[pt] O presente trabalho foi baseado no projeto ALOGTRANS desenvolvido pelo Departamento de Engenharia Industrial da Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) em conjunto com outras seis instituições. Tal projeto tem como objetivo propor alternativas para mitigar e/ou eliminar os principais gargalos logísticos presentes no escoamento da safra agrícola, de forma a reduzir os custos das operações logísticas, principalmente com a operação de transporte multimodal. Para tanto, nesse projeto, irá ser feito uma análise do fluxo de produção de soja, farelo de soja, milho e açúcar da região centro-oeste brasileira, utilizando o modelo de alocação de transportes multimodal-multiproduto através da utilização do software ALOK (desenvolvido pelo departamento de engenharia industrial da PUC–Rio). Neste contexto, o trabalho desenvolvido teve como objetivo principal atualizar os dados correlacionados com os custos de transportes utilizados como parâmetros de entrada no software ALOK. Além de descrever, de forma sucinta, o atual mercado das commodities agrícolas brasileiras, bem como, as principais rotas utilizadas para o escoamento. / [en] The present work was based on a project called ALOGTRANS developed by Industrial Engineering Department of Catholic University of Rio de Janeiro (PUC-Rio) jointly with six institutions. The purpose of such research is to propose alternatives to mitigate and/or eliminate the main logistical bottlenecks from harvest flow in order to reduce logistical operation cost, mainly by the use of multimodal transport. In such project, we analyse the production flow of soybean, soybean meal, corn and sugar from Brazilian Midwest, using multimodal-multiproduct allocation means through ALOK software (developed by Industrial Engineering Department of PUC-Rio). In this sense, the main objective of this work is to update transportation cost related data used as input to ALOK. Furthermore, we succinctly describe the present market of Brazilian commodities and also the main flow routes.

O presente trabalho aborda a temática da modelagem de redes de transporte para terminais multialocados e capacitados de carga parcelada através de métodos quantitativos aplicados em Pesquisa Operacional, onde o objetivo principal é minimizar o custo total, além de determinar a configuração ótima da rede, levandos e em consideração os custos de transporte ponto a ponto, a capacidade de processamento de carga dos hubs e os custos de alocação dos mesmos. Uma rede Hub and Spoke é configurada de forma que as estações geradoras de cargas (spoke) se comunicam única e exclusivamente com os terminais consolidadores (hubs) que, por sua vez, direcionam as cargas para as estações destino. Esta operação de consolidação gera economia de escala e é o princípio utilizado em muitos modelos de localização de redes de transporte de carga. Os modelos matemáticos utilizados para a resolução pertencem à família dos problemas de localização com custos fixos e particularmente será utilizado o método CMAHLP (Capacitated Multiple Allocation Hub Location Problem), onde para a sua resolução será utilizado o software AIMMS (Advanced Integrated Multidimensional Modeling Software) como ferramenta de apoio. / This paper addresses the issue of modeling transportation networks for multi allocated and capacitated terminals for parcel cargos through quantitative methods applied in Operational Research, where the main objective is to minimize the total cost and to determine the optimal configuration of the network, taking into account the transportation costs, the loading capacity of the hubs and the cost of allocation thereof. A Hub and Spoke network is configured in a way that the generating stations (spokes) communicate solely with consolidators terminals (hubs) which dispatch the cargo to the destination stations. This consolidation operation generates economies of scale and it is the principle used in many models of location problems. The mathematical models used for the settlement belongs to the family of location problems with fixed costs and particularly the CMAHLP (Capacitated Multiple Allocation Hub Location Problem) method is used with the AIMMS (Advanced Integrated Multidimensional Modeling Software) software, which will be used will be used as a support tool.

Performance measurement of Non-Profit Organizations (NPOs) is of increasing importance for aid agencies, policy-makers and donors. A widely used benchmark for measuring the efficiency of NPOs is the overhead cost ratio, consisting of the total money spent on administration and fundraising relative to the budget. Donors generally favor a lower overhead cost ratio as it ensures that more Money directly reaches beneficiaries. Unlike fundraising expenses, administrative costs do not contribute to advertising the actions of an NPO even though they account for a significant proportion of overhead cost. Reducing administrative expenses is a logical consequence from a financial viewpoint, but might negatively affect NPOs through the resulting administrative capacities. This phenomenon is known as "Nonprofit Starvation Cycle" This work provides an analytical framework for analyzing NPO decision making concerning administrative costs. The paper provides answers to important research questions on the optimal level of administrative spending, the influencing factors and the effects of available information on NPOs. The research shows that focusing on financial performance measurements can result in reduced utility created for NPOs. Less transparency often leads to increased utility for NPOs, but more transparency can increase NPOs' utility if the information available exceeds a certain threshold. Fluctuating donations are challenging for NPOs' planning and may Impact administrative capacities negatively.

