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Digitally Removing the Middleman for Development: Trouble Brewing in East African Tea?

11 February 2019 1 comment

How do new digital technologies enable firms to develop? One process often highlighted is disintermediation, where digital technologies allow firms to “cut out the middleman”. Exploring the Kenyan tea auction we suggest that these ideas need to be rethought. Digital technologies bring change, but may lead to more challenging conditions for smaller firms.

 

Kenya_mombasa_tea_auction_480_feb2012_2

The Mombasa auction. Source: Wikimedia Commons

 

One of the benefits often associated with digital technologies is the potential for disintermediation – or put more simply “cutting out the middleman”. This concept forms the basis for many hopes for development around digital technologies [1].

In the early days of digital technologies, it was found that they often failed to cut out the middleman due to the “digital divide” where digital skills, infrastructure quality and cost limited the use of technologies in smaller firms. But as firms have adopted technologies and with appropriate applications these foundational claims for digital development are important to revisit.

 

Digitalising the tea sector

Tea is an important export in East Africa and twice a week sellers come together in the Mombasa tea auction to trade tea with international buyers. The tea auction emerged during the colonial era, and with its antiquated traditions, slow speed, and accusations of corruption, there have been demands to move online.

An online auction would speed up the processes of trading by cutting out the middlemen in tea value chains (see below) and allowing tea producers to sell more directly to international buyers.

 

combined

Roles of middlemen in the tea value chain: The tea trade centre in Mombasa, home to the tea auction (left); tea tasting (middle); auction warehousing of tea lots (right).
Source: Photos courtesy of Laura Mann.

 

The auction seems a good fit for digital disintermediation in terms of economic models of transactions [2]. Trade is predictable with a limited number of traders and a strong sectoral governing body. With falling costs of online access in the region, a digital auction seemed viable, particularly as competitor regions such as Sri Lanka and India are already in the process of digitalising their auctions.

 

Challenges faced in the tea sector

While on paper the case seems promising, change has not taken place as expected. An “e-auction” trial was abandoned and over the past decade, digitalisation has been slow and frequently resisted.

In discussion with key stakeholders involved in the auction, we identified three challenges:

  • The nature of transactions: Tea transactions are often seen as generic and simple to trade, and so well suited to online exchange. But tea trading is becoming more complex.  Tasting the quality of tea, for example, is important to buyers who are mixing different teas together to produce retail products, and there is also a growth in value-added teas where buyers need extra information about ethical standards they want met. These factors make moving trading online more complex, where more complex factors need to be included in a digital system.
  • The types of institution: Well-established rules and governance in the tea sector limit the ability to reform the tea auction. The balance of power in sectoral bodies is often skewed towards middlemen, exactly those who might be cut out by digital technologies. This meant that any kind of reform was strongly resisted by sectoral bodies.
  • Middlemen adaptation: Eventually after much resistance, aspects of the tea auction were partially digitalised such as e-payments and digital auction catalogues. This did have an effect of reducing certain roles connected to the auction. But the intermediaries did not disappear. They adapted and took up new roles. For example, tea brokers who were previously important in facilitating payments repositioned themselves as providers of auction intelligence and price data for small tea producers.

A key finding related to these challenges was that international firms, dissatisfied with the slow pace of change, began to sidestep the auction by becoming involved in “direct sales” with selected producers, supported by digital technologies.

 

Making sense of digital disintermediation

The future for tea trade in East Africa is fragmentation which may be detrimental to smaller tea producers. Smaller tea producers were not connected enough to become part of “direct sales” with international firms. With the auction only slowly digitalising, it is falling behind as the centre of trading.

For the analysis of digital disintermediation, the case highlights the need for careful consideration of transactions: the nature of transactions, the role of institutions and potential externalities (such of adaptation of middlemen) [3]. These are factors that implementers might consider to better support small producers’ development outcomes from digitalisation – what are the institutional bodies that need to buy in? Which stakeholders should be considered? etc.

More than this though, a greater awareness of the way actors use their power as change occurs is crucial. Such an approach is very different from the abstract, economic approach normally used to explore digital disintermediation [4]. From this perspective a very different view of development emerges. In the Mombasa auction case, it has not been transformed. Through the challenges and strategic activities of more powerful actors, digital transactions are solidifying the relationships of those who are already well linked, and able to capture resources.

