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Employment Projections

Labor Force Projections Evaluation: 2012–2022

Labor force projections include population, labor force participation rates, and labor force level components. Population projections use Census Bureau resident population projections as a starting point and are benchmarked to civilian noninstitutional population (CNIP) estimates from the Current Population Survey (CPS). Participation rates are modeled using historic trends in the data. Participation rates and population projections are then multiplied together to estimate labor force levels. Errors in BLS labor force projections can therefore come from either source.


In 2003, the CPS added a new race category, “Two or more races,” and adjusted the way data on ethnicity was collected. These changes had previously limited the ability for BLS to evaluate the accuracy of our labor force projections by race and ethnicity because of inconsistencies with the historical data. BLS now has enough consistent data by race and ethnicity to estimate the naïve model using nine years of historical data. The naïve models, by sex, use 10 years of historical data, but the race and ethnicity comparisons in this round of evaluations only use nine years of historical data to estimate the naïve model.

Measuring accuracy

How often did BLS correctly project growth and decline for labor force segments?

BLS correctly projected which labor force segments would grow and which would decline 88 percent of the time.1 BLS incorrectly projected declines in the labor force for the youngest age group of ages 16-19 for both men and women. All other BLS projections matched the actual direction.

How much did BLS project the labor force to grow between 2012 and 2022?

BLS projected the labor force to grow 5.5 percent between 2012 and 2022.

How much did the labor force actually grow?

The labor force actually grew 6.0 percent between 2012 and 2022.

Accuracy of the CNIP projections

The 2022 actual CNIP was similar to the projected CNIP, with an absolute difference of about 0.5%, and dissimilarity indexes for all races of about 1.0%. (See Table 1.) The “all other groups” category for race had the highest dissimilarity indexes at 1.7% for men and 1.5% for women.2 (See Table 1a.) For ethnicity, Hispanic men had the highest dissimilarity index at 2.5%.3(See Table 1b.)

Table 1. CNIP dissimilarity indexes
All Male Female
1.0% 1.1% 0.9%
Table 1a. CNIP dissimilarity indexes by race
Race Men Women


1.2% 1.0%


1.5% 1.1%

All other groups

1.7% 1.5%
Table 1b. CNIP dissimilarity indexes by ethnicity
Ethnicity Men Women

Hispanic origin

2.5% 0.9%


Accuracy of the labor force projections

The BLS and naïve models performed similarly for the overall labor force. BLS slightly underestimated the total labor force, and the naïve model slightly overestimated the total labor force. The naïve model performed better for men, and the BLS projections performed better for women. (See Table 2.)

Table 2. Absolute percent error by sex, 2012–22 (Numbers in thousands)
Sex Actual 2022 labor force Labor force projections Absolute percent error Best performer
BLS Naïve BLS Naïve


164,288 163,450 165,134 0.5% 0.5% BLS


87,421 86,913 87,280 0.6% 0.2% Naïve


76,867 76,537 77,882 0.4% 1.3% BLS


For the naïve model, details do not sum to totals because separate models were run for Men, Women, and All.
Actual 2022 data may not match CPS data due to rounding at different aggregate levels.
The absolute percent error for the BLS and naïve models are 0.510% and 0.515%, respectively, for all sexes.

BLS outperformed the naïve model in every race by sex category. Both the BLS projections and the naïve model performed worse for Black workers and workers of all other races compared to White workers. BLS consistently overpredicted the number of White workers and consistently underpredicted the numbers of Black workers and workers of all other races. (See Table 3.)

Table 3. Absolute percent error by race and sex, 2012–22 (Numbers in thousands)
Race Sex Actual 2022 labor force Labor force projection Absolute percent error Best performer
BLS Naïve BLS Naïve


Men 68,163 68,989 69,408 1.2% 1.8% BLS


Women 57,795 57,934 59,979 0.2% 3.8% BLS


Men 10,259 9,547 9,360 6.9% 8.8% BLS


Women 10,977 10,700 10,513 2.5% 4.2% BLS

All other groups

Men 8,998 8,377 7,713 6.9% 14.3% BLS

All other groups

Women 8,094 7,903 6,909 2.4% 14.6% BLS

Note: Actual 2022 data may not match CPS data due to rounding at different aggregate levels.

BLS performed better than the naïve model at estimating the Hispanic labor force. (See Table 4.)

Table 4. Absolute percent error by ethnicity and sex, 2012–22 (Numbers in thousands)
Ethnicity Sex Actual 2022 labor force Labor force projection Absolute percent error Best performer
BLS Naïve BLS Naïve


Men 17,370 17,925 16,672 3.2% 4.0% BLS


Women 13,232 13,254 12,847 0.2% 2.9% BLS


Men 70,051 68,988 69,674 1.5% 0.5% Naïve


Women 63,635 63,283 64,857 0.6% 1.9% BLS

Note: Actual 2022 data may not match CPS data due to rounding at different aggregate levels.



1All detailed age groups for men and women.

2The "all other groups" category includes (1) those classified as being of multiple racial origin and (2) the racial categories of (2a) Asian (2b) American Indian and Alaska Native and (2c) Native Hawaiian and Other Pacific Islanders.

3There is no comparable non-Hispanic group for calculating the projected and actual CNIP dissimilarity indexes. The 2012–22 BLS Projections had data by sex and age group for White non-Hispanic origin. The 2022 actual data by sex and age group is only available for all non-Hispanic origin.

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Last Modified Date: January 19, 2024