普华永道：机器人对英国就业的影响（下） [英国媒体]

4.5 – What economic, legal and regulatory constraints might restrict automation in practice?

什么样的经济、法律和法规约束，可能会在实践中限制自动化技术替代人？

So far the analysis has focused on the technical feasibility of automation based on the characteristics of the jobs (e.g. the tasks required to be done) and their typical workers (e.g. education levels). But, in practice, we recognise that actual future levels of job automation may fall below these levels – or at least take longer to reach them than we might expect on purely technological grounds.

到目前为止，分析都是围绕技术上的可能性，基于工作的特征（比如一份工作需要做的事情）和从业者的种类（比如教育水平）。但是，在实践中，我们认识到未来实际的自动化替代人工的水平可能会低于我们的结果——或者至少需要更多时间来达到我们纯粹从技术角度所得出的结果。

Economic constraints

经济方面的约束

The first important constraint here is economic – just because it is technically feasible to replace a human worker with a robot, for example, does not mean that it would be economically attractive to do so. This will depend on the relative costs of robots (including energy inputs, maintenance and repairs) relative to human workers, as well as their relative productivity. 

首先是来自经济的约束——举个例子，从技术角度来说能做到用机器人替代人，并不意味着这样做在经济上是有吸引力的。这取决于机器人相关的成本（包括消耗的能源，保养和维修费用）与人类相比较如何，以及生产率相比较如何。

In recent years, we have seen rapid total employment growth in the UK, driven in part by relatively subdued (or negative) real wage growth.

近年来，受到相对平缓（甚至是负的）工资增长驱动，我们看到英国的总就业快速增长。

Furthermore, a relatively flexible UK labour market that has been open to migration from the EU in particular has made it a comparatively attractive option for companies in many sectors to expand by hiring more people, rather than incurring potentially large up-front costs by investing in new technologies such as AI and mobile robots, which will also seem relatively risky as they may not have been widely tested in practice.

不仅如此，一个已经向欧洲移民开放的更有弹性的英国就业市场（G19注：移民所要求的薪资水平相比当地劳动力较低），使得对于很多行业的公司来说通过招募更多的人员来进行扩张成为一个有吸引力的选择；而不必忍受可能会很巨大的前期费用，来投资人工智能和移动机器人这样的新技术。这些技术相对来说仍有风险，因为还没有在应用中广泛的测试过。

Why take the risk of such investments when there is a low risk, low cost human alternative? Such considerations would apply in sectors like transport, retail and wholesale, hotels and restaurants, and food processing.

在有低风险低成本的人类可以选择的时候，为什么冒险进行这样的投资呢？这样的考虑在运输，零售和批发，宾馆和餐馆，以及食品加工行业都适用。

Over time, however, we would expect these economic factors to become less significant as the cost of the new digital technologies falls (quite possibly very rapidly if a robotic version of Moore’s Law turns out to apply) and they become more widely adopted, leading them to seem less risky and untested by companies that were not early adopters. But it remains highly uncertain in many sectors with low current adoption of robots when the ‘tipping point’ to much higher adoption will be reached. Organisational inertia and legacy systems may push back the timing of any such shifts towards automation even if they become technically and economically feasible.

然而假以时日，我们预计这些经济因素会变得不那么显着，因为新数字技术的成本会降低（如果出现机器人版的摩尔定律，这种降低可能会非常之快）；而随着这些技术被更广泛的采用，对那些没有采用的公司来说，风险和未经测试的问题也越来越小。但是在众多目前还很少使用机器人的行业，什么时候大规模应用机器人的“临界点”会来到，还是一个未知数。组织的惰性和旧有的系统都可能会延迟转向自动化的时间点，即使在技术上和经济上都可行的情况下。

Legal and regulatory constraints

法律与法规的约束

Even if economic barriers to adopting automation can eventually be overcome, however, there could also be significant legal and regulatory hurdles to negotiate.

即使转向自动化的经济障碍最终被克服，却还有法律和法规的障碍横在前面。

In the case of driverless vehicles[10], for example, the issue of who bears the liability for accidents is a difficult one to resolve – is it the car manufacturer, the manufacturer of the sensors on the car, the provider of the computer software that makes driving decisions, or some combination of these and other suppliers? What about the liability of the human passenger if he or she is expected to take manual control of the vehicle when signalled to do so by the vehicle’s computer but failed to do so? And should driverless cars be expected to satisfy higher safety standards then human drivers if that is one of their key selling points? 

