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1

United States

Department of

Agriculture

 National

 Agricultural

 Statistics

 Service

 Research and

 Development Division

 Washington DC 20250

 March 2019

 

Author email: Benjamin.Reist@nass.usda.gov

Findings for the 2018

Agricultural Labor Base

Wage Question

Experiments

Benjamin Reist

Deputy Director

Research and Development Division

Tyler Wilson

Survey Methodologist

Survey Methodology and Technology Section

Research and Development Division

Heather Ridolfo

Survey Methodologist

Survey Methodology and Technology Section

Research and Development Division

Linda J. Young

Chief Mathematical Statistician and Director

Research and Development Division
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Acknowledgements

The Agricultural Labor Survey Team:

Shareefah Williams

Marisa Reuber

Sofiya Cherni

Cedric Reddick

Lance Daugherty

Peter Kuzila

Heather Ridolfo

Curt Stock

David Legan

Jock Summers

Kathy Ott

Franklin Duan

A Special Thanks to our Editors:

Shane T. Ball

Mark Apodaca

Jeff Bailey

Dan Beckler
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1. INTRODUCTION

The U.S. Department of Agriculture’s (USDA) National Agricultural Statistics Service (NASS)

has initiated a study to evaluate the impact that proposed changes in the questionnaire for

the Agricultural Labor Survey will have on data quality and usability. Randomized

experiments were conducted during the April and October 2018 administrations of the

Agricultural Labor Survey to evaluate two survey versions of the questionnaire: (1) the

original survey with a measure for gross wages, and (2) a modified version with measures

on gross wages, base wages, incentive/bonuses and overtime wages. The two

questionnaires are to be compared using the following measures:

 Unit response rates

 Sample size to meet NASS’s coefficient of variation (CV) targets

 Item nonresponse rates on gross wages

 Item nonresponse rates on base wages

For base wages, the three forms of weighted item nonresponse rates considered are based

on: (1) the sampling weight, (2) the nonresponse adjusted weight, and (3) the imputation

rate.

For the changes from the original questionnaire to the modified questionnaire, the

following meaningful differences/levels of each of the measures were determined before

fielding the experiment:

 Unit response rates reduction of 10% or more

 Sample size increase of 10% or more

 Item nonresponse rate on gross wages of 10% or more

 Item nonresponse rate on base wages of 40% or more

 Weighted item nonresponse rates on base wages of 25% or more

The presence of meaningful differences/levels in these measures would support the

continued use of the original survey.

This report analyzes both the April and October experiments. In Section 2, general

information about the purpose, sample design, and data collection methods of the

Agricultural Labor Survey is provided. The design of the experiment is in Section 3. The

methodology used to analyze the data from the experiment is discussed in Section 4. The

findings are presented in Section 5. The final section (6) concludes the report with overall

findings and limitations.
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2. AGRICULTURAL LABOR SURVEY

NASS conducts the Agricultural Labor Survey semiannually in April and October. The

purpose of the survey is to determine the types and number of farmworkers employed and

the wages they are paid.

Farm employment and wage statistics are used by federal, state and local government

agencies, farm organizations, and employers for many purposes, including planning,

recruitment and placement of workers, and policymaking. The agricultural wage rate is a

component of the Parity Index and is used in the establishment of minimum wage rates for

domestic and foreign agricultural workers.

The survey results are published in the semiannual Farm Labor release, issued on or

around the 21st of May and November. Both semiannual releases include regional and U.S.

level estimates for the number of hired workers, hours worked weekly, and wage rates by

type of worker. The November release also includes annual average estimates at the

regional and U.S. levels.

2.1. Sample Design

The target population for the Agricultural Labor Survey includes all U.S. farms, where a

farm is any place that “produced and sold or would normally produce and sell at least

$1,000 worth of agricultural products during the year.”

The Labor Survey is a multiple frame survey, drawing from both the NASS List Frame and

the June Area Frame. The list is an efficient sampling frame because it contains most of the

farms with hired labor. The area frame provides the completeness missing from the list.

The Labor Survey has a single stage stratified sample design. The Labor Survey has a statebased design with strata within states based on peak number of hired workers, presence of

labor-intensive commodities, and farm value of sales. The sample is drawn using simple

random sampling within state-stratum combinations.

