Management of Energy Technology for Sustainability: How to Fund Energy Technology Research and Development

Operations management methods have been applied profitably to a wide range of technology portfolio management problems, but have been slow to be adopted by governments and policy makers. We develop a framework that allows us to apply such techniques to a large and important public policy problem: energy technology R&D portfolio management under climate change. We apply a multi‐model approach, implementing probabilistic data derived from expert elicitations into a novel stochastic programming version of a dynamic integrated assessment model. We note that while the unifying framework we present can be applied to a range of models and data sets, the specific results depend on the data and assumptions used and therefore may not be generalizable. Nevertheless, the results are suggestive, and we find that the optimal technology portfolio for the set of projects considered is fairly robust to different specifications of climate uncertainty, to different policy environments, and to assumptions about the opportunity cost of investing. We also conclude that policy makers would do better to over‐invest in R&D rather than under‐invest. Finally, we show that R&D can play different roles in different types of policy environments, sometimes leading primarily to cost reduction, other times leading to better environmental outcomes.     

If You're so Smart,why Aren't You Rich? Belief Selection in Complete and Incomplete Markets

This paper provides an analysis of the asymptotic properties of Pareto optimal consumption allocations in a stochastic general equilibrium model with heterogeneous consumers. In particular, we investigate the market selection hypothesis that markets favor traders with more accurate beliefs. We show that in any Pareto‐optimal allocation whether each consumer vanishes or survives is determined entirely by discount factors and beliefs. Whereas equilibrium allocations in economies with complete markets are Pareto optimal, our results characterize the limit behavior of these economies. We show that, all else equal, the market selects for consumers who use Bayesian learning with the truth in the support of their prior and selects among Bayesians according to the size of their parameter space. Finally, we show that in economies with incomplete markets, these conclusions may not hold. With incomplete markets, payoff functions can matter for long‐run survival, and the market selection hypothesis fails.     

Competition for a Majority

We define the class of two‐player zero‐sum games with payoffs having mild discontinuities, which in applications typically stem from how ties are resolved. For such games, we establish sufficient conditions for existence of a value of the game, maximin and minimax strategies for the players, and a Nash equilibrium. If all discontinuities favor one player, then a value exists and that player has a maximin strategy. A property called payoff approachability implies existence of an equilibrium, and that the resulting value is invariant: games with the same payoffs at points of continuity have the same value and ɛ‐equilibria. For voting games in which two candidates propose policies and a candidate wins election if a weighted majority of voters prefer his proposed policy, we provide tie‐breaking rules and assumptions about voters' preferences sufficient to imply payoff approachability. These assumptions are satisfied by generic preferences if the dimension of the space of policies exceeds the number of voters; or with no dimensional restriction, if the electorate is sufficiently large. Each Colonel Blotto game is a special case in which each candidate allocates a resource among several constituencies and a candidate gets votes from those allocated more than his opponent offers; in this case, for simple‐majority rule we prove existence of an equilibrium with zero probability of ties.     

On Optimal Expediting Policy for Supply Systems with Uncertain Lead‐Times

We examine the role of expediting in dealing with lead‐time uncertainties associated with global supply chains of “functional products” (high volume, low demand uncertainty goods). In our developed stylized model, a retailer sources from a supplier with uncertain lead‐time to meet his stable and known demand, and the supply lead‐time is composed of two random duration stages. At the completion time of the first stage, the retailer has the option to expedite a portion of the replenishment order via an alternative faster supply mode. We characterize the optimal expediting policy in terms of if and how much of the order to expedite and explore comparative statics on the optimal policy to better understand the effects of changes in the cost parameters and lead‐time properties. We also study how the expediting option affects the retailer's decisions on the replenishment order (time and size of order placement). We observe that with the expediting option the retailer places larger orders closer to the start of the selling season, thus having this option serve as a substitute for the safety lead‐time and allowing him to take increased advantages of economies of scale. Finally we extend the basic model by looking at correlated lead‐time stages and more than two random lead‐time stages.     

Conditional Inference With a Functional Nuisance Parameter

This paper shows that the problem of testing hypotheses in moment condition models without any assumptions about identification may be considered as a problem of testing with an infinite‐dimensional nuisance parameter. We introduce a sufficient statistic for this nuisance parameter in a Gaussian problem and propose conditional tests. These conditional tests have uniformly correct asymptotic size for a large class of models and test statistics. We apply our approach to construct tests based on quasi‐likelihood ratio statistics, which we show are efficient in strongly identified models and perform well relative to existing alternatives in two examples.     

