i

EVALUATION OF INTEGER WEIGHTING FOR THE 1997 CENSUS OF AGRICULTURE,

by Wendy Scholetzky, Sampling and Estimation Research Section, Research Division, National

Agricultural Statistics Service, U.S. Department of Agriculture, Washington, DC 20250-2000, January

2000, Report No. RD-00-01.

ABSTRACT

The census of agriculture is an important source of statistics about the Nation’s agricultural production and

provides consistent, comparable data at the county, state, and national levels. Each census record has

weights which are used to produce totals for the entire population. The process of rounding weights to

integer values has been in place for the last several censuses. When a record’s weight is rounded to an

integer value, the totals represented by that record may or may not change dramatically. These changes

may or may not become negligible when producing totals at the state or county level. This report

compares totals calculated with the noninteger weights to the published totals (calculated with the integer

weights) for the 1997 Census of Agriculture, evaluates how different these totals are, and examines how

the differences relate to the standard error. The analysis examines a number of characteristics at both the

state and county levels. The report also examines another weighting approach where the noninteger

weights are applied to the record and the weighted data values are rounded at the record level. The

difference between totals produced with these values and the noninteger weights is calculated and

compared to the above differences.

The reasons for rounding weights, to ease data review procedures and to ensure that publication totals

add, are legitimate concerns. The author asserts that it is possible to address these two concerns and

improve the totals produced when NASS revamps the census processing system for the 2002 census.

KEY WORDS

1997 Census of Agriculture; Integer weight; Noninteger weight; t-value; Percent difference.

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

U.S. Department of Agriculture.

ACKNOWLEDGMENTS

The author would like to thank Dale Atkinson for his guidance in the development of this report, Gail

Wade for the Mapinfo tutorial, and Phil Kott for providing the standard error methodology. A special

thanks to Chadd Crouse for his tremendous assistance throughout the entire project.
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TABLE OF CONTENTS

ABSTRACT .................................................................... i

SUMMARY ................................................................... iii

INTRODUCTION .............................................................. 1

BACKGROUND ............................................................... 2

RESULTS AND DISCUSSION ................................................... 5

Differences in Totals at the State Level .......................................... 6

Differences in Totals at the County Level ........................................ 8

An Alternative Weighting Procedure .......................................... 14

RECOMMENDATIONS ........................................................ 15

REFERENCES ............................................................... 16

APPENDIX A ................................................................ 17

APPENDIX B ................................................................ 18

APPENDIX C ................................................................ 21

APPENDIX D ................................................................ 44

APPENDIX E ................................................................ 50
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SUMMARY

The census of agriculture is an important source of statistics about the Nation’s agricultural production and

provides consistent, comparable data at the county, state, and national levels. Each census record has two

weights which are used to produce totals for the entire population. The first weight is the nonresponse

weight which accounts for farm operators who did not respond to the census despite numerous attempts to

contact them. The second weight is the sample item weight which accounts for both nonresponse and

subsampling for data items that are only asked on the long form. 

The process of rounding weights to integer values has been in place for the last several censuses. When a

record’s weight is rounded to an integer value, the totals represented by that record may or may not

change dramatically. These changes may or may not become negligible when producing totals at the state

or county level. The definition of what a person considers negligible is open to debate. Rather than round

the noninteger weights to integer values, another possible approach is to apply the noninteger weights to

the record and round the weighted data values at the record level. Thus, the operation’s rounded-weighted

data values are used to produce the totals for the entire population. With this approach, alternative

methodology would have to be used for characteristics such as number of farms and demographic data.

This report compares totals calculated with the noninteger weights to the published totals (calculated with

the integer weights) for the 1997 Census of Agriculture, evaluates how different these totals are, and

examines how the differences relate to the standard error. The report also compares these differences to

the difference between totals produced with the noninteger weights and the record’s rounded-weighted

data values. The analysis examines a number of characteristics at both the state and county levels.

The reasons for rounding weights, to ease data review procedures and to ensure that publication totals

add, are legitimate concerns. The author asserts that it is possible to address these two concerns and

improve the totals produced when NASS revamps the census processing system for the 2002 census. In

reference to data review procedures, one argument for using the integer weights is that one can “easily”

obtain weighted totals for a record of interest during the data review phase. In the 1997 system, this

would be accomplished by multiplying the integer weight by each record’s data value of interest. Thus, this

manual computation is easier if the nonresponse weight is 2 rather than 1.7. However, for the 2002

system, the computer can and should perform this calculation. With this improvement, the value of the

weight is no longer relevant. In reference to the concern that the publication totals add, a combination of

the integer weights and the record’s rounded-weighted data values should be used. The integer weights

should be used to produce totals for indicator and categorical variables and for any question where the

data values are small for most farms. The record’s rounded-weighted data values should be used to

produce totals for all other characteristics. With this improvement, the integer sample item weight is no

longer necessary unless the 2002 long form contains a question that requires it. The 1997 long form

contained no such item. Once the 2002 long form is finalized, the questions will need to be evaluated to

determine whether the integer sample item weight is needed.
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INTRODUCTION

The census of agriculture is an important source of

statistics about the Nation’s agricultural

production and provides consistent, comparable

data at the county, state, and national levels. 

Census statistics are used by Congress to develop

and change farm programs, study historical

trends, assess current conditions, and plan for the

future. Many national and state programs use

census data to design and allocate funding for

extension service projects, agricultural research,

soil conservation programs, and land-grant

colleges and universities. Private industry uses

census statistics to provide a more effective

production and distribution system for the

agricultural community.

The primary purpose of the census of agriculture

is to collect information from every farm operation

in the U.S. However, weighting adjustments are

still necessary to produce totals for the entire

population. A nonresponse adjustment was

applied because some farm operators did not

respond despite numerous attempts to contact

them and a sampling adjustment was applied

because additional questions were asked to a

subset of the population. After the values of the

weighting adjustments were determined, each inscope record was assigned a noninteger

nonresponse weight and a noninteger sample item

weight (which takes into account both

nonresponse and sampling). Two weights are

assigned to each record due to the design of the

census. The nonresponse weight is used to

produce totals for data items collected from all

respondents and the sample item weight is used to

produce totals for data items collected only from

the respondents in the subset. The noninteger

nonresponse weight ranged between one and

two, inclusively; the noninteger sample item weight

ranged between one and twenty-four, inclusively,

for respondents in the subset and was equal to

zero otherwise.

To simplify certain census processes, a systematic

sampling of records was performed to round the

noninteger weights to integer values. These

integer weights were then used to produce totals

for the entire population. To assign integer

weights, a sample of records (separate from the

long form sampling) was chosen within a group of

records with the same noninteger weight, and the

integer weight was the next largest integer value

for sample records and the truncated value for

nonsample records. For example, suppose that

ten records had the same noninteger nonresponse

weight of 1.2. Then, two records (20% of the

records) were selected to receive an integer

nonresponse weight of 2 and the remaining eight

records were assigned an integer nonresponse

weight of 1.

There were two main reasons for using integer

weights. First, integer weights make the data

review process much easier. For example, during

analytical review (a census process where an

analyst can examine weighted record-level data),

the analyst can easily determine the totals

represented by the record by a quick

multiplication of each data item by the integer

weight. When the record’s weight is one (which

is true for a majority of records), this calculation is

not even needed. If a noninteger weight was used

during this process, the analyst would need to

hand-calculate the totals represented by the

record for each data item, which would slow

down the process considerably. Second, integer

weights make the publication process simpler. 

Record-level integer weights ensure that the cells

within a column or row add to the total for the

column or row. This is true both within and

across all publication tables. Additivity of cells is

extremely important because the tables are
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broken into extremely detailed cells for various

characteristics and a tremendous number of tables

are produced during the publication process (one

volume for each state plus one for the United

States). If noninteger weights were used during

this process, each cell would need to be rounded

which does not guarantee additivity of cells. For

example, noninteger weights could result in the

following scenario...there are 100 farms with less

than 1,000 acres and 50 farms with 1,000 acres

or more, but the total number of farms is equal to

151. 

Totals produced using the integer weights are

unbiased estimates of totals produced using the

noninteger weights. Therefore, in a perfect world,

these two values are equal. However, various

factors may affect the results. One possibility is

that the records chosen to receive an integer

nonresponse weight of 2 may not be a

representative sample. Returning to the previous

example, suppose that the two sample records

had 100 and 200 acres of cropland and the

average of the eight nonsample records was 400

acres. This would result in 3,800 total acres using

the integer weights compared to 4,200 total acres

using the noninteger weights. Another possibility

is that the distribution of the data is skewed,

which is common with agricultural data. For

many characteristics, the distribution is skewed to

the right since there are a lot of farms with smaller

values and a few farms with larger values. 

