United States 

Department of

Agriculture

National

Agricultural

Statistics

Service

Research and

Development Division

Washington DC 20250

RDD Research Report

RDD-08-01

January 2008

Assessing the Effect of 

Calibration on 

Nonresponse Bias in the 

2005 ARMS Phase III 

Sample Using 2002 

Census of Agriculture 

Data

Morgan S. Earp

Jaki S. McCarthy

Nick D. Schauer

Phil S. Kott

This paper was prepared for limited distribution to the research community outside the United 

States Department of Agriculture. The views expressed herein are not necessarily those of the 

National Agricultural Statistics Service or of the United States Department of Agriculture. 

i

EXECUTIVE SUMMARY

Phase III of the Agricultural Resource Management Survey (ARMS) is one of the most 

complex and detailed sample survey data collections conducted by the United States 

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

For this survey, NASS collects highly detailed, calendar year economic data from 

agricultural producers nationwide. The USDA uses ARMS data to evaluate the financial 

performance of farms and ranches, which influence agricultural policy decisions. The 

Department also uses Phase III data for objective evaluation of critical issues related to 

agriculture and the rural economy.

In September 2006 the Executive Office of the President released the Office of 

Management and Budget Standards and Guidelines for Statistical Surveys based on the 

recommendations of the Federal Committee on Statistical Methodology’s (FCSM) 

Subcommittee on Standards for Statistical Surveys. The Office of Management and 

Budget’s (OMB) new standards and guidelines for statistical surveys addressed a number 

of federal statistical agency issues, specifically response rates below 80 percent and 

analysis of unit nonresponse. The 2005 Agricultural Resource Management Survey 

(ARMS) Phase III Survey Administration Analysis (Hopper, 2006) reported a response 

rate of 70.5 percent, and thus NASS was required by OMB to conduct an analysis of 

nonresponse bias. This report focuses on fulfilling OMB’s directive, by assessing the 

effect of nonresponse bias as well as the utility of the NASS calibration process in 

adjusting for such biases. 

Records sampled for the 2005 ARMS Phase III were matched with those from the 2002 

Census of Agriculture, and means of census data were calculated for matching records 

which had also provided 2002 expenditure data for the Census. Nonresponse bias in 

ARMS data was assessed, using census data as a proxy, in terms of the degree to which 

the mean based on all sample cases versus respondent cases differed. Three means were 

computed and compared across 20 regions in order to assess relative bias: 1) the mean of 

all matching cases using base sampling weights, 2) the mean for only matching ARMS 

respondents using the same base sampling weights, and 3) the mean for matching ARMS 

respondents using the sampling weights as adjusted through calibration. 

Using 17 “study variables,” relative bias of the mean was assessed using a variation of 

the formula provided by OMB in Guideline 3.2.9. Although significant biases were 

exhibited in 9 of 17 variables using the 2005 ARMS Phase III base sampling weights, the 

2005 ARMS Phase III calibration weights were able to reduce the bias so that it was no 

longer significantly different from zero (p < .05) in almost 90 percent (8/9) of the study 

variables. For this analysis, the calibration process varied slightly from that of the 2005 

ARMS Phase III, in that egg and milk production were not included, since there were no 

comparable variables in the 2002 Census. The inability to replicate the 2005 ARMS III 

calibration process may in part account for the one variable, fertilizer expenses, still

demonstrating a significant level of bias after the use of calibrated weights. This study 

suggests that the process of calibration is an effective tool in reducing nonresponse bias 

levels, so they are no longer significantly different from zero.

ii

RECOMMENDATIONS

1. Nonresponse bias of all study variables, especially livestock purchases and 

fertilizer expenses should be reevaluated when the 2007 Census data are 

available, since this Census will contain equivalent calibration target variables 

for egg and milk production, and expenditure data for all Census respondents 

allowing for an assessment that will be consistent with the calibration targets 

used for the 2005 ARMS Phase III. 

2. Methods should be developed to assess biases not measured in this analysis, 

especially those that may exist in only a single region.1

 

1 ARMS III estimate regions include the 15 leading cash receipts states (Arkansas, California, Florida, 

Georgia, Illinois, Iowa, Kansas, Minnesota, Nebraska, North Carolina, Texas, Washington, and Wisconsin)

and the five main production regions (Atlantic, South, Midwest, Plains, and West) comprised of the 

remaining 35 states.

Assessing the Effect of Calibration on Nonresponse Bias in the 2005 ARMS 

Phase III Sample Using 2002 Census of Agriculture Data

Morgan Earp, Jaki McCarthy, Nick Schauer, & Phil Kott2 

Abstract

The United States Department of Agriculture’s National Agricultural Statistics Service 

conducts the annual Agricultural Resource Management Survey (ARMS) in three phases. 

The third phase of the ARMS collects detailed economic data which is highly sensitive, 

and thus this phase suffers from lower response rates. With the release of the 2006 Office 

of Management and Budget Standards and Guidelines for Statistical Surveys, response 

rates lower than 80 percent may not only result in nonresponse bias, but they can 

jeopardize the future of surveys carried out by federal agencies. Based on the assumption 

that the use of calibrated weights derived from appropriate targets addresses nonresponse 

bias, the effect of the 2005 Phase III ARMS calibrated weights on relative bias of the 

mean was tested for all cases sampled in the 2005 ARMS with Census 2002 expenditure 

data. 

The results showed that calibrated weights decreased bias levels so that they were no 

longer significantly different from zero for almost 90 percent of the “study variables” 

(p < .05). 

Key Words: Nonresponse; response rate; bias; calibration weights.

 

2 Morgan Earp is a survey & mathematical statistician in the USDA’s National Agricultural Statistics 

Service (NASS) – Research & Development Division (RDD), located in Room 305, 3251 Old Lee 

Highway, Fairfax, VA 22030. Jaki McCarthy provided assistance with this research while the Chief 

Research Methodologist with the USDA’s NASS – RDD. Nick Schauer provided assistance with this 

research while a mathematical statistician in the USDA’s NASS - Statistics Division. Phil Kott provided 

assistance with this research while the Chief Research Statistician with the USDA’s NASS – RDD. 
2

1. INTRODUCTION

On September 22, 2006, the Executive 

Office of the President released the 

Office of Management and Budget 

Standards and Guidelines for Statistical 

Surveys based on the recommendations 

of the Federal Committee on Statistical 

Methodology’s (FCSM) Subcommittee 

on Standards for Statistical Surveys. 

The Office of Management and Budget’s 

(OMB) new standards and guidelines for 

statistical surveys pertain to aspects of 

surveys conducted by federal statistical 

agencies. 

Federal statistical agencies, such as the 

United States Department of 

Agriculture’s (USDA) National 

Agricultural Statistics Service (NASS),

are directly affected by OMB’s new 

standards and guidelines for statistical 

surveys. One of the standards (3.2) 

issued by OMB addresses response rates 

and analysis of nonresponse bias. 

According to Standard 3.2, 

Agencies must appropriately 

measure, adjust for, report, and 

analyze unit and item 

nonresponse to assess their 

effects on data quality and to 

inform users. Response rates 

must be computed using standard 

formulas to measure the 

proportion of the eligible sample 

that is represented by the 

responding units in each study, as 

an indicator of potential 

nonresponse bias. (Office of 

Management and Budget, 2006, 

p. 14)

In 2005, the Agricultural Resource 

Management Survey (ARMS) Phase III 

response rate was 70.5 percent 

(n = 34,937), which fell below the OMB 

response rate cut off of 80 percent listed 

in Guideline 3.2.9; therefore, NASS was 

required by OMB to research the effect 

of nonresponse bias. Since the Phase II 

response rate of 80.5 percent exceeded 

OMB’s 80 percent threshold, 

nonresponse bias assessment was only 

required for Phase III, the “problem” 

stage. Guideline 3.2.9 states

Given a survey with an overall 

unit response rate of less than 80 

percent, conduct an analysis of 

nonresponse bias using unit 

response rates as defined above, 

with an assessment of whether 

the data are missing completely 

at random. As noted above, the 

degree of nonresponse bias is a 

function of not only the response 

rate but also how much the 

respondents and nonrespondents 

differ on the survey variables of 

interest. For a sample mean, an 

estimate of the bias of the sample 

respondent mean is given by: 

r nr

nr

r r t

y y

n

n

B y y y

Where: 

t

y The mean based on all 

sample cases;

r

y The mean based only on 

respondent cases;

nr y

The mean based only on 

nonrespondent cases;

n The number of cases in the 

sample; and

nnr

 The number of 

nonrespondent cases.

