[Federal Register Volume 79, Number 175 (Wednesday, September 10, 2014)]
[Notices]
[Pages 53787-53791]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2014-21241]


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DEPARTMENT OF LABOR

Bureau of Labor Statistics


Comment Request on the Local Area Unemployment Statistics Program

AGENCY: Bureau of Labor Statistics, Labor.

ACTION: Request for comments on proposed action.

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SUMMARY: The Department of Labor, through the Bureau of Labor 
Statistics (BLS) and, specifically, the Local Area Unemployment 
Statistics (LAUS)

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program, is responsible for the development and publication of State 
and local area labor force statistics. The LAUS program develops and 
issues monthly estimates of the labor force, employment, unemployment, 
and the unemployment rate for approximately 7,300 areas in the Nation. 
A major program redesign to improve the methodological basis of the 
LAUS estimates and update the geography and techniques to reflect 2010 
Census data was initially funded in FY 2011. After completion of 
various long-term research projects, the BLS plans to implement 
improvements to its estimating methods with State and area LAUS 
estimates for January 2015, to be published in March 2015.

DATES: Written comments must be submitted to the office listed in the 
ADDRESSES section of this notice on or before November 10, 2014.

ADDRESSES: Send comments to Patrick Carey, Local Area Unemployment 
Statistics, Bureau of Labor Statistics, Room 4675, Massachusetts Avenue 
NE., Washington, DC 20212 or by email to: [email protected].

FOR FURTHER INFORMATION CONTACT: Walter Sylva, Local Area Unemployment 
Statistics, Bureau of Labor Statistics, telephone number 202-691-6456 
(this is not a toll-free number), or by email to: 
[email protected].

SUPPLEMENTARY INFORMATION:

I. Introduction

    The Department of Labor, through the Bureau of Labor Statistics, is 
responsible for the development and publication of State and local area 
labor force statistics through the Local Area Unemployment Statistics 
(LAUS) program. Currently, monthly estimates of employment, 
unemployment, and the unemployment rate are prepared for approximately 
7,300 areas, including Census regions, Census Divisions, all States and 
the District of Columbia, Puerto Rico, metropolitan and small labor 
market areas, counties, cities of 25,000 population or more, and all 
cities and towns in New England regardless of population. In a multi-
year, multi-project initiative that began in FY 2011, the following 
prospective improvements to State and area labor force estimation were 
identified:

 Improve State time series estimating models by introducing:
    [cir] Model-Based Benchmarking that accounts for errors in the 
estimates
    [cir] Additivity of outlier effects that allocates level shifts to 
the appropriate State
    [cir] More efficient model structure that reduces processing time
    [cir] Enhanced smoothed seasonal adjustment procedures
 Incorporate American Community Survey (ACS) data to replace 
Census long form data that are no longer available as inputs
 Update procedures for developing other substate areas that 
employ innovative and dynamic estimating methods