Therefore, the probability of assigning an integer

weight of 2 to farms below the average is greater

than 1⁄2 and the probability of assigning an integer

weight of 2 to farms above the average is less

than 1⁄2. (If the data were normally distributed,

both probabilities would equal 1⁄2.) In this case,

there is a tendency for the integer weights to

underestimate the total, especially for small

geographic areas (i.e., county-level data).

Note that the process of rounding weights affects

the sample item weights and the nonresponse

weights differently. First, since the sample item

weights are larger in magnitude than the

nonresponse weights, there is less of an effect on

the totals. Second, since the integer sample item

weights are equal to one less frequently than the

integer nonresponse weights, more records

contribute to the adjustment. (The average

noninteger sample item weight is 3.819 and the

average noninteger nonresponse weight is 1.139.) 

In other words, records with an integer

nonresponse weight of one do not represent a

nonrespondent.

This report compares totals calculated with the

noninteger weights to the published totals

(calculated with the integer weights) for the 1997

Census of Agriculture, evaluates how different

these totals are, and examines how the differences

relate to the standard error. The author discusses

totals for a number of characteristics at both the

state and county levels. The author also examines

another possible approach where the noninteger

weights are applied to the record and the

weighted data values are rounded at the record

level (referred to as record’s rounded-weighted

data values). The difference between totals

produced with the noninteger weights and the

record’s rounded-weighted data values are

calculated and compared to the above

differences.

BACKGROUND

For more than 150 years, there has been a census

of agriculture. The first agriculture census was

taken in 1840. From 1840 to 1950, the

agriculture census was taken as part of the

decennial census. A separate mid-decade census

of agriculture was conducted in 1925, 1935, and

1945. From 1954 to 1974, a census of
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agriculture was taken for the years ending in 4 and

9. In 1976, Congress authorized the census of

agriculture to be taken for 1978 and 1982 to

adjust the data reference year so that it coincided

with other economic censuses. This adjustment in

timing established the agriculture census on a 5-

year cycle collecting data for years ending in 2

and 7. Beginning with the 1997 Census of

Agriculture, responsibility was transferred from

the U.S. Department of Commerce, Bureau of the

Census to the U.S. Department of Agriculture,

National Agricultural Statistics Service.

To reduce data collection costs for the 1997

Census of Agriculture, a screening operation was

conducted by mail and telephone to survey about

500,000 records identified as having a low

probability of being a farm. Records with no

agricultural activity were removed from the census

mail list. In December 1997, approximately

3,155,000 records were mailed a census

questionnaire and about 34,000 tagged records

(farms which were abnormal, multi-unit, in the

ARMS survey, or identified by a State Statistical

Office (SSO) for special handling) were given

directly to the SSOs for data collection. A thank

you/reminder card was mailed to everyone in

early January 1998. Nonrespondents, except for

1992 census nonrespondents and large farms,

were then sent two follow-up mailings in midFebruary and late March. Telephone calls were

made in early February to nonrespondents who

were also 1992 census nonrespondents and in

early March to nonrespondents classified as large

farms. From early April until late May, telephone

calls were made to all remaining nonrespondents

to encourage them to respond to the census and

to ensure at least a 75% response rate within each

county.

To reduce respondent burden, the census used

two forms - a long form and a short form. The

long form is the same as the short form but

contains additional questions on the usage of

fertilizers and chemicals, farm production

expenditures, value of machinery and equipment,

value of land and buildings, and farm related

income. All records classified as certainty (tagged

records; farms greater than a state-specified level

for acreage and total value of products sold

(TVP); special insert cases such as Christmas

trees and maple sap farms; farms in Rhode Island,

Alaska, and Hawaii; or records in a county that

contained fewer than 100 farms in the 1992

census) received a long form. A systematic

sample was then taken at the county level from

the remaining noncertainty records to also receive

the long form. The county’s sampling rate was

based on 1992 farm counts and was either 1-in1, 1-in-2, 1-in-4, or 1-in-6. 

Each census record has two weights which are

used to produce totals for the entire population. 

The first weight is the nonresponse weight which

accounts for farm operators who did not respond

to the census despite numerous attempts to

contact them. Information on farming status for

the nonrespondents was obtained from the 1997

Nonresponse Survey. At the end of the census

follow-up operations, a stratified systematic

sample of nonresponse records was selected for

this survey within each state (except for Alaska

and Rhode Island which required a 100%

response rate). The strata were formed based on

screener status, TVP, and number of sources

from which the record was obtained during the

development of the mail list. From the survey, the

number of census nonrespondents that operated

farms was estimated at the stratum level in each

state, and these estimates were allocated to the

county level. Within each county/stratum, the

noninteger nonresponse weight was calculated as

the total number of farms (respondents that

operated farms plus the estimated number of
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nonrespondents that operated farms) divided by

the number of respondents that operated farms. 

Strata were collapsed if the noninteger

nonresponse weight was greater than two, to

prevent an individual record from representing

more than one nonrespondent. The noninteger

nonresponse weight for each farm was then

rounded to either one or two. All farms classified

as large based on total acreage, TVP, commodity

production, or certain characteristics (i.e., value of

land and buildings, value of machinery and

equipment, farm-related income, number of

workers, etc.); all tagged records; and all farms

with uncommon commodities were assigned a

nonresponse weight of one. This was because

these records either required a 100% response

rate or could not represent a nonrespondent. 

From the remaining records, a systematic sample

of records was selected within each

county/stratum for the integer rounding; the integer

nonresponse weight was two for sample records

and one for nonsample records.

The second weight is the sample item weight

which accounts for both nonresponse and

subsampling for data items that are only asked on

the long form. Operationally, this weight was

referred to as the sample weight; the author has

expanded the name to avoid confusion with the

standard definition of a sample weight. The

sample item weight was calculated by multiplying

the noninteger nonresponse weight by the

“adjusted” sampling weight (the “adjusted”

sampling weight uses only respondents that

operated farms). Certainty farms were always

assigned an adjusted sampling weight of one. The

noncertainty farms were stratified based on TVP,

total acreage, and Standard Industrial

Classification code. Within each stratum, the

adjusted sampling weight was calculated as the

sum of the noninteger nonresponse weights for

noncertainty farms (long-form noncertainty farms

plus short-form noncertainty farms) divided by the

sum of the noninteger nonresponse weights for

long-form noncertainty farms. Strata were

collapsed if the adjusted sampling weight was

greater than twice the original sampling weight or

if the sum of the noninteger nonresponse weights

for long-form noncertainty records was less than

10. The noninteger sample item weight for each

noncertainty farm was then rounded to an integer

value. A systematic sample of records was

selected within each county/nonresponse

stratum/sample stratum for the integer rounding;

the integer sample item weight was the next

largest integer value for sample records and the

truncated value for nonsample records.

As previously stated, the purpose of the

nonresponse and sample item weights is to

produce totals for the entire population (for more

details on the weighting adjustments, refer to the

memoranda written by Swan and Scholetzky). 

The nonresponse weight is used when generating

totals for data items collected from all

respondents (the question appears on both the

long and short forms) and the sample item weight

is used when generating totals for data items

collected from a subset of all respondents (the

question appears only on the long form).

The process of rounding weights to integer values

has been in place for the last several censuses. In

1993, research was done to examine how this

process affects the aggregates by analyzing

county-level totals for three long-form

characteristics in two states (Kraus-Winters,

1993). Although that paper concentrated more

on examining the effect on the variance of the

totals, the authors concluded “the results indicate

that minor discrepancies at the strata level due to

systematic rounding accumulate to sizable

differences at the published level for some farm

characteristics.”
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This paper represents the first assessment of the

rounding process on totals computed for data

items on both the long and short forms.

RESULTS AND DISCUSSION

Before proceeding, the author will return to the

previous example to illustrate how rounding a

record’s weight to an integer value affects the

data for the individual record. Suppose that the

sample farm with 200 acres of cropland reported

100 acres of soybeans and 20 cattle (recall that

the noninteger nonresponse weight is 1.2 and the

integer nonresponse weight is 2). Using the

noninteger nonresponse weight, the record

represents 1.2 farms totaling 240 acres of

cropland, 120 acres of soybeans, and 24 cattle. 

Using the integer nonresponse weight, which is

used in producing the publication tables, the

record represents 2 farms totaling 400 acres of

cropland, 200 acres of soybeans, and 40 cattle. 

For this record, the difference between the totals

calculated with the noninteger weight and the

published totals are 0.8 farms, 160 acres of

cropland, 80 acres of soybeans, and 16 cattle.

When a record’s weight is rounded to an integer

value, the totals represented by that record may

or may not change dramatically. These changes

may or may not become negligible when

producing totals at the state or county level. The

definition of what a person considers negligible is

open to debate. One thing is clear - the

difference must be considered in relation to the

size of the total and the standard error of the total.