For a multistage (or wave) 

survey, focus the nonresponse 

bias analysis on each stage, with 
3

particular attention to the 

“problem” stages. A variety of 

methods can be used to examine 

nonresponse bias, for example, 

make comparisons between 

respondents and nonrespondents 

across subgroups using available 

sample frame variables. In the 

analysis of unit nonresponse, 

consider a multivariate modeling 

of response using respondent and 

nonrespondent frame variables to 

determine if nonrespondent bias 

exists. (Office of Management & 

Budget, 2006, p. 16)

Currently NASS calculates the 

unweighted unit response rates (RRU) 

for the ARMS based on the formula 

provided under Guideline 3.2.2 of the 

Office of Management and Budget 

Standards and Guidelines for Statistical 

Surveys: 

C R NC O e(U)

C

RRU

Where:

C = The number of completed 

cases or sufficient 

partials;

R = The number of refused 

cases;

NC = The number of 

noncontacted sample 

units known to be 

eligible;

O = The number of eligible 

sample units not 

responding for reason 

other than refusal;

U = The number of sample 

units of unknown 

eligibility, not completed; 

and

e = The estimated proportion 

of sample units of 

unknown eligibility that 

are eligible. (p. 14)

NASS sums the number of positive 

usables, out of business, and non-farms 

and calculates the percentage this sum 

represents of the total number of reports 

to calculate the response rate for ARMS 

Phase III. 

The ARMS is conducted in three phases. 

Phase I screens for potential samples for 

Phases II and III. Phase II collects data 

on cropping practices and agricultural 

chemical usage and Phase III collects 

detailed economic information about the 

agricultural operation, as well as the 

operator’s household. Phase III is the 

only phase of the ARMS with response 

rates lower than 80 percent.

Due to lower response rates with ARMS 

Phase III, the potential for nonresponse 

bias is greater there. NASS weights the 

ARMS Phase III respondent sample in 

such a way that estimated variable totals 

for a large set of items match “targets” 

determined from other sources. This is 

done through a weighting process called 

"calibration." Calibration is the process 

of adjusting survey weights so that 

certain targets are met. NASS uses 

official estimates of farm numbers, corn, 

soybean, wheat, cotton, fruit and 

vegetable acres as well as cattle, milk 

production, hogs, broilers, eggs and 

turkeys as calibration targets. For 

example, after calibration the sum of 

weights multiplied by the survey data 

will equal the NASS estimate for corn 

acres. In addition to reducing confusion 

in the user community that might result 

from NASS releasing alternative 

estimates for the same totals, calibration 
4

weighting produces 2005 ARMS Phase 

III estimates with generally lower 

variances and, hopefully reduced 

nonresponse biases. This report 

describes an ongoing research effort 

aimed at measuring the potential for 

nonresponse bias in the ARMS Phase III 

and the success or failure of calibration 

in removing it.

Nonresponse bias is very difficult to 

measure directly. Fortunately, an 

indirect measure of nonresponse bias is 

available for the 2005 ARMS Phase III,

hereafter called simply the “ARMS.”

The Census of Agriculture is a 

mandatory collection of data from all 

known agricultural operations. NASS 

has data from the Census on items of 

interest for many of the ARMS 

nonrespondents; however, the Census 

itself is incomplete. An estimated 17.90

percent of all farms are missing from the 

2002 Census Mailing List, and 12.26

percent of farms on the List failed to 

respond to the Census. Moreover, not 

all ARMS sampled farms could be 

matched to 2002 Census records. 

Nevertheless, by comparing the 2002 

Census values of ARMS respondents to 

the full sample of ARMS respondents, 

we can measure the difference between 

the average ARMS respondent and the 

average of the full sample without any 

nonresponse adjustment. Additionally, 

we hope to measure the reduction of that 

difference from using a calibrationweighting process similar to the one 

used for the 2005 ARMS. 

While the 2002 Census data do not 

perfectly match the 2005 ARMS data, 

they are correlated (see Appendix A), so 

the present evaluation will compare 

respondents on the 2005 ARMS survey 

to nonrespondents using their 2002 

Census data for each. 

