Labor Markets in the 21st Century—David Autor, Vili Lehdonvirta, Pascual Restrepo & Maria Savona#013

[Music] welcome to the interview series on the social consequences of disruptive technologies by rethinking economics and health in this interview we'll be focusing on the labor market especially how it's being affected by increasing automation innovation in disruptive technologies and the giggy coin for this i'm honored to be able to introduce four world-class experts on these topics first of all today is david alter he is a member of the american academy of arts and sciences poor professor at mit and associates head of the mit department of economics he's also co-director of the mper labor studies program and co-leader of both the mit work of the future task force and the j-pal work of the future experimental initiative israel scholarship explores the labor market impacts of technological change and globalization on job polarisation skill demands earning levels and inequality and electoral outcomes second unit today is pascal estrella he is an assistant professor at boston university in economics and a faculty research fellow at mber his current research examines the impact of technology in particular of automation on labor markets employment wages inequality the distribution of income and growth his theoretical work centers from developing microfounded models of technology choice to think about the short and long-run implications of different technologies and whether the resultant growth process is balanced some of his earlier research focuses on institutions crime violence and illegal markets directly with us today is maria savona he is a professor of economics at spru the science policy research at university of sussex and professor of applied economics at the department of economics at lewis university she was previously at the university of cambridge and the universities with sasebo and leela one her research focuses on the effects of technical change and innovation on employment and wage inequality innovation industrial policy and structural changes she recently works on political economy of data value she's a former member of the high level expert group on the impact of digital transformation on eu labour markets for the european commission fourthly and lasting with us today is vili de don vieta he is a professor of economic sociology and digital social research at the oxford union internet institute at the university of oxford he is also a former member of the european commission expert group on the online platform economy and a high level expert group on the impact of the digital transformation and eu labor markets leyland vieta is an economic sociologist with whose research focuses on digital technologies such as apps platforms and marketplaces how they are governed how they shape the organization's economic activities and with what impact to workers consumers businesses and policies and with that i'd like to go to the first question to professor arthur could you tell us more about how disruptive technologies such as ai and have been affecting labour markets in the last decades sure i mean it's a big question and it's an open research question and i've to the people on this call uh are in this meeting i will have you know more expertise than i even that pascual has worked on this uh directly i mean i think so work that passwell duronas mogul and i have done along with joe hazel who's a former phd student um uh i suggest that artificial intelligence as a starting point uh is affecting the set of tasks done by workers uh and displacing some activities in which in machines have increasing capability but we don't yet see large aggregate impacts so i think that a lot of the discussion of ai at present uh is is somewhat speculative uh and i think the reason that it's you know there's two reasons why it's it's such a focus of conversation uh one is that you know of course it's novel uh and so it's exciting um but i think more substantively is we don't have a good roadmap of the way ai will evolve and what it will be capable of doing so for you know previous generations of you know software uh and even technology it would we had a very incremental set of steps uh before you could computerize or you know write something software you had to understand all the the rules and procedures and then you had to encode those and so it was there was kind of a you know a kind of a a slow chipping away at problems but artificial intelligence because it doesn't use the same paradigm because it's not a matter of first understanding and then encoding but rather that you have a flexible technology you can in some sense infer the rules uh we don't have a sense of what it will be good at or how quickly it will get good at those things so i think there's an enormous uncertainty and so you know the work that jerome prescott pascual and joe and i have done uh is non-alarmist it says not that much actually as far as we can measure it has happened so far but i don't think we take that as comfort that nothing will happen um let me i'll just defer the question of robotics to pascal because i really think he he will and others who have a more authoritative answer that's perfect uh we actually the next question i also prepared for uh pascal could you expand as well on the uh on your research especially as professor alter also mentioned so how does it you think you focus especially in automation could you tell us more about that yeah absolutely so i see and as david already explained like you know like ai is too soon to tell but i see most of our researchers kind of like exploring previous waves of automation and trying to learn something of how was the process of adjustment were the winners and losers from these previous waves of automation and so on right so like i think that when we think about technology we as economists tend to think that technology generates winners and losers and sometimes we focus a