Increasing the Revenue of Self‐Storage Warehouses by Facility Design

Self‐storage is a booming industry. Both private customers and companies can rent temporary space from such facilities. The design of self‐storage warehouses differs from other facility designs in its focus on revenue maximization. A major question is how to design self‐storage facilities to fit market segments and accommodate volatile demand to maximize revenue. Customers that cannot be accommodated with a space size of their choice can be either rejected or upscaled to a larger space. Based on data of 54 warehouses in America, Europe, and Asia, we propose a new facility design approach with models for three different cases: an overflow customer rejection model and two models with customer upscale possibilities, one with reservation and another without reservation. We solve the models for several real warehouse cases, and our results show that the existing self‐storage warehouses can be redesigned to generate larger revenues for all cases. Finally, we show that the upscaling policy without reservation generally outperforms the upscaling policy with reservation.     

Money in Search Equilibrium,in Competitive Equilibrium,and in Competitive Search Equilibrium

We compare three market structures for monetary economies: bargaining (search equilibrium); price taking (competitive equilibrium); and price posting (competitive search equilibrium). We also extend work on the microfoundations of money by allowing a general matching technology and entry. We study how equilibrium and the effects of policy depend on market structure. Under bargaining, trade and entry are both inefficient, and inflation implies first‐order welfare losses. Under price taking, the Friedman rule solves the first inefficiency but not the second, and inflation may actually improve welfare. Under posting, the Friedman rule yields the first best, and inflation implies second‐order welfare losses.     

MODELING MODEL UNCERTAINTY

Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We point out that different assumptions about the uncertainty may result in drastically different “robust” policy recommendations. Therefore, we develop new methods to analyze uncertainty about the parameters of a model, the lag specification, the serial correlation of shocks, and the effects of real‐time data in one coherent structure. We consider both parametric and nonparametric specifications of this structure and use them to estimate the uncertainty in a small model of the U.S. economy. We then use our estimates to compute robust Bayesian and minimax monetary policy rules, which are designed to perform well in the face of uncertainty. Our results suggest that the aggressiveness recently found in robust policy rules is likely to be caused by overemphasizing uncertainty about economic dynamics at low frequencies. (JEL: E52, C32, D81)     

EFFICIENT TUITION FEES AND EXAMINATIONS

We assume that students observe only a private, noisy signal of their ability and that universities can condition admission decisions on the results of noisy tests. If the university observes a private signal of each student's ability, which is soft information, then asymmetries of information are two‐sided, and the optimal admission policy involves a mix of pricing and pre‐entry selection, based on the university's private information. In contrast, if all test results are public knowledge, then there is no sorting on the basis of test scores: Tuition alone does the job of implementing an optimal degree of student self‐selection. These results do not depend on the existence of peer effects. The optimal tuition follows a classic marginal social‐cost pricing rule. (JEL: D82, H42, I22, J24)     

Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals

This paper studies nonparametric estimation of conditional moment restrictions in which the generalized residual functions can be nonsmooth in the unknown functions of endogenous variables. This is a nonparametric nonlinear instrumental variables (IV) problem. We propose a class of penalized sieve minimum distance (PSMD) estimators, which are minimizers of a penalized empirical minimum distance criterion over a collection of sieve spaces that are dense in the infinite‐dimensional function parameter space. Some of the PSMD procedures use slowly growing finite‐dimensional sieves with flexible penalties or without any penalty; others use large dimensional sieves with lower semicompact and/or convex penalties. We establish their consistency and the convergence rates in Banach space norms (such as a sup‐norm or a root mean squared norm), allowing for possibly noncompact infinite‐dimensional parameter spaces. For both mildly and severely ill‐posed nonlinear inverse problems, our convergence rates in Hilbert space norms (such as a root mean squared norm) achieve the known minimax optimal rate for the nonparametric mean IV regression. We illustrate the theory with a nonparametric additive quantile IV regression. We present a simulation study and an empirical application of estimating nonparametric quantile IV Engel curves.     

