Factors Affecting Response to the Occupational Employment Statistics 

Survey 

Polly A. Phipps and Carrie K. Jones 

Bureau of Labor Statistics 

Phipps_p@bls.gov 

Jones_carrie@bls.gov 

Introduction 

Response rates are increasingly a source of concern for government agencies and other organizations conducting 

establishment surveys. While there has not been a consistent pattern of increasing nonresponse, in the past several decades it 

has become more difficult to achieve and maintain high response rates (Christianson and Tortora, 1995, Interagency Group 

on Establishment Nonresponse, 1998). While establishment surveys conducted by the government have higher response 

rates on average than other organizations, government agencies are not exempt from low response rates or declines in 

response to ongoing surveys. Given the concern with response rates, one would expect to find a substantial literature on who 

participates in establishment surveys and who does not. However, as many have noted, the literature on establishment survey 

participation is fairly small, and quantitative studies are even less common. Our interest is in contributing to the literature by 

exploring survey participation through an analysis of establishment characteristics and survey design and administration 

factors. Ultimately, we hope that understanding these factors will provide direction on how to address nonresponse and 

improve the quality of survey estimates. 

For our analyses, we use the Occupational Employment Statistics survey (OES), a bi-annual establishment survey measuring 

occupational employment and wage rates for wage and salary workers by industry for the U.S., States, certain U.S. 

Territories, and Metropolitan Statistical Areas within States. This voluntary survey of establishments with one or more 

employees is conducted by State employment workforce agencies in cooperation with the Bureau of Labor Statistics. While 

the response rate achieved by OES is quite high at approximately 76 percent, there is considerable variation by state, 

industry, and establishment size (Jones, 1999). 

We first review the literature on establishment survey response and nonresponse, and then provide background on the OES 

sample, data collection procedures, and state survey administration. Next, we describe OES response rates over time by 

major areas of interest, including establishment size, size of metropolitan area, and industry groups, and we describe patterns 

of nonresponse, including survey refusals. Finally, using data from the May 2006 OES survey, the Quarterly Census of 

Employment and Wages, and a survey of state administrative practices, we use logistic regression models to predict the 

likelihood of survey response. We test the effect of a number of conceptual factors on response to the 2006 OES survey, 

including establishment characteristics, such as establishment age, multi-establishment firm status, industry, size, location; 

and survey design and administration factors, including survey form type, nonresponse followup strategies, State staff 

composition, experience, and turnover, and selection into other BLS surveys. A small percentage of OES data are collected 

centrally by the BLS national and regional offices. Given the differences in collection methods, we do not use it in this 

paper, and for the same reason, we exclude U.S. Territories. 

Establishment Survey Response and Nonresponse 

There is a large literature on household survey response rates, and while the corresponding establishment survey literature is 

not as extensive, it covers many of the same topics. These include nonresponse standards (Hidiroglou, Drew and Gray, 

1993), nonresponse trends (Interagency Group on Establishment Nonresponse, 1998), procedures or experiments designed to 

increase response (Moore and Baxter, 1993), and post-survey adjustments for nonresponse (Sommers, Riesz, and Kashihara 

2004), among others. The establishment survey literature is limited in the number of studies that analyze the likelihood of 

participation using establishment and survey administration characteristics. An excellent example of this type of research in 

household surveys is Groves’ and Couper’s (1998) analysis of factors that influence participation using six household surveys 

and decennial census records. As a conceptual framework, the authors use features of the study population that are not under 
 

 

 

 

 

 

 

 

 

the control of the survey researcher (social environment and household characteristics), and features under researcher control 

(survey design and interviewer characteristics) to explore survey response. 

Several studies have set out a theoretical framework and proposed factors that influence the likelihood that establishments 

will respond to a survey request (Tomaskovic-Devey, Leiter, and Thompson, 1994; Willimack, Nichols, and Sudman, 2002). 

Willimack, Nichols, and Sudman (2002) have proposed a theory for establishment survey response that includes factors 

affecting the external environment, the business, the respondent, and the survey design; the components are shown in Exhibit 

1. Using Groves and Couper’s (1998) conceptual framework they identify factors that are and are not under the control of the 

survey researcher. Those not under researcher control include the external environment, the business, and respondent 

characteristics. Survey research organizations can control sample and instrument design, other types of survey materials 

(e.g., instructions), contact strategies, mode of administration, and timing, among others. They base their theory on 

qualitative exploratory research conducted by the U.S. Census Bureau to study the reporting process in large multi-unit firms. 

Willimack and colleagues propose under the external environment that weak economic conditions can affect participation, 

since fewer staff may be available to complete a survey, and businesses may be more reluctant to disclose information. 

Survey climate, i.e., the number of survey requests a business receives, may affect response, as well as other reporting 

requirements that are a higher priority than survey participation. They consider data availability a strong component of 

response, and related to a number of factors, including business characteristics such as size, type, industry, ownership, and 

the availability of staff to respond to mandatory and voluntary surveys. Respondent characteristics include having authority 

to provide data or delegate the task, capacity or knowledge of the data, and motivation to attend to the task. The authors find 

that of the survey design characteristics, particularly mode of administration, contact during high workload time periods, and 

contact strategies to prenotify or identify respondents are important for unit response rates. Overall, Willimack and 

colleagues propose that businesses weigh the burden of the survey response against business goals in their decision on 

whether to participate, and that the external environment, business, respondent, and survey design are factors in the weighing 

of survey burden and business goals. 

Exhibit 1. Business Survey Participation (Willimack et al., 1998) 

 

 

 

External environment Business Respondent Survey design 

Economic conditions Data availability Authority Sample 

Survey climate ---Business characteristics Capacity Survey topic 

Legal/regulatory 

requirements. 

---Organizational structure Motivation Instrument design 

 ---Management needs Mode of administration 

 ---Regulatory reporting Time schedules

 Environmental 

dependence 

 Contact strategies

Company policy Respondent identification 

Resource availability Legal authority 

Survey sponsor

Confidentiality 

 

 

 

Out of researcher control Under researcher control 

Tomaskovic-Devey and colleagues (1994) propose that organization complexity is related to authority and capacity to 

respond to a survey, and the organizational environment influences the capacity and motive to respond. Their view of 

authority, capacity, and motivation is associated with the larger organization, rather than concentrated with the respondent. 

Authority to respond can be formal or informal, organizational capacity refers to practices and processes tied to assembling 

the requested information, and there can be individual or organizational motives regarding information disclosure. They test

their theory using survey data from a North Carolina employment and health survey and establishment public records. The 

authors find that establishments that are subsidiaries, large, have higher profits, and have greater sales concentration are less 

likely to respond, while establishments with high R and D intensity, and in price, safety regulated, and publicly-traded 

industries, are more likely to respond. They did not find industry categories significantly associated with response after 

controlling for organizational factors. They conclude that motive measures are most important in explaining response: 

establishments in profitable and concentrated markets are more independent of their environment and less likely to respond to 

a survey request, while price, safety regulated, and publicly traded industries have a higher motive to cooperate and shape 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

public opinion. For capacity measures, they argue that increased establishment size is a reflection of dispersal of information 

and less capacity to respond. 

The conceptual frameworks for establishment survey response have had very limited testing. One of the reasons for the lack 

of empirical studies is likely a limited number of explanatory variables available in the survey data. An exception to this is a 

study by Potter (2000) analyzing nonresponse characteristics using the 1996 Nursing Home component of the Medical 

Expenditure Panel Survey. In this study, market, establishment, and survey administration characteristics were tested. 

Market characteristics included a state-level Medicaid reimbursement measure, and county-level data items for the 

establishment location: rural/urban, market environment (hospital and nursing home beds per capita and per population 75 

and above, percent for profit nursing home beds), and county health status (influence mortality rate). Characteristics of the 

nursing home establishment included type of ownership, number of beds and residents, and federal certification for 

reimbursement under Medicare. Survey design characteristics were twofold: endorsement by the state nursing home 

association and interviewing field cost strata. Interviewer characteristics included demographics, work experience and 

caseload. Significant predictors of nonresponse included two market measures: a flat rate Medicaid reimbursement, as 

opposed to more generous reimbursement method, and counties with a lower supply of hospital beds. Related to market 

measures, whether the nursing home was hospital based increase the likelihood of response, as did location in the Midwest, 

compared to the northeast, south, or west. One survey design characteristic --areas requiring an overnight stay to collect data 

compared to larger clusters of cases--increased response. And a number of interviewer characteristics were associated with 

lower nonresponse, including white interviewers, interviewers with some college, and greater interviewer experience. 

An analysis of the schools and staffing establishment survey sponsored by the National Center for Educational Statistics 

(1997) used logistic regression analysis to predict response for public schools. Univariate analyses showed that minority 

enrollment, region, urban/rural location, school level, size and type significantly affected response. However, the 

multivariate analysis found only three factors -- school level, size, and type—with a significant effect on nonresponse. The 

researchers found that secondary schools were more likely to respond than combined secondary and elementary schools, and 

elementary schools only; small schools were more likely to respond than larger schools; and schools that offer regular 

instruction, as opposed to special instruction in vocational, special, or alternative courses were more likely to respond. 

Several other studies have focused on establishment nonresponse. Sommers and colleagues (Sommers, Riesz, and Kashihara, 

2004) found that establishment employment, state, industry, age of firm, single or multi-unit firm, urban/rural county, and 

average wage were significant in predicting response. Tulp, Hoy, Kusch and Cole (198) used an experimental design to test 

the effect of mandatory and voluntary reporting. They found that mandatory reporting is more effective in obtaining higher 

response for establishments overall, and for establishments new to the survey and establishments who had previous survey 

exposure under mandatory conditions. Respondent identification has been explored by Moore and Baxter (1993), who found 

mixed results for use of contact name, i.e., small business with a contact name had higher response, particularly in wholesale, 

finance/real estate/insurance and small service sectors; while having or not having a contact did not affect large business 

response. 

While conceptual frameworks have been offered and some empirical studies have explored participation in establishment 

surveys, many more studies are necessary to understand the dimensions of establishment survey participation. In fact, a 1998 

Interagency Group on Establishment Nonresponse listed research on the characteristics and correlates of nonresponse as an 

area in need of development. This analysis is the first step in exploring and attempting to model OES survey participation. 

OES Background 

The Occupational Employment Statistics (OES) survey is primarily a mail survey. Data are collected by the State Workforce 

agencies, in cooperation with the Bureau of Labor Statistics, US Department of Labor. OES data are collected by analysts in 

State government offices. For survey administration purposes the State OES offices are grouped into six regions. Each 

region has a BLS office, and BLS personnel are assigned to guide, monitor, and assist the State OES offices. 

Respondents report the number of employees by occupation and wage ranges. The occupational employment and wage data 

from sampled establishments are used to calculate employment estimates for nearly 800 occupations annually for the 50 

States, the District of Columbia, Puerto Rico, the US Virgin Islands, and Guam as well as the nation as a whole. OES also 

produces employment and wage estimates for Metropolitan Statistical Areas (MSAs) as well as specific industries. 

Occupations are classified using the Standard Occupational Classification (SOC) system while industries are classified using 

the North American Industry Classification System (NAICS). 
 

 

 

 

The OES Sample 

The survey is conducted over a rolling 6-panel semi-annual (or 3-year) cycle. Each panel’s sample contains approximately 

200,000 establishments. Over the course of a 6-panel cycle, approximately 1.2 million establishments are sampled. When 

possible, non-government establishments are only sampled once every six panels. A census of Federal government, 

executive branch only, is taken for every panel. A census of State government units is taken every November. 

The sample is drawn from a universe of about 6.5 million establishments across all non-farm industries. The sample is 

stratified by geography, industry, and employment size. The sample frame comes from Unemployment Insurance (UI) 

reports filed by almost all establishments. Only establishments in Guam as well as the railroad industry are exempt from 

mandatory UI filing; the frame for those units is obtained elsewhere. 

Data Collection 

The OES survey collection instrument consists of 97 industry-specific survey forms used for medium and large sized firms 

and one open-ended survey form used for smaller firms. Respondents report employment data by occupation across 12 wage 

bands, using a matrix format. The industry-specific forms have occupations already printed on the form and range in length 

from 16 to 24 pages, as shown in Exhibit 2. In addition, there is one 32-page form for colleges and universities and a 44-

page form for government units. The occupations on each form are selected based on industry staffing patterns derived from 

previously collected data. Most survey forms cover a 3-digit NAICS industry. However, there are some forms that, due to 

heterogeneous staffing patterns, cover only a 4-digit NAICS industry. The 4-page open-ended form, in Exhibit 3, has space 

for respondents to write-in the occupations found in their forms. This form is used primarily for small size establishments, 

and each state defines their own values for “small”; the top value ranges from 9 to 99 employees, depending on state. 

Exhibit 2. Example of occupation found on an industry-specific form 

 

 

 

Exhibit 3. Example of space found on the open-ended write-in form 

The OES survey is initially mailed out to almost all establishments in the sample. The initial mailing is done by a central 

mail facility and occurs as close to the survey reference date as possible; either November 12th or May 12th. Three follow-up 

mailings are sent to non-respondents at approximately 3-4 week intervals. The initial mailing as well as the first two followup mailings use a mix of industry-specific survey forms with occupations already printed on them for the larger firms as well 

as the open-ended form for the smaller establishments. The last mailing uses only the open-ended survey form regardless of 

establishment size. Telephone follow-up calls are made to non-respondents. Some data for larger establishments is collected 

via personal visits. Other modes of collection include email, phone-in, facsimile, and electronic media such as disc or tape. 