lot on the winners like as society as a whole becomes more wealthy and richer and so on and those are the benefits of technology but i would say that a lot of my research with their own and now with david has has also been about like trying to understand who are the losers and trying to quantify how big are the losses in terms of wages and gainful employment opportunities so for example in the case of robotics we've analyzed this question by looking at local labor markets in the u.s adopting robots and we found that in these communities where more robots are being adopted you see some declines in employment and wages more recently we have been looking at what happens to workers who tend to specialize in those industries where you're seeing more automation and at the jobs that are precisely the targets of this automation and what we have documented is that these workers have experienced less wage growth and less and uh declining their employment opportunities during the last 40 years so at some level like i think that that the evidence that we put together complement with other evidence kind of like points out to the fact that there are losers from technology i guess that like a bigger and more difficult question is what are these pockets of people who are losing what are they translating to aggregate losses in employment or aggregate the clients in wages and that's something that we don't really have an answer to that but again like i said that right now we do know that there are losers and we have kind of like improved our thinking on our knowledge on who these who these people who are losing are and how much are they losing these technologies professor savannah i think that relates nicely to your work for instance the horizon 2020 from the project which you have been coding in the pillars or pathways to inclusive labor markets projects how does it relate to the comments of professor alder professor verstappen yeah thanks quinn yes i mean following from my esteemed colleagues i think the ambition of pillars was just started actually so we're going to work on this for the next three years is to go a little bit beyond this the two main focus of economies which are basically the extent to which um tasks have been replaced by robotization and ai and so on which is uh obviously a crucial point but also the issue of kill mismatch for instance and the idea would be to go beyond these two issues to look more in that into the effect of past automation waves on industries so we are interested in the heterogeneity of industry and the extent to which they are exposed to automation ai and robotization some industry will be more prone to adopt um to be exposed to uh automation some others will not and therefore the idea would be to understand how the impact of this will affect uh differences across the industry but also differences across regions because ideally we take into account the initial specialization of regions and the extent to which automation would lead to a industrial transformation both within regions and to the extent they will be industries based in these regions will be involved into a global value chain so we know that some effect of automation and uh and digitalization more in general um would be the extent to which global value chain and trades will change their function specialization so we're interested in basically these type of effects which go a little bit beyond um the the main crucial issues that colleagues have been uh looking at and also we take into account the effect on migration so ideally at the individual level these events this phenomena would lead to a global migration as well depending on on what kind of skills will be affected so that's more or less the ambition of this project and and we are very keen into as david was mentioning we're very keen into trying to understand what would be the impact of future waves of automation technologies on on industries so that's more or less the ambition and i look forward to sharing more thank you a professor can you tell us more about this relates to your research in projects such as the eye labor project sure thanks cohen so one way of putting it is that if we can look at the way automation and the adoption of technology influences labor demand demand for for different types of skills some skill demand for some skills is going down and the demand for some other skills is going up but we can also look at the effects of technology on labor market processes themselves so the matching of of demand with supply and the management of that labor supply so for instance the gig economy food delivery apps uber and so and it's something that didn't really exist almost 10 years ago but now is a massive sector of well i wouldn't say massive but it's a significant segment of the labor market in many countries that employs a lot of people and a lot of technology a lot of automation is being used in these platforms to manage people and to match supply with demand and what we're looking one of the things we're looking at and have been looking at uh for a while is how that impacts on things like job quality so technology introduction of technology into these labor market processes could make things more flexible for workers so it could allow work to be um fitted uh better into different life circumstances for students people who have caring responsibilities and so on compared to strictly nine to five jobs but it can also result in uncertainty in unpredictability and inability to make plans in the inability to to raise a family for instance because you don't know if you're going to be earning enough next week so um i think it's important to look at the effects of automation not just on on labor demand but also on on the matching and and management of labor thank you uh in the second half of the interview i would like to focus on a topic which i think combines each of your work which is how to create more shared prosperity it seems to me that uh there is increasing discussion whether the increasingly digital labor markets and technologies as ai and robotics is actually leading to increasing shared prosperity in kind of increased shared uh sorry increased uh um sorry it's not so much but increased disparity so to say for instance a recent uh recent book was released uh recently by their own smoke titled um redesigning ai and the main theme in this book is that there seems to be changes needed in a way in which we're implementing technologies as ai because the current wave implementation is not being to share prosperity so was trevor hoping that in the remainder of the interview we could focus on the changes in it in both how we are implementing technologies as ai the institutions surrounding them and to ditch the labor market especially the gig economy and for that i firstly had a question to professor arthur could you tell us more about the changes which you think are needed to create more shared prosperity uh for instance some of the work you've been doing at the mit work sure so first of all you know the um we've seen four decades of you know rising disparities and rising inequality and this is this is true in all advanced economies although to different degrees and technology has certainly played a role in this though not exclusively as well you know globalization has been important institutions have been important but clearly the the set of technologies that we've been working with over the last four decades have been ones that have amplified the productivity uh and increased the scarcity of people with you know analytical and interpersonal and high levels of educational skills and made people with lower levels education more replaceable and more redundant and uh and so this is a challenge that we have been facing and not very successfully in terms of uh how of shaping the direction of technology in a way that is more complementary and you know it's something i think is sort of uh kind of undergirding this discussion but i don't think any of us has quite said is it you know it's not the technologies don't just show up and then we just have to deal with it we choose which technologies to invest in in some extent which characteristics they have and this is something that earlier workers emphasize that you know the emergence of of new work is in some sense a function of a variety of investments that we make and recent work that i've done with anna solomon's and brian segmeller it sort of shows that you can see where newmark emerges and partly as a function of what innovations are we're making and even where demand is growing we see new skills and specialization so the direction of investment and innovation has been highly focused on basically instruments information computing electronics and those things seem to be particularly complementary to highly educated workers so i think so i think i think there are two things to keep in mind when you think about the prosperity one is uh affecting the investments we make that shape the tools we're using uh and the way that they will create work create new work replace work and then the other of course is channeling the rising productivity that comes from that toward shared prosperity we productivity growth has not been particularly impressive uh at all uh in you know in most advanced economies really since the mid-1970s but that productivity growth which we've seen has been uh highly the fruits of that have been highly concentrated have been uh you know have have risen have you know accrued mostly to the top so we need to think about you know simultaneously the way we produce and and uh and the you know the kind of structure of work but also the set of norms and institutions that channel though that uh the fruits of that those investments uh uh in a way that we you know feel good about or do not feel good about and i would say the united states sort of stands out as a country that has you know been extremely laissez-faire in uh in in managing or not managing the uh consequences and i think has generated an enormous amount of social dissatisfaction uh and frustration and uh it's i would say the inaction has been costlier than potentially than the set of interventions we might have made uh to to uh you know attenuate or ameliorate that process professor zabona it seems to me that this also relates to the type of science technology and innovation policies which you are pursuing as a country can you give an example of policies which you could pursue to create more shared prosperity in the way that we innovate thanks let me let me allow me a small premise to this reply if i may um usually innovation policy has been understood and has been crafted in a way that uh its main objective is to maintain incentives to innovate in in the understanding that at some point innovation is is gonna produce a social good or or or is gonna be a a public good and to some extent it is to the extent that it feeds the um the knowledge stock of societies and economies now what has been possibly overlooked and i think pascual was hinting this was hinting at this uh earlier on is that there is always a side effect and that in most cases there is a side effect or sort of a trade-off that policy needs to mitigate so i am a firm believer in the importance of public policy to direct the effects of innovation and i think the way we should um understand the way policy should be crafted is a sort of com in its complexity so um taking into account issues of innovation policy without talking about industrial policy or redistributed policies is going to be a a a lose-lose situation so this is my premise towards what concerns innovation policy so in a nutshell we have innovation might be good but there are side effects we need to mitigate this effect and this is something that it is important to incorporate in any policy decision um now if i may possibly a possibly an orthodox example of what i mean is for