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1

 United States 

 Department of 

 Agriculture 

 

 National 

 Agricultural 

 Statistics 

 Service 

 Research and 

 Development Division 

 Washington DC 20250 

 September 2018 

 (Revised -March 2019) 

 

 

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

Preliminary Findings for 

the April 2018 

Agricultural Labor Base 

Wage Question 

Experiment 

Benjamin M. Reist 

Deputy Director 

Research and Development Division 

Tyler Wilson 

Survey Methodologist 

Survey Methodology and Technology Section 

Research and Development Division 

Shane T. Ball 

Section Head 

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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1. INTRODUCTION 

The U.S. Department of Agriculture’s (USDA’s) 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, 

and incentive/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 

• Three forms of a weighted item nonresponse rates on base wages: 

o Using the sampling weight 

o Using the nonresponse adjusted weight 

o The imputation rate 

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

of the experiment. The following are the meaningful difference/levels of each of the measures: 

• Unit response rates reduction of 10 percent or more 

• Sample size increase of 10 percent or more 

• Item nonresponse rate on gross wages of 10 percent or more 

• Item nonresponse rate on base wages of 40 percent or more 

• Weighted item nonresponse rates on base wages of 25 percent or more 

This is a preliminary report of some of the results of the experiment in the April 2018 

administration of the Agricultural Labor Survey. 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 described in Section 3. The methodology used to 

analyze the data from the experiment is discussed in Section 4. The preliminary findings are 

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

limitations and implications for the 2018 October experiment. A final report will be produced 

after the results from the October 2018 survey are published. 

2. AGRICULTURAL LABOR SURVEY

NASS conducts the Agricultural Labor Survey twice annually. The purpose of the survey is to 

determine the types and number of farm workers 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 
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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 semi-annual Farm Labor release, issued around the 21st 

of May and November. Both semi-annual 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 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 stratified random design. Strata within states are based on peak number 

of hired workers, presence of 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 coefficients of variation (CV) of the estimated quarterly gross wage rates of 1% at 

the national level and of 2% at the agricultural labor region level. The sample is allocated to meet 

or exceed these targets. 

2.2. Data Collection 

A self-administered paper questionnaire 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 reported 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 
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and October. Phone and field enumerations begin the Sunday after the reference week, and 

continue for approximately two and a half weeks. 

2.2.1. California 

NASS and the California Employment Development Department (EDD) have a joint cooperative 

agreement to conduct a monthly California Agricultural Labor Survey by mail. In the months 

that the national survey is conducted (October and April), the NASS sample is used in both the 

NASS and EDD preliminary estimates, while the EDD supplemental sample is used only to 

enhance EDD’s regional estimates. In the other months, California’s entire list sample 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 

Agricultural Service operations. The California State 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 portion of the California sample that is used for the 

Agricultural Labor Survey Estimates. 

3. EXPERIMANTAL 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 to collect information on gross 

wages, base wages and incentive/overtime wages. 

Version 1 is referred to as the control; version 2 is the treatment. For the study, in addition to the 

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

October 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 version 2; the remaining five 

April replicates were assigned to version 1. 

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

strategies. 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 five 

DCCs. 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 in California. Additionally, the Wyoming DCC conducted phone follow-up for the

California sample for both versions. 
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4. METHODOLOGY 

The experiment was evaluated using the following criteria mentioned in the Introduction: 

• 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 

• Three forms of a weighted item nonresponse rates on base wages: 

o Using the sampling weight 

o Using the nonresponse adjusted weight 

o The imputation rate 

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

of the experiment as follows: 

• Unit response rates reduction of 10 percent or more 

• Sample size increase of 10 percent or more 

• Unweighted item nonresponse rate on gross wages of 10 percent or more 

• Unweighted item nonresponse rate on base wages of 40 percent or more 

• Weighted item nonresponse rates on base wages of 25 percent or more 

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

comparisons between the two versions of the questionnaire, logistic regression was performed to 

assess whether the differences in the rates between the two versions were statistically significant. 

The other measure only pertains to version 2, so no statistical testing can be performed. 

4.1.Unit Nonresponse 

For unit nonresponse rates, three logistic models were used to evaluate the statistical significance 

of the differences between responses at the regional and national levels. 

Separate analyses were conducted for January and April. Because the base wage question was 

not asked in California, the January model did not include any cases from California. In April, 

two models were used, one that included the cases from California and one that excluded the 

cases from California. This second model was used since the California data collection is 

substantively different from the rest of the United States as discussed in section 2.2.1. All three 

of the models used version, region and version crossed with region as the predictors. 

