Variance Estimation for the Occupational Requirements 

Survey

Bradley D. Rhein1, Chester H. Ponikowski1

1U.S.Bureau of Labor Statistics, 2 Massachusetts Ave., NE, Room 3160, 

Washington, DC 20212

Abstract

The Occupational Requirements Survey (ORS) is an establishment survey conducted by 

the Bureau of Labor Statistics (BLS) for the Social Security Administration (SSA). The 

survey collects information on the vocational preparation and the cognitive and physical 

requirements of occupations in the U.S. economy, as well as the environmental conditions 

in which those occupations are performed. Due to a desire to maximize the number of units 

collected in the survey, the 2016 sample consists of two independent, multi-stage samples, 

each with its own set of variance strata. The calculation of the estimated variances for 

means, percentiles, and percentages involves a modified version of the Balanced Repeated 

Replication (BRR) method called Fay’s BRR method. The paper describes the formation 

of the replicate half-samples based on the assignment of the variance strata, reflecting the 

sampling variability of the two complex samples. Finally, the paper addresses the easy 

adaptation of the variance procedure when ORS transitions to the use of a single complex 

sample.

Key Words: variance estimation, establishment survey, balanced repeated replicates, 

Fay’s BRR, variance strata

1. Introduction

In the summer of 2012, the Social Security Administration (SSA) and the Bureau of Labor 

Statistics (BLS) signed an interagency agreement, which has been updated annually, to 

begin the process of testing the collection of data on occupations. As a result, the 

Occupational Requirements Survey [1] (ORS) was established as a test survey in late 2012. 

The goal of ORS is to collect and publish occupational information that will replace the 

outdated data currently used by SSA. More information on the background of ORS can be 

found in the next section. All ORS products will be made public for use by non-profits, 

employment agencies, state or federal agencies, the disability community, and other 

stakeholders.

The ORS collects close to 70 data elements related to the occupational requirements of a 

job. The following four groups of information are being collected:

• Physical demand characteristics/factors of occupations (e.g., strength, hearing, or 

stooping)

• Specific vocational preparation requirements, which include educational 

requirements, experience, licensing, and certification and post-employment 

training
• Mental and cognitive demands of work

• Environmental conditions in which the work is completed

The survey plans to publish all estimates that meet the reliability and confidentiality 

criteria. Somewhere between three and eighteen estimates will be calculated for each of 

the 70 ORS data elements. Around 920 total estimates could be calculated for a single 

occupation or occupational group. Estimate types include the percentage of workers in a 

given category, mean, percentiles (10%, 25%, 50%, 75%, and 90%), and the mode.

This paper describes the ORS variance estimation processes. Section 2 provides 

background information on the Occupational Requirements Survey. Section 3 summarizes 

the ORS sample design. Section 4 summarizes the ORS data elements and types of 

estimates that are being calculated for each. Section 5 details the process for calculating 

the variance estimates, including to the creation of half-samples, the adjustment of final 

quote weights depending on the variance strata assignment, the use of a Hadamard Matrix 

to form the replicate half-samples, and the formulas for calculating the variance. The paper 

ends with a conclusion and a summary of how the process transitions when the survey with 

two complex sample designs becomes a survey with just one complex sample design.

2. Background Information on ORS

In addition to providing Social Security benefits to retirees and survivors, the Social 

Security Administration (SSA) administers two large disability programs, which provide 

benefit payments to millions of beneficiaries each year. Determinations for adult disability 

applicants are based on a five-step process that evaluates the capabilities of workers, the 

requirements of their past work, and their ability to perform other work in the U.S. 

economy. In some cases, if an applicant is denied disability benefits, SSA policy requires 

adjudicators to document the decision by citing examples of jobs the claimant can still 

perform despite restrictions (such as limited ability to balance, stand, or carry objects) [2]. 

For over 50 years, the Social Security Administration has turned to the Department of 

Labor's Dictionary of Occupational Titles (DOT) [3] as its primary source of occupational 

information to process the disability claims [4]. SSA has incorporated many DOT 

conventions into their disability regulations. However, the DOT was last updated in its 

entirety in the late 1970’s, although a partial update was completed in 1991. Consequently, 

the SSA adjudicators who make the disability decisions must continue to refer to an 

increasingly outdated resource because it remains the most compatible with their statutory 

mandate and is the best source of data at this time.

When an applicant is denied SSA benefits, SSA must sometimes document the decision by 

citing examples of jobs that the claimant can still perform, despite their functional 

limitations. However, since the DOT has not been updated for so long, there are some jobs 

in the American economy that are not even represented in the DOT, and other jobs, in fact 

many often-cited jobs, no longer exist in large numbers in the American economy. 

SSA has investigated numerous alternative data sources for the DOT, such as adapting the 

Employment and Training Administration’s Occupational Information Network (O*NET) 

[5], using the BLS Occupational Employment Statistics program (OES) [6], and 

developing their own survey. SSA was not successful with any of these potential alternative 
data sources and turned to the National Compensation Survey [7] program at the Bureau 

of Labor Statistics.

3. ORS Sample Design Summary

The ORS sample design is a 2-stage stratified sample of establishments from the entire 

nation. The frame was developed from the BLS Quarterly Census of Employment and 

Wages (QCEW) database [8] with railroads added. Stratification is by industry and 

ownership, directly, and also implicitly by region. Private industry and State and local 

government establishments will be included, and industries are defined by the North 

American Industry Classification System (NAICS) [9]. Allocation is proportional to 

establishment employment size. Establishments are selected from each stratum by 

systematic probability proportional to employment size (PPS) sampling. Jobs (quotes) are 

then sampled from the selected establishments by PPS. ORS samples will follow a threeyear rotation, though this rotation cycle may change, depending on research concerning 

how often the requirements of work change. For more details on this design, see 

“Occupational Requirements Survey Sample Design” by Ferguson, et al. [10].

In order to maximize the number of units collected during the first year of production, the 

ORS sample includes an additional 2,227 establishments from a sample that has recently 

rotated out of the National Compensation Survey (NCS). This NCS sample, initiated in 

2011 and updated through September of 2015, was selected using PPS at three stages:

OMB-defined areas, establishments, and then occupations within the selected 

establishments. The sampling frame was developed from the QCEW at the end of 2010. 

Stratification for the sampling of establishments was by area and industry (defined by 

NAICS). Allocation was proportional to establishment employment size. For more details, 

please see the section on the current design from “Update on the Evaluation of Sample 

Design Issues in the National Compensation Survey” by Ferguson, et al. [11].

So, the production sample for the first year of ORS consists of two samples selected under 

two different sample designs.

4. ORS Data Elements and Possible Estimates Summary

ORS is designed to capture occupational information on educational requirements, 

cognitive and physical demands, and exposures to environmental conditions. An extensive 

description of ORS data elements and how estimates for each element will be calculated 

can be found in the paper “Estimation Considerations for the Occupational Requirements 

Survey” [12]. Information on estimation processing can be found in the paper “Estimation 

Processes Used in the Occupational Requirements Survey” [13].

Many of the ORS data elements have percentage of workers, mean, percentiles, and mode

estimates for each occupational definition. For example, one ORS data element measures 

the amount of time during a typical day that a worker, such as a nurse, spends stooping.

Occupational definitions are derived from the Standard Occupational Classification 

Manual (SOC) [14]. Physical demands, such as stooping, are captured in hours and are also 

converted to percent of the day, and so mean and percentile estimates (10%, 25%, 50%, 

75%, and 90%) are calculated for both hours and percent of the day. Also, the hours of 

time spent stooping fall within an SSA-established category, and so a percentage of 

workers estimate is calculated for each category. SSA defines five categories by a range of 
hours spent performing an activity – not present, seldomly, occasionally, frequently, and 

constantly. Finally, the mode of the categories is identified, marking the eighteenth 

estimate related to stooping.

