Automation is likely to impact on developing countries in different ways to the way automation affects high-income countries. The poorer a country is, the more jobs it has that are in principle automatable because the kinds of jobs common in developing countries—such as routine work—are substantially more susceptible to automation than the jobs that dominate high-income economies. This matters because employment generation is crucial to spreading the benefits of economic growth broadly and to reducing global poverty. We argue that the rise of a global “robot reserve army” has profound effects on labor markets and structural transformation in developing countries, but rather than causing mass unemployment, AI and robots are more likely to lead to stagnant wages and premature deindustrialization. As agricultural and manufacturing jobs are automated, workers will continue to flood the service sector. This will itself hinder poverty reduction and likely put upward pressure on national inequality, weakening the poverty-reducing power of growth, and potentially placing the existing social contract under strain. How developing countries should respond in terms of public policy is a crucial question, affecting not only middle-income developing countries, but even the very poorest countries.
KeywordsAutomation Digitization Labor-saving technology Developing countries Economic development Jobs
A specter is haunting the industrialized and developing world—the specter of automation. 1.8 bn jobs or two-thirds of the current labor force of developing countries are estimated to be susceptible to automation from today’s technological standpoint, according to the World Bank (2016). Employment generation is crucial to spreading the benefits of economic growth broadly and to reducing global poverty. And yet, emerging economies face a contemporary challenge to traditional pathways to employment generation: automation, digitization, and labor-saving technologies.
A broad range of international agencies have recently flagged such issues relating to the future of employment, and the consequences of automation and deindustrialization in their global reports (ADB, 2018; Hallward-Driemeier & Nayyar, 2017; ILO, 2017; IMF, 2017; UNCTAD, 2017; UNDP, 2015; UNIDO, 2016; World Bank, 2013, 2016) and the International Labor Organization (ILO) has launched a Global Commission on the Future of Work. Employment prospects have also come into sharp focus because of the contested experiences of “premature deindustrialization” (Palma, 2005; Rodrik, 2016) and weakening employment elasticities of growth.1
There is currently significant and rising interest in these issues in the scholarly community (see e.g. Acemoglu & Restrepo, 2017; Arntz, Gregory, & Zierahn, 2016; Grace, Salvatier, Dafoe, Zhang, & Evans, 2017; Mishel & Bivens, 2017; Mokyr, Vickers, & Ziebarth, 2015; Roine & Waldenström, 2014), in the reports of international agencies (see references above), and in the private sector too (Frey, Osborne, & Holmes, 2016; McKinsey Global Institute, 2017a, 2017b; PWC, 2017; World Economic Forum, 2017). Moreover, the topic has also captured the public interest, reflected by a mushrooming of media reports and popular science books on the issues (e.g. Avent, 2017; Brynjolfsson & McAfee, 2011, 2014; Harari, 2016; Srnicek, 2017, to name but a few). Despite this increasing interest, the effects of automation in particular remain highly contestable and understudied with respect to developing economies, given that most research has focused on high-income Organisation for Economic Co-operation and Development (OECD) countries such as the United States.
These are, however, not only OECD country issues (see discussion of Ahmed, 2017). The World Bank (2016, pp. 22f.) estimates that “the share of occupations that could experience significant automation is actually higher in developing countries than in more advanced ones, where many of these jobs have already disappeared.” However, they note that the impact will be moderated by wage growth and the speed of technology adoption. There are numerous estimates of job displacement and much in the way of gray literature. However, these estimates are based on contestable assumptions and analysis of developing countries is often limited.
Furthermore, in contrast to a widespread narrative of technological unemployment, a more likely impact in the short-to-medium term at least is slow real-wage growth in low- and medium-skilled jobs as workers face competition from automation. This will itself hinder poverty reduction and likely put upward pressure on national inequality, weakening the poverty-reducing power of growth, and potentially placing the existing social contract under strain, or even possibly limiting the emergence of more inclusive social contracts. How developing countries should respond in terms of public policy is a crucial question, affecting not only middle-income developing countries, but even the very poorest countries given the automation trends in agriculture.
1.2 The Contribution and Structure of This Book
In light of the above, the objective of this book is to do the following: First, to outline a set of schools on economic development and revisit the Lewis model of economic development; second, to sketch the contemporary context of deindustrialization and tertiarization in the developing world; third, to survey the literature on automation; and in doing so discuss definitions and determinants of automation in the context of theories of economic development and assess the empirical estimates of employment-related impacts of automation; fourth, to characterize the potential public policy responses to automation and fifth, to highlight areas for further research in terms of employment and economic development strategies in developing countries.
The book is structured as follows. We set the scene in Part I (Chapters 2 and 3). We discuss the context for contemporary economic development in the developing world. Specifically, Chapter 2 gives an overview of schools of economic development theory and revisits the Lewis model of economic development. Chapter 3 then outlines the contemporary context of deindustrialization and tertiarization in the developing world to set the scene.
In Part II we focus on the emergence of automation and the drivers, implications for economic development and issues for developing countries. Chapter 4 discusses the trends in technology and discusses definitions and determinants of automation. Chapter 5 discusses the effect of automation on economic development and employment in developing countries from a theoretical perspective. Further, it analyzes existing empirical forecasts of automatability and global patterns. Chapter 6 considers the public policy responses proposed. Finally, Chapter 7 concludes and highlights areas for further research in terms of employment and economic development strategies in developing countries.
Heintz (2009) examines employment growth and the productivity growth rate in 35 countries between 1961 and 2008, and finds that increases in the productivity growth rate slow down the rate of employment growth, and that this pattern is getting stronger over time. In the 1960s, a one percentage point increase in the growth rate of productivity reduced employment growth by just 0.07 percentage points. However, in the 2000s, that same one percentage point increase in the growth rate of productivity reduced employment growth by a substantial 0.54 percentage point. Several possible explanations are as follows: (i) it could be that increases in productivity over time are reducing the employment elasticity of growth; (ii) it could be that the proportion of wage labor is increasing; or (iii) it could be that increases in real wages, employers’ social contributions, or strengthening labor institutions are raising unit labor costs and dampening employment creation, though this is ambiguous in empirical studies. A meta-review of 150 studies of labor institutions (Betcherman, 2012) covering minimum wages, employment protection regulation, unions and collective bargaining, and mandated benefits) with an emphasis on studies in developing countries, found that in most cases, effects are either modest or work in both directions in terms of productivity.
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