This paper describes and analyzes Brazilian monetary policy since the beginning of inflation targeting, in 1999. The main hypothesis of the paper is that inflation targeting emerged as the best macroeconomic policy in Brazil in the late 1990s because, in controlling inflation, it ends up stabilizing both the real exchange rate and GDP growth, although not necessarily at an optimal level. Despite this limitation, inflation targeting has proved to be the best framework for Brazilian monetary policy.
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In Brazil, power comes mostly from hydroelectric plants, which makes the country’s energy supply heavily dependent on the rainy season.
All inflation numbers mentioned in this paper refer to consumer prices, measured by the IPCA index, on an annual basis.
Inflation reached 6.4% in 2014.
Throughout this paper, I define the nominal exchange rate as the domestic price of foreign currency, that is, for example, the amount of BRL per US dollar. The real exchange rate is this nominal rate multiplied by the ratio of foreign to domestic prices, with the former defined by the BCB based on the composition of Brazilian exports. As a result, an increase in the exchange rate means a depreciation of the domestic currency and vice versa.
MPOG stands for Ministério do Planejamento, Orçamento e Gestão and CCPR stands for Casa Civil da Presidência da República. Formally, the BCB is still a government agency subordinated to the MoF, but its President has a cabinet-level position. In practice, the BCB functions as an independent agency. The CCPR is headed by the President’s Chief of Staff, who also has a cabinet-level position.
Brazilian Presidential Decree 3.088, of 1999. For the beginning of inflation targeting in Brazil, see Bogdanski et al. (2001).
IOF stands for Imposto sobre Operações Financeiras. The IOF law states what can be taxed and the maximum rates on each operation. Below these ceilings, the President can change the IOF rates without Congress approval.
This JEO gained importance after Brazil’s ‘Fiscal Responsibility Law’ (Lei Complementar 101), adopted in 2000.
This practice does not happen without political noise and, in recent years, there have been many Congressional attempts to limit the JEO’s powers.
For a more detailed analysis of the Brazilian economy during this period, see Giambiagi et al. (2011).
All growth rates are annual rates.
Power prices rose 17.3% in 2001, while the IPCA index rose 7.7%.
After the economy absorbed the maxi-depreciation of the BRL in 1999, GDP growth accelerated to 4.3% in 2000, but the recovery was short-lived.
The slowdown in Argentina also pulled Brazil’s GDP down during this period.
Assuming zero GDP growth in 2014.
Credit expansion took the form of subsidized government loans to its banks, especially to its development bank, which in turn lent the funds at subsidized rates to the market.
All interest rates were calculated as the cumulative rate over the past 12 months.
The BCB’s data on the expected – ‘ex ante’ – real interest rate starts only in 2001, but it shows almost the same pattern, with a one-year lead of the expected rate over the effective rate. To simplify the exposition, I will focus only on the effective real interest rate.
This period marked the peak of the ‘currency-war’ speech of Brazilian authorities, which not surprisingly coincided with the peak in the Brazilian terms of trade, pulled by high commodity prices. Until 2011, most FX actions by the MoF were focused on taxing short-term capital inflows, with limited impact on the exchange rate. Only when the MoF changed course and started to tax the net exposure in FX derivatives markets did the BRL/USD move significantly.
This marked the end of the ‘currency war’, at least for Brazilian authorities.
The VAR model with 12 lags is shown in the statistical appendix. All series used in the econometric model are available from the author upon request.
For a previous estimate of the exchange-rate pass-through, see Belaisch (2003).
More formally, recall that, in log levels, the change in the real exchange rate is the change in the nominal rate plus foreign inflation minus domestic inflation. Therefore, if the variation in the real exchange rate is 0.2 and the variation in domestic prices is 0.14 times the variation in the nominal exchange rate, the variation in domestic prices is 0.2 minus foreign inflation times 0.14/0.86. For a foreign inflation rate of 0.02, the result is 0.18 × 0.14/0.86=2.9%.
As we commented in the previous section, the Brazilian authorities relied on government subsidies to smooth the price adjustment in some regulated markets in 2012–14, which in turn reduced its primary balance.
For the details of this model, see Barbosa-Filho (2014).