II. Background

    A hierarchy of estimation methods is used to produce the State and 
area labor force estimates, based in large part on the availability and 
quality of data from the Current Population Survey (CPS), the official 
measure of the labor force for the Nation. Labor force estimates are 
generated for the nine Census Divisions utilizing time series models 
and are controlled to National estimates. State estimates also are 
developed using time series models and are controlled to Division 
estimates. Finally, substate estimates are developed by means of a 
building-block approach using locally available data and are controlled 
to State estimates.
    Improved Time Series Models. The estimates for States, the District 
of Columbia, New York City and the Los Angeles Metropolitan Division, 
and their respective balances of New York State and California are 
developed using signal-plus-noise models. These models rely heavily on 
monthly CPS data as well as current wage and salary employment 
estimates from the Current Employment Statistics (CES) program and 
claims data from State unemployment insurance (UI) programs.
    There are signal-plus-noise models for five additional substate 
areas and their respective balances of State. The areas are: The 
Chicago-Naperville-Joliet, IL metropolitan division; the Cleveland-
Elyria-Mentor, OH metropolitan area; the Detroit-Warren-Livonia, MI 
metropolitan area; the Miami-Miami Beach-Kendall, FL metropolitan 
division; and the Seattle-Bellevue-Everett, WA metropolitan division. 
As with the State and Census Division models, these area models are 
based on the classical decomposition of a time series into trend, 
seasonal, and irregular components. A component to identify and remove 
CPS sampling error is also included. Area models, like the Census 
Division models, are univariate in design in that only the historical 
relationship of the CPS is considered--UI claims data and CES 
employment data are not used each month in the estimation process.
    The monthly estimates of employment and unemployment utilize a 
tiered approach to estimation known as real-time benchmarking. Model-
based estimates (using a univariate form) are developed for the nine 
Census Divisions that geographically exhaust the Nation. These 
estimates are controlled to the National levels of employment and 
unemployment. State model-based estimates are then made and controlled 
to the Census Division estimates. In this manner, the monthly State 
employment and unemployment estimates will add to the National levels, 
precluding differences between the sum of States and the National 
estimates, and National shocks related to the business cycle or 
outliers like September 11 will be addressed in real time. Monthly pro-
rata factors for each Census Division are used to adjust the sum of the 
States within each Census Division to sum to the Division totals. 
Census Divisions also use pro-rata factors to ensure that they sum to 
the Nation. Substate estimates, including the area and balance-of-State 
models noted above, are controlled directly to the State totals, which 
are themselves controlled to the National CPS via the Census Division 
models.
    The new time series models introduce the following major 
improvements: (1) Model-based benchmarking, (2) additivity of outlier 
effects, (3) new model structure, and (4) enhanced smoothed seasonal 
adjustment procedure.
    The improved models will directly produce estimates that 
automatically sum to Census Division controls and thus eliminate the 
need for the external pro-rata factors currently in use to benchmark 
State estimates to their Census Divisions. During the benchmarking 
process the new models account for the errors inherent in each facet of 
the estimating procedure. These include State-specific CPS sampling 
error, State model prediction errors based on historical patterns, 
errors in the estimates used as a benchmark (Census Division & 
National), and the relation of these errors to the overall size of the 
benchmark discrepancy. This approach provides greater flexibility 
(monthly benchmarking adjustments will vary by State and by type of 
series), smoother monthly adjustment factors, and improved reliability 
measures.
    Another important improvement is that the new models allow for the 
additivity of outlier effects. Outlier estimates will be separated from 
the benchmarking process, resulting in the outliers being specific to 
where they occurred. Level shifts and onetime outliers will not be 
spread across all States within a Census Division so as

