Farm Labor Methodology and Quality Measures - May 2023

Farm Labor Methodology and Quality
Measures
ISSN: 2167-1893

Released May 24, 2023, by the National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United States Department of
Agriculture (USDA).

Agricultural (Farm) Labor Survey Methodology

Scope and Purpose: The NASS Agricultural (Farm) Labor Survey provides the basis for employment and wage estimates
for all workers directly hired by United States farms and ranches (excluding Alaska), for each of four quarterly reference
weeks. Selected annual average estimates are summarized from the associated quarterly estimates.
The employment and wage estimates published support USDA and Department of Labor programs, and are used by
additional federal, state, and local government agencies, educational institutions, farm organizations, and private sector
employers of farm labor. The Agricultural Labor Survey was conducted in cooperation with the Department of Labo r
from year 2012 through the temporary suspension of the program in September 2020.
The NASS Agricultural Labor Survey is typically conducted semi-annually in April and October, in all surveyed states
except California. For California, these state-level data are collected on a quarterly basis, as part of the California
Employment Development Department (EDD) monthly labor program.
During the NASS Agricultural Labor Survey April data collection, data for both January and April reference weeks are
collected. During the October data collection, data for both July and October reference weeks are collected. The quarterly
reference week is the Sunday to Saturday period which includes the 12th day of the month.
Estimates published include number of hired workers during each quarterly reference week, the average hours worked,
and average wage rates by type of worker. Estimates are published for the United States, each of 15 multi-state labor
regions, and the single-state regions of California, Florida, and Hawaii.
Survey Timeline: Data collection typically begins the week following the April or October reference week and extends
approximately one week beyond the end of the month, for all surveyed states except California. In California, EDD data
collection begins shortly after the reference week in each of the four surveyed quarters and typically extends three to four
weeks beyond the end of the month. NASS Regional Field Offices (RFOs) have about five business days following the
NASS semi-annual data collection period, to complete editing and analysis, execute the summary, and evaluate the survey
results at the state level for each member state. The Agricultural Statistics Board performs a national review, reconciles
state-level evaluations to regional and national estimates, and prepares the official estimates for release in about eight to
ten business days. Official Farm Labor estimates are typically published in May (for the January and April quarters) and
November (for the July and October quarters, and annual average estimates).
Sampling: The target population for the Agricultural Labor Survey program is all farms and ranches with $1,000 or more
in agricultural sales (or potential sales), excluding Alaska farms. NASS uses a dual frame approach, consisting of list
frame and area frame components, to provide complete coverage of this target population.
The list frame includes all known agricultural establishments. A profile, called control data, of each establishment is
maintained on the list frame to allow NASS to define list frame sampling populations for specific surveys and to employ
efficient sampling designs. The primary control datum for farm labor is the peak number of workers value, the most
recently reported annual peak number of hired workers for each record. List frame records with positive peak number of
worker control data are included in the list frame farm labor population. The California list frame labor population defined
by positive peak number of workers alone is sufficiently complete, due to the collaboration with the California EDD. For

all other states, records without peak number of worker control data that have a calculated farm value of sales of at least
$10,000, many of which are expected to employ agricultural workers, are also part of the list frame farm labor population.
In total, the list frame farm labor population includes approximately 1.1 million United States farms and ranches.
The area frame contains all land in the United States (except Alaska) and is therefore complete for the Agricultural Labor
Survey program. For all states, land is stratified according to intensity of agriculture using satellite imagery. The land in
each stratum is divided into segments of roughly one square mile. Segments are optimally allocated and sampled to
effectively measure crops and livestock. The sampled segments are fully enumerated each June during the NASS June
Area Survey, in all states except Hawaii. All farms and ranches found operating tracts in enumerated segments are
checked to see if they are included in the list frame farm labor population. The farms and ranches that are not included in
the list frame labor population, called nonoverlap tracts, are eligible for the farm labor nonoverlap sample.
The farm labor list frame sample is selected using a hierarchical stratified sampling design with strata defined by peak
number of farm workers or calculated farm value of sales. The sample is a panel sample and is designed to achieve a
United States level coefficient of variation of four percent of the point estimate for all hired workers and one percent of
the ratio estimate for wage rates of all workers. The United States list frame sample size was temporarily increased to over
35,000 operations to accommodate a program expansion implemented for mid-years 2019 through 2021. Beginning with
the July and October 2021 survey, an optimal list frame sample size of over 16,000 operations was derived both in
accordance with a program contraction, and adjustment for declining survey participation rates.
The farm labor area frame nonoverlap sample is selected using a stratified sample design based on data collected during
the annual NASS June Area Survey. An area frame nonoverlap sample is selected for each surveyed state except
California and Hawaii. The California sample does not include a nonoverlap portion because the list frame is assumed to
be complete. For Hawaii, the area frame is excluded from sampling because this frame is not updated on an annual basis.
The total farm labor area frame nonoverlap sample, which includes the remaining surveyed states, consists of
approximately 1,500 sampling units.
Each farm and ranch in this combined sample is assigned an initial sampling weight. For each farm or ranch sampled from
the list frame, this weight is the inverse of the sampling fraction for the state level stratum to which the sampled farm or
ranch is assigned. For example, if a stratum has 1,000 farms in the population and 200 are sampled for this survey, each
sampled farm has a weight of five. In other words, each sampled farm represents five farms. The nonoverlap tracts
sampled to measure the labor not accounted for by the list have a weight determined by adjusting their original area frame
weight by any second stage sampling weight.
Data Collection: Data collection proceeds with utilization of NASS data collection instruments and follow-up
procedures, in all surveyed states except California. In California, the California EDD conducts data collection utilizi ng
EDD-specific instruments and follow-up procedures which are similar to NASS procedures.
Data Collection for All States Except California
For consistency across modes, the paper version is considered the master questionnaire and the Computer Assisted
Telephone Interview (CATI) and web reporting instruments are built to model the paper instrument. Questionnaire
content and format are evaluated annually through a specifications process where requests for changes are evaluated and
approved or disapproved. Input may vary from question wording or formatting to a program change involving the deletion
or modification of current questions or addition of new ones. If there are significant changes to either the content or
format proposed, a NASS survey methodologist will pre-test the changes for usability. Prior to the start of data collection,
all instruments are reviewed including the CATI and web instruments, and the web-supported Computer Assisted
Personal Interview (CAPI) instrument.
All federal data collections require approval by the Office of Management and Budget (OMB). NASS must document the
public need for the data, show the design applies sound statistical practice, ensure the data do not already exist elsewhere,
and ensure that the public is not excessively burdened. The Agricultural Labor Survey questionnaire must display an
active OMB number that gives NASS the authority to conduct the survey, a statement of the purpose of the survey and the
use of the data being collected, a response burden statement that gives an estimate of the time required to complete the

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form, a confidentiality statement that the respondent’s information will only be used for statistical purposes in
combination with other producers, and a statement saying that response to the survey is voluntary and not required by law.
In addition to asking the specific farm labor items, all instruments collect information to verify the sampled unit,
determine any changes in the name or address, identify any partners to detect possible duplication, verify the farm still
qualifies for the target population, and identify any additional operations operated by the sampled operator.
During each semi-annual data collection period, sampled farms and ranches receive a pre-survey letter explaining the
survey and that they will be contacted for survey purposes only. The letter provides a paper copy of the questionnaire to
allow respondents to respond by mail or to prepare in advance for a follow-up interview and also provides a pass code
they can use to complete the survey on the internet. All questionnaires completed on paper are returned to the NASS
National Operations Center where they are visually reviewed, and key entered. Typically, all modes of data collection are
utilized for the Agricultural Labor Survey. While mail is a low-cost mode of collection, the short data collection period,
combined with the postal delivery window, limit its effectiveness. In most years, the majority of the data are collected by
telephone follow-up interviews from NASS Data Collection Centers, using CATI. Personal interviews, conducted via
CAPI, are also typically available for large operations or those with special handling arrangements. Data collection is
coordinated for farms sampled for multiple on-going NASS surveys.
Data Collection for California
The California EDD utilizes a mail out phase with follow-up similar to the NASS procedure. All sampling units from the
NASS California labor sample receive an EDD labor questionnaire which includes the NASS Agricultural Labor Survey
questions as well as additional content. In recent years, telephone follow-up has been conducted by the associated NASS
Data Collection Center.
Because the EDD program is monthly, collection of California labor data begins shortly after each respective quarterly
reference week, and complete datasets are received at NASS by the following month. The EDD data collection period
typically extends two to three weeks beyond the concurrent NASS April and October data collection periods. For this
reason, the final California datasets for the April and October reference weeks are not fully processed until the following
NASS semi-annual survey period.
Throughout the EDD data collection period, electronic files containing labor data are regularly transmitted securely to the
NASS Pacific RFO in California, including that collected in the NASS Data Collection Center. These files are made
available to the NASS editing and analysis instruments, so that all subsequent data handling proceeds according to the
NASS data analysis and estimation program.
Survey Edit: As survey data are collected and captured, data are edited for consistency and reasonableness using
automated systems. Reported data are edited as a batch of data when first captured. The edit logic ensures the coding of
administrative data (i.e. sampled unit and other survey-level control data) follows the methodological rules associated
with the survey design. Relationships between data items (i.e. responses to individual questions) on the current survey are
verified. Some data items in the current survey are compared to data items from earlier surveys to ensure certain
relationships are logical. The edit assigns a status to each record, indicating whether or not the record passes or fails the
edit requirements for consistency and reasonableness. Records that fail edit requirements must be updated or must be
certified by an analyst to be exempt from the failed edit requirement. All records must pass edit requirements, or be
certified exempt, before further analysis and summary.
Analysis Tools: Edited Agricultural Labor Survey data are processed and analyzed with a standard interactive data
analysis tool which displays data for all reports by item. The tool provides scatter plots, tables, charts, and special
tabulations that allow the analyst to compare record level data with previously reported data for the same record, and
reported data from similar records. Atypical responses, unusual data relationships, and statistical outliers for all labor
items are revealed by the analysis tool. RFO and NASS Headquarters (HQ) staff review such relationships to determine if
they are correct. Data found to be in error are corrected, while accepted data are retained.
Nonsampling Error: Nonsampling error is present in any survey process. This error includes reporting, recording, and
editing errors, as well as nonresponse error. Steps are taken to minimize the impact of these errors, such as questionnaire
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testing, comprehensive interviewer training, validation and verification of processing systems, application of detailed
computer edits, and evaluation of the data via the analysis tool. The respondent pool is monitored and reviewed during
and after data collection, and data collection strategies modified where necessary, to continually minimize nonresponse
error.
Nonresponse Adjustment: Response to the Agricultural Labor Survey is voluntary. Some producers refuse to participate
in the survey, others cannot be located during the data collection period, and some submit incomplete reports. These
nonrespondents must be accounted for if accurate estimates of farm labor are to be made. Sample nonrespondents are
accounted for by adjusting the weights of the respondents; specifically, the initial sampling weight is revised to account
for actual response by stratum. To continue the previous example, if a list frame stratum has 1,000 farms in the population
and 200 are sampled for this survey, the initial sampling weight is five. After 180 of the original 200 respond, the weights
of the 180 will be “adjusted” to 1,000 divided by 180, or 5.56. Each response accounts for 5.56 farms, a slight increase
from the initial five farms. This global weight adjustment occurs by each assigned stratum per state, including both the
bounded strata as well as the unbounded stratum, as all strata represent homogeneous groupings of similar sized farms.
Calibration: After nonresponse-adjusted weights are generated through initial summarization, calibration adjustments are
performed if approved by the Agricultural Statistics Board representatives for the Farm Labor program, a panel of senior
statisticians and program specialists. Calibration is a weighting technique used to adjust the sampling weights on complete
survey reports so that the summarized values of a set of benchmark variables more closely approximate a derived set of
values for the population. The initial inputs to the calibration algorithm are the nonresponse-adjusted sampling weights.
The calibration algorithm, in targeting a derived set of values, is used to mitigate both the effects of highly influential
outliers, and the effects of survey nonresponse and disproportionate response across farm type and economic sales class.
The results of any necessary calibration are input to the final summary indications and model-based estimates production.
Estimators: The Agricultural Labor Survey uses “reweighted” estimators to compute direct measures of hired farm
workers. Reweighted estimators are essentially the product of non-response and calibration-based reweighting processes.
Each such point estimate, called a direct expansion, is calculated by multiplying each reported value by the final
calibrated weight and first summing to stratum totals. A variance estimate is also computed at the stratum level. The
nonoverlap tracts are treated as an additional stratum. Totals and variances are additive across strata.
Ratio estimates are also computed for many items. For example, wage rates are calculated as the ratio of total wa ges to
total hours worked. Ratio estimators use the reweighted estimator described above for the numerator and denominator
direct expansions. Both the numerator and denominator must be usable in order for a given record to be used in the ratio
estimator.
Model-Based Estimators
Starting in year 2020, model-based estimates of hired workers, average hours worked, average wage rates, and the
associated sub-items are produced to support NASS estimation processes. Statistical models are mathematical equations
that relate quantities of interest (in this case, number of workers, hours, and wage rates) to a set of important input factors.
The models used by NASS relate the direct expansions obtained from the current Agricultural Labor Survey to previous
year, same quarter official estimates. This modeling approach improves the precision of the resulting estimates. In
particular, estimates in publication cells derived from few reports become more precise than estimates derived from
survey alone.
Outliers: Both RFO and HQ statisticians conduct a review of worker and wage outliers, identified through the interactive
data analysis tool, to ensure the most accurate data and indications possible. The RFO statisticians review outliers for
states within their regions and the HQ statistician examines outliers across all states. Many outliers trace back to unique
situations that do not exist in the target population as much as the survey weight would indicate. In some cases, aging
control data result in misstratification, and this misstratification can also give rise to outliers. The survey weight assigned
to each outlier is subject to an additional adjustment during the calibration process where appropriate, before final
summary indications and model-based estimates are produced.

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Estimation: The number of hired workers, average hours worked, average wage rate data, and all associated sub-items for
each surveyed reference week are summarized from the dataset. Because identical data collection instruments are used for
all states, as well as identical editing and analysis processes, state data can be summarized to regional and national survey
point estimates. Similarly, these data are used to support state, regional, and national model-based estimates. For
estimation purposes, survey point estimates are adopted as survey indications for all data items. The summary results
provide multiple direct and ratio indications for each data series being estimated. The results also provide information
used to assess the performance of the current survey and evaluate the quality of the survey indications. Currently, RFOs
evaluate state level survey indications and submit state-specific comments for all member states to HQ. HQ executes the
regional and United States level summaries, which utilize the same estimators and produce the same indications as the
state level summaries. The associated model-based estimates are subsequently generated, using the summarized survey
indications as input.
The estimation process is facilitated at all levels with a second interactive analysis tool, which selectively displays current
and historic summary indications, model-based estimates, measures of indication and estimate quality, state level
comments, and final estimates for all published data items. The instrument generates tables and charts of this database
content, allowing statisticians to assess trends, evaluate current and historic relationships between summary indications,
model-based estimates, and final estimates, and current and historic state level comments on local conditions and data
assessment. Statisticians view and analyze all necessary database content through the instrument, to finalize the estimates.
RFO statisticians review only data and analyses associated with their member states; HQ statisticians subsequently review
all state, regional, and national data and analyses. All steps necessary to the coordination and confirmation of final
estimates are accomplished through this data tool. Additionally, the annual average estimates are calculated using this data
tool after all final quarterly estimates are established.
For the final step in the estimation process, the assembled Agricultural Statistics Board (ASB) representatives for the
Farm Labor program review the final United States level, regional, and state level summary indications and model-based
estimates and establish all final, official estimates for each surveyed reference week. As part of the semi-annual process,
the ASB also considers revised California data, and issues revisions of previously published California and United States
level estimates where appropriate. After all final quarterly estimates are established, the ASB verifies the annual average
estimates for the annual (typically November) publication, which are summarized weighted averages of the final United
States and regional level estimates for each of the four quarters.

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Quality Metrics for the Agricultural (Farm) Labor Survey
Purpose and Definitions: Under the guidance of the Statistical Policy Office of the Office of Management and Budget
(OMB), the United States Department of Agriculture’s National Agricultural Statistics Service (NASS) provides data
users with quality metrics for its published data series. The metrics tables in this document describe the performance data
for the survey contributing to the publication. The accuracy of data products may be evaluated through sampling and
nonsampling error. The measurement of error due to sampling in the current period is evaluated by the coefficient of
variation for each estimated item. The multi-component nonsampling error can be difficult to quantify and measure, but
response rates may offer a partial assessment.
Sample size is the number of observations selected from the population to represent a characteristic of the
population. Operations that did not have the item of interest or were out of business at the time of data collection
have been excluded.
Response rate is the proportion of the sample that completed the survey. This calculation follows Guideline 3.2.2 of
the Office of Management and Budget Standards and Guidelines for Statistical Surveys (Sept. 2006) and the
American Association for Public Opinion Research (AAPOR) (2015). NASS surveys use the AAPOR Response Rate
2 (RR2) formula. In-scope records for the Agricultural Labor Survey include operations that did not have workers
because labor in the reference week is transitory for smaller operations.
Coefficient of variation provides a measure of the size for the standard error relative to the point estimate and is
used to measure the precision of the results of an estimator.