instance um one of the effects that we billy and i have also written in the um high-level group report for the european commission is one of the side effects of digitalization on the labor market is its effect on mental health now mental health is extremely important so i think that for example raising funds or drafting funds for mental health in many countries would have not only mitigate the side effects of of the disruptive effects of innovation on particular categories of workers but to some extent might also lead to increase those soft skills right that are found to be very important for um low uh low skilled people so low skilled workers are increasingly depending on unique soft skills that are valued in the labor market and that in my opinion would very much benefit from a higher public expenditure and mental health uh it's a big convoluted crafting but i think the way i see policy intervention in this in this area is uh you know incorporating this complexity professor how do you see this and could you uh give examples now we can create more share prosperity especially for those working at the lower end of the gig economy thanks cohen that's uh that's a million euro question um i i would like to answer like a politician and sidestep the question of what what the exact policies are a little bit and answer that in addition to that we also need to be thinking about how to actually implement those policies because um you know a lot of the research that for instance economists like david alter here have done already points to things that should be done but that governments are not doing so as as long as that remains the case then developing even more sophisticated uh policy solutions is a little bit uh academic in them in a bad sense of the word um and i what makes it particularly tricky today the the question of how to actually implement such policy changes is that a lot of the power over working conditions and and working arrangements is now in these large technology companies that operate across many countries and so there's only so much you can you can do a lot of things by changing policies in a given country but then often the technology companies are quite good at coming up with ways of tweaking the allow their algorithms in ways that avoid um triggering provisions in specific laws when it comes to things like employment classification for instance this debate about whether a gig economy workers should be classified as regular employees and so on and so i i think that to some extent then a more effective approach might be to think how can we empower the workers themselves vis-a-vis the companies so that they are able to demand better conditions for themselves whatever they think that they need rather than policy makers necessarily coming top down and imposing policies that may or may not work for the workers and also for the companies so one of the things that the european union is now exploring which i think is a good idea is to ensuring that there are no legal barriers to gig economy workers um essentially unionizing forming forming something like a union even if they are not legally considered employees but independent contractors and by coming together forming a union or an association speaking back to the tech companies with a collective voice in that way having a little bit more equal bargaining position in the table vessel although how do you use these cds i think for instance that at the mit uh work of the future task force it was in their final report was also a similar recommendation i think you're still yes yes sorry the quite the question of worker organization i'm sorry i missed the antecedent yes yes the basically what what type of of how can we create more shared prosperity and what to which extension should for instance be worker voicing companies being aspect of that in say america where it is of course quite limited currently in the united states you know mechanisms for worker voice or worker leverage or even worker power have been hugely attenuated and there was arguably a time where you know u.s labor unions were too strong and interfere with productivity and flexibility and in some senses they had the luxury of doing so when the us was exposed to a lot less international competition but uh now the pendulum has swung completely in the other direction and this is problematic the u.s in my opinion does very much need a revitalization of worker negotiating capacity but not through the traditional mechanisms that we used the u.s

labor system is historically and uniquely adversarial among advanced economies uh in a way that is and it's and it's trench welfare right it's you know it's like it's like the equivalent of passing minimum wage law is kind of one restaurant at a time right you know it's it's a workplace level bargain it doesn't it doesn't uh involve the sector and so on so and many worker types are excluded domestic workers agricultural workers and so on so the u.s needs to experiment expand uh and revitalize negotiation i think worker negotiating power i think that's part of the answer i don't think that's all the answer but it would also reverberate to how laws are made and who is elected so it wouldn't simply be through workplaces but through a broader change in political economy so i i definitely agree with the promise of the question and i think this is a much more serious problem in the us and the uk than in most of continental europe more more so than i think in the netherlands for example thank you uh professor estrepo it seems to me that um there might be too much automation i think this was a a focus of some of uh the papers uh how do you think it also relates to some extent to to worker voice to that there might be too much automation how do you see this do you think that there is currently too much automation or that we should have say changes in the text structure or what type of things are you seeing there yeah absolutely so like i guess