The Power of Information: The ICT Revolution in Agricultural Development. Power of Information. Eduardo Nakasone, Maximo Torero, and Bart Minten *

Transcription

1 The Power of Information: The ICT Revolution in Agricultural Development Power of Information Eduardo Nakasone, Maximo Torero, and Bart Minten * Abstract We review the state of ICT and its impact on agricultural development in developing countries, documenting the rapid changes that have taken place over the last decade. Although there is still a wide gap in access between rural and urban areas, the spread of mobile phones in rural areas has led to important changes in the agricultural sector. We find that access to mobile phones has generally improved agricultural market performance at the macro level; impacts at the micro level are mixed. There is also still limited evidence regarding the impact of market information systems (MIS) delivered through mobile phones on farm prices. Similarly, the rollout of extension programs though ICTs is still in an early stage, and little research is available regarding such programs impacts. Keywords: Information and Communication Technologies; Market Price Information; Agricultural Extension; ICTs for Development * Eduardo Nakasone is a Senior Research Analyst at the International Food Policy Research Institute (e.nakasone@cgiar.org). Maximo Torero is the Director of the Markets, Trade and Institutions Division of the International Food Policy Research Institute (m.torero@cgiar.org). Bart Minten is a Senior Research Fellow at the International Food Policy Research Institute (b.minten@cgiar.org). Corresponding Author: Maximo Torero, m.torero@cgiar.org.

2 Contents 1. Introduction The Mobile Phone Revolution in Developing Countries Penetration in Rural Areas Mobile Phones and Agricultural Trade Other ICTs ICT Platforms for Agricultural Development Conceptual Framework Agricultural Market Prices Search Cost Models Models of Limited Competition Agricultural Extension Empirical Evidence Agricultural Market Prices Price Integration and Price Volatility Impact of Mobile Phones on Farm Prices and Income Impact of MIS Delivered through ICTs on Farm Prices and Income Agricultural Extension Conclusions and Implications References... 26

3 1. Introduction The agricultural sector in developing countries is usually characterized by small farmers with poor access to physical infrastructure and market and extension information. These farmers face high transaction costs and information constraints that limit optimal production and marketing choices. Information and Communication Technologies (ICTs) have significant potential to solve these shortcomings, providing cost-effective communication that allows farmers to take advantage of previously untapped trade opportunities and to learn about previously unknown innovative practices. As ICTs rapidly expand in developing countries, there is significant interest in better understanding how these technologies affect rural and agricultural households livelihoods, and how to better target efforts to use these tools most effectively. Two main difficulties exist in analyzing the impact of ICTs on agricultural development. First, ICTs affect a wide array of outcomes in addition to agriculture. Because ICTs enhance economic opportunities in a wide variety of ways, they have also been documented to have sizeable macroeconomic impacts (Roller and Waverman 2001; Torero et al. 2006; Gruber and Koutroumpis 2011). Second, ICTs encompass many different types of technologies, from computers and the internet to radio and television to mobile phones, to name just a few. Thus, the impact of ICTs will vary widely depending on which specific technology is used. Most ICT projects in developing countries are currently deployed through two channels, which are the focus of this review. The first is the impact of ICTs on market price information. In many cases, farmers in rural areas are not well-informed about prevailing market prices (Fafchamps and Hill 2008; Mitra and Sarkar 2003); they therefore may not sell in the most profitable markets

4 or may accept lower prices from middlemen, leading to misallocation of resources and inefficiencies in the agricultural supply chain. The second channel is the role of ICTs as a means to enhance farmers knowledge about improved agricultural practices and technologies. Given the high cost of traditional extensions services, ICTs could be a more effective way to increase rural productivity. We restrict our review mainly to mobile phones because they are more widespread than other forms of ICTs. We contribute to the literature in two ways. First, we analyze the extent of the ICT and mobile phone revolution in developing countries rural and agricultural areas. Second, we give a comprehensive overview of the conceptual framework for ICT analysis, as well as of the available evidence regarding the impacts of mobile phone access, Market Information Systems (MIS), and extension advice delivered through ICTs on agricultural development. Based on this analysis, we highlight gaps to be addressed in future research. 2. The Mobile Phone Revolution in Developing Countries 2.1 Penetration in Rural Areas The penetration rate of mobile phones in developing countries i.e. the ratio of mobile phone subscriptions to population has significantly expanded over the last 10 years (see Figure 1). By 2012, in Europe, Central Asia, Latin America, and the Caribbean, there were more mobile connections than people; Sub-Saharan Africa and South Asia have also seen dramatically increased phone subscriptions. However, the penetration rate as calculated above may exaggerate actual access: households might have different phones or SIM cards that would be doublecounted in this ratio (leading to ratios that exceed one). For example, a recent study finds that

