-year panel dataset of Australians, we contribute to this understanding by estimating the impact of changes in unemployment on mental health, separately by population subgroups. Our mental health measure captures psychological distress and emotional difficulties, which are often missed by infrequent event indicators such as suicides. We find that young women suffer most during economic downturns. Men and women of older ages are not significantly affected. The effects for young women are driven by those in insecure employment, and those from low socioeconomic backgrounds. Our results suggest that public health programs should emphasize the mental health of young women during economic downturns., (© 2021 John Wiley & Sons Ltd.)

Obesity, like many health conditions, is more prevalent among the socioeconomically disadvantaged. In our data, very poor women are three times more likely to be obese and five times more likely to be severely obese than rich women. Despite this strong correlation, it remains unclear whether higher wealth causes lower obesity. In this paper, we use nationally representative panel data and exogenous wealth shocks (primarily inheritances and lottery wins) to shed light on this issue. Our estimates show that wealth improvements increase weight for women, but not men. This effect differs by initial wealth and weight-an average-sized wealth shock received by initially poor and obese women is estimated to increase weight by almost 10 lb. Importantly, for some females, the effects appear permanent. We also find that a change in diet is the most likely explanation for the weight gain. Overall, the results suggest that additional wealth may exacerbate rather than alleviate weight problems., (Copyright © 2014 John Wiley & Sons, Ltd.)

Obese children tend to perform worse academically than normal-weight children. If poor cognitive achievement is truly a consequence of childhood obesity, this relationship has significant policy implications. Therefore, an important question is to what extent can this correlation be explained by other factors that jointly determine obesity and cognitive achievement in childhood? To answer this question, we exploit a rich longitudinal dataset of Australian children, which is linked to national assessments in math and literacy. Using a range of estimators, we find that obesity and body mass index are negatively related to cognitive achievement for boys but not girls. This effect cannot be explained by sociodemographic factors, past cognitive achievement or unobserved time-invariant characteristics and is robust to different measures of adiposity. Given the enormous importance of early human capital development for future well-being and prosperity, this negative effect for boys is concerning and warrants further investigation., (Copyright © 2015 John Wiley & Sons, Ltd.)

Self-assessed general health (SAH) is one of the most frequently employed health measures in social science research. Its generic nature means it captures elements of health that more guided measures cannot, and its brevity makes it possible for health information to be included in crowded multifaceted surveys. However, a shortcoming of SAH is that it provides little guidance to researchers as to what individuals are thinking of when they assess their health - when a survey respondent reports that their health is "poor", is it because they are in pain, tired, depressed, unable to climb stairs, or something else entirely? This limits the possible inference from empirical research. It also means that important determinants and consequences of health can be missed if they are only weakly reflected in SAH. Given the continued use of SAH, it is important to better understand its structure. In this paper we use household panel data from Australia to answer two related questions: (i) what components of health does SAH most strongly represent? and (ii) does the use of SAH conceal important health effects? To answer the first question, we use a detailed health instrument and take a rigorous econometric approach to identify the health dimensions most strongly reflected in SAH. To answer the second question, we estimate the causal effects of income on SAH and on disaggregated health measures using instrumental-variables models. We find that some health dimensions - especially vitality - are consistently important to an individual when they assess their health, while other dimensions are inconsequential. We demonstrate that this fact provides insight in to why some studies find weak income gradients in SAH. Instrumental-variable regression results show that shocks to household income have no effect on SAH, but strongly improve several dimensions of health that are less commonly measured., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

To what extent does poor mental health affect employment outcomes? Answering this question involves multiple technical difficulties: two-way causality between health and work, unobservable confounding factors and measurement error in survey measures of mental health. We attempt to overcome these difficulties by combining 10 waves of high-quality panel data with an instrumental variable model that allows for individual-level fixed effects. We focus on the extensive margin of employment, and we find evidence that a one-standard-deviation decline in mental health reduces employment by 30 percentage points. Further investigations suggest that this effect is predominantly a supply rather than a demand-side response and is larger for older than young workers., (Copyright © 2014 John Wiley & Sons, Ltd.)