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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.
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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- 5th December 2006 Winter Meeting - IATRC
Summary • The challenge of measuring non-tariff barriers and the Brazilian beef exports • The Econometric model • The Intervention Model • 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). 1990 to 2005 Source: ABIEC
Beef market requirements consist on barriers to trade? • Brazilian studies: • Procópio Filho (1994): sanitary and environmental issuesare 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 goodsare not perfect substitutes • there is no perfect substitution in the beef international market
Structural model for Brazilian exports SI = f (PI, PB, WI) domestic beef suply DI = g (PI, YI,)domestic beef demand • SI = volume of beef supplied by domestic market; • PI = domestic price for Brazilian beef (in R$); • PB = Brazilian beef exporting price (R$); • WI = domestic supply shifts; • DI = beef volume demanded by domestic market • YI = shifts of domestic demand;
XS = SI – DI = h (PI, PB, WI, YI) Xs 0 XD = m (PB/TC, PW, ZD) • XS = volume of Brazilian beef supplied to the international market; • XD = volume of Brazilian beef demanded by the international market; • TC = exchange rate (R$/US$); • PW = beef price of competitors in the international market (US$); and, • ZD = shift of the foreign demand of Brazilian beef. PX = PB/TC => PX= US$ price of the exported Brazilian beef
In a balanced international market, the Brazilian exports follow: X* = XS = XD • X* = equilibrium quantity of Brazilian foreign sales Reduced Forms: The equilibrium price for foreign sales X*: PB = p(PI, WI, YI, TC, PW, ZD) And the equation for exports volume is a function of: X* = H (PB, PI, TC,WI, YI, PW, ZD) (1)
Assumption: Perfectly elastic international demand PX = PB/TC = h(PW, ZD) (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 • Zt a stochastic process • Xt= the explanatory variable responsible for part of the changes occurred in Zt • Nt is the error term (residue), represented by the second term in the right side • lag b= the moment the explanatory variable starts to influence Ut • 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 Brazilianbeef exports (in natura).
Intervention variables • 1995 March: EUban 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.
Table 1. Results of Brazilian exports model. Beef special cuts to the European Union (vdtue). 1992 January - 2000 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 tradeand all thepossible 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 EconomicsESALQ- University of São PauloBrazil Sílvia Miranda: smiranda@esalq.usp.br Geraldo Barros: gscbarro@esalq.usp.br