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Rural Resilience Impact of ICTs-in-Agriculture

28 January 2020 Leave a comment

What impact do ICT-in-agriculture projects have on rural resilience?

To cope with short-term shocks (e.g. conflict, economic crisis) and long-term trends (e.g. climate change), rural areas in developing countries must become more resilient.  Yet we currently know very little about the impact that information and communication technologies (ICTs) can have on resilience-building.

To address this knowledge gap, we undertook a systematic literature review of 45 ICT4Ag cases from Africa and Asia.  We sought to understand both what the resilience impact of ICTs is, and why.

Measuring resilience using the RABIT (Resilience Assessment Benchmarking and Impact Toolkit) framework, current reported evidence suggests ICTs are strengthening rural resilience far more than weakening it.  But the impact is highly uneven.  Household resilience is built far more than community resilience, and there is a strong differential impact across different resilience attributes: equality in particular is reported as being undermined almost as much as enhanced.

In order to explain these outcomes, we developed a new conceptual model (as shown below) of the relationship between ICTs and resilience.  It highlights the importance of individual user motivations, complementary resources required to make ICT4Ag systems support resilience, and the role of wider systemic factors such as institutions and structural relations.

We make a series of recommendations for resilience policy and practice:

  • More equal focus on both household- and community-level resilience.
  • More attention to the resilience-weakening potential of ICTs.
  • Ensuring perceived utility of digital applications among rural users.
  • Encouraging use of more complex ICT4Ag systems.
  • Looking beyond the technology to make parallel, complementary changes in resource provision and development of rural institutions and social structures.

We also draw conclusions about the conceptualisation of resilience: the need for better incorporation of agency and power, and greater clarity on resilience system boundaries and indicators.

Overall, for those seeking to strengthen rural resilience through use of ICTs, the paper – “Impact of ICTs-in-Agriculture on Rural Resilience in Developing Countries” – offers new frameworks, new evidence, new practical guidance and a research agenda.

Ride-hailing Platform Asymmetries between Riders and Driver-partners in Lagos, Nigeria.

Platform companies, with the help of data, have the potential to formalise the processes of transport systems in global South cities, especially in African cities which are characterised with informal processes. For ride-hailing platforms, this will help improve safety and security for both drivers and riders and reduce the likelihood of workers who attempt to evade formal processes such as tax payments because information is accurately recorded and managed by algorithms. In our contemporary world today, data is important for several tech platforms including Uber, Airbnb and social media platforms. This process is known as datafication, which is defined as a significant feature of modern social life and society through “the drive to turn vast amounts of activity and human behaviour into data points that can be tracked, collected and analysed”1. However, there have been growing concerns about its impacts on society and user-groups at large – in this case, on platform workers/drivers in Lagos.

Registration on platforms like Uber and Bolt (Taxify) is asymmetrical for both workers and riders in Lagos. For workers, it is a stringent process which involves them providing critical details such as home address (which can be verified), vehicle details (plate numbers and vehicle numbers), licenses and at least two guarantors. For riders, registration on the platform is as simple as inputting a contact number, card details and home addresses (which cannot be verified). This has an implication on drivers’ safety, work ethic and motivation because they feel unequally treated. When data is not being equally represented on the platforms, it puts riders and platform companies in a greater position of power. Talking about drivers’ challenges, platform driver and Union president stated that:

Box 1 – Data misrepresentation
“To this present time we have 30 -35 of drivers been killed by riders because they don’t profile them well, many riders don’t use their correct names and don’t put correct information and they are collecting cars and killing people and that’s why we need government to regulate things, to open an account. Either you use BVN or use national ID or migrate their information with the road safety (use the driver’s license), so they are can know the perpetrator of these evil acts”2.

From observation, a rider can register with many accounts in search of trip discounts/bonuses or in rare instances when they may have been blocked by the platform. This makes it difficult to track riders who may have defaulted. It takes a 4.6/5 and 4.5/5-star rating for drivers to be blocked on Uber and Bolt respectively in Lagos, but until summer of 2019, there were no clear rating thresholds to discourage bad rider behaviours3. This has however not been observed in Lagos, especially on the Bolt platform, indicating a low barrier of entry and further perpetuating bad rider behaviours which demotivates drivers from working. Mr AY’s suggestion could be helpful in ensuring that riders are more accountable on platforms such that they are equally aware that bad behaviours cannot be tolerated.

The communication between platform companies and drivers in comparison to riders is also perceived as unfair to drivers, particularly during a conflict with a rider; algorithmic misinterpretation or network issue which can lead to an undercharged fare or overcharged fare especially on the Bolt platform (see figure 1); or drivers who are blocked without any clear explanation (see box 2). However, drivers highlighted that Uber is more organised and, in many cases, are more likely to reimburse drivers that may have been undercharged. This is hardly the case on the Bolt platform.

Figure 1: Undercharged fare for a Bolt trip
Source: Nigerian Platform drivers forum.
Box 2 – Poor driver relations and algorithmic misinterpretation
“…When I do fare review on Bolt, it takes them years to get back to me. Some riders will pay you that N500 (£1.1); some will tell you that it is promo and there is nothing you can do (for example see figure 1) …They have bad customer relations in our case. They would not check properly when there is a complaint from a rider but be so quick to block drivers. But in a reverse scenario, platforms respond quickly to riders” 4.
 
“The way they block drivers is something else, although some of our drivers are also funny too. But they should look at the history of the driver. Possibly, the driver might not be the issue, but you can call the driver and talk to the person…”. To summarise, While Uber does a good job sometimes in communicating and resolving issues, Mr Raz, went ahead to state how a rider reported him for being cheated for a trip which was originally N400 (£0.8), but the rider went to multiple destinations which was not accounted for. When he reported to Uber, only a fraction of his complaints was resolved 5.

These asymmetries can be resolved if algorithmic processes are closely supervised and communicated to drivers to ensure clarity. As Mr Raz advocates “… sometimes we need to interface more apart from texting on the app. The human angle should come in”. While it is a business that benefits massively from riders, drivers are equally important and should be treated fairly. The future of work hinges on the efficiency and transparency of platforms that might ensure that its processes are clear enough to create a fair working environment for its workers while meeting the needs of its customers.

References.

  1. Dencik, L., Jansen, F., Meltcafe, P. (2019). A conceptual framework for approaching social justice in an age of datafication.
  2. Fieldwork Interview with Platform Union President in Lagos Mr Ay, 2018
  3. Paul, K. (2019). Uber to ban riders with low ratings: will you pass the test? The Guardian. 1 June
  4. Fieldwork Interview with Platform Worker Mr Ed, 2019
  5. Fieldwork Interview with Platform Worker Mr Raz, 2018
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