is that the question of whether you think that there's too much automation or not depends a little bit on the definition of share prosperity that you have because like we are talking about share prosperity and how to achieve that but we need to define what share prosperity means first so if i think of shared prosperity as just a situation where as economists typically do which is a situation where everyone has enough consumption or welfare from consumption then like it's very hard to have excessive automation because automation brings some productivity gains and then the only thing that i need to do is redistribute those productivity gains right and so that's the traditional view that we economies had in the past technology it creates some winners and losers but this is always good in terms of welfare because i can always tax the winner to compensate the losers right but if you think that that's your definition of your prosperity just the ability of people to consume enough that could lead you to a situation where perhaps we have automation and we automate what 95 of the society does and we end up in a place where five percent of the population produces all of the output and the remaining 95 percent just receives a handout right so that doesn't strike me as a world where there's share prosperity i mean there's sheer consumption for sure but there's no shared prosperity so like i think that it's useful to use a different definition of shared prosperity which is a world where everyone or a broad section of society from diverse origins and with diverse skills take part in the production or in the creation of value and if i start using the definition of your prosperity then you can start thinking about why if our objective is to generate that kind of prosperity why could there be too much automation and the reason is because automation is essentially taking away opportunities for those segments of society and might not be generating other employment came from employment opportunities for them in other sectors or in other kinds of jobs so i think that that's useful because if that's the way that you think about your prosperity definitely like you need to think about different policies such as innovation policy and maria was this was talking about this so you need to be mindful that those some innovations even though they generate productivity they might move us away from that world of shared prosperity because they take employment opportunity away from some groups of society you might want to think about training and education but you know like i think that and this is something that we can discuss more but i think that there's a limit to how much training and education alone is going to get you like in the extreme like you could make all of society a computer programmer but they're still going to be five percent of society who are better programmers than the rest and so those five percent are gonna be doing all of the coding so i think that that at some point if you're if you really want a society where everyone has access to gainful employment opportunities and that's subjective you need to start thinking about innovation policy and you need to start thinking about including those concerns into what types of projects you subsidize and so on so for example like in the nsf every time that they're going to subsidize one particular project i mean i don't know how long have they have they been doing this but recently they're asking proponents of projects to also spend some time discussing what are the societal implications of the innovations that they plan to bring and i think that that's kind of like a step in the right direction we should be managing this the same way that we manage the climate right we agree that it's desirable to create green energies so we are subsidizing greener energies and we are taxing carbon and so like i guess that in this sense if we all agree that perhaps automation is not the path for a shared prosperity the way that i define it we should kind of like be redirecting innovation in another direction and david already pointed out that kind of like technology is not that it comes in one shape and that's it we decided society how we want to develop ai for example we can decide whether we want to use ai to generate other platforms that generate lots of employment for low skilled workers or we decide whether you want to use ai to keep automating more jobs in a way that we have been doing in the last four years with software and robotics so at some level it's a societal choice if i can just add one thing to what possible just said even if you thought the world in which you know all the money was made one place and then everyone received a check uh even if you thought it was desirable which i agree with the implication that it's not at all desirable it's not politically viable right it's just not feasible to have a society where all the resources are coming from one place and then somehow the state takes ownership and redistributes it and that works okay right in some sense you need people to have some income generating capability to have a functioning democracy that doesn't uh that you know in which people will agree to the the uh the redistributive outcomes so i think there are many reasons to think that we do not want to be in a setting where you know like kind of the resource course where all the money comes from oil right and then we all try to share the oil wealth that never works well thank you it seems to me that that also relates a bit to some aspects that professor leyland worked on for instance his social more sociological work uh for instance also on um the importance of work and especially in the how people earn incomes and such in the policies surrounding that could you expand on that professor i'm not sure which work here i was thinking the uh the workforce from southeastern asia on i think you focused on workers there and yeah because you expand on that