A new sample is drawn prior to the October data collection. The sample is split into eight

replicates, each of which is representative of the population. Six of the eight replicates are

randomly selected to become the sample for the October data collection. The two

replicates that are not in the October data collection and four of the six replicates from the

October data collection comprise the sample for the following April data collection.

Approximately, 14,000 operations are in the sample for each round of data collection. 

NASS targets a coefficient of variation (CV) of the estimated quarterly gross wage rates of

1.0% at the national level and of 5.0%2 at the agricultural labor region level. The sample is

allocated to meet or exceed these targets.

 

2 CA and HI have a 2.0% target and FL has a target of 4.0%
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2.2. Data Collection

Self-administered paper questionnaires and computer assisted telephone interviews

(CATI) are the primary data collection methods for the Agricultural Labor Survey, although

survey respondents may report using the NASS Agcounts website. A limited number of field

interviews are also conducted. All states use the same version of the Agricultural Labor

questionnaire, except California. Data for two quarters are collected during each

administration of the survey. Information from January and April is gathered during the

April data collection; information from July and October is gathered during the October

data collection. 

The survey reference weeks are the weeks containing the 12th day of the survey month

(January, April, July, and October). Questionnaires are mailed the first day of the Reference

Week in April and October. Phone and field enumeration begin the Sunday after the

reference week and continue for approximately 21⁄2 weeks.

2.2.1. California

The California Employment Development Department (EDD) conducts a monthly California

Agricultural Labor Survey by mail. NASS has a joint cooperative agreement with EDD

during the months that the national survey is conducted (October and April). In the other

months where the national survey is not conducted, California’s entire list sample

(supplied by NASS) is used only by EDD.

In addition to the definition of the target population described above (section 2.1),

California also collects data from Agricultural Service firms. California maintains a separate

list frame of these firms. California EDD prints and mails a questionnaire to a sample from

this list each month. During the October and April data collections, NASS conducts phone

follow-up for nonrespondents in the California sample that is used for the Agricultural

Labor Survey estimates.

3. EXPERIMENTAL DESIGN

To evaluate the effects of modifying the survey instruments to include questions about base

wages and incentive/overtime wages, NASS conducted a randomized experiment

evaluating two versions of the April 2018 administration of the Agricultural Labor Survey: 

Version 1: The original questionnaire with questions about gross wages.

Version 2: A modified questionnaire with questions designed about gross wages, base

wages, and incentive/overtime wages.

Version 1 is the control and version 2 is the treatment. Based on cognitive testing results,

version 2 was revised between the April and October surveys. For this study, in addition to

the six replicates that are typically used in April, one of the two remaining replicates from

the October 2017 administration was included, resulting in the April 2018 sample being

comprised of seven replicates. The additional replicate from the October sample and a

random selection of one of the original six replicates for the April sample were assigned to
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version 2. The remaining five April replicates were assigned to version 1. A similar design

was utilized for the October 2018 experiment.

Data for both versions were collected simultaneously using the same data collection modes

and strategies. In April, the phone follow-up for version 2 was conducted by the Oklahoma

Data Collection Center (DCC), whereas the phone follow-up for version 1 was conducted by

the other four DCCs (Arkansas, Missouri, Montana, and Wyoming). In October, all DCCs

conducted phone follow-up for both versions.

Since version 2 of the California questionnaire was not available in time for the January

administration of the Agricultural Labor Survey in California, version 2 was only collected

for April, July, and October in California. Additionally, the Wyoming DCC conducted phone

follow-up for both versions for the California sample.

3.1. Differences between April and October

In April 2018, respondents receiving version 2 were asked to report “total base wages” and

“total incentive and overtime wages.” The definition for base wages was: Base wages

include the minimum amount paid regardless of method of pay (salaried, hourly, piece rate,

etc.) but exclude incentive pay. The definition for incentive pay was: Incentive pay includes

bonuses, performance pay, hazard pay, overtime pay, etc. that is paid in addition to the base

wages.

In October 2018, the term incentive was replaced with bonus – and bonus and overtime

wages were separated into two questions (i.e., “total bonus wages,” and “total overtime

wages”). The definition for base wages was: Base wages include the minimum amount paid

regardless of method of pay (salaried, hourly, piece rate, etc.) but exclude bonus and overtime

pay. The definition for bonus pay was: Bonus pay includes performance pay, hazard pay, and

other regularly paid bonuses, etc. that are paid in addition to the base wages.

During the October 2018 data collection, a new CATI script was fielded for both versions.

The new script was designed to improve flow of the questionnaire and to encourage

interviewers to ask questions in their entirety. Interviewers were also retrained to ask the

questions as worded. The most notable changes to the CATI script were: (1) Verification
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questions added to ensure respondents were including and excluding the appropriate

types of workers (e.g., excluding contract labor, including family members); (2)

Respondents were asked the total number of workers for the reference week before

workers were broken down into specific categories; (3) Instead of asking about the work

the workers were hired to do and then presenting the worker categories as a list in the

response options, respondents were asked how many workers were hired for each

category, with each category being asked as a separate question; and (4) Edit checks were

added to make sure all workers were accounted for and not double counted.

Although bonus and overtime wages were separated on paper, they were only asked as

separate questions in CATI if the respondent indicated that the base wages were less than

gross wages. That is, if base wages equalled gross wages, respondents were asked “Did you

pay any bonus wages or overtime pay?” If they answered yes, the interviewer had to

backtrack, revise base wages, and then ask the bonus and overtime questions. If the

respondent indicated that he or she did not pay bonus or overtime, the interviewer moved

on to the next series of questions.

4. METHODOLOGY

The experiments were evaluated using the following criteria:

 Unit response rates

 Sample size to meet NASS’s coefficient of variation (CV) targets

 Item nonresponse rates on gross wages

 Item nonresponse rates on base wages

The three forms of a weighted item nonresponse rates on base wages are based on: (1) the

sampling weight, (2) the nonresponse adjusted weight, and (3) the imputation rate.

Meaningful differences/levels for each of the metrics above were determined before

fielding the experiment as follows:

 Unit response rate reduction of 10% or more

 Sample size increase of 10% or more

 Unweighted item nonresponse rate on gross wages of 10% or more

 Unweighted item nonresponse rate on base wages of 40% or more

 Weighted item nonresponse rates on base wages of 25% or more

Since the differences in unit response rates and item nonresponse rates on gross wages are

comparisons between the two questionnaires, logistic regression was used to assess

whether the differences in the rates between the two versions were statistically significant.
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The sample size measures are based on the observed CVs and response rates for each

version. These are used to estimate the minimum sample that would be needed to meet

each CV target.

The results for the April administration of the Agricultural Labor Survey in California have

been withheld from this analysis. This was done since evidence suggests that the

difference in the unit response rates were heavily influenced by how the experiment was

conducted and/or how the data were captured and processed. This also was supported by

the July and October administrations that showed more reasonable differences in unit

response rates. Results for the April administration in California can be found in the

Preliminary Labor Report released in the fall of 2018 (Reist et. al, 2018).

4.1. Unit Nonresponse

For unit nonresponse rates, a logistic model was used to evaluate the statistical significance

of the differences between responses at the regional and national levels. This model was fit

on the operation level data from all four months for which data were collected. The

response variable was an indicator of a valid response. The model predictors were version,

region, and month. The two-way and three-way interactions were included in the model.

4.2. Item Nonresponse Rates on Gross Wages

The model of item nonresponse rates for gross wages was similar to the models used in

section 4.1 with the following differences: (1) the response indicator was a measure of an

unusable item and (2) gross wages were collected for each worker class. In addition, an

observation was recorded for each operation and reported worker class combination.

5. FINDINGS

This section reports the findings of the 2018 Labor Survey Experiments and is organized

around five metrics.

 Unit response rates

 Sample size to meet NASS’s coefficient of variation (CV) targets

 Item nonresponse rates on gross wages

 Item nonresponse rates on base wages

 Weighted item nonresponse rates on base wages

5.1. Unit Response Rates

The table below shows the p-values for the nonresponse models described in section 4.1.
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The p-values in red signify the effects that were statistically significant at the 5% level. The

significant interaction between region and month corroborates the known regional

differences in response rates seen historically and based on the changes described in the

data collections between the April and October administrations described in section 3.1.

