Dix-Carneiro and Kovak Trade Reform and Regional Dynamics industry-specific wage effects over long periods of time.

Slow factor adjustment can therefore rationalize a steady growth in regional wage and employment effects of liberalization. It is worth noting that agglomeration economies in manufacturing are similar in spirit, and could yield similar patterns (see, for example, Ellison and Glaeser (1997), Rosenthal and Strange (2004), Greenstone, Hornbeck and Moretti (2010), and Kline and Moretti (2014)). The important common element of the two mechanisms is that they can both generate dynamics in local labor demand that persist for long periods of time following a one-time shock. We focus our theoretical interpretation on sluggish factor adjustment because, as we discuss below, evidence on the regional number of establishments, establishment entry and exit rates, and job creation and destruction rates is supportive of the sluggish factor adjustment hypothesis, and Dix-Carneiro (2014) shows that it can quantitatively explain the patterns we observe.

# **4.2 Empirical Approach**

Following the model just described, we define the “regional tariff change,” or _RTCr_ , as our empirical measure of liberalization’s effect on local labor demand. This measure corresponds to the weightedaverage regional price change in (1), where we utilize only the variation in prices that is driven by trade liberalization.

where _i_ indexes tradable goods industries. To calculate the _βri_ , we measure _λri_ as industry _i_ ’s initial share of region _r_ formal employment and _φi_ as one minus the initial wagebill share of industry value added in industry _i_ .<sup>19</sup> _τi_ is the tariff rate in industry _i_ , and _d_ represents the long difference from 1990-1995, the period of Brazilian trade liberalization.

Because Brazilian local labor markets differ substantially in the industry distribution of their employment, the weights _βri_ vary across regions. Figure 2 demonstrates how variation in industry mix leads to variation in _RTCr_ . The figure shows the initial industry distribution of employment for the region with the most negative value, Colatina, the second largest city in Esp ́ırito Santo state, and the most positive value, Paranatinga, in central Mato Grosso state. The industries on the x-axis are sorted from the most negative to the most positive tariff change. Colatina has more weight on the left side of the diagram, particularly in the apparel and food processing industries. Paranatinga produces agricultural goods and wood products almost exclusively, both of which faced more positive tariff changes. Thus, although all regions faced the same set of tariff changes across

> 19We calculate formal employment shares using the 1991 Census, as it provides a more detailed industry classification than that available in RAIS. We initially use _formal_ shares because of our focus on formal sector outcomes and because workers can easily leave formal employment, but returning appears to be quite difficult (Dix-Carneiro 2014). As we show in section 5.3, results are very similar when using overall employment shares, including both formal and informal employment. We also use overall shares when studying outcomes outside the formal sector in Section 7. The _φi_ are calculated using IBGE national accounts data.

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