of the mobility costs _Ce_ and _Cu_ . This strategy follows the conditional choice probability (CCP) approach of Hotz and Miller (1993). More details about the SMM estimation including the estimation of _η_ and _ν_ are in on-line Appendix A.

Results are reported in Table 2. Standard errors for the estimates are computed analytically, as in Bloom (2009). The estimated capital adjustment costs are sizeable and signicantly dierent � from zero (panel A). We estimate a xed cost _γ_ 1 = 0 _._ 038 . This is a substantial cost since it implies that the xed cost of adjustment is about 3.8 percent of the average plant-level capital value. The estimated coecient for the quadratic adjustment cost parameter ( _γ_ �2 ) equals 0.18. Using the quadratic adjustment cost function and a steady state investment rate equal to the depreciation rate ( _I/K_ = _δ_ = 0 _._ 0991 ), the estimated parameter implies an adjustment cost relative to the average plant-level capital of 0.175 percent. Finally, our estimate of the transaction costs ( � _γ_ 3 = 0 _._ 840 ) implies that resale of capital goods would incur a loss of about 16 percent of its original purchase price.<sup>11</sup> The estimates of the labor mobility costs are in panel B of Table 2. The SMM estimates are � � _Ce_ = 3 _._ 72 and _Cu_ = 4 _._ 22 . The estimated variance is _ν_ = 2 _._ 63 . All our estimates are statistically signicant. On average, a worker wishing to switch sectors would pay a mobility cost equivalent to 3.72 times his annual wage earnings. Instead, a worker moving from non-employment to employment would pay a higher cost of 4.22 times the annual wage. These estimated costs are high, revealing large labor friction in Argentine labor markets. Our estimates are lower than those reported in Artuc, Chaudhuri, and McLaren (2010) for the U.S., where the average moving cost is around 6.565, and _ν_ is 1.884. Allowing for job quality terms _η_ , Artuç and McLaren (2015) estimate more modest _C_ , ranging from as low as 0.99 to as high as 1.54 (with _ν_ =0.257), also for the U.S. Finally, estimating a comprehensive model that allows for worker heterogeneity with Brazilian data, DixCarneiro (2014) nds median mobility costs ranging from 1.4 to 2.7, which are smaller than ours (though Dix-Carneiro's estimates show large dispersion across the population).<sup>12</sup>

> 11Our estimates of capital adjustment cost parameters for Argentina can be directly compared with those in Cooper and Haltiwanger (2006) for the U.S. as we use the same specications. Cooper and Haltiwanger (2006) estimate comparable xed costs ( _γ_ 1<sup>_US_</sup> = 0 _._ 039 ), but lower quadratic adjustment costs ( _γ_ 2<sup>_US_</sup> =0.049) and lower irreversibilities ( _γ_ 3<sup>_US_</sup> = 0 _._ 975 ). This implies that capital is more exible in the U.S. than in Argentina, especially in terms of the irreversibility cost of investment. These dierences, as well as the magnitudes of the estimates, are, however, sensible and plausible. Bloom (2009) reports larger values for the partial irreversibility cost, with capital reselling losses of 42.7 percent, and for the quadratic adjustment cost parameter (0.996). The xed cost parameter _γ_ 1 , which is estimated in terms of annual sales (instead of average capital), is 1.1 percent. Note that these results are not directly comparable to ours because of some dierences in specication.

> 12In Panel C of Table 2, we report the parameters of the wage process. They support the conjecture that wages follow an AR(1) and that they make a discrete (positive or negative) jump of an order of magnitude between 0 and

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