perhaps does it also relate to this context well i can talk about that uh in that research but let me try and think of how to make it uh relevant here well i mean one thing okay here's here's something that's interesting because um you know a lot of this and i i totally agree with with what professor walter there was saying and professor rastapo as well the one additional challenge that digital technologies are bringing to the table is that sometimes the work crosses national boundaries so we studied people in in southeast asia and sub-saharan africa my my colleagues at the oxford internet institute and myself who earn income from employers in european countries and the united states canada and japan and rich oecd economies doing work like software development web design graphic design data entry and so on and some virtual assistant services and they are matched with the employers through online platforms and when the work crosses national boundaries like this it becomes quite tricky to regulate it in the context of um essentially world system that's based on on these territorial jurisdictions units of regulation um there are some first of all there's practical legal issues which is the employer's country or the workers country which uh laws should be applied um and even if and the lawyers can't figure this out and they can tell you which is the correct answer but even if if you know that you ought to be applying the law of one country then can you in practice enforce those laws so for example you know i spoke with a virtual assistant in the philippines and let's say this uh virtual assistant doesn't get you know he has a london-based client the client doesn't pay uh wage theft very common in these markets unfortunately is it feasible really for the the filipino virtual assistant to take the client to court in london no it's it's even if it was legally possible it's economically not viable so there's a there's a kind of institutional void in a way in these transnational markets and the platform companies are stepping in these digital platforms they're stepping in and they're filling that void to some extent so they're providing rules and they're providing rule enforcement that allows these transnational markets to function and what that means for us when we think about policy is that it's not necessarily sufficient to just think of policy within a territorial jurisdiction you also need to think about okay well how can we influence policy uh in these transnational sort of virtual jurisdictions this is how does that relate to your work i was when really was mentioning this i was actually um thinking about it's it's related to digital transformation and it is related to the governance of data so this is something that it's it's increasingly um important it's increasingly debated that the eu versus us and in china um jurisdiction and regulations it is important because um there is a lot to be um understood in terms of redistribution of data value and if we think uh that this is mentioned for example in in the high-level report that we produce with billy but it's there's a lot to be done for example personal data are individual data are then gathered at the platform level and they become property as a database of the of the company and uh these race issues related to property rights for instance issues related to digital cross-borders data flows um and all this needs to be assessed and it to some extent it has to do with with the rights and the agency that people have on their own personal data that then become uh so much valuable for uh for large tech and i think this is there's a whole gap that needs to be um unpacked here and in this respect we need so much um interdisciplinary expertise i think we need uh from economies to lawyers and to sociologists and and so on um so this is one for example one of the examples that i meant earlier where policy is so complex and needs to be incorporating a different kind of expertise because it needs to tackle different issues at stake so a lot more on the political economy of data well that's a little data could you expand on some of the recommendations of the high-level expert group on the impact of the digital transformation which you and savannah go on could you expand on some of the tips which you made for the european union what are the most important changes well um let me pick one i mean we made what 10 different recommendations professor savannah was was a part of that group with me and one of them was related to this ensuring that there's no legal impediments to gig workers forming union like associations i'm bargaining collectively but another one that's related to data that was just discussed is this idea of what we termed the digital single window which would be a facility for eu governments to receive data from platform companies directly for the purposes of taxation and social insurance administration so at the moment there is a risk that as as i mentioned earlier it's not just that robotization automation is replacing workers but it's replacing managers it's replacing a labor market intermediaries and there is a risk that as that happens and the labor markets sort of move online and they become increasingly digitalized then the state falls out of the loop because the state has hooks built into sort of data hooks built into conventional labor market processes and conventional management processes where the state gets down how much people are earning and what their employment status is like which is very important especially in european welfare states where we rely on on taxes and and relying that data to produce data for the purposes of of social insurance for pensions and for unemployment insurance and so on and now if the labor market moves into this new digital sort of infrastructure and that data those data access hooks are left behind then you have a problem where the uh there might be tax under reporting and in fact we know