Transcript Intervention Models Applied to Measure Sanitary and

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
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). 1990 to 2005
1800
Mil
ton 1600
ela
das 1400
eq
uiv
1200
ale
nte
1000
car
caç 800
a
600
400
200
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Processed
Source: ABIEC
In natura
Total
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
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)
XD = m (PB/TC, PW, ZD)
Xs  0

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 function
w( B)
 ( B)
Zt  c 
X t b 
at
 ( B)
 ( B)
Intervention Variable
 ( B)
 t b
 ( B)
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 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). 1992 January - 2000
December. Series in level
R 2 = 0,88
Model: F(9,97) = 79,05*
Variable
Dependent variable = LVDTUE
Test “t”
Coefficient
Constant
18,04*
4,62
ltxreal t-1
0,72*
2,51
lvdtue t-1
0,44*
6,92
Lrpbrarg
-0,10
-0,47
lrbras t-1
-0,90**
-2,20
lprdiant t-1
-0,23
-0,99
Lpbreal
-1,06*
-3,09
lvxarg t-1
-0,46*
-3,04
Seasonality
0,23*
4,23
Trend
0,0075*
3,77
Modelo: Q(24,1) = 24,89*
R 2 = 0,92
Variável dependente =
1
LVDTUE
Variável
Constante
AR(1)
N_SAZ{0}2
N_SAZ{1}
N_SAZ{2}
N_SAZ{3}
N_SAZ{4}
N_SAZ{5}
N_SAZ{6}
N_SAZ{7}
N_SAZ{8}
N_SAZ{9}
N_SAZ{10}
N_LTXREAL{1}
N_LVXARG{1}
N_LPBREAL{1}
N_LRPBRARG{0}
N_LPRDIANT{1}
N_LRBRAS{1}
N_TREND
N_D0195{0}
N_D0195{1}
N_D0396{1}
N_D0396{2}
N_D0396{3}
N_D07{0}
N_D07{1}
N_D07{2}
Teste “t”
Coeficiente
25.62*
0.28**
0.13
0.008
0.12
0.46*
0.62*
0.60*
0.70*
0.43*
0.19
0.15
0.08
0.59
-0.48**
-0.80
-0.23
-0.47
-1.62**
0.002
-0.76*
0.52**
-0.01
-0.35
0.03
0.32***
1.42*
-0.44
4.41
2.16
1.28
0.05
0.89
3.15
3.92
3.10
3.57
2.24
1.26
1.19
0.73
1.27
-2.47
-1.46
-0.77
-1.50
-2.17
0.12
-2.90
2.13
-0.06
-1.27
0.12
1.75
4.41
-1.39
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%
-
+1
Jan/95
Fev/95
0
(0,52)
- 0.76
-1
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: [email protected]
Geraldo Barros: [email protected]