The following characteristics were considered in

the analysis: 

Value of Land and Buildings

Farm Production Expenditures

Total Value of Products Sold

Land in Farms

Acres of Harvested Cropland

Acres of Corn for Grain Harvested

Bushels of Corn for Grain Harvested

Acres of Wheat for Grain Harvested

Bushels of Wheat for Grain Harvested

Acres of Soybeans Harvested

Bushels of Soybeans Harvested

Acres of Cotton Harvested

Bales of Cotton Harvested

Total Cattle and Calves

Dollars Received for Dairy Products

Total Hogs and Pigs 

Layers 20 Weeks Old and Older

Broilers and Other Meat-Type Chickens Sold

Farms Operated by Blacks and Other Races

Farms Operated by Females

Farms Operated by Persons of Hispanic Origin

The first two characteristics are long-form data

items and the remaining characteristics are data

items on both the long and short forms. In

addition to these characteristics, the number of

farms was examined. Number of farms is a

unique variable since the data value is equal to

one for every record. Therefore, in this case, the

theory behind integer weighting dictates that totals

produced using the noninteger weights are equal

to totals produced using the integer weights. As

expected, there was no difference between the

number of farms calculated with the noninteger

and integer nonresponse weights at either the state

or county levels.

Note that the number of farms was also published

using the integer sample item weight. The totals

produced using the noninteger and integer

nonresponse weights were equal to the number of

farms calculated with the noninteger sample item

weight (this is true because the “adjusted”

sampling weight is used when calculating this

weight). However, there was a difference

between these totals and the number of farms
6

calculated with the integer sample item weight. 

The author is not sure why this resulted; it could

have occurred because of the small number of

farms within a county/nonresponse stratum/sample

stratum or because the rounding methodology

wasn’t consistent within the weighting program. 

Whatever the reason, the differences are not large

compared to the number of farms in the state. 

The three largest differences were 53 farms in

New York, 35 farms in West Virginia, and 25

farms in Maryland. At the county level, the

differences were small. But, both the state and

county differences could be troublesome to a data

user. One example of this appears on page C-16

of Table F in the United States publication, where

the two totals are adjacent to each other. A data

user might improperly conclude that these

differences were an error. 

In this report, the author refers to the totals

calculated with the integer weights as the

published totals. This terminology is not literal;

totals for the above characteristics do not appear

in the census publication when the total was

suppressed due to confidentiality

concerns or the commodity was not published for

that state. The analysis done for this report

concentrated more on comparing the county-level

totals than the state-level totals. This is because

the differences are not as predominant at the

state-level and because the primary purpose of

the census is to produce county-level totals. The

report first presents a brief discussion of the statelevel totals and then a more in-depth analysis of

the county-level totals.

Differences in Totals at the State Level

The t-value was used to evaluate the difference

between the published and noninteger totals at the

state level. A t-value was determined by

subtracting the noninteger total from the published

total and dividing this quantity by the standard

error of the difference. The standard error was

calculated using the formula described in

Appendix A and the t-value was compared to a

two-tailed Student t distribution. When the tvalue for a characteristic exceeded the threshold

for 90% significance, the difference in the statelevel totals was defined to be “large.” Table 1

lists the states that were determined to have large

differences for each characteristic.
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Table 1: State Totals with Statistically Significant Differences

Characteristic States

Value of Land and Buildings

Farm Production Expenditures

Total Value of Products Sold

Land in Farms

Acres of Harvested Cropland

Acres of Corn for Grain Harvested

Bushels of Corn for Grain Harvested

Acres of Wheat for Grain Harvested

Bushels of Wheat for Grain Harvested

Acres of Soybeans Harvested

Bushels of Soybeans Harvested

Acres of Cotton Harvested

Bales of Cotton Harvested

Total Cattle and Calves

Dollars Received for Dairy Products

Total Hogs and Pigs

Layers 20 Weeks Old and Older

Broilers and Other Meat-Type Chickens Sold

Farms Operated by Blacks and Other Races

Farms Operated by Females

Farms Operated by Persons of Hispanic Origin

CO, KY*, LA, MD*, MI*, MT, NC*, OK**, SC, VT

ND

AZ**

CA*, GA*, OK

AZ*, AR, CT, HI*, ME, NC*, SD*

CO, DE, KS, LA, NH, ND, VT**

DE*, KS, NH, ND, VT**, WY*

IA, MS, NV*, NY**

FL, IA, MS, NV**, NY**

AL, OK

None

MO

MO*, NM

KY, VT*, WI

DE, IL*, LA**, MS*, UT, WY*

LA**, NM*, OH, TN**

MD, NH*, PA*, VT*, WI

KS**, ME, WI*

AZ

*

, KY*, NM

CO*, GA, HI*, ME, MN*, ND**, SD*

IN, KY*, MD*, MI**, MN*, NM, OH**, OR*, WI*,

WY*

 * Greater than or equal to a 95% significance level.

 ** Greater than or equal to a 99% significance level.

There were several situations where the t-value

was just less than a 90% significance level. Since

state totals for these are large enough to be

potentially of practical importance, the author also

considers the difference in the state-level totals to

be large for: total value of products sold in SD

with a t-value of 1.631, acres of corn for grain

harvested in AR with a t-value of 1.617, acres of

wheat for grain harvested in GA and NE with tvalues of 1.627 and 1.611, bushels of wheat for

grain harvested in MI with a t-value of 1.626,

acres of soybeans harvested in PA with a t-value

of 1.618, acres of soybeans harvested in PA with

a t-value of 1.625, total cattle and calves in FL

with a t-value of 1.632, and total hogs and pigs in

AZ with a t-value of 1.644.

Interpretation of the results presented in Table 1

depends on the reader. For example, a person

may not be concerned with a significant difference

in a state when the characteristic is a small

percentage of the national total. On the other

hand, another person may be concerned that the

process of rounding weights results in a significant

difference for any characteristic at the state level. 

The difference and percent difference can be used

to help evaluate the importance of the statistical

significance. The percent difference is computed

by subtracting the noninteger total from the

published total, dividing by the noninteger total,

and converting this to a percentage. To illustrate

this methodology, the author will discuss two

states which were significant at the 99% level. 
8

For acres of corn for grain harvested in Vermont,

the difference was -147 acres and the percent

difference was -1.75 percent. For broilers and

other meat-type chickens sold in Kansas, the

difference was -3,507 chickens and the percent

difference was -9.10 percent. A listing of all

state/characteristic combinations that are

statistically significant at the 90% level is provided

in Appendix B.

Although not specifically presented in a table, it

should be noted that it is possible for a state to

have a large percent difference between the

published and noninteger totals that is not

statistically significant. For example, for acres of

corn for grain in Nevada, the difference was 50

acres and the percent difference was 14.45 but

the t-value was 0.901. For broilers and other

meat-type chickens sold in Colorado, the

difference was 2,082 and the percent difference

was 21.13 but the t-value was 0.917. 

Differences in Totals at the County Level

As stated earlier, a more comprehensive analysis

was performed on totals at the county level. The

analysis was performed on all 21 characteristics

for every county in the 48 contiguous states. 

“Small” counties were identified for each

characteristic and excluded from the analysis. 

Originally, a county was defined as “small” when

the total for the characteristic was less than 1% of

the state total. However, this definition resulted in

eliminating too many counties in states with a large

number of counties (i.e., Texas). In order to

account for the variation in the number of counties

among states, the concept of “expected county

contribution” was adopted. Expected county

contribution is defined as the multiplicative inverse

of the state’s number of counties times the state

total for the characteristic. So, a county was

defined as “small” when it contained less than the

expected county contribution. For example, if a

county in Texas contained 20,000 acres of

cotton, then it was considered to be small since

20,000 is less than the inverse of the number of

counties (254-1) times the total number of acres of

cotton in Texas (5,221,561). Using this

procedure, the definition of a small county for

number of acres of cotton in Texas was 20,557

rather than the original definition of 52,215. In

addition to the concept of expected county

contribution, for the three demographic variables,

a county was determined to be “small” when the

county’s sum of the integer nonresponse weights

was less than three.

After eliminating small counties from the analysis,

two statistics were used to evaluate the difference

between the published and noninteger totals. 

First, a t-value was calculated by subtracting the

noninteger total from the published total and

dividing this quantity by the standard error of the

difference. Like the state-level analysis, the

standard error was calculated using the formula

described in Appendix A, and the t-value was

compared to a two-tailed Student t distribution. 

When the t-value for a characteristic exceeded

the threshold for 90% significance, the difference

in the county-level totals was defined to be

“large.” Second, a percent difference was

computed by subtracting the noninteger total from

the published total, dividing by the noninteger

total, and converting this to a percentage. Unlike

the t-value, this method did not use a specific

cutoff to define a “large” difference in the countylevel totals. Instead, the analysis focused on

counties with the largest percent differences for

each characteristic.

Various tables and maps were created in an effort

to show the key results of the analysis. Results

from each method are presented as well as results

from combining the methods. While interpretation
9

of these results depends on the reader, the author

has attempted to present an unbiased

summarization of the analysis.