The percentage of total responses returned via each collection mode for the May 2006 panel is shown in Exhibit 4. 

Exhibit 4. Respondent Collection Mode, May 2006 

Collection Mode Percent Collection Mode Percent 

Mail 71.9% Personal visit 0.2% 

Electronic unspecified 3.6% Diskette, CD, DVD 0.6% 

Hard copy printout 0.9% E-mail 7.1% 

Phone Call 11.8% Fax 3.9% 
 

 

 

 

 

 

 

 

 

 

 

 

 

State Survey Administration 

State agencies follow general federal guidelines in conducting the OES survey, but states are allowed flexibility and in turn, 

utilize different practices and procedures. In addition, state sample sizes vary dramatically. For example, Wyoming, with a

sample of 743 establishments accounts for .4 percent of the OES sample, while California, with 15,691 establishments in the 

sample, accounts for 8.8 percent (See Appendix 1, Table 1). Since states vary in size and practice, we gathered information 

about states and state survey administration. These data were provided by the regional offices and included information on 

staff composition, staff vacancies, size of the staff, management structure, and procedures used during the May 2006 survey 

panel. The full set of results is shown in Appendix 1, Table 2, and we highlight some results below. 

Personnel is an important part of survey administration. In May 2006, the number of full-time equivalent positions in States 

funded by BLS ranged from 1.3 to 18, with an average of 5.1 positions. On average, about three out of five positions were 

managerial or professional positions. About 60 percent of state personnel in management positions had over six years of 

OES experience, while approximately 32 percent of non-management staff had over six years of experience. Approximately 

35 percent of states had some unfilled positions during the May 2006 panel, and 22 percent used staff from other programs, 

while only six percent hired temporary staff. 

As discussed, states can utilize different survey procedures. The timing of telephone nonresponse followup varies by states: 

Approximately 58 percent of states begin telephone followup calls after the first survey mailing, 24 percent after the second 

mailing, and 20 percent after the third or fourth mailing. Over 40 percent of states mail a nonresponse follow up letter to 

potential respondents at some point in survey administration – about 18 percent of states mail it to all nonrespondents, while 

25 percent of states target specific firms or industries for the letter. Over 75 percent of states did not experience mail or other 

major survey administrative problems in the May 2006 panel. 

Historical OES Response Rates 

OES response rates are quite high and fairly consistent over time, as shown in Exhibit 5. For most years the response rate for 

the November panel is slightly higher when compared to the May panel. This boost is due in part to the inclusion of State 

government data in November panels. State government data is often quite large and easier for the State office to collect 

from their co-workers in the State’s personnel office. Response rates for the May panel show a small decline from 78.4 to 

76.5 percent from 2003 to 2006. 

Exhibit 5. OES Response Rates for Recent Panels 

78.1% 

78.4% 

77.7% 

77.9% 78.1% 

76.8% 

77.9% 

76.5% 

70.0% 

72.0% 

74.0% 

76.0% 

78.0% 

80.0% 

Nov 2002 May 2003 Nov 2003 May 2004 Nov 2004 May 2005 Nov 2005 May 2006 

Panel/Year 
 

 

 

 

 

 

 

 

 

 

Response Rates by Employment Size 

Response rates grouped by the size of the establishments show that small establishments have much higher rates than large 

establishments, up to 30 percentage points difference. Exhibit 6 also shows small declines in the response rates over time in 

establishments with five to 49 employees, but a less consistent trend in larger firms. In fact, firms with 250 to over 1,000 

employees show some increases in the response rates over time. It is assumed that larger firms are more likely to have the 

technology to provide data by means of electronic filing and they are more likely to use it for completing the OES survey. In 

addition, many of the establishments in the larger size classes have staff dedicated to completing government forms and 

surveys (Willimack et al., 2002). Also, many State offices have diligent analysts who seek out a contact person in large 

establishments and work at creating and maintaining a cooperative relationship and rapport with the contact in order to 

facilitate the collection of the data. 

Exhibit 6. OES Response Rates by Establishment Size Class 

0.0% 

10.0% 

20.0% 

30.0% 

40.0% 

50.0% 

60.0% 

70.0% 

80.0% 

90.0% 

100.0% 

1-4 5-9 10-19 20-49 50-99 100-249 250-499 500-999 1000+ 

Size Class 

Nov 2002 

May 2003 

Nov 2003 

May 2004 

Nov 2004 

May 2005 

Nov 2005 

May 2006 

Response rates for State offices collecting OES data range between 57 percent and 91 percent, shown in Exhibit 7 and also in 

Table 1 in the Appendix. State partners that collect the data are required to meet a 75 percent response rate in each panel, in 

either establishments or employment, as well as in each Metropolitan Statistical Area (MSA). In Oklahoma, North Carolina 

and South Carolina, responding to the OES survey is mandatory. OES response rates mapped out across the nation do not 

reveal any geographic pattern or indication of survey administration differences. Looking at the six regional office territories 

(Boston, Philadelphia, Atlanta, Chicago, Dallas, and San Francisco) also does not reveal any clear pattern that might indicate 

survey administration differences. However, Atlanta and Dallas regional offices have the highest response rates, with 

Chicago third overall (Appendix 1, Table 3), which could indicate survey administration differences or also regional 

differences, as the south and midwest often have higher response rates than the northeast and west. 
 

 

 

 

 

 

 

 

 

 

 

 

Exhibit 7. State OES Response Rates – May 2006 

Over 85% 

78% - 85% 

75% - 77.9% 

70% - 74.9% 

Under 70% 

Response Rates by MSA 

State analysts suggest that the larger the Metropolitan Statistical Area (MSA), the harder it is to collect data. They indicate 

that establishments in larger msa’s are less likely to respond by mail and are also difficult to reach during telephone followups. Response rates by MSA shown in Exhibit 8 indicate this to be true, and only 66 percent of respondents in MSAs of one 

million or more population size reported by mail in the May 2006 panel, compared to 71.9 percent of all respondents (see 

Exhibit 4). Response rates for non- and smaller MSAs are in the lower 80s, while the larger MSAs are in the lower 70s. 

State analysts report that contacts in firms in the larger MSAs often complain that they are too busy to respond. The cultural 

environment of firms found in larger MSAs or perhaps the cultural environment of the larger MSAs themselves seems to 

influence the decision of whether or not a firm participates in the survey. State analysts report that it takes many phones calls 

and lots of persuasion to collect data from these firms. 