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not to distort the magnitude of the outlier effect.
    The new model structure uses CES and UI trend estimates as 
regressor variables to explain trend variation in the CPS. This 
produces results similar to current bivariate models but with a major 
reduction in computing time. The new structure also allows for more 
flexibility for model development over the long term.
    An enhanced smooth seasonal adjustment procedure will be utilized 
to address the presence of residual seasonality that is noticeable in 
some of the smoothed seasonally adjusted employment series. The 
smoothed seasonal adjustment (SSA) procedure was implemented in 2005 
with the third generation of models. The SSA procedure uses the 
Henderson Trend Filter to isolate the trend of the series by removing 
much of the volatility that is introduced to the State's estimates 
during the real-time benchmarking process. However, even with the 
application of the SSA there still remained some statistical evidence 
of weak residual seasonality in the SSA employment series. (The 
unemployment levels and the unemployment rates were not affected.)
    To address this concern, the fourth generation of models utilize an 
improved smoothed seasonal adjustment filter. In addition to the trend 
filter, additional weights have been added to create a seasonal filter 
as well. The enhanced procedure will continue to remove the volatility 
introduced by real-time benchmarking, while simultaneously removing all 
residual seasonality that results from benchmarking to a seasonal 
series.
    Incorporation of American Community Survey. For the 2010 Census, 
the long- and short-form questionnaires used from 1940 to 2000 were 
replaced by a single questionnaire asking 10 questions. The more 
detailed socio-economic data once obtained by the long-form 
questionnaire are now provided by the American Community Survey (ACS). 
The LAUS program had been reliant on the long form data as the basis 
for developing substate estimates for self-employed, unpaid family 
workers, private household workers, and agricultural workers throughout 
the decade. These data elements represent employment that is either not 
covered by unemployment insurance compensation programs or not included 
in the payroll survey data CES, thus the Census long form had been the 
sole source for this type of information at the local level.
    ACS data are issued on an annual basis and they do not represent a 
single point in time as did the decennial Census, which represented 
April 1 in the year that the Census was conducted. Instead ACS data are 
estimates that span 1 year, 3 years, or 5 years depending on the 
population level of each area. To ensure coverage of all LAUS 
geography, which includes areas with 25,000 population or more and all 
cities and towns in New England regardless of population, the 5-year 
estimates must be used. In addition to covering all LAUS geography, the 
5-year estimates use the largest sample size and are the most 
statistically reliable of the ACS estimates. However, since they 
represent a 5-year span they cannot be directly used to develop current 
monthly estimates.
    The most current source of the needed data inputs is the CPS which 
does not have the geographic detail of the ACS. The proposed 
methodology will utilize the strengths of the CPS and the ACS to 
develop monthly estimates of self-employed, unpaid family, and private 
household workers (collectively known as ``all-other'' employment) and 
agricultural workers at the needed level of geography.
    Enhanced procedures for developing other substate areas. Utilizing 
ACS data to replace the Census long form data facilitated the 
enhancement of some of the substate methodologies making up the 
building-block approach used to develop independent substate estimates. 
Revisions are proposed for the methodology of adjusting place-of-work 
data to a place-of-residence basis, the estimation of what is known as 
``all-other'' employment, the estimation of agricultural employment, 
and the estimation of agricultural unemployment not covered by 
unemployment insurance. In addition, substate estimates will be 
developed at the county level rather than the labor market area level. 
A brief discussion of the new methodologies is below.
    Place-of-Work Residency Adjustment. The LAUS program uses the same 
labor force concepts as the CPS. Thus employment inputs from the CES 
and Quarterly Census of Employment and Wages (QCEW) programs, which are 
based on place-of-work, must be adjusted to reflect the worker's place 
of residence per the CPS. To accomplish this, Dynamic Residency Ratios 
(DRRs) are applied to CES and QCEW employment inputs for LAUS 
estimation. This methodology assumes that resident employment in an 
area is a function of the relationship between employed residents and 
jobs not only in that area, but in other areas within commuting 
distance. The procedure is more dynamic than the use of a single 
residency ratio insofar as job count changes in commuting areas can 
affect resident employment.
    In the past, journey-to-work data from the decennial Census were 
incorporated into the DRRs. Journey-to-work data were not available 
from 2010 Census due to the discontinuation of the long form. For the 
LAUS 2015 redesign, DRRs will be computed using ACS journey-to-work 
data in the same manner that they are computed now with one major 
modification. Currently, an area must be the destination workplace of 
at least 100 resident commuters (50 in New England) to be considered a 
potential commuter area. BLS proposes replacing these criteria with a 
percentage threshold. In the new set of DRRs, commuter areas will be 
limited to those areas that are the work destination of no less than 10 
percent of resident commuters. This will eliminate marginal commuter 
areas included in the previous methodology to account for potential 
future growth.
    The previous threshold for DRR commutation areas reflected the ten-
year span between Census journey-to-work data releases. The inclusion 
of a relatively high number of areas would accommodate any potential 
changes to commuting patterns over the ensuing decade. The new data 
source for DRRs, ACS journey-to-work data, is intended to be updated 
every five years. The increased frequency in the availability of 
commutation data will make the list of commutation areas more 
responsive to changing commuting patterns, reducing the need to include 
minor destinations which may grow in importance over time.
    Estimation of All-Other Employment. The current method uses Census 
2000 data as the starting point for the self-employed, unpaid family, 
and private household workers (known as ``All-Other Employment'') and 
moves it forward through time by applying the relationship of all-other 
employment to the nonfarm wage and salary employment estimate at the 
time of the Census.
    The new method uses the relationship of each area's share of ACS 
all-other employment to the State's total ACS all-other employment. 
This relationship is then used to allocate a monthly 5-year weighted 
average of each State's CPS estimate of all-other employment. A 
weighted average of the CPS estimate is used because, depending on the 
State's CPS sample size, the monthly estimate for this element may be 
volatile due to sampling error. This monthly 5-year weighted average 
consists of the current month's estimate averaged with the same month's 
estimate going back 4