that there is some attacks under reporting and um there might be under coverage of social insurance so we have a lot of people working in the gig economy who are making decent earnings now but what they might not realize that later on they might not be covered for pensions and then that falls on the state ultimately to to fix that problem for them and so we have a kind of liability waiting for us in the future if we don't fix that problem now and so one of the things we proposed in the scilable group report was a facility for european member states to obtain data directly from those platform companies on what people are earning and what people are are doing rather than relying on uh workers reporting it themselves and in fact um then um iron and and some colleagues we did a follow-up study on this we found that several european member states already in a national context they're building systems like this so for instance denmark has introduced an api an application programming interface the tax agency has introduced an api for platform companies to report in real time what people are earning um especially this was especially for for accommodation uh rental platforms but the plan is to expand it also to labor platforms and it turns out that the um in in a country like denmark with the people you know people like their welfare state they have good tax morale um the the platform companies and the users they actually welcome this and a lot of platform companies said that they want they wanted to get early access to this reporting interface because it made the reporting easier for their users because it meant that now the users didn't have to you know if you rent your apartment or you do some work through one of these platforms and you gain some income and in the in the present situation you're supposed to report that income to the state via some paperwork which is usually intended for um essentially uh businesses because now it's considered business income you're an independent contractor so the paperwork is quite heavy and it's not something that um you know a gig worker who does a little bit of gig work as side income necessarily knows how to fill in and it's quite a big compliance cost to fill in that paperwork versus a system where the platform actually reports directly that income to the state it makes it super easy for the workers so this this policy was very much welcomed in denmark and we proposed that something like this should be done on on a union level vessel [Music] beginning of this answer he also talked a bit about automation in this context how do you see this what do you think about these comments sorry i didn't i didn't get the last part of the question what do you think about these comments especially at the beginning yeah so i i mean like i'm not a specialist in in terms of data and i think that most of his comments were about like how to handle data and all of that so i'll i'll have to pass on that question because like i i have to say that i agree with him because he's the expert on this thank you um i think we're already quite close to time so i think it might be good to move towards the closing questions and i think it's best to do this in the in the same order as the beginning statements and give them a bit more time so we can also go a bit more in detail so the we always have the same question at the end of this interview and says if you could say one thing to students in economic relations to the topics we discussed today what will that be and a question i first said for professor otto well let's see i mean i i think you know an important thing that the pasquale emphasized in his comments is that many of us did is there's so much we have to learn and we really you know we we are very far from a subtle understanding these questions they're momentous and it's not just a question of research but also they influence what we do and they influence they it's not just you know many people think when they're trying to study quote the future that they're trying to it's an act of prediction but in fact the future is something that's in our control and so it's not just a matter of anticipating it's a matter of shaping and the way we do that is by you know understanding what we've done in the past and what levers we have to uh you know produce the outcomes we want going forward and so i think it's it's more than a just a kind of a scholarly exercise it's a really vital exercise i also want to emphasize one point that probably didn't come out as clearly from this conversation which is you know economists in particular have gone from the position of being you know uh un uh you know with no caveats incredibly positive about technology and automation uh to being quite pessimistic about it without necessarily achieving an appropriate balance between those two and you know we should recognize that there are there are winners and losers but the net effects are can be quite beneficial it's hard to think how we would have come through this pandemic over the last couple of years had it not been for you know online tools that allow us to carry on our work had it not been for uh the you know the incredible progress in uh medical technology that allowed most of us to be vaccinated by now or even the way that technology has led to growing prosperity around the developing world and you know it's very easy from a kind of rich country perspective to say well things aren't improving that fast not as good as they used to be but of course think about the value of mobile technology in sub-saharan africa and many developing countries where there's no close substitute for the the banking the communication the information access that's available through technology or even the way that china's rise has brought hundreds of millions of people out of poverty not only in china but throughout the developing world and that has a both uh you know kind of a political and economic component and also a