Practical Ways to Modernize the Schedule A List

Executive summary

The Department of Labor’s Schedule A list streamlines the application process for an employment-based green card in occupations with excess labor demand. It has not been updated in 33 years. In October 2023, the Department of Labor (DOL) opened a Request for Information (RFI) about how they could modernize Schedule A using an objective, transparent, and reliable method.

Key findings:

• A majority of the substantive comments recommend updating the Schedule A list. Most of these comments cite the value of Schedule A as a tool to address U.S. labor shortages and provide greater certainty and predictability to immigrants, their families, and their employers. Commenters also say they would value Schedule A as a source of vital information about labor gaps in the United States. This information would both improve the employment-based green card system and allow the government to develop more effective workforce training and reskilling programs.
• Commenters identified 38 categories of economic indicators that could be used to make determinations about the occupations on Schedule A, and provided useful information about the associated costs and benefits of each indicator. Some indicators, like wage trends, are supported by both employer and worker organizations. Others, like vacancy postings, do not have broad stakeholder support. 
• Commenters suggested new worker protections that could be paired with a Schedule A update. They included requiring Schedule A employers not to include “stay-or-pay” provisions in contracts that tie workers to their employer, or requiring employers to attest they have been in compliance with permanent labor certification (PERM) regulations for five years.

Introduction

A simple internet search will return dozens of news articles saying the U.S. economy is experiencing intractable labor shortages, in occupations ranging from construction to hospitality to healthcare to research.¹ But how can a layperson (or the federal government) distinguish legitimate labor shortages from situations where wages are simply too low to attract more workers? How can we determine which industries are experiencing the worst, longest-lasting, or damaging labor shortages?

Readers may be surprised that there is no federal method for evaluating labor shortage based on economic data. Federal and state agencies spend millions collecting data about the health of the workforce and the economy, but offer no transparent method identifying categories of employment that need more workers. This opaqueness has ramifications far beyond just research. Students have difficulty determining which fields to study to land good jobs. Schools do not know what skills their graduates need to be competitive in the job market. Companies, states, and the federal government do not know how to maximize the benefits of limited workforce training resources.

DOL² has a neglected mechanism that could be used to identify areas of severe labor shortage, called Schedule A, but it has not been updated in decades. However, for the first time in over thirty years, DOL is considering a Schedule A modernization. In late 2023, it issued an RFI asking the public for input. By the time the comment period closed in mid-May 2024, DOL received over 2,000 comments with suggestions, criticisms, and new ideas.

An RFI is a tool available to a federal department or agency in advance of notice and comment rulemaking, facilitating the ability to collect distributed knowledge among stakeholders across the country. The Administrative Conference of the United States has recommended U.S. agencies take such steps to enhance public engagement in rulemaking to avoid regulatory proposals that do not first assemble the necessary, comprehensive information needed to tackle complex problems.³ Such recommendations are intended to better ensure agencies obtain “situated knowledge” as part of rulemaking efforts – an acknowledgement that public officials benefit from having access to knowledge that is widely dispersed among stakeholders.⁴ “In particular, agencies need information from the industries they regulate, other experts, and citizens with situated knowledge of the field in order to understand the problems they seek to address, the potential regulatory solutions, their attendant costs, and the likelihood of achieving satisfactory compliance.”⁵ When departments and agencies issue an RFI, they then thoroughly analyze comments filed to make decisions on how to proceed.

This paper aims to analyze the comments submitted to DOL about Schedule A, discuss the concerns raised, and present the options DOL has to develop a reliable, data-driven methodology to assess labor shortage and finally issue an update to Schedule A. It assesses the substantive comments, discusses their recommendations for data sources, economic indicators, and methods, and identifies the pros and cons of each suggestion. 

Developing a reliable methodology for labor shortages has long been thought too difficult an undertaking. This assessment of RFI comments suggests a path forward.

Overview of Schedule A

Starting with the Immigration and Nationality Act of 1965, Congress gave the Secretary of Labor the responsibility to certify that before a foreign worker is hired by a U.S. employer, “there are not sufficient workers in the United States who are able, willing, qualified, and available at the time of application for a visa and admission to the United States and at the place to which the [foreign worker] is destined to perform such skilled or unskilled labor, and the employment of such [foreign workers] will not adversely affect the wages and working conditions of the workers in the United States similarly employed.”⁶ Shortly after the passage of this act, DOL realized that approving individual labor certifications was highly time-consuming. To combat processing delays of work visas, the Secretary of Labor developed Schedules.⁷ Schedule A pre-certified categories of employment where there were not enough U.S. workers ready, willing, and able.⁸ 

Schedule A has been updated multiple times since 1965. It included categories of employment of critical importance to the United States at the time, including people with degrees in engineering, physics, chemistry, and pharmacology, among others.⁹ The final substantial update to the Schedule A list pre-certifying categories of employment was in 1991.¹⁰ Since then, it has included only three categories: physical therapists, nurses, and a poorly understood category of foreign workers who possess “exceptional ability in the sciences, arts, or performing arts.”¹¹ According to DOL, the agency used the workforce data it collected, stakeholder feedback, and (for a few years) data from the Department of Health and Human Services (HHS) to determine which categories of employment should be included in Schedule A.¹² The exact method that DOL used to make these determinations is not publicly available. In the last 33 years, the nature of work, federal data collection, and research about labor shortage has evolved significantly.

Recent federal actions on Schedule A

In recent years, there has been growing momentum for DOL to modernize its method for Schedule A pre-certification, including recommendations from the House Select Committee on China and the National Academies of Sciences.¹³ This momentum culminated in the passage of President Biden’s October 2023 Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (AI). The Administration urged DOL to examine ways to modernize Schedule A to attract and retain talented workers as leadership in AI and other critical and emerging technologies becomes increasingly important internationally.¹⁴ 52 days later, DOL issued an RFI seeking to understand the labor market in science, technology, engineering, and mathematics (STEM) and non-STEM fields, and if Schedule A could be a tool to satisfy protracted demand.¹⁵ DOL notes that, “In the 1960s and 1970s, Schedule A was the product of an extensive process of economic and labor market analysis of employment demand and supply by the Department. Schedule A occupations were later identified through the application of multiple factors, including unemployment rates; occupational projections; evidence submitted by trade associations, employers, and organized labor; and technical reviews by federal and state staff with expertise in these areas. The occupational listings in the Schedule were reviewed and modified at regular intervals to reflect changing economic and labor market conditions and to prevent adverse effects on the wages or working conditions of U.S. workers.” However, since the Schedule A list has not been updated in over 30 years, DOL does not have comprehensive data on how employers use Schedule A designations or whether it should be expanded.¹⁶

DOL asked questions to understand whether any occupations should be added to Schedule A and why, what sources of data and methods should be used to determine labor shortage, how DOL could develop its own methodology, and how to define occupations in STEM. The full list of questions can be found in Appendix B. In subsequent sections, this paper will provide an overview and analysis of the comments DOL received in response to their RFI. All comments are publicly available on registration.gov.¹⁷

Summary of RFI results

Before DOL closed the RFI’s comment period on May 13, 2024, the agency received 2,036 public comments. 151 of those comments provided sources or substantive arguments for their opinions on the modernization of Schedule A. Of those comments, 53% support and 47% oppose updating the list.

These commenters come from a variety of sectors, including healthcare, scientific research, government, and labor. 96% of the total number of comments were submitted either anonymously or by an individual. The rest of the comments were from companies (33), professional or industry associations (23), nonprofit organizations (20), think tanks (8), governmental organizations (6), and labor unions or workers’ rights organizations (6). 18% of the comments included information that identified the commenters as being in the information technology industry. 6% of respondents were from the scientific research sector. 3% were from the policy sector. Only 1% of respondents were in healthcare. Unfortunately, 71% of all the submitters either did not mention which industries of which they were a part or were not in the industries specifically examined in this analysis (academia, agriculture, construction, defense, finance, healthcare, hospitality, information technology, policy/government, or science).

Those in support of modernizing Schedule A suggested a wide range of occupational categories to be added, such as engineering, robotics, quantum computing, biomedical research, nursing, emergency medicine, K-12 education, manufacturing, agriculture, and construction. One commenter even suggested that any occupational categories that have a PERM approval rate of 98% or higher should be added. Commenters suggested DOL use several sources of data for a Schedule A update, including both public and private sources at the national, regional, and local levels. Commenters recommended ways DOL can improve its own data collection or partner with other agencies to increase the granularity of federal data. 