5. Calculation of Variance Estimates

ORS calculates standard errors for all percentages, means, and percentiles using a modified 

version of the Balanced Repeated Replication (BRR) method, known as the Fay’s BRR 

method. The application of Fay’s BRR to the ORS sample design involves several steps.

Replicate half-samples – each using all quotes available for estimation - are constructed 

within each of the pre-defined variance strata. Once created, the replicate half-samples are 

used as input to the estimation process, and a list of replicate estimates are produced. These

replicate estimates will then be used to calculate the variance estimates for each ORS 

estimate.

Defining the Variance Strata

The 2016 ORS sample design includes two different complex survey designs: the ORSonly sample and an additional sample from the National Compensation Survey (NCS). As 

a result, the variance estimate calculation must incorporate variance strata from each 

sample design.

The ORS-only sample specifies 23 detailed industry strata in the private sector and 10 

industry strata in the government sector, plus 4 regions, making for 132 variance strata.

Definitions for the detailed industries and census regions can be found in Appendix A. 

Variance strata definitions can be found in Appendix B.

The NCS sample adds another 101 area-based variance strata. Each OMB-defined area was 

sampled among all Metropolitan Statistical Areas (MSA), Combined Metropolitan 

Statistical Areas (CMSA), micropolitan statistical areas, and county clusters. There were 

152 total areas selected, 57 with certainty. There are only 101 variance strata, due to some 

instances of multiple areas being combined into a single variance strata. The definitions for 

these variance strata can be found in Appendix C.

So, there are 233 total variance strata. However, since the number of replicates to be used 

in the calculation of variances must be a multiple of four to satisfy Fay’s BRR, the number 

of replicates run is 236.

At some point in the future, this first production sample of ORS will rotate out. When that 

happens, the variance strata representing the NCS sample will no longer be needed and can 

be dropped from the process entirely. So, after the initial production sample has rotated 

out, the remaining sample would contain 132 variance strata, given there are no other 

changes to the sample design.

Formation of Replicate Half-samples

Replicate half-samples are constructed using a Hadamard Matrix and variance strata 

assignments. Then, occupational weights are increased or decreased depending on the halfsample selection flag. All usable quotes were randomly assigned a half-sample selection 

flag during sampling and will appear in each replicate half-sample with an appropriate

weight adjustment. 
The Hadamard Matrix is an X by X table where X is equal to the number of variance strata 

used in the variance calculation. So, X will also be the number of replicates that need to be 

created and then processed. There are 236 variance strata for the first year of production –

132 corresponding to the ORS sample design, 101 corresponding to the NCS sample 

design, and another 3 to satisfy the conditions for using BRR.

Each quote has been assigned one variance stratum and one half-sample selection flag. So, 

using the Hadamard Matrix example in Figure 1 below, a quote that is in variance stratum

2 and has a half-sample selection flag of (-1) will have a decrease in weight (for replicate 

2).

Figure 1: Example Hadamard Matrix

Quotes within non-certainty establishments will have a weight adjustment of k, a constant 

that is set for BRR. ORS will use k = 0.5, so weights for quotes within non-certainty 

establishments will increase or decrease by half, depending on the half-sample selection 

flag. All quotes within a non-certainty establishment are assigned the same half-sample 

selection flag.

In establishments selected with certainty, quotes are assigned at random to the two halfsamples. So, half of the sample quotes are assigned to half-sample number 1 and the other 

half of the quotes are assigned to half-sample number 2. The weight adjustment is then 

made depending on the half-sample selection flag.

Weight adjustments occur uniquely for each replicate half-sample as a result of the 

Hadamard Matrix. All replicate half-samples are used in the next step of variance 

calculation.

