Steven Berry Publications

We examine identification of differentiated products demand when one has “micro data” linking the characteristics and choices of individual consumers. Our model nests standard specifications featuring rich observed and unobserved consumer heterogeneity as well as product/market-level unobservables that introduce the problem of econometric endogeneity. Previous work establishes identification of such models using market-level data and instruments for all prices and quantities. Micro data provides a panel structure that facilitates richer demand specifications and reduces requirements on both the number and types of instrumental variables. We address identification of demand in the standard case in which nonprice product characteristics are assumed exogenous, but also cover identification of demand elasticities and other key features when these product characteristics are endogenous and not instrumented. We discuss implications of these results for applied work.

To reduce global carbon emissions, should people harvest and use more wood or less? This question underlies the merits of policies that encourage power plants and heating facilities to burn more wood pellets and builders to construct more tall wood buildings. As one illustration of the question’s importance, the U.S. government has recently requested input on whether a lucrative tax credit for carbon-neutral electricity should apply to burning wood.

In the Carbon Costs of Global Wood Harvests, published in Nature in 2023, WRI researchers using a biophysical model estimated that annual wood harvests over the next few decades will emit 3.5-4.2 billion tons of carbon dioxide (CO2) per year. That is more than 3 times the world’s current annual average aviation emissions. These wood-harvest emissions occur because the great majority of carbon stored in trees is released to the atmosphere after harvest when roots and slash decompose; as most wood is burned directly for heat or electricity or for energy at sawmills or paper mills; and when discarded paper products, furniture and other wood products decompose or burn. Another recent paper in Nature found that the word’s remaining forests have lost even more carbon, primarily due to harvesting wood, than was lost historically by converting forests to agriculture (other studies have found similar results1). Based on these analyses, a natural climate solution would involve harvesting less wood and letting more forests regrow. This would store more carbon as well as enhance forest biodiversity.

Carbon Costs focused on the pure physical emissions from wood harvest and timber management relative to leaving forests alone. This is consistent with the approach used for decades by the IPCC and numerous other papers to estimate the emissions from new wood harvests.2 However, it differs from some papers that claim the carbon emitted to the atmosphere by harvesting and using wood should generally be ignored. These papers assume that wood is carbon neutral, just like solar or wind energy, so long as other forest tracts in a large area (often a whole country) are growing enough to keep the total amount of carbon stored in forests stable — which is true of forests in most countries. By itself, this argument makes little sense: If some parts of a country’s forests are not harvested, forests in that country overall will grow more and absorb more carbon, which reduces global warming. This rationale for carbon neutrality is roughly equivalent to claiming that a money-losing company does not lose money if a country’s companies are profitable overall.

Yet, some researchers, such as the developers of the Global Timber Model (GTM), also have a more refined argument for why harvesting wood causes low, no, or even negative emissions. In a blog and a critique submitted to Nature, their core claim is that the effect of forestry on carbon is an economic question that requires analysis using an economic model rather than a biophysical one. According to the GTM, increased wood demand for any one product leads to a range of results that can lower carbon costs; these include causing people to plant more forests, to reduce their consumption of other wood products, and to intensify forest management. The first idea, that increased wood demand leads to more forests, is related to a broader idea: that forests exist because of the demand for wood. This underlies the views of many others who see wood as carbon neutral.

The GTM is by far the most cited economic model for analyzing the carbon consequences of global wood use, so its findings could have serious policy implications. Importantly, the model has been used to claim the climate advantages of harvesting more wood for bioenergy, particularly to burn in power plants. One GTM paper estimates that substantially increasing demand for wood for bioenergy could lead to roughly 1.1 billion hectares of agricultural land being converted to forests around the world. That is an area almost four times the size of India and equal to more than 70% of current global croplands — which raises the question of where the world’s food would come from.

This dialogue, to which WRI has responded in an exchange under review at Nature, provides a useful basis for exploring the effects of wood consumption on climate change and what they mean for policy. The U.S. government has specifically asked for comments about the role of economic models in treating wood as carbon neutral or negative. Here, we take a closer look at both economic and biophysical models and what each does or doesn’t tell us about the climate consequences of using wood.

We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically endogenous market structure. We propose the use of Generalized Instrument Variables methods to identify those dynamic policy functions that are consistent with instrumental variable (IV) restrictions. Extending popular “two-step” methods, these policy functions then identify a set of structural parameters that are consistent with the dynamic model, the IV restrictions and the data. We provide computed illustrations to both single-agent and oligopoly examples. We also present a simple empirical analysis that, among other things, supports the counterfactual study of an environmental policy entailing an increase in sunk costs.

Demand elasticities and other features of demand are critical determinants of the answers to most positive and normative questions about market power or the functioning of markets in practice. As a result, reliable demand estimation is an essential input to many types of research in Industrial Organization and other fields of economics. This chapter presents a discussion of some foundational issues in demand estimation. We focus on the distinctive challenges of demand estimation and strategies one can use to overcome them. We cover core models, alternative data settings, common estimation approaches, the role and choice of instruments, and nonparametric identification.