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Breaking Down Information Barriers: the Effects of Mobile Phones on Agricultural Output.

Evidence from a Randomised Experiment in Niger.

Thesis submitted in partial fulfilment of the requirements for the

Degree of Master of Science in Economics for Development

at the University of Oxford by

June 2014

By

454708

Word Count: 9800

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Abstract

Over the past decade the growth rate of the number of mobile phones in Sub-

Saharan-Africa has outstripped any other region. At the same time its agricultural

sector has been underperforming. This paper estimates the impact of mobile

phones on agricultural outcomes at the household level in Niger. It looks

specifically at the impact of a mobile phone intervention on the total value of

agricultural produce and on the specialization of crop production, evaluated using

the Herfindahl-Hirschman index. The dataset used is collected by a randomised

intervention delivered by the Catholic Relief Service from 2009 to 2011. Villages

were selected to host the “ABC programme” and a literacy programme giving

mobile phones to groups of five to share along with lessons on their use,

individuals were randomly selected to participate. Surveys were then conducted of

the 1044 adult students who took part across 95 villages. To test the hypotheses a

Difference-in-Differences technique is used, followed by OLS estimation, to

estimate the intent to treat. Results show that the ABC programme had no impact

on the total value of agricultural produce, unless the participants initially owned a

mobile phone. This could be due to a number of constraints prohibiting the

benefits of mobile phones, such as infrastructure, credit constraints and market

imperfections. Overall this paper finds the mobile phone revolution may be less

revolutionary than previously assumed.

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CONTENTS

INTRODUCTION...........................................................................................................4

SECTION I: ECONOMIC THEORY AND MODEL SPECIFICATION............7

(i) The Channel..............................................................................................7

(ii) Model Specification................................................................................12

(iii) Hypotheses..............................................................................................14

SECTION II: DATA......................................................................................................15

(i) Intervention.............................................................................................15

(ii) Data Summary.........................................................................................15

SECTION III: RESULTS..............................................................................................19

(i) Empirical Results ...................................................................................19

(ii) Constraints ..................................................................................................27

CONCLUSION................................................................................................................29

REFERENCES................................................................................................................30

APPENDIX......................................................................................................................32

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Introduction

Economists used to treat technology as a residual term, something

immeasurable and mostly unknown; hence Solow’s famous description of this

as “the measure of our ignorance” (Solow, 1956). Over the past decade or so,

this has changed. For example, it may now be possible to use the recent

introduction of mobile phones to Sub Saharan Africa (SSA) to help us

investigate how one particular technology, Information Communication

Technologies (ICTs), has impacted growth on a macroeconomic level but also

welfare at the microeconomic level. A unique aspect of mobile phone

technology is its availability to those at the bottom of the wealth pyramid. This

is due to the low level of operation skills, initial investment and recurrent costs

required. This widespread availability allows mobile phones to be used as a

policy tool focussed on the poor; one crucial for developing economies. Roughly

60% of the world’s mobile phones are in developing countries, of which Africa

is the fastest growing mobile phone market (Zanello, 2012).

Moreover, 75% of the world’s poor live in rural areas, indicating the vitality of

the agriculture sector as a core engine for growth (World Bank, 2008). GDP

growth initiated from agriculture has twice the effect of reducing poverty as

GDP growth originating outside the agriculture sector (World Bank, 2008).

Agriculture is a prominent sector in developing economies, yet there is a

significant productivity gap between developed and developing countries.

Furthermore the value of output per worker in non-agriculture is roughly

double that in the agricultural sector (Gollin, Lagakos and Waugh, 2013). This

suggests improvements in agricultural productivity could have a significant

impact on welfare in developing economies. Mobile phones could be a

potential source of this increase in productivity.

The key research question of this paper is how mobile phones impact

agricultural outcomes, specifically the value of agricultural production and the

level of crop specialisation. There has been a recent literature focusing on the

effects of the introduction to mobile phones in SSA since the early 2000s.

Economists including Aker (2013), Zanello (2012), Batzilis et al (2010), have

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argued that mobile phones lead to a break down in information barriers in turn

leading to positive outcomes for agricultural markets. Abraham (2007) for

example states “information is power”. The literature suggests mobile phones,

through this breakdown of information barriers, enable farmers to profit-

maximise in a more efficient manner. This could be in terms of the inputs they

use and choice of crops, or through the output channel. Information can be

used to decide when and where to sell their output or receive price information

and in turn should lead to an increase in the value of output produced.

However my hypothesis is that due to a number of constraints mobile phones

do not lead to this breakdown of information barriers, and hence do no impact

on the value of agricultural produce. Another channel to investigate is whether

the programme impacted the crop mix of farmers. For example farmers may

choose to specialise more in certain types of crops that they have heard are

more profitable. The Herfindahl Hirschman Index (HHI) is used as a

measurement of specialisation to test the second hypothesis that mobile

phones do not lead to a change in specialisation.

It is difficult to measure profitability of agriculture. For example it is unclear

how to value inputs such as labour, the wage rate may be inappropriate as the

majority of smallholder labour is family. Ideally we would use the shadow

price, yet it is unclear how to calculate this. Moreover there exist hidden costs

in farmer’s budget constraints; such as travel to market and cultural barriers.

Yields are often used when looking at agricultural outcomes; however this is a

poor proxy for profitability as there is vast heterogeneity across plots (Suri,

2011). This may be why we hit a paradox of what seems to be technically

efficient crops and technologies; such as fertilizer, yet low take-ups (Duflo et al,

2008). My research contributes to the debate by focussing on the value of

agricultural production as opposed to solely the yields.

Data from Niger, collected by Aker and Ksoll (2013), is used to test the

hypotheses. Niger is ranked last on the Human Development Index rankings,

with roughly 85% of its population living on less than $2 per day. Furthermore

80% of its population works in agriculture, which yields only 40% of the

country’s GDP. With such a substantial agriculture sector and so many people

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living in poverty, any increases in productivity in this sector could lead to

significant changes in welfare. Between 2001 and 2006, mobile phone service

became available (Aker, 2010).

The data I am using to explore my research questions is the “ABC Programme”

dataset collected in Niger in the years 2009-20111. The intervention gave a

mobile phone to groups of 5 people alongside running literacy programmes

teaching the groups how to use the phone. The intervention was delivered by

the Catholic Relief Services, who assigned the ABC programme randomly to 58

of 113 villages that were already receiving a basic numeracy and literacy

programme (Aker and Ksoll 2013). Surveys were then conducted of 1044 adult

students who took part across 95 villages. The random element to the data

assists in solving for potential endogeneity issues.

