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1. Why “Leapfrogging” in Frontier Markets Isn’t Working

Author:
Bright Simons
Publication Date:
03-2019
Content Type:
Working Paper
Institution:
Center for Global Development
Abstract:
Just before the yuletide of 2018, I arrived in my native Ghana after one of my long spells away. I flipped out my phone, opened Uber, and tried to flag a ride from inside the shiny new terminal of Accra’s international airport. After a couple of false starts I gave up, walked out, and headed for the taxi stand. In the many days that followed, this ritual repeated itself with remarkable regularity. Sometimes I got the Uber, but on as many occasions, I couldn’t. The reasons for the frequent failure ranged from curious to bizarre. The “partner-drivers” would accept the request. Then they would begin to go around in circles. Sometimes they would start heading in the opposite direction. On a few occasions they would call and announce that they were “far away,” even though their registered location was visible to me on the app and their estimated time of arrival had factored into my decision to wait. It would take me a whole week to figure out that the problem wasn’t always that many Ghanaian Uber drivers couldn’t use GPS all that well, or that they were displeased with fares. There were other issues that I’d left out of my calculation, such as my payment preference, which was set to “bank card” instead of “cash.” The drivers want cash because it allows them to unofficially “borrow” from Uber and remit Uber’s money when it suits their cashflow. Though Uber offers two tiers of service, the difference in quality appeared negligible. Even on the upper tier, it was a constant struggle to find an Uber whose air conditioner hadn’t “just stopped working earlier today.” As something of a globetrotter used to seamless Uber services in European and American cities, I found the costs of onboarding onto Uber as my main means of mobility in Accra onerous. Why is a powerful corporation like Uber, reportedly valued by shrewd investment bankers at $120 billion, with $24 billion in capital raised, unable to maintain even a relative semblance of quality in its product in Ghana? And in other African cities I have visited? It may seem bleedingly obvious why heavily digitalised Facebook, Twitter, Microsoft, and Google manage to deliver fairly uniform standards of product quality regardless of where their customers are based, whilst Uber, because of its greater “embeddedness in local ecosystems” and lower digitalisation of its value chain, fails. But in that seemingly redundant observation enfolds many explanations for why the innovation-based leapfrogging narrative in frontier markets, especially in Africa, unravels at close quarters.
Topic:
Development, Science and Technology, Governance, Digital Economy, and Emerging Technology
Political Geography:
Africa

2. The Machines Are Not So Easy to Ride: Another Take on Automation

Author:
Bright Simons
Publication Date:
03-2019
Content Type:
Working Paper
Institution:
Center for Global Development
Abstract:
In 1981, a former sheep farmer took a one-week crash course in computing, had an epiphany, and teamed up with a car tyres millionaire to form DJ AI. DJ AI announced a new artificial intelligence platform for sale at $600 that could build computer programs for customers. They named their game-changer “The Last One.” All users had to do was follow a set of screen menus, plug, and play, and bingo, they could do away with all those pesky system administrators and programmers. $6 million was spent marketing this powerful piece of magic on both sides of the Atlantic, sometimes to comic effect. Such dreams of software building software, literally cutting out the middleman, have recurred regularly since the 1960s in peaks and troughs, but we are still waiting. From the late 90s onwards, however, new data-driven approaches to automation, particularly so-called deep learning, and the involvement of many of the world’s smartest and most loaded companies, have begun to convince many level-headed analysts that this time it is going to be different. The technologies, we are told, can learn, and so it is about time we paid critical attention to the pace at which they are already and could even further turn upside down the world of work as we know it. Some of the most elegant attempts to evaluate and categorise the impact of these new capabilities on employment and income inequality can be found in papers by David Autor and his co-authors on labour market polarisation and the effect of computerisation on the market demand for skills. More than a decade after these papers were written, their core ideas and the schemas they proposed continue to inspire the framing of the issues in influential circles, making them the most cited in their writers’ corpus. The Economist is right to describe Autor’s seminal work as enormously influential. The elegance and rigorous use of data in these two persuasive treatises are not, however, enough to prevent one major convenient generalisation from weakening their key arguments. The generalisation in question emanates from the conflation of several different patterns of computerisation with “automation,” the replacement of human actors in the chain of work, which is then used as a proxy for technology diffusion and infusion into various modes of work, following in a tradition that also encompasses Goldin and Katz’s equally elegant formulation of the automation question as one of a contention between returns to skills versus returns to algorithms. Having taken “automation” as the predominant form in which modern technology manifests itself in the workplace, Autor and his collaborators then proceed to construct a spectrum of possibilities for technology’s infusion: complement, substitute, or bypass (CSB). In the CSB paradigm, modern technology in the workplace tends to complement super-skilled, high-earning, workers, in complex, adaptive, operations, thereby boosting their productivity and bargaining power; substitute for the contribution of most medium-skilled workers in many routine tasks, thus depressing their wage potential; and bypass low-skilled workers, such as drivers, waiters, and janitors, thus rendering their fate somewhat indeterminate even if their numbers grow. It is not difficult to see why Autor et al.’s extensive use of crosswalking across census-based industrial classification schemes and the Dictionary of Occupational Titles should encourage this neat stratification. Once something is coded, it acquires a hardness that confers rigour and opacity.
Topic:
Science and Technology, Automation, and Emerging Technology
Political Geography:
Global Focus