hunger and food security

Hunger and Food Security

Major challenges we are facing today

Maximo Torero

([email protected])

Monday 14th March, 12:30 – 14:00

Lunchtime Conference External Cooperation Infopoint

Rue de la Loi 43, Ground floor

Real Price Evolution in US$ 2015

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

19

60

M0

11

96

1M

01

19

62

M0

11

96

3M

01

19

64

M0

11

96

5M

01

19

66

M0

11

96

7M

01

19

68

M0

11

96

9M

01

19

70

M0

11

97

1M

01

19

72

M0

11

97

3M

01

19

74

M0

11

97

5M

01

19

76

M0

11

97

7M

01

19

78

M0

11

97

9M

01

19

80

M0

11

98

1M

01

19

82

M0

11

98

3M

01

19

84

M0

11

98

5M

01

19

86

M0

11

98

7M

01

19

88

M0

11

98

9M

01

19

90

M0

11

99

1M

01

19

92

M0

11

99

3M

01

19

94

M0

11

99

5M

01

19

96

M0

11

99

7M

01

19

98

M0

11

99

9M

01

20

00

M0

12

00

1M

01

20

02

M0

12

00

3M

01

20

04

M0

12

00

5M

01

20

06

M0

12

00

7M

01

20

08

M0

12

00

9M

01

20

10

M0

12

01

1M

01

20

12

M0

12

01

3M

01

20

14

M0

12

01

5M

01

20

15

USD

pe

r M

etr

ic T

on

s

Soybeans (US$/mt)

Maize (US$/mt)

Periods of Excessive Volatility

Note: This figure shows the results of a model of the dynamic evolution of daily returns based on historical data going back to 1954 (known as the Nonparametric Extreme Quantile (NEXQ) Model). This model is then combined with extreme value theory to estimate higher-order quantiles of the return series, allowing for classification of any particular realized return (that is, effective return in the futures market) as extremely high or not. A period of time characterized by extreme price variation (volatility) is a period of time in which we observe a large number of extreme positive returns. An extreme positive return is defined to be a return that exceeds a certain pre-established threshold. This threshold is taken to be a high order (95%) conditional quantile, (i.e. a value of return that is exceeded with low probability: 5 %). One or two such returns do not necessarily indicate a period of excessive volatility. Periods of excessive volatility are identified based a statistical test applied to the number of times the extreme value occurs in a window of consecutive 60 days.

Source: Martins-Filho, Torero, and Yao 2010. See details at http://www.foodsecurityportal.org/soft-wheat-price-volatility-alert-mechanism.

2014

Please note Days of Excessive volatility for 2014 are through March 2014

2015

Different problems but same policies

GLOBAL CHALLENGE

Source: Johan Rockstrom: Let the environment guide our development

Growing

Human

Pressure

Climate change

Ecosystem

decline

Surprise

6

Bigger population in urban areas will demand

more and better food

36%

POPULATION GROWTH

Change in population by

region 2010-2100

(millions)

182 millions

97 millions

-63 millions

2,552 millions

432 millions

29 millions

Africa: Younger

Asia and Europe: Older

Source:UN 2011

Calorie consumption vs total Cereal

Equivalent Consumption

0.5

11

.52

(to

ns/

cap

ita/y

ea

r)

0

50

00

10

00

01

50

00

(kca

l/cap

ita/d

ay)

0 10000 20000 30000 40000 50000Real GDP(PPP) per capita in 2005 int. $ 1980-2009

China Calorie Consumption Fitted Calorie Consumption

China CE Consumption Fitted CE Consumption

Source: Fukase, E. and Martin, W. (2015)

Different types of childhood malnutrition (abstract)

-

200

400

600

800

1,000

1,200

Africa south of the Sahara

South Asia

Developing Countries

Slow decline in malnourishment.

Alarming increase in obesity.

Stunted children (millions)

0

10

20

30

40

50

60

1990 1995 2000 2005 2010 2015 2020

Overweight & obese children (millions)

Source: FAOSTAT3 (http://faostat3.fao.org/download/D/FS/E).

Source: UN in de Onis, M, M. Blössner and E. Borghi. 2010. Global prevalence and

trends of overweight and obesity among preschool children. American Journal of

Clinical Nutrition 92:1257–64.

(http://www.who.int/nutgrowthdb/publications/overweight_obesity/en/).