Interestingly, the interaction between version and month was not significant indicating

that the changes described, which exclusively affected version 2 (see section 3.1), did not

have an effect on unit response rates.

The interaction between version and region was significant, indicating that the response

rate differences in versions varied significantly across regions. However, none of the 2018

regional responses were above the meaningful level of a reduction of 10% or more (see

Table A1). The lack of significance between version, region, and month indicated that the

response rate differences in versions do not vary significantly across region-month

combinations.

5.2. Item Nonresponse Rates for Gross Wages

The p-values for the item nonresponse models described in section 4.3 are displayed in the

table below.
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Table 2: Gross Wage Item Nonresponse (Unusable Reports) Model.

Predictor p-value

Version 0.9432

Region <0.0001

Month <0.0001

Version*Region 0.0603

Region*Month <0.0001

Version*Month 0.8837

Version*Region*Month <0.0001

The p-values in red signify the effects that are statistically significant at the 5% level. In

contrast to the unit nonresponse, the three-factor interaction of version, region, and month

is significant for the item nonresponse in gross wages. This indicates that the difference in

the gross wage nonresponse (unusable report) rates between the two versions depended

on the month and region as illustrated in figures 1 and 2.

Figure 1: Version 1 Percentage Unusable Reports by Region.

0%

20%

40%

60%

80%

Version 1

January April July October
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Figure 2: Version 2 Percentage Unusable Reports by Region.

This three-factor interaction is largely due to the major differences in item nonresponse

rates between versions for the region/data collection period (January/April versus

July/October) combinations in the Cornbelt II region, the Mountain III region, and the

Pacific region. For the Cornbelt II region, the differences in versions was significant in the

January/April data collection, but not for the July/October data collection. In contrast, for

the Mountain III and the Pacific regions, the differences were not significant in the

January/April data collection, but were significant for the July/October data collection.

In January, one region had an increase in the gross wages item nonresponse rates above

the 10% threshold (Delta 10.6%; see Table 2A). In April, two regions (Southern Plains

15.6% and Pacific 13.8%) had an increase in the gross wages item nonresponse rates above

the 10% threshold. In July and October, only Mountain III had an increase in the gross

wages item nonresponse rate above the 10% threshold (July 30.6% and October 17.1%). It

should be noted that in some regions decreases in the gross wages item nonresponse rate

are significant, specially, one region in April and July and four regions in October. 

5.3. Sample Size Increase

The sample sizes needed to meet NASS’s CV targets for gross wages at the regional and

national levels, based on the version 2 response rates, are shown in Tables A3 (appendix).

The national sample size would not need to be increased by more than 10% in any of the

four months of the survey. However, examining the average percent change in sample size

needed over 4 quarters, both the Appalachian I and Southern Plains would need

approximately 15% and 21% increases, respectively. For additional percentage change by

region for each month, see Table A3.

5.4. Base Wage Item Nonresponse

Four measures of base wage item nonresponse were considered, which are:

0%

20%

40%

60%

80%

Version 2

January April July October
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1. Unweighted item nonresponse rates

2. Weighted item nonresponse rates using sampling weights

3. Weighted item nonresponse rates using nonresponse adjusted weights

4. Imputation rate (i.e. the proportion of the estimate of total base wages imputed)

The definition of each of these rates is given in Table A8 (appendix). One issue with using

traditional weighted item nonresponse rates (i.e., 2 and 3), for populations with a highly

skewed outcome variable, is that the sampling weight is usually inversely related to the

estimate. To reduce sampling errors, which have higher likelihood of having large values

(i.e., base wages), they are tested at a higher rate than units that do not. Traditional

weighted item nonresponse rates give more weight to missing values with higher weights,

which in general have lower base wages. For more information about this issue in business

and establishment surveys and some alternative measures, see Thompson and Oliver

(2012). 

The U.S. Census Bureau uses imputation rates for business and establishment surveys as an

alternative to a weighted item nonresponse rate. This rate is the proportion of the estimate;

in this case it is total base wages, which is imputed (see appendix D3-B, U.S. Census Bureau

Statistical Quality Standards, 2013). The imputation rate takes into consideration both

weight and the amount of base wages either reported or imputed for the operation.