Exhibit 8. OES Response Rates by MSA Size 

OES Response Rates by Size of MSA 

64.0% 

66.0% 

68.0% 

70.0% 

72.0% 

74.0% 

76.0% 

78.0% 

80.0% 

82.0% 

84.0% 

86.0% 

Non-MSA Small Sm-Med Med-Lg Large Very Lg 

MSA Size Categories 

Nov 2002 

May 2003 

Nov 2003 

May 2004 

Nov 2004 

May 2005 

Nov 2005 

May 2006 
 

 

 

 

 

 

 

 

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Response Rates by Industry 

Response rates by industry groups show some differences (Exhibit 9), but not nearly the differences seen by size class or 

msa. The information services industry has the lowest response rates while other services and government show the highest 

response rates. In recent November panels the response rates for information services and finance, insurance, and real estate 

(FIRE) have been slightly higher than in May panels. Informal interviews suggest that this could be attributed to good 

timing; those industries, especially FIRE, tend to be involved in closing out their fiscal year accounting during the November 

collection period and find it easier to submit data they are already working on. 

Exhibit 9. OES Response Rates by Industry 

0.0% 

10.0% 

20.0% 

30.0% 

40.0% 

50.0% 

60.0% 

70.0% 

80.0% 

90.0% 

100.0% 

Nov 2002 

May 2003 

Nov 2003 

May 2004 

Nov 2004 

May 2005 

Nov 2005 

May 2006 

Nonresponse Rates 

Nonrespondents to the OES include establishments that do not mail back the survey form, those that communicate their nonparticipation (refusals), those that do not return phone calls, and those that submit incomplete employment data. 

Nonresponse rates have been consistent over time; they have varied between 21.6 percent and 23.5 percent. 

State OES nonresponse rates vary a great deal: between 9 percent and 43 percent. Anecdotally State analysts attribute the 

differences in the level of nonresponse to the size of the sample the State must collect, the number of larger MSAs the State 

has (which State analysts believe negatively impacts the likelihood an establishment will agree to participate), and the 

number of larger units in the State samples. Regional personnel also cite varying levels of expertise, different State 

operational practices, and personnel shortages and issues as additional factors that affect nonresponse. 

Nonresponse is lowest for firms with less than five employees. Informal interviews with State analysts suggest that this is at 

least partially attributed to State analysts’ preferences for smaller firms. Contacting appropriate payroll personnel in these 

units is often easier. These firms also have smaller amounts of data so they are easier to code into the OES system. During a 

push to meet the mandatory 75 percent response rate, States will often concentrate on collect data from the smallest 

establishments. Nonresponse peaks when surveying larger establishments; those with 250 to 999 employees. Nonresponse 

subsides slightly with the largest establishments, those with more than 1000 employees. 
 

 

 

 

 

 

 

 

 

 

 

Panel/Year 

% of 

Sample 

% of 

Nonresponse 

Refusal Rates 

Establishments that communicate their desire to not participate in the OES Survey are classified as refusals. In recent panels 

refusal rates range between 2.7 percent and 3.7 percent of the sample. As a portion of the overall nonresponse rate, refusals 

have ranged between 12.5 percent and 16.6 percent. 

Exhibit 10. Refusal Rates 

Nov 2002 3.0% 13.7% 

May 2003 3.3% 15.5% 

Nov 2003 3.4% 15.3% 

May 2004 3.5% 15.7% 

Nov 2004 2.7% 12.5% 

May 2005 3.5% 15.0% 

Nov 2005 3.7% 16.6% 

May 2006 3.5% 15.1% 

State refusal rates, shown in Exhibits 11, range between 0.0 percent and 11.8 percent. There is substantial anecdotal evidence 

that lower refusal rates and lower non-response rates in general are tied to the expertise and “people skills” of individual State 

analysts. 

Refusal rates mapped out across the nation again do not reveal any geographic or regional pattern, as shown below. 

Exhibit 11. State Refusal Rates – May 2006 

Less than 1% 

1% - 2.9% 

3% - 5.9% 

More than 6% 

not available 

Refusal rates by establishment size class for the May 2006 panel range between two and seven percent, as shown in Exhibit 

12. Refusals by establishment size class show a directly proportional relationship. Similar to the reverse observed with 

overall response rates, refusal rates slightly decrease for the largest establishments. This may be due to larger establishments 

having personnel assigned to complete government paperwork and reports. 
 

 

 

 

 

 

 

200204

200302

200304

200402

200404

200502

200504

200602

Exhibit 12. Percentages of OES Sample Resulting in Refusals by Establishment Size Class 

OES Refusal Rates by Establishment Size Class 

0.0% 

2.0% 

4.0% 

6.0% 

8.0% 

10.0% 

12.0% 

14.0% 

1-4 5-9 10-19 20-49 50-99 100-249 250-499 500-999 1000+ 

Establishment Size Class 

Multivariate Data and Analysis 

To analyze survey participation, we use data from the 2006 May OES panel. We include establishments in the sample that 

are collected by BLS partners in the United States, which covers 179,000 establishments in 50 states and the District of 

Columbia. We exclude one industry and one group of multi-establishment firms, due to different data collection procedures: 

the federal government, which is centrally collected by the BLS national office us, and establishments whose data are 

centrally collected by regional offices through a special arrangement with some multi-establishment firms. 

We organize our analyses using the framework outlined by Willimack and colleagues, that of establishment or business, 

survey administration, and external environment characteristics. We are not able to include respondent characteristics, but 

hope to do so in future analyses. Respondent characteristics, such as contact name, title, and department, are in overlapping 

OES text data fields and are difficult to separate. Exhibit 13 includes the variables that we have available to test under each 

area. 

In addition to OES establishment characteristics discussed earlier (employment size, industry, metropolitan statistical area 

size), we include five additional establishment characteristics listed in the first column of Exhibit 14 in the analysis from the 

BLS Quarterly Census of Employment establishment frame tied to the collection of unemployment insurance tax data. The 

additional items include whether an establishment is part of a multi-establishment firm that crosses states and/or is part of a 

multi-establishment firm within the state, how many state unemployment insurance accounts are attached to the multiestablishment, whether the establishment provides support services to other establishments in a firm, and the age of the firm 

(measured by the first unemployment insurance liability date). The response rates for these data items in the May 06 panel 

are reported in Table 3 in the appendix. To summarize: lower response rates are found for multi-establishment firm either 

across states or within a state and establishments providing support services for a firm, and higher response is observed as an 

establishment increases in age. 