Run Estimation for each Replicate and then Calculate the Variance

Once the replicate half-samples are established and the occupational replicate weights have 

been adjusted, estimation is run for each of the 236 replicate half-samples. Variances are 

calculated for each estimate, using the sum of the differences between the full sample 

estimate and each of the 236 replicate estimates. The full sample estimates were calculated 

using the final occupational weights. The variance formula is as follows:

Formula 1 – Variance Calculation
 











 

 

 











R

r

c Ycr Yc

R k

V Y

1

2

2

ˆ ˆ

1

ˆ 1

where:











 V Yc

ˆ

ˆ

= Variance estimate for ORS estimate c 

c = ORS estimate (about 920 estimates covering 70 ORS data elements)

R = Number of replicates (236)

k = a constant where 0 



k < 1 (the value of k is 0.5)

cr Y

ˆ

= Estimate c from replicate r

Yc

ˆ

= Estimate c from the full sample

Standard errors are simply the square root of the variance. Relative standard errors are 

calculated by dividing the standard error of an estimate by the corresponding estimate 

value.

7. Conclusion and Next Steps

ORS will provide a measure of accuracy for every estimate produced. For means, 

percentages, and percentiles, a standard error will be calculated using Fay’s BRR method.

The first production sample is a combination of two different complex samples. As a result, 

two sets of variance strata must be used to calculate the variance estimates. Once the initial 

sample rotates out, only one sample design will remain and there will be fewer variance 

strata to process when calculating the variance estimates.

References/Footnotes

[1] Occupational Requirements Survey, http://www.bls.gov/ors/.

[2] Social Security Administration, Occupational Information System Project, 

http://www.ssa.gov/disabilityresearch/occupational_info_systems.html.

[3] U.S. Department of Labor, Employment and Training Administration (1991), 

“Dictionary of Occupational Titles, Fourth Edition, Revised 1991”

[4] Occupational Information Development Advisory Panel, 2010, 

http://www.socialsecurity.gov/oidap/index.htm

[5] U.S. Department of Labor, O*Net Online, http://www.onetonline.org/

[6] Bureau of Labor Statistics, Occupational Employment Statistics Program, 

http://www.bls.gov/oes/

[7] National Compensation Survey, http://www.bls.gov/ncs/.

[8] Quarterly Census of Employment and Wages, http://www.bls.gov/cew/.

[9] North American Industry Classification System, 

http://www.census.gov/eos/www/naics/.

[10] Ferguson, Gwyn R., McNulty, Erin. 2015. Occupational Requirements Survey 

Sample Design. In JSM proceedings, Economic Data: CPI, PPI, NCS Section. 

Alexandria, VA: American Statistical Association.

[11] Ferguson, Gwyn R., Coleman, Joan L., Ponikowski, Chester H. 2011. Update on 

the Evaluation of Sample Design Issues in the National Compensation Survey. In 
JSM proceedings, Government Statistics Section. Alexandria, VA: American 

Statistical Association.

[12] Rhein, Brad, Ponikowski, Chester, and McNulty, Erin. 2014. Estimation 

Considerations for the Occupational Requirements Survey. In JSM proceedings, 

Government Statistics Section. Alexandria, VA: American Statistical 

Association. 2134-2146.

[13] Rhein, Bradley D., Ponikowski, Chester H. 2015. Estimation Processes Used in 

the Occupational Requirements Survey. In JSM proceedings, Government 

Statistics Section. Alexandria, VA: American Statistical Association.

[14] Standard Occupational Classification System, http://www.bls.gov/soc/.

Any opinions expressed in this paper are those of the authors and do not constitute 

policy of the Bureau of Labor Statistics or the Social Security Administration.
Appendix A – ORS Detailed Industry and Census Region Definitions

ORS Detailed Industry Definitions

Private Industry

Cell NAICS Codes Industry

21A 21 Mining

23A 23 Construction

31A 31-33 (including 336411) Manufacturing

22A 22 Utilities

42A 42 Wholesale Trade

44A 44-45 Retail Trade

48A 48-49 Transportation and Warehousing

51A 51 Information

52A 52(excl 524) Finance (Rest of)

52B 524 Insurance

53A 53 Real Estate, Renting, Leasing

54A 54 Professional, Scientific, Technical

55A 55 Management of Companies/Enterprises

56A 56 Admin., Support, Waste Management

61A 61(excl 6111-6113) Educational Services (Rest of)

61B 6111 Elementary and Secondary Schools

61C 6112,6113 Junior Colleges, Colleges & Universities

62A 62(excl 622,623) Health Care, Social Assistance (Rest of)

62B 622 Hospitals

62C 623 Nursing and Residential Care Facilities

71A 71 Arts, Entertainment, Recreation

72A 72 Accommodation and Food Services

81A 81(excl 814) Other Services (except Public Admin.)