Table 2 presents the number of nonsmall counties

with statistically significant differences. For each

characteristic, the table shows the number of

nonsmall counties that were significant at the three

most commonly-used levels and the percentage of

statistically significant nonsmall counties at the

90% level.

The three significance levels are shown to give a

measure of how different the published total is

from the noninteger total. The number of counties

at the 95% and 99% significance levels are

subsets of the previous significance level. For

example, 100 counties are significant at the 90%

level for value of land and buildings; 44 of these

100 counties are

significant at the 95% level. The last column, the

percentage with significant differences, is shown

to give a measure of the number of differences. 

This percentage was calculated by dividing the

number of nonsmall counties that were significant

at the 90% level (shown in column three) by the

total number of nonsmall counties for the

characteristic (shown in column two) and

multiplying by 100. Farms operated by persons

of Hispanic origin had the largest percentage of

significant counties and farm production

expenditures had the smallest. The two long-form

characteristics performed much better than the

characteristics collected on both the long and

short forms. This is not surprising since the

process of rounding weights has less of an effect

on the totals as the weight increases, and the

sample item weights 

Table 2: Number of Nonsmall Counties with Statistically Significant Differences

Characteristic

Number of

Nonsmall

Counties

Significance Level % with

Significant

$ Differences 90% $ 95% $ 99%
10

Value of Land and Buildings

Farm Production Expenditures

Total Value of Products Sold

Land in Farms

Acres of Harvested Cropland

Acres of Corn for Grain Harvested

Bushels of Corn for Grain Harvested

Acres of Wheat for Grain Harvested

Bushels of Wheat for Grain Harvested

Acres of Soybeans Harvested

Bushels of Soybeans Harvested

Acres of Cotton Harvested

Bales of Cotton Harvested

Total Cattle and Calves

Dollars Received for Dairy Products

Total Hogs and Pigs

Layers 20 Weeks Old and Older

Broilers and Other Meat-Type Chickens Sold

Farms Operated by Blacks and Other Races

Farms Operated by Females

Farms Operated by Persons of Hispanic Origin

1285

1033

1073

1307

1160

932

885

853

824

849

825

282

276

1170

713

706

362

296

644

1073

643

100

23

125

133

126

131

129

111

111

91

96

37

35

114

113

111

57

25

130

148

199

44

15

77

71

65

76

76

56

52

51

53

17

13

66

56

55

30

9

87

83

103

13

5

19

11

15

29

23

8

10

13

11

4

2

23

19

18

6

0

28

20

18

7.78

2.23

11.65

10.18

10.86

14.06

14.58

13.01

13.47

10.72

11.64

13.12

12.68

9.74

15.85

15.72

15.75

8.45

20.19

13.79

30.95

are larger in magnitude than the nonresponse

weights.

An interesting subset of the counties tallied in

Table 2 is the collection of those counties that

represented a substantial percentage of the state

total. Table 3 provides the number of nonsmall

counties with statistically significant differences,

where the county represented more than 5

percent of the state total. Again, for each

characteristic, the table shows the number of

nonsmall counties that were significant at the three

most commonly-used levels and the percentage of

statistically significant nonsmall counties at the

90% level.

The explanation of the columns in this table is the

same as for Table 2, with the exception of the last

column. The percentage of significant counties

was calculated by dividing the number of nonsmall

counties that were significant at the 90% level and

contributed more than 5 percent of the state total

(shown in column three) by the total number of

nonsmall counties for the characteristic that

represent more than 5 percent of the state total

(shown in column two) and multiplying by 100. 

These percentages can be compared to the

percentages in Table 2 to evaluate whether or not

the significant differences occurred in more

prominent counties. For example, acres of corn

for grain harvested increased from
11

Table 3: Number of Nonsmall Counties with Statistically Significant Differences 

that Represent More Than 5 Percent of the State Total

Characteristic

Number of

Counties

(nonsmall

and $5%)

Significance Level % with 

Significant

Differences $

90%

$

95%

$

99%

Value of Land and Buildings

Farm Production Expenditures

Total Value of Products Sold

Land in Farms

Acres of Harvested Cropland

Acres of Corn for Grain Harvested

Bushels of Corn for Grain Harvested

Acres of Wheat for Grain Harvested

Bushels of Wheat for Grain Harvested

Acres of Soybeans Harvested

Bushels of Soybeans Harvested

Acres of Cotton Harvested

Bales of Cotton Harvested

Total Cattle and Calves

Dollars Received for Dairy Products

Total Hogs and Pigs

Layers 20 Weeks Old and Older

Broilers and Other Meat-Type Chickens Sold

Farms Operated by Blacks and Other Races

Farms Operated by Females

Farms Operated by Persons of Hispanic Origin

115

127

136

106

126

159

176

187

180

121

121

94

90

126

182

178

151

137

102

101

91

2

0

15

12

11

33

27

16

15

11

18

12

12

15

36

26

24

14

18

9

16

0

0

9

5

7

19

16

9

10

7

11

7

6

10

18

12

13

5

11

4

10

0

0

2

2

2

8

4

2

2

1

2

2

1

4

3

5

2

0

4

1

6

1.74

0.00

11.03

11.32

8.73

20.75

15.34

8.56

8.33

9.09

14.88

12.77

13.33

11.90

19.78

14.61

15.89

10.22

17.65

8.91

17.58

14.06 to 20.75 percent while farms operated by

persons of Hispanic origin decreased from 30.95

to 17.58 percent.

County-level maps of the absolute percent

differences for the 48 contiguous states were

produced to analyze the differences for each

characteristic. The absolute percent difference

was used to decrease the number of ranges. 

Maps were used as the data analysis tool since

map presentation was the most efficient method

for displaying a lot of information (i.e., 3,069

counties and 21 characteristics). The maps are

included in Appendix C. The ranges are the same

for characteristics with a common theme (i.e.,

corn, wheat, soybeans, and cotton acreages and

production have the same ranges). Two maps

containing different ranges are displayed for acres

of harvested cropland so this characteristic can be

compared to individual crop acreages as well as

other key characteristics. 

The county-level percent differences were the

smallest for the two long-form characteristics
12

and total value of products sold. Land in farms

and acres of harvested cropland had more

counties with a percent difference in the top two

ranges than did these three characteristics. The

maps looked reasonable for the crop

characteristics, but the ranges were also higher

than those for the other characteristics. The maps

for the livestock characteristics were extremely

different from the crop characteristics. With the

exception of cattle and calves, there were more

counties in the top two ranges and fewer counties

in the bottom two ranges. With the exception of

farms operated by females, the maps for the

demographic characteristics showed the most

counties in the top two ranges, even with the

increase in the ranges.

To supplement the maps, Table 4 contains the

number of nonsmall counties that had a percent

difference in the top two ranges. For each

characteristic, the table shows the total number of

nonsmall counties and the number of nonsmall

counties that were statistically significant at each

level.
13

Table 4: Number of Nonsmall Counties with a Percent Difference in the Top Two Ranges

Characteristic Total

Significance Level

< 90% $ 90% $ 95% $ 99%

Value of Land and Buildings

Farm Production Expenditures

Total Value of Products Sold

Land in Farms

Acres of Harvested Cropland

Acres of Corn for Grain Harvested

Bushels of Corn for Grain Harvested

Acres of Wheat for Grain Harvested

Bushels of Wheat for Grain Harvested

Acres of Soybeans Harvested

Bushels of Soybeans Harvested

Acres of Cotton Harvested

Bales of Cotton Harvested

Total Cattle and Calves

Dollars Received for Dairy Products

Total Hogs and Pigs

Layers 20 Weeks Old and Older

Broilers and Other Meat-Type Chickens Sold

Farms Operated by Blacks and Other Races

Farms Operated by Females

Farms Operated by Persons of Hispanic Origin

6

0

1

21

15

21

15

34

28

35

31

2

2

2

30

68

51

53

117

1

305

5

0

0

4

4

11

11

25

23

30

27

2

2

1

17

61

44

51

100

0

228

1

0

1

17

11

10

4

9

5

5

4

0

0

1

13

7

7

2

17

1

77

1

0

1

12

7

5

4

5

3

2

1

0

0

1

7

4

5

1

3

1

16

1

0

0

4

2

1

1

1

1

1

1

0

0

1

1

2

0

0

0

0

0

The reader must be careful when using Table 4 to

make comparisons across characteristics, since

the top two ranges vary by characteristic. The

top two ranges contain percent differences greater

than or equal to 2% for the first five

characteristics, greater than or equal to 20% for

the last three characteristics, and greater than or

equal to 5% for the remaining characteristics. 

Also, the number of nonsmall counties that were

not significant at the 90% level is included in

column three of this table. This column plus the

number of nonsmall counties that were significant

at the 90% level (shown in column four) equals

the total number of nonsmall counties in column

two. Again, the number of counties at the 95%

and 99% significance levels are subsets of the

previous significance level. A listing of all 192

nonsmall county/characteristic combinations that

are statistically significant at the 90% level is

provided in Appendix D. The listing contains the

percent difference and t-value as well as the

percentage of the state total that the county

represents.

