Healthy Living in Hard Times

Ruhm, Christopher J.

doi:10.3386/w9468

Cited by 257 publications

(417 citation statements)

References 34 publications

(13 reference statements)

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“…However the notion that increases in non-labor time may actually be good for health is supported by several studies that find increases in unemployment to be associated with reductions in overall mortality rates (Tapia Granados, 2005, Ruhm, 1995, Ruhm, 2000, Ruhm, 2003, Ruhm, 2005, Gerdtham and Ruhm, 2006. A common explanation for the latter is that healthy lifestyles are also countercyclical: joblessness increases physical activity among the habitually inactive, as well as weight loss among the severely obese.…”

Section: Introductionmentioning

confidence: 72%

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Unemployment Insurance and Physical Activity

Cylus¹

2017

Human Capital and Health Behavior

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Unemployment insurance (UI) reduces the opportunity cost of leisure, but it is unknown whether this additional leisure time is physically active. To obtain unbiased estimates of the effect of UI on physically active leisure participation, I exploit changes in UI program legislation across US states and time. Using nationally representative monthly data between 2003 and 2010 from the Behavioral Risk Factor Surveillance System (BRFSS) and the American Time Use Survey (ATUS), I find evidence that both state UI eligibility expansions and increases in maximum allowable state UI benefits coincide with greater probability of physical activity among the recently unemployed. Based on point estimates, state UI eligibility expansions increased the probability of physical activity participation by 8 to 10 percentage points among the unemployed with less than a high school education, while a 10 percent increase in the maximum allowable state UI benefit increased the probability of physical activity by 0.3 to 0.6 percentage points among the unemployed who have completed high school or some college.

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Section: Introductionmentioning

confidence: 72%

“…For example, a one percentage point increase in US state unemployment rates has been associated with a sizeable 1.5% increase in physical activity and a 1.4% decrease in severe obesity at the population level (Ruhm 2005).…”

Section: Introductionmentioning

confidence: 99%

Unemployment Insurance and Physical Activity

Cylus¹

2017

Human Capital and Health Behavior

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“…As discussed in Section 2, measuring the effects of the intensity of local copper output instead of simply examining the difference between mining cities and non-mining areas in average behavior before and during the boom is particularly important in this setting because of substantial variation across mines in the intensity of the boom. This approach is virtually identical to that in Ruhm (2000), Ruhm (2003), Dehejia and Lleras-Muney (2004), and Ruhm (2005), studies that examine the effects of state-level unemployment rates on health in the United States. Likewise, this approach is similar in spirit to the difference-in-differences approaches in Bleakley (2007), Fortson (2009), Fortson (2010, and Lucas (2010), approaches that focus on estimating the effects of health shocks using variation in the shock on the intensive margin.…”

Section: Empirical Strategymentioning

confidence: 99%

Economic booms and risky sexual behavior: Evidence from Zambian copper mining cities

Wilson

2012

Journal of Health Economics

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Existing studies suggest that individual and household level economic shocks affect the demand for and supply of risky sex. However, little evidence exists on the effects of an aggregate shock on equilibrium risky sexual behavior. This paper examines the effects of the early twenty-first century copper boom on risky sexual behavior in Zambian copper mining cities. The results indicate that the copper boom substantially reduced rates of transactional sex and multiple partnerships in copper mining cities. These effects were partly concentrated among young adults and copper boom induced in-migration to mining cities appears to have contributed to these reductions.JEL classification: I18, J10, O12, O40

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“…A number of regression-based correction procedures have been proposed (Bolton-Smith et al, 2000;Spencer et al, 2002;Plankey et al, 1997). In the economics literature, researchers (Cawley, 2004;Chou et al, 2004;Ruhm, 2005) have regressed clinical measures of weight (height) on a quadratic of the corresponding self-reported variable using National Health and Nutrition Examination Survey (NHANES) data and then used the resulting regression estimates to predict actual weight (height) as a function of the self-reported values in a target data set (such as the NLSY) containing only the latter. 17 We found that our results were not substantively altered by using this procedure.…”

Section: Datamentioning

confidence: 99%

Age, socioeconomic status and obesity growth

Baum

Ruhm

2009

Journal of Health Economics

210

156

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Abstract:We use panel data from the National Longitudinal Survey of Youth (NLSY) to examine how body weight changes with age for a cohort moving through early adulthood, to investigate how the age-obesity gradient differs with socioeconomic status (SES) and to study channels for these SES disparities. Our results show first that weight increases with age and is inversely related to SES during childhood. Second, the obesity gradient widens over the lifecycle, consistent with research on other health outcomes. Third, a substantial portion of the -effect‖ of early life conditions operates through race/ethnicity and the translation of advantaged family backgrounds during childhood into higher levels of subsequent education. By contrast, little of the SES gap appears to propagate through household composition, family income or health behaviors. Fourth, adult SES has independent effects after controlling for childhood status.

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Healthy Living in Hard Times

Cited by 257 publications

References 34 publications

Unemployment Insurance and Physical Activity

Unemployment Insurance and Physical Activity

Economic booms and risky sexual behavior: Evidence from Zambian copper mining cities

Age, socioeconomic status and obesity growth

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