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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