The hypotheses are tested using a number of Difference-in-Difference models

with varying specifications. Aker and Ksoll (2013) show that the use of mobile

phones in this sample had a positive impact on the number of crops produced,

however this paper shows it had little impact on the value of agricultural

output and on crop specialisation (HHI). Results hold when the data is

stratified by region and gender. Results show however that the ABC

programme did have a positive impact on the total value of agricultural

produce for individuals who owned a mobile phone prior to the intervention,

or those with access to a market. The lack of average positive effects on the

agricultural outcomes, suggest that there are a number of constraints

preventing the benefits of mobile phones. These could include infrastructural

constraints, cultural constraints and credit constraints. Despite the enthusiasm

surrounding mobile phones in recent literature, these results suggest that the

mobile phone revolution may be less revolutionary than previously thought.

The paper is structured as follows: firstly the economic theory linking mobile

phones to agricultural produce is outlined, followed by the model specification

used to test the hypotheses, followed by a description of the data, the empirical

results and finally a conclusion.

1 Jenny Aker kindly shared her dataset and STATA programmes from her recent

paper Aker and Ksoll (2013) on which I based my paper furthering their research.

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Section I: Economic Theory and Model

Specification

(i) The Channels

There exist two potential channels through which mobile phones can impact

agricultural output. The first is the input channel: information on input options,

availability and prices leads to better input choices. Increased connection with

the input market leads to higher yields and reduced costs (Mittal, Mehar,

2012). Mobile phones break down information barriers, informing the farmer

how to better profit maximise and increase productive efficiency. Aker (2010b)

adds that farmers are able to find private information about agricultural

technologies with a lower than average search cost; improving farmer’s

learning. The reduction in search costs for this input information is shown in

Figure 1. The second channel is the output channel; output information leads to

better selling decisions, in terms of market locations, output prices and supply

decisions (Mittal, Mehar, 2012). This increases market efficiency and lowers

search costs. The overall impact could consist of a mixture of both of these

channels.

Overall there has been a limited amount of empirical research showing the

impact of mobile phone technology on agricultural output. Lio and Liu (2006)

look at the macroeconomic perspective and estimate agricultural production

functions for 81 countries from 1995 to 2000. They create an ICT variable

including both internet and mobile phone access. By using FGLS estimation

techniques they find ICT has a positive significant effect on agricultural

productivity.

There exist a variety of approaches to studying the impact of mobile phones at

a microeconomic level. Aker and Ksoll (2013) use the ABC dataset to find the

effect of the mobile phone intervention on agricultural yields and crop choice in

the paper from which this research builds upon. They use a treatment effects

analysis to find that mobile phones have a statistically significant effect of the

number of crops grown. They found the intervention led to a 0.34 increase in

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Figure 1: Cost of Searching for Information, per search in Niger

Source: Aker, (2010b), Pg 24

the number of crops grown, significant at the 1% level. This effect decreases to

0.27 when fixed effects are included, and significance falls to the 5% level.

They also find the ABC programme leads to a statistically significant increase

in the likelihood of growing Okra, although no other crops were affected. When

stratifying the data by region they find the more industrial region of Dosso is

the only region impacted by the intervention and grows a greater variety of

crops. Furthermore they find females are impacted more by the intervention,

and grow a greater variety of crops. Specifically they are more likely to grow

Peanut, Okra and Vouandzou. They also find that the impact of the programme

is statistically significant only if the individual owned a mobile phone prior to

the intervention.

Although there are a few positive, statistically significant results from Aker and

Ksoll (2013) research, the effects depend on the model specification and some

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yield the ABC programme and mobile phones to be insignificant. Extending the

Aker and Ksoll research by focussing on different dependent variables will be a

useful exercise to determine the success of the intervention, and the benefits

mobile phones in Niger, along with checking the robustness of their results.

Among this literature there has often been a lack of focus on profitability of

crops, and more focus on simply production and yields. Despite collecting

price information Aker and Ksoll (2013) did not investigate the impact of the

ABC intervention and mobile phones on profitability of agricultural

production. Profitability is extremely difficult to calculate, and due a lack of

input data this paper will instead analyse the impact of mobile phones on the

value of agricultural produce. By comparing the Aker and Ksoll’s (2013)

results from quantities of production with results from value of production, we

generate an indirect method of learning about prices, and how these impact

farmers’s decision making. We also get a closer look at how the intervention

and mobile phones impacted the welfare of the farmers. Aker and Ksoll’s

(2013) research is also furthered by looking at the impact of the innovation on

an HHI-based measure of production specialisation.

Mittal and Mehar (2012) suggest that mobile phones lead farmers to diversify

and invest in high-value crops. Therefore we would expect the value of

agricultural produce to increase as a result of mobile phones, and the HHI, the

degree of specialisation, to decrease. They suggest that mobile phones improve

the spread of information to the agricultural sector alongside limiting

asymmetric information issues (Mittal and Mehar, 2012). Access to

information improves farmer’s decision making, improving allocative and

technical efficiency, which can lead to a further increase in technology

including fertilizer and machinery. The information needs of farmers include

what to plant, which seed varieties to use, contextual information and prices.

Mobile phones can help meet some of these needs and bridge the information

gap and reduce information asymmetry between large and small farms (Mittal

and Mehar, 2012). By using farmer surveys from 2011 they find the benefits

are; increased market connection, better prices and increased yield, as shown

in Figure 2. For example, Muto and Yamano (2009) find increases in sale prices

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of farmers by almost 20% due to mobile phones reducing asymmetric

information between farmers and traders (Muto and Yamano, 2009). Hence

suggesting mobile phones lead to an increase in the value of agricultural

produce.

Figure 2: Benefits of Mobile Based Information (Mittal and Mehar, 2012)

Source: Mittal and Mehar (2012) pg 234

There have been a variety of methods and models used to investigate the

impacts of mobiles phones on agricultural outcomes in the literature so far.

Zanello (2012) models the output marketed supply of different crops, where

the supply of each is determined by household characteristics, production

surplus, prices and fixed transaction costs of buying and selling. This model

predicts the role of ICT increases output market supply by reducing the

transaction costs. It is found that getting market information via mobiles has a

positive and significant effect on market participation, and radios have a

positive impact on the quantity traded (Zanello, 2012). Zanello’s results show

that households receiving price information via mobile phone reduce the

number of trades. He suggests this may be due to the farmer trying to maximise

profits from less transactions (Zanello, 2012). In this framework, mobile

phones are more likely to affect market participation by reducing search costs,

whereas radios seem to impact quantity traded and sales.

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Alternatively Nkegbe (2012) derives a stochastic frontier model to study the

effect of ICTs on production. The model is used to estimate production

frontiers, which take into account the stochastic characteristics of agricultural

production. The crop output of the farm is estimated using a vector of inputs

and a random term that accounts for measurement error and technical

efficiency. Their empirical analysis concludes there is potential to improve

productivity through increasing technical efficiency, which could be achieved

through ICTs.

Abraham (2007) suggests that mobile phones impact output through the

output channel. He uses evidence from the fishing industry in India to show

using mobile phones fisherman respond quicker to market demand, markets

become more efficient and risk and uncertainty is reduced. This leads to

greater market integration and surplus from increased productivity. Prices are

vital market signals transmitting all the information agents need to coordinate

resources efficiently (Abraham, 2007). When this mechanism is blocked

information costs increase, and signal response will be slower (Abraham,

2007). Economic theory, specifically “the law of price”, dictates that across

different areas there should be price equalisation of homogeneous goods net

transport costs (Abraham, 2007). You can therefore use price dispersion as a

measure of inefficiency in the market. For example Aker (2010) shows that the

roll out of mobile phones in Niger leads to a reduction of price dispersion by

10%.

In general a number of econometric issues arise when studying impacts of

mobile phones. Aker and Mbiti (2010) suggest that identification in past

literature suffers from endogeneity problems. For example, as welfare

increases people have more access to mobile phones, hence isolating causality

causes difficulty. Mobile phone rates are also subject to measurement error and

finding exogenous variables for the use of mobile phones is a challenge (Aker

and Mbiti, 2010). In this paper these endogeneity issues are resolved through

the use of data from a randomised intervention.

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As the past literature shows there are a variety of different approaches that can

be used to investigate the impact of mobile phones on agricultural outcomes.

This paper follows the strategy of Aker and Ksoll (2013) by using a Difference-

in-Differences model and treatment effect analysis and will focus the effect of

mobiles phones on the total value of agricultural output produced and the HHI.

(ii) Model Specification

The primary hypothesis is that mobile phones, after having received the

literacy course and the ABC intervention, had no impact on the total value of

agricultural produce due to a number of constraints. By assuming that any

selection into a treatment village is driven by characteristics whose effects on

agricultural outcomes is constant over time we can make use of a Difference-in-

Differences model. We can estimate the effects of mobile phones by comparing

changes in outcomes in treatment and control groups. The following

Difference-in-Difference models are used to investigate the hypotheses:

(1) 𝑌𝑖𝑣𝑡 = 𝛽0 + 𝛽1𝑚𝑜𝑏𝑖𝑙𝑒𝑣𝑡 + 𝛽2𝑙𝑖𝑡𝑒𝑟𝑎𝑐𝑦𝑣𝑡 + 𝛽3𝑚𝑜𝑏𝑖𝑙𝑒𝑣𝑡 ∗ 𝑙𝑖𝑡𝑒𝑟𝑎𝑐𝑦𝑣𝑡 +

𝛽4𝑐𝑜ℎ𝑜𝑟𝑡𝑣 + 𝛾𝑿′𝑖𝑣𝑡 + 𝜃𝑡 + 𝑢𝑖𝑣𝑡 ,

(2) 𝑌𝑖𝑣𝑡 = 𝛽0 + 𝛽1𝑚𝑜𝑏𝑖𝑙𝑒𝑣𝑡 + 𝛽2𝑙𝑖𝑡𝑒𝑟𝑎𝑐𝑦𝑣𝑡 + 𝛽3𝑚𝑜𝑏𝑖𝑙𝑒𝑣𝑡 ∗ 𝑙𝑖𝑡𝑒𝑟𝑎𝑐𝑦𝑣𝑡 +

𝛽4𝑐𝑜ℎ𝑜𝑟𝑡𝑣 + 𝛾𝑿′𝑖𝑣𝑡 + 𝜃𝑅 + 𝜃𝑡 + 𝑢𝑖𝑣𝑡 ,

(3) 𝑌𝑖𝑣𝑡 = 𝛽0 + 𝛽1𝑚𝑜𝑏𝑖𝑙𝑒𝑣𝑡 + 𝛽2𝑙𝑖𝑡𝑒𝑟𝑎𝑐𝑦𝑣𝑡 + 𝛽3𝑚𝑜𝑏𝑖𝑙𝑒𝑣𝑡 ∗ 𝑙𝑖𝑡𝑒𝑟𝑎𝑐𝑦𝑣𝑡 +

𝛽4𝑐𝑜ℎ𝑜𝑟𝑡𝑣 + 𝛾𝑿′𝑖𝑣𝑡 + 𝜃𝑉 + 𝜃𝑡 + 𝑢𝑖𝑣𝑡,

t=2009, 2010, 2011,

𝑚𝑜𝑏𝑖𝑙𝑒𝑣𝑡 is a dummy variable equal to one if the individual lived in an ABC

selected village and hence received the mobile phone programme, and zero

otherwise for village v and time t. 𝑙𝑖𝑡𝑒𝑟𝑎𝑐𝑦 𝑣𝑡 is a dummy variable equal to one

if they had received the literacy programme that year. Hence the interaction

term 𝑚𝑜𝑏𝑖𝑙𝑒𝑣𝑡 ∗ 𝑙𝑖𝑡𝑒𝑟𝑎𝑐𝑦 𝑣𝑡; the treatment variable, is a dummy variable equal

to one if the individual received the ABC mobile phone programme and the

literacy programme that year and zero otherwise. 𝑿𝑖𝑣𝑡 is the vector of control

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variables; gender and year dummies, for individual i in village v at time t . 𝜃𝑅

are sub-region effects used for stratification before randomisation, 𝜃𝑉 are

village fixed effects. 𝑢𝑖𝑣𝑡 is the idiosyncratic error, for individual i, village v and

time t. 𝑐𝑜ℎ𝑜𝑟𝑡𝑣 is a dummy variable equal to one if the village received the

literacy programme in 2010, zero if it received it in 2009. Year fixed effects are

also used for different survey rounds; 𝜃𝑡 . The standard errors are clustered at

the village level in all models.

The first dependent variable used in models (1) to (3) will be total value of

agriculture in current US dollars. This variable is created by multiplying the

crop yield with the last price received at market. The second dependent

variable used when looking at crop specialisation is HHI (which is defined

below). 𝛽3 is the primary coefficient of interest, which represents the effect of

being assigned to a village receiving the ABC intervention after having

received the literacy course. This represents the intent to treat (ITT) impact; of

being assigned to treatment. The ITT can be used in the presence of imperfect

compliance; individuals may have been assigned to the mobile phone

intervention but chose not to attend. It is assumed firstly that conditional on

the time invariant fixed effects, whether the individuals were placed in the

mobile phone intervention or not, is independent of the potential agricultural

outcomes, discussed in Section II(i). Secondly both the treatment and control

groups have the same time trends. This is not that strong an assumption in this

case seeing as the treatment community was randomly selected.

Three Difference-in-Differences models are used; models (1) to (3) as shown

above. The empirical strategy of this paper is to follow Aker and Ksoll’s (2013)

model specifications (2) and (3). This paper extends their analysis by also

including model (1), without fixed effects as a matter of comparison. Model (2)

includes sub regional fixed effects to control for the level of randomisation,

and model (3) controls for village level fixed effects. This strategy helps us

control for the impact of unobserved heterogeneity at the regional level and at

the village level, moving us closer towards observing the causal effect of the

mobile phone intervention on agricultural outcomes. Controls for all three

regressions include gender, year dummies and the cohort dummy. The

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Difference-in-Differences model is a preferred strategy as it controls for

potential selection effects, such as the access to market differences between

ABC and non-ABC villages. A number of robustness checks are completed,

stratifying the data by crop type, gender, region, baseline mobile phone

ownership and a proxy for access to market. These are important to determine

any heterogeneous treatment effects.

Aker and Ksoll (2013) look at the impact of the ABC programme on the

number of crops grown. I further this research by looking at specialisation

through calculating the HHI; a measure of crop specialisation at the household

level. Mittal and Mehar (2012) suggest that mobile phones lead farmers to

diversify and invest in high-value crops. Diversification would suggest that the

HHI would decrease. Alternatively if individuals used mobile phones to find

out the most profitable crop to invest in and instead chose to specialise the

HHI would increase. Yet constraints may limit this impact. The HHI in this

context is the sum of the squares of agricultural income shares derived from

different crop production, as shown in equation (4). This yields a

specialisation index ranging from zero; completely dis-specialized, to one;

entirely specialised. In equation (4) Pit is the value of production of the ith crop

in year t; an estimate for the income of agricultural produce from each crop.

(4) ∑ [𝑃𝑖𝑡

∑ 𝑃𝑖𝑡]

2

= HHI

(iii) Hypotheses

𝐻0: 𝛽3=0 𝐻1: 𝛽3≠0

Using models (1) – (3) the first hypothesis of this paper is that mobile phones

in the ABC intervention, having received the literacy programme, do not impact

agricultural outputs, specifically the value of agricultural produce due to a

number of constrains; 𝐻0 above. My second hypothesis, again following model

(1) – (3), is that the intervention has not impacted the HHI; representing crop

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specialisation. The models test both these hypotheses using a t-test of

𝐻0 against 𝐻1 .The data I will use to test the hypotheses is outlined below.

Section II: Data

i) Intervention

From 2009 to 2011 the Catholic Relief Services (CRS) delivered an adult

education programme consisting of eight months of literacy and numeracy

training. The CRS selected 134 villages to be trained, this was reduced to 113

villages that had mobile phone coverage, and half the villages were then

randomly assigned to a treatment year of 2009 or 2010. Within each year

villages were randomly allocated the basic non-ABC training (55 villages) or

the ABC training (58 villages). Individuals within the villages had to be eligible

to undertake the training, which required illiteracy, willingness, and

membership to a producers’ association and were chosen by lottery if there

were too many eligible candidates. The ABC programme added a mobile phone

module to the education package, teaching the adults how to use a mobile

phone. Mobile phones were distributed to be shared by five students each.

ii) Data Summary

The data I am using to test the hypotheses is the ABC Programme dataset

collected in Niger in the years 2009-2011. This dataset was collected and used

by Aker and Ksoll (2013) to investigate the impacts of mobile phones on

agricultural outcomes. A baseline, midline and end line survey was taken to

look at the impacts of the ABC programme. Surveys were conducted of 1044

adult students who took part across 95 villages. The individuals were chosen

by random sampling after sorting based on village and gender. The surveys

included sections on demographics, economic activity, price information, and

mobile phone use. Attrition seems not to be an issue in the data, less than 2% of

individuals did not complete the midline, and less than 1% did not complete

the endline.

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Table 1 and Table 2 show some useful descriptive statistics presented by Aker

and Ksoll (2013). The tables compare ABC and non-ABC villages at the baseline

providing evidence of the randomised village selection process, using t-tests on

the mean of the variables for both groups. The tables show intervention was

relatively successful in creating similar treatment and control groups as there

are few statistically significant differences between the treatment and non-

treatment groups, indicated in column 3. Table 1 shows average number of

crops cultivated since last season was 5, the most common crop produced was

millet, the most popular crop sold was Cowpea. Table 2 shows average

household size was 8 and roughly 30% adults had any form of education.

Almost 30% of the sample owned a mobile phone before the intervention.

There are only a number of significant differences in production; cowpea,

vouandzhu and gombo at the 5% level and millet being significant at the 10%

level. Despite only a few statistically significant differences, the Difference-in-

Differences specification protects against the possibility of non-randomisation

by assuming that selection into treatment is driven by characteristics whose

effects on agricultural outcomes is constant over time. One significant

difference between treatment and control that could be of worry is that the

ABC selected households were more likely to have market access in their

village, statistically significant at the 10% level. This could be correlated with

treatment and the total value of agricultural produce and is a limitation of the

analysis if the change in market access is not constant over time. One way to

manage this is to stratify the data on access to a market. Due to data limitations

a proxy variable is used to complete this analysis; whether the individual has

visited a market in the last week. Results are shown in the next section. For a

further robustness check I use propensity score matching to see if having

visited a market last week predicts treatment, results show it may be an issue.

Table A1 in the appendix describes the list of variables used in analysis.

Table 1: Baseline Outcomes: Descriptive Statistics (Aker and Ksoll, 2013)

ABC

Mean

Non-ABC

Mean

Difference ABC-non-ABC

General outcomes

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Number of agricultural crops cultivated in past season 5.500 5.616 -0.025 (0.113)

Number of cash crop markets visited 2.671 2.700 0.009 (0.112) Market price Gombo 260.400 259.574 18.192 (18.171) Market price Voandzhou 217.213 345.406 -146.572 (145.114) HH follows market prices 0.743 0.759 -0.017 (0.027)

Production

Millet 0.992 1.000 -0.006 (0.004)* Sorghum 0.799 0.781 0.014 (0.025) Cowpea 0.942 0.95 0.000 (0.013) Peanut 0.552 0.569 0.000 (0.035) Gombo 0.554 0.614 -0.057 (0.028)** Vouandzhou 0.653 0.672 0.014 (0.03)

Sales

Millet 0.352 0.361 -0.013 (0.029) Sorghum 0.089 0.090 0.010 (0.022) Cowpea 0.662 0.743 -0.061 (0.031)** Oseille 0.176 0.197 -0.013 (0.330) Peanut 0.469 0.505 -0.036 (0.040) Gombo 0.226 0.272 -0.052 (0.039) Vouandzhou 0.26 0.341 -0.082 (0.033)**

Quantity sold

Millet 192.235 185.934 39.031 (29.097) Sorghum 65.101 63.571 7.624 (12.490) Cowpea 164.533 162.111 10.65 (16.317) Peanut 373.692 350.612 -14.184 (53.261) Gombo 24.742 37.064 -7.630 (5.857) Vouandzhou 116.733 141.368 -22.584 (20.250)

Notes: Column 1 presents the mean for ABC villages, Column 2 presents the mean for non-ABC villages. Column 3 reports the coefficient from a regression of the dependent variable on an indicator variable for ABC and sub-region fixed effects to account for randomization. Results are robust to omitting the sub-region fixed effects. Standard errors are clustered at the village level. ***, **, * denote statistical significance at the 1, 5, 10 percent levels, respectively.