At the national level, none of the four measures had a meaningful level of item nonresponse

for base wages for July and October (Tables A4-A7). Only the imputation rate in April had a

meaningful level of item nonresponse at the national level (without California). However,

some regions did attain a meaningful level of base wages item nonresponse. For each

measure, the percentage of the regions that had a meaningful level of item nonresponse in

base wages is displayed below.

Table 3: Number of Regions for Which Item Nonresponse Rates for Base Wages above 

Threshold

Month Unweighted Sampling Weight Nonresponse Weight Imputation Rate

January 3 (17.6%) 5 (29.4%) 4 (23.5%) 2 (11.8%)

April 2 (11.8%) 7 (41.2%) 6 (35.3%) 3 (22.2%)

July 3 (16.7%) 5 (27.8%) 5 (27.8%) 2 (11.1%)

October 3 (16.7%) 5 (27.8%) 5 (27.8%) 3 (16.7%)

In three of the four measures observed in April, the issue of item nonresponse on base

wages was substantially higher when compared to the three other months. In addition, all

months (January, April, July, and October) identified by the imputation rate indicated that

item nonresponse may be an issue for operations that have (1) high weights and (2) lower
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base wages. Only one region-month combination (Pacific, April) had all four measures rise

to a meaningful level. Finally, only one region (Pacific) was above the threshold of any of

the measures (unweighted item nonresponse rate) in all four months.

6. CONCLUSION

The current Agricultural Labor Survey collects gross wages. It has been proposed that the

base wage (i.e., base pay) and incentives (e.g., overtime pay) also be collected. To assess the

potential impact of such a change, a new version of the questionnaire was developed to

acquire this additional information and was then compared to the current version. Based

on the metrics evaluated, few differences exist between the two versions. However, more

qualitative research is needed to determine how best to ask operators about both base and

incentive pay.

6.1. Limitations

In the April administration of the survey, the treatment sample was administered in one

DCC, whereas the control sample was distributed between the remaining four DCCs. Thus,

interviewer effects are confounded within version, which could explain some of the

differences or lack of differences found in response rates and usable data. For the October

administration, all the DCCs were used to administer both versions. 

Evidence from recent behavior coding research on interviewers, during the January and

April data collection processes, indicates a consistent lack of adherence to reading the base

wage and incentive/overtime questions. Some interviewers reverted to the original

questionnaire and did not read the new questions. Based on the behavior coding and prior

cognitive testing, the CATI instrument has been redesigned to address some of the issues

interviewers were having administering this survey over the phone and separating

overtime wages and incentives (see section 3.1). Finally, interviewer training was updated

to provide clearer guidance on how to administer the questionnaire. Behavior coding is

currently being performed for the October data collection, so these results are currently

unavailable.

California conducts the Agricultural Labor Survey differently than other regions or states.

In California, the survey is administered monthly by the California Employment

Development Division. Response is encouraged by mail or fax, although some interviews

are conducted by telephone from Wyoming and questions are read from a paper version. In

addition, the new questionnaire was not tested in January and there is evidence that

differences in the unit response rates were probably caused by how the experiment was

conducted or how the data were captured. Because January was not tested, there is not a

full year of data to evaluate in California.

6.2. Final Summary

The results of the 2018 Agricultural Labor Survey Experiment on the inclusion of the new

wage questions are:

 No significant impact in response rates between survey versions at the national level.
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 No significant impact in item nonresponse for gross wages at the national level.

 Some significant and meaningful negative impacts for some region-month combinations

but significant positive impacts were observed as well.

 Based on the four study months, no meaningful increases in sample size are needed at the

national level to meet CV targets on gross wages.

 Four of the 18 regions require a meaningful increase in sample size for at least one

month.

 For only one month, the imputation rate shows a meaningful level of item nonresponse

for base wages at the national level (April without California).

 None of the other three item nonresponse rates rise to a meaningful level at the national

level for any month.

 Measures of item nonresponse show a consistent meaningful level of item nonresponse

for base wage in the Pacific region.

In conclusion, little evidence (two measures with meaningful difference at the national

level for any month) was found that suggests a negative impact of using an alternative

questionnaire. However, some areas warrant additional research and evaluation. Two of

the measures showed a meaningful difference at the national level for any month. The

imputation rate in April might not have been at a meaningful level at the national level if

data from California could be incorporated. This leaves the sample size increases needed

to meet CV targets on gross wages as the only measure that has a meaningful difference at

the national level.

Some significant and meaningful differences were observed at the regional level. However,

the Pacific region is the only region that had a consistent meaningful difference for any of

the measures across all months.
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References

Thompson, K. J. and Oliver, B. E. (2012). Response Rates in Business Surveys: Going

Beyond the Usual Performance Measures. Journal of Official Statistics, 28, 221-237.

U.S. Census Bureau (2013) U.S. Census Bureau Statistical Quality Standards. Washington, DC.

available at https://www.census.gov/content/dam/Census/about/about-thebureau/policies_and_notices/quality/statistical-quality-standards/Quality_Standards.pdf

(accessed 9/27/2018) 

Reist, B., Wilson, T., Ball, T. S. and Young, L.J. (2018) Preliminary Findings for the

Agricultural Labor Base Wage Question Experiment. USDA – NASS
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Appendix

Table A1: Unit Response Rate Differences

Region

January April July October 2018

Diff p-value Diff p-value Diff p-value Diff p-value Diff p-value

California April 1.2 0.8126 -0.7 0.7399 0.2 0.9560

Florida 2.9 0.9450 2.9 0.9434 3.2 0.4763 4.6 0.3295 3.5 0.4579

Hawaii -1.3 0.7808 -1.6 0.7405 -0.8 0.6589 -0.8 0.6589 -1.2 0.0120

Northeast I 3.5 0.9220 3.5 0.922 -9.4 0.0080 -9.4 0.0080 -3.3 0.1756

Northeast II -0.8 0.8991 0.5 0.6981 -7.8 0.0878 -7.4 0.1056 -4.2 0.3365

Appalachian I 0.5 0.9439 1.2 0.8134 -5.2 0.1865 -3.8 0.3292 -1.9 0.4230

Appalachian II 2.9 0.8413 1.5 0.8816 2.9 0.3251 1.4 0.5456 2.2 0.0027

Southeast 7.5 0.1398 7.7 0.1295 6.9 0.1812 8.3 0.0942 7.2 0.0270

Lake -4.7 0.0845 -4.7 0.0845 -3.2 0.6506 -3.2 0.6484 -3.9 0.4299

Cornbelt I 5.5 0.1027 6.0 0.0769 -3.1 0.2991 -2.6 0.3672 1.3 0.1191

Cornbelt II 2 0.8762 1.2 0.9746 6.3 0.1095 5.7 0.1446 3.9 <.0001

Delta -3.7 0.2882 -4.0 0.2544 -13.0 0.0006 -14.0 0.0002 -8.8 0.1066

Northern Plains 1.3 0.6216 1.6 0.553 3.5 0.2726 3.4 0.2872 2.5 0.0499

Southern Plains 0.6 0.9900 0.0 0.8913 6.5 0.0443 6.4 0.0477 3.7 0.4593

Mountain I 7.6 0.4334 7.9 0.4044 0.3 0.9316 -0.3 0.9316 3.6 0.4276

Mountain II -8.3 0.0680 -8.3 0.0680 2.7 0.3732 4.4 0.2458 -2.3 0.0918

Mountain III -7.9 0.1293 -7.5 0.1446 1.5 0.8038 2.2 0.8924 -3.0 0.4897

Pacific -1.3 0.4097 0.1 0.6198 -2.9 0.9450 -2.5 0.9865 -1.7 0.4586

US without CA 0.7 0.4496 0.0 0.5069 0.0 0.4798 0.0 0.6295 -0.1 0.194

United States -0.6 0.0388 -0.6 0.0387 -1.0 0.1984
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Table A2: Item Nonresponse Rate Differences for Gross Wages

