Social Security and Retirement: Evidence From the Canada Time Series

This study examines whether social security influences the aggregate retirement rate in Canada. The life—cycle model of individual behavior provides the foundation for this study. The model indicates how social security can affect an individual's decision to retire. Further, the model is used to specify the variable that measures this effect. This variable is social security wealth which equals the present value of the social security benefits to which an individual is entitled. The model for individual retirement decisions is used to construct a model for the aggregate retirement rate. Time series data from Canada include a measure of social security wealth that matches the specification given by the life—cycle model. The estimate of the model yields evidence that social security induces retirement. An increase in social security wealth of approximately $2300 per capita measured in 1971 dollars has been estimated to raise the retirement rate by 5 to 6 points. The effect of the creation of the Canada and Quebec Pension Plan was to raise the retirement rate by 1.5 points in 1967.

Some skeptical observations on real business cycle theory

Business cycles

Government consumption, taxation, and economic activity

The authors use a stylized model of the economy to analyze how permanent and temporary increases in government expenditure--and the timing of taxation used to finance them--affect aggregate output and other variables that describe the economy.Expenditures, Public ; Deficit financing

The optimal accumulation of human capital over the life cycle / BEBR No.553

Includes bibliographical references (p. 28)."This paper summarizes the important contributions of the new life cycle human capital literature and demonstrates that many of these results can be derived more simply than in their original presentations. Within three period discrete-time framework it is demonstrated how the optimal pattern of human capital investment over the life cycle depends upon the choice of the objective function, the life cycle of leisure, and the extent of nonmarket benefits of human capital. The paper offers sufficient conditions for the optimality of a profile of monotonically declining investment activity over the life cycle.

A Century of Work and Leisure

hours, long-run trends, schooling

A clinically relevant method of analyzing continuous change in robotic upper extremity chronic stroke rehabilitation

BACKGROUND: Robots designed for rehabilitation of the upper extremity after stroke facilitate high rates of repetition during practice of movements and record precise kinematic data, providing a method to investigate motor recovery profiles over time.OBJECTIVE: To determine how motor recovery profiles during robotic interventions provide insight into improving clinical gains.METHODS: A convenience sample (n = 22), from a larger randomized control trial, was taken of chronic stroke participants completing 12 sessions of arm therapy. One group received 60 minutes of robotic therapy (Robot only) and the other group received 45 minutes on the robot plus 15 minutes of translation-to-task practice (Robot + TTT). Movement time was assessed using the robot without powered assistance. Analyses (ANOVA, random coefficient modeling [RCM] with 2-term exponential function) were completed to investigate changes across the intervention, between sessions, and within a session.RESULTS: Significant improvement (P < .05) in movement time across the intervention (pre vs post) was similar between the groups but there were group differences for changes between and within sessions (P < .05). The 2-term exponential function revealed a fast and slow component of learning that described performance across consecutive blocks. The RCM identified individuals who were above or below the marginal model.CONCLUSIONS: The expanded analyses indicated that changes across time can occur in different ways but achieve similar goals and may be influenced by individual factors such as initial movement time. These findings will guide decisions regarding treatment planning based on rates of motor relearning during upper extremity stroke robotic interventions