Table 4 can be compared to Table 2 to evaluate

whether or not the significant differences occurred

in the top two ranges for the characteristics. For

example, Table 2 shows that the percentage of

significant counties out of all nonsmall counties for

land in farms was 10.18 percent while Table 4

shows the percentage of significant counties out of

all nonsmall counties with a percent difference

greater than or equal to 2% was 80.95 percent

(17 divided by 21). On the other hand, the same
14

comparison for farms operated by persons of

Hispanic origin shows that the percentage of

significant counties was 30.95 percent in Table 2

and 25.25 percent in Table 4.

A miscellaneous observation for Appendix D is

that Screven, GA had seven characteristics with a

percent difference in the top two ranges (the next

closest county had four). For each characteristic,

Screven represented between 1 and 4 percent of

the state total. All characteristics except for one

were statistically significant at the 95% level.

Again, an interesting subset of the counties tallied

in Table 4 is the collection of those counties that

represented a substantial percentage of the state

total. Table 5 provides a listing of the counties

that had a percent

difference in the top two ranges, and where the

county represented more than 5 percent of the

state total. Since this subset is only 16 counties,

the county names are given with the associated

percent difference, percentage of state total, and

t-value (note that these counties are also listed in

Appendix D). If the characteristic does not

appear in the table, then there were no counties

that met these criteria.

Although the same counties appear for acres and

bushels of corn for grain harvested, this was not

always the case when examining harvested acres

and production for a crop. For example, for

acres and bushels of wheat for grain harvested,

Appendix D shows that Peach, GA was

statistically significant for acres but not bushels

while Morgan, GA was
15

Table 5: Nonsmall Counties with a Percent Difference in the Top Two Ranges 

that Represent More Than 5 Percent of the State Total

Characteristic State Name County Name

Percent

Difference

% of State

t-value

Acres of Corn for Grain

Harvested

Montana Rosebud 9.89 5.50 2.203

Vermont Rutland 5.91 8.51 2.239

Bushels of Corn for Grain

Harvested

Montana Rosebud 8.77 5.16 2.243

Vermont Rutland 5.03 9.07 2.271

Dollars Received for Dairy

Products

Kansas Sedgwick 5.99 5.73 2.138

Wyoming Lincoln 5.25 42.20 2.039

Total Hogs and Pigs New Mexico Roosevelt 11.55 6.26 2.514

Layers 20 Weeks Old and

Older

Mississippi Leake 6.30 7.76 2.005

Mississippi Simpson 6.29 5.23 2.196

Nevada Washoe 11.55 12.23 2.071

Broilers and Other MeatType Chickens SoldColorado Larimer 8.47 10.32 2.162

Kansas Reno 9.84 16.46 1.796

Farms Operated by Blacks

and Other Races

Massachusetts Barnstable 20.00 11.11 1.921

Farms Operated by

Persons of Hispanic Origin

Maine Penobscot 20.00 11.11 1.710

Maryland Frederick 27.27 16.47 1.677

Wyoming Fremont 21.05 17.56 2.067

statistically significant for bushels but not acres. 

For the two demographic variables, the percent

difference for Barnstable, MA and Penobscot,

ME are a little misleading. These counties were

not small enough to be eliminated from the

analysis, but the weighted number of farms is only

4 (not true for the other two counties). Note that

some of these counties contributed substantially to

the state-level totals being statistically significant. 

For example, for dollars received for dairy

products, Lincoln, WY represented 42.20

percent of the state total and was statistically

significant at the 95% level. Table 1 shows that

Wyoming is statistically significant at the 95%

level for this characteristic. 

An Alternative Weighting Procedure

As previously stated, one of the two main reasons

for using integer weights is to make the publication

process simpler. Record-level integer weights

ensure the table cells within a column or row add

to the total for the column or row. However,

there is another possible way to address this

concern. Rather than round the noninteger

weights to integer values, another possible

approach is to apply the noninteger weights to the

record and round the weighted data values at the

record level. Thus, the operation’s weighted data

values are rounded and these values are used to

produce the totals for the entire population. 

To illustrate, recall the previous example where

the farm had 200 acres of cropland, reported 100

acres of soybeans, and 20 cattle. Using the
16

noninteger nonresponse weight of 1.2, the record

represented 1.2 farms totaling 240 acres of

cropland, 120 acres of soybeans, and 24 cattle. 

With the exception of 1.2 farms, no rounding is

required because the noninteger weight multiplied

by the individual data values are already integer

values. This is not a common occurrence in

practice. A more applicable example would be

that the noninteger nonresponse weight for this

record is a noninteger value rounded to six

decimal places, say 1.212684. For this example,

the record’s weighted data values are 1.212684

farms totaling 242.5368 acres of cropland,

121.2684 acres of soybeans, and 24.25368

cattle. After the rounding these data values, the

record represents 1 farm totaling 243 acres of

cropland, 121 acres of soybeans, and 24 cattle. 

For this record, the differences between the totals

calculated with the noninteger weight and the

record’s rounded-weighted data value are

minimal.

Totals produced using the record’s roundedweighted data values are unbiased estimates of

totals produced using the noninteger weights. 

This procedure performs better than the integer

weighting procedure because the two factors

mentioned earlier do not affect this procedure (not

having a representative sample or the distribution

of the data being skewed). However, the

downfall of using this procedure is that it does not

work well when producing totals for indicator or

categorical characteristics. For example, the

average noninteger nonresponse weight is

approximately 1.139. Therefore, for variables

such as number of farms and the demographic

characteristics (Blacks and Other Races,

Females, Hispanic Origin), the record’s roundedweighted data value will round down to one most

of the time and thus underestimate the total.

To examine this methodology on 1997 census

data, the author selected three states (Georgia,

Texas, and Virginia) which showed fairly large

differences between totals calculated with the

noninteger weights and the published totals. For

each characteristic, the difference between the

totals calculated with the noninteger and integer

weights is compared to the difference between the

totals calculated with the noninteger weights and

rounded-weighted data values. This analysis was

done at both the state and county levels. An

overall summary of these differences is presented

in Appendix E. The table shows the differences

at the state level along with the minimum and

maximum differences at the county level. As

expected, these results show that the totals

produced using the rounded-weighted data values

are more precise than the totals produced using

the integer weights except for the demographic

characteristics and number of farms.

RECOMMENDATIONS

The reasons for rounding weights, to ease data

review procedures and to ensure that publication

totals add, are legitimate concerns. The author

asserts that it is possible to address these two

concerns and improve the totals produced when

NASS revamps the census processing system for

the 2002 census. 

1) Automate the weights into the data

review procedures.

One argument for using the integer weights

is that one can “easily” obtain weighted

totals for a record of interest during the

data review phase. In the 1997 system,

this would be accomplished by multiplying

the integer weight by each record’s data

value of interest. Thus, this manual

computation is easier if the nonresponse
17

weight is 2 rather than 1.7. However, for the

2002 system, the computer can and should

perform this calculation. With this

improvement, the value of the weight is no

longer relevant. 

2) Use a combination of the integer weights

and the record’s rounded-weighted data

values.

 

Another argument for using the integer weights

is to ensure that publication totals add. Integer

weights should be used to produce totals for

indicator and categorical variables. These

variables include number of farms as well as

variables for the questions concerning type of

organization, corporate structure, and

characteristics and occupation of the operator

(which includes demographic characteristics). 

In addition, the integer weights should be used

for any question where the data values are

small for most farms. For the 1997 census,

the only example of this is the question

concerning injuries or deaths. The record’s

rounded-weighted data values should be used

to produce totals for all other characteristics. 

Here, the noninteger weight is applied to the

record and the weighted data values are

rounded at the record level. With this

improvement, the integer sample item weight

should no longer be necessary since the 1997

long form did not contain a question that

requires it.

3) Evaluate the 2002 questionnaires to

determine which weight to use.

Once the 2002 long and short forms are

finalized, the questions will need to be

evaluated to determine which weight to use

during the summarization process. In

particular, the questions on the 2002 long form

will determine whether the integer sample item

weight is needed.
18

REFERENCES

Swan, Carolyn, “1997 Nonresponse Weighting:

Nonresponse Survey Sampling Specification,”

NASS Internal Memo, April 1999.

Scholetzky, Wendy, “1997 Nonresponse

Weighting: Creation of Final Nonresponse

Weighting Short Record File,” NASS Internal

Memo, April 1999.

Swan, Carolyn, “Estimation of In-Scope

Nonrespondents and Calculation of Initial

Noninteger Nonresponse Weights at

County/Nonresponse Stratum Level,” NASS

Internal Memo, April 1999.

Scholetzky, Wendy, “1997 Nonresponse

Weighting: Final Nonresponse Weighting

Specification,” NASS Internal Memo, April

1999.