Signaling and Election Motivations in a Voting Model with Common Values and Responsive Candidates

In this paper we focus on strategic voting behavior when both an election and a signaling motivation affect voters' behavior. We analyze a model of elections with two candidates competing on a one‐dimensional policy space. Voters are privately and imperfectly informed about a common shock affecting the electorate's preferences. Candidates are assumed to choose policy in response to information gleaned from election results and according to exogenous factors that may lead to polarization in candidates' policy choices. We analyze a subset of symmetric equilibria in which strategies are symmetric to candidates' names and private signals (CSS equilibria). We show that signaling and election motivations pull voters to vote in different directions. We provide conditions that show the relation between the amount of information aggregated in the election and the motivation that influences voting behavior the most. Finally, we show that when candidates are responsive and polarized, all CSS equilibria are inefficient in the limit.     

A DRAWBACK OF ELECTORAL COMPETITION

In most major democracies there are very few parties compared to the number of possible policy positions held by voters. We provide an efficiency rationale for why it might be appropriate to limit the proliferation of parties. In our model, the larger the number of parties, the greater the inefficiency of the outcome of electoral competition. The reason is that, when the number of parties increases, electoral incentives push each party to focus its electoral promises on a narrower constituency, and then special interest policies replace more efficient policies that have diffuse benefits. The analysis provides a possible explanation for the existence of institutional features that limit the extent of electoral competition: thresholds of exclusion, runoff electoral systems, and majoritarian two‐party political systems. (JEL: D82, L15)     

Simple Efficient Contracts in Complex Environments

This paper studies a general model of holdup in a setting encompassing the models of Segal (1999) and Che and Hausch (1999) among others. It is shown that if renegotiation is modeled as an infinite‐horizon noncooperative bargaining game, then, with a simple initial contract, an efficient equilibrium will generally exist. The contract is robust in the sense that it does not depend on fine details of the model. The contract gives authority to one party to set the terms of trade and gives the other party a nonexpiring option to trade at these terms. The difference from standard results arises because the initial contract ensures that the renegotiation game has multiple equilibria; the multiplicity of continuation equilibria can be used to enforce efficient investment.     

Endogenous Completeness of Diffusion Driven Equilibrium Markets

We study the existence of dynamic equilibria with endogenously complete markets in continuous‐time, heterogenous agents economies driven by diffusion processes. Our main results show that under appropriate conditions on the transition density of the state variables, market completeness can be deduced from the primitives of the economy. In particular, we prove that a sufficient condition for market completeness is that the volatility of dividends be invertible and provide higher order conditions that apply when this condition fails as is the case in the presence of fixed income securities. In contrast to previous research, our formulation does not require that securities pay terminal dividends, and thus allows for both finite and infinite horizon economies.     

Using Adaptive Sparse Grids to Solve High‐Dimensional Dynamic Models

We present a flexible and scalable method for computing global solutions of high‐dimensional stochastic dynamic models. Within a time iteration or value function iteration setup, we interpolate functions using an adaptive sparse grid algorithm. With increasing dimensions, sparse grids grow much more slowly than standard tensor product grids. Moreover, adaptivity adds a second layer of sparsity, as grid points are added only where they are most needed, for instance, in regions with steep gradients or at nondifferentiabilities. To further speed up the solution process, our implementation is fully hybrid parallel, combining distributed and shared memory parallelization paradigms, and thus permits an efficient use of high‐performance computing architectures. To demonstrate the broad applicability of our method, we solve two very different types of dynamic models: first, high‐dimensional international real business cycle models with capital adjustment costs and irreversible investment; second, multiproduct menu‐cost models with temporary sales and economies of scope in price setting.     

Design of Extended Warranties in Supply Chains under Additive Demand

We study the design of extended warranties in a supply chain consisting of a manufacturer and an independent retailer. The manufacturer produces a single product and sells it exclusively through the retailer. The extended warranty can be offered either by the manufacturer or by the retailer. The party offering the extended warranty decides on the terms of the policy in its best interest and incurs the repair costs of product failures. We use game theoretic models to answer the following questions. Which scenario leads to a higher supply‐chain profit, the retailer offering the extended warranty or the manufacturer? How do the optimum price and extended warranty length vary under different scenarios? We find that, depending on the parameters, either party may provide better extended warranty policies and generate more system profit. We also compare these two decentralized models with a centralized system where a single party manufactures the product, sells it to the consumer, and offers the extended warranty. We also consider an extension of our basic model where either the manufacturer or the retailer resells the extended warranty policies of a third party (e.g., an independent insurance company), instead of offering its own policy.     