technological component so i think we should we should simulate we should you know simultaneously realize that we are creating challenges for ourselves they're important we have control over them uh and there are many ways to do this wrong but simultaneously we wouldn't want to not face this problem we wouldn't want to not have the opportunity to use these technologies and use them well and clearly there's there's enormous potential for benefit and a lot of good is occurring it's often unfortunately the case that the people who are the beneficiaries are not the same ones who are as the people who are on the you know side of of being affected directly by automation thank you uh professor sniper for you the same question if there's one thing you could say and feel free to expand a bit here uh to students in economics right to the topic to discuss today what will that be yeah so i think that i would basically say every single word that david just said but i would add some more notes on top of that so the first thing that i would tell the students who are interested in these topics is that these are some of the most important and consequential questions that an economist or in general an academic can be thinking about these days because as david point pointed out these are questions about kind of like what kind of future we want to live in so yes we tend to study the past because it's the only thing that we observe but we're starting the past in order to learn about our future and that i think is very consequential and very important and the second thing that i would point out to students is that you know like at least in the economics profession we tend to have a view of technology where we put all technologies together into a single thing and just call it technology and think that that's always good for prosperity or for share prosperity as we have been discussing and i think that that you know like my invitation for students is to kind of like open that black box of technology there are many different technologies and technologies do very different things so there are there's the technology that for example powers one of the platforms that can hire thousands of workers during the pandemic right that's one type of digital technology and we want to understand what are the effects of that particular technology and whether that technology is being used in the right way there's other technologies that are really just about automation and there are other technologies that are about new materials new products so you know like we tend to think of like our we tend to call a lot of things automation we tend to call a lot of things technology i think that it is useful to start distinguishing the fact that technology is very rich and that different technologies do different things and have different implications for society and so so we shouldn't be just talking about like automation in general or technology in general there are many different types of technologies that we need to understand so that's part of my invitation to students let's open the black box of technology it's not just kind of like a pfp a solo residual thing that makes the economy grows different technologies do different stuff thank you very much for you the same question if there's one thing you could say to students in economics watching today what will that be yeah i think my recommendation follows nicely from what pasquale was saying and my recommendation would be um try not to become slave of disciplinary protocols i mean many economies have all of us have some sort of disciplinary sense of belonging right that leads us to have a sort of standardized protocols of research and my recommendation would be try to get getting to cross boundaries like if you if you get interested for example in um ethical concerns about ai just or regulation about ai try to read law and incorporate law into your research if you're interested for example in as i said one of the issues that is going to grow i think is in the mental impact mental health impact of digitalization and trying to expand on issues that are related to psychology or sociology if you're interested for example in data governance or and so on just try to expand in different areas or if you want to unpack as pasqual usa what kind of automation we're talking about if you're interested in trying to understand what kind of task is going to be replaced from a surgeon replacing a broken bone then try to read engineering journals on that particular type of of products and this to me is is the one recommendation that i would that i would have trying to be interdisciplinary starting from one well-known discipline but try to enlarge to something that allows you to follow your curiosity and your research um instinct that's all thank you i think this is something we really hear throughout the series for you uh professor linonderta for you the very final question this the same one as the other other panelists if there's one thing you could say to students in economics question today what would that be we all have a limited number of years on this planet and we spend a lot of those years working um but time use researchers so sociologists who study how we spend our lives uh tell us that we spend over the course of our lives as much time on unpaid work on domestic chores on caring for for other people as we do in the paid labor market and so far this whole future of work discussion has focused solely on the paid labor market so next let's think about how about that other half of work that work that happens in the domestic sphere and that's just as vital for the reproduction of society thank you thank you each for your time this was the final episode of the series and i want to thank each of the panelists also throughout the entire series so much for their time it was a very interesting conversation thank you [Music] you

2021-07-04 22:37

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