這篇論文示主要討論在信息不完全情況下的無線網絡資源分配的兩個問題。傳輸節點不固定和信道狀態的不確定性將影響資源分配的選擇。因為不完整的信息，或在這些情況下可能無法確切獲得準確的信息，將對資源優化配置產生一定的影響。對於不完整信息對無線網路的影響，其中用戶的移動性和信道增益的不確定性，將是本論文中討論的兩個主要問題。 / 本論文的第一部分是關於移動傳輸節點的資源分配問題。我們主要分析上行系統的移動用戶，其中每個用戶會盡量優化他或她自己的功效函數，以達到最佳的性能。此外，我們提出對移動用戶的功率分配優化的方案當所有信道信息可用的時候。此外，我們提出了幫助每個用戶預測信道信息總幹擾來優化功效函數，當信息不完備的時候。這形成了一個不完全信息博弈。我們提出了預測的規則，幫助動態預測總幹擾。我們採用了卡爾曼濾波器來處理測量噪聲 我們也說明了利用預測來優化的功效函數和由完整的信息得出的之間的差異。此外，從動態規劃，我們在預測的基礎上給出一個動態的功率分配方案。 / 第二部分討論了在資源分配時，當有關信道增益是不完整時的不確定規劑。我們主要考慮認知無線電模型。在第二部分中，我們考慮，當二級用戶的干擾不會超過一定限制時，他們能夠使用共享的頻率的情況。我們首先利用約束幹擾的限制進行建模，使得二級用戶的干擾，即使在最壞的情況下，也不會超過限制，這將有助於他或她以避免不可行的解決方案。然後，我們擴展我們的概率約束條件來代表不確定性的干擾限制。由於概率約束一般都是難以解決的，而基於無線信道的衰落效應，有關變量的完整的信息是很難獲得。我們重新將概率約束條件轉化為隨機期望約束條件。利用樣本平均近似法，我們提出了隨機學習算法，以幫助次級用戶從主要用戶那裡獲得反饋信息，最大限度地提高自己的功效函數。此外，我們分析了在認知無線電網絡定價條件的頻譜共享方案。我們展示的聯合優化配置方案，幫助次級用戶從主要用戶購買頻譜和優化功率。當有信道增益的不確定性時，二級用戶希望最大限度地提高功效函數的期望值並且採用相對穩定的採購策略追求最佳平均收益。它是一個有鞍點的隨機優化問題。我們展示一個分佈式的隨機算法，以幫助二級用戶更新資源分配策略。在一些實際的情況下，為了減少計算複雜性和希望實施越來較為容易，我們利用迭代平均來為二級用戶進行資源配置。 / Two main issues of resource allocation in wireless networks with incomplete information are addressed in this thesis. Transmission node is not fixed in the wireless system and uncertainties of the channel states would also affect the choices of resource allocation, since full information cannot be provided or may not be exact under these scenarios. For incomplete information in wireless networks, mobility of the users and uncertainties of channel gains are two main issues that would be considered in this thesis. / The first part of this thesis is concerning the resource allocation problems with mobile trans- mission nodes. We consider mobile users in an up-link system. We analyze the mobile system where each user would try to maximize his or her own utility to achieve the best performance. Besides, we propose a power allocation scheme for the mobile users when all channel information is available. We show that our model can form a game. Moreover, we illustrate that each user would expect to predict the aggregate interference to maximize the utility when channel information is incomplete. It can be shown that this forms a game with incomplete information. We demonstrate the prediction rules which help predict the aggregate interference dynamically. We apply the Kalman filter to tackle measurement noises. We also illustrate the bound on the difference between the utility with prediction and that with complete information. Moreover, applying dynamic programming, we give a dynamic power allocation scheme based on the predictions. / The second part discusses the issue of uncertain programming in resource allocation when information about channel gains is incomplete. We mainly consider the model of cognitive radio networks. We introduce a resource allocation scheme for secondary users with spectrum sharing in a cognitive radio network. Secondary users can exploit the spectrum owned by primary links when their interference level does not exceed certain requirements. We first model the interfer- ence constraints as robust constraints such that secondary users would satisfy the interference constraints even under the worst cases, which would help him or her to avoid the unfeasible solutions. We then extend our consideration of the interference constraints as chance constraints to represent uncertainties. Since chance constraints are generally difficult to solve and full in- formation about the uncertain variables is not available due to the fading effects of wireless channels, we reformulate the constraints into stochastic expectation constraints. With sample average approximation method, we propose stochastic distributed learning algorithms to help secondary users satisfy the constraints with the feedback information from primary links when maximizing the utilities. Moreover, we introduce a resource allocation scheme for secondary users to share spectrum and optimize usage of power with pricing. Secondary users need to buy spectrum from primary users. In the