EXPANSIONARY AUSTERITY? INTERNATIONAL EVIDENCE

This paper investigates the short‐term effects of fiscal consolidation on economic activity in OECD economies. We examine contemporaneous policy documents to identify changes in fiscal policy motivated by a desire to reduce the budget deficit and not by responding to prospective economic conditions. Using this new dataset, our estimates suggest that fiscal consolidation has contractionary effects on private demand and GDP. By contrast, estimates based on conventional measures of the fiscal policy stance used in the literature support the expansionary fiscal contractions hypothesis but appear to be biased toward overstating expansionary effects.     

Sovereign Cat Bonds and Infrastructure Project Financing

We examine the opportunities for using catastrophe‐linked securities (or equivalent forms of nondebt contingent capital) to reduce the total costs of funding infrastructure projects in emerging economies. Our objective is to elaborate on methods to reduce the necessity for unanticipated (emergency) project funding immediately after a natural disaster. We also place the existing explanations of sovereign‐level contingent capital into a catastrophic risk management framework. In doing so, we address the following questions. (1) Why might catastrophe‐linked securities be useful to a sovereign nation, over and above their usefulness for insurers and reinsurers? (2) Why are such financial instruments ideally suited for protecting infrastructure projects in emerging economies, under third‐party sponsorship, from low‐probability, high‐consequence events that occur as a result of natural disasters? (3) How can the willingness to pay of a sovereign government in an emerging economy (or its external project sponsor), who values timely completion of infrastructure projects, for such instruments be calculated? To supplement our treatment of these questions, we use a multilayer spreadsheet‐based model (in Microsoft Excel format) to calculate the overall cost reductions possible through the judicious use of catastrophe‐based financial tools. We also report on numerical comparative statics on the value of contingent‐capital financing to avoid project disruption based on varying costs of capital, probability and consequences of disasters, the feasibility of strategies for mid‐stage project abandonment, and the timing of capital commitments to the infrastructure investment. We use these results to identify high‐priority applications of catastrophe‐linked securities so that maximal protection can be realized if the total number of catastrophe instruments is initially limited. The article concludes with potential extensions to our model and opportunities for future research.     

Looking Upstream: Optimal Policies for a Class of Capacitated Multi‐Stage Inventory Systems

We consider a multi‐stage inventory system with stochastic demand and processing capacity constraints at each stage, for both finite‐horizon and infinite‐horizon, discounted‐cost settings. For a class of such systems characterized by having the smallest capacity at the most downstream stage and system utilization above a certain threshold, we identify the structure of the optimal policy, which represents a novel variation of the order‐up‐to policy. We find the explicit functional form of the optimal order‐up‐to levels, and show that they depend (only) on upstream echelon inventories. We establish that, above the threshold utilization, this optimal policy achieves the decomposition of the multidimensional objective cost function for the system into a sum of single‐dimensional convex functions. This decomposition eliminates the curse of dimensionality and allows us to numerically solve the problem. We provide a fast algorithm to determine a (tight) upper bound on this threshold utilization for capacity‐constrained inventory problems with an arbitrary number of stages. We make use of this algorithm to quantify upper bounds on the threshold utilization for three‐, four‐, and five‐stage capacitated systems over a range of model parameters, and discuss insights that emerge.     

Optimal Inventory Control with Dual‐Sourcing,Heterogeneous Ordering Costs and Order Size Constraints

We consider a dual‐sourcing inventory system, where procuring from one supplier involves a high variable cost but negligible fixed cost whereas procuring from the other supplier involves a low variable cost but high fixed cost, as well as an order size constraint. We show that the problem can be reduced to an equivalent single‐sourcing problem. However, the corresponding ordering cost is neither concave nor convex. Using the notion of quasi‐convexity, we partially characterize the structure of the optimal policy and show that it can be specified by multiple thresholds which determine when to order from each supplier and how much. In contrast to previous research, which does not consider order size constraints, we show that it is optimal to simultaneously source from both suppliers when the beginning inventory level is sufficiently low. We also show that the decision to source from the low‐cost supplier is not monotonic in the inventory level. Our results require that the variable costs satisfy a certain condition which guarantees quasi‐convexity. However, extensive numerical results suggest that our policy is almost always optimal when the condition is not satisfied. We also show how the results can be extended to systems with multiple capacitated suppliers.