Can Data Revolution Improve
Food Security? Evidence
from ICT technologies
Maximo Torero
m.torero@cgiar.org
International Food Policy Research
Institute
Brussels Policy Briefing No. 40
Data: the next revolution for ACP
countries
Example 1
Excessive volatility
Page 2
Periods of Excessive Volatility
201
4
Please note Days of Excessive volatility for 2014 are through March 2014
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
Example 2
Global Hunger Index
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Example 3
Mobile Banking
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Connectivity
Content
Capability
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Billions
Cellular Phone subscription and Population
8
7
6
5
4
3
2
1
0
Population
Cellular phones
Source: Mobile phone subscriptions are from the International Telecommunication Union (ITU) and country categories are
from the World Bank.
Page 9
Cellular Phone subscription per 100 inhabitants
in Developing Countries, by Region *
1.2
MENA
1
LAC
0.8
OECD
ECA
0.6
EAP
0.4
0.2
SSA
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
SA
2000
0
SSA
* EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA= Middle East and North Africa; SA = South Asia; and SSA =
Sub-Saharan Africa. High-Income (OECD and non-OECD) are excluded from the sample.
Source: Mobile phone subscriptions are from the International Telecommunication Union (ITU) and country categories are from the World Bank.
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Source: Nakasone, Torero and Minten (2013). “The Power of Information: The ICT Revolution in Agricultural Development”.
IFPRI.
Percentage of Households that Own a Mobile Phone,
by Residence Area
Bolivia (2007) a/.
Brazil (2009) a/.
Colombia (2010) a/.
Ecuador (2010) a/.
Mexico (2007) a/.
Peru (2010) a/.
India (2011) b/.
Bangladesh (2010) c/.
Tanzania (2010) d/.
Kenya (2010) e/.
South Africa (2008 / 09) f/.
Liberia (2009) g/.
Malawi (2010) h/.
Ghana (2010) i/.
Nigeria (2009) j/.
Egypt (2008) k/.
Ehtiopia (2011) l/.
Uganda (2011) m/.
Senegal (2011) n/.
Mozambique (2011) o/.
Nepal (2011) p/.
Zimbabwe (2011) q/.
Rwanda (2010) r/.
Cambodia (2010) s/.
China (2010) t/.
% Urban
77.6%
83.3%
90.2%
82.9%
66.6%
82.2%
76.0%
82.7%
77.5%
71.9%
87.5%
69.0%
72.7%
63.4%
88.3%
54.1%
65.2%
86.8%
95.4%
66.8%
91.6%
90.1%
71.8%
90.1%
76.3%
% Rural
18.7%
53.2%
71.7%
59.7%
45.0%
47.1%
51.2%
56.8%
34.2%
55.0%
82.0%
20.7%
32.3%
29.6%
60.3%
27.8%
12.8%
53.1%
81.7%
20.0%
71.9%
48.0%
35.1%
56.2%
60.7%
% All
57.0%
78.8%
86.0%
75.5%
55.2%
70.4%
59.2%
63.7%
45.4%
59.8%
85.7%
43.2%
39.0%
47.7%
70.6%
40.5%
24.7%
59.4%
88.4%
34.1%
74.7%
62.2%
40.3%
61.9%
67.9%
Source: Nakasone,
Torero and Minten
(2013). “The Power of
Information: The ICT
Revolution in
Agricultural Page 11
Development”. IFPRI.
Comparación Internacional de los costos de una paquete básico
de telefonía móvil (prepago) en 2009 US $ PPP
Source: Hernan Galperin, Broadband Prices in Latin America and the Caribbean, Working Paper #15 (Buenos Aires, Argentina: Universidad de San Andrés, 2013).
Notes: PPP = purchasing power parity. Prices include taxes. Equipment and connection costs are not included. The low-volume basket includes 30 outgoing calls and 33 SMSs per month. The following
structure of calls is assumed: local to fixed phones (15%), national (7%), mobile in-network (48%), mobile out-of-network (22%), and voice mail (8%). The estimations assume that 48% of calls take place
during peak times, 25% in off-peak times, and 27% during the weekends. The following duration of calls is assumed (in minutes): 1.5 for local and national, 1.6 for mobile on-net, 1.4 for mobile off-net, and
0.8 for voice box. The tariffs are prorated according to the market shares of each operating company.
