UI on LAUS

7/28/2019 UI on LAUS

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How Is Minnesota’s

Unemployment Rate Estimated?

Steve Hine, Director

Labor Market Information Office

MN DEED

651-259-7396

[email protected]



Possible approaches to estimation:

Survey or Census-based estimation – ask (a

sample of) the population

Time Series Models – present depends on the past

We use a combination of these two approaches

• Current Population Survey (CPS) is used for LAUS• Econometric model is used to extract ‘signal’ from

‘noisy’ CPS data



Some Important Background:

• Each state works in cooperation with the BLSto produce unemployment estimates

• This program is called LAUS – Local AreaUnemployment Statistics

• Fed/state cooperative programs providestandard methodology, resources, economiesof scale, comparability across states

• Produces data for 7,300 unique geographiesacross the country – focus here is on state-level methods and results



Current Population Survey

• Conducted each month by the Census Bureauon behalf of the BLS

• Comprised of ~76,000 households nationally

each month; about 1,700 in MN having ~2,500individuals ≥ 16

• Designed to provide official estimates of household employment and unemployment

for the nation and annual averages for allstates, NOT for state and sub-state areas

• Also provides conceptual framework and

crucial inputs for state LAUS estimation



CPS Continued

• Survey asks a set of detailed questions meantto identify workforce status of working ageindividuals in sampled households

• Uses the week of each month that containsthe 12th as the reference period (survey isconducted the week of the 19th)

• Surveyed households are rotated in and out of

the sample in an overlapping manner – eachhousehold is in for four months, out for eight,back in for four



Identifying Labor Force Status

1. LAST WEEK, DID YOU DO ANY WORK FOR (EITHER)

PAY (OR PROFIT)?

– If ‘Yes’, individual is classified as employed, if

‘No’, ask …

2. LAST WEEK, DID YOU DO ANY UNPAID WORK

IN THE FAMILY BUSINESS OR FARM

– If ‘Yes’ and ≥ 15 hours, person is employed, if ‘No”, ask …



3. LAST WEEK DID YOU HAVE A JOB EITHER FULLOR PART-TIME, INCLUDING ANY JOB FROMWHICH YOU WERE TEMPORARILY ABSENT?

– If ‘Yes’, person is employed, if ‘No’, ask …

4. LAST WEEK, WERE YOU ON LAYOFF FROM AJOB?

– If ‘Yes, ask …

5. HAS YOUR EMPLOYER GIVEN YOU A DATE TORETURN TO WORK?

– If ‘Yes’, person is unemployed, if ‘No’, ask …

6. HAVE YOU BEEN DOING ANYTHING TO FIND WORKDURING THE LAST 4 WEEKS?

– If ‘No’, person is Not In The Labor Force (NLF), if ‘Yes’, ask …



7. WHAT ARE ALL OF THE THINGS YOU HAVE DONE TO

FIND WORK DURING THE LAST 4 WEEKS?

1 CONTACTED EMPLOYER DIRECTLY/INTERVIEW2 CONTACTED PUBLIC EMPLOYMENT AGENCY

3 CONTACTED PRIVATE EMPLOYMENT AGENCY

4 CONTACTED FRIENDS OR RELATIVES

5 CONTACTED SCHOOL/UNIVERSITY EMPL CENTER

6 SENT OUT RESUMES/FILLED OUT APPLICATION

7 CHECKED UNION/PROFESSIONAL REGISTERS

8 PLACED OR ANSWERED ADS

9 OTHER ACTIVE

10 LOOKED AT ADS

11 ATTENDED JOB TRAINING PROGRAMS/COURSES

12 NOTHING13 OTHER PASSIVE

And if person provides an active job search method,

person is unemployed, otherwise NLF



Employed Unemployed Not in the labor force

Each person of

working age in

sampled

households is

classified as one

of three …



We can then derive …

• Population = Employed + Unemployed + NLF

• Labor Force = Employed + Unemployed

• LFPR = Labor Force/Population• Emp/Pop Ratio = Employment/Population

• Unemployment Rate = Unemployed/Labor Force



• There are also many other questions meant to

identify characteristics of the employed,

unemployed, and non-participants such as:

– What are the demographic characteristics (age

race, gender, marital status) of these individuals

– What are their educational levels, occupations,industry, and full-time/part-time status and why

– Why did they lose their job and how long ago

– If they aren’t looking for re-employment, why not



State Rate Methodology (simplified)

Small state samples in the CPS introduce high

variability (large sampling errors) into the sampleestimates The sampling methodology (4 months in, 8

months out, 4 months in) introduces serialcorrelation (persistence across time) into theseerrors

months 1 2 3 4 5 6 7 8% overlap 75 50 25 0 0 0 0 0

months 9 10 11 12 13 14 15 16% overlap 0 12.5 25 37.5 50 37.5 25 12.5

Time Series Models are used to reduce themagnitude of these errors – to account for factthat the CPS contains a “signal” of the true rate,but also “noise” due to sampling error that can in

part be estimated from past errors



For employment and unemployment, the CPSestimates are taken to be comprised of unknownbut estimable Signal and Noise components:

• CPSt = St + Nt

Each of these components is also modeled:• St = tXt + Tt + SEAt;

tXt = coefficient*(UI continuing claims rate for unemployment rate

model, CES employment rate for household employment model)Tt = estimated trend component

SEAt = estimated seasonal component

• Nt = 1Nt-1 + 2Nt-2 + 3Nt-3 + 4Nt-4 + 5Nt-5 + …. + nNt-n

What we want to report is the signal component St of the survey observation CPSt; the rest of theobservation is the noise Nt due to sampling error

Data for statewide estimates are seasonally

adjusted via X-12 ARIMA process



Compare CPS Rate to LAUS Rate (NSA)

0

1

2

3

4

5

6

7

8

9

10

2006 2007 2008 2009 2010 2011 2012 2013

CPS-based rate

LAUS rate

2



Noise = CPS - Signal

-1

-0.5

0

0.5

1

1.5

2

2006 2007 2008 2009 2010 2011 2012 2013

Noise



• The original not-seasonally adjusted (NSA) signal are

then benchmarked to division and national models to

ensure that the sum of states equals the national

total (MN in West North Central)• If the sum of all divisions (groups of states) is 0.91 of

national employment all division totals are multiplied

by the inverse so the total yields a sum equal to thenation

• The process is repeated for states; if sum of state

employment for a division is 1.02 of the division

employment, then employment is multiplied by the

inverse

• Then process is repeated for unemployment



Once we have a CPS signal

• Seasonal adjustment: this is an X-12 ARIMAstatistical model to isolate the “true” change

independent of predictable seasonal trends and

relies on historical data.

Not done yet…

0

1

2

3

4

5

6

7

8

9

10

2 0 1 3 - 0 5

2 0 1 3 - 0 2

2 0 1 2 - 1 1

2 0 1 2 - 0 8

2 0 1 2 - 0 5

2 0 1 2 - 0 2

2 0 1 1 - 1 1

2 0 1 1 - 0 8

2 0 1 1 - 0 5

2 0 1 1 - 0 2

2 0 1 0 - 1 1

2 0 1 0 - 0 8

2 0 1 0 - 0 5

2 0 1 0 - 0 2

2 0 0 9 - 1 1

2 0 0 9 - 0 8

2 0 0 9 - 0 5

2 0 0 9 - 0 2

2 0 0 8 - 1 1

2 0 0 8 - 0 8

2 0 0 8 - 0 5

2 0 0 8 - 0 2

2 0 0 7 - 1 1

2 0 0 7 - 0 8

2 0 0 7 - 0 5

2 0 0 7 - 0 2

2 0 0 6 - 1 1

2 0 0 6 - 0 8

2 0 0 6 - 0 5

2 0 0 6 - 0 2

Minnesota Unemployment Rates: SA and NSA

SA

NSA



Smoothing• The seasonally

adjusted series has

an additional step;it’s “smoothed” to

reduce volatility

• In the current

series (beforeannual processing)

this is done with

weighted averages

applied to the

current and

previous 6 months

of data



CPS vs. the Final SSA Rate

0

1

2

3

4

5

6

7

8

9

10

1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5

2006 2007 2008 2009 2010 2011 2012 2013

CPS Unemp Rate

SSA Unemp Rate



www.positivelyminnesota.com/laus



Back to ‘Other’ CPS Data Elements

• Beyond just classifying individuals asemployed or not, many other questions are

asked, allowing for other measures … some

examples from May 2013:Females ≥ 16, all races, population 2,133,300

Females ≥ 16, all races, employed 1,326,600

Females ≥ 16, all races, unemployed 71,400

Females ≥ 16, all races, LFPR 65.5%

Females ≥ 16, all races, unemployment rate 5.1%



Males ≥ 16, black, population 95,500

Males ≥ 16, black, employed 57,700

Males ≥ 16, black, unemployed 13,500

Males ≥ 16, black, LFPR 74.5%

Males ≥ 16, black, unemployment rate 18.9%

Labor force, all ages and races 2,959,500

‘Officially’ unemployed 161,600

Discouraged Workers 8,400

Other Marginally Attached Workers 25,000

Working part-time for economic reasons 145,300

‘Official’ Unemployment rate (U3) 5.5%

Unemp. rate inc. discouraged workers (U4) 5.7%

Unemp. rate inc. other marginally attached (U5) 6.5%

Unemp. Rate inc. part-time workers (U6) 11.4%



Minnesota’s Unemployment Rate Measures

0

2

4

6

8

10

12

14

16

2007 2008 2009 2010 2011 2012 2013

U3

U4

U5

U6



0

5

10

15

20

25

2002 2004 2006 2008 2010 2012

16-19

Black

Hispanic

Male

Female

White

Minnesota Unemployment Rates by Demographic



Summary • Estimating the state’s unemployment rate

(and other LAUS data) is a joint effort of BLS,the states’ LMI offices, and the Census Bureau

• CPS identifies numerous labor market

characteristics of survey respondents• Estimates involve ‘signal/noise’ analysis of CPS

data – based in part on UI claims data

• Then estimation involves seasonaladjustment, benchmarking and smoothing

UI on LAUS

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