Concerns about the treatment of foreign nurses who have used Schedule A to come to the United States were a frequent topic of discussion and produced recommendations about how DOL can reduce fraud and abuse in the employment-based visa system. The RFI also produced a robust list of economic indicators DOL should use if it undertakes the development of a methodology to update Schedule A. These indicators ranged from ways to measure job openings, unemployment rates, and wages to the working conditions, training opportunities, and demographics of the current workforce. Beyond just updating the Schedule A list, commenters also had recommendations about improving the process to obtain a prevailing wage determination, expanding special handling, and creating an outside consultative body of experts who would analyze labor shortage questions in the future.

In order to best consider and understand the numerous substantive comments filed in response to the RFI, we discuss comments on possible Schedule A revisions in the following groupings:

1. Should DOL modernize Schedule A?
2. Recommended methodologies to update Schedule A
3. Recommended worker protections
4. Recommended data sources
5. Defining STEM

Should DOL modernize Schedule A?

In this section we describe commenters’ feedback as to whether DOL should modernize Schedule A. First, we discuss criticisms of Schedule A modernization. We then discuss support for Schedule A modernization. Lastly, we briefly outline the occupational categories that commenters specifically recommended should be added to Schedule A.

Arguments against modernizing Schedule A

While a majority of substantive comments supported an update to Schedule A, a significant minority were skeptical of the need for an update. In this section, we distinguish between two categories of arguments deployed by those skeptical of a Schedule A update: a) shortage-denying arguments and b) methodology critiques. As we discuss, we think both contribute important insights for DOL and should inform the methodology they eventually adopt.

The first category of criticism is shortage-denying arguments. These arguments presume that we can use existing sources of evidence to determine whether labor shortage conditions prevail, but assert that the evidence would not support the existence of labor shortage. By presuming that we have sufficient evidence to make such judgments, this kind of argument is consistent with the Department adopting a transparent, evidence-based, and outcome-neutral methodology to identify labor shortage. Indeed, many of the comments that fall into this category provide useful information about what that methodology could look like. 

Many shortage-denying commenters expressed concerns that a Schedule A update will be captured by self-interested firms that peddle false claims of a shortage. It is precisely for this reason that a transparent, objective, and reliable process will be critical: to evaluate which claims are legitimate and which are not. Many of the comments denying specific claimed shortages identify precisely the categories of evidence and even the specific indicators that DOL should be looking at to make determinations across all occupations.

For example, the Center for Immigration Studies (CIS) suggests that “DOL should base any determination of a ‘labor shortage’ on wage trends,” in conjunction with “the number of working-age Americans who possess relevant degrees and the share of such degree holders who currently work in the pertinent occupations.”¹⁸ The Economic Policy Institute (EPI) echoes some of CIS’s recommendations, concluding that “rising wages and the wages offered in an occupation are a key — if not the primary — indicator of whether a shortage exists in an occupation.”¹⁹ The view that wage trends are critically important is also shared by the International Federation of Professional and Technical Engineers (IFPTE), which points to wage growth and lagged wage growth, in both nominal and real terms, as compelling evidence.²⁰ Given essentially universal support for the informative value of wage trends, we suggest these be the core of any methodology DOL adopts.

AFL-CIO’s comment suggests using the layoff rate as a negative indicator of labor shortage conditions, which also seems quite reasonable.²¹ In any attempt to measure this, care should be taken not to conflate quits or job-to-job transitions (i.e., workers quitting to find better jobs) with layoffs. EPI also suggests enrollment rates among U.S. citizens and legal permanent residents (LPRs) in STEM field degree programs.²² On this last point, we caution that while supply of STEM workers has increased, demand has also increased over the period. The question for DOL must always be if demand rises faster than supply can increase to meet it — and how the market fares during the long training period. 

At the same time, many commenters warn against heavy reliance on indicators like job openings or the average number of days positions are open, as these may simply reflect bad offers with low pay or poor conditions. CIS also cautions that DOL should not infer too much from low unemployment rates, which may mask people leaving the labor force altogether if they cannot find a job.²³ This caution is shared by the AFL-CIO, which notes that relatively lower unemployment is consistent with STEM graduates having education and skills that let them get work in other fields, rather than a shortage of STEM jobs.²⁴

Other commenters similarly skeptical that labor shortages exist suggest using unemployment, but with caveats. EPI and the Institute for Sound Public Policy (IfSPP) both argue that unemployment on its own is a misleading indicator, and must be compared to historical within-occupation unemployment rather than across-occupation unemployment.²⁵ In its comment, EPI points out that unemployment rates associated with full employment (and recessions) vary significantly across occupations. They therefore argue that DOL should use occupation-specific full employment rates (i.e., the rate of unemployment when the aggregate economy is in full employment) as a baseline for comparison.

We agree that comparing the economy-wide full employment rate to every occupation may overstate the extent of shortage conditions. In Help Wanted, we suggest choosing a threshold for inclusion in Schedule A by targeting the number of workers in occupations with less than 1.8% unemployment (well below than the 2.8% IfSPP identifies for computer workers, or the 2% EPI suggests).²⁶ But the business cycle affects occupations differently, and full employment across the aggregate economy does not necessarily imply there is full employment in every occupation. Therefore, we cannot deduce that an occupation is at full employment if and only if it has lower unemployment than it does when the aggregate economy is at full employment. If an occupation has high barriers to entry, in an industry unexposed or anticorrelated with the business cycle, shortage conditions may persist even during a recession. Meanwhile, another occupation may not face shortage conditions even when the aggregate economy is at full employment. Put another way, if conditions in the aggregate economy simply coincided with occupation-specific conditions, then occupational characteristics would not be needed.

In short, arguments that deny the existence of shortages share implicit assumptions with arguments that broadly indicator-based approaches should be used. Both arguments often provide important recommendations about which indicators DOL should find reliable. These arguments will be useful for DOL to study in constructing a credible, evidence-based methodology. 

Schedule A skeptics like the AFL-CIO, EPI, and CIS generally agreed that wage trends are the most important and reliable indicator of shortage conditions, while vacancies and general employment trends are less reliable. We recommend that DOL adopt a method that places significantly more weight on the indicators that Schedule A-skeptical commenters find convincing, and places less weight on indicators skeptics suggest are unreliable. 

The second category of arguments consists of methodology critiques. These arguments doubt the feasibility and reliability of particular methodologies that DOL may be considering. Some of these critiques point to important drawbacks of certain methodologies that can simply be avoided. Others point to serious tradeoffs that DOL should carefully weigh and that we believe can ultimately be addressed. We urge DOL not to consider the mere existence of drawbacks to a proposal in isolation, or to compare proposals against imagined perfection. Rather drawbacks must be compared against the drawbacks of the opaque status quo. 

For example, EPI poses four methodological critiques that it believes support inaction on Schedule A. EPI identifies four weaknesses with an indicator-based methodology of the sort used by other developed countries: lags in data, lack of robustness, unharmonized data, and the importance of “bottom-up” data.²⁷

EPI’s comment states that “data available to the Department for determining a shortage are far too old and out of date to credibly establish a current and/or future shortage.” But the data available today are no less frequent than when Congress first conceptualized the use of Schedule A. In fact, there is better data infrastructure today, with more diverse sources, larger sample sizes, better statistical and integrity methods, and more frequent updates. Delays in PERM processing also impose lags comparable to lags in federal data collecting. Nevertheless, remaining lags in data can be accounted for if the Department ensures identified shortage occupations are persistent and the list is not subject to volatile swings. As we discussed above, there is often a tradeoff between granularity of data and frequency of updates, to which the Department has to strike a balance. To the extent lagging data is a concern, the Department can still update the list by considering occupations that have been persistently identified in back-to-back years, or by including lagged variables to ensure identified occupations face structural shortage conditions. Green cards — facilitating permanent immigration — are more conducive to persistent, long-lasting shortages rather than transient ones. Finally, the Department could use an ensemble of sources, incorporating less granular data that is updated more frequently, like the Current Population Survey (CPS).

