Essays On CEO Compensation: New Evidence On The Managerial-Power Vs. Optimal Contracting Debate

This dissertation analyzes the effect of market analysts’ expectations of share prices (price targets) on executive compensation. It examines how well the estimated effects of price targets on compensation fit with two competing views on determining executive compensation: the arm’s length bargaining model, which assumes that a board seeks to maximize shareholders’ interests, and the managerial power model, which assumes that a board seeks to maximize managers’ compensation (Bebchuk et al. 2005). The first chapter documents the pattern of CEO pay from fiscal year 1996 to 2010. The second chapter analyzes the Institutional Broker Estimate System Detail History Price Target data file, which that reports analysts’ price targets for firms. I show that the number of price target announcements is positively associated with company share price’s volatility, that price targets are predictive of changes in the value of stocks, and that when analysts announce positive (negative) expectations of future stock price, share prices change in the same direction in the short run. The third chapter analyzes the effect of price targets on executive compensation. I find that analysts' price targets alter the composition of executive pay between cash-based compensation and stock-based compensation. When analysts forecast a rise (fall) in the share price for a firm, the compensation package tilts toward stock-based (cash-based) compensation. The substitution effect is stronger in companies that have weaker corporate governance. The fourth chapter explores the effect of the introduction of the Sarbanes-Oxley Act (SOX) in 2002 and its reinforcement in 2006 on the options granting process. I show that the introduction of SOX and its reinforcement eliminated the practice of backdating options but increased “spring-loading” of option grants around price targets announcements. Overall, the dissertation shows that price targets provide insights into the determinants of executive pay in favor of the managerial power model.

Rural governance and livelihoods systems diversification in the Western Balkans: comparative case studies of Bosnia and Herzegovina, Montenegro and Serbia

Agriculture is still important for socio-economic development in rural areas of Bosnia, Montenegro and Serbia (BMS). However, for sustainable rural development rural economies should be diversified so attention should be paid also to off-farm and non-farm income-generating activities. Agricultural and rural development (ARD) processes and farm activity diversification initiatives should be well governed. The ultimate objective of this work is to explore linkages between ARD governance and rural livelihoods diversification in BMS. The thesis is based on an extended secondary data analysis and surveys. Questionnaires for ARD governance and coordination were sent via email to public, civil society and international organizations. Concerning rural livelihood diversification, the field questionnaire surveys were carried out in three rural regions of BMS. Results show that local rural livelihoods are increasingly diversified but a significant share of households are still engaged in agriculture. Diversification strategies have a chance to succeed taking into consideration the three rural regions’ assets. However, rural households have to tackle many problems for developing new income-generating activities such as the lack of financial resources. Weak business skills are also a limiting factor. Fully exploiting rural economy diversification potential in BMS requires many interventions including improving rural governance, enhancing service delivery in rural areas, upgrading rural people’s human capital, strengthening rural social capital and improving physical capital, access of the rural population to finance as well as creating a favourable and enabling legal and legislative environment fostering diversification. Governance and coordination of ARD policy design, implementation and evaluation is still challenging in the three Balkan countries and this has repercussions also on the pace of rural livelihoods diversification. Therefore, there is a strong and urgent need for mobilization of all rural stakeholders and actors through appropriate governance arrangements in order to foster rural livelihoods diversification and quality of life improvement.

Kinematics and statics of cable-driven parallel robots by interval-analysis-based methods

In the past two decades the work of a growing portion of researchers in robotics focused on a particular group of machines, belonging to the family of parallel manipulators: the cable robots. Although these robots share several theoretical elements with the better known parallel robots, they still present completely (or partly) unsolved issues. In particular, the study of their kinematic, already a difficult subject for conventional parallel manipulators, is further complicated by the non-linear nature of cables, which can exert only efforts of pure traction. The work presented in this thesis therefore focuses on the study of the kinematics of these robots and on the development of numerical techniques able to address some of the problems related to it. Most of the work is focused on the development of an interval-analysis based procedure for the solution of the direct geometric problem of a generic cable manipulator. This technique, as well as allowing for a rapid solution of the problem, also guarantees the results obtained against rounding and elimination errors and can take into account any uncertainties in the model of the problem. The developed code has been tested with the help of a small manipulator whose realization is described in this dissertation together with the auxiliary work done during its design and simulation phases.

Vision-Based Solutions for Human-Robot Collaboration in Industrial Workcells

Industrial robots are both versatile and high performant, enabling the flexible automation typical of the modern Smart Factories. For safety reasons, however, they must be relegated inside closed fences and/or virtual safety barriers, to keep them strictly separated from human operators. This can be a limitation in some scenarios in which it is useful to combine the human cognitive skill with the accuracy and repeatability of a robot, or simply to allow a safe coexistence in a shared workspace. Collaborative robots (cobots), on the other hand, are intrinsically limited in speed and power in order to share workspace and tasks with human operators, and feature the very intuitive hand guiding programming method. Cobots, however, cannot compete with industrial robots in terms of performance, and are thus useful only in a limited niche, where they can actually bring an improvement in productivity and/or in the quality of the work thanks to their synergy with human operators. The limitations of both the pure industrial and the collaborative paradigms can be overcome by combining industrial robots with artificial vision. In particular, vision can be exploited for a real-time adjustment of the pre-programmed task-based robot trajectory, by means of the visual tracking of dynamic obstacles (e.g. human operators). This strategy allows the robot to modify its motion only when necessary, thus maintain a high level of productivity but at the same time increasing its versatility. Other than that, vision offers the possibility of more intuitive programming paradigms for the industrial robots as well, such as the programming by demonstration paradigm. These possibilities offered by artificial vision enable, as a matter of fact, an efficacious and promising way of achieving human-robot collaboration, which has the advantage of overcoming the limitations of both the previous paradigms yet keeping their strengths.

PCM-based in-memory computing: architectures, circuits and applications

Analog In-memory Computing (AIMC) has been proposed in the context of Beyond Von Neumann architectures as a valid strategy to reduce internal data transfers energy consumption and latency, and to improve compute efficiency. The aim of AIMC is to perform computations within the memory unit, typically leveraging the physical features of memory devices. Among resistive Non-volatile Memories (NVMs), Phase-change Memory (PCM) has become a promising technology due to its intrinsic capability to store multilevel data. Hence, PCM technology is currently investigated to enhance the possibilities and the applications of AIMC. This thesis aims at exploring the potential of new PCM-based architectures as in-memory computational accelerators. In a first step, a preliminar experimental characterization of PCM devices has been carried out in an AIMC perspective. PCM cells non-idealities, such as time-drift, noise, and non-linearity have been studied to develop a dedicated multilevel programming algorithm. Measurement-based simulations have been then employed to evaluate the feasibility of PCM-based operations in the fields of Deep Neural Networks (DNNs) and Structural Health Monitoring (SHM). Moreover, a first testchip has been designed and tested to evaluate the hardware implementation of Multiply-and-Accumulate (MAC) operations employing PCM cells. This prototype experimentally demonstrates the possibility to reach a 95% MAC accuracy with a circuit-level compensation of cells time drift and non-linearity. Finally, empirical circuit behavior models have been included in simulations to assess the use of this technology in specific DNN applications, and to enhance the potentiality of this innovative computation approach.