Source: Aker and Ksoll (2013) pg 20

Table 2: Baseline Descriptive Statistics (Aker and Ksoll, 2013)

ABC

Mean

Non-ABC

Mean

Difference ABC-non-ABC

Panel A: Socio-Demographic Characteristics

Age of respondent (in years) 37.146 37.892 -0.407 (0.936) Number of household members 8.319 8.431 0.009 (0.256) Number of adult household members 3.913 4.108 -0.121 (0.141) Number of female household members 4.131 4.151 0.038 (0.137) Percent children with education 0.268 0.279 0.000 (0.018) Percent adults with education 0.302 0.317 -0.013 (0.021) Number of asset categories owned 4.979 4.990 -0.031 (0.097)

18

Household owns mobile phone (1=Yes, 0=No) 0.295 0.297 -0.003 (0.027) Respondent has access to mobile phone 0.795 0.762 0.042 (0.022)* Used mobile to talk about trade in Niger 0.092 0.105 -0.007 (0.022) Respondent is member of village level association 0.640 0.634 0.008 (0.026)

Panel B: Agro-Pastoral Production

Household experienced drought in past year 0.612 0.643 -0.055 (0.044) Farming is respondent's primary occupation 0.862 0.880 -0.019 (0.016) Respondent member of a farmers' association 0.407 0.356 0.048 (0.033) Household received training in agricultural marketing 0.042 0.033 0.014 (0.011) Number of agricultural crops cultivated in past season 5.500 5.616 -0.025 (0.113) Livestock is a source of household income 0.908 0.900 0.000 (0.017) Number of livestock categories owned by household 3.177 3.121 0.053 (0.074) Household has sold livestock since previous harvest 0.583 0.542 0.059 (0.029)**

Panel B: Agro-Pastoral Marketing

Village has any market 0.330 0.170 0.154 (0.090)* Village has a livestock market 0.210 0.150 0.045 (0.079)

Nobs (households) 521 520 Nobs (villages) 48 47

Notes: Column 1 presents the mean for ABC villages, Column 2 presents the mean for non-ABC villages. Column 3 reports the coefficient from a regression of the dependent variable on an indicator variable for ABC and sub-region fixed effects to account for randomization. Results are robust to omitting the sub-region fixed effects. Standard errors are clustered at the village level presented in parentheses. ***, **, * denote statistical significance at the 1, 5, 10 percent levels, respectively.

Source: Aker and Ksoll (2013) pg 27

There are a few other limitations to consider. Firstly as mentioned before due

to the possibilities of non-compliance we can only look at the ITT as opposed to

the preferred Average Treatment Effect (ATE). Furthermore we could expect a

negative selection effect, as to be eligible for the programme you had to be

illiterate. This could lead to a positive biased ITT if we expect the intervention

to have more impact on the uneducated. Alternatively this could lead to the ITT

being biased downwards if we think the intervention instead will have a

stronger impact on the educated. In this case I think the latter is more

appropriate, hence a possible reason for our insignificant findings, also

supporting the fact that individuals who already owned a mobile phone made

better use of the intervention. The dataset also includes limited input variables

hence the decision to follow the empirical strategy of Aker and Ksoll (2013)

using treatment analysis rather than other models in the literature that use

production functions.

19

Section III: Results

(i) Empirical Results

The first results presented are from testing Hypothesis 1: mobile phones in the

ABC programme, after receiving the literacy programme, have had no impact

on total value of production due to a number of constraints. As outlined in the

empirical strategy three Difference-in-Difference models are used, the second

and third controlling for regional and village fixed effects respectively. Table 3

shows results using total value of agricultural produce in current US dollars as

the dependent variable. The coefficient of interest is Mobile*Literacy which

indicates whether the individual has received both the mobile phone and

literacy programme. Regression 1 shows that the programme led to an increase

of $68.31 of the total value of produce, statistically significant at the 10% level.

This is a relatively large economic effect, seeing as the mean at baseline of total

value of agricultural produce was $209. However, when we include fixed effects

the statistical significance of this result disappears, suggesting that there may

be unobserved heterogeneity in regression 1, at both the regional and village

level, causing upward bias. Regression 2, including fixed effects at the regional

level which controls for the level of randomisation, tells us that the ABC

programme increased the total value of agricultural produce by $50.60, again a

relatively large economic effect; however this result is no longer statistically

significant, and may still include bias from unobserved heterogeneity at the

village level. When we control for village fixed effects in regression 3, we see a

further decrease in the ITT estimation to $34 and again a statistically

insignificant result. These insignificant effects could be due to a number of

constraints of mobile phone benefits which are discussed in more detail in

section III(ii). All specifications show that being female reduces the total value

of agricultural produce by around $30, with regression (2) and (3) yielding

statistically significant results at the 10% level. The R squared for the first

model is particularly low, at 0.01 suggesting the other two specifications may

be more appropriate as they explain more of the variation in the data, after

controlling for fixed effects.

20

Table 3: Average Program Effects on Total Value of Agricultural Produce in Current US$

(1) (2) (3)

Mobile * Literacy 68.31* 50.60 34.00

(36.31) (39.54) (32.7)

Female -29.00 -31.50* -27.00*

(17.80) (17.20) (15.56)

Baseline mean 209 209 209

Controls Yes Yes Yes

Cohort dummy Yes Yes Yes

Sub-region fixed effects No Yes No

Village fixed effects No No Yes

Number of observations 3021 3,021 3,021

R2 0.01 0.10 0.10

Notes: Column 1 to 3 refer to the Difference-in-Difference models (1) to (3) in section I(ii). ***, **, * denote statistical significance at the 1, 5 and 10 percent levels, respectively. Robust standard errors are used, clustered at the village level. Controls include mobile, literacy, and year dummies.