4.2.Item Nonresponse Rates on Gross Wages 

The models of item nonresponse rates for gross wages are similar to the models presented in 

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

unusable item. (2) Each operation can report multiple worker classes. Because gross wages are 

collected for each worker class, an observation is recorded for each operation and reported 
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worker class combination. (3) Finally, since there can be multiple reports of gross wages by an 

operation, a random effect could be used to account for any interoperation correlation. However, 

almost 50 percent of operations only report one worker class, making the random effect for 

operation non-estimable. Not using this random effect leads to an increase in the variances, 

resulting in more conservative tests. 

4.3.Additional Metrics 

The other three metrics are calculated and compared to the levels specified above. 

5. FINDINGS 

This section reports the preliminary findings of the April 2018 Labor Survey Experiment and is 

organized around the five metrics laid out in the introduction. 

• 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. 

Nonresponse Models 

Predictor January April 

U.S. without California U.S. without California All of the U.S.

Version 0.4496 0.5069 0.0463

Region <0.0001 <0.0001 <0.0001

Version*Region 0.4409 0.4023 <0.0001

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

results of the U.S. response rate analysis without California are similar for both January and 

April. The lack of a significant interaction between version and region indicates that the response 

rate differences in versions do not vary significantly across regions. Although there is a 

significant difference in response rates among regions, the response rates between the two 
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versions is not significant (see Table A1 for more detail). In April, when California is included in 

the model version, region and the interaction between version and region are statistically 

significant at the 5 percent level. The 23.9 percent reduction in the California unit response rate 

for the treatment compared to the control is the only regional difference between versions that is 

significant at the 5 percent level (p = <0.0001) (see Table A2). It is also the only reduction in 

regional level response that is above the meaningful level of a reduction of 10 percent or more. 

The decrease in California led to a statistically significant decrease in the U.S. unit response rate 

although the observed decrease of 1 percent was well below the 10 percent reduction considered 

meaningful. In summary, the only region with an observed decrease in unit nonresponse 

exceeding 10 percent from version 1 to version 2 is California, which occurred in April. 

 

5.2.Sample Size Increase 

The sample size changes needed to meet CV targets for total gross wages (both regional and 

national) based on the version 2 response rates, are displayed in Tables A3 in the appendix. To 

meet the sample size for the national sample, the total size would need to be increased nationally 

by 9 percent based on January estimated CVs and 8 percent based on April estimated CVs. Most 

sample increases fall below the 10 percent meaningful increase. 

The Southern Plains and Appalachian I regions are the only regions that need an increase in 

sample in either April or January. Four regions in January and two regions in April could reduce 

their sample sizes and still meet the CV targets. 

5.3.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. 

Gross-wages Unusable Reports Models 

Predictor January April 

U.S. without California U.S. without California All of the U.S.

Version 0.9742 0.2751 0.9698 

Region <0.0001 0.0004 0.0006

Version*Region 0.1832 0.0001 0.0002

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

January, neither the interaction between version and region nor the main effect of version is 

significant. However, the Delta region had a 10.6 percent increase in item nonresponse, which 
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was significant at the 5 percent level (p = 0.0085). Since this is an increase larger than 10 

percent, this increase is considered meaningful (see Table A4 in the appendix). 

For April, whether considering the United States with or without California, there is a significant 

interaction between version and region. Three regions had a significant increase in item 

nonresponse for gross wages at the 5 percent confidence level, the Delta region at 8.9 percent (p

= 0.0166), the Pacific region at 13.8 percent (p < 0.0001) and the Southern Plains region at 15.6 

percent (p = 0.0163). The increases in the Pacific and Southern Plains regions are considered 

meaningful because they are larger than 10 percent (see Table A4 in the appendix). 

5.4.Base Wage Item Nonresponse 

For measures of base wage, the following item nonresponse rates were considered: 

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 in the appendix. One issue with using 

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

outcome variables is that the sampling weight is usually inversely related to the contribution 

record of the estimate. This is because to reduce sampling error, units that have a higher 

likelihood of having large values, in this case wages, are sampled at a higher rate than units that 

do not. A traditional weighted item nonresponse rate gives more weight to missing values with 

higher weights that 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 an item nonresponse rate for business and establishment surveys as 

an alternative to a weighted item nonresponse rate (see appendix D3-B of the U.S. Census 

Bureau Statistical Quality Standards, 2013). This rate is the proportion of the estimate that is 

based on imputed values. 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 show a meaningful level of item nonresponse for 

base wages. In April, only the imputation rate rises to a meaningful level for both the United 

States with or without California at 28.4 percent and 26.6 percent respectively.

