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Job Openings and Labor Turnover Survey
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JOLTS Experimental State Estimates Methodology

The JOLTS sample of 16,000 establishments does not directly support the production of sample based state estimates. However, state estimates have been produced by combining the available sample with model-based estimates, and smoothed by taking a 3-month moving average. These data are experimental. As such, they have not been subject to the same level of review as the current official JOLTS national and regional estimates. BLS is inviting data users to comment on both the methodology used to produce these estimates and on the usefulness of these data. The eventual goal is to produce and provide JOLTS state-level estimates on a monthly basis.

These estimates consist of four major estimating models; the Composite Regional model (an unpublished intermediate model), the Synthetic model (an unpublished intermediate model), the Composite Synthetic model (published historical series through the most current benchmark year), and the Extended Composite Synthetic model (published current-year monthly series). The Composite Regional model uses JOLTS microdata, JOLTS regional published estimates, and Current Employment Statistics (CES) employment data. The Composite Synthetic model uses JOLTS microdata and Synthetic model estimates derived from monthly employment changes in microdata from the Quarterly Census of Employment and Wages (QCEW), and JOLTS published regional data. The Extended Composite Synthetic extends the Composite Synthetic estimates by ratio-adjusting the Composite Synthetic by the ratio of the current Composite Regional model estimate to the Composite Regional model estimate from one year ago.

The Extended Composite Synthetic model (and its major component—the Composite Regional model) is used to extend the Composite Synthetic estimates because all of the inputs required by this model are available at the time monthly estimate are produced. In contrast, the Composite Synthetic model (and its major component—the Synthetic model) can only be produced when the latest QCEW data are available. The plan is to use Extended Composite Synthetic model estimates to extend the Composite Synthetic model estimates during the annual JOLTS re-tabulation process. The extension of the Composite Synthetic model using current data-based Composite Regional model estimates will ensure that the Composite Synthetic model estimates reflect current economic trends.

The following outlines each model in a non-technical summary format. Each model is summarized separately, and answers the following:

  • What is the approach attempting to do?
  • What data inputs are used in the approach?
  • How does the approach attempt to use that data?
  • What data outputs are produced by the approach?
  • What limitations does the approach have?
  • What more needs to be done?

Composite Regional Model

What Approach?

The Composite Regional approach calculates state-level JOLTS estimates from JOLTS microdata using sample weights, and the adjustments for non-response (NRAF). The Composite Regional estimate is then benchmarked to CES state-supersector employment to produce state-supersector estimates. The JOLTS sample, by itself, cannot ensure a reasonably sized sample for each state-supersector cell. The small JOLTS sample results in quite a number of state-supersector cells that lack enough data to produce a reasonable estimate. To overcome this issue, the state-level estimates derived directly from the JOLTS sample are augmented using JOLTS regional estimates when the number of respondents is low (that is, less than 30). This approach is known as a composite estimate which leverages the small JOLTS sample to the greatest extent possible and supplements that with a model-based estimate. Previous research has found that regional industry estimates are a good proxy at finer levels of geographical detail. That is, one can make a good prediction of JOLTS estimates at the regional-level using only national industry-level JOLTS rates. The assumption in this approach is that one can make a good prediction of JOLTS estimates at the state-level using only regional industry-level JOLTS rates.

In this approach, the JOLTS microdata-based estimate is used, without model augmentation, in all state-supersector cells that have 30 or more respondents. The JOLTS regional estimate will be used, without a sample-based component, in all state-supersector cells that have fewer than five respondents. In all state-supersector cells with 5–30 respondents an estimate is calculated that is a composition of a weighted estimate of the microdata-based estimate and a weighted estimate of the JOLTS regional estimate. The weight assigned to the JOLTS data in those cells is proportional the number of JOLTS respondents in the cell (weight=n∕30, where n is the number of respondents).

What data inputs?

  • All JOLTS microdata records
  • All weights from JOLTS estimation (final weights that account for sampling weight, NRAF, agg-codes, etc.)
  • JOLTS published regional rates estimates (regional JO, H, Q, LD, and TS rates)
  • CES state-supersector employment

How are data used?

  1. All JOLTS microdata are weighted using final weights. A weighted estimate is made for each JOLTS respondent.
  2. Counts are made for each state-supersector cell.
  3. Each JOLTS respondent is paired with its regional rate estimate for all variables.
  4. Based on the count of respondents in the state-supersector cell the JOLTS respondent belongs to, a Composite Model Weight (CMW) is calculated.
    1. If the count is>30, then the CMW for the respondent data=1. The CMW for the regional estimate=0.
    2. If the count<5, then the CMW for the respondent data=0. The CMW for the regional estimate=1.
    3. If the count is 5–30, then the CMW for the respondent data=n∕30, where n is the number of respondents. The CMW for the regional estimate=1-n∕30.
  5. The state-level rate estimate is therefore the final weighted respondent-based JOLTS rate times the CMW added to the regional rate times the CMW, benchmarked to CES state-level estimate:
    1. FINAL ESTIMATE=CES STATE EMP×((final weight JOLTS rate×CMW)+(regional rate×CMW))
  6. To stabilize the estimate, the sum of state Composite Regional estimates within each region is then benchmarked to the published JOLTS regional estimates.

How are outputs produced?

  • This model produces state-level estimates of JO, H, Q, LD, and TS. These estimates provide estimates for the most current month of estimates and can be produced during monthly JOLTS estimation production.

What are the limitations?

  • JOLTS data are somewhat volatile at the national and regional levels due to the small sample size which in turn results in volatile state estimates.
  • The Composite Regional estimates can vary substantially from Composite Synthetic estimates for states that exhibit seasonal employment patterns that differ substantially from the JOLTS region to which they belong. For example, Alaska has a pronounced seasonal employment pattern that differs from the West region in which it resides. Consequently, the Composite Regional estimates derived using West region JOLTS rates substantially understate the JOLTS rates in that state.

What more is needed?

These estimates are based upon a model. There is, as of yet, no methodology in place that can produce an estimate of error for the estimates the model produces. Research on a methodology to produce an error estimate is currently underway.

The Composite Regional supersector estimates are summed across state industry supersectors to the nonfarm level.

Synthetic Model

What approach?

The Synthetic model differs fundamentally from the Composite Regional model. The Synthetic approach does not use JOLTS microdata but rather it uses data from the QCEW that have been linked longitudinally (Longitudinal Database—LDB), the QCEW-LDB. The Synthetic model attempts to convert QCEW-LDB monthly employment change microdata into JOLTS job openings, hires, quits, layoffs and discharges, and total separations data.

What data inputs?

  • All monthly employment changes for each record on the QCEW-LDB
  • JOLTS published regional estimates (regional JO, H, Q, LD, and TS)

How are data used?

  1. Every record on the QCEW-LDB is classified as expanding, contracting, or stable based on monthly employment change.
    1. For expanding records, the amount of employment growth is converted to JOLTS hires. They are given no separations.
    2. For contracting records, the amount of employment decline is converted to JOLTS separations. They are given no hires.
    3. For stable records, no attribution of JOLTS hires or separations is made.
  2. The entire QCEW-LDB is summarized to the US Census regional level.
  3. The QCEW-LDB regional summary is ratio adjusted to the JOLTS published regional estimate for hires and total separations.
    1. For each region, the ratio of QCEW-LDB based regional hires and total separations to JOLTS published hires and total separations is calculated (Ratio-H for hires and Ratio-TS for total separations).
    2. Each record on the QCEW-LDB within each US Census region will have their converted JOLTS data multiplied by Ratio-H and Ratio-TS, by region.
      1. For expanding records, the amount of employment growth is then: (JOLTS hires×Ratio-H). They remain with no separations.
      2. For contracting records, the amount of employment decline is then: (JOLTS separations×Ratio-TS). They remain with no hires.
      3. For stable records, they remain with no JOLTS hires or separations.
  4. To produce state-level estimates, sum the regional hires×Ratio-H by state to produce a state-level JOLTS hires estimate and sum the TS×Ratio-TS by state to produce a state-level JOLTS total separations estimate.

How are the outputs produced?