process, secondary users also enhance the utilization of the unused bandwidth by primary users. We first demonstrate the resource allocation scheme when full information about channel gains is available. When there are uncertainties of channel gains, secondary users would like to maximize the expected value of the utilities to pursue the best benefits on average with relatively stable buying strategies. It can be shown that it is a stochastic optimization problem with saddle points. We demonstrate a Distributed Stochastic Algorithm to help secondary users update their resource allocation strategies. For some practical scenarios, to reduce computation complexity and make implementation easy, we illustrate an Iterate Average from Distributed Stochastic Algorithm for secondary users. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhou, Kenan. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 123-134). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivations --- p.1 / Chapter 1.2 --- Contributions and Outline of the Thesis --- p.2 / Chapter 2 --- Background Study --- p.5 / Chapter 2.1 --- Slow and Flat Fading Wireless Channel --- p.5 / Chapter 2.2 --- Cognitive Radio Networks --- p.7 / Chapter 2.3 --- Multiple-Access Channel --- p.8 / Chapter 2.4 --- Mobility Model --- p.10 / Chapter 2.5 --- Convex Optimization --- p.12 / Chapter 2.6 --- Uncertain Programming --- p.13 / Chapter 2.7 --- Game Theory --- p.14 / Chapter Part I --- Resource Allocation in Wireless Networks with Mobility --- p.16 / Chapter 3 --- Resource Allocation with Mobile Users in an Up-link System --- p.19 / Chapter 3.1 --- System Model --- p.20 / Chapter 3.2 --- Power Allocation for Mobile Users with Complete Information --- p.22 / Chapter 3.3 --- Power Allocation with Incomplete Information --- p.26 / Chapter 3.3.1 --- Bound on the difference between the utility with prediction and that with complete information --- p.27 / Chapter 3.3.2 --- Prediction Scheme for Incomplete Channel Information --- p.30 / Chapter 3.3.3 --- Power Allocation with Dynamic Programming --- p.32 / Chapter 3.4 --- Numerical results and discussion --- p.35 / Chapter 3.4.1 --- Simulation Model --- p.35 / Chapter 3.4.2 --- Numerical Results --- p.36 / Chapter 3.5 --- Chapter Summary --- p.42 / Chapter 3.6 --- Appendices --- p.44 / Chapter 3.6.1 --- Proof of Theorem 3.1 --- p.44 / Chapter 3.6.2 --- Proof of Theorem 3.2 --- p.47 / Chapter Part II --- Resource Allocation inWireless Networks with Uncertain Programming --- p.48 / Chapter 4 --- Resource Allocation with Robust Optimization --- p.52 / Chapter 4.1 --- System Model --- p.52 / Chapter 4.2 --- Resource Allocation with Robust Optimization Approach --- p.54 / Chapter 4.3 --- Trade-Off Between Robustness and Performance --- p.59 / Chapter 4.4 --- Numerical results and discussion --- p.62 / Chapter 4.4.1 --- Choice of the Penalty Function --- p.62 / Chapter 4.4.2 --- Simulation Model --- p.63 / Chapter 4.4.3 --- Simulation Results --- p.64 / Chapter 4.5 --- Chapter Summary --- p.65 / Chapter 5 --- Resource Allocation with Chance Constraints --- p.67 / Chapter 5.1 --- System Model --- p.68 / Chapter 5.2 --- Power Allocation with Complete Information about Probabilistic Constraints --- p.69 / Chapter 5.3 --- A Stochastic Approximation Approach Based on the Outage Event --- p.72 / Chapter 5.3.1 --- Feasibility of the Stochastic Approximation Method --- p.74 / Chapter 5.3.2 --- Stochastic Distributed Learning Algorithm I (SDLA-I) --- p.76 / Chapter 5.3.3 --- Stochastic Distributed Learning Algorithm II (SDLA-II) --- p.80 / Chapter 5.4 --- Numerical Results and Discussion --- p.82 / Chapter 5.4.1 --- Examples of uk(.) for Simulation --- p.82 / Chapter 5.4.2 --- Simulation Model --- p.83 / Chapter 5.4.3 --- Simulation Results and Discussions --- p.84 / Chapter 5.5 --- Chapter Summary --- p.86 / Chapter 5.6 --- Appendices --- p.88 / Chapter 5.6.1 --- Proof of Lemma 5.3 --- p.88 / Chapter 6 --- Priced Resource Allocation