When looking at the impact of the intervention on specific crops results show

again there is no statistically significant effect on the value of produce. This is a

useful exercise as the ABC programme may have heterogeneous effects on

different crop types and hence we may have observed the cancelling out of a

number of different effects in our previous regressions. These regressions were

run with the Difference-in-Differences model (2), controlling for regional

effects, to control for the level of randomisation. Table 4A shows the results

found by Aker and Ksoll (2013) of the impact of the ABC programme on

quantities of specific crops produced. Table 4B repeats the analysis using value

of the produce in current US Dollars as the dependent variable. The results

show that the programme had no significant effect on either the quantity or

value of the produce. Millet however is worth noting as the effect is relatively

large, seeing as the programme increased the value of millet produced by $48

and the baseline mean was $209, yet this is statistically insignificant. The lack

of statistically significant effects on either the quantity or value of the crops

produced could lead us to question the impact of the intervention and indeed

the impact of mobile phones.

21

Table 4A: Impact of ABC Program on Quantity Produced of Specific Crops,

Aker and Ksoll (2013)

Table 4B: Impact of ABC Program on Total Value of Specific Crops produced

in current $US

Mill et

Sorg hum

Cow pea

Pea Nut

Vouand zou

Mill et

Sorg hum

Cow pea

Pea nut

Vouand zou

Mobile * Literacy 193.62 -7.16 13.84 2.33 15.42 48.00 3.12 -10.35 -0.89 9.42

(133.28) (18.87) (16.45) (21.79) (13.11) (37.73) (2.13) (10.28) (2.88) (0.21)

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Cohort

Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Region fixed

effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Obs 2,991 2,991 2,991 2,991 2,991 3021 3021 3021 3021 3021

R2 0.18 0.07 0.19 0.34 0.33 0.01 0.03 0.02 0.07 0.10

Notes: The table present results the Difference-in-Differences model (2) from section I(ii). Sub-regional fixed effects control for the level of randomization. ***, **, * denote statistical significance at the 1, 5 and 10 percent

levels, respectively. Controls include gender and year dummies. Robust standard errors clustered at the village level.

Heterogeneous effects of the ABC intervention need to be considered; the

effects may depend on your characteristics, hence the data is stratified by

region, gender, baseline mobile phone ownership and access to market. Aker

and Ksoll (2013) find that when stratifying the sample the ABC programme has

a positive and significant effect on the number of crops produced for females,

individuals living in the region Dosso and individuals who owned a mobile

phone at baseline. This analysis is repeated looking at the total value of

agricultural produce in tables 5A-5D. Stratifying by region for both Dosso and

Zinder the ABC programme is found to have no statistically significant effect on

the total value of produce in current $US, shown in table 5A. The magnitude of

the coefficients overall seem to be not that economically significant in Zinder,

although in Dosso in regression (1) with no fixed effects the programme led to

an increase in total value of production by $102.40, an almost doubling,

although this was not statistically significant. The magnitude of this effect also

decreases as fixed effects are used, suggesting unobserved heterogeneity at the

regional and village level leading to an upward bias in the ITT. Moreover Aker

and Ksoll (2013) find that the ABC programme had a significant effect on the

22

number of crops produced by females. When we repeat the analysis using total

value of produce we find that the ABC programme had no statistically

significant effect for females as shown in Table 5B. However in regression (4),

the programme shows to have a statistically significant result at the 10% level

for males only, leading to an increase in value of $66.25. Although this result

weakens in both economic and statistical significance again once fixed effects

are used.

Table 5A: Impact of ABC on Total Value of Production Current $US by region

Dosso Zinder

(1) (2) (3) (4) (5) (6)

Mobile * Literacy 102.40 64.07 42.14 21.79 26.67 15.72

(62.14) (70.43) (77.10) (22.53) (20.32) (19.29)

Controls Yes Yes Yes Yes Yes Yes

Cohort Dummy Yes Yes Yes Yes Yes Yes

Sub-region fixed effects No Yes No No Yes No

Village fixed effects No No Yes No No Yes

Number of observations 1565 1,565 1,565 1,456 1,456 1,456

R2 0.02 0.04 0.08 0.03 0.06 0.12

Notes: Columns 1-3 report results for Dosso following model specifications (1) to (3) in section I(ii). Columns 4-6 represent models (1) to (3) respectively, reporting results for Zinder. ***, **, * denote statistical significance at the 1, 5 and 10 percent levels, respectively. Controls used are mobile, literacy, gender and time dummies. Robust standard errors clustered at the village level.

Table 5B: Impact of ABC on Total Value of Production Current $US by gender

Females Males

(1) (2) (3) (4) (5) (6)

Mobile * Literacy 71.11 50.00 29.23 66.25* 54.27 37.75

(53) (54.34) (61.87) (38.45) (41.98) (45.78)

Controls Yes Yes Yes Yes Yes Yes

Cohort Dummy Yes Yes Yes Yes Yes Yes

Sub-region fixed effects No Yes No No Yes No

Village fixed effects No No Yes No No Yes

Number of observations 1,538 1,538 1,538 1,483 1,483 1,483

R2 0.01 0.07 0.13 0.01 0.08 0.14

23

Notes: Columns 1-3 report results for females following model specifications (1) to (3) in section I(ii). Columns 4-6 represent models (1) to (3) respectively, reporting results for

males. ***, **, * denote statistical significance at the 1, 5 and 10 percent levels, respectively. Controls used are mobile, literacy, gender and time dummies. Robust standard errors

clustered at the village level.

Aker and Ksoll (2013) find that mobile phones have a significant effect on the

number of crops produced for individuals that initially owned a mobile phone. I

repeat the analysis for the total value of crops produced stratified by baseline

mobile phone ownership and find all specifications yield positive, statistically

significant results, shown in Table 5C. The ABC programme led to an increase on

the total value of produce of $160.66, $141.88 and $155.72 , for individuals that

owned a mobile phone before the intervention, significant at the 1%, 5% and

5% level, for models (1)-(3) respectively. The effect reduces slightly when fixed

effects are used yet still these are economically significant results compared to a

baseline mean of only $209. This suggests a heterogeneous treatment effect; the

ABC programme was more effective for those that initially owned a mobile

phone. This can be used to support the fact that a number of constraints exist

limiting the potential benefits of mobile phones. Arguably those that were able

to overcome those constraints already and hence owned a mobile phone were in

better stance to make use of the ABC intervention.