Undernourished

people (millions)

0

50

100

150

200

250

300

1990 1995 2000 2005 2010 2015 2020

Source: de Onis, M, M. Blössner and E. Borghi. 2011

http://www.who.int/nutgrowthdb/publications/Stunting1990_2011.pdf.

Africa

Asia


Africa

Asia


WATER STRESS RISK

2.5

US$9.4 TRILLION

Source: Veolia Water & IFPRI 2011.

BILLION PEOPLE

TODAY Total population living in water scarce areas

Global GDP generated in water scarce regions

US$63 TRILLION

Total population living in water scarce areas

4.7 BILLION PEOPLE

90%

570%

By 2050

Global GDP generated in water scarce regions

52% 49% 45%

36% 39% 22%

population grain production

global GDP

HEAVY TOLL ON RAINFED MAIZE WITH

CLIMATE CHANGE

Global yields projected 30% lower in 2050 compared to no climate change

Source: IFPRI IMPACT simulations. (HadGEM2, RCP 8.5)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

2010 2015 2020 2025 2030 2035 2040 2045 2050Source: IFPRI IMPACT 3.2 Projections.

FOOD PRICES INCREASE WITHOUT CLIMATE

CHANGE; EVEN HIGHER WITH CLIMATE

CHANGE

No climate change

Average with climate change

With climate change - range across models

(Indexed to 1 in 2010)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

2010 2015 2020 2025 2030 2035 2040 2045 2050

Cereals Roots/tubers

2010 = 1 2010 = 1

Sources: 1969-71 to 1999-2001 from Alexandratos 2006; 2010-2050 from IFPRI's IMPACT 3.2 Projections.

Per capita food consumption grows.

Africa and South Asia catching up.

0

500

1000

1500

2000

2500

3000

3500

4000

World Industrial countries Developing countries South Asia Africa south of the Sahara

Per capita food consumption (kcal/person/day) 1979/1981 2010 2050

0

100

200

300

400

500

600

700

800

900

2010

2050, No Climate Change

2050, With Climate Change

Source: IFPRI IMPACT 3.2 Projections.

Improved progress on hunger, but too slow.

Climate change increases hunger.

Undernourished people (millions)

Developing countries South Asia Africa south of the Sahara

A continuous trend towards

internationalization of food markets

1975 1985 1995 2005 2015

18.2%

13.9% 12.3%

19.1%

16.1%

Share of produced calories crossing an international border

Globalization and/or Regionalization

-

2,000

4,000

6,000

8,000

10,000

12,000

Average distance (km) travelled by imported calories

1995 2000 2005 2010

Evolution by region of the price support

through border measure

-60.0%

-50.0%

-40.0%

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

Africa Asia Eastern Europe LAC HIC World

Average Nominal Rates of Assistance (NRA) through Border Measure

1975 1985 1995 2005

Cumulative number of preferential trade

agreements (PTAs) in force

Note: it includes notified and non-notified PTAs by country group

But mega trade deals are becoming strategic

TPP

USA

TTIP

TISA

Vietnam Brunei Singapore Malaysia

Australia Canada Taiwan Japan Chile Mexico Peru South Korea New Zealand

Norway Switzerland Iceland Israel Uruguay Hong Kong Costa Rica Colombia Panama Paraguay Turkey

Austria Bulgaria Belgium France Czech Republic Ireland Denmark

Germany Estonia Portugal Romania Poland Netherlands Lithuania UK

Sweden Luxembourg Slovenia Hungary Slovenia Italy Spain

Greece Finland Malta

Croatia Lativia Cyprus

Economic Slowdown

Comparison of 2012 and 2015 GDP growth

projections for 2017 (selection of countries)

Source: World Economic Outlook (2015 and 2012) - IMF

Economic Slowdown

World Commodity Price Projections (2015)

Source: World Economic Outlook (2015 and 2012) - IMF

40

60

80

100

120

140

2013 2014 2015 2016 2017

Pri

ce In

dex

10

0 =

20

11

Year Crude Oil (petroleum), simple average of three spot prices; Dated Brent, West Texas Intermediate, and the Dubai Fateh, US$ per barrelCommodity Natural Gas Price Index includes European, Japanese, and American Natural Gas Price IndicesCommodity Coal Price Index includes Australian and South African CoalWheat, No.1 Hard Red Winter, ordinary protein, FOB Gulf of Mexico, US$ per metric tonneMaize (corn), U.S. No.2 Yellow, FOB Gulf of Mexico, U.S. price, US$ per metric tonneRice, 5 percent broken milled white rice, Thailand nominal price quote, US$ per metric tonneSoybeans, U.S. soybeans, Chicago Soybean futures contract (first contract forward) No. 2 yellow and par, US$ per metric tonnePalm oil, Malaysia Palm Oil Futures (first contract forward) 4-5 percent FFA, US$ per metric tonneBeef, Australian and New Zealand 85% lean fores, FOB U.S. import price, US cents per poundPoultry (chicken), Whole bird spot price, Georgia docks, US cents per pound