EPI also criticizes indicator-based methodologies for not being sufficiently robust — in other words, for being sensitive to assumptions. Specifically, they cite fluctuations in the resulting Schedule A list when adjusting parameters on the Institute for Progress’s (IFP) interactive Create Your Own Data-Driven Update to Schedule A tool.²⁸ We believe they overstate the extent of the sensitivity. As an exercise, we can assign each of the 11 parameter values completely at random to simulate 1,000 Schedule A cases, and compare the resulting lists. What we get is a robust list of core occupations that are relatively insensitive to particular assumptions. For example, electrical and electronics engineers, surgeons, and astronomers and physicists all show up in at least two-thirds of the simulations. Meanwhile, most occupations robustly do not show up in the simulations. Over 88% of occupations show up in fewer than half of our simulations. 20% of occupations, like welding/soldering/brazening workers and waiters and waitresses, do not show up in a single one. If there is strong uncertainty around a parameter, DOL can build robustness checks into its methodology. Additionally, sensitivity may not matter if we have good reasons to choose our particular assumptions. The point is not to randomly assign weights, but to use available evidence to pick reasonable assumptions. Many of the RFI responses provide significant helpful evidence about which assumptions to pick, how to weight different indicators, and so on. 

EPI also points to a lack of occupational harmonization across data sources. While we agree that data sources can be improved, a variety of data sources also provides an opportunity to use converging lines of evidence. EPI cites the example of CPS and the Occupational Employment and Wage Statistics (OEWS) using different definitions, noting the crosswalk is not a one-to-one mapping. Ultimately, many government functions have to rely on data that do not have perfect harmonization. We do not think this is an existential threat to evidence-based decision-making. 

As to the lack of “bottom-up” data sources, this too poses tradeoffs that the Department should consider. A benefit of including bottom-up sources is that it may ensure the legitimacy of results. However, we note that in conversations with the UK’s Migration Advisory Committee (MAC) members, they cautioned that these are the most costly to produce and analyze, while providing the MAC the least value, since they are produced by biased sources like employers and hard to corroborate and compare.

Benefits of updating Schedule A

It is possible to update Schedule A to address severe labor shortage in the present day, and in fact, updating the list aligns with the intent of Congress when the Immigration and Nationality Act of 1965 was passed. The intent was that DOL should certify that “there are not sufficient workers who are able, willing, qualified, and available at the time of application” (emphasis added).²⁹ However, in fiscal year 2024, it generally takes 400 to 560 days to complete the entire PERM process, leaving aside the waiting period for petitions and applications at the Department of Homeland Security (DHS) and the Department of State after PERM is completed.³⁰

Additionally, a majority of workers getting employment-based green cards are adjusting from a temporary work visa, like an H-1B. When a worker is on an H-1B, it is very difficult to change employers or job duties, leaving these workers more vulnerable to poor treatment. However, under current law, individuals whose adjustment of status to an employment-based green card has been pending for over 180 days are eligible to change employers more easily, but the clock does not start until after DOL certifies the PERM application.³¹

Modernizing Schedule A could support several of the federal government’s priorities. For example, the CHIPS and Science Act of 2022 provided $52 billion in funding to support the growth of the semiconductor manufacturing industry in the United States.³² The act also spurred $450 billion in private investments to increase U.S. manufacturing capacity.³³ However, for this funding to be successfully deployed, more workers need to be hired. The Semiconductor Industry Association predicts in its comment that if this need is not addressed, 1.4 million new jobs requiring STEM technical proficiency risk going unfilled.³⁴ The development of quantum computing technologies has also been highly important. Congress and the Trump and Biden Administrations have prioritized quantum sciences with the creation and support of the National Quantum Initiative (as a result of the passage of the National Quantum Initiative Act of 2018,³⁵ its expansion via the National Defense Authorization Act (NDAA) for Fiscal Year (FY) 2022,³⁶ and by the CHIPS and Science Act of 2022).^{37 38} However, as the Center for a New American Security³⁹ and the Quantum Economic Development Consortium (QED-C) note,⁴⁰ the difficulty in recruiting enough workers threatens U.S. national security.

This competition for STEM talent is not happening in a vacuum. Competition with China has been a central issue for both the Trump and Biden Administrations. It has also become a concern of Congress, as noted by the House Select Committee on the Chinese Communist Party. In its report Reset, Prevent, Build: A Strategy to Win America’s Economic Competition with the Chinese Communist Party, the committee recommends that Schedule A be updated to “add relevant occupations critical to national security and emerging technology,” and that “Schedule A be updated continuously to reflect the dynamic job market and current market conditions and demands in certain industries.”⁴¹ A modernized Schedule A can help bolster the United States’ position on the global stage, by making the country’s processes to recruit international talent more predictable and certain. The importance of attracting international talent has been recognized in the Biden Administration’s Executive Order on AI and its order for DOL to publish the very RFI we are discussing in detail in this report.⁴²

Evidence has already shown that the number of STEM graduates outside of the United States is growing rapidly. The Center for Security and Emerging Technology (CSET) reported that by next year, it is estimated that China will produce more than 77,000 STEM PhD graduates per year compared to about 40,000 in the United States.⁴³ The Quantum Economic Development Consortium (QED-C) notes that in 2021, the European Union graduated 113,000 students in quantum-relevant fields, versus 55,000 graduates in the United States that same year.⁴⁴ 

A growing proportion of STEM graduates in the United States are in the country on a temporary visa, and will require a green card if they are to stay and contribute to our scientific and technological goals in the long term. In its comment, the American Physical Society⁴⁵ cites another CSET study noting that between 2000 and 2019, foreign-born students accounted for more than 40% of the 500,000 STEM PhDs awarded, and 46% of the doctoral degrees in physics.⁴⁶ In 2021, NCSES found that more than half of graduate students studying critical STEM fields, such as AI (50 percent); electrical, electronics, communications, and computer engineering (61 percent); and computer science (66 percent) were on temporary visas.⁴⁷ And current immigration policies are actively making it more difficult to attract and retain this talent. One survey of scientists at research-intensive universities found that 90% of respondents agreed and that the unpredictability and uncertainty of the system hindered the development of the scientific workforce and the competitiveness of high-tech industries.⁴⁸

The addition of specific occupations to Schedule A

Many commenters suggested specific occupations be added. We do not endorse or reject any of these occupational recommendations, but are simply reporting on the results collected by DOL. 166 comments provided these suggestions, with 117 suggesting occupations requiring a high level of skills.

The table below outlines the categories in which commenters suggested adding specific occupations. It is important to note that these authors’ analysis categorized recommended occupations into eight groups. Seven are listed in the table below (one group, “Mathematics,” receiving no recommendations). The category “Other non-STEM” included occupations that are not defined as hospitality, healthcare, or construction, such as accountants, teachers, meat and seafood processors, furniture manufacturers, agricultural commodity producers, veterinarians, and founders and/or owners of startups. The category “Other STEM” included occupations such as biomedical researchers, trust and safety roles, and “occupations that are in the United States’ critical national interest.”

Occupational categories recommended in RFI responses

Within these categories, commenters suggested a wide range of occupations to be added to Schedule A, both high- and low-skill. In STEM, commenters recommended adding:

• Industrial engineers
• Automation and robotics occupations
• Electrical engineers
• Quantum technology workers
• Biomedical researchers
• AI engineers
• Atmospheric and space scientists
• Astronomers and physicists
• Natural science managers
• Environmental engineers
• Workers with degrees or experience in occupation the federal government has designated as critical and emerging technologies

In healthcare, commenters recommended adding:

• Direct care occupations
• Registered nurses
• Nurse practitioners and nurse midwives
• Physical therapists
• Pharmacists
• EMTs and paramedics
• Surgeons
• Psychologists
• Counselors
• Audiologists
• Diagnostic-related technologists and technicians
• Doctors serving in federally designated Medically Underserved Areas/Health Professional Shortage Areas

Other suggested occupations included:

• K-12 teachers
• Construction workers
• Hospitality workers
• Furniture manufacturers
• Agricultural commodity producers
• Veterinarians
• Accountants
• Urban and regional planners
• Training and development managers
• Architectural and engineering managers
• Founders and owners of startups and existing businesses
• L-1B intracompany transferees with specialized knowledge

Recommended methodologies to update Schedule A

Indicator-based approaches

One of the more common types of methodologies that have been developed to assess labor shortage uses economic indicators. These indicators capture many different economic conditions that can exacerbate, or alleviate, excess labor demand. In some cases, an indicator-based approach is combined with a more qualitative method to incorporate feedback from employers, business associations, governments, workers and workers’ rights organizations, and members of the general public. The next section will provide additional details on qualitative, or nominations-based, assessments.