以无人驾驶汽车为例[注10]，在交通事故中由谁负责任就是一个很难解决的问题——是汽车制造商，还是汽车传感器的制造商，还是做出驾驶决定的计算机软件的开发者，还是上述多方共同担责，还是汽车的其他供应商？是不是乘客也应担责，如果他或她在行车电脑发出信号需要人工控制的时候没有这样做？另外既然安全是无人驾驶汽车的主要卖点之一，那是不是应为无人驾驶汽车制定比人类驾驶更高的安全标准？

In the long run, we would expect these kind of legal and regulatory barriers to be overcome in those industries where automation makes economic sense and is technically feasible. But there may often be powerful vested interests arguing against too rapid an advance in automation, so it may well be that realisation of the full potential automation may occur significantly later than the early 2030s timescale we assume in this report (in line with the original FO study).

长期来说，我们认为这些类型的法律与法规障碍，在那些自动化在经济上有利可图、在技术上可行的行业里终将被克服。但是恐怕会有强大的既得利益者去反对自动化太快太深入的进行，所以自动化实现、发挥出全部的潜力的日子，很可能要明显晚于我们在本文中所认为的2030年代早期这个时间表（见FO原始研究相关段落）。

4.6 – What offsetting job and income gains might automation generate?

自动化能带来什么样的补偿性的工作机会以及收入增长？

Another key caveat noted earlier in this article is that we have focused so far on estimating the potential job losses from automation. In practice, however, there should also be significant gains from these technologies in terms of:

我们之前在本文中声明过，到目前为止我们专注于对自动化造成的潜在的岗位损失作出估计。然而在实践中，自动化技术在以下方面也会来带明显的好处：

• completely new types of jobs being created related to these new digital technologies; and

创造出与这些新数字技术相关的全新的工作种类；并且

• more significantly in quantitative terms, the wealth from these innovations being recycled into additional spending, so generating demand for extra jobs in less automatable sectors where humans retain a comparative advantage over smart machines.

在数量方面更显着的是，这些创新带来的财富循环成为额外的消费，并在那些人类对于智能机器保持相对优势、不那么容易被自动化的行业产生额外的工作需求。

These offsetting gains are not easy to quantify, but in an earlier PwC study[11] with Carl Frey, we estimated that around 6% of all UK jobs in 2013 were of a kind that did not exist at all in 1990. Moreover, in London, this proportion rose to around 10% of all jobs. These were mostly related to new digital technologies such as computing and communications. Similarly, by the 2030s, 5% or more of UK jobs may be in areas related to new robotics/AI of a kind that do not even exist now. It is very difficult to know what these new types of jobs will be in advance, but past experience suggests that there will be some job gains from this source, albeit probably significantly less than the around 30% potential job losses from automation discussed above.

这些补偿性的工作机会不容易量化，但是在早前普华永道与卡尔弗雷的研究中[注11] ，我们估计2013年英国所有职位中，有约6%是在1990年时根本不存在的工作种类。更有甚者，在伦敦，这个比例高达10%。这些主要是新数字技术比如计算机和通讯方面的工作。与此类似，到2030年，英国可能将会有5%或者更多的现在没有的机器人/人工智能相关的领域的工作。现在难以提前预测到这都会是些什么样的工作，但是过去的经验告诉我们确实会有，虽然其数量可能会大大少于之前所讨论过的自动化带来的30%的潜在岗位流失。

The more significant offsetting factor is that these new automated technologies will boost productivity considerably over time[12]  (if not, they will not be adopted on economic grounds). This will generate extra incomes, initially for the owners of the intellectual and financial capital behind the new technologies, but feeding into the wider economy as this income is spent or invested in other areas. This additional demand will generate increased jobs and incomes in sectors that are less automatable, including healthcare and other personal services where robots may not be able to totally replace, as opposed to complement and enhance, workers with the human touch for the foreseeable future[13] .

更加显着的补偿效应是，这些新自动化技术将会随着时间推而移提高生产力[注12]  （如果不能做到这一点，它们也就不会在经济上被采用）。这将带来额外的收入，最初是给这些新技术背后的知识产权人以及金融资本，然后随着这些收入被花费或者投资在其他领域而扩散到更广泛的经济领域。额外的需求将给不容易自动化的行业带来新增的工作机会和收入，包括在可预见的未来机器人难以完全替代的医疗和其他个人服务，在这里人类不是作为补充和增益，而是做有人情味的工作者[注13]。

The historical evidence suggests that this will eventually lead to:

如果参考以历史，最终的结果是：

• broadly similar overall rates of employment for human workers, although with different distributions across industry sectors and types of jobs than now;

总体人类工人就业率变化不大，虽然与现在相比在行业和工作种类的分配上会有变化。

• higher average real income levels across the country as a whole due to higher overall productivity;

由于整体生产率的提高，在全国范围内，平均的实际收入水平会提高。

• but quite possibly also a more skewed income distribution with a greater proportion going to those with the skills to thrive in an ever more digital economy – this would put a premium not just on education levels when entering the workforce, but also the ability to adapt over time and reskill throughout a working life.