Economic Slowdown

Projections of Effects: Global Poverty

Source: Laborde and Martin (2016)

Economic Slowdown Projections of Effects: Net and gross movements into and out of

poverty, Scenario 1, Percentage points, Total Population.

Source: Laborde and Martin (2016) Note: Poverty is defined by the $1.90 PPP 2011 threshold.


poverty, Scenario 2, Percentage points, Total Population.



poverty, Scenario 2, Percentage points, Farmer Population.


Regional Challenges: Africa South of the

Sahara

Africa in Global Trade After the long decline of the ‘70s-’90s, a reversed trend:

0

100

200

300

400

500

600

700

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Bn

s U

SD

SSA TOTAL TRADE

Agriculture All goods

In 15 years, total trade for SSA has been multiplied by 6, agricultural trade by 4.6. In comparison, global trade multiplied by 3.4 and agricultural trade by 2.9.

0.0%

1.0%

2.0%

3.0%

4.0%1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

SSA SHARE IN GLOBAL TRADE Agriculture All goods

Heterogeneous Performance on Global

Agricultural Markets

-150%

-100%

-50%

0%

50%

100%

150%

200%

250%

% IN

CR

EASE

IN G

LOB

AL

MA

RK

ET S

HA

RE

Decomposition of export performance (selected countries) between 1995 and 2007

Domestic Performance (competitivness) Geographical Specialization Sectoral Specialization

Source: Bouet, Deason and Laborde (2014)

Explaining a country’s performance and defining the right benchmark: • Being specialized in the right

products? • Being specialized in the booming

markets? • Improving its own

competitiveness?

During this period, exports have: • decreased by 20 M USD for C.A.R

(bad performance in absolute and relative terms).

• increased by 150 M USD for Uganda (bad performance in relative terms).

• increased by 88 M USD for Rwanda (good performance in absolute and relative terms).

http://www.ifpri.org/publication/promoting-agricultural-trade-enhance-resilience-africa




• Need to differentiate short term variation and medium/long term modification of the trend.

• Different policy responses on both energy and agricultural, and macroeconomic policies.

• In SSA, weak institutions, capital, financial and insurance markets: incremental costs of volatility.

• Energy and food prices: high level of distortions, and huge heterogeneity of policies within the continent.

• From energy to food prices: many links (inputs, fertilizers, transports, biofuels).

Implications of Changing Prices and

Demand for Energy and Food

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00Aggregated welfare impact of a world price

shock

AGRI -15% Extraction -15% ExtractionAgri -15%

Looking at the Past

• Increased regional integration, especially when looking at the nutritional contents of trade flows.

African Imports Africa Asia Europe LAC NorthAmerica Oceania

Dollars (value)

1990-1995 6.77% 17.26% 37.90% 9.96% 24.79% 3.31%

2002-2007 12.39% 19.81% 35.23% 15.97% 13.68% 2.93%

kCal

1990-1995 3.09% 14.23% 23.81% 10.44% 44.81% 3.62%

2002-2007 7.05% 20.38% 27.06% 19.45% 21.63% 4.43%

African Exports Africa Asia Europe LAC NorthAmerica Oceania

Dollars (value)

1990-1995 7.99% 16.79% 67.32% 0.61% 6.95% 0.34%

2002-2007 15.15% 14.86% 62.51% 0.53% 6.10% 0.84%

kCal

1990-1995 13.80% 26.20% 49.96% 2.99% 6.59% 0.46%

2002-2007 31.41% 29.21% 34.03% 0.92% 4.19% 0.23%

1/3 of the calories exported by Africa, go to Africa

Role of African intra-trade over the previous decade has more than doubled.

Shift in external suppliers among Americas.

Source: Bouet, Deason and Laborde (2014)





Outlook Modeling and Analysis

Important changes within SSA:

• Potential evolution in agri-food system value-added in Africa: potential increase by about USD 300 million (constant 2007 USD) between 2013 and 2030 (or 85%) in the business as usual scenario.