Scholetzky, Wendy, “1997 Census of Agriculture

- Sample Weighting Specifications,” NASS

Internal Memo, April 1999.

Kraus, Melinda and Winters, Franklin, “An

Evaluation of Rounding Techniques Used to

Obtain Estimated Totals,” American Statistical

Association, 1993 Proceedings of the

International Conference on Establishment

Surveys, pp. 893-898.
19

APPENDIX A. FORMULA FOR CALCULATING THE STANDARD ERROR OF THE

DIFFERENCE

The following formula was used for calculating the standard error of the difference between the totals

generated using integer and noninteger weights:

Standard Error of Difference ' j

J

j'1

j

H

h'1

nhj

nhj & 1 j

n hj

i'1

[wihj(yihj &  ̄yh(w)) & aihj(yihj &  ̄yh(a))]

2

where

nhj = the number of in-scope, interviewed records in stratum h in county j

wihj = the noninteger weight associated with record i in stratum h in county j

aihj = the integer weight associated with record i in stratum h in county j

yihj = the unweighted data value for record i in stratum h in county j

 ̄y = the weighted average total for stratum h in county j using the noninteger weights h(w)

 ̄y = the weighted average total for stratum h in county j using the integer weights h(a)

H = the number of nonresponse strata in the county for characteristics on both the long 

 and short forms

 or

= the number of sample strata in the county for long-form characteristics

J = the number of counties in the state for state-level totals

 or

= 1 for county-level totals
20
21

APPENDIX B. LISTING OF STATES WITH STATISTICALLY SIGNIFICANT DIFFERENCES

AT THE 90% LEVEL

Characteristic State Name

Percent

Difference Difference t-value

Value of Land and

Buildings

Colorado 0.27 53,982,465 1.789

Kentucky -0.09 -17,501,972 -1.960

Louisiana 0.21 19,204,512 1.790

Maryland 0.25 16,990,601 2.036

Michigan 0.14 22,722,570 2.024

Montana -0.15 -25,874,963 -1.712

North Carolina -0.14 -26,145,535 -2.259

Oklahoma -0.18 -35,755,465 -2.675

South Carolina -0.22 -14,636,941 -1.907

Vermont -0.36 -6,858,242 -1.869

Farm Production

Expenditures

North Dakota -0.05 -1,187,380 -1.732

Total Value of

Products Sold

Arizona -0.11 -2,180,052 -3.358

Land in Farms California 0.40 111,717 2.287

Georgia -0.25 -26,798 -2.327

Oklahoma 0.12 41,009 1.673

Acres of Harvested

Cropland

Arizona -0.26 -2,492 -2.440

Arkansas 0.13 10,164 1.941

Connecticut -0.56 -793 -1.687

Hawaii -0.21 -213 -2.091

Maine -0.45 -1,804 -1.745

North Carolina -0.18 -7,833 -1.964

South Dakota -0.15 -21,711 -2.002

Acres of Corn for

Grain Harvested

Colorado -0.37 -3,397 -1.814

Delaware 0.63 989 1.708

Kansas -0.20 -4,976 -1.646

Louisiana -0.42 -1,725 -1.898

New Hampshire -1.46 -18 -1.800

North Dakota -0.35 -2,044 -1.659

Vermont -1.75 -147 -2.940
Characteristic State Name

Percent

Difference Difference t-value

22

Bushels of Corn for

Grain Harvested

Delaware 0.81 126,614 2.208

Kansas -0.21 -748,562 -1.807

New Hampshire -1.26 -1,622 -1.816

North Dakota -0.37 -203,715 -1.747

Vermont -1.58 -15,061 -2.791

Wyoming -1.58 -100,573 -1.979

Acres of Wheat for

Grain Harvested

Iowa -2.36 -535 -1.701

Mississippi -0.58 -901 -1.818

Nevada -1.28 -247 -2.150

New York -0.93 -1,137 -3.118

Bushels of Wheat for

Grain Harvested

Florida -2.35 -14,088 -1.716

Iowa -2.61 -24,231 -1.766

Mississippi -0.49 -31,983 -1.713

Nevada -1.11 -21,468 -2.816

New York -0.72 -46,268 -2.710

Acres of Soybeans

Harvested

Alabama 0.81 2,537 1.731

Oklahoma -0.81 -2,654 -1.879

Acres of Cotton

Harvested

Missouri -0.37 -1,457 -1.661

Bales of Cotton

Harvested

Missouri -0.43 -2,401 -2.125

New Mexico -0.66 -754 -1.895

Total Cattle and

Calves

Kentucky -0.23 -5,500 -1.736

Vermont 0.48 1,483 2.292

Wisconsin -0.19 -6,492 -1.793

Dollars Received for

Dairy Products

Delaware 1.32 250,408 1.666

Illinois 1.33 3,329,056 2.359

Louisiana 1.21 1,243,682 2.655

Mississippi -0.97 -816,420 -2.549

Utah 0.25 496,490 1.675

Wyoming -2.59 -262,555 -2.140

Total Hogs and Pigs Louisiana -2.11 -439 -2.905

New Mexico -5.08 -327 -2.399

Ohio -0.43 -7,418 -1.773

Tennessee -1.21 -3,933 -4.743
Characteristic State Name

Percent

Difference Difference t-value

23

Layers 20 Weeks Old

and Older

Maryland 0.81 33,018 1.645

New Hampshire -0.39 -721 -2.077

Pennsylvania 0.24 57,715 2.064

Vermont 0.34 864 1.965

Wisconsin -0.32 -12,011 -1.804

Broilers and Other

Meat-Type Chickens

Sold

Kansas -9.10 -3,507 -2.627

Maine -0.40 -807 -1.699

Wisconsin -0.13 -36,844 -2.098

Farms Operated by

Blacks and Other

Races

Arizona -2.63 -14 -2.490

Kentucky 2.92 20 2.168

New Mexico 0.99 21 1.681

Farms Operated by

Females

Colorado 1.04 33 2.034

Georgia -0.75 -32 -1.726

Hawaii 2.33 21 2.167

Maine 1.99 16 1.911

Minnesota -1.09 -40 -2.173

North Dakota 2.85 37 2.875

South Dakota -1.60 -24 -2.360

Farms Operated by

Persons of Hispanic

Origin

Indiana -2.93 -7 -1.853

Kentucky 3.58 14 2.054

Maryland 8.97 7 2.195

Michigan -3.78 -11 -3.036

Minnesota 4.84 12 2.166

New Mexico -0.69 -24 -1.730

Ohio -3.77 -12 -2.750

Oregon -2.48 -13 -2.190

Wisconsin -3.46 -9 -2.054

Wyoming 6.50 8 2.122
24

APPENDIX C. COUNTY-LEVEL MAPS OF PERCENT DIFFERENCES
44

APPENDIX D. LISTING OF COUNTIES WITH PERCENT DIFFERENCES IN THE TOP TWO

RANGES AND STATISTICALLY SIGNIFICANT DIFFERENCES AT THE 90% LEVEL

Characteristic State Name County Name

Percent

Difference

% of

State t-value

Value of Land and

Buildings

Georgia Oglethorpe 2.01 0.69 2.600

Total Value of Products

Sold

Tennessee Williamson 2.12 1.32 2.386

Land in Farms Alabama Coffee 2.29 2.15 2.004

Alabama Monroe 3.28 1.55 2.107

California Humboldt 5.77 2.11 2.426

California Mendocino 3.76 2.31 1.777

Georgia Bacon 2.51 0.66 1.831

Georgia Franklin -3.90 0.72 -3.168

Georgia Jackson -3.04 0.73 -1.867

Georgia Jeff Davis -2.65 0.67 -1.925

Missouri Laclede 2.17 1.10 2.395

Montana Cascade -2.02 2.46 -3.877

Nebraska Buffalo 2.00 1.36 2.336

Nebraska Rock 2.02 1.39 1.999

North Carolina Franklin 2.15 1.50 2.230

Ohio Clinton 2.13 1.58 2.683

Oregon Gilliam 2.72 4.26 1.862

Texas Live Oak 3.41 0.40 2.257

West Virginia Wood -2.09 1.93 -3.346

Acres of Harvested

Cropland

Georgia Screven 2.65 1.77 2.628

Kentucky Barren 2.03 1.83 2.427

Kentucky Butler 3.31 0.92 2.198

New York Delaware 2.29 1.84 2.161

North Carolina Rowan 2.17 1.00 1.690

Pennsylvania Centre 3.12 1.94 2.950

Pennsylvania Columbia 2.15 1.62 1.920

Tennessee Williamson 2.55 1.30 1.786

Texas Baylor 2.22 0.51 1.743

Texas Haskell -2.04 1.02 -2.492
Characteristic State Name County Name

Percent

Difference

% of

State t-value

45

West Virginia Wood -2.41 2.02 -2.498

Acres of Corn for

Grain Harvested

Alabama Marion 8.52 1.66 1.914

Florida Walton 8.57 2.06 1.705

Georgia Jeff Davis -5.87 0.97 -1.668

Georgia Screven 6.37 2.56 2.947

Montana Rosebud -9.89 5.50 -2.203

Texas Bexar -5.33 0.66 -2.226

Texas Cameron 5.93 0.85 1.976

Vermont Rutland -5.91 8.51 -2.239

Virginia Lancaster 6.36 1.31 1.727

Virginia Page 5.90 1.07 1.835

Bushels of Corn for

Grain Harvested

Georgia Screven 5.92 2.31 2.673

Montana Rosebud -8.77 5.16 -2.243

Texas Cameron 6.51 0.74 2.071

Vermont Rutland -5.03 9.07 -2.271

Acres of Wheat for

Grain Harvested

Florida Santa Rosa -5.40 3.99 -2.076

Georgia Peach -7.01 0.93 -1.717

Georgia Screven 6.08 1.12 2.038

Georgia Taylor -5.14 0.65 -1.727

Indiana Randolph 6.61 1.73 2.991

Iowa Benton -10.91 1.11 -1.683

Iowa Linn -14.35 1.86 -1.741

Iowa Page -10.14 1.76 -2.371

Virginia Middlesex 6.30 1.60 2.157

Bushels of Wheat for

Grain Harvested

Georgia Morgan -5.76 0.74 -1.901

Georgia Screven 5.24 1.21 2.115

Indiana Randolph 6.77 1.81 2.970

Iowa Benton -11.00 1.05 -1.682

Iowa Page -9.62 1.52 -2.414

Acres of Soybeans

Harvested

Georgia Screven 5.15 4.11 2.311

Georgia Telfair -12.72 0.66 -1.807

Pennsylvania Snyder 5.15 1.72 1.676

Texas Red River 10.27 4.56 1.725

Virginia Middlesex 6.53 1.69 2.625
Characteristic State Name County Name

Percent

Difference

% of

State t-value

46

Bushels of Soybeans

Harvested

Florida Holmes -6.98 2.72 -1.694

Georgia Telfair -20.75 0.65 -1.732

Pennsylvania Snyder 6.44 1.88 1.821

Virginia Middlesex 7.29 1.34 3.267

Total Cattle and Calves Georgia Thomas -5.94 0.73 -2.916

Dollars Received for

Dairy Products

Illinois Douglas 16.99 1.09 1.734

Illinois Ogle 10.64 1.95 1.707

Kansas Sedgwick 5.99 5.73 2.138

Kentucky Edmonson 9.20 0.88 1.683

Mississippi Marion 8.57 4.55 1.659

Mississippi Pearl River -5.47 1.39 -1.972

Nebraska Boone -6.05 1.39 -1.706

Nebraska Saunders -10.25 1.17 -2.086

South Dakota Bon Homme -6.42 1.85 -3.146

South Dakota Gregory 9.19 2.80 1.788

Tennessee Williamson 6.86 1.84 2.005

West Virginia Ohio -9.18 3.17 -2.364

Wyoming Lincoln -5.25 42.20 -2.039

Total Hogs and Pigs Alabama Madison -6.67 1.73 -1.765

Florida Hernando -12.15 1.74 -1.703

Idaho Gem -5.29 2.35 -1.793

Louisiana Calcasieu -7.86 2.88 -2.654

New Mexico Roosevelt -11.55 6.26 -2.514

Texas Fayette -5.00 1.07 -2.102

Wisconsin Outagamie -5.34 1.79 -3.478

Layers 20 Weeks Old

and Older

Alabama Cleburne -6.78 1.60 -1.970

Arkansas Yell -7.18 2.63 -1.767

Mississippi Jefferson Davis -17.64 2.56 -2.003

Mississippi Leake -6.30 7.76 -2.005

Mississippi Simpson -6.29 5.23 -2.196

Missouri Pettis -8.06 1.88 -1.829

Nevada Washoe -11.55 12.23 -2.071
Characteristic State Name County Name

Percent

Difference

% of

State t-value

47

Broilers and Other

Meat-Type Chickens

Sold

Colorado Larimer -8.47 10.32 -2.162

Kansas Reno -9.84 16.46 -1.796

Farms Operated by

Blacks and Other

Races

Arkansas Clark 25.00 2.14 1.975

Georgia Baldwin 28.57 0.68 1.734

Georgia Monroe 37.50 0.83 1.965

Indiana Allen -25.00 1.74 -1.725

Indiana Lake -25.00 1.74 -1.645

Indiana Putnam -20.00 2.33 -1.927

Iowa Lee -20.00 3.25 -1.727

Massachusetts Barnstable -20.00 11.11 -1.921

Minnesota Clearwater -20.00 2.04 -1.863

Minnesota Morrison -20.00 2.04 -1.944

Minnesota Otter Tail 40.00 3.57 1.663

Nebraska Douglas -25.00 1.59 -1.649

Nebraska Sioux -25.00 1.59 -1.741

Nebraska Thurston -20.00 4.23 -2.105

Ohio Ashtabula 37.50 3.45 1.806

Wisconsin Pierce -20.00 2.17 -1.956

Wisconsin Washburn -25.00 1.63 -1.646

Farms Operated by

Females

North Dakota Pierce 20.83 2.17 2.105

Farms Operated by

Persons of Hispanic

Origin

Alabama Jackson -25.00 1.61 -1.683

Alabama Shelby -20.00 2.15 -1.653

Alabama Washington -20.00 2.15 -1.946

Georgia Cobb -25.00 0.96 -1.971

Georgia Turner -25.00 0.96 -1.717

Illinois Bureau -20.00 1.38 -1.961

Illinois Calhoun -25.00 1.04 -1.870

Illinois Champaign -20.00 1.38 -1.976

Illinois Effingham -20.00 1.38 -1.726