Table 5C: Impact of ABC on Total Value of Production Current $US by baseline phone ownership

Baseline mobile No baseline mobile

(1) (2) (3) (4) (5) (6)

Mobile * Literacy 160.66*** 141.88** 155.72** 27.24 7.25 -18.89

(55.32) (55.56) (69.75) (40.46) (45.67) (49.75)

Cohort Dummy Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes

Sub-region fixed effects No Yes No No Yes No

Village fixed effects No No Yes No No Yes

Number of observations 892 892 892 2120 2,120 2,120

R2 0.04 0.08 0.16 0.01 0.07 0.12

Notes: Columns 1-3 report results for those owning a mobile phone at baseline following model specifications (1) to (3) in section I(ii). Columns 4-6 represent models (1) to (3) respectively, reporting results for those without a baseline mobile phone. ***, **, * denote statistical significance at the 1, 5 and 10 percent levels, respectively. Controls used are

24

mobile, literacy, gender and time dummies. Robust standard errors clustered at the village level.

As mentioned earlier Table 2 showed access to market was significantly

different at the 10% level between the treatment and non treatment

communities. This may be correlated with unobserved variables that impact

the total value of agricultural produce. Therefore as a robustness check we can

stratify the data by access to market. Due to data limitations instead of

stratifying by market access a proxy variable is used; a dummy variable equal

to 1 if the individual visited a market last week and 0 if not. This is not an ideal

proxy variable however it can be assumed there is some positive correlation

between this and the unobserved variable market access. Table 5D shows that

the ABC programme had more of an impact on individuals who had visited a

market in the last week, hence were more likely to have access to a market at

baseline. In regressions (1) to (3) the programme increased total value of

produce by $134, $131 and $112 respectively, both at the 10%, 5% and 5%

significance level. These are economically significant results, over a 50%

increase in the value of production. This suggests that individuals with better

access to markets realise the benefits from mobile phones more than those

without, again suggesting there are constraints existing that limit the potential

gains from the intervention; market access being one of them. This also

suggests that one of the assumptions of treatment analysis, the similarity

between ABC and non-ABC villages, may lead to bias in our results seeing as the

access to market determines the impact of the ABC programme. To verify

whether this limitation holds a propensity score is created to investigate

whether having visited a market last week predicts treatment. Results for the

Probit regression are shown in Table 6, the dependent variable is the

probability of being in treatment where treatment is considered to have

occurred when the individual has received both the ABC and literacy

programme. This robustness check verifies the treatment has been delivered

randomly as gender, region and mobile phone ownership do not predict

treatment. The coefficients cannot be directly interpreted; however whether

the individual visited a market last week seems to have had some predictive

25

power, at the 1% level. If we do not assume that the change in market access

was constant over time, this limitation must be considered when determining

the usefulness of our results.

Table 5D: Impact of ABC on Total Value of Production Current $US by visit market in last week

Market Visit No Market Visit

(1) (2) (3) (4) (5) (6)

Mobile * Literacy 134.12*** 131.26** 111.58** 15.01 -6.66 -13.96

(44.48) (42.61) (44.23) (56.36) (64.20) (76.47)

Cohort Dummy Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes

Sub-region fixed effects No Yes No No Yes No

Village fixed effects No No Yes No No Yes

Number of observations 1314 1314 1314 1490 1,490 1,490

R2 0.02 0.09 0.17 0.01 0.06 0.11

Notes: Columns 1-3 report results for those having visisted a market in the past week following model specifications (1) to (3) in section I(ii). Columns 4-6 represent models (1) to (3) respectively, reporting results for those who did not visit a market in the last week. ***, **, * denote statistical significance at the 1, 5 and 10 percent levels, respectively. Controls used are mobile, literacy, gender and time dummies. Robust standard errors clustered at the village level.

Table 6: Propensity Score to predict treatment using Probit Model

(1)

Region 0.02

(0.02)

Female -0.05

(0.06)

Baseline mobile phone 0.02

(0.06)

Visit market last week -0.12*

(0.06)

Number of observations 2,795

Pseudo R2 0.00 Notes: The dependent variable is the probability of being in treatment, where treatment is when the

individual receives both the literacy and ABC programme. ***, **, * denote statistical significance at

the 1, 5 and 10 percent levels, respectively.

26

Aker and Ksoll (2013) looked at the impact of mobile phones on the number of

different crops produced. This analysis can be furthered by calculating the HHI

measure for crop specialisation and using this as the dependent variable. This

could be a potential channel through which the individual increases their

welfare by maximising profits from specialisation or diversification learned

through mobile phones. However the results shown in Table 7B suggest that

the ABC intervention had no significant effect on the HHI. Table 7A reports

Aker and Ksoll’s (2013) results of the effects of the ABC programme on the

number of crops produced, using model specifications (2) and (3). They find

that the programme increases the number of crops produced by 0.34 and 0.27,

statistically significant at the 1% and 5% level respectively. Columns 3 to 5

report the regression using HHI as the dependent variable, using model

specifications (1) to (3). This is a measure of crop specialisation as opposed to

just simply the number of crops planted. When using this measure the

programme seems to have no statistically or economically significant effects on

crop specialisation of the individuals. This suggests that despite individuals

increasing the number of crops they grow, this has not fed through to increased

diversification, seeing as the income from these new crops adopted has not

displaced the majority earnings from staple crops, such as millet.

Table 7A : Average Programme Effects on Number of Crops Grown (Aker and Ksoll)

Table 7B: Average Programme Effects on HHI

(1) (2) (3) (4) (5)

Mobile * Literacy 0.34*** 0.27** -0.03 0.00 0.01

(0.12) (0.11) (0.04) (0.04) (0.04)

Female 0.12* 0.11** 0.04 0.03 0.03*

(0.06) (0.05) (0.03) (0.03) (0.02)

Cohort Dummy Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes

Sub-region fixed effects Yes No No Yes No

Village fixed effects No Yes No No Yes

Number of observations 2,991 2,991 1,928 1,928 1,928

R2 0.18 0.23 0.02 0.20 0.27

Notes: Column 1 and 2 are Aker and Ksoll's (2013) results, using number of crops grown as the dependent variable, using model specifications (2) and (3) respectively. Columns 3 to 5 use HHI as the dependent variable

and use the model specifications (1) to (3) respectively. ***, **, * denote statistical significance at the 1, 5 and 10 percent levels, respectively. Controls are mobile, literacy and year dummies. Robust standard errors clustered at

the village level.

27

(i) Constraints

The ABC Programme was a well-designed intervention and could be described

as best practice considering its random selection techniques. The empirical

strategy has also involved best practice treatment effect analysis. With the

enthusiasm surrounding mobile phones and their potential in developing

countries it is perhaps a surprise that we do not find more positive results. The

statistically insignificant results suggest that the mobile phone revolution may

be less revolutionary than previously thought. It seems this literature suffers

from “publication bias” and presents a selective set of results. There may also

be an element of over-enthusiasm surrounding the impacts of mobile phones

and the effect they can have on the poor. Aker and Mbiti (2010) state there is a

tendency for donors and development agencies to become over enthused with

the impacts of ICTs, and invest before they have fully analysed the effects.

