Spatial Economic Analysis in Data-Rich Environments

Controlling for spatial effects in micro-economic studies of consumer and producer behavior necessitates a range of analytical modifications ranging from modest changes in data collection and the definition of variables to dramatic changes in the modeling of consumer and producer decision-making. This paper discusses conceptual, empirical, and data issues involved in modeling the spatial aspects of economic behavior in data rich environments. Attention is given to established and emerging agricultural economic applications of spatial data and spatial econometric methods at the micro-scale. Recent applications of individual and household data are featured, including models of land-use change at the urban-rural interface, agricultural land values, and technological change and technology adoption.Research Methods/ Statistical Methods, C21, Q10, Q12, Q15, Q56,

A Climate-Change Policy Induced Shift from Innovations in Energy Production to Energy Savings

We develop an endogenous growth model with capital, labor and energy as production factors and three productivity variables that measure accumulated innovations for energy production, energy savings, and neutral growth. All markets are complete and perfect, except for research, for which we assume that the marginal social value exceeds marginal costs by factor four. The model constants are calibrated so that the model reproduces the relevant trends over the 1970-2000 period. The model contains a simple climate module, and is used to assess the impact of Induced Technological Change (ITC) for a policy that aims at a maximum level of atmospheric CO2 concentration (450 ppmv). ITC is shown to reduce the required carbon tax by about a factor 2, and to reduce costs of such a policy by about factor 10. Numerical simulations show that knowledge accumulation shifts from energy production to energy saving technology.Induced technological change, Environmental taxes, Partial equilibrium

Does Endogenous Technical Change Make a Difference in Climate Policy Analysis? A Robustness Exercise with the FEEM-RICE Model

Technical change is generally considered the key to the solution of environmental problems, in particular global phenomena like climate change. Scientists differ in their views on the thaumaturgic virtues of technical change. There are those who are confident that pollution-free technologies will materialize at some time in the future and will prevent humans from suffering the catastrophic consequences of climate change. Others believe that there are inexpensive technologies already available and argue the case for no-regret adoption policies (e.g. subsidies). Others again believe that the process of technological change responds to economic stimuli. These economic incentives to technological innovation are provided not only by forces that are endogenous to the economic system, but also by suitably designed environmental and innovation policies. In this paper, we consider and translate into analytical counterparts these different views of technical change. We then study alternative formulations of technical change and, with the help of a computerized climate-economy model, carry out a number of optimization runs in order to assess what type of technical change plays a role (assuming it does) in the evaluation of the impact of climate change and of the policies designed to cope with it.Climate policy, Environmental modeling, Integrated assessment, Technical change

An Agent Based Market Design Methodology for Combinatorial Auctions

Auction mechanisms have attracted a great deal of interest and have been used in diverse e-marketplaces. In particular, combinatorial auctions have the potential to play an important role in electronic transactions. Therefore, diverse combinatorial auction market types have been proposed to satisfy market needs. These combinatorial auction types have diverse market characteristics, which require an effective market design approach. This study proposes a comprehensive and systematic market design methodology for combinatorial auctions based on three phases: market architecture design, auction rule design, and winner determination design. A market architecture design is for designing market architecture types by Backward Chain Reasoning. Auction rules design is to design transaction rules for auctions. The specific auction process type is identified by the Backward Chain Reasoning process. Winner determination design is about determining the decision model for selecting optimal bids and auctioneers. Optimization models are identified by Forward Chain Reasoning. Also, we propose an agent based combinatorial auction market design system using Backward and Forward Chain Reasoning. Then we illustrate a design process for the general n-bilateral combinatorial auction market. This study serves as a guideline for practical implementation of combinatorial auction markets design.Combinatorial Auction, Market Design Methodology, Market Architecture Design, Auction Rule Design, Winner Determination Design, Agent-Based System

Learning by Doing vs Learning by Researching in a Model of Climate Change Policy Analysis

Many predictions and conclusions in climate change literature have been made on the basis of theoretical analyses and quantitative models that assume exogenous technological change. One may wonder if those policy prescriptions hold in the more realistic case of endogenously evolving technologies. In previous work we modified a popular integrated assessment model to allow for an explicit role of the stock of knowledge which accumulates through R&D investment. In our formulation knowledge affects the output production technology and the emission-output ratio. In this paper we make progress in our efforts aimed to model the process of technological change. In keeping with recent theories of endogenous growth, we specify two ways in which knowledge accumulates: via a deliberate, optimally selected R&D decision or via experience, giving rise to Learning by Doing. We simulate the model under the two versions of endogenous technical change and look at the dynamics of a number of relevant variables.Climate Policy, Environmental Modeling, Integrated Assessment, Technical Change

Mechanisms of Endogenous Institutional Change

This paper proposes an analytical-cum-conceptual framework for understanding the nature of institutions as well as their changes. In doing so, it attempts to achieve two things: First, it proposes a way to reconcile an equilibrium (endogenous) view of institutions with the notion of agents’ bounded rationality by introducing such concepts as a summary representation of equilibrium as common knowledge of agents. Second, it specifies some generic mechanisms of institutional coherence and change -- overlapping social embededdness, Schumpeterian innovation in bundling games and dynamic institutional complementarities -- useful for understanding the dynamic interactions of economic, political, social and organizational factors.

Induced Technological Change Under Carbon Taxes

We develop an economic partial equilibrium model for energy supply and demand with capital and labor as production factors, and endogenous technological change through learning by research and learning by doing. Our model reproduces the learning curve typical for (bottom-up) energy system models. The model also produces an endogenous S-curved transition from fossil fuel energy sources to carbon-free energy sources over the coming two centuries. We use the model to study changes in fossil fuel and carbon-free energy use and carbon dioxide emissions induced by carbon taxes. It is shown that induced technological change accelerates the substitution of carbon-free energy for fossil fuels substantially, and can increase by factor 5 the cumulative emission reductions achieved through a carbon tax over the period 2000-2100.Induced technological change, Environmental taxes, Partial equilibrium, Learning by doing

Quantifying knowledge exchange in R&D networks: A data-driven model

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. This data is used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers which are able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in 8 dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate $\mu$ for an alliance duration $\tau$. Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiency $\mathcal{\hat{C}}_{n}$. This is a new measure, that takes also in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm's position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiency $\mathcal{\hat{C}}_{n}$. Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.Comment: 35 pages, 10 figure