The Economy and Digitalization – Opportunities and Challenges
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DECEMBER 2015 MÅRTEN BLIX The economy and digitalization – opportunities and challenges Arkitektkopia AB, Bromma, 2015 Arkitektkopia Författare: Mårten Blix Email: [email protected] www.martenblix.com Denna studie är gjord på uppdrag av Svenskt Näringsliv. Författaren står själv för allt innehåll. This report is written on behalf of the Confederation of Swedish Enterprise. The author is alone responsible for all content. THE ECONOMY AND DIGITALIZATION – OPPORTUNITIES AND CHALLENGES Preface If a software robot, such as IBM’s Watson – or some future variant thereof – were to write a report on the economic implications of digitalization, what would it look like? Unlike the author, it would be able to scan all the current works that reference auto- mation and technology, from Keynes and the Luddites to the myriad of reports from think tanks, management consultancies, and armchair philosophers. In this I fear the robot would win in terms of sheer volume and endurance. What would the scan look like? It would find the current economic literature a little unsure of how to tackle the issue of digitalization, recently scarred from having misread and otherwise having been blindsided by the financial crisis in 2007. Could economists once again get it wrong? Perhaps the first step for the software robot would be to specify keywords to use in the search. In his book Superintelligence: Paths, Dangers, Strategies, Nick Bostrom discusses the path to artificial intelligence (AI), but for the foreseeable future, robots are not true AI and the keywords would have to be supplied by a human being, such as myself. Indeed, if computers were true AI, it is unclear why they would want to write a report about the economic effects of digitalization in the first place. Taking the economists’ approach of focusing on incentives, we might suppose that AI robots would be most concerned about the steady supply of electricity and raw materials for replacement parts, just as human beings are preoccupied with extending the span and enhancing the quality of their lives. After having been given the keywords to search for, the robot would scan the literature and arrange the arguments in some order. Using statistical techniques and amassing frequencies, it would compile a list of data up the current day with all the arguments that support various positions. For example, the argument that robots will take over is supported by facts x, y, and z and so on. In the jargon of the economic profession, this kind of search might be called data mining, which is very useful for finding corre- lations and discovering trends. But it also brings several pitfalls. On the plus side, the robot software would not be afraid of amassing evidence that did not support a particular position. As long as the program it follows is neutral, it stands to reason that the output would be neutral as well. A human being writing a report inevitably has prior beliefs (as Bayesians might say) that slip into the writing and influence the report; we might call them biases. All writings, from newspaper articles to books to academic papers are to some extent colored by prior beliefs, as is the selection of reading material. For example, a person who selects one newspaper rather than another makes a choice to be more interested in the prior belief of that newspaper. It is then perhaps no surprise that that the early robot-written texts tend to be in areas of sports and competition, where the presenta- tion of goals and times compared to the past are the key outcomes. So, having an unbiased report would surely be a good thing? It is not clear that even if the researcher – or the robot software – is unbiased that this would automatically make the report unbiased. If the main driving force for finding results would be the frequency of how many times various keywords are mentioned, then there is always 1 THE ECONOMY AND DIGITALIZATION – OPPORTUNITIES AND CHALLENGES the risk that frequently mentioned nonsense would be assigned high value (or in the jargon, a high probability mass), whereas the really good stuff might be discarded. In my reading of the literature, I keep coming back to some of the great writings and thinkers – Keynes, Schumpeter, Coase – that stand the test of time; even when they are wrong, they are wrong in helpful ways that give us the wherewithal to think better about the issues that matter. Of course, no robot would dismiss Keynes, but what other good material might fall below the radar? In the end, I would be quite happy to let robot software do the scut work of a literature review, not unlike how we send out spacecraft to amass data from faraway planets while we go about our lives, so that I could focus on the other stuff: structuring the report, trying to make abstract ideas understandable, and addressing policy