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Summary The general health check is one of the most common preventive healthcare measures in many countries. In this study, we propose an empirical approach which jointly models the decision to obtain a general health check and healthcare utilization, tackling the self‐selection problem by using eligibility to obtain a health check for free as an instrumental variable. Eligibility has some exogenous variations by design and this helps us to partial out the effect of general health checks from self‐selection biases. We apply the model to a large 12‐year panel data set provided by the Korean National Health Insurance Service. We find that participation in the general health check increases healthcare utilization and ignored self‐selection generates substantial upward bias in the estimates. We also find that the health check effect shows noteworthy heterogeneity across gender and income groups. Before health checks, healthcare utilization of males and people in low income groups is lower than those of females and people in high income groups respectively. However, these become comparable across different groups after health checks. This finding implies that general health checks can be an effective vehicle for health equity.

A Bayesian statistical method to detect turning points in the leading composite index is introduced. Under the assumption of causal priority of the leading composite index to the business cycle, the turning points in business cycles are predicted by detection of them in the index. The underlying process of the leading composite index is described by a dynamic linear model with random level and slope, where the random slope is distorted by a random shock a t each turning point. The turning point is detected by obtaining a large value of the posterior probability that one of the previous slope components has undergone a major change. The intensity of the change causing a turn in the business cycle is quantified by estimating the size of the random shock. The application of the results to the US leading composite index are compared with results of earlier studies. KEY WORDS Business cycle Turning point Leading composite index Dynamic linear model Kalman filter Random shock Slope change

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