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Employment Projections
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Labor Force Projections Evaluation: 2006–2016

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 2016 projections, BLS used the categories from before 20001, which are not comparable to the categories published in 2016.2

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 96 percent of the time.3

How much did BLS project the labor force to grow between 2006 and 2016?

BLS projected the labor force to grow 8.5 percent between 2006 and 2016.

How much did the labor force actually grow?

The labor force actually grew 5.1 percent between 2006 and 2016.

Accuracy of the CNIP projections

The 2016 actual CNIP was similar to the projected CNIP, with an absolute difference of about one percent, and similar dissimilarity indexes.

Table 1. CNIP dissimilarity indexes

Race

Sex

Dissimilarity index

All

All

1.3%

All

Male

1.5%

All

Female

1.3%

 

Accuracy of the labor force projections

In general, the BLS and naïve models both overestimated the number of workers in the 2016 labor force. Across the detailed demographic groups, neither model clearly outperforms the other.

Table 2. Absolute percent error by age group

Age

Actual 2016 labor force

Labor force projection

Absolute percent error

Best performer

BLS

Naïve

BLS

Naive

16 and 17

2,127

2,026

1,563

4.7%

26.5%

BLS

18 and 19

3,763

3,870

3,578

2.8%

4.9%

BLS

20 and 21

5,400

4,976

5,731

7.9%

6.1%

Naïve

22 to 24

9,913

9,979

9,114

0.7%

8.1%

BLS

25 to 29

18,100

18,893

17,657

4.4%

2.4%

Naïve

30 to 34

17,420

18,396

17,642

5.6%

1.3%

Naïve

35 to 39

16,784

17,303

17,032

3.1%

1.5%

Naïve

40 to 44

16,036

16,351

16,175

2.0%

0.9%

Naïve

45 to 49

16,910

17,413

17,170

3.0%

1.5%

Naïve

50 to 54

16,999

17,669

17,549

3.9%

3.2%

Naïve

55 to 59

15,584

16,201

16,327

4.0%

4.8%

BLS

60 and 61

5,188

5,259

5,315

1.4%

2.5%

BLS

62 to 64

5,693

5,828

6,009

2.4%

5.6%

BLS

65 to 69

5,367

5,641

5,928

5.1%

10.5%

BLS

70 to 74

2,254

2,435

2,347

8.0%

4.1%

Naïve

75 to 79

1,007

1,253

1,048

24.4%

4.0%

Naïve

80 and over

644

737

548

14.4%

15.0%

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

BLS

Naïve

All

All

5.8%

6.0%

BLS

All

Male

6.9%

8.3%

BLS

All

Female

8.3%

7.4%

Naïve

 

Table 4. Weighted mean absolute percent error, labor force

Race

Sex

Weighted mean absolute percent error

Best performer

BLS

Naïve

All

All

3.8%

3.6%

Naïve

All

Male

4.4%

3.6%

Naïve

All

Female

4.3%

6.1%

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 2006–16 labor force projections.

Table 5. Labor force dissimilarity indexes

Race

Sex

BLS

Naïve

Best performer

All

All

0.9%

1.5%

BLS

All

Male

1.7%

1.6%

Naïve

All

Female

1.7%

3.1%

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: August 1, 2018