  • State-level JOLTS estimates for hires and total separations come directly from the model outlined above.
    • Synthetic job openings are a function of the ratio of industry-regional job openings and hires. This ratio of published job openings to hires is applied to model hires estimates to derive model job opening estimates. Ratio-adjusting the JOLTS model hires and separations to the regional published JOLTS hires and separations estimates ensures that the JOLTS published churn rate is fully accounted for.
      • JOLTS synthetic JO formula
    • Synthetic quits and layoffs and discharges are a function of the relative percentage of the individual components of total separations at the industry-regional level. The relative percentages of each component are applied to the model separations estimates to derive model quits and layoffs and discharges.
      • JOLTS synthetic quits ratio
      • JOLTS synthetic L&D ratio
      • JOLTS synthetic quits formula
      • JOLTS synthetic L&D formula

What are the limitations?

  • This approach is NOT meant to model individual QCEW-LDB data records. It would not be prudent to use this approach to model small populations (30 or fewer establishments). The model works best at the state-level, and while it is possible to model smaller populations, there potentially is a reduction in the strength of the model proportionate to the reduction in the size of the population being modeled
  • The model does generate state-level job openings and separations breakouts. However, these estimates are based upon ratios that are common across the region to which a state belongs. If there are significant differences in the ratio of job openings to hires or separations breakouts for any particular state (or set of states) within a region, the model cannot detect that and estimates will not reflect those differences.
  • Since the model is based on QCEW-LDB data, the model cannot produce current state-level estimate since QCEW-LDB data lags current JOLTS estimation production by 6–9 months.

What more is needed?

These estimates are based upon a model. There is, as of yet, no methodology in place that can produce any estimate of error for the estimates the model produces. Research on a methodology to produce an error estimate is currently underway. The Synthetic model may be augmented in the future with the Census Bureauís QWI series of hires and separations.

Composite Synthetic Model

What approach?

The Composite Synthetic model is nearly identical to the Composite Regional model. The primary difference is the use of the Synthetic model estimates (described in the first section) rather than JOLTS published regional estimates when there is an insufficient amount of JOLTS microdata to produce a state-supersector estimate.

Just like the Composite Regional approach, the JOLTS microdata-based estimate is used in all state-supersector cells that have 30 or more respondents. However, in contrast to the Composite Regional approach, the Composite Synthetic approach uses the Synthetic estimate in all state-supersector cells that have fewer than five respondents. In all state-supersector cells with 5–30 respondents an estimate is calculated that is a composition of a weighted estimate of the microdata-based estimate and a weighted estimate of the Synthetic estimate. The weight assigned to the JOLTS data in those cells is proportional the number of JOLTS respondents in the cell (weight=n∕30, where n is the number of respondents).

The Composite Synthetic supersector estimates are summed across state-supersectors to the nonfarm level. Composite Synthetic estimates are averaged across 3 months, creating a 3-month moving average.

What data inputs?

  • All JOLTS microdata records
  • All weights from JOLTS estimation (final weights that account for sampling weight, NRAF, agg-codes, etc.)
  • Synthetic estimates (regional JO, H, Q, LD, and TS rates)
  • JOLTS regional-level estimates (to benchmark the state estimates)
  • CES state-supersector employment

How are data used?

  1. All JOLTS microdata are weighted using final weights. A weighted estimate is made for each JOLTS respondent.
  2. Counts are made for each state-supersector cell.
  3. Each JOLTS respondent is paired with its Synthetic rate estimate for all variables.
  4. Based on the count of respondents in the state-supersector cell the JOLTS respondent belongs to, a Composite Model Weighted (CMW) estimate is calculated.
    1. If the count is>30, then the CMW for the respondent data=1. The CMW for the Synthetic estimate=0.
    2. If the count<5, then the CMW for the respondent data=0. The CMW for the Synthetic estimate=1.
    3. If the count is 5–30, then the CMW for the respondent data=n∕30, where n is the number of respondents. The CMW for the Synthetic estimate=1−n∕30.
  5. The state-level rate estimate is therefore the final weighted respondent-based JOLTS rate times the CMW added to the Synthetic rate times the CMW, benchmarked to CES state-level estimate:
    1. FINAL ESTIMATE=CES STATE EMP×((final weight JOLTS rate×CMW)+(synthetic rate×CMW))
  6. To stabilize the estimate, the sum of state Composite Synthetic estimates within each region is then benchmarked to the published JOLTS regional estimates.

How are outputs produced, and what are the limitations?

  • This model produces state-level estimates of JO, H, Q, LD, and TS. These estimates cannot be produced without lag.

What more is needed?

These estimates are based upon a model. There is, as of yet, no methodology in place that can produce any estimate of error for the estimates the model produces. Research on a methodology to produce an error estimate is currently underway.

Extended Composite Synthetic Model

What Approach?

The Extended Composite Synthetic model is designed to project the Composite Synthetic forward until QCEW-LDB data are available to produce Composite Synthetic estimates. The Composite Synthetic estimates are extended using the ratio of the current Composite Regional state industry estimate to the Composite Regional state industry estimate from one year ago.

This approach ensures that the Extended Composite Synthetic state estimates reflect current JOLTS regional and industry-level economic conditions. The Extended Composite Synthetic estimates reflects current JOLTS state economic conditions to the extent that sufficient JOLTS microdata are available.

What data inputs?

  • The historical series of Composite Synthetic model estimates at the state-industry-level
  • The historical series of Composite Regional model estimates at the state-industry-level

How are data used?

The Composite Synthetic model estimates are produced at a lag since QCEW-LDB data are only available at a 6–9 month lag relative to JOLTS data. The Composite Regional model estimates, in contrast, are not produced at a lag and are available concurrent with JOLTS data. Therefore, Composite Synthetic estimates can be extended by ratio-adjusting the Composite Synthetic estimates by the ratio of current Composite Regional estimates to the Composite Regional estimates from one year ago at the state-industry-level as follows:

Extended Composite Synthetic Model formula

Where

  • extended composite synthetic state industry estimate for month t is the Extended Composite Synthetic state industry estimate for month t
  • composite synthetic state industry estimate for month t-12 is the Composite Synthetic state industry estimate for month t-12 (one year ago)
  • composite regional state industry estimate for month t is the Composite Regional state industry estimate for month t
  • composite regional state industry estimate for month t-12 is the Composite Regional state industry estimate for month t-12 (one year ago)

State-level estimates are produced by summing the Extended Composite Synthetic estimates over industry.

How are outputs produced, and what are the limitations?

  • This model will produce state-level estimates of JO, H, Q, LD, and TS. These estimates are produced without lag. The methodology allows the Extended Composite Synthetic data to reflect current economic trends at the CESID Industry-Region level. The projection reflects current state economic trends where sufficient JOLTS microdata are available.

Sample allocation

What is the sample size allocation for the inputs used to produce the JOLTS state estimates?

The JOLTS experimental state estimates sample allocation table below provides a snapshot of the sample used in the state estimates. Sample are utilized in both components of the model. The sample component incorporates JOLTS MSA respondent data. The model component incorporates JOLTS regional-level respondent data, CES Metro Area respondent data, and QCEW establishment counts.

SAMPLE ALLOCATION: For State Estimator Components
FIPS CodeStateJOLTS State Respondents[1]JOLTS Regional Respondents[2]QCEW Establishments[3]CES State respondents[4]
20182019201820192018201920182019