Region

January April July October

Diff p-value Diff p-value Diff p-value Diff p-value

California -0.6 0.3991 -5.4 0.0006

Florida -3.7 0.4648 -9.1 0.0673 7.9 0.0916 2.4 0.6252

Hawaii -15.3 0.9752 -8.8 0.1819 6.2 0.1445 6.7 0.1308

Northeast I 7.4 0.0650 6.3 0.0522 -2.5 0.5350 -15.9 0.0007

Northeast II 0.1 0.9789 -5.7 0.1907 4.6 0.2721 3.1 0.3688

Appalachian I -1.2 0.7828 3.6 0.2061 -4 0.2235 -2.4 0.4341

Appalachian II 0.1 0.9935 -0.1 0.9771 -2.1 0.5384 0.1 0.9214

Southeast 0.1 0.9836 -0.5 0.901 -2 0.591 -4.2 0.3113

Lake -1.4 0.5744 -2.7 0.226 -4.5 0.465 -2.8 0.6028

Cornbelt I -4.7 0.1601 -6.2 0.0911 2.2 0.8605 -3 0.355

Cornbelt II 1.1 0.8248 1.9 0.6522 -24.4 0.0069 -11.7 0.0863

Delta 10.6 0.0085 8.9 0.0165 -3.3 0.3253 -4.2 0.1894

Northern Plains -3.8 0.5466 -3.8 0.522 -16 0.9646 -11.6 0.0342

Southern Plains 8.4 0.2276 15.6 0.0163 -0.6 0.7561 -10.6 0.0124

Mountain I -2.7 0.4875 -1.3 0.7329 2 0.679 0 0.7517

Mountain II -4.4 0.5534 -6.9 0.3218 8.1 0.503 6.2 0.6145

Mountain III -2.8 0.4943 -7 0.1232 30.6 <.0001 17.1 <.0001

Pacific 9.8 0.0079 13.8 <.0001 -5.5 0.2990 -7.5 0.1636

US without CA 0.2 0.9742 0.2 0.9628 0.2 0.2751 -2.3 0.0020

United States -0.7 0.9617 -3.0 0.0008
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Table A3: Percent Increase in Sample Size Needed

Region January April July October 4 Quarter Percent Average Change

California 0% 0% 0% 0% 0

Florida 0% 0% 71% 26% 0

Hawaii -56% -20% -47% -13% -38

Northeast I 0% 0% 0% 0% 0

Northeast II 0% 0% 0% -38% 0

Appalachian I 87% 0% 9% 0% 15

Appalachian II 0% 0% 0% 0% 0

Southeast 0% 0% 0% 0% 0

Lake 0% 0% 0% 0% 0

Cornbelt I -35% 0% 0% 0% 0

Cornbelt II 0% 0% 0% 0% 0

Delta 0% 0% 0% 0% 0

Northern Plains 0% 0% 0% 0% 0

Southern Plains 230% 166% 0% 0% 21

Mountain I 0% 0% 0% 0% 0

Mountain II -25% 0% 0% 0% 0

Mountain III -57% -60% 26% 0% -27

Pacific 0% 0% 0% 0% 0

United States 9% 8% -4% -3% -3
19 | Page

Table A4: Unweighted Item Nonresponse Rate for Base Wages

Region January April July October

California 28.0 36.2

Florida 20.3 18.6 29.4 27.5

Hawaii 20.7 30.0 29.3 29.3

Northeast I 19.2 22.1 23.5 8.8

Northeast II 41.5 34.0 20.2 13.2

Appalachian I 15.9 22.4 23.6 25.0

Appalachian II 11.1 9.1 23.8 25.6

Southeast 51.6 48.6 12.3 11.1

Lake 26.3 24.2 21.3 19.5

Cornbelt I 29.2 21.6 22.7 22.8

Cornbelt II 34.3 29.3 32.4 20.5

Delta 27.8 25.6 12.2 9.5

Northern Plains 33.3 28.6 3.2 3.4

Southern Plains 15.4 17.2 16.3 9.7

Mountain I 30.8 28.9 30.9 34.5

Mountain II 5.6 0.0 47.6 48.1

Mountain III 3.6 1.8 67.6 51.5

Pacific 46.3 56.7 44.7 41.9

US without CA 27.1 26.6 35.1 33.6

United States 26.8 26.1
20 | Page

Table A5: Weighted Item Nonrespose Rate for Base Wages (Sample Weight)