 

This post summarises a recent book chapter: ‘Making Sense of Digital Disintermediation and Development: The Case of the Mombasa Tea Auction’ by Chris Foster, Mark Graham and Timothy Mwolo Waema.

The chapter is part of the new MIT Press book ‘Digital Economies at Global Margins’. The book is available as an open-access PDF from the IDRC website

 

 

Notes:

[1] A good example is the World Development Report (2016) on ‘Digital Dividends’, but many other projects often uncritically assume similar concepts.

[2] In economics, disintermediation is often associated with transaction costs, and an analysis of how digital technologies change aspects of transactions costs: information costs (gathering information about transactions) and coordination cost (co-ordinating the exchange of goods) (e.g. Wigand 1997).

[3] The study of transaction costs can be split into two differing perspectives. The “neoclassical approach” focussing on the mechanics of transactions such as coordination and information costs, and “property rights approaches” which explore wider aspects of transactions such as rules, regulations and externalities (Allen 1999). We suggest that digital disintermediation has been too focussed on narrow “neoclassical” perspectives to date.

[4] Contemporary institutional analysis often explores political power and settlements in shaping institutions. We also stress this aspect here, highlighting the importance of power in shaping institutions, and in turn the outcomes of digital disintermediation.

 

 

References:

Allen, D.W. (1999) Transaction Costs, in Encyclopedia of Law and Economics, B. Bouckaert & G. De Geest (eds), Edward Elgar, Cheltenham, UK, pp. 893–926.

Wigand, R.T. (1997) Electronic Commerce: Definition, Theory, and Context. The Information Society, 13(1), pp. 1–16.

World Bank (2016) World Development Report 2016: Digital Dividends, World Bank, Washington, D.C.

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How Many Platform Workers Are There in the Global South?

29 January 2019 Leave a comment

In developing countries, there has been a rapid increase in the gig economy and in the presence of digital labour platforms: defined as “a set of digital resources – including services and content – that enable value-creating interactions between consumers and individual service-providing workers”[1].

But how many workers actually work for such platforms?

I am not going to provide a reliable answer to that question but I will give some kind of ballpark figure.

We start by dividing out two types of platform work: digital gig work that involves digitisable tasks like data entry, writing copy, web design, accounting, etc; and physical gig work that involves a physical task like taxi driving, food delivery, domestic work, etc.  A previous estimate[2], updated to account for growth, would be that there were something like 10 million active digital gig workers in the global South at the start of 2019 (and around ten times that number registered on digital labour platforms but with 90% of them inactive).

So how many physical gig workers are there?  I’m going to break this down by continent since the extent of physical gig work seems to vary significantly between the three main continents of the global South.

Africa

Calculations here are based on extrapolations from just two economies, and seek to take account of wealth and population[3].  Current research for the Fairwork project estimates around 30,000 physical gig workers in South Africa; about half in taxi-driving and the rest mainly in delivery and domestic work.  Estimates for Nigeria[4] plus re-use of some of the same ratios found in South Africa, suggest 20,000 such workers.  Accounting for GDP per capita and population suggests around 60 workers per US$1,000 GDP/capita and per 1 million population; i.e. per US$1bn GDP.  Multiplying up to the overall GDP of Africa produces an estimate of c.130,000 physical gig workers in Africa.  However, given there are at least 100,000 in Egypt alone, we can at least double that to 250,000.

Asia

Similar calculations can be undertaken in Asia, based on numbers associated with platforms in India and Indonesia.  Extrapolating from estimates for taxi-driving and food delivery platforms in India[5], I estimate around 2 million physical gig workers in India.  For Indonesia[6], the figure is closer to 1 million.  Accounting for GDP suggests around 800 workers per US$1bn of GDP.  Multiplying up to the overall GDP of Asia (excluding Japan) produces an estimate of roughly 18 million physical gig workers in developing Asia.

However, there is an alternative approach, which is to exclude China in this calculation, which produces a figure of 9 million, and then take at face value claims that Didi Chuxing employs 21 million physical gig workers in China[7].  This would lead to an estimate of 30 million physical gig workers in developing Asia.