 

2. METHODS

Our analytical data set consists of census 

values for farms sampled for the ARMS 

that also provided 2002 expenditure data 

on the Census. In the 2002 Census, only 

a sample of farms received the long 

version that asks the expenditure data. 3

2002 Census data were available for 

81.4% of all 2005 ARMS III sampled 

operations. However, only the 48% of 

those which had completed the Census 

long forms with expenditure data were 

included in this analysis.4

The base sample weights (each farm’s 

ARMS sample weight before calibration

multiplied by its Census sample weight) 

for the subset of farms responding to the 

ARMS were calibrated so that the final 

weighted totals computed from them 

equaled the raw weighted total computed 

from the entire matched set for the 

following variables: cattle, corn, cotton, 

pigs, soybeans, wheat, fruit, vegetables, 

broilers, and turkeys.5 Each of these 

target variables plus egg and milk 

production was used operationally to 

calibrate the ARMS data. 

As in the operational program, the 

ARMS respondent subset was calibrated 

independently in 20 regions. These 

included the 15 leading cash receipts 

states (Arkansas, California, Florida, 

 

3 Only “sample farms” were used for this 

analysis.

4 The match rate for 2005 ARMS Phase III was 

significantly higher for nonrespondents (86.5%) 

than for respondents (79.2%) (z = 16.04, p < 

.05).

5 The sample weight was used to determine 

which operations were to report expenditure data 

from (USDA, 2002).
5

Georgia, Illinois, Indiana, Iowa, Kansas, 

Minnesota, Missouri, Nebraska, North 

Carolina, Texas, Washington, and 

Wisconsin). The remaining 33 states 

(Alaska and Hawaii are not sampled for 

ARMS) were grouped using the five 

production regions: 1) Atlantic, 

2) South, 3) Midwest, 4) Plains, and 

5) West.

Our analysis focuses on 17 specific 

variables collected on both the ARMS 

and the Census: total acres, total sales, 

acres rented, cropland acres, total 

production expenses, crop expenses, 

seed expenses, fertilizer expenses, 

chemical expenses, livestock purchases, 

feed purchases, hired labor expenses, 

machinery and equipment value, 

government payments, operator’s age, 

operator’s race, and farm type. 

Letting 

r

y

denote the 2002 Census 

preliminary-sample or calibrated-sample 

mean among the ARMS respondent 

subset for a study variable, and 

t

y

denote the corresponding preliminarysample mean among the entire matched 

sample, it is a simple matter to compute 

the relative bias of the former with 

respect to the latter, relBias =

t

r t

y

y y

. 

The statistical significance of this value 

is much harder to assess since the 

samples on which 

r

y

and 

t

y

are based 

are complex and overlapping. 

Fortunately, we can easily test the 

persistence or absence of a systematic 

bias across the 20 regions. To this end, 

we compute the following measure of 

bias of an ARMS-respondent mean 

(before or after calibration) with respect 

to the Census mean in every region: 

M = log( ) log( )

r t

y y

 = log r

t

y

y

 =

t

r t

y

y y

log 1 

 

t

r t

y

y y

This measure is conveniently symmetric, 

log( ) log( ) [log( ) log( )] t r r t y y y y

, while 

retaining the scale-invariance property of 

the relative bias (i.e., multiplying the 

reported item value on each farm by a 

fixed factor does not affect the overall

relative bias). 

The bias measure M for a study variable 

in a region can be treated as an 

independent random variable. The null 

hypothesis of no bias (again, either 

before or after calibration) can be tested 

against an alternative hypothesis of a 

persistent bias (p%) across all the 

regions. The conventional t test based 

on the 20 observations (one per region) 

is asymptotically normal under both the 

null and alternative hypotheses. We 

follow the standard practice of 

approximating the distribution of this 

test statistic with a Student’s t having 19 

degrees of freedom. This may lead to 

liberal inferences (the inappropriate 

rejection of the null hypothesis when it 

is true) because the M-values for the 

study variable may not be normally

distributed with a common variance 

across regions. Nevertheless, by taking 

logs we create a test statistic that is more 

nearly normal and homoscedastic than 

relative biases would be. 

A sign and a ranked-sign test of the 20 

paired observations for a study variable 

before and after calibration was 
6

conducted. The sign test is not as 

powerful as the other two tests (i.e., it 

more often fails to find that M is 

significantly different from 0 when, in 

fact, there is a persistent bias across the 

regions), but it assumes neither that M is 

normal nor homoscedastic. The signedrank test assumes the latter but not the 

former. We include it in our results for 

completeness.

3. RESULTS AND DISCUSSION 

Our results are summarized in Table 1. 

