Who’s Employed?
An in Depth Comparison of
Employment Data Sources
Gregory Giaimo, PE
Samuel Granato, PE
Andrew Hurst
The Ohio Department of Transportation
Division of Planning
Presented at
The 14th Transportation Planning Applications Conference
May 6, 2013
Overview
• Motivation
• Macro View-QCEW vs. BEA Control Totals for Data Expansion
• Micro View-QCEW vs. Purchased Data for Possible Replacement
Motivation
• For Travel Modeling Want Employment Data With:
• Accuracy (correct employment/employers)
• Completeness (all employment/employers)
• Spatial Precision (geocodable address of individual employers at actual
place of business activity)
• Temporal Consistency (no defunct businesses, contain new businesses
extant on the supposed date of the dataset)
• Categorization (correct NAICS or similar)
• Disaggregate (individual employer records allows data checking, finer
TAZ disaggregation and future travel demand models (particularly
freight) will include disaggregate attraction end modeling including
business synthesizers similar to current household synthesizers)
• There Area a Number of Potential Employment Data
Sources
Motivation
• QCEW (Quarterly Census of Employment and Wages)
• Regulatory dataset for Federal unemployment insurance
• Pros: cheap, regulatory basis implies it is complete and temporally consistent for
covered sectors
• Cons: confidentiality restrictions, uncovered sectors for those exempt from
Federal unemployment insurance laws (sole proprietors, small farms, railroads,
military, small non-profits, student workers, elected officials etc.), sub-county
location must be geocoded by user from mailing addresses (regulations only
require correct county and ability to mail a bill), single site reporting for multisite businesses, government particularly poor
• BEA (Bureau of Economic Analysis)
• Dataset maintained by Federal Government for Macro-Economic Analysis
• Pros: based on QCEW but enhanced with other administrative sources such as
income tax data to provide complete and temporally consistent data
• Cons: Only aggregate county level data available
Motivation
• LEHD (Longitudinal Employer-Household Dynamics)
• Census Bureau product based on QCEW and linked with ACS data
• Pros: Same pros as other QCEW based sources, no confidentiality restrictions or
costs, in addition dataset provides linkages between employee residences and
employer locations
• Cons: Same pros as other QCEW based sources, plus no employer records only
aggregate employment, Census Bureau masking, a PUMS-like product for
employment would alleviate some of this constraint
• Private Sources (InfoGroup’s InfoUSA/ReferenceUSA, Dun &
Bradstreet’s Global Commercial Database etc.)
• Several firms assemble employment data, primarily for resale for business
marketing purposes, they use phone directories and other publicly available
sources and then enhance and verify it with their staff
• Pros: Good spatial precision, few of the multi-site problems in QCEW,
reasonably complete
• Cons: Cost, lack of regulatory basis means incompleteness is ill-defined,
temporal consistency is poor because primary purpose of dataset makes it more
likely that defunct businesses are retained
Motivation
• Since 2000 ODOT has utilized QCEW as its primary source of employment
data, confidentiality requirements mean model employment data can’t be
given out freely creating some logistical issues with the models and
consultant contracts, also the latest confidentiality agreement includes
stricter personal liability making some hesitant to sign
• Ohio library system has a license for Infogroups’s ReferenceUSA, allowing
state agencies to query 50 records at a time, based on this data, ODOT also
received a small area sample of their InfoUSA database for this study
• ODOT Economic Development and Planning Offices also recently purchased
two separate version of the Dun and Bradstreet database for their own
purposes (largely due to QCEW confidentiality limits)
