We are the first to confirm that sufficient cost convexity in a Stackelberg model generates profitable mergers between two leaders and between two followers. Moreover, the degree of convexity required for leaders to merge is generally far smaller than that required for followers. Most...

This paper examines the set of surplus maximizing mergers in a model of mixed oligopoly. The presence of a welfare maximizing public firm reduces the set of mergers for which two private firms can profitably merge. When a public firm and private firm merge, the changes in welfare and profit...

"Returning to the contention that convex costs provide a resolution to the merger paradox, we show that for reasonable degrees of convexity, the minimum market share needed for merger to be profitable remains close to that associated with linear costs. Moreover, convex costs do not eliminate the...

This paper models the behavior of team members in a consistent conjectures equilibrium. When subject to scale economies, team members produce more than Nash and when subject to scale diseconomies, they produce less than Nash. Moreover, even when effort levels of team members are perfect...

This paper identifies the unique strategic issues of cross-border mergers in a mixed oligopoly showing that the presence of a welfare maximizing public firm increases the incentive for such mergers. The well-known merger paradox that two-firm mergers are rarely profitable is substantially...

This paper identifies the unique strategic issues of cross-border mergers in a mixed oligopoly showing that the presence of a welfare maximizing public firm increases the incentive for such mergers. The well-known merger paradox that two-firm mergers are rarely profitable is substantially...

We are the first to confirm that sufficient cost convexity in a Stackelberg model generates profitable mergers between two leaders and between two followers. Moreover, the degree of convexity required for leaders to merge is generally far smaller than that required for followers. Most...