但是很有可能收入分配也会更加扭曲，更大的份额会被那些拥有技能可以在前所未有的数字化经济里仍能发展的人所占有—— 这将不仅仅取决于人进入职场时的教育水平，还需要有能力去适应形势并且在整个职业生涯里不断的学习新技能。

4.7 – What implications might these trends have for public policy?

这些趋势对于公共政策来说意味着什么？

The latter point raises important public policy issues. The less controversial one is that the government, working with employers and education providers, should invest more in the types of education and training that will be most useful to people in this increasingly automated world. Exactly how to identify the skills that will be required and develop the training is much more complex of course – for many people, this will involve an increased focus on vocational training[14] that is constantly updated to stay one step ahead of the robots. It also requires better matching of workers to the new opportunities that will arise in an increasingly digital economy. But the principle of investing more in education, skills and retraining seems widely accepted.

上一章最后一条，对于公共政策来说是重大的挑战。有共识的一点是，政府应该与雇主和教育部门一起，在那些对人们在这个日益自动化的世界中更有用处的教育与培训类别上加大投资。当然，如何准确识别哪些技能更加急需并为此开发培训课程，就更加复杂了——对很多人来说，这将包括更加注重不断更新的职业培训[注14]以保持领先机器人一步，还需要与这个更加数字化的经济中所产生的新工作机会相匹配。但是加大教育、技能和再培训这个总的原则是被广泛接受的。

Central and local government bodies also needs to support digital sectors that can generate new jobs, for example through place-based strategies centred around university research centres, science parks and other enablers of business growth. This place-based approach is one of the key themes in the government’s new industrial strategy and its wider devolution agenda. It also involves extending the latest digital infrastructure beyond the major urban centres to facilitate small digital start-ups in other parts of the country.

中央及地方政府实体也需要支持那些可以产生新工作岗位的数字领域，比如通过基于场所的策略，围绕大学研究中心，科学园区，以及其他能够推动产业成长的方式做文章。这种基于场所的方法是政府的新工业策略以及广泛放权议程的关键性主题。这还包括，把最新的数字基础设施从中心城市扩展到国内其他地方去以帮助小型数字技术新创企业。

More controversial is whether governments should intervene more directly to redistribute income[15]. In particular, the idea of a universal basic income (UBI) has gained traction in Silicon Valley and elsewhere as a potential way to maintain the incomes of those who lose out from automation and (to be hard headed about it) whose consumption is important to keep the economy going. The problem with UBI schemes, however, is that they involve paying a lot of public money to many people who do not need it, as well as those that do. As such the danger is that such schemes are either unaffordable or destroy incentives to work and generate wealth, or they need to be set too low to provide an effective safety net.

更具争议的是，政府是否应该直接干预收入再分配[注15] 。尤其现在全民基本收入(UBI)（G19注：社会福利）的理念已经在硅谷和其他地方获得关注，作为一种可以维持那些在自动化进程中被淘汰者的收入的潜在方式，或者说白了，让经济保持运行需要这些人的消费。然而问题是，全民基本收入计划把很多属于公众的钱给了很多并不需要人，虽然有的人是真的需要。危险在于，这样的计划要么难以承受（G19注：在经济上太昂贵）或者摧毁了人们通过工作来创造财富的热情，要么就只能标准设置的很低以致于并不能构建成一个有效的安全网。

Nonetheless, we are now seeing practical trials of UBI schemes in a number of countries around the world including Finland, the Netherlands, some US and Canadian states, India and Brazil. The details of these schemes vary considerably, and it is beyond the scope of this article to review them in depth, but it seems likely that more pilot schemes of this kind will emerge around the world and that they will come on to the policy agenda in the UK as well. For the moment, the need to reduce the UK budget deficit may be a significant barrier to any such scheme on a national level, as well as concerns about the social acceptability of giving people ‘money for nothing’. But the wider question of how to deal with possible widening income gaps arising from increased automation seems unlikely to go away.

虽然如此，我现在看到世界上一些国家已经进入了全民基本收入计划的实际测试，包括芬兰，荷兰，美国和加拿大的一些州，印度和巴西。以上国家地区的具体实施细节差异非常大，深度检视它们超出了本文所能讨论的范围，不过看上去，越来越多这类的试点方案在全球出现，而且这也将会提到英国的政策议程上来。目前，从国家层面上来说，英国减少预算赤字的需要对任何此类计划来说都是一个重大的障碍；“白给钱”（G19注：给特定人群）的公众接受度也是一个问题。但是更广泛的问题，如何应对日益加剧的自动化可能带来的收入鸿沟，这个问题是不会凭空消散的。

4.8 – Summary and conclusions

总结与结论

Our analysis suggests that around 30% of UK jobs could potentially be at high risk of automation by the early 2030s, lower than the US (38%) or Germany (35%), but higher than Japan (21%).