with Stochastic Optimization --- p.90 / Chapter 6.1 --- System Model --- p.91 / Chapter 6.2 --- Price-Based Optimization with Complete Information --- p.94 / Chapter 6.3 --- Price-Based Stochastic Optimization with Uncertainties --- p.96 / Chapter 6.4 --- Distributed Stochastic Algorithms for the Price-Based Stochastic Optimization --- p.100 / Chapter 6.4.1 --- Iterate Averages of DSA --- p.104 / Chapter 6.5 --- Numerical Results and Discussion --- p.106 / Chapter 6.5.1 --- Simulation Model --- p.106 / Chapter 6.5.2 --- Numerical Results --- p.107 / Chapter 6.6 --- Chapter Summary --- p.112 / Chapter 6.7 --- Appendices --- p.113 / Chapter 6.7.1 --- Proof of Lemma 6.1 --- p.113 / Chapter 6.7.2 --- Proof of Proposition 6.1 --- p.114 / Chapter 6.7.3 --- Proof of Lemma 6.3 --- p.114 / Chapter 6.7.4 --- Proof of Lemma 6.4 --- p.115 / Chapter 6.7.5 --- Proof of Proposition 6.2 --- p.116 / Chapter 7 --- Conclusion and Future Work --- p.117 / Chapter 7.1 --- Conclusion --- p.117 / Chapter 7.2 --- Future Work --- p.119 / Chapter 7.2.1 --- Joint Power and Channel Access Scheduling for Mobile Users --- p.119 / Chapter 7.2.2 --- Power Control for Heterogeneous Mobile Users --- p.120 / Chapter 7.2.3 --- More on Uncertain Programming in Cognitive Radios --- p.120 / Chapter 7.2.4 --- Transmissions in Complex Networks with Uncertainties --- p.121 / Chapter 7.2.5 --- Secure Transmissions in Wireless Networks with Uncertainties --- p.122 / Bibliography --- p.123

This study examines the role of tertiary education in the development of a knowledge based economy, with particular reference to developing countries and in the particular case of the University of Botswana (UB). It presents evidence to suggest that the commodification of knowledge is important to the fulfilment of this role and that if UB were to embrace this notion, it would help focus the fragmented approach of business practices in the University. The study also asks whether the resource allocation model at UB supports commodification of knowledge and examines what alternative approaches could be adopted, concluding that the adoption of a formulaic resource allocation model would better support this aim. A case study approach was adopted especially for its potential to capture explanatory and descriptive data. The three phases of study used were firstly the documentary research where reports were inspected and classified into categories. The second phase concentrated on the semi-structured interviews with members of the senior management staff at UB that served to clarify, confirm, refute and/or corroborate documentary research outcomes. The third phase of the study combined the data collected from the different sources. In combining both quantitative and qualitative data this thesis took advantage of data complementarity, facilitation and triangulation. The findings of the study show that UB embraces the notion of knowledge based economy through advancing the intellectual and human resource capacity of the nation and the international community. However the definition of commodification was not uniformly understood across UB faculties. The study also finds that UB did not seem to have a resource allocation model but had a budgeting system that did not support commodification of knowledge. While the human resource accounted for 67% of the total budget of the institution there was no staff allocations model to manage it. The inadequate database was found to be a major contributing factor which was exacerbated by lack of clear business processes in certain critical areas. The principles underpinning commodification of knowledge could be traced in UB policies even though there was a need to strengthen systems, processes and quality control mechanisms to facilitate and support data collection and establishment of database. A practical system which has the potential to help establish a systemic approach that would interrogate business process in UB is proposed.

