**Equilibrium Effects.** Even in the presence of unbiased estimated parameters, one additional problem remains. The parameters are estimated on data extracted from a given economy, with given prices and policy parameters. Evaluating a policy, or even simply trying to give an interpretation to a given return, requires imagining or simulating a counterfactual environment where some of these fundamentals change. For example, suppose to have estimated returns to schooling in a country with 12 years of average completed schooling. A relevant policy question may be asking what the average wage or the overall wage distribution in the country would be when the average years of schooling completed in the population is increased to 16. The answer is not obvious since it cannot simply use the wage regression parameters estimated in the pre-policy environment. If more people complete more education, the supply of skilled workers in the labor market increases. But if the supply increases, the wages for skilled workers will fall. As a result, the pre-policy returns cannot be simply transferred to the post-policy environment.

These general equilibrium effects are difficult to fully incorporate in empirical micromodel while they are more common in many macro-models performing quantitative exercises using calibrated or estimated parameters.<sup>22</sup> Still, interesting examples exist. Kahanna (2016) uses a large expansion of the public-school system together with a model of human capital accumulation. Both elements are embedded in a model of the labor market for skills in order to study returns to schooling once general equilibrium effects are taken into account. The estimated returns are in the usual range (13.4% return for an additional year of education) but the equilibrium impact of adding more educated work force to the labor market is shown to depress the returns to skill reducing the economic benefit of the human capital investment.

If general equilibrium effects are important when considering large scale policies, they may be less crucial when the policy intervention is more limited. But even if this is the case, there can still be partial equilibrium effects due to changes in workers' behavior. Two examples from the literature on search models of the labor market<sup>23</sup> estimated on micro data [Eckstein and Wolpin (1995); Flinn and Mullins (2015)] illustrate this point. They assume that that markets are not completely competitive so that it requires time and effort to meet a firm offering a job.

Eckstein and Wolpin (1995) study returns to schooling in the US. They make the point that accepted wages may be different from wages offered to the agents during the search process. Returns estimated from wage regressions such as equation [8] use observed wages, i.e. wages at which agents have accepted to work. However, each agent may have received and rejected many wage offers before the one observed in the data. Agents reject wage offers in the hope of receiving a better offer at the next meeting with an employer. How selective workers are in accepting a wage offer depends on the schooling level but also on

> 22 The debate dates back at least to the so called Lucas critique [Lucas (1976)]. Recent examples include Dynamic Stochastic General Equilibrium (DSGE) models (see Sbordone et al. (2010) for a review aimed at policy makers) and macro search model of the labor market incorporating endogenous job creation (see Rogerson et al. (2005) for a review.)

> 23 See for example the reviews by Rogerson et al. (2005) focusing on macro search and by Eckstein and van den Berg (2007) focusing on empirical micro search.

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