The following table shows the percentage of the regions that show a meaningful level of item 

nonresponse in base wages (base wages pertains only to version 2 so no statistical testing was 

performed). 
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 Item Nonresponse Rate 

Month Unweighted Sampling Weight 

Nonresponse 

Weight Imputation Rate 

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

April 3 (16.7%) 8 (44.4%) 7 (38.9%) 4 (22.2%) 

The four measures show that item nonresponse to base wages was a bigger issue in April 

compared to January (see Tables A6 and A7). Additionally, in both January and April, fewer 

regions were identified by the imputation rate when compared to either of the weighted 

measures. This implies that item nonresponse is more of an issue for operations with the larger 

weights and lower levels of base wages. Finally, there are only two instances where all four 

measures rise to a meaningful level, the California and Pacific regions in April. These regions 

had April imputation rates of 59.2 percent and 58.2 percent, respectively. 

6. CONCLUSION 

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

wage, e.g. base pay, as well as incentives (including overtime pay), also be collected. To assess 

the potential impact of such a change, a new version of the questionnaire with questions to 

acquire the additional information was compared to the current version. In this report, statistical 

findings indicate very few differences in response rates between versions at the national level. In 

addition, some results in the amount of usable data collected for base wages indicate possible 

issues that will require further research. As is true with any experiment performed in an 

operational environment, there were limitations that need to be addressed. 

6.1. Limitations 

The treatment survey sample was administered in one call center, whereas the control survey 

sample was evenly distributed between the remaining four call centers. Thus, interviewer effects 

are confounded with version, which could explain some of the differences or lack of differences 

found in response rates and usable data. 

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 

questions. Some interviewers were found reverting to the original base wage wording and not 

reading the new questions. A final report on this research is pending. 

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. This led to two analyses 
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for the April survey: (1) analyzing all of the United States and (2) considering separate analyses 

for California and the rest of the United States. 

In the upcoming October 2018 Agricultural Labor Survey Experiment, the first two limitations 

have been addressed. To allow for an evaluation of potential call-center effects, both versions of 

the questionnaire will be administered in each of the data collection centers. Additionally, based 

on the behavior coding and prior cognitive and usability 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. Finally, interviewer training was 

updated to provide clearer guidance on how to administer the questionnaire. 

6.2. Final Summary 

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

wage question for the United States, excluding California are: 

1. Response Rates 

• No significant impact in response rates between survey versions 

• No significant impact in item nonresponse for gross wages at the national level 

although some differences are observed at the regional level 

• Significant and meaningful impacts in item nonresponse rates for gross wages for 

a few regions 

• At the national level, only one of the four measures of item nonresponse for base 

wages rose to a meaningful level in April and none for January. 

• The national samples would need to be increased to meet NASS CV targets, but

this increase is less than 10 percent 

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

nonresponse for base wage in the Pacific region. 

Even though the new version of the questionnaire satisfied the criteria for implementation for all 

but one of the national measures, there is evidence that there could be some issues between 

versions at the regional level when reporting base wages. This is particularly true in California 

and the Pacific region. Regional level analysis will need to be a focus of the 2018 October 

Agricultural Labor Base Wage Experiment. Additionally, more research needs to be done to 

better understand why the new version of the questionnaire performed so poorly in California. 

 
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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) 
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Appendix

Table A1: January Overall Unit Response Rate Treatment versus Control by Region 

Region 

Sample Size Response Rate 

Control Treatment Control Treatment Difference P-value 

Florida 419 152 53.0% 55.9% 2.9% 0.9220

Hawaii 351 124 47.3% 46.0% -1.3% 0.9450

Northeast I 591 209 46.7% 50.2% 3.5% 0.7808

Northeast II 445 152 52.8% 52.0% -0.8% 0.8991

Appalachian I 546 184 53.8% 54.3% 0.5% 0.9439

Appalachian II 633 220 58.9% 61.8% 2.9% 0.8413

Southeast 553 175 58.2% 65.7% 7.5% 0.1398

Lake 696 233 56.6% 51.9% -4.7% 0.0845

Cornbelt I 753 260 52.6% 58.1% 5.5% 0.1027

Cornbelt II 639 231 52.6% 54.5% 2.0% 0.8762

Delta 669 198 66.8% 63.1% -3.7% 0.2883

Northern Plains 895 288 49.1% 50.3% 1.3% 0.6216

Southern Plains 913 269 50.7% 51.3% 0.6% 0.9900

Mountain I 347 122 48.1% 55.7% 7.6% 0.4334

Mountain II 296 110 57.4% 49.1% -8.3% 0.0680

Mountain III 285 102 53.0% 45.1% -7.9% 0.1293

Pacific 536 184 45.9% 44.6% -1.3% 0.4097

U.S. without CA 9,567 3,213 53.3% 53.9% 0.7% 0.4496

 
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Table A2: April Overall Unit Response Rate Treatment versus Control by Region 