The survey administration characteristics listed in column 2 in Exhibit 13 originate in both the OES data and from the 2006 

May panel state questionnaire discussed earlier. From the OES data, we use BLS region, state sample size, whether the 

survey is mandatory in a state, and whether an unstructured or industry-specific form was sent by the state partner. From the 

QCEW, we use a data item that indicates if any wage or employment data were missing, imputed or of problematic quality, to 
 

 

 

 

 

 

 

 

 

 

 

 

 

Establishment Characteristics Survey administration/design External environment

indicate a pattern of problem reporting for the establishment. Also from the QCEW, we are able to determine if the 

establishment was in the sample for another BLS survey, the Current Employment Statistics Survey, to assess potential 

burden. Response rates for these data items are listed in Table 3 of the appendix and show that being in the CES sample, 

having missing or imputed UI/QCEW data, and receiving an industry specific form (the latter is highly associated with 

employment size) reduces response rates, while mandatory state surveys have higher response rates. From the state 

questionnaire, we use data items on staffing, data collection practices and problems, and state government events, listed in 

Table 2 in the appendix. 

Exhibit 13 

Employment size BLS Region In CES sample 

Industry State sample size Employment size 

MSA size State mandatory survey MSA size 

State multi-establishment Survey form type Significant state economic 

change 

Number of State UI accounts Missing or imputed UI wage 

or employment data 

State population change 

05-06 

U.S. multi-establishment In CES sample State revenue change 05-06 

Provides auxiliary support 

services to other company 

establishments 

Staff number, composition, 

experience 

Establishment age Staff reductions, use of nonregular staff 

Data collection practices 

 Nonresponse followup timing

 Survey administration 

problems 

State govt/agency events 

We have few variables to measure the external environment of the establishment, including whether the establishment is in 

the ces sample to measure the survey environment, employment size, msa, whether there were significant state economic 

events as measured by the state questionnaire, the state population change from 2005-06 from Bureau of the Census data, and 

2005-06 state general fund revenue change from the Association of State Budget Officers. 

We use logistic regression models to fit the response outcomes, which predict the probability of whether the establishment 

responded or not. We provide the chi-square statistics and significance levels for each data item, and the exponentiated value 

of the coefficient, which can be interpreted as an odds ratio (values greater than one indicate an improvement in response). 

Finally, we compare different models using the rescaled R2

 for variance explained and the likelihood ratio. 

Establishment Model Results 

Exhibit 14 provides the Chi-square statistics and significance levels of the variables in the establishment model. Employment 

size, followed by industry , and whether or not the establishment is part of a multi-state firm have the largest Chi-square 

values, with employment size much larger than other variables. This is followed by the age of a firm, MSA size, number of 

state UI accounts, whether the establishment is part of a state mult-unit firm, and auxiliary status. The odds ratios, shown in 

Exhibit 15, show that having an employment size of less than 100 increases the likelihood of response. Being outside or in 

metropolitan statistical area with a lower population increases the probability of response compared to the most populous 

MSA; however, it is not a linear trend, due to a higher likelihood of response from establishments in MSAs with 500-999,000 

persons. The results for industry show that information and finance have lower probability of response than local 

government establishments. One can speculate that many of the white-collar industries within the finance and information 

are likely to have fairly well developed records systems for reporting data. While all industries have a lower probability of 

response than local government, a number of service industries –education and health, leisure and hospitality, and all other 

services have a higher probability of reporting compared to manufacturing, an industry with a history of strong recordskeeping practices. Establishment age, perhaps associated with better reporting capabilities and more established staffing, 

increases the likelihood of response, as does the number of state UI accounts associated with an establishment. However, 

being part of a multi-unit firm, either across states or within a state reduces the likelihood of response compared to single unit 

establishments, as does providing support services to other establishments in a firm. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Establishment Model -

Degrees of

Freedom Chi Square Pr > Chi Sq

Parameters Pr > 

ChiSq

Odds 

Ratio Parameters Pr > ChiSq Odds 

Ratio

Exhibit 14 

Employment Size 5 5302.71 <.0001 

MSA 5 149.70 <.0001 

Industry 10 1418.57 <.0001 

Multi-State unit 1 1272.05 <.0001 

State Multi-unit 1 55.80 <.0001 

No. of state UI accounts 1 91.64 <.0001 

Auxiliary status 1 12.83 0.0003 

Establishment age 1 348.15 <.0001 

Exhibit 15 

Intercept .0879 1.17 Industry 

Employment Size Nat res, mining <.0001 0.47 

1-9 <.0001 4.25 Construction <.0001 0.58 

10-49 <.0001 2.01 Mfg <.0001 0.55 

50-99 <.0001 1.38 Trade, trans, utility <.0001 0.56 

100-249 .1268 1.10 Information <.0001 0.32 

250-999 .7570 1.02 Finance <.0001 0.42 

1000+ 1.0 Prof & bus <.0001 0.50 

MSA Educ, health <.0001 0.75 

Not MSA <.0001 1.43 Leisure, hospitality <.0001 0.64 

50-149,999 <.0001 1.31 Other services <.0001 0.63 

150-249,999 <.0001 1.29 Local government 1.0 

250-499,999 <.0001 1.25 US multi-unit 

500-999,999 <.0001 1.33 Single unit <.0001 1.85 

1,000,000+ 1.0 Multi unit 1.0 

Establishment age <.0001 1.01 State multi-unit 

No. state UI accounts <.0001 1.01 Single-unit <.0001 1.12 

Auxiliary status Multi unit 1.0 

Not Auxiliary 0.0003 1.20 

Auxiliary 1.0 

Survey Administration Model Results 

Exhibits 16 and 17 display the results of the survey administration model. Survey form type, which is highly associated with 

the employment size of the firm, has a very high Chi-square value with the longer, industry form having a much lower 

probability of response, controlling for all other survey administration variables. Establishments in states that do not have a 

mandatory survey have a lower probability of response, as would be expected. Whether or not an establishment is in the CES 

sample is not significant in this model. BLS regional results may reflect administrative practices, but also could reflect 

geographic or cultural differences in reporting. The data, show that establishments in Atlanta, Philadelphia and Chicago 

regions have a higher probability, and Boston and Dallas have a lower probability of response compared to San Francisco. 

Having a higher percent of managerial staff and no decrease in staff positions increases the likelihood of response, but other 

staffing variables do not show the same pattern. The number of positions, having staff and managers with greater than four 

years of experience, unfilled positions, and using temporary or staff from other programs lowers the probability of response. 

