Robert S. Pindyck’s research while affiliated with Massachusetts Institute of Technology and other places

How does sustainable consumption depend on productivity growth, the size and growth rate of the population, and uncertainty over these growth rates? I address these questions using a model in which productivity and population growth are stochastic and human lives can have (positive or negative) intrinsic social value. I show how sustainable consumption depends on expected rates of productivity and population growth, the volatility of those rates, and the dependence of welfare on population. For plausible parameter values, sustainable consumption is well below the optimal welfare-maximizing level. This raises a question: given its cost, should sustainability be a social objective? (JEL E21, E22, E23, J11, Q01)

Most of the literature on the economics of catastrophes assumes that such events cause a reduction in the stream of consumption, as opposed to widespread fatalities. Here we show how to incorporate death in a model of catastrophe avoidance, and how a catastrophic loss of life can be expressed as a welfare-equivalent drop in consumption. We examine how potential fatalities affect the policy interdependence of catastrophic events and “willingness to pay” (WTP) to avoid them. Using estimates of the “value of a statistical life” (VSL), we find the WTP to avoid major pandemics, and show it is large (10% or more of annual consumption) and partly driven by the risk of macroeconomic contractions. Likewise, the risk of pandemics significantly increases the WTP to reduce consumption risk. Our work links the VSL and consumption disaster literatures.

How should a firm price a new product for which little is known about demand? We propose a simple and practical pricing rule for new products where demand information is limited. The rule is simple: Set price as though the demand curve were linear. Our pricing rule can be used if three conditions hold: the firm can estimate the maximum price it can charge and still expect to sell some units, the firm need not plan in advance the quantity it will sell, and marginal cost is known and constant. We show that if the true demand curve is one of many commonly used demand functions, or even a more complex (randomly generated) function, the firm can expect its profit to be close to what it would earn if it knew the true demand curve. We derive analytical performance bounds for a variety of demand functions, calculate expected profit performance for randomly generated demand curves, and evaluate the welfare implications of our pricing rule. We show that with limited demand information (maximum price and marginal cost), our simple pricing rule can be used for new products while often achieving a near-optimal performance. We also discuss the limitations of our method by identifying cases where our pricing rule does not perform well. This paper was accepted by Joshua Gans, business strategy.

jats:title>Abstract Most of the literature on the economics of catastrophes assumes that such events cause a reduction in the stream of consumption, as opposed to widespread fatalities. Here we show how to incorporate death in a model of catastrophe avoidance, and how a catastrophic loss of life can be expressed as a welfare-equivalent drop in consumption. We examine how potential fatalities affect the policy interdependence of catastrophic events and ‘willingness to pay’ (WTP) to avoid them. Using estimates of the ‘value of a statistical life’ (VSL), we find the WTP to avoid major pandemics, and show that it is large (10% or more of annual consumption) and partly driven by the risk of macroeconomic contractions. Likewise, the risk of pandemics significantly increases the WTP to reduce consumption risk. Our work links the VSL and consumption disaster literatures.</jats:p

An estimate of the social cost of carbon (SCC) is crucial to climate policy. But how should we estimate the SCC? A common approach uses an integrated assessment model (IAM) to simulate time paths for the atmospheric CO 2 concentration, its impact on temperature, and resulting reductions in GDP. I have argued that IAMs have deficiencies that make them poorly suited for this job, but what is the alternative? I present an approach to estimating an average SCC, which I argue can be a useful guide for policy. I rely on a survey of experts to elicit opinions regarding (1) probabilities of alternative economic outcomes of climate change, but not the causes of those outcomes; and (2) the reduction in emissions required to avert an extreme outcome, i.e., a large climate-induced reduction in GDP. The average SCC is the ratio of the present value of lost GDP from an extreme outcome to the total emission reduction needed to avert that outcome. I discuss the survey instrument, explain how experts were identified, and present results. I obtain SCC estimates of $200/mt or higher, but the variation across experts is large. Trimming outliers and focusing on experts who expressed a high degree of confidence in their answers yields lower SCCs,$80 to $100/mt, but still well above the IAM-based estimates used by the U.S. government.

In environmental economics, the marginal external cost of emitting a pollutant determines the optimal abatement policy, which might take the form of an emissions tax. But the marginal external cost is often difficult to estimate. This is especially the case when it comes to climate change; estimates of the social cost of carbon (SCC) range from around $10 per metric ton to well over$200 per metric ton, and there has been little or no movement toward a consensus number. Partly as a result, rather than an SCC-based carbon tax, climate policy has focused on a set of targets that would put limits on temperature increases or atmospheric CO2 concentrations, which in turn imply targets for emission reductions. Economics, however, can tell us little about whether such targets are socially optimal. I discuss the trade-off between taxes versus targets as the focus of policy, explain why it has been so difficult to estimate a marginal SCC, and suggest an approach to estimating an average SCC through the use of expert elicitation. I argue that such an approach could serve as the basis for a harmonized carbon tax.

In recent articles, I have argued that integrated assessment models (IAMs) have flaws that make them close to useless as tools for policy analysis. IAM-based analyses of climate policy create a perception of knowledge and precision that is illusory, and can fool policy-makers into thinking that the forecasts the models generate have some kind of scientific legitimacy. But some have claimed that we need some kind of model, and that IAMs can be structured and used in ways that correct for their shortcomings. For example, it has been argued that although we know little or nothing about key relationships in the model, we can get around this problem by attaching probability distributions to various parameters and then simulating the model using Monte Carlo methods. I argue that this would buy us nothing, and that a simpler and more transparent approach to the design of climate change policy is preferable. I briefly outline what that approach would look like.