Latin America

Here, I’ve taken a simpler approach based on some national and continent-wide estimates of taxi driving[8] and then re-using ratios from the South Africa work.  This produces an estimate of something like 2 million physical gig workers in Latin America.

Summary

The basis for these estimates is flimsy, and the extrapolations are worse, so please attach a strong health warning to this material.  Better still, come up with some improved statistics.  But my ballpark figure is that there are at least 30 million platform-based gig workers in the global South; 10 million digital and just over 20 million physical.  And that the figure could be more than 40 million, which would be around 1.5% of the global South workforce.

A proportion of these workers are not relying on this as their primary source of income.  For digital gig workers, this number is anything from two-thirds to a half[9].  It may be somewhat less for the physical gig economy, so another ballpark would be that around 15-20 million workers in developing countries are relying on digital platforms for their primary source of income.

(Annual turnover is an issue for another day but, globally and summing figures for the digital gig economy[10] and main physical gig platforms Uber[11] and Didi Chuxing[12], it must be at least US$50bn.)

 

[1] Adapted from Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—Platforms and Infrastructures in the Digital Age, Information Systems Research, 29(2), 381-400

[2] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[3] An alternative approach would seek to extrapolate in terms of numbers of Internet users but that is correlated with GDP, and the figures still point to a strong under-representation of Africa in platform labour and strong over-representation of China.  Put another way, factors other than wealth and Internet access are needed to explain national differences in the proportions working in the platform economy.

[4] E.g. https://www.vanguardngr.com/2018/08/uber-monthly-passenger-base-in-nigeria-hits-267000/ and https://technext.ng/2018/08/17/max-ng-3-5-things-should-know-about-ride-hailing-platform/

[5] E.g. https://qz.com/india/1385653/uber-ola-drivers-pay-the-price-for-indias-fuel-price-rise/ and https://www.livemint.com/Companies/cYbdfsYk93HFhMuC0XgaNN/Swiggy-Zomato-hike-delivery-boy-salaries-as-competition-gro.html and https://economictimes.indiatimes.com/small-biz/startups/newsbuzz/zomato-swiggy-and-ubereats-paying-higher-cash-on-delivery/articleshow/65142563.cms

[6] e.g. http://buscompress.com/uploads/3/4/9/8/34980536/riber_7-s1_sp_h17-051_59-67.pdf and https://www.thejakartapost.com/academia/2018/11/21/the-gig-economy-and-skills-traps-in-indonesia.html

[7] E.g. https://technode.com/2018/03/19/didi-1-5-billion-abs/ and https://www.sustainabletransport.org/archives/6317

[8] E.g. https://www.reuters.com/article/us-uber-brazil/uber-rival-apps-join-forces-in-brazil-to-stem-tide-of-regulation-idUSKBN1D71KE and https://www.ft.com/content/7bf04e08-1d63-11e8-aaca-4574d7dabfb6 and https://www.globalfleet.com/en/smart-mobility/latin-america/news/chile-imposes-regulations-ride-hailing-companies and https://www.forbes.com/sites/jonathanmoed/2018/12/20/is-uber-operating-illegally-in-its-fastest-growing-region/#74c69e161925

[9] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[10] Heeks, R. (2017) Decent Work and the Digital Gig Economy, GDI Development Informatics Working Paper no.71, University of Manchester, UK

[11] E.g. https://www.cnbc.com/2018/08/15/uber-q2-2018-revenue-bookings-slow-slightly.html

[12] E.g. https://kr-asia.com/losing-300m-in-2017-didi-chuxing-wants-to-turn-a-profit-in-2018-amid-fierce-competition

Social Media Analytics for Better Understanding of the Digital Gig Economy

27 April 2018 2 comments

Owing to the proliferation of digital platforms facilitating online freelance work such as Upwork, Fiverr and Amazon Mechanical Turk, the number of digital gig workers has been continuously increasing worldwide. In 2015, there were as many as 48 million digital gig workers [1]; between 2016 and 2017, a 25% increase in the number of such workers was reported [2].

Digital gig work is indeed attractive to many, with a number of benefits that such independent workers are perceived to enjoy, e.g., flexible working hours, reduced transportation costs, wide range of projects to choose from. However, there exist potentially distressing issues, e.g., lack of job security, tough competition, substandard wages, which are especially pronounced in developing country settings [3]. Whereas traditional media such as news were unable to pinpoint or bring attention to these concerns, social media analysis–done manually by Cision in 2017–provided a window to the thoughts of independent workers which led to the fine-grained identification of issues that they are faced with [4].

