Social Capital,Labour Markets,and Job‐Finding in Urban and Rural Regions: comparing paths to employment in prosperous cities and stressed rural communities in Canada1,2

This paper compares paths to employment (job‐finding) in prosperous cities and economically‐stressed rural communities in Canada. Since the pioneering work of Mark Granovetter (1973; 1974 ), sociologists have investigated the role of social capital in job‐finding (specifically, the use of strong and weak social ties to find out about employment opportunities). To date, however, there have been few direct comparisons of job‐finding in urban and rural settings (see Lindsay et al., 2005 ; Wahba and Zenou, 2005 ). Using data from two major surveys and a qualitative interview project, we uncover several important differences in urban and rural paths to employment. First, we find that both strong and weak ties are used more frequently by rural residents to find a job, while city‐dwellers rely more often on formal or impersonal means. Second, we find much stronger evidence of differentiation within rural regions. Long‐time rural residents are much more likely to use strong and weak ties to find employment than are newcomers. However, rural residents who used weak ties as paths to employment have significantly lower incomes. None of these patterns are evident in the cities. Together, these findings lead us to conclude that job‐finding in rural settings is strongly affected by constraints – in the labour market and in social capital resources – that are not present in cities.     

Middlemen,fair traders,and poverty

We propose a spatial model of producer market access where local middlemen reap market power due to match friction, and fair traders enter to present an alternative. The model features location as a key determinant of the impact of fair trader entry on the market share of fair traders, the distribution of consumer willingness to pay between middlemen and producers, and intra- / inter-regional poverty incidence. For governments who wish to minimize the poverty gap, our results support directing resources to subsidize fair trade organizations, and/or to producers with no access to markets, rather than to local middlemen intermediaries.     

Comparison of cancer slope factors using different statistical approaches.

The U.S. Environmental Protection Agency's cancer guidelines ( USEPA, 2005 ) present the default approach for the cancer slope factor (denoted here as s*) as the slope of the linear extrapolation to the origin, generally drawn from the 95% lower confidence limit on dose at the lowest prescribed risk level supported by the data. In the past, the cancer slope factor has been calculated as the upper 95% confidence limit on the coefficient (q*₁) of the linear term of the multistage model for the extra cancer risk over background. To what extent do the two approaches differ in practice? We addressed this issue by calculating s* and q*₁ for 102 data sets for 60 carcinogens using the constrained multistage model to fit the dose‐response data. We also examined how frequently the fitted dose‐response curves departed appreciably from linearity at low dose by comparing q₁, the coefficient of the linear term in the multistage polynomial, with a slope factor, s_c, derived from a point of departure based on the maximum liklihood estimate of the dose‐response. Another question we addressed is the extent to which s* exceeded s_c for various levels of extra risk. For the vast majority of chemicals, the prescribed default EPA methodology for the cancer slope factor provides values very similar to that obtained with the traditionally estimated q*₁. At 10% extra risk, q*₁/s* is greater than 0.3 for all except one data set; for 82% of the data sets, q*₁ is within 0.9 to 1.1 of s*. At the 10% response level, the interquartile range of the ratio, s*/s_c, is 1.4 to 2.0.     

An Exploratory Study of Variations in Exposure to Environmental Tobacco Smoke in the United States

There is considerable interest in assessing exposure to environmental tobacco smoke (ETS) and in understanding the factors that affect exposure at various venues. The impact of these complex factors can be researched only if monitoring studies are carefully designed. Prior work by Jenkins et al. gathered personal monitor and diary data from 1,564 nonsmokers in 16 metropolitan areas of the United States and compared workplace exposures to ETS with exposures away from work. In this study, these data were probed further to examine (1) the correspondence between work and away-from-work exposure concentrations of ETS; (2) the variability in exposure concentration levels across cities; and (3) the association of ETS exposure concentrations with select socioeconomic, occupation, and lifestyle variables. The results indicate (1) at the population level, there was a positive association between ETS concentrations at the work and away-from-work environments; (2) exposure concentration levels across the 16 cities under consideration were highly variable; and (3) exposure concentration levels were significantly associated with occupation, education, household income, age, and dietary factors. Workplace smoking restrictions were associated with low ETS concentration levels at work as well as away from work. Generally, the same cities that exhibited either lower or higher away-from-work exposure concentration levels also showed lower or higher work exposure concentration levels. The observations suggest that similar avoidance characteristics as well as socioeconomic and other lifestyle factors that affect exposure to ETS may have been in operation in both away-from-work and work settings.     

Semicompeting risks in aging research: methods,issues and needs

A semicompeting risks problem involves two-types of events: a nonterminal and a terminal event (death). Typically, the nonterminal event is the focus of the study, but the terminal event can preclude the occurrence of the nonterminal event. Semicompeting risks are ubiquitous in studies of aging. Examples of semicompeting risk dyads include: dementia and death, frailty syndrome and death, disability and death, and nursing home placement and death. Semicompeting risk models can be divided into two broad classes: models based only on observables quantities (class \(\mathcal {O}\) ) and those based on potential (latent) failure times (class \(\mathcal {L}\) ). The classical illness-death model belongs to class \(\mathcal {O}\) . This model is a special case of the multistate models, which has been an active area of methodology development. During the past decade and a half, there has also been a flurry of methodological activity on semicompeting risks based on latent failure times ( \(\mathcal {L}\) models). These advances notwithstanding, the semicompeting risks methodology has not penetrated biomedical research, in general, and gerontological research, in particular. Some possible reasons for this lack of uptake are: the methods are relatively new and sophisticated, conceptual problems associated with potential failure time models are difficult to overcome, paucity of expository articles aimed at educating practitioners, and non-availability of readily usable software. The main goals of this review article are: (i) to describe the major types of semicompeting risks problems arising in aging research, (ii) to provide a brief survey of the semicompeting risks methods, (iii) to suggest appropriate methods for addressing the problems in aging research, (iv) to highlight areas where more work is needed, and (v) to suggest ways to facilitate the uptake of the semicompeting risks methodology by the broader biomedical research community.     

Selecting a test level for random effects

A method is presented for selecting an a-level to use when testing for group difference in a one-way classification random effects model. The a-level is chosen to make the power of the test equal to .5 when the parameters are such that between group mean square and total mean square are equally good minimum expected squared error estimators of the variance of y the estimator of the mean