State and Local Government

Cell NAICS Codes Industry

10L 21, 23, 31-33 Goods-Producing

20L 42, 44-45, 48-49, 22 Trade, Transportation, and Utilities

30L 6111 Elementary and Secondary Education

40L 6112, 6113 Colleges and Universities

50L 61 excl. 6111-6113 Rest of Education

60L 622 Hospitals

70L 623 Nursing Homes

80L 62 excl. 622-623 Rest of Health and Social Services

90L 92 excl. 928 Public Administration

99L 51, 52-53, 54-56, 71-72, 81 Other Service-producing

Census Region Definitions

Census 

Region 

Code

Census 

Region 

Name States Included

1 Northeast ME, CT, MA, NH, RI, VT, NJ, NY, PA

2 South AL, KY, MS, TN, DE, DC, FL, GA, MD, NC, SC, VA, WV, AK, LA, OK, TX

3 Midwest IL, IN, IA, MI, WI, OH, KS, MN, MO, NE, ND, SD

4 West AZ, CO, ID, MT, NV, NM, UT, WY, AK, CA, HI, OR, WA
Appendix B – ORS-only Variance Strata Definitions

Ownership 

Sector

Census 

Region

ORS 

Detailed 

Industry 

Stratum

Variance 

Stratum 

Number

Ownership 

Sector

Census 

Region

ORS 

Detailed 

Industry 

Stratum

Variance 

Stratum 

Number

Private 1 21A 345 Private 3 61C 411

Private 2 21A 346 Private 4 61C 412

Private 3 21A 347 Private 1 62A 413

Private 4 21A 348 Private 2 62A 414

Private 1 22A 349 Private 3 62A 415

Private 2 22A 350 Private 4 62A 416

Private 3 22A 351 Private 1 62B 417

Private 4 22A 352 Private 2 62B 418

Private 1 23A 353 Private 3 62B 419

Private 2 23A 354 Private 4 62B 420

Private 3 23A 355 Private 1 62C 421

Private 4 23A 356 Private 2 62C 422

Private 1 31A 357 Private 3 62C 423

Private 2 31A 358 Private 4 62C 424

Private 3 31A 359 Private 1 71A 425

Private 4 31A 360 Private 2 71A 426

Private 1 42A 361 Private 3 71A 427

Private 2 42A 362 Private 4 71A 428

Private 3 42A 363 Private 1 72A 429

Private 4 42A 364 Private 2 72A 430

Private 1 44A 365 Private 3 72A 431

Private 2 44A 366 Private 4 72A 432

Private 3 44A 367 Private 1 81A 433

Private 4 44A 368 Private 2 81A 434

Private 1 48A 369 Private 3 81A 435

Private 2 48A 370 Private 4 81A 436

Private 3 48A 371 Government 1 10G 437

Private 4 48A 372 Government 2 10G 438

Private 1 51A 373 Government 3 10G 439

Private 2 51A 374 Government 4 10G 440

Private 3 51A 375 Government 1 20G 441

Private 4 51A 376 Government 2 20G 442

Private 1 52A 377 Government 3 20G 443

Private 2 52A 378 Government 4 20G 444

Private 3 52A 379 Government 1 30G 445
Ownership 

Sector

Census 

Region

ORS 

Detailed 

Industry 

Stratum

Variance 

Stratum 

Number

Ownership 

Sector

Census 

Region

ORS 

Detailed 

Industry 

Stratum

Variance 

Stratum 

Number

Private 4 52A 380 Government 2 30G 446

Private 1 52B 381 Government 3 30G 447

Private 2 52B 382 Government 4 30G 448

Private 3 52B 383 Government 1 40G 449

Private 4 52B 384 Government 2 40G 450

Private 1 53A 385 Government 3 40G 451

Private 2 53A 386 Government 4 40G 452

Private 3 53A 387 Government 1 50G 453

Private 4 53A 388 Government 2 50G 454

Private 1 54A 389 Government 3 50G 455

Private 2 54A 390 Government 4 50G 456

Private 3 54A 391 Government 1 60G 457

Private 4 54A 392 Government 2 60G 458

Private 1 55A 393 Government 3 60G 459

Private 2 55A 394 Government 4 60G 460

Private 3 55A 395 Government 1 70G 461

Private 4 55A 396 Government 2 70G 462

Private 1 56A 397 Government 3 70G 463

Private 2 56A 398 Government 4 70G 464

Private 3 56A 399 Government 1 80G 465

Private 4 56A 400 Government 2 80G 466

Private 1 61A 401 Government 3 80G 467

Private 2 61A 402 Government 4 80G 468

Private 3 61A 403 Government 1 90G 469

Private 4 61A 404 Government 2 90G 470

Private 1 61B 405 Government 3 90G 471

Private 2 61B 406 Government 4 90G 472

Private 3 61B 407 Government 1 99G 473

Private 4 61B 408 Government 2 99G 474

Private 1 61C 409 Government 3 99G 475

Private 2 61C 410 Government 4 99G 476
Appendix C – NCS Variance Strata Definitions