Region January April July October

California 27.4 33.1

Florida 10.1 8.9 24.3 16.6

Hawaii 15.9 40.7 35.9 32.3

Northeast I 14.4 13.1 16.2 7.2

Northeast II 35.3 31.9 16.1 13.4

Appalachian I 5.2 14.7 33.9 27.3

Appalachian II 27.3 25.8 17.2 21.3

Southeast 32.3 25.7 21.3 15.8

Lake 22.0 22.3 16.2 15.2

Cornbelt I 14.5 13.3 21.4 9.1

Cornbelt II 31.7 29.5 6.1 5.7

Delta 21.7 23.4 19.2 18.6

Northern Plains 13.8 11.9 0.8 0.8

Southern Plains 23.0 18.2 10.2 19.5

Mountain I 34.1 44.4 7.2 8.8

Mountain II 16.0 0.0 20.0 34.4

Mountain III 2.9 0.6 75.5 58.5

Pacific 16.2 27.0 33.4 21.0

US without CA 20.4 19.8 23.1 20.6

United States 20.6 18.6
21 | Page

Table A6: Weighted Item Nonrespose Rate for Base Wages (Nonresponse Weight)

Region January April July October

California 27.3 32.9

Florida 12.1 10.4 23.8 17.3

Hawaii 15.1 42.1 37.6 33.4

Northeast I 11.8 11.8 18.4 7.4

Northeast II 37.3 32.1 9.0 7.9

Appalachian I 5.7 14.9 34.1 27.0

Appalachian II 24.3 22.5 20.3 23.8

Southeast 34.2 27.1 13.7 12.0

Lake 22.1 21.9 20.0 19.5

Cornbelt I 16.5 15.0 20.2 8.4

Cornbelt II 30.7 30.2 12.8 6.9

Delta 23.1 24.5 16.8 17.4

Northern Plains 13.7 11.7 1.5 1.5

Southern Plains 22.3 18.0 10.2 18.9

Mountain I 40.9 50.3 8.3 9.9

Mountain II 11.5 0.0 20.1 36.3

Mountain III 2.5 0.7 74.3 56.7

Pacific 23.0 33.3 32.8 24.4

US without CA 20.7 20.3 21.6 19.4

United States 19.8 17.7
22 | Page

Table A7: Imputation Rate for Base Wages

Region January April July October

California 24.7 35.7

Florida 0.0 0.0 22.1 20.5

Hawaii 7.8 13.7 44.7 27.1

Northeast I 10.5 13.5 0.0 0.0

Northeast II 4.7 4.3 0.1 0.4

Appalachian I 5.1 5.2 16.0 20.6

Appalachian II 14.3 14.2 12.7 12.8

Southeast 0.0 0.0 9.5 6.6

Lake 2.6 6.6 0.2 0.1

Cornbelt I 18.9 11.8 0.0 0.0

Cornbelt II 12.7 14.3 11.5 1.7

Delta 0.0 0.6 0.0 0.0

Northern Plains 47.3 45.1 0.0 0.0

Southern Plains 9.0 8.7 0.0 1.8

Mountain I 21.5 29.8 47.6 34.2

Mountain II 8.0 7.3 12.1 6.3

Mountain III 8.9 3.8 24.2 10.7

Pacific 44.8 58.2 4.9 8.3

US without CA 13.4 26.6 9.8 8.5

United States 13.7 15.7
23 | Page

Table A8: Item Nonresponse Formulas

Type Definition

Unweighted

∑iεF ∑jεRi

uij

r

Sampling Weight

∑ ∑ wi

S

iεF jεRi

uij

∑ ∑ wi

S

jεRiiεF

Nonresponse Adjusted Weight

∑ ∑ wi

A

iεF jεRi

uij

∑ ∑ wi

A

jεRiiεF

Imputation Rate

∑ ∑ wi

Auijt iεF jεRi

ij

∑ ∑ wi

A

t iεF jεRi

ij

F = the set of farms who responded

Ri = the set of rows for farm i

uij = the indicator that value of the given item is unusable in row j for farm i

r = the total number of rows across all responding farms

wi

S = the sampling weight for farm i

wi

A = the sampling weight for farm i

tij = the value of the given item given item in row j for farm i (Note: this could be imputed)