当我们听到自动化和人工智能取代工作, 时，似乎一场技术海啸将以更高的效率之名广泛消灭工人,。但麻省理工学院经济学家与人合着的一项研究显示，自 1980 年以来美国的动态明显不同。
When we hear about automation and artificial intelligence replacing jobs, it may seem like a tsunami of technology is going to wipe out workers broadly, in the name of greater efficiency. But a study co-authored by an MIT economist shows markedly different dynamics in the U.S. since 1980.
公司不是为了追求最大生产力,而实施自动化，而是经常使用自动化来取代那些专门获得“工资溢价,”的员工，这些员工比其他同类工人赚取更高的工资。实际上, 这意味着自动化经常降低未受过大学教育的工人的收入，这些工人的薪水比大多数具有类似资格的员工要高。
Rather than implement automation in pursuit of maximal productivity, firms have often used automation to replace employees who specifically receive a “wage premium,” earning higher salaries than other comparable workers. In practice, that means automation has frequently reduced the earnings of non-college-educated workers who had obtained better salaries than most employees with similar qualifications.
这一发现至少有两个重大意义。一方面，, 自动化对美国收入不平等加剧的影响甚至比许多观察家意识到的还要大。与此同时，, 自动化对生产率的提升却表现平平,，这似乎是由于企业将重点放在控制工资上，而不是寻找更多技术驱动的方法来提高效率和长期增长。
This finding has at least two big implications. For one thing, automation has affected the growth in U.S. income inequality even more than many observers realize. At the same time, automation has yielded a mediocre productivity boost, plausibly due to the focus of firms on controlling wages rather than finding more tech-driven ways to enhance efficiency and long-term growth.
“自动化目标效率低下,” 麻省理工学院的 的 Daron Acemoglu, 合著者发表了一篇详细介绍该研究的 结果的论文。 “特定行业、职业或任务中工人的工资越高,，自动化对公司的吸引力就越大。” 理论上,他指出,公司可以有效地实现自动化。但他们并没有,，而是强调它是一种削减工资的工具,，这有助于他们自己的内部短期数字，但没有建立最佳的增长路径。
“There has been an inefficient targeting of automation,” says MIT的 Daron Acemoglu, co-author of a published paper detailing the study的 results. “The higher the wage of the worker in a particular industry or occupation or task, the more attractive automation becomes to firms.” In theory, he notes, firms could automate efficiently. But they have not, by emphasizing it as a tool for shedding salaries, which helps their own internal short-term numbers without building an optimal path for growth.
该研究估计，1980 年至 2016 年收入不平等增长的 52% 是由自动化造成的,，其中约 10 个百分点是由于企业取代了一直赚取工资溢价的工人而造成的。在此期间，这种针对某些员工的低效定位抵消了自动化带来的 60-90% 的生产力收益。
The study estimates that automation is responsible for 52 percent of the growth in income inequality from 1980 to 2016, and that about 10 percentage points derive specifically from firms replacing workers who had been earning a wage premium. This inefficient targeting of certain employees has offset 60-90 percent of the productivity gains from automation during the time period.
“It的 美国生产力提高相对缓慢的可能原因之一,，尽管我们’拥有数量惊人的新专利, 和新技术,” Acemoglu 说。 “那么你看看生产力统计数据,，他们相当可怜。”
“It的 one of the possible reasons productivity improvements have been relatively muted in the U.S., despite the fact that we’ve had an amazing number of new patents, and an amazing number of new technologies,” Acemoglu says. “Then you look at the productivity statistics, and they are fairly pitiful.”
论文, “自动化和租金耗散:对工资,不平等,和生产率,”的影响出现在《经济学季刊》五月印刷版上。作者是 MIT; 研究所教授 Acemoglu, 和耶鲁大学经济学副教授 Pascual Restrepo,。
The paper, “Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity,” appears in the May print issue of the Quarterly Journal of Economics. The authors are Acemoglu, who is an Institute Professor at MIT; and Pascual Restrepo, an associate professor of economics at Yale University.
追溯到 2010 年代, Acemoglu 和 Restrepo 联合开展了许多关于自动化及其对就业, 工资, 生产率, 和企业增长的影响的研究。总的来说, 他们的研究结果表明，1980 年后自动化对劳动力的影响比许多其他学者认为的更为显着。
Dating back to the 2010s, Acemoglu and Restrepo have combined to conduct many studies about automation and its effects on employment, wages, productivity, and firm growth. In general, their findings have suggested that the effects of automation on the workforce after 1980 are more significant than many other scholars have believed.
为了进行当前的研究,，研究人员使用了许多来源的数据,，包括美国人口普查局统计数据, 该局的数据的 美国社区调查, 行业数据, 等等。 Acemoglu 和 Restrepo 分析了 500 个详细的人口群体,，按五个教育水平, 以及性别, 年龄, 和种族背景排序。该研究将这些信息与对美国 49 个行业, 变化的分析联系起来，以详细了解自动化对劳动力的影响。
To conduct the current study, the researchers used data from many sources, including U.S. Census Bureau statistics, data from the bureau的 American Community Survey, industry numbers, and more. Acemoglu and Restrepo analyzed 500 detailed demographic groups, sorted by five levels of education, as well as gender, age, and ethnic background. The study links this information to an analysis of changes in 49 U.S. industries, for a granular look at the way automation affected the workforce.