Variance 

Stratum Sampled Locality

124 Atlanta-Sandy Springs-Gainesville, GA-AL CSA

125 Boston-Worcester-Manchester, MA-NH CSA

126 Buffalo-Niagara-Cattaraugus, NY CSA

127 Chicago-Naperville-Michigan City, IL-IN-WI CSA

128 Cincinnati-Middletown-Wilmington, OH-KY-IN CSA

129 Cleveland-Akron-Elyria, OH CSA

130 Columbus-Marion-Chillicothe, OH CSA

131 Dallas-Fort Worth, TX CSA

132 Dayton-Springfield-Greenville, OH CSA

133 Denver-Aurora-Boulder, CO CSA

134 Detroit-Warren-Flint, MI CSA

135 Hartford-West Hartford-Willimantic, CT CSA

136 Houston-Baytown-Huntsville, TX CSA

137 Huntsville-Decatur, AL CSA

138 Indianapolis-Anderson-Columbus, IN CSA

139 Los Angeles-Long Beach-Riverside, CA CSA

140 Louisville-Elizabethtown-Scottsburg, KY-IN CSA

141 Milwaukee-Racine-Waukesha, WI CSA

142 Minneapolis-St. Paul-St. Cloud, MN-WI CSA

143 New York-Newark-Bridgeport, NY-NJ-CT-PA CSA

144 Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA

145 Pittsburgh-New Castle, PA CSA

146 Raleigh-Durham-Cary, NC CSA

147 Sacramento--Arden-Arcade--Truckee, CA-NV CSA

148 San Jose-San Francisco-Oakland, CA CSA

149 Seattle-Tacoma-Olympia, WA CSA

150 Washington-Baltimore-No. Virginia, DC-MD-VA-WV 

CSA

151 Albany-Schenectady-Troy, NY

152 Austin-Round Rock, TX

153 Birmingham-Hoover, AL

154 Charlotte-Gastonia-Concord, NC-SC

155 Grand Rapids-Wyoming, MI

156 Honolulu, HI

157 Jacksonville, FL
Variance 

Stratum Sampled Locality

158 Kansas City, MO-KS

159 Las Vegas-Paradise, NV

160 Memphis, TN-MS-AR

161 Miami-Fort Lauderdale-Miami Beach, FL

162 Nashville-Davidson--Murfreesboro, TN

163 New Orleans-Metairie-Kenner, LA

164 Oklahoma City, OK

165 Omaha-Council Bluffs, NE-IA

166 Orlando, FL

167 Phoenix-Mesa-Scottsdale, AZ

168 Portland-Vancouver-Beaverton, OR-WA

169 Providence-New Bedford-Fall River, RI-MA

170 Richmond, VA

171 Rochester, NY

172 Salt Lake City, UT

173 San Antonio, TX

174 San Diego-Carlsbad-San Marcos, CA

175 St. Louis, MO-IL

176 Tampa-St. Petersburg-Clearwater, FL

177 Tulsa, OK

178 Virginia Beach-Norfolk-Newport News, VA-NC

179

Bangor, ME

Springfield, MA

180

Johnstown, PA

State College, PA

181

York-Hanover, PA

Atlantic City, NJ

