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In this paper, we build a complete information bargaining model of collective negotiation that can explain delays in reaching agreements. We structurally estimate the model using firm-level data for large Spanish firms. For this type of firm, the assumption of complete information seems a sensible one, and it matches the collective bargaining environment better than the one provided by private information models. The specification of the model with players having different discount factors allows us to measure their relative bargaining power, a recurrent question in the theory of bargaining. Our model replicates the data on delays at the sectoral and aggregate level. We also find that both entrepreneurs and workers have high discount factors, and no evidence that entrepreneurs have greater bargaining power, as usually assumed.

This volume comprises 12 chapters, each accounting for a particular aspect of worker well-being. Among the issues addressed are: employee compensation, job loss, disability, health, gender, education, contract negotiation, and macroeconomic labor policy. In discussing these issues, the volume provides answers to a number of important questions. For example, why do smaller, newer companies do a better job matching CEO pay to profits than old, established corporations? Why do firms hire outside contractors rather than produce all goods internally? Which demographic groups are most prone to job losses? Can self-reported health predict which workers become disabled? How does AIDS affect the supply of nurses? What does marital status have to do with the glass ceiling? Does retiring from work increase one’s mental health? Does domestic violence drive women to work more? Do higher educational subsidies lead to more schooling than larger educational rates of return? Do different firm and worker discount rates lead to longer contract negotiations? And finally, how robust are estimated effects of public policy to changes in data definition? In short, the volume addresses a number of important policy-related research issues on worker well-being facing labor economists today.

The purpose of this paper is to describe the specification language TML for adaptive mission plans that the authors designed and implemented for the open-source framework Aerostack for aerial robotics.

The TML language combines a task-based hierarchical approach together with a more flexible representation, rule-based reactive planning, to facilitate adaptability. This approach includes additional notions that abstract programming details. The authors built an interpreter integrated in the software framework Aerostack. The interpreter was validated with flight experiments for multi-robot missions in dynamic environments.

The experiments proved that the TML language is easy to use and expressive enough to formulate adaptive missions in dynamic environments. The experiments also showed that the TML interpreter is efficient to execute multi-robot aerial missions and reusable for different platforms. The TML interpreter is able to verify the mission plan before its execution, which increases robustness and safety, avoiding the execution of certain plans that are not feasible.

One of the main contributions of this work is the availability of a reliable solution to specify aerial mission plans, integrated in an active open-source project with periodic releases. To the best knowledge of the authors, there are not solutions similar to this in other active open-source projects. As additional contributions, TML uses an original combination of representations for adaptive mission plans (i.e. task trees with original abstract notions and rule-based reactive planning) together with the demonstration of its adequacy for aerial robotics.