Chemical expenses, machinery and 

equipment value, government payments, 

acres rented, farm type, fuel and oil 

expenses, operator’s age, and cropland 

acres (Variables 1-8) do not exhibit 

significant biases using either calibrated 

or uncalibrated weights. Although 

chemical expenses (Variable 1) did not 

exhibit significant bias, significantly less 

bias was exhibited using the calibrated 

weights versus the uncalibrated weights.

In almost 90 percent (8/9) of the study 

variables (9-17) exhibiting persistent 

biases using the base sample weights, 

calibration weighting is able to reduce 

the bias so that it was no longer 

significantly different from zero (9-16) 

(p < .05) according to the t-test, and in 

50% of these variables we saw a 

significant reduction in bias levels (9-12)

(p < .05) according to the paired t-test. 

After calibration, only one study 

variable, fertilizer expenses has a 

significant bias (p < .05) according the ttest (but not according to the sign test). 

The bias of livestock purchases is 

indicated to be the largest of the study 

variables. Using only the sampling 

weights, it was highly significant in 

terms of each of the test statistics. After 

calibration, while still large in 

magnitude, the indicated bias was 

reduced to the point that it was 

statistically insignificant according to all 

the test statistics. For this variable, 

calibration does reduce the bias 

significantly if not completely. 

4. CONCLUSION

ARMS data are used by farm 

organizations, commodity groups, 

agribusiness, Congress, State 

Departments of Agriculture, and the 

USDA. The USDA uses ARMS data to 

evaluate the financial performance of 

farms and ranches, which influence 

agricultural policy decisions. The 

Department also uses Phase III data for 

objective evaluation of critical issues 

related to agriculture and the rural 

economy; therefore, it is essential that 

measures be taken to minimize the effect 

of nonresponse bias in ARMS, 

specifically Phase III. 

In the research on adjustment for 

nonresponse bias in the 2005 ARMS 

Phase III, the 2002 Census mean 

estimates of feed purchases, total 

production expenses, total sales, seed 

expenses, livestock purchases, cropland 

expenses, total acres operated, hired 

labor expenses, and fertilizer expenses 

demonstrated significant bias using just 

the base sample weights. Although the 

magnitude of the relative bias of the 

mean estimate remained high for 

livestock purchases using the calibrated

weights, calibration reduced the 

magnitude of this bias so that it was no 

longer significant (see Table 1). 
7

Table 1: Mean Comparisons and Indicated Biases for Matching Records Using Base Sampling Weights versus Calibrated Weights 
8

Table 1 (Cont.): Mean Comparisons and Indicated Biases for Matching Records Using Base Sampling Weights versus Calibrated Weights 
9

For this analysis, the calibration process 

varied slightly from that of the 2005 

ARMS Phase III, in that egg and milk 

production were not included, since 

these data items were not collected for 

the 2002 Census. This may help to 

explain why the magnitude of the 

relative bias of the mean for livestock 

estimates in Table 1 remained high even 

after the data were calibrated. While it 

was not possible to use these as 

calibration targets in this analysis, their 

use in the ARMS III survey may reduce 

the bias for livestock purchases in 

published ARMS data. 

According to Guideline 3.2.13 of the 

Office of Management and Budget 

Standards and Guidelines for Statistical 

Surveys, NASS should 

Base decisions regarding whether 

or not to adjust or impute data for 

item nonresponse on how the 

data will be used, the assessment 

of nonresponse bias that is likely 

to be encountered in the review 

of collections, prior experience 

with this collection, and the 

nonresponse analysis discussed 

in this section. When used, 

imputation and adjustment 

procedures should be internally 

consistent, sampled on 

theoretical and empirical 

considerations, appropriate for 

the analysis, and make use of the 

most relevant data available. If 

multivariate analysis is 

anticipated, care should be taken 

to use imputations that minimize 

the attenuation of underlying 

relationships.

Due to the broadness of the ARMS 

Phase III data user community and the 

survey’s impact on agricultural policy, it 

is crucial that the calibration process 

effectively adjusts for nonresponse bias. 

Assuming that the adjustment process is 

even more effective than demonstrated 

here (particularly for livestock purchases 

and fertilizer expenses) when all 

calibration targets (including egg and 

milk production) are available and used, 

it appears that NASS is appropriately 

addressing the issue of nonresponse bias 

in ARMS Phase III through the 

calibration process.

