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Bureau of Labor Statistics > Employment Projections > Data

Labor Force Projections Evaluation: 2004–2014

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 confidently 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 2004 projections, BLS used the categories from before 2000¹, which are not comparable to the categories published in 2014.²

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 97 percent of the time.³

How much did BLS project the labor force to grow between 2004 and 2014?

BLS projected the labor force to grow 10 percent between 2004 and 2014.

How much did the labor force actually grow?

The labor force actually grew 6 percent between 2004 and 2014.

What contributed to the difference?

BLS projected labor force participation rates that were slightly too high, on average. In particular, men aged 25–34 and women aged 45–61 had lower labor force participation rates than BLS projected.

Accuracy of the CNIP projections

The 2014 actual CNIP was similar to the projected CNIP, with an absolute difference of less than one percent, and relatively low dissimilarity indexes.

Table 1. CNIP dissimilarity indexes
Race	Sex	Dissimilarity index
All	All	1.2%
All	Male	1.4%
All	Female	1.3%

Accuracy of the labor force projections

In most cases, the naïve model outperformed the BLS projections.

Table 2. Absolute percent error by age group
Age	Actual 2014 labor force	Labor force projection		Absolute percent error		Best performer
Age	Actual 2014 labor force	BLS	Naïve	BLS	Naive	Best performer
16 and 17	1,971	2,135	1,853	8.3%	6.0%	Naïve
18 and 19	3,683	4,108	4,008	11.5%	8.8%	Naïve
20 and 21	5,455	5,593	6,302	2.5%	15.5%	BLS
22 to 24	10,186	10,322	9,808	1.3%	3.7%	BLS
25 to 29	17,091	18,273	17,677	6.9%	3.4%	Naïve
30 to 34	17,108	18,482	17,993	8.0%	5.2%	Naïve
35 to 39	15,931	16,422	16,377	3.1%	2.8%	Naïve
40 to 44	16,574	16,923	16,924	2.1%	2.1%	BLS
45 to 49	16,638	17,612	17,370	5.9%	4.4%	Naïve
50 to 54	17,424	17,915	18,374	2.8%	5.5%	BLS
55 to 59	15,167	15,503	15,653	2.2%	3.2%	BLS
60 and 61	4,964	4,902	5,040	1.2%	1.5%	BLS
62 to 64	5,371	5,224	5,331	2.7%	0.7%	Naïve
65 to 69	4,774	5,058	5,124	5.9%	7.3%	BLS
70 to 74	2,071	1,884	1,894	9.0%	8.6%	Naïve
75 to 79	877	1,034	779	17.9%	11.2%	Naïve
80 and over	635	711	395	12.0%	37.8%	BLS

Examining the mean absolute percent errors and weighted mean absolute percent errors shown below in tables 3 and 4 reveals minor differences in the performance of the BLS projections and the naïve model. 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	6.1%	7.5%	BLS
All	Male	7.5%	8.1%	BLS
All	Female	7.1%	7.7%	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	4.5%	4.5%	BLS
All	Male	5.3%	4.6%	Naïve
All	Female	4.2%	5.2%	BLS

Dissimilarity indexes provide another measure of accuracy, but as with the measures presented above, BLS projections performed similarly to the naïve model for the 2004–14 labor force projections.

Table 5. Labor force dissimilarity indexes
Race	Sex	BLS	Naïve	Best performer
All	All	1.3%	1.4%	BLS
All	Male	2.1%	1.6%	Naïve
All	Female	1.2%	2.4%	BLS

Note

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

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

³Male and female, all detailed age groups.

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Last Modified Date: August 1, 2018