FAO International Technical Consultation on Low …...Global market context Short term market...
Transcript of FAO International Technical Consultation on Low …...Global market context Short term market...
FAO International Technical Consultation on Low Levels of GM Crops in International
Food and Feed Trade
FAO, Rome 20 – 21 March 2014
Global market context ◦ Short term market movements
◦ Medium term projections
◦ Policy challenges
◦ Trade policy and global market impacts
Analyzing the effects of LLP/AP on trade flows ◦ Patterns of LLP/AP incidents
◦ Econometric analysis
Looking forward ◦ Further research needs
Market assessments
Market indicators
Major policy developments
http://www.amis-outlook.org/
6
0.0
50.0
100.0
150.0
200.0
250.0
FAO Food Price Index 1961-2014
Nominal
Price Index
Deflated
Price Index
• Joint OECD-FAO report
• Model based projection, not forecast
• 10 year horizon
• Major temperate commodities
• Global coverage
GDP growth ◦ Recovery at different paces
Population growth ◦ Urbanisation, changing diets
Oil prices
Exchange rates
Market and trade policy ◦ Trends, assumptions
9
-70%
-60%
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
Projected change between 2010/12-2022
Egypt
South Africa
Brazil
Japan
EU
India
China
Rus. Fed.
Indonesia
Argentina
Growth in global agricultural production is slowing - projected at 1.5% annually in coming decade
Consumption increasing ◦ But response to income growth is low in many countries
Strong prices (expected to remain firm) are prompting investments into production capacity and technology (but not by all)
Constrained by structure of agriculture, high energy costs, limitation on land and water, tighter environmental regulations
Emerging economies remain agricultural growth leaders
10
0.8
1
1.2
1.4
1.6
1.8
2
Ind
ex
= 1
in
20
00
W.Europe
N.America
Oceania
E.Europe&C.Asia
N.Afr&M.East
SSA
O.Asia
L.America
Index based on constant 2004-06 dollars
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
20
00
= 1
OECD
BRICS
LDC
RestofWorld
China
Index of consumption based on constant
12
1%
9% 11% 8% 6%
81%
42% 37%
2% 8% 9% 6%
15%
66%
41% 35%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
OECD BRICS LDC Rest of World
Consumption change 2003-12 Income change 2003-12
Consumption change 2013-22 Income change 2013-22 13
0
100
200
300
400
500
600
700
20
00
20
02
20
04
20
06
20
08
20
10
20
12
20
14
20
16
20
18
20
20
20
22
USD
/t Rice
Coarse Grains
Wheat
Oilseeds
14
-60
-40
-20
0
20
40
60
80
Bil
lio
ns
of
Co
nst
an
t $2
00
4-0
6
N.America
L.America
Oceania
W.Europe
E.Europe&C.Asia
SS Africa
MENA
Other Asia
0
5000
10000
15000
20000
25000
2001/02
2002/03
2003/04
2004/05
2005/06
2006/07
2007/08
2008/09
2009/10
2010/11
2011/12
2012/13e
2013/14e
USD
million
Cereal import bill – 2001/02 – 2013/14e
LDCs
NFIDCs
-10
0
10
20
30
40
50
60
2001/02
2002/03
2003/04
2004/05
2005/06
2006/07
2007/08
2008/09
2009/10
2010/11
2011/12
2012/13e
2013/14e
%
Commercial cereal import and food aid volumes
(NFIDC+LDC)
Food aid (% total
imports)
Commercial
Imports (% change
over 2001/02)
Food security
– Availability, access and stability concerns. Greater focus on domestic markets in pursuit of national FS objectives
Exporters: ₋ Export restrictions to moderate domestic price increases
Importers: ₋ Tackling higher food import bills ₋ Productivity increases/food self-sufficiency more in focus ₋ Enabling higher response by smallholders more in focus ₋ Importance of stage of development now recognized
But trade policy is not just about Food Security - Minimizing rural-urban income differentials - Export led growth: getting the balance right
And trade barriers are not just tariffs − Food safety −TBT
‣ Impact on global markets determined by:
₋ Way in which increase in production handled by producing country
₋ Size of any surplus that finds its way onto the international market
₋ Characteristics of that market ...
