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Why do corporate governance law and practice differ across countries? This paper explains how wage-setting institutions influence ownership structures and investor protection laws. In particular, we identify a nonmonotonic relationship between the level of centralization in wage-bargaining institutions and the level of ownership concentration and investor protection laws. As wage setting becomes more centralized, ownership concentration within firms at first becomes more, and then less, concentrated. In addition, the socially optimal level of investor protection laws is decreasing in ownership concentration. Thus, as wage-setting institutions become more centralized, investor protection laws become less and then more protective. This explanation is consistent with the observable pattern of wage-setting structures, ownership concentration, and investor protection legislation across developed countries. While agreeing with recent research that highlights labor as an important corporate stakeholder in shaping corporate governance, a focus on bargaining structures can resolve an important puzzle this research confronts, namely, why Scandinavian countries with higher than average labor strength also have higher than average investor protection legislation.

Purpose Participant engagement in an online physical activity (PA) intervention is described and baseline factors related to engagement are identified. Design Longitudinal Study Within Randomized Controlled Trial. Setting Online/Internet. Sample Primary care patients (21-70 years). Intervention ActiveGOALS was a 3-month, self-directed online PA intervention (15 total lessons, remote coaching support, and a body-worn step-counter). Measures Engagement was measured across six outcomes related to lesson completion (total number and time to complete), coach contact, and behavior tracking (PA, sedentary). Self-reported baseline factors were examined from seven domains (confidence, environment, health, health care, demographic, lifestyle, and quality of life). Analysis General linear and nonlinear mixed models were used to examine relationships between baseline factors and engagement outcomes within and across all domains. Results Seventy-nine participants were included in the sample (77.2% female; 74.7% white non-Hispanic). Program engagement was high (58.2% completed all lessons; PA was tracked ≥3 times/week for 11.3 ± 4.0 weeks on average). Average time between completed lessons (days) was longer than expected and participants only contacted their coach about 1 of every 3 weeks. Individual predictors related to health, health care, demographics, lifestyle, and quality of life were significantly related to engagement. Conclusion Examining multiple aspects of engagement and a large number of potential predictors of engagement is likely needed to determine facilitators and barriers for high engagement in multi-faceted online intervention programs.