By reading all 2,036 comments submitted to the RFI, we have compiled the 38 economic indicators that commenters recommended be included in labor shortage analysis. The indicators vary in level of granularity and availability, but can be grouped into several buckets. Those buckets are: price data; employment data; talent pipelines; hirings, layoffs, and turnover; migration; and organized labor. The tables for each bucket of indicators can be found in Appendix C, and include the commenters that suggested each indicator, the possible sources of data for each indicator, and their availability.

Some of the most commonly suggested indicators include the retention rate of workers, the average time to fill positions, wage increases at a rate greater than inflation, company level of investment in workforce training, and job-to-job flows to measure how many people switch jobs within an occupational category. The most common suggested indicator was forecasted occupation employment growth relative to total employment growth. Many commenters suggested using the Bureau of Labor Statistics’ (BLS) Employment Projections for this purpose. However, as explained in further detail below, the projections are not meant to be used to forecast labor demand or supply, and are generated under the assumption that there are no shortages of labor.

Many commenters recommended using indicators that would be sourced from internal company data, such as quit rates, the number of qualified applicants responding to job openings, the rate of job offers being declined, and the number of qualified foreign applicants versus U.S. applicants for an occupation. Internal company data could fill informational holes that appear in federal databases, and provide rich context on local and regional talent pipelines.

Nevertheless, there were multiple suggested indicators that could be measured using only publicly available data. BLS’ Job Openings and Labor Turnover Survey (JOLTS) and the Census Bureau’s CPS could satisfy indicators such as the rate of layoffs, number of job openings, number of workers hired versus open positions, unemployment rates, and age of the current workforce. Both proponents and opponents of updating Schedule A suggested that wage trends are among the most reliable indicators of shortage conditions.

An example of a purely indicator-based approach is the Help Wanted Index from IFP.⁴⁹ The Index uses ten indicators, weighted evenly, to create a “score” for each of almost 400 occupational categories. Those indicators were chosen to strike a balance that is responsive to both long-term and short-term changes in labor demand, and include percentage change in the median wage over one year and over three years, job vacancy postings per worker, percentage change in employment over one year, percentage change over three years in median weekly paid hours worked, labor force non-participation, income premium, unemployment rate, three-year lagged unemployment rate, and job-to-job transition rate over one year.⁵⁰

Christophe Combemale of Valdos Consulting LLC developed a similar methodology with Andrew Reamer and Jack Karsten of George Washington University, but these authors instead divided up the indicators into four separate labor tests for labor demand, labor supply, workforce training, and worker transitions, which would apply after an occupation is nominated (an approach described in the next subsection).⁵¹ The demand test is intended to map labor demand across the country according to occupations, skills, and educational attainment. Using data from OEWS, online job postings, and state-based training infrastructure, DOL would aggregate labor demand by at least the state level into a central, public map of in-demand occupations. Next, DOL would conduct the supply test to determine if labor demand is being met by the domestic labor market. This test would include an analysis of online job postings data, UI records, real wage data controlled for productivity gains, and data on relative wages to similar occupations. The training test aims to answer whether labor demand can be “credibly met in a timely fashion” by workforce training programs. This test requires that DOL establish a clear time horizon over which training or job transitions might be expected to meet demand, including a reasonable amount of time for the administration of Schedule A. This analysis would require an understanding of the training programs available that could deliver relevant occupational certifications. Information from the Survey of Earned Doctorates (SED) and Workforce Innovation and Opportunity Act (WIOA) training provider data would be useful in the analysis. If the training time required exceeds the amount of time it takes to update Schedule A by a set amount, it would indicate that it might be useful to include that occupation on Schedule A. DOL would then conduct the transition test, asking if labor demand could be “credibly met” if workers transitioned into the examined occupation from occupations with similar skills. This test would be informed by Carnegie Mellon University’s Workforce Supply Chain Initiative.⁵² The test would also incorporate data on realized worker transitions from CPS, unemployment insurance (UI) records, and private sources, similarities in skills between the examined occupation and adjacent occupations, and similarities in the occupations’ industries.

Both of these methods, the Help Wanted Index and the four tests, are intended to be conducted by DOL itself. This can be beneficial because DOL has access to a lot of economic data from federal and state governments. However, there is an administrative burden that accompanies such a model and the agency would want to also incorporate outside feedback into their analysis.

To mitigate any administrative burden, other commenters suggested that DOL establish an independent body of experts to develop and carry out the methodology.⁵³ These experts in both economics and workers’ rights issues, would apply for an appointment to the committee and all of their decisions would be made with public data and be accessible to outside stakeholders. This structure is similar to the MAC’s process that the United Kingdom previously used for its own labor shortage assessments.⁵⁴

DOL already has a committee model that could work well for establishing this body of experts called the Workforce Information Advisory Council (WIAC).⁵⁵ Established by WIOA in 2014, the WIAC is a committee of workforce and labor experts who represent “a broad range of national, state, and local data and information users and producers.”⁵⁶ The members are appointed by the Secretary for three-year terms and cannot be appointed for more than two consecutive terms.⁵⁷ The committee gives advice to the Secretary of Labor to improve the U.S. workforce and labor market information systems and better understand the challenges that workers face in the United States today.

Nominations-based approaches

Beside indicator-based methods, the other major category of methodological recommendations were for processes that are driven by stakeholders requesting that occupations be listed on Schedule A. A nominations-based approach relies on testimony from the organizations that are steeped in labor supply and demand issues and that understand how the labor market is behaving in their local communities.

Many commenters recommended that DOL take inspiration from DHS’ method for updating its STEM Designated Degree Program List.⁵⁸ This list includes the fields of study that DHS has determined are STEM. International students applying for a STEM OPT extension, must be studying one of the listed fields to qualify.⁵⁹ Members of the public can nominate fields to be added to the list, with an annual feedback deadline of August 1. The Student Exchange Visitor Program (SEVP) within DHS evaluates each nomination to see if the fields are “generally considered to be… STEM degree[s] by recognized authorities, including input from educational institutions, governmental entities and non-governmental entities.”⁶⁰ SEVP also examines the National Center for Education Statistics’ (NCES) definitions of the nominated fields and any supporting materials provided by the nominators.

Going a step further, one commenter suggested that all nominations submitted to DOL be posted publicly, and that DOL allow stakeholders to comment on those nominations and provide their own data to support or argue against them.⁶¹ After collecting all of the nominations and feedback, DOL would analyze the submissions, compare nominations with their own data, and issue an updated Schedule A list.

It was also recommended that DOL could develop a process similar to the one used for updating Appendix A to the Preamble-Education and Training Categories by O*NET-Standard Occupational Classification (SOC).⁶² In 2021, DOL issued a Federal Register Notice announcing that it was planning on updating the appendix to better mesh with the 2018 SOC codes and the O*NET database.⁶³ The announcement gave stakeholders an opportunity to provide information to DOL about how professional and non-professional job titles have changed over time, and established a predictable cadence for future updates.

DOL also received recommendations that it look at its own past processes to update Schedule A efficiently. Specifically, DOL could take inspiration from its Reduction in Recruitment (RIR) standards to help determine which occupational categories should be eligible for Schedule A.⁶⁴ When RIR was active, employers reached out to their respective state workforce agencies and informed agencies that they had already tried to recruit for an occupation and were unsuccessful. The employers would submit evidence of their past recruitment and ask for RIR. Unlike the current system, the state workforce agencies would make the decision as to whether an employer could use RIR. To help modernize Schedule A, DOL could have the states identify which occupations did not have enough U.S. workers as they best understand the labor needs in their local communities.

Lastly, a subset of recommended nomination-based approaches build upon DOL’s current labor certification systems for green cards and H-1Bs. For example, a group of commenters recommended examining PERM data and adding any occupations to Schedule A that are approved 98% of the time when employers submit PERM applications.⁶⁵ Such a method relies on data that DOL already knows well, and does not require additional data collection. It also allows the list to be quickly updated in predictable intervals. Another recommendation is that only a true labor market test at the initial point of hire is the best way to identify labor shortage. This test would occur at the time of filing either an I-129 petition for a nonimmigrant worker or an I-140 immigrant petition for a foreign worker, and would require that employers provide proof of good faith, real-world recruitment efforts.⁶⁶ This method, like the current processes for PERM and Labor Condition Applications (LCAs), puts much of the administrative burden on the employer to prove that they were unable to find U.S. candidates. It could also modernize current processes by removing the requirement to publish job advertisements in the newspaper. However, the commenter did not include a recommendation for the threshold that would qualify an occupation as scarce.

Other recommendations

Commenters did not just provide feedback on how DOL could update Schedule A. They also proposed ways that the agency could improve other parts of the employment-based immigration process.