• Taken with the public availability of LEHD and BEA data this provided an
opportunity and need for ODOT to compare and contrast data sources
Macro-View
• Macro-View will focus on QCEW
vs. BEA
• Expand QCEW to BEA to account
for:
1. Ungeocoded QCEW (records do
travel modelers no good if not
located)
2. Uncovered employment sectors
3. Sole proprietors (most important)
4. Difference between 1st Qtr. QCEW
and annual average BEA
Total Employment
Employees Percent
QCEW Geocoded
4765940
74%
QCEW Total
4909538
76%
BEA Wage
5199216
81%
BEA Total
6451236
100%
Ohio Employment Sources
7000000
6000000
• Important to expand by county
and industry as will be shown
5000000
BEA Proprietors
4000000
Extra BEA Wage
3000000
Ungeocoded
2000000
Geocoded
1000000
0
Employees
QCEW vs. BEA
Industry Level QCEW vs. BEA
QCEW
BEA
Employers
Employees
County
INDUSTRY GeocodedUngeocoded
%Geocoded
GeocodedUngeocoded
%Geocoded
Total
Allocated %Allocated%QCEWofBEA
AG/FISH/FOREST
1150
47
96%
11770
128
99%
91078
84038
92%
13%
MINNING
709
83
90%
9885
462
96%
27895
19410
70%
37%
UTILITIES
894
86
91%
29659
1946
94%
20765
17853
86%
152%
CONSTRUCTION
22411
2235
91%
150915
6822
96%
296852
291608
98%
53%
MANUFACTURING
16008
524
97%
608488
2580
100%
648564
647290
100%
94%
WHOLESALE 15815
7228
69%
193657
21674
90%
236906
226113
95%
91%
RETAIL
35467
1080
97%
536292
4922
99%
671615
671615
100%
81%
TRANS/WAREHOUSE
8000
763
91%
183774
3288
98%
215452
196664
91%
87%
INFORMATION 3730
913
80%
86949
5673
94%
93023
92724
100%
100%
FINANCE/INS 16390
1292
93%
203054
6198
97%
331883
331377
100%
63%
REAL ESTATE/RENT
9642
696
93%
55617
1679
97%
234520
233849
100%
24%
PROF/TECH SERVICES
24846
4983
83%
227422
16112
93%
367974
355874
97%
66%
MGMT SERVICES1531
215
88%
106652
1344
99%
113014
110997
98%
96%
ADMIN/SUPPORT
13990
SRV
2470
85%
248063
17312
93%
387132
383296
99%
69%
EDUCATION
6419
324
95%
456385
5389
99%
147691
137663
93%
313%
HEALTH CARE/SOCIAL
26928
858
97%
805857
14069
98%
830432
778222
94%
99%
ARTS/REC
3739
300
93%
56763
2282
96%
119530
119412
100%
49%
ACCOMODATION/FOOD
22412
529
98%
413534
3468
99%
443910
443303
100%
94%
OTHER SERVICES
22661
1390
94%
146197
3370
98%
338268
337561
100%
44%
PUBLIC ADMIN 6850
1153
86%
234043
24569
90%
834732
834732
100%
31%
UNCLASSIFIED 547
309
64%
964
311
76%
0
0
0
Total
260139
27478
90% 4765940
143598
97% 6451236 6451236
100%
76%
QCEW vs. BEA
• There are significant
differences so it’s
worth delving a bit
deeper
QCEW Geocoding
• Mostly automated but manual passes on large employers
(hence while only 90% of employers geocoded, 97% of
employment)
• Geocoding not even across industry categories or counties
• ODOT spent a lot of time fixing multi-site employers,
especially school districts which now appear in Ohio’s official
file
QCEW Geocoding Percentages
100%
90%
80%
70%
60%
50%
40%
30%
20%
Employers
10%
Employees
UNCLASSIFIED
PUBLIC ADMIN
OTHER SERVICES
ACCOMODATION/FOOD
ARTS/REC
HEALTH CARE/SOCIAL
EDUCATION
ADMIN/SUPPORT SRV
MGMT SERVICES
PROF/TECH SERVICES
REAL ESTATE/RENT
FINANCE/INS
INFORMATION
TRANS/WAREHOUSE
RETAIL
WHOLESALE
MANUFACTURING
CONSTRUCTION
UTILITIES
MINNING
AG/FISH/FOREST
0%
BEA Characteristics
• While BEA industry and county marginal totals add up, the
joint distribution values do not due to limitations in the
sources BEA uses to fill in QCEW gaps
• Hence if you are expanding to industry/county
totals you need to use an Iterative
Proportional Fitting routine (i.e. Fratar) to
account for the unallocated employment (not
all industries/counties equal in this regard)
• BEA data has different (and much higher) sole
proprietor rate for farm than other types
BEA Percent Allocated to Counties
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
BEA Proprietor Rates
Farm
83%
Private
21%
Government
0%
Comparing QCEW/BEA
• BEA adds many commission only
employees in NAICS 50 categories,
particularly real estate so you should
expect high expansion factors here
• ODOT uses Q1 QCEW so we get high
expansion factors in seasonal industries
