How the psychosocial work environment of motor vehicle mechanics may influence coping with musculoskeletal symptoms

A number of studies have shown that musculoskeletal symptoms are related to the physical and psychosocial work environments. Workers with musculoskeletal symptoms are often advised to cope by such measures as changing working technique, using lifting equipment and discussing health and environmental problems with supervisors and colleagues. Intervention studies at the individual level, however, have shown that such advice has limited effects in reducing the prevalence of musculoskeletal symptoms. The hypothesis for this study was that negative social and organizational factors may prevent workers from implementing such coping strategies. All 103 motor vehicle mechanics surveyed in 12 different garages responded to a questionnaire on coping with musculoskeletal symptoms and the psychosocial work environment. Positive and significant relationships were shown between how mechanics coped with their musculoskeletal symptoms and such psychosocial factors as work demands, social support, control, manager's involvement in health and safety work, and whether the garages had regular meetings between management and workers. Achieving positive results from preventive work through back schools and other ergonomic interventions seems to require that an organization with positive attitudes towards health and safety work be created before such interventions are implemented.     

Historical Sociology and the Prism of Biography: Lillian Wineman and the Trade in Dakota Beadwork, 1893–1929

Qualitative Sociology - How can researchers learn about the social lives of people and cultures who leave little or no written record of their lives? This article introduces the idea that one...     

Threshold Autoregression with a Unit Root

This paper develops an asymptotic theory of inference for an unrestricted two‐regime threshold autoregressive (TAR) model with an autoregressive unit root. We find that the asymptotic null distribution of Wald tests for a threshold are nonstandard and different from the stationary case, and suggest basing inference on a bootstrap approximation. We also study the asymptotic null distributions of tests for an autoregressive unit root, and find that they are nonstandard and dependent on the presence of a threshold effect. We propose both asymptotic and bootstrap‐based tests. These tests and distribution theory allow for the joint consideration of nonlinearity (thresholds) and nonstationary (unit roots). Our limit theory is based on a new set of tools that combine unit root asymptotics with empirical process methods. We work with a particular two‐parameter empirical process that converges weakly to a two‐parameter Brownian motion. Our limit distributions involve stochastic integrals with respect to this two‐parameter process. This theory is entirely new and may find applications in other contexts. We illustrate the methods with an application to the U.S. monthly unemployment rate. We find strong evidence of a threshold effect. The point estimates suggest that the threshold effect is in the short‐run dynamics, rather than in the dominate root. While the conventional ADF test for a unit root is insignificant, our TAR unit root tests are arguably significant. The evidence is quite strong that the unemployment rate is not a unit root process, and there is considerable evidence that the series is a stationary TAR process.     

The economics of adoption of children from foster care

Federal initiatives since 1996 have intensified the efforts of states to achieve adoption for children in foster care. For many waiting children, the path to adoption is long. The authors offer an economic analysis of adoption from foster care, with an emphasis on the reasons why achieving the goal of adoption for all waiting children may be so difficult. The authors then estimate the determinants of adoptions from foster care across the states using data for fiscal years 1996 and 1997. Adoption assistance subsidy rates stand out as the most important determinant of adoptions from foster care, followed by use of alternatives (e.g., intercountry adoption). Adoptive matching on the basis of race does not appear to prevent adoptions from foster care in the aggregate, leaving flaws in the matching process, such as a lack of information and difficulty using the Interstate Compact on the Placement of Children (ICPC), as a primary reason why children wait.     

Identifiability and estimation of recursive max‐linear models

We address the identifiability and estimation of recursive max‐linear structural equation models represented by an edge‐weighted directed acyclic graph (DAG). Such models are generally unidentifiable and we identify the whole class of DAG s and edge weights corresponding to a given observational distribution. For estimation, standard likelihood theory cannot be applied because the corresponding families of distributions are not dominated. Given the underlying DAG, we present an estimator for the class of edge weights and show that it can be considered a generalized maximum likelihood estimator. In addition, we develop a simple method for identifying the structure of the DAG. With probability tending to one at an exponential rate with the number of observations, this method correctly identifies the class of DAGs and, similarly, exactly identifies the possible edge weights.     

Inferring beliefs as subjectively imprecise probabilities

We propose a method for estimating subjective beliefs, viewed as a subjective probability distribution. The key insight is to characterize beliefs as a parameter to be estimated from observed choices in a well-defined experimental task and to estimate that parameter as a random coefficient. The experimental task consists of a series of standard lottery choices in which the subject is assumed to use conventional risk attitudes to select one lottery or the other and then a series of betting choices in which the subject is presented with a range of bookies offering odds on the outcome of some event that the subject has a belief over. Knowledge of the risk attitudes of subjects conditions the inferences about subjective beliefs. Maximum simulated likelihood methods are used to estimate a structural model in which subjects employ subjective beliefs to make bets. We present evidence that some subjective probabilities are indeed best characterized as probability distributions with non-zero variance.