Limitations of this analysis include: 

1) Inability to replicate the 2005 ARMS 

Phase III calibration process exactly; 

2) Inability to assess farms not covered 

or responding to the Census of 

Agriculture or for which expenditure 

data were not available; 3) Inability to 

recognize localized biases in the ARMS 

data (tests were limited to persistent 

biases across regions); and 

4) Assessment of nonresponse bias was 

conducted using 2002 data as opposed 

to the 2005 data, since Census data is 

only available every five years.

Knowing that the analyzed data come 

from the 2002 Census and not from the 

2005 ARMS Phase III Survey does not 

limit, but strengthens the analysis. It 

allows us to focus entirely on the impact 

of the nonresponse per se. 

5. RECOMMENDATIONS

Based on the results of the present study, 

the following recommendations are

offered: 

1. Nonresponse bias of all study 

variables, especially livestock 

purchases and fertilizer 

expenses should be reevaluated 
10

when the 2007 Census data are 

available, since this Census 

will contain equivalent 

calibration target variables for 

egg and milk production, and 

expenditure data for all Census 

respondents allowing for an 

assessment that will be 

consistent with the calibration 

targets used for the 2005 

ARMS Phase III. 

2. Methods should be developed to 

assess biases not measured in 

this analysis, especially those 

that may exist in only a single

region. 

6. REFERENCES

Hopper, R. (2006). 2005 Agricultural 

Resource Management Survey 

(ARMS) Phase III Survey 

Administration Analysis. Census and 

Survey Division Staff Report SAB06-14, United States Department of 

Agriculture, National Agricultural 

Statistics Service.

Kott, P.S. (2005), “Using Calibration 

Weighting to Adjust for 

Nonresponse and Coverage Errors” 

Survey Methodology 32: 133-142.

United States Department of 

Agriculture. (2002). 2002 Census of 

Agriculture, Vol. 1, Appendix C. 

Washington, DC: U.S. Department 

of Agriculture.

United States Department of Education. 

(2003). National Center for 

Education Statistics Statistical 

Standards. Washington, DC: U.S. 

Department of Education.

United States Executive Office of the 

President. (2006). Office of 

Management and Budget Standards

and Guidelines for Statistical 

Surveys. Washington, DC: U.S. 

Executive Office of the President.
11 Census Census Census Census Census Census Census Census Census Census Census Census

APPENDIX A: Census 2002 and ARMS Phase III 2005 Correlations & Scatter 

Plots

Table A1: Census 2002 and ARMS III 2005 Variable Correlations with Outliers

r r 2Scatter Plots

Total Acres Operated .84805

( n = 9,258 )

.71919

 

ARMS III

Acres Rented .35984

( n = 9,258 )

.12948

 

ARMS III

Cropland Acres .83219

( n = 9,258 )

.69254

 

ARMS III

Total Production Expenses .78981

( n = 9,258 )

.62380

 

ARMS III

Seed Expenses .57695

( n = 9,258 )

.33287

 

ARMS III

Fertilizer Expenses .69688

( n = 9,258 )

.48564

 

ARMS III

Chemical Expenses .60506

( n = 9,258 )

.36610

 

ARMS III

Crop Expenses .66189

( n = 9,258 )

.43810

 

ARMS III

Livestock Purchases .43729

( n = 9,258 )

.19122

 

ARMS III

Feed Purchases .75983

( n = 9,258 )

.57734

 

ARMS III

Hired Labor Expenses .84178

( n = 9,258 )

.70859

 

ARMS III

Fuel & Oil Expenses .68970

( n = 9,258 )

.47569

 

ARMS III
12 Census Census Census Census

Machinery & Equipment .44797

( n = 9,258 )

.20068

 

ARMS III

Government Payments .26941

( n = 9,258 )

.07258

 

ARMS III

Operator’s Age .56132

( n = 9,258 )

.31508

 

ARMS III

Farm Type .80645

( n = 9,258 )

.65036

 

ARMS III

1. All correlations were significant at the .05 level.

2. Correlations were only estimated for ARMS respondents.

3. Outliers were flagged using DFFITS, Cook’s D, and studentized residuals and are shown in red.
13 Census Census Census Census Census Census Census Census Census Census Census Census Census Census

Table A2: Census 2002 and ARMS III 2005 Variable Correlations without Outliers

r r 2Scatter Plots

Total Acres Operated .95246

( n = 9,174 )

.90718

 

ARMS III

Acres Rented .79452

( n = 9,164 )

.63126

 

ARMS III

Cropland Acres .94193

( n = 8,834 )

.88723

 

ARMS III

Total Production Expenses .83579

( n = 9,014 )

.69854

 

ARMS III

Seed Expenses .68555

( n = 9,092)

.46998

 

ARMS III

Fertilizer Expenses .77179

( n = 8,946 )

.59566

 

ARMS III

Chemical Expenses .75377

( n = 9,026 )

.56817

 

ARMS III

Crop Expenses .81138

( n = 9,036 )

.65834

 

ARMS III

Livestock Purchases .41659

( n = 9,159 )

.17355

 

ARMS III

Feed Purchases .75001

( n = 8,977 )

.56252

 

ARMS III

Hired Labor Expenses .88404

( n = 9,017)

.78152

 

ARMS III

Fuel & Oil Expenses .75420

( n = 8,963 )

.56882

 

ARMS III

Machinery & Equipment .61434

( n = 8,910 )

.37741

 

ARMS III

Government Payments .43480

( n =9,007 )

.18905

 

ARMS III
14 Census Census

Operator’s Age .84767

( n = 8,582 )

.71854

 

ARMS III

Farm Type .80645

( n = 9,258)

.65036

 

ARMS III

1. All correlations were significant at the .05 level (n = 19,483).

2. Correlations were only estimated for ARMS respondents.
15

Figures A1-A2: Census 2002 versus ARMS III 2005 Total Acres Operated Census 2002 

– Total Acres Operated (Acres)

Scatter Plot of Total Acres Operated with Outliers

0

100000

200000

300000

400000

500000

600000

0 100000 200000 300000 400000 500000 600000

ARMS III 2005 – Total Acres Operated (Acres)

Census 2002 

–Total Acres Operated (Acres)

Scatter Plot of Total Acres Operated without Outliers 

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

0 20000 40000 60000 80000 100000

ARMS III 2005 – Total Acres Operated (Acres)
16

Figures A3-A4: Census 2002 versus ARMS III 2005 Acres Rented Census 2002 

– Acres Rented (Acres)

Scatter Plot of Acres Rented with Outliers 

0

100000

200000

300000

400000

0 10000 20000 30000 40000 50000 60000 70000 80000 90000

ARMS III 2005 – Acres Rented (Acres)

Census 2002 

– Acres Rented (Acres)

Scatter Plot of Acres Rented without Outliers 

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

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ARMS III 2005 – Acres Rented (Acres)
17

Figures A5-A6: Census 2002 versus ARMS III 2005 Cropland Acres Census 2002 

– Cropland Acres (Acres)

Scatter Plot of Cropland Acres with Outliers 

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ARMS III 2005 – Cropland Acres (Acres)

Census 2002 

– Cropland Acres (Acres)

Scatter Plot of Cropland Acres without Outliers 

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ARMS III 2005 – Cropland Acres (Acres)
18

Figures A7-A8: Census 2002 versus ARMS III 2005 Total Production Expenses Census 2002 

– Total Production Expenses (Dollars)

Scatter Plot of Total Production Expenses with Outliers 

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ARMS III 2005 – Total Production Expenses (Dollars)

Census 2002 

– Total Production Expenses (Dollars)

Scatter Plot of Total Production Expenses without Outliers 

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ARMS III 2005 – Total Production Expenses (Dollars)
19

Figures A9-A10: Census 2002 versus ARMS III 2005 Seed Expenses Census 2002 

– Seed Expenses (Dollars)

Scatter Plot of Seed Expenses with Outliers 

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ARMS III 2005 – Seed Expenses (Dollars)

Census 2002 

– Seed Expenses (Dollars)

Scatter Plot of Seed Expenses without Outliers 

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ARMS III 2005 – Seed Expenses (Dollars)
20

Figures A11-A12: Census 2002 versus ARMS III 2005 Fertilizer Expenses Census 2002 

– Fertilizer Expenses (Dollars)

Scatter Plot of Fertilizer Expenses with Outliers 

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ARMS III 2005 – Fertilizer Expenses (Dollars)

Census 2002 

– Fertilizer Expenses (Dollars)

Scatter Plot of Fertilizer Expenses without Outliers 

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ARMS III 2005 – Fertilizer Expenses (Dollars)
21

Figures A13-A14: Census 2002 versus ARMS III 2005 Chemical Expenses Census 2002 

– Chemical Expenses (Dollars)

Scatter Plot of Chemical Expenses with Outliers 

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ARMS III 2005 – Chemical Expenses (Dollars)

Census 2002 

– Chemical Expenses (Dollars)

Scatter Plot of Chemical Expenses without Outliers 

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ARMS III 2005 – Chemical Expenses (Dollars)
22

Figures A15-A16: Census 2002 versus ARMS III 2005 Crop Expenses Census 2002 

– Crop Expenses (Dollars)

Scatter Plot of Crop Expenses with Outliers 

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ARMS III 2005 – Crop Expenses (Dollars)

Census 2002 

– Crop Expenses (Dollars)

Scatter Plot of Crop Expenses without Outliers 

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ARMS III 2005 – Crop Expenses (Dollars)
23

Figures A17-A18: Census 2002 versus ARMS III 2005 Livestock Purchases Census 2002 

– Livestock Purchases (Dollars)

Scatter Plot of Livestock Purchases with Outliers

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ARMS III 2005 – Livestock Purchases (Dollars)

Census 2002 

–Livestock Purchases (Dollars)

Scatter Plot of Livestock Purchases without Outliers 

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ARMS III 2005 – Livestock Purchases (Dollars)
24

Figures A19-A20: Census 2002 versus ARMS III 2005 Feed Purchases Census 2002 

– Feed Purchases (Dollars)

Scatter Plot of Feed Purchases with Outliers

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ARMS III 2005 – Feed Purchases (Dollars)

Census 2002 

–Feed Purchases (Dollars)

Scatter Plot of Feed Purchases without Outliers 

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ARMS III 2005 – Feed Purchases (Dollars)
25

Figures A21-A22: Census 2002 versus ARMS III 2005 Hired Labor Expenses Census 2002 

– Hired Labor Expenses (Dollars)

Scatter Plot of Hired Labor Expenses with Outliers

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ARMS III 2005 – Hired Labor Expenses (Dollars)

Census 2002 

–Hired Labor Expenses (Dollars)

Scatter Plot of Hired Labor Expenses without Outliers 

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ARMS III 2005 – Hired Labor Expenses (Dollars)
26

Figures A23-A24: Census 2002 versus ARMS III 2005 Fuel & Oil Expenses Census 2002 

– Fuel & Oil Expenses (Dollars)

Scatter Plot of Fuel & Oil Expenses with Outliers

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ARMS III 2005 – Fuel & Oil Expenses (Dollars)

Census 2002 

– Fuel & Oil Expenses (Dollars)

Scatter Plot of Fuel & Oil Expenses without Outliers 

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ARMS III 2005 – Fuel & Oil Expenses (Dollars)
27

Figures A25-A26: Census 2002 versus ARMS III 2005 Machinery & Equipment Value Census 2002 

– Machinery & Equipment Value (Dollars)

Scatter Plot of Machinery & Equipment Value with Outliers

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ARMS III 2005 – Machinery & Equipment Value (Dollars)

Census 2002 

– Machinery & Equipment Value (Dollars)

Scatter Plot of Machinery & Equipment Value without Outliers 

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ARMS III 2005 – Machinery & Equipment Value (Dollars)
28

Figures A27-A28: Census 2002 versus ARMS III 2005 Government Payments Census 2002 

– Government Payments (Dollars)

Scatter Plot of Government Payments with Outliers

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ARMS III 2005 – Government Payments (Dollars)

Census 2002 

– Government Payments (Dollars)

Scatter Plot of Government Payments without Outliers 

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ARMS III 2005 – Government Payments (Dollars)
29

Figures A29-A30: Census 2002 versus ARMS III 2005 Operator’s Age Census 2002 

– Operator’s Age (Years)

Scatter Plot of Operator’s Age with Outliers

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ARMS III 2005 – Operator’s Age (Years)

Census 2002 

– Operator’s Age (Years)

Scatter Plot of Operator’s Age without Outliers 

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ARMS III 2005 – Operator’s Age (Years)
30

Figures A31-A32: Census 2002 versus ARMS III 2005 Farm Type Census 2002 

– Farm Type (Nominal)

Scatter Plot of Farm Type with Outliers

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ARMS III 2005 – Farm Type (Nominal)

Census 2002 

– Farm Type (Nominal)

Scatter Plot of Farm Type without Outliers 

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ARMS III 2005 – Farm Type (Nominal)
31