One can speculate that staff and managers with the greatest tenure and not having unfilled positions could be associated with 

burn out, and that using staff outside of regular staffing could be associated with inexperience. However, more testing of 

staffing variables using different cut-offs is important to understanding the patterns. The timing of first telephone followup 

calls indicates that calling after the first mailing is most important in predicting response. Other survey administration 
 

 

 

 

 

 

 

 

 

 

 

 

Survey Admin Model Degrees of Freedom Chi Square - Pr > Chi Sq

Parameters Pr > 

ChiSq

Odds 

Ratio Parameters Pr > ChiSq Odds Ratio

BLS Region 

variables are difficult to assess, for example, the greater the number of data collection practices utilized, the lower the 

probability of response. It may be that states with more difficulty historically in reaching higher response rates utilize more 

of the practices. Also, establishments in states reporting no survey administrative problems and no state government or 

agency events have a lower probability of response. It may be that states reporting those types of problems, particularly 

administrative problems, are those that have efficiently identified problems and feel comfortable relating them to regional 

personnel (who collected the state questionnaire data). 

Exhibit 16 

BLS Region 5 430.37 <.0001 

State sample size 1 95.84 <.0001 

State mandatory survey 1 444.00 <.0001 

Survey form type 1 5099.04 <.0001 

Missing/imputed UI data 1 23.45 <.0001 

CES sample 1 1.29 <.2566 

Staff FTE positions 1 165.59 <.0001 

Percent Managerial 1 348.15 <.0001 

Has staff with 4+ yrs exp 1 112.58 <.0001 

Has managers with 4+ yrs exp 1 62.32 <.0001 

Unfilled positions 1 176.04 <.0001 

Used non-regular staff 1 132.18 <.0001 

Had decrease in FTE 1 74.32 <.0001 

No. of data collection practices 1 27.56 <.0001 

Timing of telephone followup 3 267.62 <.0001 

Survey admin problems 1 18.68 <.0001 

State govt/agency events 1 52.84 <.0001 

Exhibit 17 

Intercept <.0001 11.57 No staff with 4+ yrs exp <.0001 1.27 

Staff with 4+ yrs exp 1.0 

Boston <.0001 .83 No managers with 4+ yrs exp <.0001 1.14 

Philadelphia <.0001 1.28 Managers with 4+ yrs exp 1.0 

Atlanta <.0001 1.62 No unfilled positions <.0001 .72 

Chicago <.0001 1.28 Unfilled Positions 1.0 

Dallas .2928 .97 No non-regular staff <.0001 0.77 

San Francisco 1.0 Used non-regular staff 1.0 

State sample size <.0001 1.0 No decrease in FTE <.0001 1.29 

Not mandatory state <.0001 .38 Had decrease in FTE 1.0 

Mandatory state 1.0 No. of data collection practices <.0001 0.95 

Industry form <.0001 .42 Phone followup begins after 

Unstructured form 1.0 1

st mailing <.0001 1.34 

No missing/imputed UI data <.0001 1.13 2

nd mailing <.0001 .82 

Missing/imputed 1.0 3

rd mailing <.0001 1.08 

Not in CES sample <.0001 1.02 4

th mailing 1.0 

CES sample 1.0 No admin problems <.0001 .90 

No. FTE positions <.0001 .91 Admin problems 1.0 

Percent Managerial <.0001 1.48 No state gov/agency events <.0001 .84 

State gov/agency events 1.0 
 

 

 

 

 

 

 

 

 

 

 

Establishment Model -

Degrees of

Freedom Chi Square Pr > Chi Sq

Parameters Pr > 

ChiSq

Odds 

Ratio Parameters Pr > ChiSq

Odds 

Ratio

External Environment Model Results 

The external environment model shows that employment size has the greatest impact, as measured by the chi-square values, 

as shown in Exhibit 18. Employment size in one form or another has the greatest impact overall in the models, and we hope 

to investigate the role of size further in future analyses, using interactions. Clearly, the size of the establishment affects the 

reporting environment, with larger establishments having more government reporting, and perhaps less commitment to 

completing voluntary surveys. Increasing MSA size decreases the probability of response, again with the exception of the 

500-999,000 category. Establishments in larger MSAs may operate in a cultural environment that reduces the likelihood of 

response, although one can speculate that establishments in smaller MSA might face more difficult economic conditions that 

could discourage response. We are exploring adding other economic survey data items that might measure market 

competitiveness by industry and msa, which could improve this model. Significant state economic changes reduced the 

likelihood of response, state population change had a small effect, while state revenue changes were not significant. Finally, 

not being in the CES sample lowers the probability of response, which is a item that was insignificant in the survey 

administration model. This is somewhat contrary to expectation in that establishments operating in a survey environment that 

is demanding in reporting requirements might be less likely to participate. However, one can argue that greater reporting 

requirements are handled best by those that face the greatest burden, given staffing and records keeping capabilities. 

Exhibit 18 

In CES sample 1 43.08 <.0001 

Employment size 5 6297.28 <.0001 

MSA size 5 745.18 <.0001 

Significant state economic 

change 1 345.01 

<.0001 

State population change 05-06 1 74.94 <.0001 

State revenue change 05-06 1 1.59 0.2076 

Exhibit 19 

Intercept .3428 .94 MSA 

Not MSA <.0001 1.51 

Not in CES .89 50-149,999 <.0001 1.34 

In CES sample 1.0 150-249,999 <.0001 1.32 

Employment Size 250-499,999 <.0001 1.27 

1-9 <.0001 3.93 500-999,999 <.0001 1.32 

10-49 <.0001 1.89 1,000,000+ 1.0 

50-99 <.0001 1.28 

No significant 

economic change <.0001 1.65 

100-249 .8933 .99 

Significant 

economic change 1.0 

250-999 .0502 .89 State pop change <.0001 1.0 

1000+ 1.0 

State revenue 

change 0.2076 1.0 

Full Model Results 

Exhibits 20 and 21 show the results of the inclusion of all variables in the three models. The direction of nearly all the

variables remains the same, with a few minor exceptions. Employment size has the largest chi-square value, although 

smaller, the trend is consistent in that establishments with fewer than 100 employees are more likely to respond. Other items 

with large values tied to the establishment include whether the establishment is part of a firm that crosses states and industry, 

as well as establishment age and MSA. These variables are in the same direction as in the establishment model, in that multistate status decreases, but age increases the probability of response. Information and finance have the lower probability of 
 

 

 

 

 

 

 

 

 

 

 

Full Model -

Degrees of

Freedom Chi Square Pr > Chi Sq

response, while many of the services are more likely to respond than mining, construction, manufacturing, and transportation 

industries, but overall all industries have a lower response than local government. MSA results are mixed, with not being 

located in an msa being associated with the highest response. Survey administration results are very similar, with region, 

mandatory survey, form type, telephone followup timing, unfilled positions and use of non-regular staff having a large 

impact. Telephone follow timing shows a trend of the greatest probability of response after the first mailing, with the second 

and third mailings also increasing response, compared to followup after the final mailing. External environment variables are 

in the same direction, with significant state economic factors having a high chi-square value. 