Region 

Sample Size Response Rate 

Control Treatment Control Treatment Difference P-value 

California 706 259 66.0% 42.1% -23.9% <.0001

Florida 419 152 53.7% 56.6% 2.9% 0.9434

Hawaii 351 124 47.6% 46.0% -1.6% 0.7405

Northeast I 591 209 46.7% 50.2% 3.5% 0.9220

Northeast II 445 152 52.8% 53.3% 0.5% 0.6981

Appalachian I 546 184 53.7% 54.9% 1.2% 0.8134

Appalachian II 633 220 59.9% 61.4% 1.5% 0.8816

Southeast 553 175 58.0% 65.7% 7.7% 0.1295

Lake 696 233 56.6% 51.9% -4.7% 0.0845

Cornbelt I 753 260 52.9% 58.8% 6.0% 0.0769

Cornbelt II 639 231 52.9% 54.1% 1.2% 0.9746

Delta 669 198 67.1% 63.1% -4.0% 0.2545

Northern Plains 895 288 49.1% 50.7% 1.6% 0.5531

Southern Plains 913 269 51.3% 51.3% 0.0% 0.8914

Mountain I 347 122 47.8% 55.7% 7.9% 0.4044

Mountain II 296 110 57.4% 49.1% -8.3% 0.0680

Mountain III 285 102 52.6% 45.1% -7.5% 0.1446

Pacific 536 184 45.0% 45.1% 0.1% 0.6198

U.S. without CA 9,567 3,213 53.4% 54.1% 0.7% 0.5069

United States 10,273 3,472 54.3% 53.2% -1.0% 0.0463

 
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Table A3: Percentage Change in Sample Sizes Needed to Meet CV Targets

Region

% Difference 

January April 

California N/A 0

Florida 0 0

Hawaii -56 -20

Northeast I 0 0

Northeast II 0 0

Appalachian I 87 0

Appalachian II 0 0

Southeast 0 0

Lake 0 0

Cornbelt I -35 0

Cornbelt II 0 0

Delta 0 0

Northern Plains 0 0

Southern Plains 230 166

Mountain I 0 0

Mountain II -35 0

Mountain III -57 -60

Pacific 0 0

United States 9 8

 
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Table A4: Item Nonresponse for Gross Wages January 2018 

Region 

Total Rows Percent of Rows with Missing Gross Wages 

Control Treatment Control Treatment Difference P-Value 

Florida 224 68 17.0% 13.2% -3.7% 0.4649

Hawaii 222 29 15.3% 0.0% -15.3% 0.9752

Northeast I 266 52 6.0% 13.5% 7.4% 0.0650

Northeast II 185 42 7.0% 7.1% 0.1% 0.9789

Appalachian I 249 44 8.0% 6.8% -1.2% 0.7828

Appalachian II 163 36 5.5% 5.6% 0.1% 0.9935

Southeast 206 62 16.0% 16.1% 0.1% 0.9836

Lake 231 79 3.9% 2.5% -1.4% 0.5745

Cornbelt I 193 67 6.2% 1.5% -4.7% 0.1602

Cornbelt II 121 35 7.4% 8.6% 1.1% 0.8248

Delta 251 73 7.2% 17.8% 10.6% 0.0085

Northern Plains 234 39 16.7% 12.8% -3.8% 0.5467

Southern Plains 166 26 10.8% 19.2% 8.4% 0.2276

Mountain I 152 39 5.3% 2.6% -2.7% 0.4875

Mountain II 120 18 10.0% 5.6% -4.4% 0.7329

Mountain III 172 56 8.1% 5.4% -2.8% 0.3219

Pacific 255 67 5.1% 14.9% 9.8% 0.1233

U.S. without CA 3,410 754 9.2% 9.4% 0.2% 0.9742

 
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Table A5: Item Nonresponse for Gross Wages April 2018 