182

Reading, PA

Allentown-Bethlehem-Easton, PA-NJ

183

Fort Walton Beach-Crestview-Destin, FL

Ocala, FL

184

Fayetteville, NC

Hickory-Lenoir-Morganton, NC

185

Wilmington, NC

Charleston-North Charleston, SC

186

Salisbury, MD

Sarasota-Bradenton-Venice, FL
Variance 

Stratum Sampled Locality

187

Columbia, SC

Roanoke, VA

188

Tallahassee, FL

Greensboro-High Point, NC

189

Greenville, SC

Palm Bay-Melbourne-Titusville, FL

190

Bloomington, IN

Youngstown-Warren-Boardman, OH-PA

191

Wausau, WI

Muskegon-Norton Shores, MI

192

Elkhart-Goshen, IN

Rockford, IL

193

Toledo, OH

Holland-Grand Haven, MI

194

Madison, WI

Bloomington-Normal, IL

195

Auburn-Opelika, AL

Jackson, MS

Mobile, AL

196

Tuscaloosa, AL

Knoxville, TN

197

Springfield, MO

Sioux City, IA-NE-SD

198

Lincoln, NE

Iowa City, IA

Cedar Rapids, IA

199

Brownsville-Harlingen, TX

El Paso, TX

200

Monroe, LA

Amarillo, TX

201

Corpus Christi, TX

Little Rock-North Little Rock, AR

202

Baton Rouge, LA

Lafayette, LA

203

Great Falls, MT

Billings, MT

204 Tucson, AZ; Albuquerque, NM
Variance 

Stratum Sampled Locality

205

Fort Collins-Loveland, CO

Reno-Sparks, NV

206

Medford, OR

Visalia-Porterville, CA

207

Salem, OR

Fresno, CA

208

Salinas, CA

Kennewick-Richland-Pasco, WA

209

Caledonia, Orleans Counties, VT

Claremont, NH

210

Bedford, Fulton, Juniata Counties, PA

Meadville, PA

Corning, NY

211

Fannin, Gilmer, Lumpkin Counties, GA

Lee, Norton City, Wise Counties, VA

212

Mount Airy, NC

Clarksburg, WV

213 Palatka, FL; Lancaster, SC

214

Carroll, Jo Daviess Counties, IL & Lafayette County, WI

Sanilac County, MI

215 Quincy, IL-MO and Logansport, IN

216 Wooster, OH; Manitowoc, WI

217 Murray, KY and Starkville, MS

218

Claiborne, Franklin, Jefferson, Wilkinson Counties, MS

Paducah, KY-IL

219

Graham, Norton, Osborne, Phillips, Rooks, Smith, KS

Atchison, Holt, Mo & Johnson, Nemaha, etc., NE

220 Emporia, KS and Ottumwa, IA and Brainerd, MN

221 Miami, OK and Muskogee, OK

222

Baylor, Briscoe, etc., TX

Fayette, Lee, TX

223

Kalispell, MT

Nogales, AZ

Esmeralda, Lyon, Mineral, NV

224

Ferry, Okanogan Counties, WA

Centralia, WA