Large-scale scenarios such as search-and-rescue operations, agriculture, warehouse, surveillance, and construction consist of multiple tasks to be performed at the same time. These tasks have non-trivial spatial distributions. Robot swarms are envisioned to be efficient, robust, and flexible for such applications. We model this system such that each robot can service a single task at a time; each task requires a specific number of robots, which we refer to as 'quota'; task allocation is instantaneous; and tasks do not have inter- dependencies. This work focuses on distributing robots to spatially distributed tasks of known quotas in an efficient manner. Centralized solutions which guarantee optimality in terms of distance travelled by the swarm exist. Although potentially scalable, they require non-trivial coordination; could be computationally expensive; and may have poor response time when the number of robots, tasks and task quotas increase. For a swarm to efficiently complete tasks with a short response time, a decentralized approach provides better parallelism and scalability than a centralized one. In this work, we study the performance of a weight-based approach which is enhanced to include spatial aspects. In our approach, the robots share a common table that reports the task locations and quotas. Each robot, according to its relative position with respect to task locations, modifies weights for each task and randomly chooses a task to serve. Weights increase for tasks that are closer and have high quota as opposed to tasks which are far away and have low quota. Tasks with higher weights have a higher probability of being selected. This results in each robot having its own set of weights for all tasks. We introduce a distance- bias parameter, which determines how sensitive the system is to relative robot-task locations over task quotas. We focus on evaluating the distance covered by the swarm, number of inter- task switches, and time required to completely allocate all tasks and study the performance of our approach in several sets of simulated experiments.

Area exploration and information gathering are one of the fundamental problems in mobile robotics. Much of the current research in swarm robotics is aimed at developing practical solutions to this problem. Exploring large environments poses three main challenges. Firstly, there is the problem of limited connectivity among the robots. Secondly, each of the robots has a limited battery life which requires the robots to be recharged each time they are running out of charge. Lastly, the robots have limited memory to store data. In this work, we mainly focus on the memory and energy constraints of the robot swarm. The memory constraint forces the robots to travel to a centralized data collection center called sink, to deposit data each time their memory is full. The energy constraint forces the robots to travel to the charging station called dock to recharge when their battery level is low. However, this navigation plan is inefficient in terms of energy and time. There is additional energy dissipation in depositing data at the centralized sink. Moreover, ample amount of time is spent in traveling from one end of the arena to the sink owing to the memory constraint. The goal is that the robots perform data gathering in the least time possible with the optimal use of energy. Both the energy and time spent while depositing data at the sink act as an additional overhead cost to this goal. In this work, we propose to study an algorithm to tackle this scenario in a decentralized manner. We implement a dynamic task allocation algorithm which accomplishes the goal of exploration with data gathering by assigning roles to robots based on their memory buffer and energy levels. The algorithm assigns two sets of roles, to the entire group of robots, namely: Role A is the data gatherer, a robot which does the task of workspace exploration and data gathering, and Role B is data relayer, a robot which does the task of data transportation from data gatherers to the sink. By this division of labor, the robots dynamically decide which role to choose given the contradicting goals of maximizing data gathering and minimizing energy loss. The choice of a robot to perform the task of data gathering or data relaying is the key problem tackled in this work. We study the performance of the algorithm in terms of task distribution, time spent by the robots on each task and data throughput. We analyze the behavior of the robot swarm by varying the energy constraints, timeout parameter as well as strategies for relayer choice. We also test whether the algorithm is scalable.

Doctor of Philosophy / Department of Agricultural Economics / Timothy J. Dalton / Economic indicators (price, income, taste, and preference) and non-economic (information, time and equipment, food quality and safety) indicators are key elements of the food environment that need further investigation in developing countries.The main objective of this thesis is to evaluate the effect of these factors on consumer behavior in West Africa, especially in Niger and Burkina Faso. The first essay analyzes the implications of world cereal price shocks on rural household welfare in Burkina Faso by establishing a link between farmers and world markets. The approach is grounded in agricultural household modelling with the world price for cereals, transmitted to farmers, through local producer and consumer prices. Household net welfare after a price shock is derived as a function of behavioral responses to local price change induced by the international price shock.The main result of this analysis is that the increase in prices during the period from 2006 to 2014 is translated to welfare improvement ranging from 0.02 percent for 2006 to 0.06 percent for 2011 for farmers in Burkina Faso. The second essay assesses urban consumers' preference for food quality attributes of value-added cereal products in Niamey, Niger. It combines qualitative and quantitative methods to evaluate the effect of quality attributes on consumers' food choice. A particular focus is placed on assessing consumers' marginal willingness-to-pay (WTP) for quality attributes in an experimental setting. The evaluation accounted for taste and preference heterogeneity inherent to consumers’ responses to changes in quality attributes. The results suggest market demand inferred from significant marginal WTP for the nutritional quality attribute as measured by the expiration date, the presence of micronutrients, and the country of origin of the product. In addition, demand is found to be highly heterogeneous across consumers socio-demographic and economic characteristics. As a result, better communication and appropriate targeting by food processors and policymakers could be an additional tool to enhance food quality and diet through the market. Finally, the third essay theoretically and empirically assesses the impact of a time-saving food attribute on consumer’s food choice in urban areas of Niger. The theoretical assessment relied on a ``Beckerian’’ time allocation model to derive how a time-saving food product affects consumers' utility and food choice. The empirical approach combines hedonic tasting, random utility and a latent class framework to identify taste heterogeneity patterns underlying consumers' choice. Both the hedonic and latent class models confirm the theoretical prediction that a time-saving characteristic can either increase or decrease the demand for food that embodies the attributes. A significant market segment of about 38% includes consumers with a positive valuation of the time-saving product, highlighting the potential of this attribute to increase consumers welfare, reduce energy use and prevent food preparation-related health issues.