As part of the currently ongoing Social Media Analytics Research and Teaching @ Manchester (SMART@Manchester) project funded by the University of Manchester Research Institute (UMRI), we aim to automatically gain insight into people’s perceptions of digital gig work, based on their posts on social media platforms such as Twitter and Facebook, as well as on review sites such as Glassdoor.

Specifically, we wish to test the currently prevailing assumption that digital gig work is experienced differently in the Global South compared to the Global North. Workers tend to make comparisons with their local benchmarks (i.e., office-based work), and it is believed possible that in the Global North, digital gig work is worse than prevailing benchmarks, whereas in the Global South it is better.

The following are some of the research questions that will be addressed as part of this case study.

  1. How do digital gig workers feel about their jobs?
  2. Which topics pertaining to decent work standards do they frequently talk about?
  3. Are there any differences—in terms of sentiments and topics—across different geographic locations, or across genders?

The first question can be answered by opinion mining while the second is addressable by topic identification. To determine whether there are differences with respect to opinions and topics, between the Global North and South or between genders, results from opinion mining and topic identification need to be combined with social media content metadata (e.g., geographic locations). 

In the way of opinion mining, we are currently investigating the use of an automatic emotion identification tool called Illuemotion which was developed by University of Manchester final-year Computer Science student, Elitsa Dimova. The web-based tool, a screenshot of which is provided below, is underpinned by a neural network model that analyses tweets to determine the most dominant emotions expressed, which can be any of anger, fear, joy, love, sadness, surprise and thankfulness.

The image below shows one of the tweets directly fetched by the tool from Twitter (via their API) when supplied with “#upwork” as input query. The tweet, which speaks of hidden dangers of being a digital gig worker, was detected by Illuemotion as expressing sadness and fear. One of our next steps is to apply the tool on a collection of thousands of tweets to allow us to analyse them across different geographic regions as well as genders.

As we are analysing data that pertains to human emotion, ethical considerations are being taken into account, especially bearing in mind that we also do not wish to compromise any of the digital gig workers who are social media users. For example, many Twitter users are unaware that what they post publicly can be used to identify or (reverse) look them up. They also have a right to be forgotten (i.e., they can delete their posts as well as their accounts). Overall what this means for us researchers who make use of their data is that in scholarly publications, we should provide only aggregated results and ensure that we do not include any identifiable information. These and other ethical considerations were discussed in detail in the recently concluded symposium in the Academy of Management Specialised Conference on Big Data entitled, “Ethical and Methodological Considerations for Management Research in the Digital Economy” held at the University of Surrey from the 18-20th April.

As well as two other SMART@Manchester case studies, the above described research questions on perceptions of digital gig work and our proposed approaches will be presented in the upcoming 4th International Workshop on Social Media World Sensors (Sideways 2018) co-located with the 15th European Semantic Web Conference to be held in Heraklion, Crete, Greece from the 3rd-7th June.

References:

[1] Kuek, S.C. et al. (2015) The Global Opportunity in Online Outsourcing. World Bank, Washington, DC. Available at: http://documents.worldbank.org/curated/en/138371468000900555/The-global-opportunity-in-online-outsourcing

[2] Lehdonvirta, V. (2017) The online gig economy grew 26% over the past year, The iLabour Project, Oxford Internet Institute. Available at: http://ilabour.oii.ox.ac.uk/the-online-gig-economy-grew-26-over-the-past-year/

[3] Heeks, R. (2017) Decent Work and the Digital Gig Economy: A Developing Country Perspective on Employment Impacts and Standards in Online Outsourcing, Crowdwork, etc, Centre for Development Informatics, Global Development Institute, University of Manchester. Available at: http://hummedia.manchester.ac.uk/institutes/gdi/publications/workingpapers/di/di_wp71.pdf

[4] Rubec, J. (2017) Study: The Dark Side of the Gig Economy, Cision. Available at: https://www.cision.com/us/2016/12/the-dark-side-of-the-gig-economy/

Industry 4.0 to Digital Industrialisation: When Digital Technologies meet Industrial Transformation

23 April 2018 1 comment

As digital technologies increasingly permeate all aspects of our physical world, many believe that we are moving into a hyper-connected, intelligent society and economy. One of the emerging concepts underpinning this potential transformation is the Fourth Industrial Revolution, or Industry 4.0.

