The Rise of the Robot Reserve Army: Automation and the Future of Economic Development, Work, and Wages in Developing Countries

Schlögl, Lukas; Sumner, Andy

doi:10.2139/ssrn.3208816

Cited by 33 publications

(21 citation statements)

References 43 publications

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“…• Robotics and automation in the developing world-Commentators have pointed out the potential for robotics and automation in developing world agriculture (e.g. Tarannum et al 2015;Schlogl and Sumner 2018;Kushwaha et al 2016), but research is scarce. Farm labour is increasingly hard to find everywhere in the world, including developing countries.…”

Section: Discussionmentioning

confidence: 99%

Economics of robots and automation in field crop production

et al. 2019

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This study reviewed research published after 1990 on the economics of agricultural mechatronic automation and robotics, and identified research gaps. A systematic search was conducted from the following databases: ScienceDirect, Business Source Complete, Wiley, Emerald, CAB Abstract, Greenfile, Food Science Source and AgEcon Search. This identified 4817 documents. The screening of abstracts narrowed the range to a dataset of 119 full text documents. After eligibility assessment, 18 studies were subjected to a qualitative analysis, with ten focused on automation of specific horticultural operations and eight related to autonomous agricultural equipment. All of the studies found some scenarios in which automation and robotic technologies were profitable. Most studies employed partial budgeting considering only costs and revenues directly changed by the introduction of automation or robotics and assuming everything else constant. None examined cropping system changes, or regional and national impacts on markets, trade and labour demand. The review identified a need for in-depth research on the economic implications of the technology. Most of the studies reviewed estimated economic implications assuming that technology design parameters were achieved and/or based on data from prototypes. Data are needed on the benefits and problems with using automation and robotics on farm. All of the studies reviewed were in the context of agriculture in developed countries, but many of the world's most pressing agricultural problems are in the developing world. Economic and social research is needed to understand those developing country problems, and guide the engineers and scientists creating automation and robotic solutions.

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Section: Discussionmentioning

confidence: 99%

Economics of robots and automation in field crop production

et al. 2019

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“…Within the context of AI/R technologies, however, this so-called Lewis inflection point may no longer be reached in emerging economies if there were to evolve an unlimited "robot reserve army" which competes with the labour force of the poor (Schlogl and Sumner 2018). In adaption of the Lewis model of economic development, Schlogl and Sumner (2018) use a framework in which the potential for automation creates "unlimited supplies of artificial labor" particularly in the agri-cultural and industrial sectors due to technological feasibility. This is likely to push labour into the service sector, leading to a further expansion of the already large and low-productive service-sector employment.…”

Section: Ai/robotics Effects For the Poor In Employment Small Businementioning

confidence: 99%

AI/Robotics and the Poor

Braun¹,

Baumüller²

2021

Robotics, AI, and Humanity

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Artificial intelligence and robotics (AI/R) have the potential to greatly change livelihoods. Information on how AI/R may affect the poor is scarce. This chapter aims to address this gap in research. A framework is established that depicts poverty and marginality conditions of health, education, public services, work, small businesses, including farming, as well as the voice and empowerment of the poor. This framework identifies points of entry of AI/R, and is complemented by a more detailed discussion of the way in which changes through AI/R in these areas may relate positively or negatively to the livelihood of the poor. Context will play an important role determining the AI/R consequences for the diverse populations in poverty and marginalized populations at risk. This chapter calls for empirical scenarios and modelling analyses to better understand the different components in the emerging technological and institutional AI/R innovations and to identify how they will shape the livelihoods of poor households and communities.

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“…Deep learning has more recently made great progress in such applications as speech and language understanding, computer vision, and event and behavior prediction (Goodfellow et al, 2016). These rapid technological advances and the promise of automation and human-intelligence augmentation (Jordan, 2019) reignited debates on AI's impact on jobs and markets (Brynjolfsson et al, 2018;Samothrakis, 2018;Schlogl and Sumner, 2018) and the need for AI governance (Aletras et al, 2016;Benjamins et al, 2005).…”

Section: Ai: Machine Learning and Nlpmentioning

confidence: 99%

Deep Learning for Political Science

Chatsiou¹,

Mikhaylov

2020

The SAGE Handbook of Research Methods in Political Science and International Relations

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IntroductionPolitical science, and social science in general, have traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. More recently, increasingly available quantities of data are being combined with improved algorithms and affordable computational resources to predict, learn, and discover new insights from data that is large in volume and variety. New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories and evaluating the impact of interventions and programs in a more dynamic and effective way.Applications using large volumes of structured and unstructured data are becoming common in government and industry, and increasingly also in social science research.

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The Rise of the Robot Reserve Army: Automation and the Future of Economic Development, Work, and Wages in Developing Countries

Cited by 33 publications

References 43 publications

Economics of robots and automation in field crop production

Economics of robots and automation in field crop production

AI/Robotics and the Poor

Deep Learning for Political Science

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