Exhibit 20 

Establishment 

Employment Size 5 2034.35 <.0001 

MSA 5 218.85 <.0001 

Industry 10 1097.05 <.0001 

Multi-State unit 1 1303.82 <.0001 

State Multi-unit 1 39.87 <.0001 

No. of state UI accounts 1 84.99 <.0001 

Auxiliary status 1 11.48 0.0007 

Establishment age 363.05 <.0001 

Survey administration 

BLS Region 5 382.58 <.0001 

State sample size 1 4.58 .0323 

State mandatory survey 1 153.58 <.0001 

Survey form type 1 123.50 <.0001 

Missing/imputed UI data 1 39.99 <.0001 

CES sample 1 31.95 <.0001 

Staff FTE positions 1 17.46 <.0001 

Percent Managerial 1 35.31 <.0001 

Has staff with 4+ yrs exp 1 99.83 <.0001 

Has managers with 4+ yrs exp 1 52.28 <.0001 

Unfilled positions 1 176.04 <.0001 

Used non-regular staff 1 112.53 <.0001 

Had decrease in FTE 1 15.51 <.0001 

No. of data collection practices 1 36.32 <.0001 

Timing of telephone followup 3 41.19 <.0001 

Survey admin problems 1 16.80 <.0001 

State govt/agency events 1 13.69 .0002 

Significant state economic 

change 1 148.41 

<.0001 

State population change 05-06 1 70.01 <.0001 

State revenue change 05-06 1 81.28 <.0001 
 

 

 

 

 

 

 

 

Parameters Pr > ChiSq Odds Ratio Parameters Pr > ChiSq Odds Ratio

Employment Size

MSA 

Industry 

Auxiliary status 

US multi-unit 

State multi-unit 

Exhibit 21 

Intercept .3642 1.13 BLS Region 

Boston <.0001 1.22 

1-9 <.0001 3.67 Philadelphia <.0001 1.80 

10-49 <.0001 1.91 Atlanta <.0001 1.99 

50-99 <.0001 1.40 Chicago <.0001 1.91 

100-249 .0643 1.12 Dallas <.0001 1.27 

250-999 .7026 1.03 San Francisco 1.0 

1000+ 1.0 State sample size <.0001 1.0 

Not mandatory <.0001 .54 

Not MSA <.0001 1.25 Mandatory 1.0 

50-149,999 <.0001 1.18 Industry form <.0001 .81 

150-249,999 <.0001 1.22 Unstructured form 1.0 

250-499,999 

<.0001 

1.12 

No missing/imputed UI 

data <.0001 1.19 

500-999,999 <.0001 1.22 Missing/imputed 1.0 

1,000,000+ 1.0 Not in CES sample <.0001 .90 

CES sample 1.0 

Nat res, mining <.0001 .47 No. FTE positions <.0001 .97 

Construction <.0001 .57 Percent Managerial <.0001 1.25 

Mfg <.0001 .54 No staff with 4+ yrs exp <.0001 1.28 

Trade, trans, utility <.0001 .56 Staff with 4+ yrs exp 1.0 

Information 

<.0001 

.32 

No managers with 4+ 

yrs exp <.0001 1.13 

Finance 

<.0001 

.42 

Managers with 4+ yrs 

exp 1.0 

Prof & bus <.0001 .50 No unfilled positions <.0001 .73 

Educ, health <.0001 .77 Unfilled Positions 1.0 

Leisure, hospitality <.0001 .64 No non-regular staff <.0001 .74 

Other services <.0001 .62 Used non-regular staff 1.0 

Local government 1.0 No decrease in FTE <.0001 1.13 

Establishment age <.0001 1.01 Had decrease in FTE 1.0 

No. state UI accounts <.0001 1.01 

No. of data collection 

practices <.0001 .94 

Phone followup begins 

after 

Not Auxiliary unit .0007 1.19 1

st mailing <.0001 1.37 

Auxiliary unit 1.0 2

nd mailing <.0001 1.18 

3

rd mailing <.0001 1.18 

Single-unit <.0001 1.88 4

th mailing 1.0 

Multi unit 1.0 No admin problems <.0001 .90 

Admin problems 1.0 

Single-unit <.0001 1.11 

No state gov/agency 

event .0002 .90 

Multi unit 1.0 State gov/agency event 1.0 

No significant economic 

change <.0001 1.8 State pop change <.0001 1.0 

Significant economic 

change 1.0 State revenue change <.0001 1.0 
 

 

 

 

 

 

 

Model Max-rescaled R 

Square Likelihood ratio Pr > Chi Sq

Model Testing 

Comparing the models in Exhibit 22, we see that the establishment model explains more of the variation and has a better fit 

than the survey administration or external environment models. The establishment model includes data items theoretically 

not under the control of the survey organization, thus its strength poses a dilemma in how to proceed to address nonresponse. 

All of the first three models are heavily influenced by the effect of size, either through the employment size itself or the 

survey form type, in the case of the survey administration model. We hope to disentangle the meaning of employment size 

through future analysis of interactions with industry, msa, establishment age, and multi-establishment status. There may be 

proxy variables at an industry level that measure establishment characteristics such as data availability and staffing resource

patterns we could utilize; the latter data are likely to be found in the OES. We also can explore and reconceptualize variables 

in the survey administration model, include additional information we are gathering on the modes that are more likely to be

offered to respondents by each state (such as email reporting), and conduct further analysis of form type through interactions 

with establishment and survey administration variables. In addition, we plan further analysis of state sample size, which was 

significant in the survey administration model, and its interactions with industry, msa, and employment size in the future. 

Since survey design and administration is theoretically under the control of the survey researcher, it is a critical area for 

further research. For the external environment model, we are investigating additional data items that might better capture 

economic conditions and the legal and regulatory climate associated with states, msa and industries. 

The full model, reduces the effect of size somewhat, and with all variables from the other models included explains about 12 

percent of the variance, a substantial increase over the models focusing on only one conceptual areas of survey participation. 

While we have much more to explore in model testing and alternative variable construction, it appears that each conceptual 

area -- the establishment, survey administration and external environment -- is important in explaining participation in the 

OES survey. 