我们的分析结论是，到2030年代早期，英国大约有30%的工作岗位有潜在高风险被自动化，低于美国的38%和德国的35%，但高于日本的21%。

The risks appear highest in sectors such as transportation and storage (56%), manufacturing (46%) and wholesale and retail (44%), but lower in sectors like health and social work (17%).

风险在运输与仓储(56%)，制造业(46%) 和批发与零售(44%)等行业最高，但是在医疗与社会工作(17%)等行业较低。

For individual workers, the key differentiating factor is education. For those with just GCSE-level education or lower, the estimated potential risk of automation is as high as 46% in the UK, but this falls to only around 12% for those with undergraduate degrees or higher.

对于个体就业者来说，主要的区别因素是教育。对于英国中等教育或者以下学历者，其岗位有潜在高风险被自动化的比例估计高达46%，但是对于大学及以上学历这来说风险则只有12%。

However, in practice, not all of these jobs may actually be automated for a variety of economic, legal and regulatory reasons.

然而在现实中，因为各种各样的经济、法律与法规的原因，不是所有这些预估会发生的岗位自动化都都会实际发生。

Furthermore new technologies in areas like AI and robotics will both create some totally new jobs in the digital technology area and, through productivity gains, generate additional wealth and spending that will support additional jobs of existing kinds, primarily in services sectors that are less easy to automate.

此外，人工智能和计算机等领域的新技术，既会在数字技术领域产生出全新的工作岗位来，也会通过生产率的增长来创造额外的财富和消费，使得现有一些种类的工作增加就业机会，主要在那些不容易被自动化的服务行业。

The net impact of automation on total employment is therefore unclear. Average pre-tax incomes should rise due to the productivity gains, but these benefits will probably not be evenly spread across income groups. The pay premium for higher education and non-automatable skills will also probably rise ever higher.

因此，自动化对于总就业的净影响难以预估。平均税前收入将因为生产率增长而上升，但是这些利益并不太可能会均等的在不同收入人群中分配。而人们为了更高的教育水平以及学习不被自动化淘汰的技能，所花费的费用将会的越来越多。

There is therefore a case for some form of government intervention to ensure that the potential gains from automation are shared more widely across society through policies in areas like education, vocational training and job matching. Some form of universal basic income scheme might also be considered though this does face problems relating to affordability and potential adverse incentive effects that would need to be addressed.

所以有理由采取一些形式的政府干预，来保证自动化所带来的潜在利益能更加广泛的在整个社会中分享，比如通过在教育、职业培训以及就业分配等领域的政策。某种形式的全民基本收入计划也应列入考虑，虽然这面对经济上能否承担以及逆向激励效果（G19注：指培养吃福利的懒汉）相关的问题需要解决。

以下为原文注：

[10] For a more detailed discussion of these issues, see PwC Strategy&’s 2016 Connected Car report here: http://www.strategyand.pwc.com/reports/connected-car-2016-study

对于此问题的细节讨论，见普华永道思略特咨询公司的《2016年联网汽车报告》。

[11]  C. Frey and J. Hawksworth (PwC, 2015): http://www.pwc.co.uk/assets/pdf/ukeo-regional-march-2015.pdf
[12] See, for example, this 2015 PwC report on the potential productivity benefits of service robots:
http://www.pwc.com/us/en/technol ... ivity-platform.html

可参考普华永道2015年关于机器人带来的潜在生产率增长报告。

[13] Of course, eventually, we may reach the science fiction scenario where robots become indistinguishable in all ways from humans. At present, that seems likely to be much further off than the early 2030s time horizon we are focusing on in this study, though this could always change given recent rapid advances in AI and robotics.

当然，终有一天我们会到达科幻小说里的场景，机器人变得与人类难以区分。目前来说这远远超出我们在本研究中所针对的2030年代早期这个时间点，虽然这也说不定，因为近期以来人工智能与机器人技术上在快速的进步。

[14]  An area where the UK lags well behind countries like Germany as highlighted in our 2016 Young Workers Index report here:
http://www.pwc.co.uk/services/ec ... -workers-index.html

在这个领域英国明显落后于德国等国家，我们在2016年年轻工人指数报告中提过。

[15] Another idea here is the recent suggestion of Bill Gates to tax robots where these displace human labour. However, it is not clear that such a specific tax on investment in robots would be economically efficient. Other labour-saving technologies do not face such specific taxes, so why single robots out for such treatment and potentially lose productivity gains from such innovation and investment? 

另有一个思路是比尔盖茨最近提出的建议，向机器人取代人类劳工征税。然而，向机器人领域的投资征收特别税在经济上是否有效是一个问题。其他节省人力的技术并没有被特别征税，那为什么专门针对机器人技术？这会使得这些创新与投资所带来的生产力增长化为乌有。