difficult for individuals (workers, firms) to internalize those gains. There is an externality that is not accounted for in the individuals’ decisions concerning employment, technology adoption, and so on. Conceptually, it is not hard to understand what the issues are. What is less clear is how to incorporate these features into the TAC models in order to use them for this purpose. As a crude and simple example, suppose we decide to model green growth policies as a cost shock so that the cost of the firm is now higher (perhaps in selected sectors of the economy) because firms need to improve their capital equipment, retool it to make it environmentally more efficient, etc. As I just argued, it wouldn’t be too hard to incorporate these features into some of the TAC models and to simulate how the economy will respond to this specific example of a hypothetical green growth policy. But if we model this as a negative cost shock, the implications will be lower profitability, lower wages, lower employment, and, overall, lower welfare. But it would be incorrect to conclude that green growth policies lead to lower welfare, because there are social benefits that are being unaccounted for. We could amend the utility function of the individual to include a term related to the “environment” and link the size of the cost shock to this piece of the utility function. This idea is indeed implementable. The conceptual issues are clear, but working out all the full details empirically is not straightforward.

The last point I want to raise is data constraints in developing countries. To the extent that the green growth initiative focuses on developing countries, it is important to discuss whether any of the TAC models can be estimated (or at least calibrated) using data from developing countries. The modern approach exploits microdata as much as possible. Fortunately, micro-data have become increasingly available across the developing world and it is now easy to find firm surveys or household surveys for many countries. However, there are limitations. Studies such as Dix-Carneiro’s rely on individual records of workers who are tracked along their working life. This type of administrative data is not, unfortunately, so widespread. Instead, the method in Artuc, Chaudhuri and McLaren (2010) requires panel data to compute flows of workers between sectors and interindustry wage differentials. While this type of data is generally more readily available, sufficiently large panels to estimate the model with a decent degree of precision are probably harder to come by than one would a priori think. In fact, in Artuc, Lederman and Porto (2011), while working on creating a mapping of labor mobility costs in the developing world, we have encountered enormous difficulties in finding the needed panel data. To overcome this limitation, we develop a modified version of the labor mobility cost model that could be estimated with repeated cross-section (instead of panels). One of the goals of the project is to facilitate applied research in developing countries on the topic of adjustment costs to trade and, if possible, to green growth.

For some questions such as for example the role of capital adjustment costs or firm’s costs of adjusting employment, firm-level data is also needed. The rich data used by Bloom (2009) or Cooper and Haltiwanger (2006) is seldom available in developing countries. Many middle-income countries do have rich panels of firms (Argentina, Chile, Mexico, are examples). The investment climate data, which covers most countries of interest, is perhaps not suitable for this type of analysis.

# **6. Other Relevant Adjustments**

There is a large literature on responses to trade reforms/shocks. Clearly, firms adjust labor and capital and also output and prices. Harrigan and Barrows (2009) provide a detailed account of the responses of world trade to the elimination of the U.S. Multi-Fiber Trade Arrangement (MFA). Research has also shown how firms adjust the skill intensity of production (Brambilla, Lederman, and Porto, 2011), adopt new technologies (Bustos, 2011a), upgrade quality (Verhoogen, 2008), invest in R&D (Bustos, 2011b), 23