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Coefficient of Variation for Modeled Estimate of Gross Wage Rate by Type of Worker – United States
Reference week

Field workers

Livestock workers

Field and livestock combined

(percent)

(percent)

(percent)

April 10-16, 2022 ...............................

0.6

0.9

0.5

April 9-15, 2023 .................................

0.8

1.1

0.7

January 9-15, 2022 ...........................

0.7

1.0

0.6

January 8-14, 2023 ...........................

0.9

1.2

0.7

Farm Labor Sample Size and Response Rate – Regions and United States: April 10-16, 2022 and
April 9-15, 2023
Regions

Sample size

Response rate

2022

2023

2022

2023

(number)

(number)

(percent)

(percent)

Northeast I ....................................
Northeast II ...................................

927
957

914
927

38.6
36.6

42.0
36.2

Appalachian I ................................
Appalachian II ...............................

700
932

698
923

51.9
54.6

47.4
48.8

Southeast ......................................
Florida ...........................................

777
795

801
821

54.1
45.3

41.9
40.3

Lake ...............................................

1,005

999

39.2

42.9

Cornbelt I ......................................
Cornbelt II .....................................

1,277
1,523

1,272
1,506

43.1
42.5

43.8
41.4

Delta ..............................................

1,125

1,138

48.1

46.7

Northern Plains .............................

1,324

1,332

40.6

38.4

Southern Plains ............................

1,438

1,475

49.2

49.1

Mountain I .....................................
Mountain II ....................................
Mountain III ...................................

704
454
439

675
414
430

39.1
45.6
32.6

42.8
49.8
37.0

Pacific ............................................
California 1 ....................................

717
1,098

722
850

30.0
45.3

34.6
53.5

Hawaii ...........................................

575

573

48.5

47.1

United States ................................

16,767

16,470

43.9

43.6

1

January-April 2022 sample size remained at pre-reduction level due to region-specific sampling constraints.

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Farm Labor Sample Size and Response Rate – Region and United States: January 9-15, 2022 and
January 8-14, 2023
Regions

Sample size
2022

Response rate
2023

(number)

2022

(number)

2023

(percent)

(percent)

Northeast I .....................................
Northeast II ....................................

927
957

914
927

38.9
36.7

41.9
36.1

Appalachian I ................................
Appalachian II ...............................

700
932

698
923

52.0
54.6

47.3
48.6

Southeast ......................................
Florida ............................................

777
795

801
821

53.5
45.4

41.9
40.4

Lake ...............................................

1,005

999

39.3

42.9

Cornbelt I .......................................
Cornbelt II ......................................

1,277
1,522

1,272
1,506

43.5
42.4

43.8
41.6

Delta ..............................................

1,125

1,138

47.7

46.6

Northern Plains .............................

1,324

1,332

40.3

38.4

Southern Plains ............................

1,438

1,475

49.2

48.9

Mountain I .....................................
Mountain II ....................................
Mountain III ...................................

704
454
439

675
414
430

39.3
46.0
32.1

43.1
50.0
36.7

Pacific ............................................
California 1 .....................................

717
1,098

722
850

30.1
55.9

34.5
61.6

Hawaii ............................................

575

573

48.3

47.1

United States ................................

16,766

16,470

44.6

44.0

1

8

January-April 2022 sample size remained at pre-reduction level due to region-specific sampling constraints.

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Coefficient of Variation for Modeled Estimate of All Hired Workers and Gross Wage Rate – Region and
United States: April 10-16, 2022 and April 9-15, 2023
All hired workers

Regions

2022

Gross wage rate
2023

(percent)

2022

(percent)

2023

(percent)

(percent)

Northeast I ..................................................
Northeast II .................................................

7.5
7.8

7.9
8.1

1.8
2.0

1.9
1.7

Appalachian I ..............................................
Appalachian II .............................................

9.5
8.8

11.9
9.3

1.6
2.1

2.7
2.4

Southeast ....................................................
Florida .........................................................

7.5
11.3

8.6
10.9

1.5
1.8

2.8
2.1

Lake .............................................................

7.6

7.3

1.7

2.2

Cornbelt I ....................................................
Cornbelt II ...................................................

7.0
8.3

8.3
8.7

1.7
1.8

1.8
2.7

Delta ............................................................

7.8

8.4

1.5

1.6

Northern Plains ...........................................

7.7

8.0

1.6

1.4

Southern Plains ..........................................

7.6

10.0

2.2

3.6

Mountain I ...................................................
Mountain II ..................................................
Mountain III .................................................

8.8
9.3
9.8

9.3
11.0
7.7

1.9
2.1
2.1

1.5
3.3
2.1

Pacific ..........................................................
California .....................................................

9.7
4.5

11.4
5.6

1.5
0.9

1.8
1.2

Hawaii .........................................................

9.4

9.0

2.8

3.3

United States ..............................................

2.4

2.9

0.5

0.7

Coefficient of Variation for Modeled Estimate of Hired Workers and Gross Wage Rate by Standard
Occupational Classification (SOC) System – United States: April 10-16, 2022 and April 9-15, 2023
Title

SOC code

All hired workers

Gross wage rate

2022

2023

2022

2023

(percent)

(percent)

(percent)

(percent)

Graders and sorters, agricultural products ......................................................
Agricultural equipment operators .....................................................................
Farmworkers, crop, nursery, and greenhouse ................................................
Farmworkers, farm, ranch, and aquacultural animals .....................................
Agricultural workers, all other ...........................................................................
Packers and packagers, hand ..........................................................................

(45-2041)
(45-2091)
(45-2092)
(45-2093)
(45-2099)
(53-7064)

18.1
5.0
4.6
4.8
10.2
11.9

17.1
6.1
4.9
5.6
11.5
15.9

2.6
1.2
0.8
1.1
1.7
2.3

3.0
1.2
1.0
1.3
2.1
2.6

Farmers, ranchers, and other agricultural managers ......................................
First-line supervisors of farming, fishing workers ............................................

(11-9013)
(45-1011)

6.2
6.7

6.7
7.5

2.2
1.8

2.1
1.9

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Coefficient of Variation for Modeled Estimate of All Hired Workers and Gross Wage Rate – Region and
United States: January 9-15, 2022 and January 8-14, 2023
All hired workers

Regions

2022

Gross wage rate
2023

(percent)

2022

(percent)

2023

(percent)

(percent)

Northeast I ...................................................
Northeast II ..................................................

6.6
8.7

7.0
9.1

2.0
2.4

2.1
2.4

Appalachian I ..............................................
Appalachian II .............................................

9.0
10.0

13.3
10.0

1.7
2.4

3.3
2.8

Southeast ....................................................
Florida ..........................................................

8.6
13.0

9.7
9.4

1.9
1.6

3.0
2.3

Lake .............................................................

8.3

8.0

1.8

2.2

Cornbelt I .....................................................
Cornbelt II ....................................................

7.6
10.4

8.8
11.4

2.3
2.1

2.1
2.9

Delta ............................................................

8.5

8.7

1.9

1.8

Northern Plains ...........................................

8.5

9.1

1.7

1.7

Southern Plains ..........................................

8.4

9.7

2.1

3.6

Mountain I ...................................................
Mountain II ..................................................
Mountain III .................................................

9.2
10.2
11.7

10.0
12.4
12.6

2.3
2.3
2.0

2.0
3.5
2.3

Pacific ..........................................................
California .....................................................

10.9
4.0

9.4
5.5

2.1
1.0

2.4
1.4

Hawaii ..........................................................

9.3

9.6

3.1

3.2

United States ..............................................

2.5

2.9

0.6

0.7

Coefficient of Variation for Modeled Estimate of Hired Workers and Gross Wage Rate by Standard
Occupational Classification (SOC) System – United States: January 9-15, 2022 and January 8-14, 2023
Title

All hired workers

SOC code

Gross wage rate

2022

2023

2022

2023

(percent)

(percent)

(percent)

(percent)

Graders and sorters, agricultural products ......................................................
Agricultural equipment operators .....................................................................
Farmworkers, crop, nursery, and greenhouse ................................................
Farmworkers, farm, ranch, and aquacultural animals ....................................
Agricultural workers, all other ...........................................................................
Packers and packagers, hand .........................................................................

(45-2041)
(45-2091)
(45-2092)
(45-2093)
(45-2099)
(53-7064)

30.4
5.5
3.9
5.1
10.0
12.1

16.6
5.3
5.2
5.5
11.8
13.8

2.6
1.0
0.8
1.1
1.7
3.2

2.0
1.8
5.5
1.1
1.1
1.5

Farmers, ranchers, and other agricultural managers .....................................
First-line supervisors of farming, fishing workers ...........................................

(11-9013)
(45-1011)

5.6
6.5

7.0
8.3

2.3
1.9

2.5
1.4

10

Farm Labor Methodology and Quality Measures (May 2023)
USDA, National Agricultural Statistics Service

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