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 Please note Days of Excessive volatility for 2014 are through March 2014 201 4 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 Page 4
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Example 3 Mobile Banking Page 6
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Connectivity Content Capability Page 8
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
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Cellular Phone subscription per 100 inhabitants in Developing Countries, by Region * 1.2 1 0.8 OECD ECA LAC MENA 0.6 0.4 EAP 0.2 0 SA 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. Source: Nakasone, Torero and Minten (2013). The Power of Information: The ICT Revolution in Agricultural Development. Page 10 IFPRI.
Percentage of Households that Own a Mobile Phone, by Residence Area % 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% 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.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Ratio of Broadband Subscriptions to Population 0.12 0.1 0.08 EAP ECA 0.06 LAC 0.04 0.02 SSA SA MENA 0 Source: Nakasone, Torero and Minten (2013). The Power of Information: The ICT Revolution in Agricultural Development. Page 14 IFPRI.
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Connectivity Content Capability Page 16
ICT Impact on agriculture Extension services Market information Policy environment, laws, and regulations Natural resources and geography Page 17
Institutional arrangement for a simple price information system Source: Hernanini (2007), World Page Bank 18
Flow of information and Institutional agreements for virtual markets Page 19 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. Page 20
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. Page 23
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
Connectivity Content Capability Page 25
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! Page 29