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years, with more weight placed on the more current estimates. This 
technique borrows strength from prior estimates while preserving 
seasonal trends.
    Estimation of Agricultural Employment. The current method uses the 
Census 2000 data as the base and moves the estimate forward using a 
monthly change factor based on a State's membership in a multi-State 
agricultural region.
    The new method for estimation of agricultural employment uses a 
similar approach as the all-other employment method. A monthly 5-year 
weighted average of each State's CPS estimate of agricultural 
employment is developed and allocated to substate areas using each 
areas' share of the State's total ACS agricultural employment. This 
method is State-specific and eliminates the need for an agricultural 
regional factor.
    Estimation of non-covered agricultural unemployment. This is an 
optional procedure that is currently utilized by 19 States. The current 
procedure uses an indirect approach for the estimation of agricultural 
unemployment not covered by unemployment insurance. It assumes that 
there is unemployment associated with employment and that the 
unemployment rate in non-covered agriculture is related to the rate of 
unemployment in covered sectors of the economy. To estimate non-covered 
agricultural employment, the annual average of covered agricultural 
employment from the QCEW program is subtracted from the covered 
agricultural employment estimate that is developed each month (as 
described in the prior section). Seasonal factors derived from CPS 
agricultural data from 1977-1982 are applied to account for 
seasonality.
    The new method replaces the annual average QCEW covered 
agricultural employment with a 1-year lagged monthly estimate of 
agricultural employment from the QCEW and eliminates the potentially 
outdated seasonal factors. Use of a 1-year lagged monthly estimate will 
incorporate seasonal trends into the estimate, simplifying the 
calculation and making it more responsive to long-term changes in 
seasonal patterns.
    New procedure for estimating employment and unemployment at the 
county level. Labor market areas (LMAs) are independently estimated 
using a building block approach that incorporates the new methods 
discussed above and other methods still currently in use. The 
employment component is comprised of non-agricultural wage and salary 
employment, all-other employment and agricultural employment. While the 
unemployment component is derived by summing the estimates of non-
covered agricultural unemployment (if applicable), total unemployment 
insurance (UI) continued claims without earnings, unemployed exhaustees 
and unemployed entrants into the labor force.
    The current procedure consists of first developing these 
independent substate estimates at the LMA level and then disaggregating 
them into counties and cities. With the exception of non-agricultural 
wage and salary employment, all inputs for estimating the components of 
employment and unemployment are readily available at the county level 
(Minor Civil Division (MCD) level in New England, MCDs being cities and 
towns). Aggregating these more geographically detailed data into LMAs 
is an unnecessary step that results in the distortion of these data 
when they are reallocated backed to the county level or MCD level, 
particularly for some of the unemployment components.
    The new method proposes to first develop the independent substate 
estimates at county level and then sum them to their appropriate LMA. 
This approach will result in more accurate estimates and will allow 
better operational flexibility for future updates to the geographic 
definitions of LMAs as counties (MCDs in New England) are the basic 
component of LMA geographic definitions issued by the Office of 
Management (OMB), as well as for small labor market areas as defined by 
the BLS.
    The current method estimates the labor force in LMAs, which are 
defined to comprise one or more counties (MCDs in New England). 
Employment and unemployment inputs are entered at the LMA level. In a 
multi-county LMA, county unemployment estimates are disaggregated from 
the LMA using the share of UI claims for the experienced unemployed, 
the share of the 16-19 population for unemployed new entrants, and the 
share of the 20+ population for unemployed re-entrants.
    The new procedures discussed above for estimating the employment 
components of all-other employment and agricultural employment produce 
these estimates at the county level. The non-agricultural wage and 
salary employment component, which is provided by the CES and the QCEW 
programs, is generally available at the LMA level and must be allocated 
into the counties that comprise the LMA. This will be accomplished by 
using ACS non-agricultural wage and salary employment ratios derived 
from the most recent ACS five-year dataset to distribute the CES/QCEW 
LMA data to its component counties (and MCDs in New England). This step 
is not needed for single county LMAs.
    All of the necessary inputs for estimating unemployment are already 
available at the county (and MCD) level. The new procedure results in 
more accurate county estimates by estimating the level of persons who 
remain unemployed after exhausting their eligibility for unemployment 
insurance benefits (known as exhaustees) at the county level and by 
avoiding the disaggregation of entrants from interstate LMAs.
    In the current method, if a layoff event occurs in a county that is 
part of a multi-county LMA, the exhaustees later associated with this 
event are not necessarily assigned to the county where the layoff 
occurred. This is because estimates of persons who have exhausted their 
eligibility for further UI benefits are disaggregated to a county using 
that county's share of persons who continue to be eligible for 
benefits. Using the new county-based methodology, each county will have 
its own independently estimated number of exhaustees, which will make 
it unnecessary to disaggregate exhaustees from the LMA level.
    In addition, unemployed entrants to the labor force are allocated 
from a Statewide control total to the intrastate parts of interstate 
LMAs using ratios based on annually updated population data from the 
Census Bureau. These entrants are then summed into their respective 
interstate LMAs before being disaggregated again using ratios based on 
population data specific to each interstate LMA. In some cases this 
two-step process has the effect of reallocating entrant unemployment 
estimates across State lines. Using the new county-based methodology, 
each county will be allocated its share of entrants in one step.
    Detailed descriptions of the current and Redesign approaches are 
available at the above address and at the BLS LAUS Web site http://www.bls.gov/lau/home.htm.

II. Desired Focus of Comments

    This notice is a general solicitation of comments from the public.
    Comments submitted in response to this notice will be summarized 
and included in the Notice of Decision on this proposal.


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    Signed at Washington, DC, this 29th day of August 2014.
Eric Molina,
Acting Chief, Division of Management Systems, Bureau of Labor 
Statistics.
[FR Doc. 2014-21241 Filed 9-9-14; 8:45 am]
BILLING CODE 4510-24-P