Region 

Total Rows Percent of Rows with Missing Gross Wages 

Control Treatment Control Treatment Difference P-Value 

California 1,048 266 0.4% 0.0% -0.4% 0.9761

Florida 221 69 16.3% 7.2% -9.1% 0.0674

Hawaii 223 30 12.1% 3.3% -8.8% 0.1820

Northeast I 299 77 5.4% 11.7% 6.3% 0.0522

Northeast II 224 54 9.4% 3.7% -5.7% 0.1908

Appalachian I 287 67 3.8% 7.5% 3.6% 0.2061

Appalachian II 180 55 5.6% 5.5% -0.1% 0.9771

Southeast 236 74 12.7% 12.2% -0.5% 0.9010

Lake 269 94 3.7% 1.1% -2.7% 0.2260

Cornbelt I 227 76 8.8% 2.6% -6.2% 0.0911

Cornbelt II 147 41 5.4% 7.3% 1.9% 0.6523

Delta 363 86 8.5% 17.4% 8.9% 0.0166

Northern Plains 254 42 15.7% 11.9% -3.8% 0.5220

Southern Plains 176 29 8.5% 24.1% 15.6% 0.0163

Mountain I 174 45 5.7% 4.4% -1.3% 0.7329

Mountain II 136 24 11.0% 4.2% -6.9% 0.3219

Mountain III 171 57 10.5% 3.5% -7.0% 0.1233

Pacific 330 90 3.9% 17.8% 13.8% <.0001

U.S. without CA 3,917 1,010 8.5% 8.7% 0.2% 0.7897

United States 4,965 1,276 6.7% 6.9% 0.1% 0.8499

 
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Table A6: Item Nonresponse for Base Wages January 2018 

Item Nonresponse Rates 

Region Total 

Rows 

Unweighted Sampling 

Weight 

Nonresponese 

Weight 

Imputation 

Rate 

Florida 69 20.30% 10.1% 12.1% 0.0%

Hawaii 29 20.70% 15.9% 15.1% 7.80%

Northeast I 52 19.20% 14.4% 11.8% 10.50%

Northeast II 41 41.50% 35.3% 37.3% 4.70%

Appalachian I 44 15.90% 5.2% 5.7% 5.10%

Appalachian II 36 11.10% 27.3% 24.3% 14.30%

Southeast 62 51.60% 32.3% 34.2% 0.0%

Lake 80 26.30% 22.0% 22.1% 2.60%

Cornbelt I 65 29.20% 14.5% 16.5% 18.90%

Cornbelt II 35 34.30% 31.7% 30.7% 12.70%

Delta 72 27.80% 21.7% 23.1% 0.0%

Northern Plains 39 33.30% 13.8% 13.7% 47.30%

Southern Plains 26 15.40% 23.0% 22.3% 9.00%

Mountain 39 30.80% 34.1% 40.9% 21.50%

Mountain II 18 5.60% 16.0% 11.5% 8.00%

Mountain III 55 3.60% 2.9% 2.5% 8.90%

Pacific 67 46.30% 16.2% 23.0% 44.80%

National (no CA) 829 27.10% 20.4% 20.7% 13.40%
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Table A7: Item Nonresponse for Base Wages April 2018

Item Nonresponse Rates 

Region 

Total 

Rows Unweighted

Sampling 

Weight 

Nonresponese 

Weight 

Imputation 

Rate 

California 265 60.0% 60.4% 60.8% 59.2%

Florida 70 18.6% 8.9% 10.4% 0.0%

Hawaii 30 30.0% 40.7% 42.1% 13.7%

Northeast I 77 22.1% 13.1% 11.8% 13.5%

Northeast II 53 34.0% 31.9% 32.1% 4.3%

Appalachian I 67 22.4% 14.7% 14.9% 5.2%

Appalachian II 55 9.1% 25.8% 22.5% 14.2%

Southeast 74 48.6% 25.7% 27.1% 0.0%

Lake 95 24.2% 22.3% 21.9% 6.6%

Cornbelt I 74 21.6% 13.3% 15.0% 11.8%

Cornbelt II 41 29.3% 29.5% 30.2% 14.3%

Delta 86 25.6% 23.4% 24.5% 0.6%

Northern Plains 42 28.6% 11.9% 11.7% 45.1%

Southern Plains 29 17.2% 18.2% 18.0% 8.7%

Mountain 45 28.9% 44.4% 50.3% 29.8%

Mountain II 24 0.0% 0.0% 0.0% 7.3%

Mountain III 56 1.8% 0.6% 0.7% 3.8%

Pacific 90 56.7% 27.0% 33.3% 58.2%

U.S. without CA 1,008 26.6% 19.8% 20.3% 26.6%

United States 1,273 33.5% 23.1% 24.5% 28.4%

 

 
19 | P a g e

Table A8: Item Nonresponse Formulas 

Type Definition 

Unweighted 

∑ ∑



	

Sampling Weight 

∑ ∑ 




 



∑ ∑ 






Nonresponse Adjusted Weight 

∑ ∑ 




 



∑ ∑ 






Imputation Rate 

∑ ∑ 



  



∑ ∑ 





  



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)