issues of how the economy works and how it affects people. In the future, they might be able to help with these things too, but then I would likely feel more inclined to overrule the robot. I would especially like the robot to create all my charts and put the references in the correct order and format – and I doubt I am alone there. Just as machines in the past made the need for arduous and dangerous physical work redundant across broad activities – mining and road/railway construction, for instance – we can only hope that some of the hitherto inevitably dreary parts of writing – or the equivalent in other professions – might be easier. How I try to address the policy issues raised by digitalization is the outcome of this report. I believe the amassing and structuring of data is an important phase but that it should not distract from scenario or policy analysis. Human beings are not particularly good at forecasting far into the future and there is no reason to believe robots would be better. Instead, one of our strengths is to have scenario analyses that outline different future paths – often taking somewhat extreme roads to more clearly illustrate the implications of going in some particular direction – in order to raise the issues con- fronting policymakers. Ultimately, the question is what kind of policies roughly support good outcomes and avoid the really bad ones. It is especially important to think about policies that are grounded in the way our institutions currently work because systems change slowly and, absent a crisis, there is little chance of public acceptance of major upheavals. Since we cannot be completely sure what the right policy is anyway, slow changes may be a good thing, while always being open to adjusting the path if it turns out prior beliefs were ill-advised. Perhaps as a matter of conscience, I should also state my own prior beliefs. I find it hard to believe doomsday predictions of a future without work, as outlined in any number of recent books, for example Martin Ford’s Rise of the Robots. People adjust and new jobs are created; this happened despite the fears of the Luddites during the Industrial Revolution. As stressed by David Autor, most of the benefits from digitaliza- tion come from viewing the complementarity in work between computers and people. But the worry is that the period of adjustment may be tough. In particular, it might be hard for broad groups in society – the lower and middle classes – as the changes brought by digitalization are both wide in scope and fast. To be sure, in contrast to the adjustments that occurred during the Industrial Revolution – affecting workers, artisans, and even the aristocracy – we now have social safety nets such as unemploy- ment insurance and sick pay; in Sweden, we have one of the most expansive social safety nets in the world and so the issues confronting us are different from those in the US, where the social safety nets are riddled with holes. 2 THE ECONOMY AND DIGITALIZATION – OPPORTUNITIES AND CHALLENGES I believe the question we should be asking is how institutions in different countries need to adapt to make the structural change from digitalization as unrugged as pos- sible. This involves social safety nets, how wage bargaining works, the rules governing how firms operate, and the education system. In contrast to life during the Industrial Revolution, people today have much higher expectations on government, and life is not just about surviving but also about quality – living the “good life.” The British television drama Downton Abbey, set in the latter years of the Industrial Revolution illustrates well the clash between the old and new. We are now entering an age faced with our own, digital, version of this challenge. In writing this report, I have received useful input and suggestions from many people but all opinions expressed are my own. I would especially like to thank Jonas Arnberg, Stig Bengtsson, Jan Brockmann, Hugo Brändström, Jörgen Bödmar, Mia Chennell, Stefan Eklund, Eva Erlandsson, Ann-Marie Fransson, Stefan Fölster, Dan Grannas, Christine Grahn, Ola Landström, Christian Levin, Erik Ljungberg, Björn Lundqvist, Anders Hektor, Carl-Gustaf Leinar, Assar Lindbeck, David Mothander, Magnus Nytell, Claudia Olsson, Ulf Pehrsson, Paul Palmstedt, Patrik Regårdh, Therese Rosen Löfstedt, Pär Nygårds, Anna Sabelström, Fredrik Sand, Christian Sandström, Rene Summer, Åsa Sterte, Magnus Thynell, Fredrik Söderqvist, Markus Tibblin, Katarina Tobe, Anita Vahlberg, and Urban Wass. A special thanks to all those that have commented on various aspects in the report: Marianna Blix Grimaldi, Anna Breman, Jimmy Boumediene, Lena Carlsson, Gabriela Chirico Willstedt, Enrico Deiaco, Irene Ek, Ingemar Eriksson, Robert Gidehag, Magnus Henrekson, Fredrik Heyman, Pehr-Johan Norbäck, Lars Persson, Jesper Roine, Eva Udden Sonnegård, Victor Snellman, Susanne Spector, Daniel Wiberg, and Sara Öhrvall.