And beyond:

• SSA share in global food trade will reach 4.3% by 2030 (compared to 3% today, and 2.2% in 2000).

0

10

20

30

40

50

60

70

80

90

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

Bn

s U

SD, c

on

stan

t

SSA Agri-food exports

ECOWAS CEMAC COMESA SACU

0

10

20

30

40

50

60

70

80

90

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

Bn

s U

SD, c

on

stan

t

SSA Agri-food imports


Source: MIRAGRODEP model simulations, Bouet, Deason and Laborde (2014)





Per Capita Net Agricultural Trade Flows by Region

-150.00

-100.00

-50.00

0.00

50.00

100.00

150.00

2013 2030 2013 2030 2013 2030 2013 2030 2013 2030 2013 2030

AFRICA CEMAC COMESA ECOWAS SACU UMA

USD

pe

r C

apit

a, N

et

trad

e f

low

s

Vegetable Oil Vegetables & FruitsSugar FibersOilseeds Processed FoodCash Crops Meat, whiteMeat, red Fish ProductsDairy Products Cereals


• Complementarity in terms of potential and needs at the continental level shows large potential for intra-trade growth; some targeted initiatives may be needed (vegetable oils, food processing).

• The continental agri-business net trade deficit will increase from six dollars per capita to 12 dollars per capita.


How Will Intra-African Trade Perform?

• Under a business as usual scenario? +122% in average

• Which levers could we use to reach the CAADP target (+200% from 2014 to 2025, Malabo Declaration)?

– Addressing trade policy barriers

– Improving infrastructure


CEMAC

COMESA

ECOWAS

SACU

0%

50%

100%

150%

200%

SACU ECOWAS COMESA CEMAC

% increase in intra-SSA trade between 2013 and 2030 CEMAC COMESA ECOWAS SACU

CEMAC 67% 148% 80% 88%

COMESA 148% 146% 179% 116%

ECOWAS 80% 179% 136% 137%

SACU 88% 116% 137% 111%



Trade Policy Barriers for Expanding Trade in

Africa

Huge potential for an ambitious trade facilitation agenda:

• Free circulation of goods still not achieved within custom unions (intra-trade still affected by MFN tariffs, double taxation, etc.)

• Numerous fees and bribes

• Administrative burden

• Inefficiency of checkpoints (delays)

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%


Average import tariffs on agri-food imports

Applied to non SSA countries Applied to SSA countries

Despite regional integration, intra-African trade still affected by:

• significant tariffs;

• the need to address between trade barriers between blocs;

• external pressure to liberalize markets with third countries (EPA with the EU: SADC and ECOWAS should sign this year);

• instability/uncertainty regarding some trade policies

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Rice Wheat Yams Beef(carcass)

Chicken(cuts)

Milk Powder

Tariffs on selected products

CEMAC ECOWAS COMESA SACU

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Pow

er

Inte

rnati

onal call

Wate

r

Road f

reig

ht

Inte

rnet

dia

l-up

Mobile t

ele

phone

Rat

io o

f p

rice

s Infrastructure Barriers: Several Times More

Expensive than Elsewhere

Source: AICD – African Infrastructure Country Diagnostic

Challenge 1: Improve

efficiency or shift of

potential frontier

Yields are vey low

Spatial Patterns (annual avg. 2005-07)

Labor Land

Source: Benin, et.al (2011). Trends and Spatial Patterns in Agricultural Productivity in Africa 1961-2010, ReSAKSS.

Intensity of agricultural research spending and capacity, 1981–2008 (E.g. Zambia)

STOCHASTIC PROFIT FRONTIER

C

Production of maize

Production of wheat

Frontier of possibilities of production

Frontier of possibilities of production increases

Challenge 2: We need to

value externalities positive

or negative

Pricing water

A CONTINUOUS TREND TOWARDS INTERNATIONALIZATION OF FOOD MARKETS

1975 1985 1995 2005 2015

18.2%

13.9% 12.3%

19.1%

16.1%

Share of produced calories crossing an international border

Are we pricing the water?