Available income for telecommunications in Brazil (5% of income) by
income decile
Fuente: H. Galperin, Tarifas y Brecha de Asequibilidad de los Servicios de Telefonía Móvil en América Latina y el Caribe (Lima, Peru: Diálogo Regional sobre
Sociedad de la Información, 2009), 22.
Note: R$ = Brazilian real.
Ratio of Broadband Subscriptions to
Population
0.12
ECA
0.1
EAP
0.08
0.06
LAC
0.04
MENA
2012
2011
SA
2010
2008
2007
2006
2005
2004
2003
2002
2001
0
2000
0.02
2009
SSA
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Source: Nakasone, Torero and Minten (2013). “The Power of Information: The ICT Revolution in Agricultural Development”.
IFPRI.
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Connectivity
Content
Capability
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ICT Impact on agriculture
 Extension services
 Market information
 Policy environment, laws, and regulations
 Natural resources and geography
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Institutional arrangement for a simple
price information system
Source: Hernanini (2007), World
PageBank
18
Flow of information and Institutional
agreements for virtual markets
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Source: Hernanini (2007), World
Bank
Have ICTs been adapted to low-income
countries, and have they had an impact?
• Information is an indispensable ingredient in decision
making for livelihood of households.
• Potential gains for rural households:
• time and cost saving
• more and better information, leading to better decisions and reduction of
transaction costs (Stigler, 1961; Stiglitz, 1985 and 2002)
• greater efficiency, productivity, and diversity(Leff, 1984; Tschang et al.,
2002; Andrew et al., 2003).
• lower input costs and higher output prices and information on new
technologies (Gotland, et al, 2004)
• expanded market reach
 Previous work trying to measure the consumer surplus:
Saunder et al. 1983, Bresnahan, 1986, Saunders,
Warford and Wellenius 1994, etc.
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Results at the Micro Level
Results at the Micro Level
Results on extension
 ICT’s can also play a role in reducing the three
main constraints traditional extension services:
• First, poor infrastructure increases the cost of
extension visits,
• Second, the need to follow up information and
feedback
• Finally, traditional extension is plagued by principalagent and institutional problems.
 Aker (2011) also claims that ICTs can also make
farmers better able access to private information
from their own social networks.
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Results on extension
 Fafchamps and Minten (2012) look at the effect of using SMS with
crop advisory tips (offered for one crop chosen by the farmer) and
local weather forecasts. They found no effect of the information
for any of these outcomes.
 Cole and Fernando (2012) conduct an impact evaluation of the
Avaaj Otalo (AO) program among cotton farmers in Gujarat, India.
They find that households who benefited from AO shift their
pesticides from hazardous to more effective ones. Their results
also suggest that beneficiaries are more likely to harvest cumin (a
high-value cash crop)
 Fu and Akter (2012) investigate the impact of a program called
“Knowledge Help Extension Technology Initiative” (KHETI) in
Madhya Pradesh, India. Those in the KHETI group increased
their awareness and knowledge towards extension services,
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compared to a control group.
Connectivity
Content
Capability
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Kids and ICTs for Extension
•
Traditional Agricultural
Extension: costly, hard to
reach remote areas,
accountability of extension
workers.
•
ICTs can solve many of
these shortcomings.
•
Problem:
Computer-illiterate adult
population in rural areas.
Agricultural
extension
Parents
Kids
Kids and ICTs for Extension: design
• High School students in the northern highlands of
Peru
• Identified the most severe problems for farmers:
blight (potato), flea beetle (potato), earworm (corn),
bloating (guinea pigs), and cold (chicken).
• Cost-effective and simple mechanisms.
• Randomize information among students whose
farms are affected by these problems.
Kids and ICTs for Extension: Example
(molasses trap for corn earworm
Final Comments
• We need significant innovation in data collection to
improve access to farmers and consumers
• Three C’s of ICTs: Connectivity, Capability to
use it, and Content are essential
• Governments need better data for proper decisions
• ICTs can have an important impact in linking
smallholders and SMEs to markets
• Still we have a significant access gap!
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