Exhibit 22 

Establishment .1042 12678.03 <.0001 

Survey Administration .0649 7882.13 <.0001 

External environment .0775 9458.22 <.0001 

Full Model .1229 15048.14 <.0001 
 

 

 

 

State FIPS N Sample 

Percent 

Response

Rate 

State FIPS N Sample 

Percent 

Response

Rate 

APPENDIX 

Table 1. Sample Distribution and Response Rate by State, May 2006 panel 

Alabama 01 3619 2.0 82.5 Missouri 29 4096 2.3 80.7 

Alaska 02 691 .4 73.7 Montana 30 1023 .6 84.0 

Arizona 04 2529 1.4 78.1 Nebraska 31 1647 .9 91.3 

Arkansas 05 2323 1.3 80.6 Nevada 32 1453 .8 73.1 

California 06 15691 8.8 74.4 New 

Hampshire 

33 1641 .9 80.2 

Colorado 08 3352 1.9 77.1 New Jersey 34 5260 2.9 56.6 

Connecticut 09 3097 1.7 70.8 New Mexico 35 1408 .8 89.9 

Delaware 10 825 .5 76.5 New York 36 8513 4.8 74.8 

District of 

Columbia 

11 453 .3 73.7 North Carolina 37 5401 3.0 91.4 

Florida 12 8943 5.0 77.1 North Dakota 38 926 .5 77.4 

Georgia 13 4321 2.4 82.9 Ohio 39 7671 4.3 74.6 

Hawaii 15 949 .5 78.1 Oklahoma 40 2431 1.4 79.1 

Idaho 16 1183 .7 75.8 Oregon 41 2876 1.6 76.9 

Illinois 17 5545 3.1 71.2 Pennsylvania 42 7679 4.3 74.5 

Indiana 18 4842 2.7 80.8 Rhode Island 44 954 .5 75.4 

Iowa 19 2912 1.6 82.6 South Carolina 45 2975 1.7 84.7 

Kansas 20 2276 1.3 77.9 South Dakota 46 966 .5 91.1 

Kentucky 21 2855 1.6 75.7 Tennessee 47 3518 2.0 75.0 

Louisiana 22 3283 1.8 75.0 Texas 48 11379 6.7 74.8 

Maine 23 1408 .8 78.3 Utah 49 1718 1.0 79.0 

Maryland 24 2902 1.6 74.3 Vermont 50 839 .5 86.3 

Massachusetts 25 4460 2.5 74.0 Virginia 51 4563 2.6 73.5 

Michigan 26 5298 3.0 65.6 Washington 53 3858 2.2 76.6 

Minnesota 27 3540 2.0 77.9 West Virginia 54 1692 1.0 74.9 

Mississippi 28 1888 1.1 89.5 Wisconsin 55 4585 2.6 76.0 

Wyoming 56 743 .4 89.6 
 

 

BLS funded FTE 

BLS Funded FTE-Management/ 

Professional Staff 

Change in FTE positions from 

November 2005 panel 

Number of Managers

Table 2. State and District Survey Results, May 2006 (N=51) 

Number of Staff 

Mean 5.1 < 1 – 3 years experience 43% 

Standard Deviation 3.3 4 - 6 years experience 25% 

Range 1.3 – 18.0 > 6 years experience 32% 

Sum 259 Total 100% 

Data Collection Practices 

Mean 3.4 

Used BLS spreadsheet for address 

refinement 57% 

Standard Deviation 3.0 Used address refinement postcards 31% 

Range .5 – 13.0 Used email for data collection 98% 

Sum 171.5 

Collected multi units separately from 

centralized mailings 43% 

Use staff from other programs? 22% 

Mailed nonresponse letter to all 

nonrespondents 18% 

Missing 1 

Mailed nonresponse letter to some 

nonrespondents 25% 

Hire temporary staff? 6% Nonresponse Telephone Followup 

Missing 2 After 1st mailing 57% 

After 2nd mailing 24% 

Increase 4% After 3rd mailing 16% 

Decrease 22% After 4th mailing 4% 

About the same 75% Survey Administration Problems 

Unfilled positions 35% Late mail delivery 5% 

Mean .47 Other mail problems 12% 

Standard Deviation .78 Other survey admin problems 6% 

Range .3 -3.4 None of the above 76% 

State Events 

< 1 – 3 years experience 23% Significant economic changes 4% 

4 – 6 years experience 17% Significant administrative changes 12% 

> 6 years experience 59% Agency restructuring 8% 

Total 100% Other agency transitions or moves 18% 
Table 3. Distribution of Variables and Final Response Rate (N=179,000) 

 

 

 

 

 

 

 

 

 

 

N % of Total Response

Rate N % ofTotal 

Response

Rate 

All States/DC 179000 76.5 Industry 

Nat res, mine 

Constr. 

1803

14768 

 1.0 

8.3 

76.4

Emp Size Class 79.7

1-4 28925 16.2 89.5 Mfg 

Trade, trans, 

17129 9.6 74.9

5-9 29826 16.7 84.9 utility 41469 23.2 77.1

10-19 32405 18.1 79.5 Information 3978 2.2 64.6

20-49 39236 21.9 73.1 Finance 12210 6.8 72.9

50-99 23718 13.3 67.6 Prof & bus 25429 14.2 72.5

100-249 15411 8.6 61.8 Educ, health 

Leisure, 

27485 15.4 78.6

250-499 5722 3.2 60.3 hospitality 21265 11.9 76.6

500-999 2328 1.3 59.4 Other serv 9584 5.4 83.8

1000+ 1429 .8 62.6 Local govt 3880 2.2 82.1

MSA 

Not MSA 36833 20.6 81.9 

Multiple state 

unit 

Single state 

unit 

51665 

127335

24.4 

 75.6 

65.0

80.2 

State multiple 

50-149,999 14105 7.9 81.7 unit 49878 27.9 70.3

State single 

150-249,999 13808 7.7 80.7 unit 129122 72.1 78.9

State multiple 

250-499,999 20712 11.6 78.6 UI acct 29530 16.5 69.4

State single 

500-999,999 

1,000,000+ 

19113 

74427 

10.7 

41.6 

78.0 

71.1 

UI accts 149470 83.5 77.9

Auxiliary 

service estab 

Non-aux est. 

1958

177042

 1.1 

 98.9 

61.8

76.7 

Regional 

Offices 

Boston 21792 11.7 75.2 

CES sample 

overlap 

No overlap 

18611 

160389

10.4 

 89.6 

72.2

77.0 

Mandatory 

Philadelphia 24510 13.1 70.3 state 10807 6.0 86.8

NonAtlanta 35253 18.7 81.7 mandatory 16893 94.0 75.9

Missing/impu 

Chicago 39427 21.2 76.0 ted UI data 9667 5.4 73.1

No miss/imp 

Dallas 

San Francisco 

35444 

30689 

19.0 

16.3 

78.1 

75.3 

UI data 169333 94.6 76.7

Industry form 

Unstructured 

form 

87304 

91696 

48.7 

51.2 

68.9

Estab. Age 83.8

Mean 

Quartile 1 

Median 

Quartile 3 

44750 

44750 

44750 

44750 

Years 

13.2 

4.1 yrs 

9.2 yrs 

18.9 yrs 

74.5 

74.9 

77.2 

79.5 
 

 

 

 

 

 

 

 

 

 

 

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