最终,，该分析不仅使学者们能够估计因自动化而消失的工作岗位总量,，还可以估计其中有多少是公司非常专门试图消除某些工人的工资溢价。
Ultimately, the analysis allowed the scholars to estimate not just the overall amount of jobs erased due to automation, but how much of that consisted of firms very specifically trying to remove the wage premium accruing to some of their workers.
除其他发现,之外，该研究还表明，在受自动化影响的工人群体中,，薪资范围,的70-95%的工人受到的影响最大，这表明高收入员工在这一过程中首当其冲。
Among other findings, the study shows that within groups of workers affected by automation, the biggest effects occur for workers in the 70th-95th percentile of the salary range, indicating that higher-earning employees bear much of the brunt of this process.
正如分析表明的那样，收入不平等总体增长的约五分之一,可归因于这一唯一因素。
And as the analysis indicates, about one-fifth of the overall growth in income inequality is attributable to this sole factor.
“I 认为这是一个很大的数字,” 与长期合作者麻省理工学院的西蒙·约翰逊和芝加哥大学的詹姆斯·罗宾逊分享了 2024 年诺贝尔经济学奖的 Acemoglu, 说道。
“I think that is a big number,” says Acemoglu, who shared the 2024 Nobel Prize in economic sciences with his longtime collaborators Simon Johnson of MIT and James Robinson of the University of Chicago.
该研究还阐明了公司管理者, 的一个基本选择，但这一选择却被忽视了。想象一种自动化— 呼叫中心技术,，例如—，它实际上对企业来说可能效率低下。即便如此，, 公司经理有动力采用它, 降低工资, 并监督生产率较低的企业，但增加了净利润。
The study also illuminates a basic choice for firm managers, but one that gets overlooked. Imagine a type of automation — call-center technology, for instance — that might actually be inefficient for a business. Even so, firm managers have incentive to adopt it, reduce wages, and oversee a less productive business with increased net profits.
明显, 自 1980 年以来，美国经济似乎一直在发生这种情况: 更高的盈利能力并不等同于更高的生产率。
Writ large, some version of this seems to have been happening to the U.S. economy since 1980: Greater profitability is not the same as increased productivity.
“这两件事是不同的,” Acemoglu 说。 “您可以在降低生产效率的同时降低成本。”
“Those two things are different,” says Acemoglu. “You can reduce costs while reducing productivity.”
事实上, Acemoglu 和 Restrepo 目前的研究让人想起已故麻省理工学院经济学家 Robert M. Solow, 的一项观察，他在 1987 年写道, “除了生产力统计数据之外，你可以在任何地方看到计算机时代。”
Indeed, the current study by Acemoglu and Restrepo calls to mind an observation by the late MIT economist Robert M. Solow, who in 1987 wrote, “You can see the computer age everywhere but in the productivity statistics.”
在这种情况下, Acemoglu 观察到, “ 如果管理者可以将生产率降低 1%，但增加利润, 他们中的许多人可能会对此感到满意。这取决于他们的优先事项和价值观。因此，我们论文的另一个重要含义是，边缘的良好自动化与不太好的自动化捆绑在一起。”
In that vein, Acemoglu observes, “If managers can reduce productivity by 1 percent but increase profits, many of them might be happy with that. It depends on their priorities and values. So the other important implication of our paper is that good automation at the margins is being bundled with not-so-good automation.”
需要明确的是, 这项研究并不一定意味着自动化程度越低越好。某些类型的自动化可以提高生产力并促进良性循环，使公司赚更多的钱并雇用更多的工人。
To be clear, the study does not necessarily imply that less automation is always better. Certain types of automation can boost productivity and feed a virtuous cycle in which a firm makes more money and hires more workers.
但目前, Acemoglu 认为, 自动化的复杂性尚未得到足够清晰的认识。也许了解自 1980, 以来美国自动化, 的广泛历史模式将有助于人们更好地掌握涉及— 的权衡，而不仅仅是经济学家,，还有公司经理, 工人, 和技术专家。
But currently, Acemoglu believes, the complexities of automation are not yet recognized clearly enough. Perhaps seeing the broad historical pattern of U.S. automation, since 1980, will help people better grasp the tradeoffs involved — and not just economists, but firm managers, workers, and technologists.
“重要的是它是否融入了人们’的思维，以及我们对自动化,在不平等,生产力和劳动力市场影响方面的整体评估的定位,” Acemoglu说。 “所以我们希望这项研究能够推动这一目标。”
“The important thing is whether it becomes incorporated into people的 thinking and where we land in terms of the overall holistic assessment of automation, in terms of inequality, productivity and labor market effects,” Acemoglu says. “So we hope this study moves the dial there.”
或者,，正如他的结论, “通过更仔细地校准自动化的类型和程度,，并以更能提高生产力的方式，我们可能会错失潜在的更好的生产力提升。 的 都是一个选择, 100%.”
Or, as he concludes, “We could be missing out on potentially even better productivity gains by calibrating the type and extent of automation more carefully, and in a more productivity-enhancing way. It的 all a choice, 100 percent.”