in 1997 (Figure 1). Among the markets in our sample, only 16 percent of markets received some type of road improvement between 1999 and 2008, with the majority of this improvement occurring in 2007/2008, toward the end of our sample period and well after mobile phone coverage was introduced into these markets. 19This table is similar to the table presented in Aker (2010), with the exception of the definition of the “mobile phone coverage” variable.

20 These tests do not test for other time-varying unobserved factors that might be simultaneously correlated with mobile phone coverage and the outcomes of interest, such as road quality, landline coverage, donor funding, and private sector investment. Some of these factors, such as changing relations with France in the north and an increase in anti-terrorism activity, either occurred outside of our study sample (e.g., in the Saharan desert) or outside of our study period. Other changes occurring in Niger over this time (such as the introduction of Chinese investment into Niger or increased aid in the wake of the 2005 food crisis) would need to be correlated with an increase in mobile phone rollout during our time period. Although Chinese investment could be a potentially important confounding factor, the growth of this investment occurred after most of the markets in our sample had mobile phone coverage. 21 Aker (2010) also included cross-border markets in the specification. This is not possible for producer price dispersion, as producer price data are not available from cross-border markets. Thus, all of the regressions using are only for markets within Niger.

22 We also conducted the heterogeneity analysis by the market’s landline status prior to mobile phone coverage and found no statistically significant effects. The results are available upon request.

23 The Niger Agricultural Market Information System (AMIS) defines four different types of markets: producer, consumer, wholesale, and border. These categories are not mutually exclusive and are open to interpretation. Here, we regard as surplus markets those that are primarily classified as producer markets (i.e., those markets that are located in surplus regions and serve as major trading points for farmers to sell their produce).

24 For the gross trade margin to change, consumer prices should fall, producer prices should rise, or both. The relative price change is akin to a standard tax incidence question: if the short-run price elasticity of demand is larger than the short-run price elasticity of supply, the average consumer price will fall by more than the average producer price rises. The demand for staple food is, in all likelihood, price inelastic. In contrast, supply may be more price elastic if farmers store their output or have alternative uses (e.g., cowpea cakes). Without independent evidence on short-run price elasticities in Niger, we are unable to make strong predictions either way. However, we expect no systematic effect on producer and consumer price differences within the same market because mobile phone coverage is unlikely to affect the spatial allocation of trade within a single market.

25 To answer this question precisely, we could use trader-level data on gross margins at different levels of the supply chain. Although we have trader-level data over a two-year period, we do not have these data over the full period of our sample.

26Restricting the sample to consumer price data during 1999–2007, we find that millet consumer prices decrease in deficit markets by 1.3-2.8 percent, with a statistically significant effect. The millet results are consistent with Aker (2008).

27It is also conceivable that our categorization of surplus and deficit markets is imprecise. A market’s “surplus” or “deficit” status may vary across seasons and years. In the absence of time-varying data on trade flows, we can only categorize markets based on the average trading patterns, which might be an oversimplification. This categorization is similarly true for a market’s status as a collection, retail, or wholesale market, which is correlated with its categorization as surplus or deficit.

28 There are only 411 observations in Panel A (Table 9) because the question was only asked of farmers who had sold the relevant commodities since the previous harvest (and thus could report a producer price). We did not impute the missing values with a zero price.

29 Our results differ from those of Goyal (2010), who finds that internet kiosks increase producers’ soybean prices in India. The technology is different from mobile phone coverage because it provides both price and quality information to farmers.

30 Aker and Ksoll (2013) report similar findings: a mobile phone-based education intervention (which was randomized at the village level) occurring between 2009 and 2011 increased farmers’ access to price information but did not change farmers’ marketing behavior or the farm-gate price received.

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