A central concern in economics is explaining the allocation of resources, its consequences for economic activity and the distribution of the associated economic revenues. This dissertation contains three essays examining the average and distributional effects of reallocations resulting from liberalization reforms or trade shocks. Chapter 1 examines the distributional effects of trade liberalization. A vast literature demonstrates that liberalization is associated with higher wage inequality. Nearly the entire literature considers comparative statics or steady states, which ignore dynamics and of necessity feature monotonic changes. I address these limitations by developing a micro-founded model that emphasizes the dynamics of reallocation between heterogeneous firms and workers in the presence of costly labor adjustments. Trade liberalization provides firms both new export markets and new sources of competition. Expanding high-paying firms increase wages to recruit better workers faster. Workers at firms threatened by competition accept wage cuts to delay their employers' exit and keep their job. This provides novel implications for both aggregate and within-firm inequality across a distribution of firm types. I show that key mechanisms of the model are consistent with a wide range of facts, some of which being examined in greater details in chapter 2. Results from the calibrated model suggest an overshooting of inequality on the path to a new steady state. This is consistent with evidence based on an event study of recent liberalization episodes. Inequality appears to peak about six years after liberalization, with one-fourth of the overshooting disappearing in the following ten years. Chapter 2 investigates the effects of firm growth on hiring and separations. I contribute to the literature on worker flows by studying the wages and characteristics of new and separated workers. First, I show that separations are an essential and robust component of firm growth. I argue that this may be the result of a more intense search for better matches at faster growing firms. Second, I find that wage offers to new hires increase with firm hiring rates. This is partly the result of the selection of more experienced workers. However fixed unobservable and variable observable worker characteristics cannot fully explain this relationship: the residual wage of new hires is significantly associated with the firm hiring rate. We interpret this as direct evidence of the firm-level upward-sloping labor supply curve predicted by the canonical models. We provide estimates of the slope of the curve using an instrumental variable approach to control for supply shocks. We find that a 10% increase in the hiring rate results in a wage increase of 1%. In chapter 3 Jaromir Nosal, Jonathan Vogel and I ask the following: What is the contribution of industry reallocation and productivity changes to the economic gains resulting from banking deregulation? How does local industrial structure determine the outcomes of banking deregulation? This chapter uses the staggered reforms of the banking sector in the U.S. between 1977 and 1997 to empirically investigate these questions. In the private sector, we show that the deregulation-induced reallocation of workers was directed towards industries with lower GDP per worker. Moreover, employment gains were associated with a reduction in productivity. Nevertheless we find that these effects are offset by across the board within-industry productivity gains. In addition, total output and aggregate productivity increased because of the reallocation of workers out of unemployment, self-employment and non-private industries towards the more productive private sector. Finally we find that initial industry mix can explain up to one third of the variation in state aggregate responses.

We consider a pure exchange economy with asymmetric information where individual behavior exhibits ambiguity aversion along the line of maximin expected utility decision making. For such economies we introduce different notions of maximin value allocations. We also introduce a strong notion of (maximin) incentive compatibility. We prove existence and incentive compatibility of the maximin value allocation, when the economy's state space is either finite or non-finite. In the latter case, we provide two different existence results: assuming first countable and then uncountable infinitely many states of nature of the world. We conclude that unlike the Bayesian value allocation approach, incentive compatibility is related to efficiency rather than to direct exchange of information.