1

Alabama145135 3,086 2,979 127,057 129,836 14,790 14,880

2

Alaska3329 2,034 1,933 22,064 22,346 2,658 2,690

4

Arizona169163 2,034 1,933 163,385 168,006 10,685 10,780

5

Arkansas8570 3,086 2,979 90,170 91,348 6,657 7,390

6

California887822 2,034 1,933 1,596,644 1,636,051 80,219 80,150

8

Colorado181184 2,034 1,933 205,521 213,236 9,669 9,870

9

Connecticut140119 1,893 1,862 120,353 122,420 8,326 8,330

10

Delaware2927 3,086 2,979 33,266 34,339 2,388 2,390

11

District of Columbia4441 3,086 2,979 40,421 41,459 1,680 1,860

12

Florida473465 3,086 2,979 681,386 704,202 42,642 39,580

13

Georgia263255 3,086 2,979 264,580 270,797 25,136 24,710

15

Hawaii3137 2,034 1,933 41,573 42,600 2,960 3,120

16

Idaho5354 2,034 1,933 62,622 66,589 4,884 4,880

17

Illinois405381 2,153 2,096 367,357 370,118 23,844 24,200

18

Indiana219179 2,153 2,096 166,974 167,895 13,207 13,790

19

Iowa113107 2,153 2,096 102,019 103,255 9,976 10,060

20

Kansas119105 2,153 2,096 88,549 88,903 7,844 7,660

21

Kentucky10896 3,086 2,979 123,587 122,392 8,792 9,120

22

Louisiana143123 3,086 2,979 131,654 133,076 10,094 10,630

23

Maine4447 1,893 1,862 50,574 51,244 4,876 5,210

24

Maryland156138 3,086 2,979 172,964 175,946 10,016 10,360

25

Massachusetts249248 1,893 1,862 263,123 268,730 14,884 15,790

26

Michigan264275 2,153 2,096 250,360 254,321 15,477 15,930

27

Minnesota195175 2,153 2,096 178,398 180,442 10,448 10,530

28

Mississippi8177 3,086 2,979 73,953 73,484 7,303 7,620

29

Missouri186195 2,153 2,096 207,228 212,816 15,701 16,180

30

Montana5054 2,034 1,933 50,002 50,062 3,960 3,950

31

Nebraska6576 2,153 2,096 72,663 72,252 5,601 5,930

32

Nevada10790 2,034 1,933 82,599 82,582 4,322 4,360

33

New Hampshire4855 1,893 1,862 53,095 54,109 4,484 4,520

34

New Jersey274270 1,893 1,862 261,193 265,349 17,243 17,020

35

New Mexico7670 2,034 1,933 60,617 62,972 6,273 6,500

36

New York636639 1,893 1,862 612,862 603,777 39,969 38,940

37

North Carolina271279 3,086 2,979 280,304 286,747 24,763 24,950

38

North Dakota3849 2,153 2,096 31,175 31,153 3,087 3,140

39

Ohio323327 2,153 2,096 301,400 305,330 27,764 27,920

40

Oklahoma114103 3,086 2,979 110,791 111,779 8,580 8,730

41

Oregon106121 2,034 1,933 150,988 154,578 12,984 12,840

42

Pennsylvania451425 1,893 1,862 365,599 368,424 25,550 27,540

44

Rhode Island3338 1,893 1,862 37,458 39,239 2,099 2,290

45

South Carolina119105 3,086 2,979 138,022 141,227 10,510 10,410

46

South Dakota3239 2,153 2,096 33,419 33,943 2,842 2,980

47

Tennessee143144 3,086 2,979 164,216 168,859 12,706 13,110

48

Texas667664 3,086 2,979 692,217 713,990 48,805 50,380

49

Utah107100 2,034 1,933 107,457 111,204 7,765 7,880

50

Vermont1821 1,893 1,862 25,789 26,021 2,554 2,540

51

Virginia201208 3,086 2,979 271,736 272,382 17,881 19,940

53

Washington194178 2,034 1,933 240,720 243,936 14,193 12,990

54

West Virginia4449 3,086 2,979 51,091 51,522 5,792 6,260

55

Wisconsin194182 2,153 2,096 173,764 181,594 11,287 11,630

56

Wyoming4031 2,034 1,933 26,322 26,948 3,074 2,990

00

Total US9,1668,864 9,166 8,870 10,021,281 10,205,830 689,244 697,450

Footnotes:

[1]JOLTS Sample Units used in Sample Component of the Composite & Extended Composite Models

[2]JOLTS Sample Units used in Model Component of the Composite & Extended Composite; the Total is the sum of the four regions

[3]QCEW Establishments used in the Model Component of the Synthetic and Composite Synthetic Model

[4]CES UI Sample Units used in Model Component of the Composite Synthetic & Extended Composite Models

What more is needed?

These estimates are based upon a model. There is, as of yet, no methodology in place that can produce any estimate of error for the estimates the model produces. Research on a methodology to produce an error estimate is currently underway.

Last Modified Date: August 19, 2020