We need to recognize carbon as a global externality and value carbon through carbon trade

Challenge 3: We need to

be resilient to climate

change and weather

shocks

https://www.climate.gov/news-features/blogs/enso/november-el-ni%C3%B1o-update-it%E2%80%99s-small-world

Ranking of August-October El Niño episodes (ONI) since 1950

El Niño Risks

El Niño Risks: January to March 2015

El Niño Risks: October – December 2015

Cereal Production in selected regions (million tons)

2012/13 2013/14 2014/15 2015/16*

2015 / Average 2012-2014 (% change)

World 2,267.0 2,474.9 2,501.1 2,467.5 2.2 SSA 121.4 123.1 124.9 117.2 -4.8 Central Africa 7.5 6.7 6.7 6.9 -1.4 East Africa 38.1 38.9 42.1 40.0 0.8 Southern Africa 28.5 31.1 29.5 23.7 -20.2 West Africa 47.3 46.4 46.5 46.6 -0.4 North Africa 32.2 36.2 32.6 36.5 8.6 East Asia 495.0 508.9 512.3 526.2 4.1 South Asia 324.7 332.4 333.4 318.8 -3.4 Southeast Asia 144.8 148.0 146.5 145.2 -0.8 Europe 292.0 318.3 343.6 323.2 1.7 Central America & Caribbean 6.5 6.8 6.3 6.5 -0.7 Middle East 57.9 65.9 55.5 66.0 10.5 North America 436.9 533.1 527.2 517.3 3.7 South America 169.9 168.0 173.9 167.5 -1.8 Others 185.8 234.2 244.9 242.9 9.6

Source: USDA, *= forecasted estimates

Maize and wheat price data in selected markets

Region Market Current Price ($/KG)

Current price compared to 2012-14 average (% change)

East Africa

Ethiopia, Addis Ababa, Wheat 0.465 15.3%

Ethiopia, Addis Ababa 0.217 -25.6%

Uganda, Kampala 0.240 -4.4%

LAC

El Salvador, San Salvador 0.413 22.8%

Guatemala, Guatemala City 0.350 4.3%

Honduras, National Average 0.430 38.2%

Nicaragua, National Average 0.400 29.5%

Southern Africa

Malawi, National Average 0.253 6.5%

Mozambique, Maputo 0.300 -30.1%

South Africa, Randfontein 0.223 -4.7%

Zimbabwe, Harare 0.565 -32.6% Source: Authors’ calculation based on FAO GIEWS data Notes: All prices for maize unless otherwise indicated. Real prices using IMF CPI deflator, prices are averages for the period October to December for all years presented.

Challenge 4: Is not only

supply!

ADDITIONAL DEMAND FOR BIOMASS

Growing population

Growing income

Need for alternative to fossil carbon chains

Increased production

Reduced supply for final consumers

Reduced supply for

intermediate consumers

New Demand for crops

Increase in yield and

area, extension of

cropland, and reduction

of other crops

GROWING DEMAND

Additional food demand

Additional Bioenergy demand

Additional industrial

Hunger?

Substitution effects

Feed

Other sectors (agrifood,

cosmetics)

Substitution effects Biomass demand

OVERALL IMPACT

By 2020: illustration with biofuels 1st generation

23.2%

22.1%

2.7%

10.0%

43.3%

15.7%

15.0%

10.1%

2.0%

13.9%

1.4%

2.4%

7.7%

1.7%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0%

MAIZE

SUGAR CROPS

WHEAT

PALM OIL

RAPESEED OIL

SOYBEAN OIL

SUNFLOWER OIL

Share of the crop (all use) in totalHARVESTED cropland

Production devoted to biofuels

Source: Laborde, 2011 But only 16% of world area devoted to biofuels

SUPPLY AND DEMAN

SUPPLY AND DEMAND

• Huge opportunity for smallholders

• Huge potential for contract farming

• But we need an appropriate regulation framework

Challenge 5: Economic

Growth is not enough

Prevalence of Undernourishment

Economic Growth is not enough

A 10% increase in GDP/PC

leads to a 6% reduction in

stunting

Source: Ruel and Alderman, 2013

Income Growth Can Have Unintended

Consequences of Increasing Risks of Overweight

and Obesity

A 10% increase in GDP/PC

leads to a 7% increase in

overweight and obesity in women

Source: Ruel and Alderman, 2013

Final Remarks

Agriculture is critical for

Employment

Economic development

Food Security

Important changes in key drivers

Demand drivers changing rapidly

Land constraints

Water constrains

Climate change

Huge opportunity

But we need proper regulatory

environment

Gains in efficiency and potential

Increase value added

SAI

Needs to be inclusive

63

Features

SSA.foodsecurityportal.org

Thanks!

hunger and food security

Presentations & Public Speaking