REGIONAL SHORT-RUN EFFECTS OF TRADE LIBERALIZATION IN BRAZIL

Abstract

We use a single-country multi-regional computable general equilibrium model to evaluate regional short-run impacts of reduction in import tariffs resulting from recent free trade area agreements, on poverty and distribution of income in Brazil. Results show that trade can reduce inter-regional income inequality, but poor urban households lose with trade liberalization. Trade policy alone is not sufficient for achieving more equitable income distribution goals in Brazil. Without greater investment in human and physical capital, incomes in most regions of Brazil are likely to lag behind incomes in the South/Southeast, the most developed regions in the country.

Keywords:

• Trade liberalization
• Income distribution
• CGE models
• Brazil

Notes

¹ The Free Trade Area of Americas was initially intended to include all South, Central and North-American countries, and the main regulations and agreements in different sectors still in debate and negotiations. However, the FTAA missed the targeted deadline of 2005 as planned. There was a planned summit called the Fifth Summit of the Americas in Trinidad and Tobago in 2009, which once again was not successful in reaching an agreement to the FTAA.

² It is true that the analysis of the poverty due to trade liberalization can be more general than the pattern of trade restrictions across countries. See Winters (2002) for more details.

³ Short- to medium-run here refers to the period where some of the factors of production are not fully fixed.

⁴ Of course some other factors can affect the long-term responses of investments and the overall success of the trade reform as well, such as the economic and political environment of the country, since the degree of credibility of the reform plays an important role in this process. For more details, see Rodrik (1992) and Mehlum (2002).

⁵ Even though Ando and Meng (2009) use a spatial CGE model applied to China, the model also takes into account regional and sectoral production distributions and prices differentials.

⁶ According to the Demographic Census 2000 (IBGE, 2000), low income here represents people whose total monthly earnings are less than twice the minimum wage, approximately US$140.

⁷ For more details about the recent changes in the Gini coefficient in Brazil see Hoffman and Ney (2008).

⁸ According to information from the World Bank, South Africa and Malawi are the countries with the highest degree of income inequality, with a Gini coefficient respectively of 0.62 and 0.61. Brazil is the third in this list (Barros et al., 2001).

⁹ More recent data are not available because the Bureau of Brazilian Statistics (IBGE) no longer updates the Brazilian input–output tables. It is not crucial as the main structure of the Brazilian economy has slowly changed in the last few decades. However, the main elasticities used in the CGE model come from recent estimations from different sources.

¹⁰ See Liew (1984) for a good evaluation of both ‘top-down’ and ‘bottom-up’ approaches. Higgs et al. (1988) give a third procedure that consists of a hybrid of both ‘top-down’ and ‘bottom-up’ approaches.

¹¹ For more details and explanation about this approach, see, Robinson, Cattaneo and El-Said (1998, 2000).

¹² Lofgren et al. (2002), Thurlow and Van Seventer (2002) and Wobst (2002). Mathematical description of the regional model can be seen in Bittencourt (2004).

¹³ For more details about this model, see Lofgren et al. (2001).

¹⁴ Description of parameters and variables can be seen in the Appendix. For a detailed description of the model see Bittencourt (2004).

¹⁵ Government transfers indexed to the CPI make the model homogeneous of degree zero in prices.

¹⁶ Government saving is defined as the difference between current government revenues and current government expenditures.

¹⁷ Brazilian exchange rate policy in recent years allows flexible exchange rate fluctuations within a band controlled and determined by the Central Bank.

¹⁸ In general, the average nominal import tariff in Brazil is around 13%, as noted by Estevadeordal et al. (2000), Leipziger et al. (1997), and Monteagudo and Watanuki (2002). Some sectors present, on average, low levels of protection, but there are some specific products with very high import tariffs. For instance, the industry average import tariff is around 10.6%, but the import tariff for vehicles is 39%, and for clothing and shoes is 18.3%.

¹⁹ The results from both scenarios were robust to changes in the Armington elasticities and in the structure of the CGE model. However, they were very sensitive to changes in the macroeconomic closures as expected.

²⁰ Horticultural, forest, and industrial commodities have large increases in exports after eliminating import tariffs.

²¹ We used 2000 repetitions and a 5% significance level.

²² The real exchange rate in our model is represented by the relative prices of traded and non-traded goods.

²³ H-H index was the only index to indicate that the within-region inequality is the most important component to explain the overall inequality. This result reflects how this component is calculated, which includes the product of individual income and the square of a coefficient of variation. That is, if the income is highly concentrated, the within-region inequality tends to be larger than the between inequality, which seems to be the case in Brazil.

²⁴ The low value for transvariation was not surprising due to the SAM disaggregation, since the labor income comes from activities specified by region, with no overlap from sources of income.

²⁵ An increase in the value of the exchange rate in our model represents a devaluation.