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Objective: The study examined the factorial validity of the Maslach Burnout Inventory (MBI) for social work research and practice. Method: Factor structure and longitudinal invariance of the MBI were tested using structural equation modeling techniques with a random sample of 475 state-registered social workers in California. Results: The original three-factor model was superior over other competing factor models. Investigation of the second-order factor model supported the presence of the common burnout factor and indicated depersonalization and emotional exhaustion were core components of burnout. Longitudinal factorial invariance was not achieved for personal accomplishment. Conclusion: The findings suggest the applicability of the MBI in a longitudinal research with careful inference regarding personal accomplishment and highlight the importance of depersonalization in social worker burnout.

Abstract.  This paper analyses how domestic sluggish capital movement can affect multilateral trade negotiations between countries. In multilateral trade talks, including the current Doha Round of trade talks organized by the World Trade Organization, countries take steps to liberalize even though they seem to be moving towards the ultimate free trade equilibrium. This paper argues that when capital moves sluggishly between sectors in an economy, there are cases in which countries do not want to move to the ultimate free trade equilibrium immediately. Instead, they find it more beneficial if they simultaneously move gradually, with their tariffs lowered step by step.

Previous research suggests that the likelihood of runaway episodes among children in out‐of‐home care varies across different communities/regions. However, the potential regional variation has rarely been reflected in attempts to understand runaway episodes in out‐of‐home care systems. The current study examines the effects of child characteristics, family characteristics and child welfare system‐related characteristics on the likelihood of runaway episodes among children in out‐of‐home care, while accounting for county‐level variations in the risk of runaway behaviours. The authors employed multilevel analyses using data on children aged 12–17 from the 2009 AFCARS database. Results demonstrate that the likelihood of runaway episodes varied across counties. Accounting for county variation, children's ages, gender, diagnosed clinical conditions, family structures, number of removals, number of placements, removal manner, and case plan goals significantly predicted runaway status. Implications are discussed.

Anxiety is a pervasive emotional state that tends to arise in situations involving uncertainty due partly to social and contextual issues including competition, economic disparity, and social insecurity. Thus, distribution of aggregate emotions, such as in anxiety, may reveal an important picture of otherwise invisible social processes in which individuals interact with local and global opportunities, constraints, and potential threats. The aim of this study is to present a computational approach to the dynamic distribution of anxiety extracted from natural language expressions of users of Twitter, a popular global social media platform. We develop an unsupervised machine learning procedure based on a naive Bayes model to classify contents of anxiety, estimate the degree of anxiety, and construct a geographic map of spatiotemporal distribution of anxiety. To validate our mapping results, a multilevel statistical analysis was performed to examine how anxiety distribution is correlated with other district-level sociodemographic statistics such as rates of birth and early divorce. Implications for further research and extension are discussed.