Besides PERM, the other major piece of the DOL process for obtaining an employment-based green card is the Prevailing Wage Determination (PWD). Employers are required to pay foreign workers the prevailing wage, or “the average wage paid to similarly employed workers,” for the occupational category in the area that the job will take place.⁶⁷ Unfortunately, obtaining a PWD can take months. At the beginning of August 2024, DOL stated that it was processing PWDs that were submitted between eight and 11 months prior.⁶⁸ One way commenters thought DOL could shorten PWD process times would be to offer expedited processing for a fee, similar to the premium processing system used by the U.S. Citizenship and Immigration Services (USCIS).⁶⁹ For certain forms, USCIS will process within 15 business days when paid a premium processing fee. If the agency is unable to meet that deadline, the fee is returned to the petitioner.⁷⁰ This fee helps pay for additional processing capacity at USCIS and, if implemented at DOL, could do the same. Another idea is to create an expedited process for obtaining PWDs that is similar to DOL’s processing of LCAs for H-1Bs.⁷¹ Once submitted, DOL processes LCAs within seven business days.

One commenter recommended that DOL expand special handling to permit employers to use previously completed competitive recruitment to hire workers with a STEM Master’s or PhD from a U.S. institution.⁷² Special handling is currently only available for colleges and universities hiring foreign workers for teaching positions, allowing them to satisfy the labor certification process with a recruitment that looks for the most qualified candidate for the position.⁷³ In contrast, the typical PERM process requires that employers look for a “minimally qualified” U.S. candidate.

Many of the commenters discussed how DOL should collect outside feedback for a regular update of Schedule A.⁷⁴ Historically, DOL issued a RFI or a Notice of Proposed Rulemaking (NPRM) every time that it has updated Schedule A, but RFIs are time-consuming to issue, and repeatedly going through the rulemaking process would be highly burdensome to the overburdened staff at DOL. One commenter suggested that DOL issue one NPRM to lay out the entire process for how DOL would collect stakeholder feedback, evaluate data, and publish updates to Schedule A. Then, all future updates would be made without notice-and-comment rulemaking. The recommended intervals for updates were every two, three, or five years.⁷⁵ Regularly updating Schedule A would provide much-needed predictability and certainty to the system for both employers and employees. It would also avoid the situation that DOL is currently experiencing, in which Schedule A is over 30 years out of date.

Recommended worker protections

A common concern among workers’ rights advocates is that the expansion of the Schedule A list could lead to increased rates of fraud and abuse.⁷⁶ Foreign nurses, who have been eligible for Schedule A for decades, have been the victims of well-documented abuses, particularly by staffing firms. The National Employment Lawyers Association (NELA) and the National Institute for Workers’ Rights (NIWR) cite several court cases about these abuses in their comment.⁷⁷ When recruiting foreign nurses, the sponsors assume some expenses, such as visa processing fees, licensing exam fees, and airfare. Some firms try to recoup those costs by including “training repayment provisions” or “stay-or-pay provisions” in the nurses’ contracts.⁷⁸ These provisions require that the foreign nurse work for the staffing firm’s clients for a certain number of years, or else the nurse is required to pay sometimes upward of $100,000 plus the firm’s legal fees as a penalty.⁷⁹ Any foreign nurse that comes into the United States with Schedule A is given a green card, which affords the nurses all the same working rights as U.S. citizens, including the ability to change jobs whenever they want. Workers’ advocates argue that the “stay-or-pay provision” significantly infringes on the foreign nurses’ working rights and traps them in jobs that are unsafe or abusive. There are other common contractual provisions in foreign nurse contracts that can lead to abuse, including lengthy non-competes, forced arbitration, and non-disclosure or confidentiality agreements with hefty penalties if the foreign nurse speaks out about unsafe or abusive working conditions.

Workers’ rights advocates knowledgeable about the abuses foreign nurses have experienced recommend in their comments that DOL introduce guard rails to Schedule A to reduce the likelihood that future nurses (and any other future workers coming to the country as a result of the list) are mistreated by their employers. NELA and NIWR recommended that DOL state clearly on its website and in its informational materials about Schedule A that provisions with a high likelihood for abuse, such as non-competes, forced arbitration, non-disclosure agreements, breach or penalty fees, or training repayment requirements, are not permitted in the employment contracts for Schedule A workers.⁸⁰ They also recommend that fact sheets be provided to Schedule A foreign workers informing them of their working rights at many points during the recruitment and hiring process, including at embassy interviews, upon receipt of an offer letter, and also as part of the employment contract. If an employer fails to comply with these requirements, NELA and NIWR recommend that DOL’s Office of Foreign Labor Certification (OFLC) bars those employers from using Schedule A in the future.⁸¹

Other commenters suggested additional guard rails that could be applicable to any occupation. The recommendations include:

• Requiring employers to submit documentation to DOL’s Employment and Training Administration (ETA) before being eligible to hire through Schedule A. The documentation would attest that the company has been in compliance with PERM regulations for the past five years.⁸²
• Requiring that before an employer is eligible to hire through Schedule A, they maintain a workforce in which more than half of the workers are U.S. workers.⁸³
• Requiring employers to advertise their Schedule A-eligible jobs on the internet, instead of in a newspaper, like a traditional PERM application.⁸⁴

Another suggestion some commenters make is to apply Schedule A only to petitions filed for OEWS Level 3 and Level 4 jobs to ensure all Schedule A beneficiaries are paid more than the median wage for their occupation.⁸⁵ EPI points out that most PERMs are for Level I and II jobs and hence will be below the median wage. While fixing the PWD system is outside the scope of Schedule A reform, a Schedule A update could feasibly take this into account in a number of ways. For example, the Department could use the ACS or other data sources that include age and work history data to disaggregate occupations by inferred level of experience. 

However, a problem with limiting Schedule A to Level 3 and Level 4 is that it assumes that immigrants should be paid more than similarly situated Americans. Median wages for an occupation are simply not the market wage for all individual jobs, and may be above the market wage for early-career jobs. DOL should consider whether lower experience Level 1 or Level 2 jobs in occupations with sufficiently rising wages and other indicators of labor shortage should require PERMs to protect workers. The median wage for an occupation is not the market wage for an individual job.

Sources of data

A diverse group of 21 commenters thought deeply about which data sources could be useful for DOL in an assessment of labor shortage. These recommendations were submitted by universities, professional and industry associations, state-based nonprofits, research-based nonprofits, think tanks, companies, labor unions, and one anonymous contributor. This section will identify each data source recommended by these commenters and describe their scope and constraints.

Data sources suggested in RFI responses

Federal data sources

Department of Labor

Quarterly Census of Employment and Wages (QCEW)

Conducted by the BLS, the QCEW publishes each quarter a count of employment and wages as reported by employers. The data is available at county, state, national, and Metropolitan Statistical Area (MSA) levels by industry as classified by the North American Industry Classification System (NAICS).⁸⁶ It measures the number of businesses, number of workers, and quarterly wages for positions covered by state UI and for federal positions covered by the Unemployment Compensation for Federal Employees program.⁸⁷ Several categories of workers are excluded from this survey, including business owners, unpaid family members, certain farm and domestic workers, certain railroad workers, elected officials in the Executive and Legislative branches, members of the armed forces, and workers who have earned no wages during the survey period because of “work stoppages, temporary layoffs, illness, or unpaid vacations.”⁸⁸ This survey was suggested by Combemale, Reamer, and Karsten.⁸⁹

One of the major benefits of the QCEW is that it produces new data frequently and throughout the year. This can be helpful because much labor data is published annually and many months after the survey period has closed. The survey has also been running for many decades, with the data being classified by industry since 1938 and can be broken down into county-level sections. The geographic granularity would be an asset to labor shortage evaluations because economies are very diverse across the country. However, the fact that the survey is only classified by industry is a drawback to an assessment of labor shortage at the occupational level. 

Current Employment Statistics (CES)

Recommended by Combemale, Reamer, and Karsten, the CES is a monthly survey that records the employment, hours, and earnings estimates of workers in nonfarm jobs. It is based on employers’ payroll records.⁹⁰ The richest data has been published by BLS since 1990, but aggregate industry data can be found back to 1939. Employment rates are published for both the private and public sectors, but hours and earnings are only published for the private sector. The survey encompasses about 119,000 businesses and government agencies with about 629,000 individual worksites in the United States.⁹¹ CES data is classified according to NAICS.

Similar to QCEW, CES publishes frequently and is, according to BLS, the “first economic indicator of current economic trends each month.”⁹² It is capable of measuring several indicators which would be important to an assessment of labor shortage, including earnings trends and wage-push inflation, short-term fluctuations in demand, and levels of industrial production. But, also like QCEW, the survey collects data only at the industry and national levels

Employment Projections

The DOL Employment Projections (EP) are developed annually by BLS and estimates what the U.S. labor market will look like ten years in the future.⁹³ The EPs cover 300 industries and 800 detailed occupations and are based on data from OEWS, CPS, and CES surveys. BLS classifies the occupations in the EPs using the 2018 SOC codes and classifies industries using NAICS. BLS creates each projection by examining the labor force, output, and other economic measures by consumer sector and product, industry output, employment by industry, and employment by occupation.

Many commenters cited BLS’ EPs in their arguments, and several (including Combemale, Reamer, and Karsten,⁹⁴ the Bipartisan Policy Center,⁹⁵ and the U.S. Chamber of Commerce)suggested using them in a labor shortage assessment.⁹⁶ While it is understandable that commenters gravitated toward this data source to argue both for and against a method to assess labor shortage, BLS itself makes it clear that these projections cannot be used to predict labor shortages or surpluses.⁹⁷ BLS explains that the EPs assume the labor market is in equilibrium, “where overall labor supply meets labor demand except for some degree of frictional unemployment.”⁹⁸ The agency continues by noting that the urge to predict shortages or surpluses with the projections comes from an incorrect comparison of total employment and total labor force projections: “The total employment projection is a count of jobs and the labor force projection is a count of individuals. Users of these data should not assume that the difference between the projected increase in the labor force and the projected increase in employment implies a labor shortage or surplus.”⁹⁹ For this reason, EPs were not included in the Institute for Progress’s Help Wanted Index,¹⁰⁰ and we do not recommend using them in a DOL assessment of labor shortage.

Department of Commerce

National Income and Product Accounts (NIPAs)

The NIPAs are published by the Department of Commerce’s Bureau of Economic Analysis (BEA). The NIPAs are part of a trio of U.S. national economic accounts which also include the Industry Economic Accounts (IEAs) and the Financial Accounts of the United States. BEA uses the NIPAs to answer three questions:

• What is the output of the economy and its size, composition, and use?
• What are the sources and uses of national income?
• What are the sources of savings to provide for investment in future production?¹⁰¹

As part of the NIPAs, BEA estimates the number of workers in each industry every year at the state, county, metropolitan, and micropolitan level.¹⁰² NIPA data is available back to the 1940s.

A benefit of the NIPA estimates is that they are commonly used to evaluate the condition of the U.S. economy.¹⁰³ A drawback is that they do not focus on the condition of the U.S. labor market and only make assessments at the national level.

Employment Multipliers

Combemale, Reamer, and Karsten suggest use of BEA’s employment multipliers.¹⁰⁴ These multipliers are calculated by BEA to relate how much regional spending translates into jobs. Based on its Regional Input-Output Modeling Service II, the department can estimate how spending changes output, employment, labor earnings, and ultimately labor demand.

Census Bureau

Current Population Survey (CPS)

Recommended by Combermale, Reamer, and Karsten,¹⁰⁵ IFP,¹⁰⁶ and the American Immigration Lawyers Association (AILA) and American Immigration Council (AIC),¹⁰⁷ the CPS is a joint effort between the Census Bureau and BLS. It is one of the oldest and biggest surveys in the United States and measures vital monthly labor force statistics.¹⁰⁸ It measures a slew of aspects of the U.S. population, such as school enrollment rates, median annual earnings by field, health insurance coverage, poverty rates, populations of various minorities, educational attainment, voting registration rates, and fertility.¹⁰⁹ To collect all this data, the Census Bureau surveys 60,000 households each month.

When attempting to evaluate labor shortage, using CPS data has some important benefits. Data is published much more frequently than other economic surveys and there are many years of historical data publicly available. It also, as stated above, collects data about many different pieces of the labor market, giving researchers a wide, detailed view of potential indicators of labor shortage.

However, several commenters pointed out shortcomings of CPS data. The AFL-CIO Department for Professional Employees noted that CPS data does not capture state, regional, and national trends for the STEM educational pipeline and workforce specifically.¹¹⁰ EPI states that there is not enough harmonization between CPS and other agency data sources, like OEWS, ACS, and CES.¹¹¹ CPS and these other sources use slightly different SOC codes for occupational categories and try to remedy these differences with crosswalk documents.¹¹² Nevertheless, the crosswalks do not provide a one-to-one mapping between the different occupational categories and that can create difficulties in conducting labor market analyses that incorporate some or all of these data sources. EPI also states in its comment that CPS occupational categories do not have clear definitional boundaries. For example, there are some catch-all categories in CPS data, such as “Computer Occupations, All Other,” or “Engineers, All Other.” EPI notes that 18% of computing occupations fall under the former category and 25% of engineers fall under the latter, a significant amount that can hamper the ability to properly assess labor needs in specific occupations. Additionally, the categorization system for OEWS conflicts with CPS’ “All Other” buckets, with the percentage of occupations in both of the OEWS “All Other” categories for computing and engineering is only 9%.

Business Trends and Outlook Survey (BTOS)

The Bipartisan Policy Center (BPC) recommended incorporating the BTOS into a methodology to update Schedule A.¹¹³ The BTOS was launched in 2022 and is designed “to measure business conditions on an ongoing basis.”¹¹⁴ It improves upon the Small Business Pulse Survey, which measured changing business conditions during major events, like hurricanes and the COVID-19 pandemic. It collects data from about 1.2 million single-location employer businesses (except for farms) every two weeks on the sector, state, and metropolitan statistical area level.¹¹⁵ Employers are asked to reflect on the growth of their business over the past two weeks and estimate the business’ performance over the next six months.

BTOS data could be useful for an analysis of labor shortage because it takes into account the employers’ opinions about the success of their businesses and of near- and medium-term challenges. However, the data is all self-reported, and the survey is entirely voluntary. It is also a very new survey, having been launched just two years ago, so there is not much historical data to analyze. Lastly, all of the questions asked in the survey are qualitative and less able to be integrated easily into a methodology that relies on quantitative data.

American Community Survey (ACS)

The ACS publishes annually and covers a wide variety of aspects of the U.S. population. Some of the topics ACS covers include computer and internet use, citizenship status, educational attainment, fertility, undergraduate degree field, school enrollment, income, poverty status, employment status, industry, occupation, race, age, and sex, among others.¹¹⁶ The Census Bureau contacts over 3.5 million households in the United States to gather data.¹¹⁷ In the 20th century, the Census was divided into a short form and a long form, which was only given to a subset of the population. After 2000, the long form of the Census became the annual ACS.¹¹⁸

Use of the ACS was recommended by several commenters to the RFI, such as IFP,¹¹⁹ CIS,¹²⁰ Combemale, Reamer, and Karsten,¹²¹ and AILA and AIC.¹²² ACS data is highly detailed and measures many of the economic and educational indicators which would be important in an analysis of labor shortage. Its annual publication and almost 25 years of data also give researchers the opportunity to reliably assess economic conditions over many years and through several economic crises, like the COVID-19 pandemic and the Great Recession. While it has a larger sample size and more detailed information than many other sources, it is not updated as frequently or current as other sources (like the CPS), which prevents it from being an up-to-the-month or up-to-the year snapshot.

Post-Secondary Employment Outcomes (PSEO)

Recommended by Combemale, Reamer, and Karsten,¹²³ PSEO data is generated via a partnership between Census Bureau researchers, universities, university systems, state departments of education, and state labor market information offices.¹²⁴ It looks at the employment outcomes and earnings of university graduates by degree level, area of study, institution of higher education, and state. Data is generated by pairing university transcripts with a national database of jobs.¹²⁵

This project captures information about a very important aspect of the labor market: how successful graduates are at getting jobs in their fields of study and if their earnings are growing. Growth of wages, especially of recent graduates, is important to many of the RFI commenters, including EPI,¹²⁶ the Center for Immigration Studies,¹²⁷ AFL-CIO¹²⁸ and their Department for Professional Employees,¹²⁹ and IFPTE.¹³⁰

However, the major drawback to using PSEO data is that it does not cover the whole United States or even every university in the states covered by the survey. In fact, it only includes 28 states and coverage within states greatly varies. Rhode Island has the least coverage, with only 4% of the state’s university graduates included. Virginia has the greatest coverage at 87% of graduates.¹³¹ Additionally, PSEO data only includes graduates who have earned “at least the annual equivalent of full-time work at the prevailing federal minimum wage” and have worked “three or more quarters in a calendar year.”¹³²

National Science Foundation (NSF)

National Survey of College Graduates (NSCG)

NSF’s National Center for Science and Engineering Statistics (NCSES) partners with the Census Bureau to carry out this survey. The RAND Corporation recommended its use in labor shortage analyses.¹³³ It is published every other year, with the next one being released January 2025.¹³⁴ The survey collects data from college graduates living in the United States during the survey week who have at least a bachelor’s degree and are younger than 76.¹³⁵ It began collecting data in 1993 and, as of 2021, sampled about 164,000 people. The NSCG looks at the demographics of college graduates, their educational history, employment status, degree field, and occupation.

The benefits of using NSCG data for an analysis of labor shortage include the fact that it has a focus on STEM occupations in particular, and that it includes detailed data about all factors related to the STEM talent pipeline. NSF compiles tables of graduates by major, employment status, demographics (including citizenship status), earnings, and even job satisfaction. The job satisfaction data could contribute to better understanding of STEM workers’ experiences in the labor force. As several commenters suggest,¹³⁶ if workers are not satisfied with their jobs (low pay, poor working conditions, limited career mobility, etc.) and that sector is experiencing unmet labor demand, that could indicate that the sector could do more to attract workers before turning to hiring international workers.

There are a few drawbacks to using this survey. It only publishes data every other year, which can make it difficult to accurately assess labor shortage on a more frequent basis. It also does not survey individuals who either have some college education or professional certifications, otherwise known as the skilled technical workforce (STW). Occupational categories are also not as granular as in other surveys, including categories such as “biological, agricultural, and other life scientists,” “computer and mathematical scientists,” “management and administration fields,” and “health.”

National Training, Education, and Workforce Survey (NTEWS)

This survey, launched in 2022 and recommended by the RAND Corporation,¹³⁷ collects data on people 16 to 75 years old and focuses especially on the skilled technical workforce (workers with some college, or professional certifications).¹³⁸ It examines work experience programs, types of credentials, employment characteristics, demographic characteristics, and education enrollment and attainment. NSF uses the data to evaluate the relationship between workers’ credentials and their employment outcomes. It will be published every other year and is meant to supplement the NSCG and the Survey of Doctorate Recipients.¹³⁹ The sample size is about 43,200 people. NSF expects to publish the first tranche of data in December 2024.

One major benefit of this survey is that it covers STW occupations exclusively. These jobs play a huge role in the success of the U.S. economy. However, the survey has not published its first set of data yet, so there could be some unforeseen operational, thematic, and coverage hurdles to iron out in the coming years before it is useful for labor shortage analyses.

Survey of Earned Doctorates (SED)

The SED is recommended by Combemale, Reamer, and Karsten for future labor shortage analyses.¹⁴⁰ It is an annual census of all recipients of research doctorates from U.S. institutions of higher education. The SED collects information on recipients’ educational history, graduate funding sources, educational debts, plans post graduation, and demographic data, including citizenship status.¹⁴¹ NCSES, in partnership with the National Institutes of Health (NIH), the Department of Education (ED), and the National Endowment for the Humanities (NEH), have conducted this survey since 1957. The sample size each year is about 50,000 people.

One of the benefits of the SED is that it has many decades of historical data. It also surveys all recipients of research doctorates in the United States, not just a subset of people like many of the other surveys detailed in this section. It also covers a population of people who presumably are going into occupations that require a lot of training and time for which to prepare. If this data flags indications of labor shortage, it would be highly useful for a methodology to update Schedule A. Occupations with very long lead times may be the best suited for hiring internationally to address excess labor demand.

A drawback of this survey is that because it looks only at doctorate recipients, it evaluates only a very small subset of the U.S. workforce.

Survey of Doctoral Recipients (SDR)

Recommended by Combemale, Reamer, and Karsten,¹⁴² the SDR provides specific data on characteristics of science, engineering, and health research doctorate recipients from U.S. institutions who are under the age of 76.¹⁴³ NSF partners with NIH to collect information such as recipients’ educational history, employment status, degree field, occupation, and demographic information. This survey is published every other year and has been conducted since 1973.¹⁴⁴ For its last iteration, the SDR’s sample size was 125,938 people.

The pros and cons of this survey are similar to that of the SED. One major difference is that the SED collects information on a broader range of degree fields, both STEM and non-STEM, than the SDR.

Science and Engineering Indicators report

The Science and Engineering Indicators were recommended by the National Science Board (NSB).¹⁴⁵ The NSB serves as part of the leadership of NSF and “identifies issues that are critical to NSF’s future, approves NSF’s strategic budget directions and the annual budget submission to the Office of Management and Budget, and approves new major programs and awards.” It also acts “as an independent body of advisors to both the President and the Congress on policy matters related to science and engineering and education in science and engineering.”¹⁴⁶ The NSB compiles detailed reports as part of the Science and Engineering Indicators about the scope and vitality of STEM fields in the United States.¹⁴⁷ The reports include:

• Elementary and Secondary STEM Education;
• Higher Education in Science and Engineering;
• The STEM Labor Force: Scientists, Engineers, and Skilled Technical Workers;
• Research and Development: U.S. Trends and International Comparisons;
• Publications Output: U.S. and International Trends;
• Academic Research and Development;
• Invention, Knowledge Transfer, and Innovation;
• Production and Trade of Knowledge- and Technology-Intensive Industries; and
• Science and Technology: Public Perceptions, Awareness, and Information Sources.

To compile the reports, NSB integrates information collected in surveys conducted by national statistical agencies and by other countries.¹⁴⁸ Some of the data are collected by companies, governments, and private organizations as part of their internal activities.

The Indicators reports contain extensive detail about the STEM educational pipeline and workforce, and include information that would not otherwise be available for researchers. It also includes international data, which is rare among other U.S. statistical agencies’ surveys.

However, these reports do not forecast future outcomes in STEM and do not model the dynamics of science and engineering sectors.¹⁴⁹ The reports are also only published every other year.

Department of Education

Teacher Shortage Area (TSA) Reports

The Office of Postsecondary Education collects data from state representatives to develop the TSA reports. ED intends for these reports to be used by incoming education workers where school districts may be hiring new faculty, administrators, and other educators across the country.¹⁵⁰ The reports are published each school year and include a wide range of occupational focus areas, such as core subjects, drivers education, world languages, English as a second language, and special education. This data source was recommended by the Chicago Public Schools.¹⁵¹

One benefit is that there is TSA historical data going back to 1990 for every state and territory in the United States. However, this data does not track the labor needs of institutions of higher education.

State-based data sources

State wage records

State Unemployment Insurance offices collect wage information to aid in providing unemployment benefits to workers. This data, collected by BLS quarterly, is a rich source of information about how much people are being paid and their employers within each participating state.¹⁵² There are currently 30 states participating in the Wage Records Program.¹⁵³ While research has been conducted with this data, it is not publicly available on BLS’ website.¹⁵⁴ This source was recommended by Combemale, Reamer, and Karsten.¹⁵⁵

Workforce board in-demand occupation lists

As recommended by Combemale, Reamer, and Karsten, “local workforce boards compile lists of occupations that meet in-demand criteria based on employment and wage growth” in order to receive federal workforce funding.¹⁵⁶ These lists are supplemented by knowledge of local labor markets and sometimes nominations from employers. These sources of data could be compiled by DOL and displayed online as a map of which occupations are considered in demand in each state and provide valuable information to labor researchers. However, as it is not currently aggregated, significant preparation would be needed before this analysis is possible.

Private data sources

World Economic Forum (WEF) Future of Jobs Report

This report was launched by WEF in 2016 and it “explores how jobs and skills will evolve” on a global scale. It is “based on unique survey data that details the expectations of a cross-section of the world’s largest employers related to how socioeconomic and technology trends will shape the workplace of the future.”¹⁵⁷ The report is published roughly every other year, with the most recent being published April 2023. Unlike many other data sources described above, this source does include actual projections of job creation, displacement, and specific disruptions to skills in the near future. The fact that it is also a global assessment can be a valuable supplement to an analysis of labor shortage in the United States and how it could be impacted by the international economy. However, the information is not detailed enough to be incorporated into an analytical, quantitative process for assessing labor shortage. This source was recommended by BPC.¹⁵⁸

Jobs and Employment Data Exchange (JEDx)

Recommended by Combemale, Reamer, and Karsten,¹⁵⁹ JEDx aims to develop “a public-private approach for collecting and using standards-based jobs and employment data.”¹⁶⁰ It is organized by the U.S. Chamber of Commerce Foundation and the T3 Innovation Network. While the goals of JEDx are promising for future labor shortage analyses, the initiative is still developing a roadmap and has not released any data as of the writing of this report.

