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Bureau of Labor Statistics > Employment Projections > Data

Labor Force Projections Evaluation: 2008–2018

Labor force projections begin with civilian noninstitutional population (CNIP) estimates from the U.S. Census Bureau, to which BLS applies our projected labor force participation rates. Errors in BLS labor force projections can therefore come from either source. For more information, refer to our evaluation methodology.

Limitations

In 2000 the Census added a new race category, "Two or more races," which limits BLS' ability to evaluate the accuracy of our labor force projections by race. Due to the limited historical data available for this new category when BLS produced the 2018 projections, BLS used the categories from before 2000¹, which are not comparable to the categories published in 2018.²

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 about 91 percent of the time.³

How much did BLS project the labor force to grow between 2008 and 2018?

BLS projected the labor force to grow 8.2 percent between 2008 and 2018.

How much did the labor force actually grow?

The labor force actually grew 5.0 percent between 2008 and 2018.

Accuracy of the CNIP projections

The 2018 actual CNIP was similar to the projected CNIP, with an absolute difference of about 0.3 percent, and dissimilarity indexes around one percent.

Table 1. CNIP dissimilarity indexes
All	Male	Female
1.1%	1.3%	1.0%

Accuracy of the labor force projections

In general, the BLS and naïve models both overestimated the number of workers in the 2018 labor force. Across the detailed demographic groups, neither model clearly outperforms the other.

Table 2. Absolute percent error by age group
Age	Actual 2018 labor force	Labor force projection		Absolute percent error		Best performer
Age	Actual 2018 labor force	BLS	Naïve	BLS	Naïve	Best performer
16 and 17	2,133	1,648	1,196	23%	44%	BLS
18 and 19	3,753	4,221	3,778	12%	1%	Naïve
20 and 21	5,354	5,495	5,333	3%	0%	Naïve
22 to 24	9,745	9,768	9,585	0%	2%	BLS
25 to 29	18,893	18,592	18,125	2%	4%	BLS
30 to 34	17,881	18,222	17,937	2%	0%	Naïve
35 to 39	17,491	18,168	18,018	4%	3%	Naïve
40 to 44	16,129	16,619	16,459	3%	2%	Naïve
45 to 49	16,873	17,295	17,128	3%	2%	Naïve
50 to 54	16,438	17,047	16,918	4%	3%	Naïve
55 to 59	15,679	16,706	16,803	7%	7%	BLS
60 and 61	5,422	5,619	5,696	4%	5%	BLS
62 to 64	6,253	6,429	6,588	3%	5%	BLS
65 to 69	5,592	6,213	6,536	11%	17%	BLS
70 to 74	2,615	2,832	2,962	8%	13%	BLS
75 to 79	1,081	1,237	1,233	14%	14%	Naïve
80 and over	746	800	727	7%	3%	Naïve

Examining the mean absolute percent errors and weighted mean absolute percent errors shown below in tables 3 and 4 reveals differences in the performance of the BLS projections and the naïve model, with the BLS projections performing more accurately in the aggregate groups (total, male, female) than the naïve model, with the exception of the male aggregate in table 4. The smaller weighted mean absolute percent errors indicate that the BLS projections and the naïve model both made smaller errors in the larger population cohorts.

Table 3. Mean absolute percent error, labor force
Race	Sex	Mean absolute percent error		Best performer
Race	Sex	BLS	Naïve	Best performer
All	All	6.4%	7.3%	BLS
All	Male	5.9%	7.1%	BLS
All	Female	7.8%	9.0%	BLS

Table 4. Weighted mean absolute percent error, labor force
Race	Sex	Weighted mean absolute percent error		Best performer
Race	Sex	BLS	Naïve	Best performer
All	All	4.0%	4.2%	BLS
All	Male	3.7%	3.7%	Naïve
All	Female	5.0%	6.0%	BLS

Dissimilarity indexes provide another measure of accuracy. As with the measures presented above, BLS projections performed more accurately than the naïve model for the 2008-18 labor force projections in the aggregate groups.

Table 5. Labor force dissimilarity indexes
Race	Sex	BLS	Naïve	Best performer
All	All	1.2%	1.8%	BLS
All	Male	1.2%	1.4%	BLS
All	Female	2.4%	3.2%	BLS

Note

¹These categories are: White, Black, Asian, Hispanic, White Hispanic, and White non-Hispanic.

²These categories are: White, Black, Asian, Two or more races, Hispanic, and White non-Hispanic.

³Male and female, all detailed age groups.

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Last Modified Date: March 16, 2020