Intervention Models Applied to Evaluate Impacts of Sanitary and Technical Barriers to Trade Sílvia H. G. de Miranda Geraldo S.A. de C. Barros ESALQ – University of Sao Paulo 2- 5 th December 2006 Winter Meeting - IATRC

Summary 1. The challenge of measuring non-tariff barriers and the Brazilian beef exports 2. The Econometric model 3. The Intervention Model 4. Results and Concluding Remarks

Introduction Challenge: the measurement of impacts of sanitary and technical trade barriers Laird (1996) and Beghin and Bureau (2001): a search of methods Inventory models; coverage and frequency indexes; CGE models, tariff equivalents, gravity models etc Only a few studies in developing countries Beef sector: one of the most affected

International beef market Brazil - Since 2004: the major exporter 2005: US$ 3.1 billion of exports Other world’s largest exporters: USA, Australia, New Zealand, Argentina and EU a huge protectionism Competition and the Pacific Rim market: quality requirements

Brazilian beef exports, by type (1000 thousand tons carcass-equivalent) to 2005 Source: ABIEC

Beef market requirements consist on barriers to trade? Brazilian studies: Procópio Filho (1994): sanitary and environmental issues are used to decrease prices Ferraz Filho (1997): sanitary rules affect exporting growth rates of companies; Lima, Miranda & Galli (2005): Brazil is not participating in a beef market amounting to US$ 7.5 billion

Relevance Hypothesis Sanitary and technical events affect Brazilian beef exports, on quantity or prices, or even both Objective This study proposes a (econometric + intervention) methodology to measure the impacts of sanitary or technical events on the Brazilian beef exports.

Econometric Model for External Beef Sales A reduced form model is estimated based on a structural model; Assumptions: the imported and domestic goods are not perfect substitutes there is no perfect substitution in the beef international market

Structural model for Brazilian exports S I = f (P I, P B, W I )domestic beef suply D I = g (P I, Y I,) domestic beef demand S I = volume of beef supplied by domestic market; P I = domestic price for Brazilian beef (in R$); P B = Brazilian beef exporting price (R$); W I = domestic supply shifts; D I = beef volume demanded by domestic market Y I = shifts of domestic demand;

X S = S I – D I = h (P I, P B, W I, Y I ) Xs  0 X D = m (P B /TC, P W, Z D ) X S = volume of Brazilian beef supplied to the international market; X D = volume of Brazilian beef demanded by the international market; TC = exchange rate (R$/US$); P W = beef price of competitors in the international market (US$); and, Z D = shift of the foreign demand of Brazilian beef. P X = P B /TC => P X = US$ price of the exported Brazilian beef

In a balanced international market, the Brazilian exports follow: X* = X S = X D X* = equilibrium quantity of Brazilian foreign sales Reduced Forms: The equilibrium price for foreign sales X*: P B = p(P I, W I, Y I, TC, P W, Z D ) And the equation for exports volume is a function of: X* = H (P B, P I, TC,W I, Y I, P W, Z D ) (1)

Assumption: Perfectly elastic international demand P X = P B /TC = h(P W, Z D ) (2) a) OLS to estimate the reduced forms b) Residual analysis to identify outliers: - application of a Box-Jenkins model; - the residues as the dependent variable

Transfer Function and Intervention Variable Transfer functionIntervention Variable ω(B) = moving average operator with l terms  (B) = an auto-regressive operator with m terms Z t a stochastic process X t = the explanatory variable responsible for part of the changes occurred in Z t N t is the error term (residue), represented by the second term in the right side lag b = the moment the explanatory variable starts to influence U t intervention variabel  t

Representation of intervention variables A special case of Transfer function Pulse or step Vandaele (1983): dynamic effects of intervention variables

Data From 1992 January to 2000 December In natura exports – to EU* Corned beef – to EU and US A Survey: 10 exporting slaughterhouses were visited: In 2000, these companies were responsible for 70.1% (value) and 66.5% (volume) of the Brazilian beef exports (in natura).

Intervention variables 1995 March: EU ban temporarily SP and MG beef exports; 1996 March: EU bans imports from UK; 1998: March: FMD outbreaks in Mato Grosso do Sul State – BR; May: RS and SC states declared free from FMD with vaccination; June: partial opening to the UK beef exports to EU; October: FMD outbreak in Naviraí/MS; 2000 May: Argentina, RS and SC were recognized as FMD free zones without vaccination by the OIE; August: FMD outbreaks in Jóia/RS; September: FTAA lifted bans on Argentinean in natura beef exports because of FMD problems.

Results

Table 1. Results of Brazilian exports model. Beef special cuts to the European Union (vdtue) January December. Series in level

Table 2. Results of Box-Jenkins model for Brazilian beef exports, special cuts to the EU (vdtue). January 1992 to December 2000

Intervention Model January 1995 statistically significant: shock defined as (m,l,d) = (0,1,0), where m is the auto-regressive component, l is the moving average component and d is the lag. The result shows an immediate intervention impact, valued in a decrease of 0.76% on vdtue; in t+1 a positive effect on exports, decreasing it in 0.52% Figure – Sketch on the pattern of the intervention variable (step) effects on Brazilian beef exports to the EU (vdtue ) for January 1995.

Concluding remarks: beef market and intervention analysis Economic variables were the most significant: expected effects There is evidence that Brazilian beef exporters face a non perfeclty elastic demand in the EU market: Brazil affects prices Sanitary events had some significant impacts on quantity and prices of Brazilian beef exports But It was not possible to explain all the significant residues (outliers)

Concluding remarks: about modelling The intervention model requires detailed knowledge about the determinants of trade and all the possible relevant events that can affect the sector’s performance Some additional comments: What is the proper pattern of the intervention function in each specific case? Regionalized effects? The occurrences coming just after a previous event analyzed can reduce its original impacts. Update of this study

CEPEA – Center for Advanced Studies on Applied Economics ESALQ- University of São Paulo Brazil Sílvia Miranda: Geraldo Barros: