metadata: authors: 0: author: Aker, Jenny C.
1: author: Fafchamps, Marcel
count: Western Africa,Niger
docna: 0: docna: Mobile phone coverage and producer markets :
            evidence from West Africa
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Telecommunications Infrastructure,Information Technology,International Trade and Trade Rules,Transport Services
teratopic: Information and Communication Technologies,Informatics
abstracts: cdata!: Mobile phone coverage has expanded
            considerably throughout the developing world, particularly
            within sub-Saharan Africa. Existing evidence suggests that
            increased access to information technology has improved
            agricultural market efficiency for consumer markets and
            certain commodities, but there is less evidence of its
            impact on producer markets. Building on the work of Aker
            (2010), this paper estimates the impact of mobile phone
            coverage on producer price dispersion for three commodities
            in Niger. The results suggest that mobile phone coverage
            reduces spatial producer price dispersion by 6 percent for
            cowpea, a semi-perishable commodity. These effects are
            strongest for remote markets and during certain periods of
            the year. The introduction of mobile phone coverage has no
            effect on producer price dispersion for millet and sorghum,
            two staple grains that are less perishable and are commonly
            stored by farmers. There are no impacts of mobile phone
            coverage on traders' gross margins or producer price
            levels, but mobile phone coverage is associated with a
            reduction in the intra-annual price variation for cowpea.
            These results are potentially explained by the fact that
            farmers engage in greater storage for storable commodities
            such as millet and sorghum.
geo_reg_key: 80475,80468,80473,80474
repnme: repnme: Mobile phone coverage and producer markets
            : evidence from West Africa
display_title: Mobile phone coverage and producer
            markets : evidence from West Africa
listing_relative_url: /research/2014/07/19898909/mobile-phone-coverage-producer-markets-evidence-west-africa
topicv3: Information and Communication Technologies
            m1326624 133,Crop Production m1326627 174,Transport m1326661 135,Trade m1326924 698,Mobile Phone m1505675 3092
publishtoextweb_dt: 2014-07-01T00:00:00Z
historic_topic: Information and Communication Technologies,Informatics
no_of_pages: 43
model_extractions: WPS6986 Policy Research Working Paper 6986 Mobile Phone Coverage and Producer Markets Evidence from West Africa _Jenny C. Aker Marcel Fafchamps_ Development Economics Vice Presidency Development Policy Department July 2014
Policy Research Working Paper 6986

# **Abstract**

Mobile phone coverage has expanded considerably throughout the developing world, particularly within sub-Saharan Africa. Existing evidence suggests that increased access to information technology has improved agricultural market efficiency for consumer markets and certain commodities, but there is less evidence of its impact on producer markets. Building on the work of Aker (2010), this paper estimates the impact of mobile phone coverage on producer price dispersion for three commodities in Niger. The results suggest that mobile phone coverage reduces spatial producer price dispersion by 6 percent for cowpea, a semi-perishable commodity. These effects are strongest for remote markets and during certain periods of the year. The introduction of mobile phone coverage has no effect on producer price dispersion for millet and sorghum, two staple grains that are less perishable and are commonly stored by farmers. There are no impacts of mobile phone coverage on traders’ gross margins or producer price levels, but mobile phone coverage is associated with a reduction in the intra-annual price variation for cowpea. These results are potentially explained by the fact that farmers engage in greater storage for storable commodities such as millet and sorghum.

This paper is a product of the Development Policy Department, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Jenny.Aker@tufts.edu and fafchamp@stanford.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Mobile Phone Coverage and Producer Markets: Evidence from West Africa**

Jenny C. Aker and Marcel Fafchamps<sup>∗</sup>

**JEL Classification Codes:** JEL O1, O3, Q13

**Keywords:** Africa, Information, Information Technology, Market Performance, Search Costs, Niger.

*Jenny C. Aker (corresponding author): The Fletcher School and Department of Economics, Tufts University, 160 Packard Avenue, Medford, MA 02155. Email: Jenny.Aker@tufts.edu. Marcel Fafchamps: Freeman Spogli Institute for International Studies, Stanford University, 616 Serra Street, Stanford CA 94305. Email: fafchamp@stanford.edu. This research was partially funded by Rocca Dissertation Fellowship, the Ford Foundation, and UC Berkeley’s CIDER. We are grateful for comments from three anonymous referees, Reena Badiana, Joanna Upton, and participants at the Center for the Study of African Economies (CSAE) and American Economic Association (AEA) conferences. All errors are our own. A supplemental appendix to this article is available at http://wber.oxfordjournals.org/.
# **Introduction**

Price information plays an important role in arbitrage behavior and market efficiency. Access to such information has improved significantly in developed countries, especially with the introduction of online databases (Autor 2001; Anderson and Magruder 2012). In sub-Saharan Africa, long distances and limited infrastructure have historically made obtaining such information costly. The spread of mobile phone coverage over the past decade has significantly reduced the costs of obtaining such information, thereby enabling consumers, traders, and producers to send and receive information more quickly and cheaply (Aker and Mbiti 2010). Although a growing body of evidence suggests that this increased access to information has improved consumer market efficiency, there is more limited evidence on its impact on producer markets, which form an important segment of the population, especially in developing countries.

Between 2001 and 2008, mobile phone networks were phased in throughout Niger. By 2008, more than 44 percent of the population and 90 percent of the agricultural markets in our sample had mobile phone coverage (Aker 2010; authors’ calculations from GSMA). As coverage remained relatively limited in rural areas by 2008, the introduction of mobile phones primarily reduced traders’ costs of obtaining market information (Aker 2010), and, to a lesser extent, farmers who transported their output to local markets for sale.

In earlier work, Aker (2010) finds that the rollout of mobile phone coverage in Niger reduced millet consumer price dispersion by 10-16 percent. Building upon that work, this paper estimates the impact of mobile phone coverage on the spatial producer price dispersion for three agricultural commodities in Niger. We first develop a simple model showing how the circulation of information among traders is likely to improve spatial arbitrage and reduce price dispersion in producer markets. These effects should be strongest for perishable and semi-perishable commodities when inter-temporal arbitrage is largely absent. However, the theoretical predictions related to producers’ prices and traders’ gross margins are ambiguous, as they depend on the market structure and relative elasticities of supply and demand for a particular commodity.

2
We test these theoretical predictions using market-level panel data on producer prices for millet, sorghum, and cowpea in Niger between 1999 and 2008. We find that mobile phone coverage reduces the spatial dispersion of producer prices by 6 percent for cowpea, a semi-perishable cash crop in Niger. These effects are larger for more isolated markets and during certain periods of the year. We find no effect of mobile phone coverage on producer price dispersion for millet or sorghum or on producer price levels. Nevertheless, mobile phone coverage reduced intra-annual producer price variation for cowpea by 6 percent.

We use farmer-level data to better understand the correlations between mobile phone coverage and farmers’ marketing behavior. While farmers were more likely to use information technology as a search tool, mobile phone ownership was not correlated with changes in farmers’ marketing behavior or the sales prices that they received, consistent with Fafchamps and Minten (2012) and Aker and Ksoll (2013). Yet Tack and Aker (2014) find evidence that mobile phone coverage increased traders’ search behavior over time, suggesting that most of the reductions in search costs might have accrued to traders during this period.

Our paper makes two contributions. First, the results speak to a substantial economic literature showing that information is crucial for the effective functioning of markets, both from a theoretical (Stigler 1961; Reinganum 1979; Stahl 1989) and an empirical perspective (Autor 2001; Brown and Goolsbee 2002; Jensen 2007; Aker 2010; Goyal 2010). Most of this work has focused on consumer markets, with a more recent extension to producer prices (Jensen 2007, Muto and Yamano 2009; Goyal 2010; Fafchamps and Minten 2012). However, few of these studies assess the impact of information technology on agricultural producer price dispersion, as our work does.

Second, much of the existing literature concentrates on the impact of information technology on a single good (Jensen 2007; Aker 2010; Goyal 2010). To our knowledge, only Muto and Yamano (2009) and Nakasone (2013) assess the impact of information technology on perishable and non-perishable commodities. Our study is able to measure the impact of information technology on multiple goods with varying perishability and over a long time period.

3
The rest of this paper proceeds as follows. We first provide an overview of the context and research design. Next, we present the theoretical framework. Subsequently, we present our data and then discuss the empirical strategy. Finally, we provide the main empirical results and present conclusions. **Context**<sup>**1**</sup> With an estimated 61 percent of the population living in poverty, Niger is one of the lowestranked countries on the United Nations Human Development Index (United Nations Development Program 2013). Agriculture employs more than 80 percent of the population and contributes approximately 43 percent to the GDP (Aker 2010, World Bank 2013). The majority of the population consists of subsistence farmers who depend on rain-fed agriculture and livestock as their main sources of income.

Subsistence farmers primarily cultivate millet, sorghum, and cowpea with a unimodal rainfall system. Although these commodities can, in principle, be stored for several years, a majority of the farmers and traders in our study area store for shorter periods and rarely engage in inter-annual storage (Aker 2008; Tack and Aker 2014).<sup>2</sup> Cowpea, in particular, is highly susceptible to storage insects (the cowpea weevil), with storage losses estimated to be 25 percent (Jackai and Daoust 1986). As a result, cowpea is often considered to be a semi-perishable commodity (Murdock et al. 1997).

Although a majority of farmers in our sample are net consumers, they often sell a portion of their production at some point in the year. All three commodities are traded through a system of weekly agricultural markets (Aker 2010). Farmers typically sell their agricultural products to smaller traders (e.g., retailers and intermediaries) located within their village or at a nearby weekly market (an average of 7.5 km away). These smaller traders sell to wholesalers and semi-wholesalers in local markets, who, in turn, sell to buyers (wholesalers, retailers, or consumers) in regional markets. Although an agricultural market information system has existed in Niger since the 1990s, 89 percent of traders and 75 percent of farmers stated that they primarily obtain price information through personal networks (Aker 2010).<sup>3</sup> Mobile phone service was introduced in Niger in October 2001 (Aker 2010). Three private mobile phone operators intended to provide universal coverage by 2009, with mobile phone service rolled 4
out in different markets over time. At the outset, mobile phone operators prioritized urban centers and a town’s proximity to an international border.<sup>4</sup> As a result, the capital city and regional capitals received coverage during the first three years of mobile phone rollout, followed by a quasi-experimental pattern in later years.

Aker (2010) shows the spatial rollout of mobile phone coverage by market and by year between 2001 and 2008. Mobile phone coverage increased substantially during this time, with 90 percent of the weekly markets having access to mobile phone service by 2008. The greatest increase in mobile service into more remote rural areas occurred between 2008 and 2010, after our study period.

While landlines have existed in Niger for some time, Niger has the second-lowest landline coverage in the world (World Bank 2006), and the number of landlines remained relatively stable during our study period (Figure 1).<sup>5</sup> Among all of the agricultural markets in our study, only one received new landline coverage between 1999 and 2008.

Despite a large increase in mobile phone coverage, Niger had the lowest adoption rate in Africa in 2008. There were an estimated 1.7 million mobile phone subscribers in 2008, representing 12 percent of the population (authors’ calculations from Wireless Intelligence). Yet grain traders, especially wholesalers, were some of the first adopters: A trader survey conducted in 2006 showed that approximately 30 percent owned a mobile phone and used it for their trading operations (Aker 2010; Tack and Aker 2014). In contrast, less than 5 percent of farm households owned a mobile phone at that time.<sup>6</sup>

# **Conceptual Framework**

To clarify the way in which we expect mobile phone coverage to affect producer price dispersion, we present a model of agricultural markets visited by farmers and itinerant traders. The model is based on a fair but stylized description of how agricultural traders operate in sub-Saharan Africa (Fafchamps, Gabre-Madhin, and Minten 2005).<sup>7</sup> Because each market operates for a limited duration on different days and markets are often far apart, it is difficult for farmers or traders to visit more than one market in a given day. In the absence of widespread communication devices, farmers and traders must visit a market to obtain price information 5
and are limited to one per day. For Nigerien farmers, these travel costs are substantial, as the average distance to the nearest market is 7.5 km, or 1.5 hours.<sup>8</sup> For this reason, farmers often sell their output in the village (farm-gate) or at the closest market and are reluctant to transport their output home because of transport costs. As a result, supply on any given market day is inelastic. Because each weekly market serves a large geographic area, farmers cannot coordinate supply. Consequently, supply varies randomly across market days in ways that traders cannot easily predict. If better-informed traders can select which market to visit depending upon local supply, this situation would generate a potential gain from spatial arbitrage.

The introduction of mobile phone coverage in Niger reduced the cost of obtaining information in general and information about local market conditions in particular. Aker (2010) estimated that the cost of obtaining price information from a market located 10 km away decreased by 35-50 percent with the introduction of mobile phone coverage.<sup>9</sup> This cost reduction had a greater effect on traders (compared with farmers) because traders were more likely to adopt mobile phones and operated in different weekly markets, many of which had mobile phone coverage. In contrast, mobile phone coverage did not expand significantly into more remote rural villages, farmers’ primary residences, until after 2008.

Our model captures these different features to illustrate how mobile phone coverage can increase spatial arbitrage and reduce price dispersion while keeping the average producer price unaffected.

# **_The Model_**

To provide a focus, we consider a static, symmetric, one-period model without intertemporal arbitrage. Each risk-neutral trader has _n_ markets nearby, all located at the same distance _d_ with transport cost _c_ .<sup>10</sup> Each market is reachable by _n_ traders, and each trader has a working capital of _k_ . In the morning, the trader visits a single market, _m_ , and purchases all possible quantities with working capital _k_ at the going price _pm_ . In the afternoon, the trader sells the total quantity purchased to his home market. In the morning of day _t,_ producers bring a random quantity of agricultural goods q�m to market _m._ The distribution _F(q)_ of q�m is the same in all markets and is known to traders, but the exact quantity in each market is unknown.<sup>11</sup> Let _E_ [q�m] = q� for all _m_ . Each morning, a trader must select a market _m_ among the 6
_n_ markets that he could potentially visit.<sup>12</sup> Let d̃m be the number of traders who happen to choose market n−1 _m_ on a particular day _t_ . Because traders randomize equally among all _n_ markets, d̃m~B(1, ~~)~~ , it follows n that the variance of d̃m increases in _n,_ the number of markets from which the traders must choose.

The total demand is equivalent to d̃mk, the number of traders multiplied by their individual working capital. The price in market _m_ on a given day _t_ is given by the standard supply equals demand equilibrium: d<sup>�</sup> <u>m</u> Setting _k_ = 1 by choice of units, this reduces to pm = ~~.~~ In other words, the price on a given market _n_ q�m on a given day _t_ is the same for all farmers and traders in that market, but there is spatial price variation across markets. For a given distribution _F_ ( _q_ ), the variance of pm is increasing in the variance of _dm_ and hence _n_ . The quantity that each trader _i_ purchases on a given day is which is increasing in the number of farmers who brought their output to the market that day and decreasing in the number of traders who chose market _m_ .

In the absence of temporal arbitrage, a trader must sell in his/her home market. We assume, for simplicity, that each trader sets the sales price to cover transport costs plus a unit profit margin _r_ . The sales price for trader _i_ is thus Because the profit of trader _i_ is rq�i, the trader would prefer to buy from markets with many farmers (i.e., a high q�m) and few other traders (i.e., a low d̃m). In other words, a trader could benefit from obtaining an informative signal about the realization of q�m or d̃m. Without this signal, the trader’s best response function is 7
> : q

q .

a Prd =x)=( )@ (Q-# =ft ~~-~~ j q E[q |s] > E[@

rE| ~~-+-|~~ s] +5 ~~— +~~

F(d )= , , q q =Elq q>q rq +rqcy ~~—~~ rq +rqC(Qi-n rq =rqC,s.t.q =q
=1ifq >G =0ifq <q q >q a a ~~a~~ _ 76 rq E ~~|—|s~~ | + (1-7 )qrE ~~[—~~~ e=a/a ~~=e~~ [ ~~—]~~ e ~~[=f~~

Pr(d =x) =( \(x
The system of three equations (12), (13), and (14) defines an implicit relationship between πs and the signal strength θ. It is easy to show that θ increases monotonically with πs. It follows that the equilibrium πs must rise to equilibrate the FOC. In other words, ∂πs/∂ _θ_ > 0: a more positive signal is associated with a higher probability that traders will visit market _s_ . This raises prices in surplus (low price) markets and lowers them in deficit (high price) markets, thereby reducing price dispersion between markets.

## **_Theoretical Predictions_**

This model, albeit stylized, conveys the key intuition behind the empirical analysis. The introduction of mobile phones in Niger provided traders with access to a private signal about local supply and arguably improved the quality of such signals in terms of accuracy, detail, and timing. During our study period, the technology was primarily used by traders, with more limited access to and use by farmers. The model suggests that the introduction of mobile phone coverage should 1) lead to a shift in informed traders’ attention to market _s_ (i.e., the surplus market with an informative signal); 2) raise producer prices in surplus (low price) markets and lower them in deficit (high price) markets; and 3) reduce spatial price dispersion between markets with mobile phone coverage.

These effects need not be present for storable commodities because intertemporal arbitrage sets a ceiling or a floor on the price at which farmers and traders are willing to trade and can thus reduce price dispersion even in the absence of informative signals.<sup>13</sup> This implies that the above predictions apply primarily to perishable and semi-perishable commodities for producer prices, but not necessarily to nonperishables or for consumer price markets, as is the case in Aker (2010). In this paper, we formally test the third hypothesis for non-perishable and semi-perishable commodities and provide suggestive evidence in support of the second hypothesis.

# **Data and Summary Statistics**

10
This paper uses three primary datasets. The first is a market-level monthly panel for 37 markets over a 10-year period (1999–2008) collected by the Agricultural Market Information Service (AMIS) of Niger. This dataset includes monthly producer and consumer prices for millet, sorghum, and cowpea.<sup>14</sup> A producer price observation is calculated as the average monthly price that farmers received for selling a given crop in that market. In addition, we have data on factors that may affect arbitrage, such as fuel prices, transport costs, rainfall, market latitude and longitude, and the distance and road quality between market pairs.<sup>15</sup> These data are combined with information on the location and date of mobile phone coverage in each market between 2001 and 2008, which was obtained from the mobile phone service providers.

The second dataset is a panel survey of traders and farmers interviewed in Niger between 2005 and 2007. The survey includes traders and farmers in 32 markets and 37 villages across six geographic regions of Niger. A majority of traders in Niger are male, from the Hausa ethnic group, and have never attended school. Traders search for price information in an average of 3.9 markets and buy and sell commodities in four markets. Traders have an average of 16 years’ of trading experience, and only 10 percent had changed their market since they began trading (Tack and Aker 2014). Table 1A provides summary statistics for farmers. Despite their low levels of production, on average, 25 percent of farmers sold millet, and 75 percent sold cowpea. Compared to traders, farmers traded over a smaller geographic area, selling in 1.46 markets and searching for price information in 1.5 markets.

Fewer than 5 percent of the villages in the farmer survey had mobile phone coverage between 2005 and 2007. As a result, inferences drawn from farmer-level dataset have low power. To provide additional insight into the relationship between mobile phone coverage and farmers’ marketing behavior, we also rely on a 2009 survey of 1,038 farm households from 100 villages in two regions. The survey collected data on agricultural production and marketing behavior, as well as mobile phone coverage and ownership. Table 1B provides summary statistics from this survey. Overall, many of the sociodemographic indicators are similar to those of the 2005–7 survey; households have low levels of education and are primarily from the Hausa ethnic group. Millet and cowpea are the primary crops grown 11
Yi,i =hyt Bmobile,- +X+, +0, + Ly, Ver Xie Y= fi, + Bmobilepercent, t +X y+ O.4+0,+U,,
where _Yr,t_ is the difference in maximum and minimum producer prices across markets within a region during month _t_ and _mobilepercentr,t_ is the percentage of markets within a region that have mobile phone coverage during month _t._

# **_Identification and Assumptions_**

To interpret _β_ 1 as the causal effect of mobile phone coverage on producer price dispersion, we must assume that, conditional on all covariates, _mobilejk,t_ is uncorrelated with the error term. The DD specification controls for time-invariant unobservables, but we must also assume that there are no timevarying unobservables that are correlated with mobile phone coverage and the outcomes of interest.

We formally test the validity of these identification assumptions in several ways. First, we examine whether mobile phone coverage expanded primarily into markets where the potential for improvement in market integration was the greatest. This does not appear to be the case. Mobile phone operators cited two main determinants of tower placement: whether a location was an urban center (defined as a population of more than 35,000) and whether the location was near the southern or western borders. To verify these claims, we regress a binary variable for mobile phone coverage in market _j_ at time _t_ on _j_ ’s urban status, latitude and longitude, elevation, slope, and road quality (Buys et al. 2009; Batzilis et al. 2010). The regression results appear to confirm the criteria cited by mobile phone operators (Table 2): urban areas were more likely to receive mobile coverage, as were markets with paved roads, a variable that is correlated with urban status (column 1). The eastern part of the country was also slightly more likely to receive mobile phone coverage earlier, as it had a higher density of border markets. Characteristics that are potentially correlated with high potential for market integration, such as elevation, slope, latitude, and market size, are not correlated with mobile phone expansion during our study period. These results are robust to the use of probit estimation (column 2).

Even if agricultural market performance were not an explicit rationale behind tower placement, the possibility remains that spatial producer price dispersion is correlated with pre-treatment timeinvariant or time-varying characteristics that led to the placement of mobile phone towers. To determine whether this is the case, Table 3 shows the differences in means for pre-treatment (1999–2001) outcomes 13
and covariates at the market (Panel A) and market pair level (Panel B).<sup>19</sup> Overall, the results suggest that there were few statistically significant differences in pre-treatment outcomes between mobile phone and non-mobile phone markets. Most differences in pre-treatment covariates are also not statistically significant from zero, with the exception of a market’s urban status, thereby supporting the earlier results. Although the pre-treatment differences in producer price levels and dispersion are not significantly different from zero for millet and sorghum, there is a statistically significant difference in cowpea producer prices and dispersion. However, a statistically significant difference only exists for one of the two pre-treatment years, suggesting that this was not systematic. In addition, cowpea pre-treatment price dispersion was lower in non-mobile phone markets. Hence, if our findings are biased because of the nonrandom placement of phone towers, this bias is most likely in the direction of underestimating the effect of mobile phone coverage.

The key identification assumption of equation (15) is that of parallel trends across mobile phone and non-mobile phone markets. This assumption might be violated if we do not control for time-varying characteristics, such as road quality, that are simultaneously correlated with mobile phone coverage and price dispersion. We therefore conduct a falsification test by estimating equation (15) using data from _before_ the introduction of mobile phones (Table 4). The rationale behind this test is that if mobile phone and non-mobile phone markets follow different time trends, this difference should have already been apparent before mobile phone coverage was introduced (Aker 2010). We find that the pre-intervention trends for the log of cowpea producer prices (column 3) and cowpea and sorghum producer price dispersion (columns 4 and 6) are not significantly different from zero for markets and market pairs that ever received mobile phone coverage compared with those that did not. However, there seem to be somewhat differing trends for millet and sorghum producer prices and millet producer price dispersion. This finding raises some concerns regarding the parallel trend assumption, especially for millet producer price dispersion. Nevertheless, mobile phone markets had relatively higher producer price dispersion prior to treatment, suggesting that our findings may underestimate the effects on millet producer price 14
dispersion. In addition, these differences are present during one of the pre-treatment years rather than both, suggesting that the differences are not systematic.<sup>20</sup>

# **Results**

We first present the impacts of mobile phone coverage on producer price dispersion for different commodities. Because these results might differ by market characteristics, we then present heterogeneous treatment effects by distance, road quality, period of year, and market type. We end with the results on the impact of mobile phone coverage on producer-consumer margins and producer price levels.

## **_Average Effects of Mobile Phone Coverage_**

Table 5 presents the regression results of equation (15) for cowpea (columns 1-4), millet (columns 5-8), and sorghum (columns 9-12). According to our model, we expect mobile phone coverage to reduce price dispersion for cowpea more than for millet or sorghum, as cowpea is a semi-perishable commodity. Controlling for yearly, monthly, and market pair fixed effects (column 1), we find that mobile phone coverage reduces cowpea producer price dispersion by 6.3 percent, with a statistically significant effect at the 1 percent level. This result is robust to the introduction of additional covariates that also affect producer price dispersion across markets (column 2), such as drought and transport costs. The result is also similar when including market fixed effects with the standard errors clustered by quarter (column 3). Similar to Aker (2010), we also include a binary variable equal to one when only one market in a pair has mobile phone coverage (column 4). Using the most conservative estimate of all of the specifications, the introduction of mobile phones is associated with a 6 percent reduction in cowpea producer price dispersion compared to market pairs without mobile phone coverage.<sup>21</sup> Columns 5 to 12 contain similar regressions for millet and sorghum, two less perishable commodities. Mobile phone coverage reduces millet producer price dispersion across markets by as little as 0.1 percent without a statistically significant effect. The magnitude and statistical significance of this effect is similar across all specifications (columns 5-8) and is similar for sorghum (Columns 9-12). Taken 15
together, these results are consistent with the idea that even limited intertemporal storage may act as a buffer on local market producer price fluctuations for less perishable commodities.

A concern with the producer price data used in these estimations is the number of missing observations. Because demand for staple grains and cowpea is relatively constant throughout the year, consumer price data are readily available for each market and each month (Aker 2010). In contrast, farmers in Niger do not have sufficient stocks to sell throughout the year. As a result, producer price data are not available for some markets during certain periods of the year, especially during the season immediately prior to the annual harvest.

To check the robustness of our results to potential selection bias generated by missing data, we re-estimate equation (15) in two different ways. We first use a two-stage Heckman procedure, estimating a selection equation on market-pair data and adding the resulting inverse Mills’ ratio as a separate regressor to equation (15) (Heckman 1979). These results are shown in the supplemental appendix (Tables S1 and S2, available at <u>http://wber.oxfordjournals.org/). The results for cowpea are similar in</u> magnitude and statistical significance to those reported in Table 5. We also re-estimate equation (15) using a balanced panel of market pairs that have a full set of price data for all time periods in the sample (Table S2, columns 5-8). The point estimates are slightly smaller, but the coefficient estimates remain significant at the 1 percent level.

It is important to note that our results for millet _producer_ price dispersion differ from the results of Aker (2010), who found that mobile phone coverage reduces _consumer_ price dispersion for millet by 10 percent (Aker 2010). Both findings are consistent if millet is stored primarily in production areas. When there are insufficient stocks in consumer markets, unanticipated demand shocks are a source of price fluctuation. The effect of shocks on consumer prices can only be smoothed if traders can quickly determine where to send more supplies. Using the theoretical model in reverse, by facilitating the efficient allocation of millet from producer to consumer markets, mobile phones can explain the reduction in price dispersion across consumer markets.

16
Table 6 presents results based on equation (16) using the max-min producer price spread (in CFA/kg) of markets across a region as the dependent variable (Jensen 2007). The key independent variable of interest is the intensity of mobile phone coverage within a region ( _mobilepercentr,t_ ) rather than the coverage status of a particular market or market pair. Controlling for year and region fixed effects, we find that an increase in the density of mobile phone coverage within a region leads to a reduction of 26 CFA/kg in the max-min price spread of cowpea producer prices (column 1), with a statistically significant effect at the 10 percent level. There is no statistically significant impact on the max-min producer price spread for millet (column 2) or sorghum (column 3). Although the results in Table 6 are not directly comparable to those in Table 5, they demonstrate that our findings are not an artifact of the dyadic specification _._

# **_Heterogeneous Effects of Mobile Phone Coverage_**

From our theoretical model, the effect of mobile phone coverage on producer price dispersion should be larger for markets and time periods in which access to information reduces the spatial misallocation of traders across markets. We expect coordination failures among traders to be strongest when search costs are high, i.e., when markets are distant and transport costs are large because of poor quality roads. In addition, we expect trader miscoordination to be highest at times of the year when markets are thin (i.e., outside of the harvest period). Heterogeneous effects by market type, whether surplus or deficit, depend on the commodity. For semi-perishable crops, both deficit and surplus markets experience shocks that cannot be smoothed by storage and thus benefit from better spatial arbitrage. For less perishable crops such as millet and sorghum, inter-temporal arbitrage reduces the potential benefits from spatial arbitrage. Because grain storage in Niger is undertaken predominantly by market agents near surplus markets, we would therefore expect mobile phone coverage to reduce millet and sorghum price dispersion primarily in deficit markets, with ambiguous predictions for cowpea.

Table 7 estimates the heterogeneous effects of mobile phone coverage on producer price dispersion for cowpea.<sup>22</sup> The regression specifications are similar to those in Table 5, except for the inclusion of the interaction term and the sub-group of interest. Column 1 looks at heterogeneous effects 17
by distance, a binary variable that is equal to one if markets are more than 350 km apart. Consistent with the theoretical predictions, the interaction term is negative and statistically significant at the 1 percent level, implying that mobile phone coverage reduces price dispersion by 7 percent for markets located more than 350 km apart, compared to a 5 percent reduction for markets in closer proximity. Column 2 includes an interaction term between mobile phone coverage and a binary variable for paved roads. Although the coefficient is positive and consistent with the theoretical predictions, this effect is not statistically significant at conventional levels. Looking at the impact by period of year (column 3), the coefficient on the interaction term is positive and statistically significant, suggesting that mobile phone coverage reduces producer price dispersion _less_ during the harvest period compared with other seasons, again in agreement with theoretical predictions. Finally, columns 4 and 5 introduce an interaction term between mobile phone coverage and market type (“surplus” market), which is defined as a market with surplus production.<sup>23</sup> In column 4, the surplus variable is equal to one if both markets in a pair are surplus markets and 0 otherwise. In column 5, the variable is equal to one if one market is a surplus market and the other is a deficit market. The coefficient estimates are negative, suggesting that the reduction in price dispersion is stronger in surplus markets compared with deficit markets. The magnitude of the effect is small, however, and is not statistically significant in column 5. We find qualitatively similar results for millet and sorghum (Table S3).

# **_Effects on Gross Trade Margins and Producer Price Levels_**

The previous results show that the introduction of mobile phone coverage reduced cowpea producer price dispersion, with stronger effects for markets where there was potential miscoordination among traders. With the introduction of mobile phone coverage, improved information flows are expected to reduce traders’ costs. With sufficient competition among traders, reduced trade costs should reduce the average gross trade margin, that is, the difference between average consumer and producer prices in a market.<sup>24</sup> As a result, consumer prices should fall on average, and/or producer price levels should rise. This effect should thus only affect price differences between geographically distinct surplus and deficit markets, especially if inter-temporal arbitrage is not feasible.

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To investigate whether mobile phone coverage affected gross trade margins, we estimate equation (15) using the absolute value of the difference in logged consumer prices in market _j_ (a deficit market) and logged producer prices in market _k_ (a surplus market). The sample thus only includes market pairs with a deficit and surplus market. In this specification, _β_ 1 measures the percentage reduction in the gross trade margins associated with the introduction of mobile phone coverage.<sup>25</sup> In order to understand how mobile phone coverage affected the components of the gross margin, we also estimate the following equation, (17) , with two dependent variables: (1) the log of producer prices in surplus market _j_ in month _t_ and (2) the log of consumer prices in deficit market _j_ in month _t_ . In this specification, _mobilej,t_ equal is a binary variable to one at month _t_ if market _j_ has mobile phone coverage and 0 otherwise. is a vector of control variables thought to affect producer price levels on market _j_ , such as the occurrence of drought. The _θj_ ′s are market fixed effects, controlling for geographic location, urban status, and market size, and _θ_ t are time fixed effects (either monthly or yearly) that control for time-varying aggregate factors. Standard errors are clustered at the market level. We also use the intra-annual price coefficient of variation of commodity _i_ on market _j_ at year _t_ as an alternative measure of producer welfare to producer prices.

Table 8 presents the regression results for gross trade margins and equation (17) for cowpea (columns 1-4) and millet (columns 5-8). We find no statistically significant effects of mobile phone coverage across markets on gross trade margins for cowpea (column 1), millet (column 5), or sorghum (not shown). One possibility is that the efficiency gains (i.e., economizing on search costs) were not large enough to yield a statistically significant reduction in gross margins. It is also conceivable that the net margins improved for traders but that this improvement did not occur during our study period.

Turning to the regression results for equation (17), we find no effect of mobile phone coverage on producer price levels in surplus markets for cowpea (column 2), millet (column 6), or sorghum (not shown). For deficit markets, we also do not find any statistically significant effects of mobile phone 19
coverage on consumer prices for any of the commodities.<sup>26</sup> These findings are in line with our earlier findings that gross margins did not fall, even for cowpeas. They are also consistent with our model, which predicts a reduction in the spatial variance of prices but not necessarily an effect on average prices across surplus or deficit areas.<sup>27</sup> Using the intra-annual coefficient of producer price variation as the dependent variable, we find that mobile phone coverage reduces the average intra-annual coefficient of variation by 6 percent for cowpea, with a statistically significant effect at the 1 percent level (column 4). Given that the pretreatment intra-annual coefficient of variation of cowpea price is 26 percent, this represents a significant reduction in intra-annual cowpea price risk. We find negative but not statistically significant effects for millet (column 8) and sorghum (not shown). These findings are also in line with the model predictions in that better spatial arbitrage reduces week-to-week price variation, but only when this role cannot be assumed by inter-temporal arbitrage.

A limitation of the market-level data is that mobile phone coverage at the village level was still relatively limited by 2008. Thus, we might be underpowered to estimate effects on producer prices levels. To gain more insight into this issue, we use the farm household survey data collected by one of the authors in 2009, by which time mobile phone coverage had begun to reach more rural areas. Across the 100 villages in our sample, all had mobile phone coverage by the end of 2009, and 30 percent of the farmers in these villages owned mobile phones. Although there are obvious limitations to using this crosssectional survey, the data complement the market-level data.

Our dependent variable of interest is the log of producer prices received for a variety of commodities, namely millet, sorghum, cowpea and peanut. The estimating equation is (18) , where _Y_ j is the log price of commodity _i_ received by farmer _j_ , _mobileown_ is a binary variable for whether the household owns a mobile phone, _Xj_ is a vector of farmer-specific covariates to control for factors that potentially affect mobile phone ownership and prices received (such as land ownership, asset ownership, 20
gender, and ethnicity), and _α_ is a set of village fixed effects. Farmers with a phone may access price information more easily, but this will not be reflected in a higher producer price unless farmers can use this information for arbitrage. _β_ 1 may be biased upward if mobile phone ownership is correlated with unobserved factors, such as motivation or intrinsic marketing ability.

Table 9 shows the results for equation (18). For all commodities except peanuts, mobile phone ownership is not associated with a significantly higher price received (Panel A).<sup>28</sup> These findings are similar to Fafchamps and Minten (2012), who show that a mobile phone-based price information intervention in India is not associated with a higher producer price levels.<sup>29</sup>

# **_Mechanisms_**

To understand the mechanisms behind our market-level results, we look for direct evidence of information acquisition and arbitraging behavior among different market agents. Our model suggests that mobile phone coverage improved _traders’_ access to information on producer prices in different markets, thereby facilitating spatial arbitrage. At the same time, mobile phone coverage may improve _farmers_ ’ access to information, thereby allowing them to engage in spatial arbitrage.

Tack and Aker (2014) measure the impact of mobile phone coverage on traders’ behavior, primarily focusing on their search behavior. Using the trader-level dataset, they find that the duration of mobile phone coverage is associated with an increase in traders’ numbers of search markets and the number of market contacts, with statistically significant effects. Although they do not provide direct evidence of a change in traders’ arbitrage behavior, the results do not appear to be driven by traders’ selection into mobile phone markets, changes in the composition of traders, or increased collusion among traders.

Table 9 (Panel B) provides some suggestive evidence of the impact of mobile phone coverage on farmers’ behavior using a specification similar to equation (18). Although the coefficient estimates are biased, we find that mobile phone ownership is associated with an increase in farmers’ probability of searching for price information and that mobile phones became a more useful source of such information. Unlike the trader results, farmers with mobile phones do not increase the number of search markets or the 21
number of sales and purchase markets.<sup>30</sup> This finding suggests that despite increased access to information, farmers in Niger did not change their marketing behavior.

# **Conclusion**

This paper provides some estimates of the nature, magnitude, and distribution of the effects of mobile phones on producer market performance in Niger. Although mobile phone coverage did not reach remote rural areas during the period of our sample, it reduced the spatial dispersion of producer prices for cowpea, a semi-perishable commodity. It did not affect spatial price dispersion for millet or sorghum, two storable grains. We also find a stronger reduction in producer price dispersion in remote markets and during periods when markets were thin. The reduction in price dispersion did not increase average producer prices, but it did reduce the intra-annual price variation for cowpea.

This paper provides additional empirical evidence of the importance of informative signals for market efficiency and the differential impacts by crop. Combined with the results in Aker (2010) and Tack and Aker (2014), our results indicate that the introduction of mobile phones generated net efficiency gains in agricultural markets in Niger. However, we find no evidence suggesting that these gains translate into higher average prices for the primary suppliers of these commodities, rural farmers.

These findings are central to the current debate on the role of information technology in promoting economic development. Information technology is often considered a low development priority. However, some believe that by reducing communication costs over long distances, mobile phones can reduce poverty among rural households. The results presented here indicate that the impact of technology can differ substantially by the type of crop, the type of market, and the time of year, even within the same country. Differences in impact can be linked to differences in arbitrage opportunities and market behavior for these crops and between agents.

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# **Notes**

1 This section draws heavily on Aker (2010).

> 2 Aker (2008) shows that grain traders store an average of one week, with larger traders (wholesalers) storing for an average of one month. Storage duration data were not available by commodity. Farmer-level data collected by one of the authors suggests that farmers primarily produce cowpea as a cash crop and sell soon after the harvest, thus implying limited storage, and store millet and sorghum for longer periods.

> <sup>3</sup> In surveys with agricultural traders and producers between 2005 and 2007, an overwhelming majority stated that they did not access or use price information provided Agricultural Market Information System (AMIS), primarily because of the type of data (only consumer prices are provided) and the timing of the data diffusion (the data are provided weekly, in some cases six days after a market).

4 Based on one of the author’s interviews with mobile phone service providers in Niger. The primary priority borders were those in the southern areas of the country (Nigeria, Burkina Faso, and Mali) rather than the north (Libya, Algeria).

> <sup>5</sup> Figure 1 is similar to Figure A1 Aker (2010), but it extends the data until 2008 and adds data on road quality. 6 Since 2008, mobile phone coverage and adoption have expanded considerably into rural areas in Niger. The 2009 survey conducted by one of the authors revealed that mobile phone ownership had reached 29 percent in rural areas. 7 Jensen (2007, 2010) develops a model outlining the welfare implications of costly search under exogenous supply, focusing primarily on the producer’s perspective.

8 Agricultural traders in Niger also typically relied upon personal travel to obtain price information prior to the introduction of mobile phones.

9 In 2008, a two-minute call to a market located 10 km away cost US$.50, compared to US$1 for round-trip travel using a market truck or cart.

10 This scenario encompasses two possibilities. The first is that traders and markets are placed at regular intervals on a lattice or Taurus, and traders cover partially overlapping geographical areas. The second is that _n_ traders cover the same _n_ producer markets and sell in the same consumer market.

> 11 Systematic differences in _F_ ( _q_ ) across markets would generate systematic differences in average price. However, in the first approximation, this should not affect the spatial integration of prices.

12 We only consider symmetric equilibria (thus ruling out a situation whereby traders coordinate on a public randomization device), which implies that the only Nash equilibria is for each trader to randomize among each market with equal probability.

13 To illustrate our model, assume that traders receive no information about market prices, and imagine that the agricultural commodity can be stored by farmers. Consider farmers in market _m_ who are offered a low price because few traders happen to visit market _m_ on that day. Rather than selling at a low price, they can store and sell later, when more traders visit the market. In this case, intertemporal arbitrage will smooth prices in market _m_ across time (Williams and Wright 1991). As a result, prices in different markets cannot diverge simply because the number of traders who visit each market varies in a stochastic manner.

14 Because sorghum requires more rainfall than millet, it is grown in fewer geographic locations in Niger, primarily in the southern areas of the country. As a result, sorghum price data are available for fewer markets and during fewer periods and hence are subject to more missing observations than the cowpea or millet producer price data. 15 These data were obtained from the _Syndicat des Transporteurs Routiers_ , the _Direction de la Météo_ in Niger, and the trader survey. The data are the same as those used in Aker (2010), but are extended for an additional two years (from 2006 to 2008).

16 Various dependent variables have been used in the literature to measure price dispersion. The consumer search literature has used the sample variance of prices across markets over time (Pratt, Wise, and Zeckhauser 1979), the coefficient of variation (CV) across markets (Eckard 2004; Jensen 2007), or the maximum and minimum prices across markets (Pratt, Wise, and Zeckhauser 1979; Jensen 2007). The international trade literature has used the log of the price ratio between two markets or the standard deviation of price differences across markets (Engel and Rogers 1996; Parsley and Wei 2001; Ceglowski 2003; Aker 2010). We adopt the latter approach for our core specification, but we also use the CV and the max-min as alternative specifications.

17 In all specifications, “treatment” is defined as the presence of a mobile phone tower rather than mobile phone adoption (Aker 2010).

18 A market’s urban status did not change between 1999 and 2008, so this is controlled for by including market pair and market fixed effects. Road quality in Niger was fairly stagnant during the time period under consideration. In 1995, Niger had 3,526 km of paved roads, increasing to 3,761 km in 2008, with the primary improvement occurring 23
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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