‣ Extent to which other countries are affected by a
change in global market conditions depends on their own trade status:
₋ Food exporter ₋ country’s exporting firms may face reduced prices with
implications for the farmers from which they source
₋ Food importer ₋ fall in price may be positive development if the country is in a
structural deficit for agroclimatic reasons; ₋ but may be less positive for a country with ag potential
0
10
20
30
40
50
60
Linseed Rice Rice Crackerand noodle
Maize Papaya Petfood Soybean andSoybeanProducts
Soybean Canola andoil seed
Other
Nu
mb
er
Gravity-type bilateral export flow model: ◦ Assumes that bilateral trade between partner
countries increases with size (income, population) and closeness
◦ Cross-sectional data
◦ Introduce GMO regulation index and LLP threshold
lnEij = lnα + β1lnYi + β2lnYj + β3lnDij
+β4lnReg-Indexj +β5lnLLPj +ln ɛij
GMO regulation index of importing country - Reg-Indexj ◦ existence of food, feed, and environmental regulation ◦ safety risk assessment ◦ labelling requirement ◦ LLP test requirement ◦ traceability requirement ◦ socio-economic assessment ◦ existence of zero tolerance for unauthorized GM crops ◦ food, feed, and environmental safety assessments to international guidelines ◦ restrictiveness of authorization policy ◦ testing requirement form exporting country ◦ technical capacity to detect GMOs ◦ detection methods utilized
LLP threshold of the importing country - LLPj ◦ Model 3 - LLP variable takes either the value 0.1, or 10 for countries that do
not have the threshold. ◦ Model 4 - LLP threshold takes into account reported threshold levels and
combination of factors - zero tolerance and existence of GMO regulation. ◦ Model 5 - LLP variable takes the value 0.1 for the EU members, and 1 for
other countries, controlling for EU internal trade.
0
10
20
30
40
50
60
70
80
90
100
No
rwa
y
Au
str
ia
Bu
lga
ria
Cyp
rus
De
nm
ark
Esto
nia
Fin
lan
d
Fra
nce
Ge
rma
ny
Ire
lan
d
Ita
ly
La
tvia
Lu
xe
mb
ou
rg
Ne
the
rla
nd
s
Slo
va
kia
Slo
ve
nia
Sp
ain
Sw
ed
en
Cze
ch
R.
Sa
mo
a
Mo
ng
olia
Ca
na
da
Na
mib
ia
Hu
ng
ary
To
go
Arg
en
tin
a
Ja
pa
n
Ma
laysia
Ne
w Z
ea
lan
d
Mo
za
mb
iqu
e
Th
aila
nd
Cro
atia
Au
str
alia
La
o P
DR
Bra
zil
Co
lom
bia
Cu
ba
Ph
ilip
pin
es
US
Co
sta
Ric
a
Ma
li
Ba
ng
lad
esh
Ga
mb
ia
Lith
ua
nia
Su
da
n
Bo
livia
Ira
n
Ma
da
ga
sca
r
Ecu
ad
or
DR
Co
ng
o
Ho
nd
ura
s
Ja
ma
ica
Bo
tsw
an
a
Ba
rba
do
s
Mya
nm
ar
Qa
tar
Ca
pe
Ve
rde
Nig
er
Se
ych
elle
s
Ba
ha
ma
s
Ca
mb
od
ia
Do
min
ica
n R
.
El S
alv
ad
or
So
ma
lia
GMO Regulation Index
Variable [Model 1] (GMO
regulation impact)
[Model 2] (GMO
regulation impact)
[Model 3] (LLP impact)
[Model 4] (LLP impact)
[Model 5] (LLP impact)
Constant –10.28 (–3.43***)
–10.28 (–3.43***)
–10.68 (–3.99***)
–10.73 (–3.98***)
–5.22 (–1.89*)
Ln-Yi 1.00 (10.20***)
– – – –
Ln-Yj 0.84 (9.23***)
– – – –
Ln-GDPCi –1.70 (–7.72***)
–0.69 (–3.76***)
–0.69 (–4.08***)
–0.68 (–3.94***)
–0.64 (–3.68***)
Ln-GDPCj
–0.56 (–3.43***)
0.28 (2.10**)
– – –
Ln-Pi – 1.00 (10.21***)
1.03 (10.47***)
1.01 (10.23***)
0.72 (6.62***)
Ln-Pj – 0.84 (9.23***)
0.86 (9.39***)
0.86 (9.44***)
0.81 (8.80***)
Ln-Dij –0.97 (–8.68***)
–0.97 (–8.68***)
–0.92 (–8.20***)
–0.93 (–8.35***)
–0.90 (–7.17***)
Ln-Reg-Indexj –0.49 (–1.70*)
–0.49 (–1.70*)
– – –
Ln-LLPj – –0.10 (–1.48)
–0.17 (–1.48)
–0.24 (–2.10**)
R2 0.23 0.23 0.22 0.22 0.18
F 28.21*** 28.21*** 32.63*** 33.10*** 26.03***
Schwarz B.I.C. 1468 1468 1467 1467 1481
N 582 582 582 582 582
Regulation variable is negative and significant at the 10 percent level. Implies that a more restrictive GMO regulation has a deterrent effect on maize trade flow.
Models 3 and 4 indicate that LLP does not have a significant impact on trade flows
Model 5 (lower threshold) suggests that the impact of LLP on trade flows may be significant, but not necessarily trade distorting.
The inclusion of fixed effects yielded similar results for the regulation index, but the LLP variable became positive (but significant only at the 10 percent level).
0
10
20
30
40
50
60
70
80
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Num
bers
Improved datasets required on: ◦ policies on GMOs existing between trading partners
◦ different timings for approvals etc
Further studies examining the impacts of regulations and threshold levels on global trade in the context of multiple products
When limitations of datasets overcome, impact of regulations should be analyzed in a dynamic setting