Mathematically, since the data-generating process is the same, the univariate model represents a linear transformation of a vector-error correction model containing domestic price, the nominal exchange rate and the foreign price, with the latter being exogenous. Since the focus of this paper is economic policy rather than econometrics, I restrict the statistical results to their univariate and more intuitive representation.
The econometric model is an updated version of the one presented in Barbosa-Filho (2010).
The statistical results are shown in the appendix. The model is an updated version of the one presented in Barbosa-Filho et al. (2011). By analogy with the inflation model, the univariate GDP model can be interpreted as a linear transformation of a vector error-correction model containing GDP, the domestic price, the nominal exchange rate and the foreign price, with the latter being exogenous.
For an application of the AK model to Brazil, see Bacha and Bonelli (2005).
More formally, define economic growth as g=su/p, where s is the investment–GDP ratio at current prices, u the income–capital ratio and p the relative price of investment in terms of the GDP deflator. For a given capital productivity (u), the concave-down growth curve can be justified from the fact that both s and p are positive functions of the real exchange rate.
For the substitution between the real exchange rate and institutional or structural change in accelerating growth, see Rodrik (2008).
The usual candidates in the current Brazilian economic debate are tax reform, social security reform, labor reform, higher investment in education and infrastructure, financial deepening, and prudential financial regulation – including the regulation of capital flows and derivatives operations.
Bacha, EL and Bonelli, R . 2005: Accounting for Brazil’s growth deceleration. Brazilian Journal of Political Economy 25 (3): 163–189.
Barbosa-Filho, NH . 2010: Duas não linearidades e uma assimetria. Paper presented at the 7th Economic Forum of the Getulio Vargas Foundation, São Paulo.
Barbosa-Filho, NH . 2014: A structuralist inflation curve. Metroeconomica 65 (2): 349–376.
Barbosa-Filho, NH, Silva, JA, Goto, F and Silva, B . 2011: Crescimento econômico, acumulação de capital e taxa de câmbio. In: Holland, M and Nakano, Y. (ogs). Taxa de Câmbio no Brasil: Estudos de uma perspectiva do desenvolvimento econômico. Elsevier: São Paulo.
Belaisch, A . 2003: Exchange-rate pass-through in Brazil. IMF Working paper 03/141.
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I would like to thank Roberto Frenkel, Martin Rapetti, Mario Damill, Jose Antonio Ocampo and three anonymous referees for their comments and suggestions on a previous version of this paper. The errors and opinions remain mine only.
The data series used in the models are:
IPCA: consumer-price index, monthly frequency.
GDPSA: real gross domestic product index, seasonally adjusted, quarterly frequency.
NER: nominal BRL/USD exchange rate, monthly frequency.
RER: real exchange rate, as defined by the BCB, monthly frequency.
GAP: output gap, measured by the difference between GDP and its Hodrick–Prescott trend, both series in natural logs, with a smoothing parameter of 1600.
DUMMY_POWER: dummy variable for the energy rationing of 2001, equal to 1 in the third and fourth quarters of 2001.
DUMMY_CRASH: dummy variable for the financial crash of 2008–09, equal to1 in the fourth quarter of 2008 and first quarter of 2009.
DUMMY_WCUP: dummy variable for the World Cup of 2014, equal to 1 in the second quarter of 2014.
Table A1 presents the Granger causality test between IPCA and RER, and Figure A1 presents the impulse-response function of the VAR model for these two variables. The test and the model were estimated with 12 lags based on the data from January 1998 through September 2014.
Table A2 presents the statistics of a group of models, from general to specific, of the second difference of IPCA (the change in inflation). Based on the information criteria, the most general model seems to be the best description of the error-correction process of inflation to its long-run trend. Analogously, Table A3 presents the statistics of another group of models, again from general to specific, of the second difference of GDP (the acceleration of economic growth). Based on the information criteria, the most general model seems to be the best description of the data.
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Barbosa-Filho, N. Monetary Policy with a Volatile Exchange Rate: The Case of Brazil since 1999. Comp Econ Stud 57, 401–425 (2015). https://doi.org/10.1057/ces.2015.15
- inflation targeting
- monetary policy