In reality there exist a number of constraints that may prohibit positive effects

of mobile phones on agricultural outcomes. Mittal and Tripathi, G. (2009)

suggest three major constraints to the benefits of mobile phones being realised

are; infrastructure constraints, credit constraints and capacity for risk taking.

In the ABC survey individuals were asked how many bars of mobile phone

signal they had on their mobile phone during the interview; ranging from zero

(no signal) to five bars (full signal). Figure 3 shows a histogram of the signal

and suggests that the infrastructure in Niger may not be sufficient to support

mobile phone signal, as zero bars of signal was the most commonly reported.

From India, Mittal and Tripathi (2009) find that the major infrastructure

constraints are lack of resources, inadequate irrigation, insufficient access to

markets and insufficient crop storage. Credit constraints can prevent an

individual from purchasing inputs even if they have received information about

their profitability using mobile phones. Finally for mobile phones to increase

agricultural value, farmers must be open to change and trying new farming

techniques or strategies. Hence social norms and culture may be a barrier to

the potential benefits of the ABC programme.

Figure 3: Histogram of bars of mobile phone signal from the ABC dataset

28

Price data is essential when the producer has to make a choice between several

markets to sell from (Abraham, 2007). If the producers do not have market

choice in the first place, mobile phones may have less of an impact in reducing

market inefficiencies. Abraham (2007) concludes that the impacts of mobile

phones are realised more so at the marketing end than the production end.

This is due to a relatively higher skill set at the marketing end, which could lead

to an increase in inequality in the short run within the industry. Despite mobile

phones being an intervention accessible to the poor, there may still be a

disparity of success, due mainly to differences in education.

Mittal and Mehar (2012) note in their paper that we have not yet seen the full

benefits of mobile phones, possibly due to difficulties in putting this new

knowledge into use. Indeed owning a mobile phone is not enough to increase

agricultural outcomes (Zanello, 2012). It is ensuring they are used for business

purposes in the right way that will realise their full potential. Mittal and Mehar

(2012) analysed this in greater depth through household surveys and found

that there is a need to establish supporting infrastructure and increase farmer’s

capacity do utilise the benefits from ICTs. They found constraints to increasing

farmer’s productivity including the availability of quality seed, pesticides and

fertilizers, along with labour constraints and poor infrastructure.

0.5

11

.52

2.5

Den

sity

0 1 2 3 4 5Number of bars of reception on cell phone

29

Hosman and Fife (2012) emphasise a key difference between the ability to

communicate using ICTs and using the technology to “access, process, use,

synthesize, and produce information and technology”. This represents a

difference between a communications revolution and reaching an “information

society”, that is able to not only communicate but process and generate

information from ICTs (Hosman and Fife, 2012). This requires an “information-

literate” society with unique skill sets, investments and infrastructure enabling

the full use of ICTs. Perhaps Niger and other developing countries do not have

this society yet.

Another explanation for the insignificant results is that the impact of the

intervention may be delayed due to the network effect; as the network of

mobile phones increases the benefits increase (Aker, 2010). Hence we may

have to wait until the mobile phone market in Niger grows until we see the full

benefits of the programme. This could be apparent in this intervention due to

its scattered nature across 53 villages. Mittal and Mehar (2012) suggest that

interventions reaching breadth appose to depth may suffer limitations due to

the nature of mobile phone benefits increasing with network size.

Conclusion

If our overall aim is to increase welfare of those in developing countries we

need to think carefully about how we measure the welfare of agricultural

households. It is a necessary step to move more towards estimating the welfare

of agricultural households through income and profitability as opposed to

production and yields. Although the ABC intervention shows mobile phones

had a number of successes regarding number of crops produced, it appears to

have had little impact on the value of the crops produced for those not initially

owning a mobile phone, suggesting welfare may have been less affected.

Furthermore when we look at crop specialisation as opposed to number of

crops the programme had little impact. With the intervention and analysis

following best practice it could be seen as a surprise that mobile phones had

little impact on the total value of the produce.

30

The fact that the ABC intervention did not lead to a statistically significant

effect on the value of production on average may be due to a number of

constraints; including infrastructure, credit constraints and capacity for risk

taking (Mittal and Tripathi, 2009). Market information from a mobile phone

may not be the binding constraint. Furthermore mobile phones are only

complements to a number of other investment opportunities; they should not

be seen as substitutes to other communication investment. If there is no

opportunity to invest in a higher quality seed, purchase different crops, or sell

at a higher price, then the mobile phone may have no effect. Until these

constraints are weakened, mobile phone interventions may have little impact

on agricultural outcomes. Policy makers should aim to invest in mobile phones

along with other forms of communication and infrastructure. It may be the case

that mobile phones offer an imperfect solution to deeper economic problems.

Indeed the mobile phone revolution may be less revolutionary than originally

anticipated.

It must be remembered that this is the evaluation of one intervention in one

country and it cannot be seen as proof that mobile phones do not increase the

value of agricultural produce. There are limitations to the data, including non

compliance and spillovers. Additionally country specifics in Niger must be

considered, it may be the network needs to increase before such mobile phone

interventions have a larger impact. For future research it may be useful to run

interventions having relaxed some of the constraints discussed to see if the

impacts of mobile phones increase.

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Appendix

Table A1: Variable definitions and Summary Statistics

Variable Description Obs Mean Std. Dev. Min Max

Totalvalueproduce scaled

the sum of quantity produced multiplied the price received at market last for each crop in Current US Dollars

3084 156.55 435.42 0 8064.85

HHI the Herfindahl-Hirschman index between 0 the least specialised and 1 completely specialised

1935 0.33 0.423 0 1

female dummy variable equal to 1 if the individual is a female and 0 otherwise

3021 0.51 0.5 0 1

33

region 3=Doutchi, 7=Zinder 3050 4.93 2.00 3 7

abcpost

mobile*literacy, =1 if completed both the ABC programme and literacy programme, =0 otherwise

3081 0.25 0.43 0 1

dc2010 dummy variable equal to 1 if the individual was in the 2010 cohort 0 otherwise

3084 0.46 0.50 0 1

littreat

dummy variable equal to one if the individual received the literacy programme equal to zero otherwise

3084 0.51 0.50 0 1

dy2009 dummy variable equal to one if year 2009 and zero otherwise

3081 0.34 0.47 0 1

dy2010 dummy variable equal to one if year 2010 and zero otherwise

3081 0.33 0.47 0 1

baselinecellphone Dummy variable equal to one of individual owned a mobile phone at baseline

3069 0.30 0.46 0 1

visitmarketlastweek Dummy variable equal to one if the individual had visited a market last week

2805 0.47 0.50 0 1