What is Industry 4.0?

According to the proponents of Industry 4.0, each industrial revolution has shifted manufacturing opportunities and patterns of specialisation, enabled by key technological developments as illustrated in Figure 1.

industry 4.0 timeline2

Figure 1. Industry 4.0 trajectory (source: Author based on [1] and [2])

The vision of Industry 4.0 includes digitalising all elements of industrial activities to achieve a highly flexible, distributed production and service network. Through advancements of technologies such as Artificial Intelligence (AI), advanced automation and robotics, 3D printing, big data and Internet of Things, a tighter integration of digital and physical elements will allow machine-to-machine interactions and a mode of operation that provides more efficient production. In an absolute Industry 4.0 world, every object and all machinery in the factory will be interconnected to share data and operate without much human presence [2].

This of course, is only viable when an advanced level of technological, social and economic integration occurs. Given that technologies progress at an unpredictable rate, and that their real-world applications often lead to unexpected outcomes, it is difficult to know how (or whether) industry 4.0 will manifest. Nevertheless, recent studies warn us that this industrial change can drive uneven global development even further.

Shifting focus from manufacturing to “digital industrialisation”

AI and robotics may take 800 million jobs by 2030 in the world, and emerging economies such as China and India could be hit the hardest, losing 236 and 120 million jobs by 2030 respectively [3]. The costs of operating robots and 3D printers in furniture manufacturing in the US is predicted to be cheaper than Kenyan wages in 2033 [4], indicating that the lower labour cost may no longer be the main attribute ensuring competitiveness in a global market.

Given that industrialisation has long been considered to play a vital role in economic growth of developing countries, the development implications of this transformation have been mainly discussed in manufacturing, albeit with a negative perspective: changing patterns and geography of production [2] (such as re-shoring manufacturing back to high-income countries) and technological unemployment in labour-intensive manufacturing industry [4].

However, I would like to bring more attention to the development of the “digital” side of this industrial transformation – which I will refer to as digital industrialisation. This is a work-in-progress concept that encompasses not only the technological integration of digital technologies into manufacturing, but also the extensive re-organisation of an economy to digitalise production processes.

Some work on this has already been carried out within the DIODE (Development Implications of Digital Economies) network [5], but we need more research to build a better picture of the current and future landscape: for example, how digital industrialisation can take place in small-scale, localised production networks in the global South [6] and how the economic models emerging within the digital economy (such as platform economy and gig economy) may impact innovation and manufacturing processes globally [7].

I will further argue that this impending industrial transformation is best understood as a continuous process rather than a goal to reach – something that terms such as industry 4.0 tend to project. Rather than focusing on the potential winners and losers in this race, we need to elucidate how this transformation can take place in an inclusive and sustainable manner.


[1] Lasi, H., Fettke, P., Kemper, H. G., Feld, T. and Hoffmann, M. (2014) ‘Industry 4.0.’ Business and Information Systems Engineering, 6(4), pp. 239–242.

[2] Hallward-Driemeier, M. and Nayyar, G. (2018) Trouble in the making? The future of Manufacturing-led development. Washington, DC: The World Bank.

[3] Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., Sanghvi, S., (2017) Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute.

[4] Banga, K. and te Velde, D. (2018) Digitalisation and the future of manufacturing in Africa. London: Overseas Development Institute.

[5] Bukht, R. and Heeks, R. (2017) Defining, conceptualising and measuring the digital economy. GDI Development Informatics Working Paper 68. Centre for Development Informatics, University of Manchester, UK.

[6] Seo-Zindy, R., & Heeks, R. (2017) ‘Researching the emergence of 3D printing, makerspaces, hackerspaces and fablabs in the global south: A scoping review and research agenda on digital innovation and fabrication networks‘, Electronic Journal of Information Systems in Developing Countries, 80(1), pp. 1–24.

[7] UNCTAD (2017) The ‘new’ digital economy and development, UNCTAD Technical Notes on ICT for Development no.8. Geneva: United Nations Conference on Trade And Development.

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