Traditional labor market data isn’t keeping up with jobs in the ‘frontier economy’

Current debates about AI and the labor market overwhelmingly focus on job losses.

当前关于人工智能和劳动力市场的争论绝大多数集中在失业问题上。

If you’re a student of history, this might seem counterintuitive: You’ve likely heard the argument that new technologies historically create more new jobs on net than they destroy.

如果您是历史系的学生，这可能看起来违反直觉：您可能听说过这样的论点：历史上新技术在网络上创造的新就业机会多于它们破坏的就业机会。

One reason why today’s discourse revolves around job displacement rather than creation is that we have far less clarity about what those “new jobs” are going to be.

今天的讨论围绕着工作岗位取代而不是创造工作，原因之一是我们对这些“新工作”将是什么还不太清楚。

Over the past year, as part of my upcoming book, “The New American Frontier: Job Training for the Next Technological Age,” I have been researching the jobs being created in what I call “frontier regions”—places that have been early to develop and deploy critical and emerging technologies.

在过去的一年里，作为我即将出版的新书《美国新前沿：下一个技术时代的职业培训》的一部分，我一直在研究在我所谓的“前沿地区”——那些较早开发和部署关键新兴技术的地方——创造的就业机会。

In doing this work, I came to appreciate more fully that the traditional labor market data sources many policymakers rely on were not really designed to shed light on this question, especially not in real time.

在开展这项工作时，我更加充分地认识到，许多政策制定者所依赖的传统劳动力市场数据源并不是真正旨在阐明这个问题，尤其是实时数据。

Some of this is by intention.

其中一些是有意为之。

Government statistics are meant to prioritize consistency and reliability, not to change with every new trend.

政府统计数据旨在优先考虑一致性和可靠性，而不是随着每个新趋势而改变。

For many purposes, this is a strength.

对于许多目的来说，这是一种优势。

But it also has downsides: If new or changing job categories do not show up clearly in official data, that makes it harder for people to see where new opportunities will be or what skills they should start building.

但它也有缺点：如果新的或变化的工作类别没有在官方数据中清楚地显示出来，那么人们就更难看到新的机会在哪里，或者他们应该开始培养哪些技能。

This analysis shows why traditional public data sources often struggle to capture the extent of the “frontier economy,” or the jobs critical and emerging technologies create.

这一分析说明了为什么传统的公共数据源常常难以捕捉“前沿经济”的范围，或者关键的就业机会和新兴技术所创造的就业机会。

Drawing on our experience analyzing jobs data in frontier industries, we highlight several recurring challenges and blind spots for policymakers relying on traditional labor market data.

借鉴我们分析前沿行业就业数据的经验，我们强调了依赖传统劳动力市场数据的政策制定者面临的几个反复出现的挑战和盲点。

We conclude with five ready-to-deploy strategies that can help practitioners keep better pace with the workforce development needs of new industries, even in the absence of perfect data.

最后，我们提出了五种可立即部署的策略，即使在缺乏完美数据的情况下，这些策略也可以帮助从业者更好地满足新行业的劳动力发展需求。

The examples in this piece reference several data sources and classification systems that policymakers and educators commonly use to analyze the labor market, summarized in Table 1.

本文中的示例引用了政策制定者和教育工作者通常用来分析劳动力市场的几个数据源和分类系统，如表 1 所示。

A common way state and local policymakers project workforce needs is to start with an industry using North American Industry Classification System (NAICS) codes, and then use Bureau of Labor Statistics (BLS) industry-occupation tables to estimate the composition of workers across different Standard Occupational Classification (SOC) occupations.

州和地方政策制定者预测劳动力需求的常见方法是从使用北美行业分类系统 (NAICS) 代码的行业开始，然后使用劳工统计局 (BLS) 行业职业表来估计不同标准职业分类 (SOC) 职业的工人构成。

They may then use O*NET to help them translate the SOC occupations into tasks and skills workers will need.

然后，他们可以使用 O*NET 帮助他们将 SOC 职业转化为工人所需的任务和技能。

To go beyond these official classifications in this analysis, we use individual-level resume data and job postings from Revelio Labs .

为了在本次分析中超越这些官方分类，我们使用来自 Revelio Labs 的个人简历数据和职位发布。

Revelio compiles data from more than 1 billion worker profiles, including work and education histories, along with data from job posting aggregators and company websites.

Revelio 汇集了来自超过 10 亿员工档案的数据，包括工作和教育历史，以及来自职位发布聚合器和公司网站的数据。

One of the first problems in understanding jobs in frontier industries is definitional: It’s hard to identify the right industry codes to use.

理解前沿行业工作的首要问题之一是定义：很难确定要使用的正确行业代码。

Take data centers as one example.

以数据中心为例。

In the Census Bureau’s NAICS system, companies self-classify in tax forms and surveys based on their primary business activity, or the activity that generates the most revenue.

在人口普查局的 NAICS 系统中，公司根据其主要业务活动或产生最多收入的活动在纳税表格和调查中进行自我分类。

However, data center companies vary widely in their business models, and could reasonably be classified under several different codes.

然而，数据中心公司的业务模式差异很大，可以合理地分为几个不同的代码。

For example:

例如：