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. 40Data: the next revolution for ACP

countries

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Example 1

Excessive volatility

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

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Example 2

Global Hunger Index

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Example 3

Mobile Banking

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Connectivity

Content

Capability

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Cellular Phone subscription and Population

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

*0

1000000000

2000000000

3000000000

4000000000

5000000000

6000000000

7000000000

8000000000

Population Cellular phones

Source: Mobile phone subscriptions are from the International Telecommunication Union (ITU) and country categories are from the World Bank.

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Cellular Phone subscription per 100 inhabitants in Developing Countries, by Region *

* 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.

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

0

0.2

0.4

0.6

0.8

1

1.2

SASSASSA

LAC

ECA

MENA

EAP

OECD

Source: Nakasone, Torero and Minten (2013). “The Power of Information: The ICT Revolution in Agricultural Development”. IFPRI.

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% Urban % Rural % All

Bolivia (2007) a/. 77.6% 18.7% 57.0%Brazil (2009) a/. 83.3% 53.2% 78.8%Colombia (2010) a/. 90.2% 71.7% 86.0%Ecuador (2010) a/. 82.9% 59.7% 75.5%Mexico (2007) a/. 66.6% 45.0% 55.2%Peru (2010) a/. 82.2% 47.1% 70.4%India (2011) b/. 76.0% 51.2% 59.2%Bangladesh (2010) c/. 82.7% 56.8% 63.7%Tanzania (2010) d/. 77.5% 34.2% 45.4%Kenya (2010) e/. 71.9% 55.0% 59.8%South Africa (2008 / 09) f/. 87.5% 82.0% 85.7%Liberia (2009) g/. 69.0% 20.7% 43.2%Malawi (2010) h/. 72.7% 32.3% 39.0%Ghana (2010) i/. 63.4% 29.6% 47.7%Nigeria (2009) j/. 88.3% 60.3% 70.6%Egypt (2008) k/. 54.1% 27.8% 40.5%Ehtiopia (2011) l/. 65.2% 12.8% 24.7%Uganda (2011) m/. 86.8% 53.1% 59.4%Senegal (2011) n/. 95.4% 81.7% 88.4%Mozambique (2011) o/. 66.8% 20.0% 34.1%Nepal (2011) p/. 91.6% 71.9% 74.7%Zimbabwe (2011) q/. 90.1% 48.0% 62.2%Rwanda (2010) r/. 71.8% 35.1% 40.3%Cambodia (2010) s/. 90.1% 56.2% 61.9%China (2010) t/. 76.3% 60.7% 67.9%

Percentage of Households that Own a Mobile Phone, by Residence Area

Source: Nakasone, Torero and Minten (2013). “The Power of Information: The ICT Revolution in Agricultural 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

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2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

0

0.02

0.04

0.06

0.08

0.1

0.12

SASSA

LAC

ECA

MENA

EAP

Source: Nakasone, Torero and Minten (2013). “The Power of Information: The ICT Revolution in Agricultural Development”. IFPRI.

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Connectivity

Content

Capability

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

Page 18Source: Hernanini (2007), World Bank

Flow of information and Institutional agreements for virtual markets

Page 19Source: Hernanini (2007), World Bank

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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.

Results at the Micro Level

Results at the Micro Level

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 principal-

agent 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, compared to a control group. Page 24

Results on extension

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Connectivity

Content

Capability

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

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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!