Overview

The tool shows the number of annual visas for foreign workers needed in the US economy to satisfy labor demand. The values are based on total workforce projections from the Bureau of Labor Statistics combined with the historical share of immigrants in each location and occupation.

Users first select a location (state or metropolitan area) and are provided a detailed list of occupations along with four columns of information about each occupation. The columns are:

• Occupation Centrality: This number falls between 0 and 1 and measures how central the occupation is to the US economy. The more this occupation complements all other occupations in the economy (e.g., a nurse is highly complementary to a doctor) the higher this score. See a detailed explanation on how this is measured below.
• Job Growth: This is the (percent) employment growth for a given occupation in a locality. This number can be positive (the number of workers in that occupation is expected to grow) or negative (the number of workers in that occupation is expected to shrink). See a detailed explanation on how this is measured below.
• New Visas:This number measures the number of new visas that will be required to satisfy future changes in the demand for workers in each location and occupation. This number is the sum of the growth in the number of jobs, reflected in the job growth value above, plus the projected number of annual retirements, which we take as 2.4 percent of the current number of workers in the job. The value 2.4 is the estimated number of U.S. workers turning 65 in 2024, which may undercount retirees since COVID delayed retirement for many workers, thereby raising retirement rates in recent year. This suggests our New Visas value may be a lower bound. See detailed explanation on how this is measured below.
• Skilled: This column shows whether an occupation requires extensive training (light circle) or not (dark circle). See detailed explanation on how this is measured below.

Methodology

For each location and occupation, our tool provides visual depictions of a range of variables related to future regional demand for immigrant workers. These are:

Occupational Complementarity

We also report our own measure of occupational centrality and describe the methodology here. This is meant to provide policymakers with information on the occupations that are most central to the rest of the labor market. This metric evaluates how seamlessly an occupation integrates into the existing U.S. labor market. It identifies roles that are not just crucial to the economy but that also have the potential to elevate productivity across occupations and sectors.

We characterize two occupations as being complementary if their employment shares move in the same direction within industries over time. To construct this measure, we use Lightcast Staffing Patterns Data to first calculate the change in the share of employment in each occupation within industries over the period 2009 to 2019. Next, for each occupation, we calculate the pairwise correlation between changes in its employment share and the employment shares of all other occupations. As an example, we find that the complementarity between “food and beverage serving workers” and “cooks and food preparation workers” is 0.49. This number indicates that in at least 49 percent of industries when either of the two occupations was both present and one of them grew (or shrank) as a share of total employment the other one also grew (or shrank). Occupations are considered complementary if their industry employment shares co-vary together.

We then create an overall “complementarity index”—ranging from 0 to 1—for each occupation by taking the average of all its pairwise complementarity values.

Job Growth and New Visas :

This metric aims to measure the number of visas required to satisfy changes in labor demand for each location and occupation.

The projections of changes in the total U.S. workforce and the change in annual job openings come from the Projections Managing Partnership (PMP), a Department of Labor funded organization that collects and organizes underlying state-level data. For our projections we use the reported annual average openings which are the sum of two values:

• Average annual employment change: The increase or decrease in the number of jobs associated with an occupation)
• Average annual separations: The number of workers who either leave the labor force or make a significant occupational change. PMP states that: “An example of a non- significant occupational change would a move from Teachers Assistant (25-9041) to Secondary Teacher (25-2031), staying within the same major group (indicated in the first two digits of the SOC code). A significant change would be to move from Secondary Teacher (25-2031) to Lawyer (23-1011), by changing the minor group or the broad or detailed occupation.”

Our visualization of data for US States derives from data reported by PMP. Our visualization for data at the MSA (city) level, uses the state-level data as the underlying source and allocates workforce demand to MSAs according to their share of the overall state working age population.

These workforce projections are then multiplied by the baseline (e.g., 2023) share of foreign born workers that are presently working in an occupation and locality according to the 2023 American Community Survey. This is to obtain the projected demand for immigrant visas over the time horizon. This relies on the conservative assumption that the share of foreign-born workers in each locality and occupation will remain unchanged over the next decade. In future iterations of the tool we plan to adjust this assumption by providing a range of scenarios.

Skilled Occupations

Since immigration policy tends to differ for individuals with and without a college degree, we incorporate in our projections and visualizations an indicator of the skill level of each occupation. In particular, we exploit data on the extent to which occupations require extensive training and preparation in order to characterize the background that immigrant workers will need to fill certain labor supply shortages. Here we rely on the Occupational Information Network (O*NET) developed under the sponsorship of the U.S. Department of Labor. The O*NET database contains hundreds of standardized and occupation-specific descriptors covering almost 1,000 U.S. occupations. We focus on O*NET Job Zones, which place occupations within one of five groups based on the training the occupation requires, where “Job Zone Five” requires extensive training and “Job Zone One” requires little to no training.

To illustrate, computer occupations are in Job Zone Four, as they require considerable training, whereas Grounds Maintenance Workers belong to Job Zone One where little or no training is needed.

Data Sources

We use the following data sources for our calculations and visualizations: