the transition of the economy following a (trade) policy shock and to use those simulations to assess the dynamic distributional impacts of trade reforms. The model relies on the interplay between labor demand and labor supply in a rational expectations equilibrium. The demand side is simple because labor demand is just the value of the marginal product of labor for firms that do not face any costs of adjusting either labor or capital (see Bet, Brambilla, and Porto, 2011). Labor supply to different sectors is derived from workers’ intertemporal optimization decision. In a given period, a worker produces in a sector and earns the equilibrium wage in that sector. At the end of each period, each worker has the opportunity to switch sectors, but at a cost. These costs have a common component to all workers and an idiosyncratic component that varies for each worker over time. The common costs and the parameters of the distribution of idiosyncratic costs can be estimated with panel data. To do this, the authors derive an Euler equation that represents the equilibrium condition for the workers. This equilibrium condition essentially links gross flows of workers across sectors (from sector i to sector j and also from sector j to sector i) with future intersectoral wage differentials. The Euler equation can easily be implemented as a linear regression.

The interpretation of the labor mobility costs in these papers is, in principle, broad. It is possible to argue that these costs include retraining and relocation costs as well as psychological or other nonpecuniary costs of moving (such as tedium in the current job). In addition, the parameters that characterize the labor mobility costs are identified with the correlation of job flows and wage differentials across sectors. This means that, in the data, labor market imperfections and other features of the economy (not modeled) that affect job flows, for instance, will affect the estimate of the labor mobility costs. In consequence, these costs are in some sense “reduced-form” labor mobility costs. I will explore some of their microfoundations in later sections.

A key feature of this theory is that the time-varying idiosyncratic costs allow for a gradual reallocation of workers to a trade shock. This creates a rich dynamic response to trade policy that provides a very detailed illustration of how an economy moves during the transition. In principle, while the authors use the simulation exercises to assess trade policy, I believe it is possible to set up exercises of green growth policy too. I elaborate on this idea in section 4.3 below.

The estimation often delivers extremely high average moving costs and very high variance of moving costs. Using U.S. data, for instance, Artuc, Chaudhuri and McLaren (2010) report moving costs that range from 4 to 13 times the annual wage income. Using data from Turkey, Artuc and McLaren (2010) also report moving costs equivalent to roughly 5-13 times the annual wage.<sup>7</sup> For Argentina, Bet, Brambilla and Porto (2011) report costs of around 9 times the annual wage. In all these instances, the moving costs show a very high variance indicating that, at each period, workers can be subject to very large positive or negative idiosyncratic shocks.

While the estimated costs appear too large, they typically produce a very realistic dynamic aggregate behavior in the economy. For the ease of exposition and to exemplify the results, I reproduce in Figure 3 the dynamics of labor allocations, real wages, and the present value for workers obtained in the simulations of Artuc, Chaudhuri and McLaren (2010). The adjustment of the labor market to a trade shock is sluggish: it takes several years to approach the new steady state. The import-competing sector

> 7 Note that Artuc, Chadhuri and McLaren (2010) use instruments in the estimation of the Euler equation to deal with several biases that may take place if OLS is used instead. However, because the Turkey panel is short and because the instruments are the lagged values of wages, Artuc and McLaren (2010) cannot use IV methods. Instead, they estimate OLS coefficients and fix them to account for the biases reported in Artuc, Chaudhuri and McLaren (2010).

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