(construction and arts/recreation)
UNCLASSIFIED
PUBLIC ADMIN
OTHER SERVICES
ARTS/REC
HEALTH CARE/SOCIAL
EDUCATION
ADMIN/SUPPORT SRV
MGMT SERVICES
PROF/TECH SERVICES
REAL ESTATE/RENT
FINANCE/INS
INFORMATION
TRANS/WAREHOUSE
RETAIL
WHOLESALE
MANUFACTURING
CONSTRUCTION
UTILITIES
MINNING
AG/FISH/FOREST
350%
300%
250%
200%
150%
100%
50%
0%
ACCOMODATION/FO…
Percent Total QCEW to Total BEA
• Note similarity to previous map
Comparing QCEW/BEA
• Tiny representation of
agriculture in QCEW renders
direct expansion sub-optimal
• ODOT allocates the BEA farm
proprietors based on
agricultural acreage instead
Agricultural Employment From ES202 vs Distributed Proportionally to Ag. Acreage
800
700
600
500
es202
farm
400
300
200
100
0
1
46
91
136 181 226 271 316 361 406 451 496 541 586 631 676 721 766 811 856 901 946 991 1036
Comparing QCEW/BEA
• While of minor importance, we decided to allocate some of the missing
transportation employment to rail terminals prior to expansion
Macro-View Wrap Up
• As mentioned previous, ODOT evaluated other sources beyond QCEW
• At a macro level, there are significant differences
• These are more difficult to understand at this level, so ODOT conducted
some micro analysis at several locations
Micro-View
• This presentation will focus
on one location for clarity
• A relatively recent and
growing commercial/
industrial area in the western
suburbs of Columbus
• Contains diverse mix of
employment types
• However, due to small study
area, results shown here
should not be generalized,
consider them as illustrative
only
Micro-View
• The same area
looks a bit
different
depending on
the source
• RefUSA
data only
obtained
for a
subarea
• D&B data
only
obtained
for 4+
employee
employers
Comparison Methodology
• Obtained data for (mostly) the same area
• Compared the employment records by address since no other common
unique identifier
• Combined this with detailed local knowledge and aerial imagery (study areas
were selected based on analyst knowledge)
• Necessary to determine when duplicate addresses are valid (office parks,
suite’s, corporate vs. franchise and subsidiaries often have employee’s at
same address) or when multiple occupants from different year’s are in data
• Theoretical maximum employment for an address taken as the maximum
valid employment from any of the sources (this is not necessarily the true
value since that source may have over-stated the number)
• LEHD not included in most comparison’s since it is aggregate data
Comparison Methodology
• Purchased data sources contain many duplicate businesses which need
removed prior to comparison
• More problematic for smaller employers
Comparisons
• After removal of duplicates, REFUSA and QCEW performed similarly for large
employers, REFUSA had better coverage of small employers (includes some
sole proprietors and commission employee’s not in QCEW)
• D&B didn’t perform
as well in this study
area
Harris one of the two versions of the D&B data purchased by ODOT, only had 20+ employee employers
Combining Datasets
• Employers included in purchased data and QCEW were nearly statistically
independent
• Given the 75% and 92% employer coverage in QCEW and Reference USA, one
would expect 98% coverage by combining the sources (analyst could not identify
any missing employers which implies 100% was obtained but there is certainly
some margin of error)
Number of Employers (4+ employees) by Source
140
120
100
D
RD
80
R
QRD
60
QD
QR
40
Q
20
0
QCEW
QCEW/REFUSA
QCEW/D&B
REFUSA
Number of Employers if Only Use These Sourceas
D&B
Categorization
• Categorization by industry was
similar (89% same for same
employers)
Future Direction
• Given these results and
the desire to produce
model datasets not
subject to confidentiality
constraints ODOT will
purchase employment
data and develop a
process to:
1.
2.
3.
4.
5.
Geocode
Remove duplicates
Cross match with previous
year’s data
Cross match with QCEW
Develop an employment
estimate for employer’s
identified by QCEW rather
than using value directly