Dix-Carneiro and Kovak Trade Reform and Regional Dynamics

# **A Data Appendix and Supplemental Results**

## **A.1**

Tariff data come from Kume et al. (2003), who report nominal tariffs and effective rates of protection from 1987 to 1998 using the Brazilian industry classification _N ́ıvel_ 50. We aggregate these tariffs slightly to an industry classification that is consistent with the Demographic Census data used to construct local tariff shock measures. The classification is presented in Table A1. In aggregating, we weight each _N ́ıvel_ 50 industry with its 1990 industry value added, as reported in IBGE National Accounts data. Figure A1 shows the evolution of nominal tariffs from 1987 to 1998 for the ten largest industries. The phases of Brazilian liberalization are visible (see Section 2 for a discussion and citations). Large nominal tariff cuts from 1987-1989 had little effect on protection, due to the presence of substantial nontariff barriers and tariff exemptions. In 1990, the majority of nontariff barriers and tariff exemptions were abolished, being replaced by tariffs providing equivalent protection; note the increase in tariffs in some industries in 1990. During liberalization, from 1990 to 1994, tariffs fell in all industries, then were relatively stable from 1995 onward.

## **A.2 Outcome Pre-Trends vs. Tariff Changes**

Along with regional variation in the industrial composition of employment, our analysis relies on variation in tariff cuts across industries. Here we analyze the relationship between tariff cuts during liberalization (1990-1995) and trends in industry wages and employment before liberalization, 19801991. We choose these two years in measuring pre trends because this is when Demographic Census data are available. We implemented a similar analysis using another smaller household survey, the PNAD, which is fielded yearly between Censuses. This allowed us to consider pre-trends from 19811990 and 1986-1990, avoiding overlap with liberalization in 1991. The results were very similar to the Census results presented here.

We implemented a variety of specifications, with results reported in Table A2. In all specifications, the independent variable is the proportional change in one plus the tariff rate (∆1990 _−_ 95 ln(1+ _τi_ )), shown in Figure 1. In panels A-C the dependent variable is the change in log industry earnings. Panel A uses unconditional average industry earnings; Panel B uses industry earnings residuals controlling for individual age, sex, education, and formal status; and Panel C uses average log earnings residuals controlling for these individual characteristics and region fixed effects. In panel D, the dependent variable is the change in log industry employment. Column (1) weights industries equally, and presents standard errors based on pairwise bootstrap of the t-statistic, to improve small sample properties with only 20 tradable industry observations. Column (2) uses the same estimator, but drops agriculture. Column (3) uses heteroskedasticity weights and presents heteroskedasticityrobust standard errors, which are likely understated in this small sample (MacKinnon 2011). Column (4) uses the same estimator, but drops agriculture. In all cases, the results should be seen primarily as suggestive, because the analysis only uses 20 observations.

Nearly all of the earnings estimates are negative, indicating that tariffs fell more for industries experiencing more positive wage growth prior to liberalization. The majority of the estimates are insignificantly different from zero, with the exception of weighted results in Panels A and B. These specifications heavily weight agriculture, which exhibited negative wage growth prior to liberalization and experienced essentially no tariff decline during liberalization, driving the strong negative relationship. By dropping agriculture, Column (4) confirms that the significant relationship is 75