Improvements to existing federal data sources

In addition to suggesting specific data sources, commenters had recommendations for how existing federal data sources could be improved for labor shortage analyses. If DOL wishes to use existing federal data sources, the agency can take some of the following steps recommended by various commenters to improve data collection and granularity.

• Expand JOLTS to collect monthly data at the occupational level¹⁶¹
• Partner with the Census Bureau to track state, regional, and national trends in how well students are finding jobs that align with their education and skills and the number of students pursuing different fields to gauge potential future supply of workers¹⁶²
• Expand BLS’ partnerships with other federal and state agencies, such as the state workforce boards and the Census Bureau to add additional questions to the data they already collect, including occupational data in unemployment insurance wage records,¹⁶³ and occupational questions to Census surveys¹⁶⁴

The agency could also go further by developing new sources of data to improve understanding of labor needs. Some suggestions include:

• Creating a survey of state Medicaid agencies to gauge demand for healthcare workers and the state workforce development agencies, which are in charge of local workforce training programs.¹⁶⁵
• Creating surveys that ask companies and workers’ rights groups about their internal data, including worker turnover, attrition, and retention issues, investments in workforce training and career path development for current workers, the time it takes to hire a new worker and how many days an open job remains unfilled, and worker benefits such as signing bonuses.¹⁶⁶

Defining STEM

One of the first questions that DOL asks in the RFI is how it can define STEM and which occupations should be included under the STEM umbrella. The inclusion of this question likely originates in the text of the AI Executive Order which requested DOL to examine “AI and other STEM-related occupations… across the economy, for which there is an insufficient number of ready, willing, able, and qualified United States workers.”¹⁶⁷

Of the comments that answered this question, a majority recommended that DOL define STEM broadly, using straightforward assessments to determine which occupations are STEM. The recommended assessments would define an occupation as STEM if:

• It contributes to domestic advancement of critical and emerging technologies;
• It uses “significant” levels of technical and science and engineering knowledge and do not require a bachelor’s degree;
• It is integral to scientific research and development;
• The skills needed to do the job align with those identified by broad surveys of job openings and professional profiles, such as those found on LinkedIn; or
• If it requires a degree field identified by the STEM Optional Practical Training (OPT) Designated Degree Program list.¹⁶⁸

Others recommended either a dramatic narrowing of what is considered STEM or a very specific recommendation. For example, one commenter urged DOL to include all occupations who are categorized by the BLS SOC code starting with 29, which are “Healthcare Practitioners and Technical Occupations.¹⁶⁹

Conclusion

This paper aims to provide a sensible analysis of all of the options that are available to DOL as they consider how to modernize Schedule A. We believe it clearly demonstrates that there exist many high-quality options. Stakeholders from across the country have provided recommendations for how every aspect of Schedule A could be improved, in addition to recommendations for other parts of the employment-based green card process at DOL. Perhaps equally importantly, several commenters have suggested highly actionable guard rails that DOL could implement to reduce the possibility of fraud and abuse of Schedule A-eligible workers. With this treasure trove of information, we have the best opportunity in decades to develop a transparent, data-driven process to understand where there are labor gaps in the U.S. economy and how we can best use our federal, state, and local resources to not only supplement our workforce with international talent but also strengthen domestic training and reskilling pipelines to ensure that good jobs are accessible to as many Americans as possible. We encourage DOL to carefully examine the feedback it has received as part of this RFI process, to issue a Notice of Proposed Rulemaking to outline how it will modernize Schedule A, and to update it regularly into the future. 

Acknowledgments

We would like to thank Amy Nice, Andrew Moriarity, Barbara Leen, Cecilia Esterline, Matthew La Corte, Greg Wright, Jack Malde, Sharvari Dalal-Dheini, Steven Hubbard, and Leslie Dellon for their support and sage advice in preparation for (and during the drafting of) this report.

Appendix A: Acronyms and their definitions

ABC = Associated Builders and Contractors

ACS = American Community Survey

AFL-CIO = American Federation of Labor-Congress of Industrial Organizations

AI = Artificial Intelligence

AIC = American Immigration Council

AILA = American Immigration Lawyers Association

ASU = Arizona State University

BEA = Bureau of Economic Analysis

BLS = Bureau of Labor Statistics

BPC = Bipartisan Policy Center

BTOS = Business Trends and Outlook Survey

CES = Current Employment Statistics

CIS = Center for Immigration Studies

CPA = Certified Public Accountant

CPI = Consumer Price Index

CPS = Current Population Survey

DHS = Department of Homeland Security

DOL = Department of Labor

ED = Department of Education

EO = Executive Order

EP = Employment Projections

EPI = Economic Policy Institute

ETA = Employment and Training Administration

GSS = Survey of Graduate Students and Postdoctorates in Science and Engineering

HHS = Department of Health and Human Services

HSI = Homeland Security Investigations

IEA = Industry Economic Accounts

IFPTE = International Federation of Professional and Technical Engineers

IPEDS = Integrated Postsecondary Education Data System

IfSPP = Institute for Sound Public Policy

I-O = Input-Output Accounts

JEDx = Jobs and Employment Data Exchange

JOLTS = Job Openings and Labor Turnover Survey

LCA = Labor Condition Application

LPR = Legal Permanent Resident

MSA = Metropolitan Statistical Area

NAICS = North American Industry Classification System

NCES = National Center for Education Statistics

NCSES = National Center for Science and Engineering Statistics

NELA = National Employment Lawyers Association

NIPA = National Income and Product Account

NIWR = National Institute for Workers’ Rights

NSB = National Science Board

NSCG = National Survey of College Graduates

NSF = National Science Foundation

NTEWS = National Training, Education, and Workforce Survey

OEWS = Occupational Employment and Wage Statistics

OFLC = Office of Foreign Labor Certification

OPT = Optional Practical Training

PERM = Permanent Labor Certification

PSEO = Post-Secondary Employment Outcomes

PWD = Prevailing Wage Determination

QCEW = Quarterly Census of Employment and Wages

QED-C = Quantum Economic Development Consortium

RFI = Request for Information

RIMS II = Regional Input-Output Modeling Service II

SDR = Survey of Doctoral Recipients

SED = Survey of Earned Doctorates

SEVIS = Student and Exchange Visitor Information System

SEVP = Student and Exchange Visitor Program

SOC = Standard Occupational Classification

STEM = Science, Technology, Engineering, and Mathematics

STW = Skilled Technical Workforce

TSA = Teacher Shortage Area

UI = Unemployment Insurance

WEF = World Economic Forum

WIAC = Workforce Information Advisory Council

WIOA = Workforce Innovation and Opportunity Act

Appendix B: Questions asked in DOL’s RFI on the modernization of Schedule A

DOL requested comments concerning generally:

• “Whether any STEM occupations should be added to Schedule A, and why; and
• Defining and determining which occupations should be considered as falling under the umbrella of STEM, and why.”

DOL also requested specific information regarding the following questions:

• “Besides the OEWS, ACS, and CPS, what other appropriate sources of data are available that can be used to determine or forecast potential labor shortages for STEM occupations by occupation and geographic area?
• What methods are available that can be used alone, or in conjunction with other methods, to measure presence and severity of labor shortages for STEM occupations by occupation and geographic area?
• How could the Department establish a reliable, objective, and transparent methodology for identifying STEM occupations with significant shortages of workers that should be added to Schedule A?
• Should the STEM occupations potentially added to Schedule A be limited to those OEWS occupations used in most of the recent BLS publications, or should the STEM occupations be expanded to include additional occupations that cover STW occupations?

Beyond the parameters discussed for STW occupations, should the Department expand Schedule A to include other non-STEM occupations? If so, what should the Department consider to establish a reliable, objective, and transparent methodology for identifying non-STEM occupations with a significant shortage of workers that should be added to or removed from Schedule A?”¹⁷⁰

Appendix C: Suggested economic indicators by category

Price data

Employment data

Talent pipeline data

Hiring, layoffs, and turnover data

Migration data

Organized labor data