of compensation (measured as the size of the resulting distortion relative to the gain from trade). They find that using the correct policy creates very little deadweight loss, ranging from well under 1 percent of the gains from trade to approximately 30 percent of the gains from trade, depending upon parameter assumptions and the particular scenario studied. However, using the wrong policy causes these costs to balloon, in some cases more than doubling for the same set of parameter values and the same scenario.

In many instance, a large fraction of the costs of adjustment to trade are the result of an inherent distortion or market failure in the economy.<sup>14</sup> These distortions can affect labor markets (for instance if there are taxes and regulations that affect the cost of hiring and firing workers) or capital markets (if for instance there are credit constraints). In consequence, a direct way to tackle the trade adjustment costs is to implement policies to mitigate or eradicate the main distortions in the economy. Instead of listing broad policy measures (e.g., eliminate labor market frictions, improve credit markets, provide training to displaced workers), I’d rather discuss hard-core evidence from the models of section 4. The main exponents are Cosar (2010) and Dix-Carneiro (2010).

Cosar (2010) presents a model where the costs of adjustment are generated by frictional unemployment via labor search and matching. As in Dix-Carneiro (2010), this is an overlapping generation model and human capital is accumulated on the job. The model has three key features: i) worker mobility and adjustment costs age-dependent; ii) labor markets are subject to search frictions where young and old workers search for jobs and are randomly matched with firms. Depending on their match-specific productivity draws, they continue (or separate) and share rents via Nash-bargaining; iii) workers accumulate human capital through learning-by-doing on the job and skills are not transferable across sectors. Old workers in the import-competing sector are thus more likely to bear the costs of adjustment.

Cosar (2010) calibrates this model using Brazilian data and then works out different labor market policies to deal with trade adjustment costs. He finds that sector-specific human capital is a bigger impediment to mobility than labor market frictions (search and matching). In terms of policies, he compares an unemployment insurance program (similar to one actually implemented in Brazil in 1988) and a targeted employment subsidy paid to the initial old employed in the previously protected industry conditional on working in the export-oriented industry. The results show that the unemployment insurance (actual policy) experiment leads to an output loss during the transition by hampering what the economy needs most: reallocation and skill formation in the expanding sector. On the other hand, the employment subsidy (counterfactual policy) experiment suggests that it is possible to not only redistribute income toward workers harmed by the liberalization, but also to increase net output during the transition. The subsidy mitigates the market failure due to the learning externality: the underinvestment in skill formation is especially problematic during the transition which is a time for human capital build-up in the export-oriented industry. A policy that rewards work and mobility for workers adversely affected by trade not only compensates them, but it also speeds up the transition and helps the economy reap the gains from trade earlier on.

Dix-Carneiro (2010) also explicitly models labor market policies. Unlike Cosar (2010), the author finds that the costs of mobility are more important than sector-specific experience in explaining the slow adjustment of the labor market. This is because Dix-Carneiro allows experience to be imperfectly transferable (while Cosar assumes non-transferable experience) and estimates the degree of

> 14 Note that the costs of re-tooling workers or of adapting capital as well as the efficiency losses from, for example, human capital specificity may also be significant. Recall, for example, the result in Davidson and Matusz (2010) reviewed in section 4.

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