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Trust is considered instrumental for economic growth, successful operation of public institutions and social cohesion. We explore how public trust in Icelandic institutions has developed during the recent tumultous financial times, including the failure of the Icelandic banking sector. Using data from Gallup-Iceland’s annual survey of individuals’ trust in institutions, we show that trust in general, and particularly towards political and financial institutions, evaporates following the crisis year of 2008. Although trust varies significantly among different demographic groups, the trend shows how the road to recovering trust in Icelandic institutions post-crisis has proven to be challenging and drawn-out. Apart from law-enforcement agencies, which were relatively unscathed by the financial calamities, no institution has managed to escape the drop in trust, nor have they re-established the pre-crisis level of trust in the minds of the public nearly a decade after the crisis. A notable personal post-crisis exception is the recently elected President of Iceland who has managed to improve trust in his office by the highest margin of all 15 public offices and institutions examined.

The authors examine whether or not applicants and recipients of federal disability insurance (DI) inflate their self-assessed health (SAH) problems relative to others. To do this, the authors employ a technique which uses anchoring vignettes. This approach allows them to examine how various cohorts of the population interpret survey questions associated with subjective self-assessments of health. The results of the analysis suggest that DI participants do inflate the severity of a given health problem, but by a small but significant degree. This tendency to exaggerate the severity of disability problems is much more apparent among those with more education (especially those with a college degree). In contrast, racial minorities tend to underestimate severity ratings for a given disability vignette when compared to their white peers.

The health consequences of child labor may take time to manifest themselves. This study examines whether children who began working at a young age experience increased incidence of illness or physical disability as adults. When child labor and schooling are treated as chosen without consideration of unobserved abilities or health endowments, child labor appears to have small adverse effects on a wide variety of health measures. Some adverse health consequences such as heart disease or hypertension seem unlikely to be caused by child labor. However, when we allow unobserved health and ability endowments to alter the age of labor market entry and years of schooling completed, the joint effects of child labor and schooling on health become larger while the less plausible health consequences lose significance. Results imply that delaying entry into child labor while increasing time in school significantly lowers the probability of early onset of physical ailments such as back problems, arthritis, or reduced strength or stamina. However, our methods are not able to distinguish between the health impacts of child labor from the impacts of reduced time in school.

This chapter identifies three crisis warning indicators driven from trading in emerging markets’ carry trades, and empirically examines whether these indicators could predict two major financial crises that hit the global financial markets in the last decades — The 1997–1998 Asian crisis and the 2007–2008 global crisis. The probit regression is used to examine the power of the three indicators in forecasting financial crises, using data from eight Asian emerging countries which serve as proxies for emerging markets, independent of the origination of the crisis. I use both fixed effect and random effect estimation to measure crisis impacts. The empirical results show that financial crises could have been predicted. Probit estimation show that carry trade returns can predict a financial crisis, and the estimation results are robust to both panel level and country-level analysis. These three indicators are by no means an exhaustive list of all possible predictors of financial crisis. The literature suggests other fundamental indicators of financial crises such as the current account deficit and foreign debt. However, this chapter cannot fully consider these indicators for lack of data at this point in time. Although financial crisis may be better predicted by the well-known fundamental indicators, the contribution of this chapter is simply that carry trade-related indicators can help in predicting crises.

Health scientists and urban planners have long been interested in the influence that the built environment has on the physical activities in which we engage, the environmental hazards we face, the kinds of amenities we enjoy, and the resulting impacts on our health. However, it is widely recognized that the extent of this influence, and the specific cause-and-effect relationships that exist, are still relatively unclear. Recent reviews highlight the need for more individual-level data on daily activities (especially physical activity) over long periods of time linked spatially to real-world characteristics of the built environment in diverse settings, along with a wide range of personal mediating variables. While capturing objective data on the built environment has benefited from wide-scale availability of detailed land use and transport network databases, the same cannot be said of human activity. A more diverse history of data collection methods exists for such activity and continues to evolve owing to a variety of quickly emerging wearable sensor technologies. At present, no “gold standard” method has emerged for assessing physical activity type and intensity under the real-world conditions of the built environment; in fact, most methods have barely been tested outside of the laboratory, and those that have tend to experience significant drops in accuracy and reliability. This paper provides a review of these diverse methods and emerging technologies, including biochemical, self-report, direct observation, passive motion detection, and integrated approaches. Based on this review and current needs, an integrated three-tiered methodology is proposed, including: (1) passive location tracking (e.g., using global positioning systems); (2) passive motion/biometric tracking (e.g., using accelerometers); and (3) limited self-reporting (e.g., using prompted recall diaries). Key development issues are highlighted, including the need for proper validation and automated activity-detection algorithms. The paper ends with a look at some of the key lessons learned and new opportunities that have emerged at the crossroads of urban studies and health sciences.

We do have a vision for a world in which people can walk to shops, school, friends' homes, or transit stations; in which they can mingle with their neighbors and admire trees, plants, and waterways; in which the air and water are clean; and in which there are parks and play areas for children, gathering spots for teens and the elderly, and convenient work and recreation places for the rest of us. (Frumkin, Frank, & Jackson, 2004, p. xvii)

National estimates of persons with disability are of great importance since they inform policy and program development. However, accurate estimation depends on accurate measurement, and disability measurement is still evolving. Using data from the 1994–1995 National Health Interview Survey and Disability Supplement, this study examines the relationship between functional and activity limitations and equipment use in order to characterize the influence of environmental factors on disability measurement. Our findings highlight the challenging methodologic issues related to measuring a concept of disability that reflects person–environment interactions.

A dynamic model of the evolution of health for those over the age of 50 is embedded in a structural, econometric model of retirement and saving. Effects of smoking, obesity, alcohol consumption, depression, and other proclivities on medical conditions are analyzed, including hypertension, diabetes, cancer, lung disease, heart problems, stroke, psychiatric problems, and arthritis. Compared to a population in good health, the current health of the population reduces retirement age by about one year. Including detailed health dynamics in a retirement model does not influence estimates of the marginal effects of economic incentives on retirement.