Illinois Fayette -25.00 1.04 -1.726

Illinois Macoupin -20.00 1.38 -1.666

Illinois Peoria -20.00 1.38 -1.984
Characteristic State Name County Name

Percent

Difference

% of

State t-value

48

Illinois Pike -20.00 1.38 -1.941

Illinois Shelby -20.00 1.38 -1.777

Indiana Allen -25.00 1.29 -1.719

Indiana Morgan -25.00 1.29 -1.700

Indiana Ripley -20.00 1.72 -1.895

Indiana Switzerland -20.00 1.72 -1.764

Iowa Butler -20.00 1.17 -1.753

Iowa Harrison -20.00 1.17 -1.698

Iowa Mills -20.00 1.17 -1.776

Iowa Taylor -20.00 1.17 -1.968

Iowa Union -20.00 1.17 -1.956

Iowa Washington -20.00 1.17 -1.970

Kansas Jackson -20.00 1.20 -1.871

Kansas Republic -20.00 1.20 -1.926

Kansas Washington -20.00 1.20 -1.969

Kansas Wilson -20.00 1.20 -1.686

Kentucky Hart -20.00 1.98 -2.491

Louisiana Morehouse -20.00 1.87 -1.937

Louisiana Plaquemines -20.00 1.87 -1.672

Louisiana Saint Bernard 20.00 2.80 2.115

Maine Penobscot -20.00 11.11 -1.710

Maryland Frederick 27.27 16.47 1.677

Michigan Ionia -20.00 1.43 -1.809

Michigan Oakland -20.00 1.43 -1.933

Minnesota Le Sueur 75.00 2.69 1.755

Minnesota Marshall -20.00 1.54 -1.901

Minnesota Sherburne -20.00 1.54 -1.838

Mississippi Jackson -25.00 2.01 -1.753

Missouri Camden -20.00 0.90 -1.899

Missouri Howard -20.00 0.90 -1.963

Missouri Jefferson -20.00 0.90 -1.912

Missouri Macon -20.00 0.90 -1.987

Missouri Moniteau -20.00 0.90 -1.863

Missouri Reynolds -20.00 0.90 -1.921
Characteristic State Name County Name

Percent

Difference

% of

State t-value

49

Missouri Warren -20.00 0.90 -1.845

Montana Missoula -20.00 2.31 -1.948

Nebraska Butler -25.00 1.18 -1.724

Nebraska Cheyenne -20.00 1.57 -1.857

Nebraska Nuckolls -25.00 1.18 -1.733

Nebraska Otoe -25.00 1.18 -1.736

Nebraska Seward -25.00 1.18 -1.709

Nebraska Sioux -20.00 1.57 -1.945

North Carolina Alexander -20.00 1.25 -1.647

North Carolina Davidson -20.00 1.25 -1.729

North Carolina Iredell -22.22 2.19 -2.127

North Carolina Pender -20.00 1.25 -1.697

North Dakota Oliver -25.00 2.07 -1.733

North Dakota Pembina -20.00 2.76 -1.699

North Dakota Williams -20.00 2.76 -1.731

Ohio Clermont -20.00 1.31 -1.948

Ohio Gallia -20.00 1.31 -1.704

Ohio Geauga -20.00 1.31 -1.917

Ohio Madison -20.00 1.31 -1.940

Oklahoma Payne 23.08 2.90 1.825

South Dakota Day 60.00 4.76 1.693

South Dakota Turner -20.00 2.38 -1.931

Tennessee Cannon -20.00 1.07 -1.688

Tennessee Davidson -28.57 1.33 -2.209

Tennessee Dickson -20.00 1.07 -1.915

Tennessee Mcnairy -20.00 1.07 -1.957

Virginia Frederick -20.00 1.72 -1.964

Wisconsin Clark -20.00 1.59 -1.931

Wisconsin Eau Claire -20.00 1.59 -1.923

Wisconsin Shawano -20.00 1.59 -1.986

Wyoming Fremont 21.05 17.56 2.067
50

APPENDIX E. SUMMARY OF DIFFERENCES BETWEEN STATE AND COUNTY TOTALS

FOR GEORGIA, TEXAS, AND VIRGINIA

Georgia data - 1997 census

Characteristic Level

Integer Total -

Noninteger Total

Rounded Total -

Noninteger Total

Value of Land and state -7,033,641 72

Buildings min cnty -2,892,379 -5

max cnty 2,881,849 5

Farm Production state -207,412 60

Expenditures min cnty -135,120 -8

max cnty 204,418 6

Total Value of Products state -1,410,654 180

Sold min cnty -536,106 -9

max cnty 463,555 11

Land in Farms state -26,801 303

min cnty -3,529 -13

max cnty 2,403 18

Acres of Harvested state -4,889 32

Cropland min cnty -1,393 -13

max cnty 1,723 9

Acres of Corn for state -885 35

Grain Harvested min cnty -348 -5

max cnty 620 4

Bushels of Corn for state -130,369 27

Grain Harvested min cnty -34,972 -4

max cnty 52,426 6

Acres of Wheat for state -1,777 5

Grain Harvested min cnty -354 -4

max cnty 404 3

Bushels of Wheat for state -56,259 6

Grain Harvested min cnty -18,331 -3

max cnty 19,843 3

Acres of Soybeans state -296 15

Harvested min cnty -340 -2

max cnty 706 3

Bushels of Soybeans state 1,065 27

Harvested min cnty -12,088 -2

max cnty 15,117 5

Acres of Cotton state -1,202 9

Harvested min cnty -723 -3

max cnty 697 3
Georgia data - 1997 census

Characteristic Level

Integer Total -

Noninteger Total

Rounded Total -

Noninteger Total

51

Bales of Cotton state -943 18

Harvested min cnty -995 -4

max cnty 900 5

Total Cattle and Calves state -3,759 182

min cnty -630 -15

max cnty 659 9

Dollars Received for state 93,501 -3

Dairy Products min cnty -120,013 -1

max cnty 140,939 1

Total Hogs and Pigs state 2,858 -1

min cnty -491 -4

max cnty 700 4

Layers 20 Weeks Old state -79,527 4

and Older min cnty -31,671 -4

max cnty 19,248 4

Broilers and Other state -445,904 1

Meat-Type Chickens min cnty -935,341 -1

Sold max cnty 450,302 1

Farms Operated by state 7 -151

Blacks and Other min cnty -3 -8

Races max cnty 3 1

Farms Operated by state -33 -443

Females min cnty -5 -10

max cnty 4 1

Farms Operated by state 6 -25

Persons of Hispanic min cnty -1 -1

Origin max cnty 1 0

Number of Farms state 0 -4,014

min cnty 0 -92

max cnty 0 6

Texas data - 1997 census

Characteristic Level

Integer Total -

Noninteger Total

Rounded Total -

Noninteger Total

Value of Land and state 3,016,549 316

Buildings min cnty -6,887,964 -6

max cnty 5,526,046 15

Farm Production state 1,048,512 -1

Expenditures min cnty -222,899 -10

max cnty 214,559 12
Texas data - 1997 census

Characteristic Level

Integer Total -

Noninteger Total

Rounded Total -

Noninteger Total

52

Total Value of Products state -92,594 558

Sold min cnty -501,672 -21

max cnty 990,152 37

Land in Farms state -45,143 285

min cnty -10,832 -45

max cnty 20,212 54

Acres of Harvested state -3,658 181

Cropland min cnty -4,155 -64

max cnty 2,402 60

Acres of Corn for state -451 2

Grain Harvested min cnty -766 -6

max cnty 979 6

Bushels of Corn for state -44,145 -12

Grain Harvested min cnty -145,745 -3

max cnty 99,626 3

Acres of Wheat for state -748 14

Grain Harvested min cnty -2,314 -6

max cnty 2,359 8

Bushels of Wheat for state -1,629 44

Grain Harvested min cnty -36,454 -6

max cnty 67,443 12

Acres of Soybeans state 2,107 7

Harvested min cnty -384 -2

max cnty 1,618 3

Bushels of Soybeans state 9,886 5

Harvested min cnty -11,092 -4

max cnty 24,216 2

Acres of Cotton state 2,827 25

Harvested min cnty -1,488 -4

max cnty 1,970 7

Bales of Cotton state 2,705 -5

Harvested min cnty -1,103 -4

max cnty 2,047 6

Total Cattle and Calves state 1,986 701

min cnty -1,834 -41

max cnty 1,621 45

Dollars Received for state 325,215 9

Dairy Products min cnty -274,514 -2

max cnty 354,517 2
Texas data - 1997 census

Characteristic Level

Integer Total -

Noninteger Total

Rounded Total -

Noninteger Total

53

Total Hogs and Pigs state -1,281 -147

min cnty -357 -7

max cnty 744 6

Layers 20 Weeks Old state -35,673 28

and Older min cnty -31,299 -9

max cnty 31,572 8

Broilers and Other state -324,496 -1

Meat-Type Chickens min cnty -170,131 -1

Sold max cnty 118,908 1

Farms Operated by state 3 -1,223

Blacks and Other min cnty -5 -47

Races max cnty 9 1

Farms Operated by state -51 -3,053

Females min cnty -14 -53

max cnty 11 1

Farms Operated by state 3 -1,164

Persons of Hispanic min cnty -8 -95

Origin max cnty 10 1

Number of Farms state 0 -26,925

min cnty 0 -343

max cnty 0 7

Virginia data - 1997 census

Characteristic Level

Integer Total -

Noninteger Total

Rounded Total -

Noninteger Total

Value of Land and state 720,977 128

Buildings min cnty -2,328,309 -5

max cnty 3,887,524 15

Farm Production state -76,832 34

Expenditures min cnty -116,301 -6

max cnty 196,232 6

Total Value of Products state -575,119 170

Sold min cnty -346,315 -33

max cnty 414,753 14

Land in Farms state 6,261 119

min cnty -2,538 -21

max cnty 1,933 21

Acres of Harvested state 604 -206

Cropland min cnty -582 -53

max cnty 893 15
Virginia data - 1997 census

Characteristic Level

Integer Total -

Noninteger Total

Rounded Total -

Noninteger Total

54

Acres of Corn for state 717 -27

Grain Harvested min cnty -171 -9

max cnty 283 5

Bushels of Corn for state 81,713 19

Grain Harvested min cnty -19,575 -4

max cnty 26,827 6

Acres of Wheat for state 302 7

Grain Harvested min cnty -175 -6

max cnty 259 7

Bushels of Wheat for state 2,761 11

Grain Harvested min cnty -13,882 -7

max cnty 19,456 4

Acres of Soybeans state 1,537 27

Harvested min cnty -338 -2

max cnty 617 4

Bushels of Soybeans state 22,121 3

Harvested min cnty -9,354 -3

max cnty 13,408 4

Acres of Cotton state 95 6

Harvested min cnty -86 0

max cnty 104 2

Bales of Cotton state 103 3

Harvested min cnty -112 -1

max cnty 104 2

Total Cattle and Calves state 1,085 -22

min cnty -876 -26

max cnty 786 11

Dollars Received for state -292,542 2

Dairy Products min cnty -122,914 -3

max cnty 102,213 2

Total Hogs and Pigs state 972 -56

min cnty -125 -4

max cnty 420 2

Layers 20 Weeks Old state -24,316 0

and Older min cnty -25,298 -3

max cnty 14,642 3

Broilers and Other state -493,666 1

Meat-Type Chickens min cnty -423,092 -1

Sold max cnty 65,829 1
Virginia data - 1997 census

Characteristic Level

Integer Total -

Noninteger Total

Rounded Total -

Noninteger Total

55

Farms Operated by state 11 -143

Blacks and Other min cnty -5 -19

Races max cnty 3 0

Farms Operated by state -7 -473

Females min cnty -5 -25

max cnty 9 0

Farms Operated by state 7 -12

Persons of Hispanic min cnty -1 -1

Origin max cnty 2 0

Number of Farms state 0 -4,110

min cnty 0 -191

max cnty 0 0