5 mobile phone users in developing countries own 2.2 SIM cards on average (The Economist 2012). [Figure 1 about here] In order to better understand mobile phone penetration in rural and agricultural areas, we analyze large-scale, representative household surveys in a number of developing countries. Table 1 presents the percentage of urban and rural households in 34 countries in Latin America, Asia, and Africa that own a mobile phone. As the table shows, access to mobile phones varies considerably between countries, being generally higher in Latin America and Asia than in Africa. While more than 80% of households own a mobile phone in Colombia, less than 40% do in Malawi and Mozambique, and in Ethiopia, this drops to less than 25%. On the other hand, some African countries have surprisingly good access, such as Senegal (88%) and Nigeria (71%). There are also wide gaps in access between rural and urban areas; overall, rural areas have less access. This gap is again generally smaller for Latin America and Asia than it is for Africa. [Table 1 about here] 2.2 Mobile Phones and Agricultural Trade While mobile phones have spread rapidly in developing countries, few surveys have looked at their use in agricultural marketing. The exceptions include Reardon et al. (2012), who collected data in China, Bangladesh, and India on farmers phone use for marketing transactions in major commercial rice- and potato-producing areas and Minten et al. (2013), who collected information on phone transactions for teff, a major commercial crop in Ethiopia.

6 A large number of the farmers interviewed in these areas, ranging from a high of 97% in China to a low of 27% in Ethiopia, owned mobile phones at the time of the survey (Table 2). However, only between 12% and 78% were in contact by phone with the buyer in their last marketing transaction before the sale. Thus, the use of mobile phones for market transactions, while high, is still significantly lower than would be expected from overall phone ownership. This seems partly driven by a lack of perceived benefits: the prevalence of auction systems and government rice procurement price setting in some areas of China and India seem to lower the potential advantages of mobile phone use. When farmers see clearer benefits, phone contacts are more prevalent. For example, phones are actively used in the potato trade in India, where most of the marketing transactions are done off-market and near cold stores (Reardon et al., 2012). Overall, fewer calls are made for rice than for potato transactions, possibly because rice prices are better understood and less variable because of multiple annual production periods and established government interventions. Taking a simple average of products and countries in Asia, the data show that almost a quarter of farmers in commercial zones had reached a price agreement by phone in their last transaction. For rice and potato supply chains in Dhaka, rice chains in Beijing, and potato chains in Delhi, almost all farmers who used phones contacted multiple traders before engaging in a transaction. Overall, 40% of staple suppliers in these ruralurban supply chains contacted multiple buyers per phone before their last transaction. This illustrates the extent to which access to phones is empowering farmers and changing marketing systems. The low numbers seen in Ethiopia also illustrate the large variations between countries and indicate that mobile phone penetration is in the starting phase in that country.

7 [Table 2 about here] Despite farmers low use of mobile phones for commercial transactions in some areas, mobile phones might still have important effects on the agricultural marketing system because of widespread use by agricultural traders. Figure 2 shows to what extent mobile phones are used by cereal traders in Ethiopia; the figure shows how mobile phone coverage has changed in the last decade for Ethiopia s major wholesale cereal markets. In 2000, only Addis Ababa had mobile phone coverage; by 2005, there was almost universal coverage of wholesale markets. Figure 2 further shows that mobile phone usage rates reached 100% of the traders for major cereals markets within an average of only 4-5 years after the introduction of coverage. Minten et al. (2012) found that nearly half of the traders studied reported having a landline phone at home before gaining access to a mobile phone, indicating that there was not a complete communications void in Ethiopia. Mobile phone technology has, however, improved ease of communication. More than twice as many traders and brokers use their mobile phones today for conveying price information and making deals than did so using landline phones when the latter was all that was available. Since a market s physical location matters less with mobile phone technology, traders and brokers also appear to be increasingly bypassing wholesale markets in rural areas and in urban towns (Minten et al. 2012). This evidence suggests important implications for the efficiency of cereal marketing systems. [Figure 2 about here]

8 2.3 Other ICTs Figure 3 shows how penetration of landline phones and the internet has evolved over the last decade. Until the 1990s, landlines were the predominant form of communication; however, they did not cover significant proportions of the population, due in part to higher costs. Waverman et al. (2005) find that the per-connection cost of a mobile network is 50% lower than that of a landline system. Further, Aker and Mbiti (2010) show that Kenyan firms had to wait 100 days to get a landline, and that this process usually required a bribe. Thus, it is not surprising that the number of landlines per inhabitant has either decreased or stagnated in most developing countries. As of , there were only 0.11 landlines per inhabitant in developing countries. By contrast, the internet has shown considerable expansion. The ratio of broadband subscriptions to population has reached 0.1 in Europe and Central Asia (ECA) and East Asia and Pacific (EAP), but is still below 0.03 in the Middle East and Northern Africa (MENA) and Sub-Saharan Africa. Overall, it seems that the internet only reaches a modest proportion (27%) of the population in developing countries; in fact, there are only 0.05 broadband subscriptions per inhabitant. By contrast, by 2012, there were 0.82 mobile phone subscriptions per capita in the same group of nations, many with more subscriptions than people. [Figure 3 about here] 1 Information based on the International Communications Union s (ITU) statistics: We follow the World Bank s country classification to separate developed from developing countries: Our definition of developing countries excludes all high-income (OECD and non-oecd) nations.

9 2.4 ICT Platforms for Agricultural Development GSMA s Mobile for Development Intelligence 2 compiles a list of mobile-enabled products and services for development initiatives; as of October 2013, there were 98 projects listed in the mobile agriculture sector. We analyzed 87 of these that provide direct services to rural populations 3 (Table 3). Arguably due to their larger availability, mobile phones are used in most of these projects; delivery is done mainly through SMS, although voice messages, Interactive Voice Response (IVR) systems, or mobile applications (apps) are also used. A second group of projects uses or the internet, and increasing internet access in rural areas will likely lead to an expansion of this delivery channel in the future. Most projects deliver information regarding market prices (48%) and agricultural extension (39%), combined in an important number of cases with weather advisory. Table 3 further illustrates two interesting new approaches: the virtual coordination of demand and supply (i.e. buyers and sellers posting offers in virtual marketplaces) and social networks for farmers (allowing for multiple types of information exchange). [Table 3 about here] We find that mobile phones have spread quickly in developing countries, while other forms of ICTs are significantly less important. We also note a significant gap in access between urban and rural areas, consistent across all continents. However, we find that where farmers have low access, mobile phones still impact the functioning of agricultural markets because of widespread use by traders. Moreover, we increasingly see projects using mobile phones for the delivery of extension messages; however, phones use for these purposes is still limited. We next look at the A complete list of the 87 projects we reviewed is available upon request.

10 conceptual framework, as well as the available empirical evidence, for the impact of mobile phones on agricultural markets and on agricultural extension. 3. Conceptual Framework In this section, we explore different theoretical frameworks that might explain how ICTs can affect agricultural development, focusing on the conveyance of market price information and agricultural extension advice. 3.1 Agricultural Market Prices Search Cost Models Rural areas in developing countries are more sparsely populated, have poorer infrastructure, and are less connected to markets than those in developed countries (Dorosh et al. 2010) and are usually hampered by information deficiencies. The effect of ICTs on these informational deficiencies can be modeled in a number of ways. One basic framework can be based on search cost models. Suppose that a farmer has the option of selling his harvest to different markets. If he knew the prices in different locations, he would simply choose the market where he would make the most profit (net of transportation costs). Traditionally, he might have to visit each market to collect this price information, and his decision to do so will be based on the cost of that search. If the costs are large, he might settle for a lower price rather than exploring alternative opportunities. ICTs can dramatically impact this scenario: rather than incurring travel costs to gather prices, a farmer can readily find price information through a (much cheaper) phone call or (even cheaper) SMS. Thus, ICTs lower search costs can translate into more extensive search behavior and better opportunities for farmers. It can be argued that in the long run, if farmers are

11 able to find more profitable opportunities, they can change allocation of production factors or crop patterns and increase agricultural productivity. Aker (2010) applies this same framework to agricultural middlemen (who act as intermediaries between small farmers and wholesale buyers in markets). Assume that the middleman has purchased crops from different farmers in several villages. However, he does not know the prevailing prices that wholesale buyers are paying in different markets. If ICTs reduce the cost of researching alternative prices, he will be able to find better business opportunities. Aker s model also provides some interesting insights into more general equilibrium effects: traders higher sales in markets with higher prices imply that ICTs also enhance arbitrage across markets. As supply expands where crops are most valued (and reduces where they are not), there is the potential for much larger welfare gains. The enhancement of market arbitrage can also reduce price variability if ICTs encourage trade between regions; rather than depending solely on regional supply, local prices would depend on a more aggregate measure of production. If consumers or farmers are risk-averse, there can also be welfare gains from more stable prices through the increasing use of ICTs Models of Limited Competition The role of ICTs for more efficient arbitrage across markets has also been emphasized by Jensen (2007). His model looks at traders (fishermen) in two towns. Typically, fishermen sell their daily catch in their own village. However, they can also incur a transportation cost and sell in the other town. Assuming both towns have the same demand function and if there is any production shock, price differentials should emerge and encourage trade (arbitrage) between towns. However,

12 without communication technologies, each fisherman only has information about his own catch, providing him with limited information about aggregate shocks (e.g. high fish density). If his catch is high, he might believe that the fish supply in his own village is high, which would lead to lower local prices (encouraging him to incur the transportation cost and to sell in the other village). However, he cannot fully distinguish these aggregate phenomena from other possible idiosyncratic shocks (i.e. he had a lucky day). Communication technologies would allow him to access prices in both locations. In this model, the introduction of ICTs has large potential implications for overall welfare: from the producer s side, the sum of profits in both villages is higher (because of the better opportunities for traders), while from the consumers side, as produce is traded on the market with the higher marginal valuation, aggregate surplus increases. In Jensen s model, no price information is systematically available to market participants. By contrast, others argue that agricultural markets in developing countries are mostly characterized by informed parties (usually traders) who have better access to market price information and who leverage this information asymmetry when bargaining with less informed farmers. Svenson and Yanagizawa (2008) model this relationship in a monopolistic screening framework. The farmer establishes an ex-ante menu of contracts, to which he supplies different amounts of his harvest depending on the market price. In this case, the farmer establishes these contracts in such a way that the trader has an incentive to reveal the true market price: if the trader reveals a high market price, the farmer concedes an informational premium (through lower farm-gate prices). However, if the trader reveals that the prices are low, the farmer restricts his supply, hurting the trader s profits. Thus, the contract establishes a truth-telling mechanism. Note that in this model,

13 the farmer does find out the prevailing market prices, entailing a loss of efficiency: the farmer has to either receive lower farm-gate prices or restrict his optimal sales quantities. Fafchamps and Minten (2012) provide an alternative model for this bargaining process. In their setting, an informed trader bilaterally bargains with a risk-averse farmer. The farmer has to decide whether to sell his harvest to the trader or sell it directly in the market, where he is uncertain of the price. Thus, the trader can exploit the farmer s uncertainty and risk-aversion and offer his certainty equivalent (i.e. the price that would make the farmer indifferent about going to the market or selling at farm-gate, net of transportation costs). This model predicts that the trader will profit and that this profit will depend on the farmer s degree of risk aversion and the uncertainty of market prices. Both of these models rely on the assumption that there is limited competition between middlemen. Competition among traders would ensure that they could not make profits from the transactions with farmers. However, there are a number of reasons why, in theory, they could exercise significant market power. Mérel et al. (2009) argue that high transportation costs can effectively increase middlemen s market power by limiting farmers access to other potential buyers. Barrett (1997) posits that capital constraints can prevent entry into some segments of the food supply chain (for example, those that comprise wholesale purchases, inter-seasonal storage, transportation across regions, etc.). Lopez and You (1993) argue that traders market power can be sustained through oligopsony arrangements. Fafchamps and Hill (2008) present a model where, despite free entry, traders can still maintain market power. In their model, this entry increases uninformed farmers search costs, as they have to compare prices among an increased

14 number of potential buyers. Mitra et al. (2013) find that farmers might not be able to sell their crops directly in markets if wholesale buyers only transact with traders that can offer larger volumes and are not willing to trade with smallholders. Thus, traders ability to maintain some degree of market power seems critical to models of asymmetric information and bilateral bargaining. Mitchell (2011) implicitly incorporates this idea in a two-period model, highlighting the role of market price information and competition among middlemen. In the first period, the farmer decides whether to sell his product to a trader or directly to a market. In the second period, he decides whether to stay with the same trader or to switch (though this might be costly) to a different trader. Her results suggest that a competitive environment (with low switching costs) encourages traders to make higher offers even in the absence of price information for farmers. As it becomes more difficult to switch traders, information acts as a substitute for competition to level farmers farm-gate prices. However, as the market becomes more monopsonistic, information does not matter. 3.2 Agricultural Extension There is a considerable productivity gap between developing and developed countries, with heterogeneous technology adoption presenting one possible explanation. Foster and Rosenzweig (2010) highlight the importance of understanding adoption, arguing that one mechanism by which poorer countries can catch up with richer countries is through technological diffusion. Restuccia et al. (2008) present a two-sector general equilibrium model to investigate crosscountry differences in productivity and argue that productivity gaps can be driven by barriers to the adoption of modern intermediate outputs (e.g. chemical fertilizers, pesticides, machinery,

15 improved seeds, etc.) in developing countries. Jack (2011) identifies inefficiencies in seven different areas that could hamper technology adoption in developing countries: (a) externalityrelated 4, (b) input and output markets, (c) land markets, (d) labor markets, (e) credit markets, (f) risk markets, and (g) information. He argues that one of ICTs most important roles is directly related to the reduction of information deficiencies in developing countries. Cole and Fernando (2012) find that traditional extension services face several constraints that limit their efficiency. First, poor infrastructure makes it harder and more costly to visit remote areas. Second, typical extension programs usually provide only one-time information to farmers, restricting their long-term impact. Finally, traditional extension is plagued by principal-agent and institutional problems, including a lack of accountability. Cole and Fernando argue that ICTs can overcome these problems by reducing the cost of extension visits, enabling more frequent twoway communication between farmers and agents, and improving agents accountability. Aker (2011) argues that ICTs can also provide farmers with better access to private information from their own social networks. Technology adoption has been shown to be related to observations of and learning from one s peers; thus, this could constitute an important channel for ICT use. By increasing communication linkages between individual farmers, extension agents, and research centers, ICTs can improve the flow of relevant information among all these agents. 4 There is an increasing amount of evidence that relates agricultural technology adoption to decisions made by others in the same social network or village (Foster and Rosenzweig, 1995; Munshi, 2004, Bandiera and Rasul, 2006, Conley and Udry, 2010). In this spirit, adoption does not only benefit the farmer who takes up a new technology: it also generates positive externalities to other (who might learn from an early adopter). Because these externalities are not considered by farmers, this leads to under adoption.

16 4. Empirical Evidence 4.1 Agricultural Market Prices This section first examines ICTs macro effects on market prices, measuring price integration and price volatility in markets. We then analyze two micro effects, measuring the price impact on farmers with access to mobile phones and on farmers with access to Market Information Systems (MIS) delivered through ICTs Price Integration and Price Volatility To examine ICTs role in improving market efficiency, Jensen (2007) investigates sardine markets across the state of Kerala, India. These markets were characterized by considerable price heterogeneity and wastage; however, following the introduction of mobile phones, fishermen had better information about where to take their daily catch. Through this enhanced information, they were able to arbitrage prices between markets, leading to a close adherence to the law of one price and the virtual elimination of wastage. Abrahams (2007) reaches similar conclusions about the role of mobile phones in fish markets in India. Aker (2010) analyzes the roll-out of mobile phones across markets in Niger. She finds that the introduction of mobile phones led to a 10-16% reduction in the dispersion of consumer prices for millet across markets (i.e. the absolute value of price differentials between pairs of markets) and to a 10% reduction in the coefficient of intra-annual price variation within markets. Aker (2008) argues that the main price transmission mechanism is traders behavior: traders with mobile phones are able to search for sales opportunities across more markets, reducing the variability of consumer prices. In a complementary study, Aker and Fafchamps (2013) analyze if mobile

17 phone introduction also leads to changes in producer prices in the same markets. Their results suggest that phones reduce producer price dispersion for cowpeas by 6%, with larger effects for market pairs that are farther away from each other and for those connected through unpaved roads. They also find reductions in the intra-annual coefficient of variation of farmers prices. However, they do not find any significant impacts for millet, suggesting that information may have larger effects on perishable crops (cowpeas) compared to less perishable ones (millet). Analyzing the impact of a Market Information System (MIS) on price variation, Goyal (2010) investigates the introduction of e-choupals (internet kiosks) in Madhya Pradesh, India. The kiosks were implemented by a private soybean processor in an effort to cut out intermediaries in their supply chain by providing both the price farmers would get for selling directly to the processor and the prevailing prices in local markets. The author finds that e-choupals led to a reduction in price dispersion Impact of Mobile Phones on Farm Prices and Income A number of studies have looked at the impact of mobile phone access at the farm level. The available evidence of the impact of mobile phones on agricultural farm prices and income shows overall mixed results. Some studies have found a mostly positive effect on prices and income. Beuermann et al. (2012) find that villages with mobile phone coverage in rural areas of Peru experienced a 10.8% increase in total household expenditures between 2004 and Similarly, Labonne and Chase (2009) find that mobile phone ownership increases households growth rate of per capita consumption by 11-17% among farmers in the Philippines. While the authors are

18 not able to directly test if these increases are due to access to more profitable markets or to better bargaining power against traders, they present some suggestive evidence about farmers trips to markets and trust relationships with traders. Jensen (2007) finds that the introduction of mobile phones in Kerala led to a decrease in fish prices of about 4%. This result might be attributable to enhanced arbitrage between markets; however, due to the reduction of wastage, fishermen s profits increased by an average of 8%. Muto and Yamano (2009) investigate the impact of the rapid mobile phone expansion that took place in Uganda between 2005 and They find that increases in phone coverage were associated with a higher probability of banana sales for farmers in remote areas farther away from the district center. They also document increases in the sale prices of bananas in communities with a mobile network. However, they do not find any of these effects for maize, again suggesting that information may be more important for perishable products than for less perishable ones. However, there are also a number of papers that find little or no impact on marketing outcomes. In their analysis of mobile phone expansion in Niger, Fafchamps and Aker (2013) do not find increases for farm-gate prices for either cowpeas or millet 5. Futch and McIntosh (2009) investigate the introduction of village phones in Rwanda and find that the technology did increase the proportion of farmers arranging their own transport to markets. However, ultimately, the village phones do not appear to have increased the prices received by those farmers. 5 Though, as mentioned in the previous section, they do find reductions in price dispersion of cowpeas.

19 4.1.3 Impact of MIS Delivered through ICTs on Farm Prices and Income A number of authors have further evaluated the effect of Market Information Systems (MIS) on farm prices. Two studies look at the impact of a MIS not implemented through mobile phones. Svenson and Yanagizawa (2009) study the impact of radio shows broadcasting market prices for different agricultural commodities. They find that farmers who received the price information 6 achieved a 15% higher farm-gate price for maize. The treatment also increased maize sales, thus implying an overall increase of 55% in farmers crop revenues (Svenson and Yanagizawa 2010). Goyal (2010) also finds that the introduction of e-choupals in India had a positive effect on soybean prices, with a 1-3% increase in markets located in districts where kiosks were introduced. This technology also yielded a 19% increase in soy production, leading to an overall increase of 33% in farmers net profits, most of which seems to have come from a redistribution of surpluses away from traders. Other authors look at the impact of Market Information Systems (MIS) spread specifically through mobile phones. In their impact evaluation of ESOKO (an ICT platform for agricultural information in Ghana), Nyarko et al. (2013) find that farmers with ESOKO access sold their yams at 11% higher prices than those without this service. Their evidence suggests that this effect is likely to be driven by farmers improved bargaining power against traders. However, they find no price increases for maize, cassava, or gari (a by-product of cassava), suggesting again that information might have differential effects by crop type. 6 Though the authors do not directly observe which farmers had access to the project information, their empirical strategy compares households with and without radios in districts with and without coverage MIS coverage, though a difference-indifference specification.

20 Nakasone (2013) implements a randomized controlled trial among smallholders in the Central Highlands of Peru in which a group of farmers in treatment villages received price information for different crops through SMS in six alternative reachable wholesale markets. He finds large impacts on the average prices received by farmers (11-13%), mostly driven by increases in prices for perishable crops (lima beans and peas) rather than non-perishable ones (different types of barley, corn, potatoes, and other Andean tubers). His analysis also suggests that, rather than driving farmers to sell directly in markets, information improves bargaining power with agricultural traders. Fafchamps and Minten (2012) investigate the impact of Reuters Market Light (RML), a service that provides farmers with agricultural information through mobile phones in Maharashtra, India. They implemented an experimental evaluation in which a random sample of farmers received a free RML subscription for a year. While the authors find that younger farmers received slightly higher prices for their crops, they do not find differences in average prices for farmers with RML subscriptions. They suggest that low levels of actual RML usage 7 and the fact that farmers mostly sold to a single local market may have contributed to this result. Mitra et al. (2013) collected daily price data in West Bengal for the two most important types of potatoes in local markets and in the closest metropolitan market and tested if different means of information provision generated different impacts among farmers. Out of 72 villages, one-third posted potato prices (for the two nearby local markets and the closest metropolitan market) on 7 The authors report some causes for limited usage of RML: some farmers thought they would be charged if they used RML, subscribers did not know how to activate the service (they had to indicate the crops and markets they were interested in), changes in phone number, and problems with Chinese phones that did not display messages in local languages.

21 public boards; in another third, this same information was delivered through phone calls to randomly selected farmers in the village, while the remaining third acted as a control group. They do not find price increases among farmers who received information through any of these alternative means, which may be attributable to characteristics specific to the area. They found that large wholesale buyers are not willing to buy directly from smallholders and would only negotiate with middlemen with whom they can trade larger volumes. Hence, even when farmers are more knowledgeable about prevailing market prices, information might not alter their options when negotiating with middlemen. Camacho and Conover (2011) conducted a randomized controlled trial among famers in Colombia in which a randomly assigned sample received market price and weather information through SMS to their mobile phones. The authors find that while treated farmers seem to increase their knowledge about prevailing market conditions, this does not translate into better sales prices, agricultural revenue, or household expenditures. All in all, there seems to be mixed evidence regarding the impact of improved market price information disseminated via ICTs on prices and agricultural income. The spread of mobile phones seems overall to lead to better market integration and less price volatility. On the other hand, the impact of mobile phones on farm prices and farm incomes is mixed. Finally, impact assessments of Market Information Systems delivered by mobile phones show that, overall, there is no impact on farm prices or income. Even when a positive impact was found, it was mainly limited to perishable and high-value crops.

22 These varying results might be due to characteristics specific to the studies or to the market structure in particular regions. If farmers do not have outside options for their sales, information will do little to improve their marketing outcomes. However, if there is scope for outside options and increased bargaining power, information could present potential gains. The available studies also seem to indicate that there can be considerable heterogeneous effects depending on perishability of the product, farmers initial information about prevailing market conditions, and the relationships between producers and traders. 4.2 Agricultural Extension While there are a wide array of projects using ICTs in developing countries to improve the spread of information related to improved agricultural technologies and management practices, only a few evaluations have been conducted. In their investigation of RML in India (described in Section 4.1), Fafchamps and Minten (2012) also examine the impact of RML s delivery of crop advisory tips (offered for one crop chosen by the farmer) and local weather forecasts. It might be assumed that this information would change farmers cultivation practices or reduce harvest losses; however, no effect was found for these outcomes. Cole and Fernando (2012) conduct a randomized impact evaluation of the Avaaj Otalo (AO) program among cotton farmers in Gujarat, India, delivering information through voice messages rather than SMS. This system provided both push (weekly information on weather and crop conditions) and pull (a hotline for specific advice) content. Results show that households who benefited from AO shifted their pesticide use to less hazardous, more effective products. The results also suggest that beneficiaries are more likely to harvest cumin, a high-value cash crop.

23 Fu and Akter (2012) investigate the impact of a program called Knowledge Help Extension Technology Initiative (KHETI) in Madhya Pradesh, India. While KHETI does not provide information directly to farmers, it operates through agricultural specialists who travel across villages with special mobile phones. These phones are able to record Short Dialogue Strips (SDSs), short videos depicting a particular problem faced by farmers. The specialists send these SDSs to scientists, who determine solutions for each case presented; these solutions are then passed back to the farmers. Using difference-in-differences estimations, Fu and Akter argue that those in the KHETI group 8 increased their awareness and knowledge of extension services compared to a control group. The authors also provide before-and-after comparisons for beneficiaries, indicating that they perceive KHETI as more useful, faster, and of better quality than other services. These studies highlight the heterogeneity of extension projects: one-way vs. two-way communication between farmers and agricultural specialists, SMS vs. voice messaging 9, and oral description of problems vs. pictures taken in the field. Overall, there remains however a lack of evidence regarding which services work and which do not, as most agricultural extension through ICTs is fairly recent. While farmers awareness might increase through these programs, this does not automatically translate into behavioral changes such as increased adoption of improved agronomic practices or modern inputs. 8 All households in the KHETI group were previously part of an association of poor and marginalized farmers in Madhya Pradesh. Given that the treatment and control groups may have had different characteristics to begin with, these results should be interpreted with caution. 9 Mittal et al (2013) argue that voice messages can come at unpredictable times during the day, so SMS might be more convenient. However, if there is a substantial proportion of illiterate population, voice messages can be a better dissemination tool.

24 5. Conclusions and Implications Developing countries access to mobile phones has increased considerably in the last decade. However, access to other ICTs remains rather low, and there is still room to improve mobile phone access, especially in rural areas. We find overall that access to mobile phones in rural areas seems to increase agricultural market performance, possibly through better arbitrage opportunities. Results for farm prices received in the presence of mobile phones are mixed, and most impact evaluations of agricultural Market Information Systems using mobile phones do not show any significant additional impact. Extension systems by mobile phones and other ICTs have yet to be examined fully. When we take into account: (a) the level of mobile phone penetration in the country at the time of the implementation of the interventions in the specific studies; (b) the specific characteristic of the commodity in terms of its value in the markets (i.e. low value, medium value and high value); (c) the specificity or quality of the content being provided to the farmers (general price information or information specific to the commodity); (d) and the statistical significance of the impacts the results show a rather clear pattern. First, we find that the lower the penetration the higher the level of impact, especially for medium and high value commodities, and even in one case for low value commodities. This result seems driven by significant asymmetry of price information in situations of low levels of cellular phone penetration. Secondly, as the penetration increases, and therefore there is more access to information, the type of information matters significantly more and impacts are only significant when commodities are of high value and farmers received specific price information to the commodity and the variety of it.

25 Our review allows us to suggest areas for future work. First, the information provided to farmers must be locally relevant and specific to the needs of the farmer; however, the generation of local content can be very costly. Second, information might foster adoption of agricultural techniques or changes in cropping patterns among uninformed farmers; however, other constraints (such as a lack of irrigation or credit constraints) might prevent farmers from adopting new practices even when they become aware of them (Mittal et al. 2010; Glendening and Ficarelli 2012). Third, the financial sustainability of ICT-driven extension services must be better understood and must rely less on donor funding. Fourth, how information is delivered also needs to be considered. SMS messages can be effective for simple price or weather information; however, they have data limitations (usually 160 characters) that might render them ineffective in providing more complex advice about agricultural practices or new technologies. Farmers might also need a higher level of ability or literacy to process the content of these messages. On the other hand, while voice messages might be more suitable, they are also more complicated and costly to implement.

26 References Abrahams R Mobile Phones and Economic Development: Evidence from the Fishing Industry in India. Information Technologies and International Development. 4(1): Aker JC Does Digital Divide or Provide? The Impact of Cell Phones on Grain Markets in Niger. Working Paper 154, Center for Global Development. Aker JC Information from Markets Near and Far: Mobile Phones and Agricultural Markets in Niger. Am. Econ.J.: Applied Economics. 2(3): Aker JC Dial A for Agriculture: Using ICTs for Agricultural Extension in Developing Countries. Ag.Econ. 42(6): Aker JC, Fafchamps M How does Mobile Phone Coverage affect Farm-Gate Prices? Evidence from West Africa. World Bank Economic Review. In press. Aker JC, Ksoll C, Lybbert T Can Mobile Phones Improve Learning? Evidence from a Field Experiment in Niger. Am. Econ.J.: Applied Economics. 4(4): Aker, JC, Collier, P, Vicente PC Is Information Power? Using Mobile Phones and Free Newspapers during an Election in Mozambique. Mimeo,Tufts University. Aker, JC, Mbiti IM Mobile Phones and Economic Development in Africa. J. Econ. Perspectives. 24(3): Anderson J, Feder G Agricultural Extension: Good Intentions and Hard Realities. The World Bank Research Observer. 19(1): Anderson, J, Feder G Agricultural Extension. In Handbook of Agricultural Economics, Volume 3, ed. R. Evenson, P. Pingali, 44: Elsevier Science. Bandiera, O, Rasul I Social Networks and Technology Adoption in Northern Mozambique. The Economic Journal. 116:

27 Barrett CB Smallholder Market Participation: Concepts and Evidence from Eastern and Southern Africa. Food Policy 33: Batista C, Vicente PC Introducing Mobile Money in Mozambique: Evidence from a Field Experiment. Mimeo, Nova Business School of Business and Economics. Baye, MR, Morgan J, Scholten P Information, Search and Price Dispersion. In Handbook in Economics and Information Systems, ed. T. Hendershott, 1: Elsevier Science. Beuermann, DW, McKelvey C, Vakis R Mobile Phones and Economic Development in Rural Peru. J. Dev. Studies. 48(11): Camacho A, Conover E The Impact of Receiving Price and Climate Information in the Agricultural Sector. IDB Working Paper Series No. IDB-WP-220, Inter-American Development Bank. Caselli F Accounting for Cross-Country Income Differences. In Handbook of Economic Growth, Edition 1, ed. P. Aghion, S. Durlauf, 1(9): Elsevier Science. Cole SA, Fernando AN The Value of Advice: Evidence from Mobile Phone-Based Agricultural Extension. Working Paper , Harvard Business School, Harvard University. Conley T, Udry, C Learning about a new technology: Pineapple in Ghana. Am. Econ Rev. 100(1): Courtois P, Subervie J. Farmer Bargaining Power and Market Information Services. Paper presented at CSAE Conf.: Economic Development in Africa, Oxford, UK. Dammert AC, Galdo JC, Galdo V Preventing Dengue through Mobile Phones: Evidence from a Field Experiment in Peru. Mimeo, Carleton University.

28 Davis K Extension in Sub-Saharan Africa: Overview and Assessment of Past and Current Methods and Future Prospects. Proc. Annual Meeting, Assoc. International Agriculture Extension Educators (AIAEE), 24th. Dorosh P, Wang H-G, You L, Schmidt E Crop production and road connectivity in Sub- Saharan Africa: A spatial analysis. Policy Research Working Paper Series 5385, World Bank. Evenson RE Economic impacts of agricultural research and extension. In Handbook of Agricultural Economics, ed. BL Gardner, GC Rausser, 1A: Elsevier Science. The Economist (2012). Global Mobile Usage: SIM Earth. Oct. 19. Fafchamps M, Minten B Impact of SMS-Based Agricultural Information on Indian Farmers. World Bank Economic Review. 26(3): Fafchamps M, Hill RV Price Transmission and Trader Entry in Domestic Commodity Markets. Econ. Dev. and Cultural Change 56(4): Foster A, Rosenzweig M Learning by doing and learning from others: Human capital and technical change in agriculture. J. Pol. Econ. 103(6): Foster AD, Rosenzweig MR "Microeconomics of technology adoption." Ann. Rev.Econ. 2: Freund C, Weinhold D The Internet and International Trade in Services. Am. Econ. Rev. 92(2): Fu X, Akter S The Impact of ICT on Agricultural Extension Services Delivery: Evidence from the Rural e-services Project in India. TMD Working Paper Series No. 046, University of Oxford, Department of International Development.

29 Futch MD, McIntosh CT Tracking the Introduction of the Village Phone Product in Rwanda. Inf. Tech. Internat l. Dev. 5(3): Glendenning CJ, Ficarelli PP The Relevance of Content in ICT Initiatives in Developing Countries. International Food Policy Research Institute Discussion Paper 01180, IFPRI Gollin D, Lakagos D, Waugh W The Agricultural Productivity Gap in Developing Countries. CGEB Working Paper Series No. 03, Center for Global Economy and Business, NYU Stern School of Business. Goyal A Information, Direct Access to Farmers and Rural Market Performance in Central India. American Economic Journal: Applied Economics 2(3): Gruber H, Koutroumpis P Mobile Telecommunications and the Impact on Economic Development. Econ. Pol. 26(67): Helpman E General Purpose Technologies and Economic Growth. Cambridge, MA: The Massachusetts Institute of Technology. Howard PN, Duffy A, Freelon D, Hussain M, Mari W, Mazaid M Opening Closed Regimes: What was the Role of Social Media during the Arab Spring? Project on Information Technology and Political Islam, Working Paper , University of Washington. Islam N, Saha JK Country Paper on Household ICT Access and Individual ICT Usage in Bangladesh. Mimeo, Bangladesh Bureau of Statistics. Jack K Constraints on the adoption of agricultural technologies in developing countries. White paper, Agricultural Technology Adoption Initiative, J-PAL (MIT) and CEGA (UC Berkeley).