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PUBLIC USE MICRODATA SAMPLE OF THE 1850 UNITED STATES CENSUS OF POPULATION : User's Guide and Technical Documentation

by Steven Ruggles and Russell R. Menard

and Lisa Dillon Matthew Mulcahy

William C. Block Todd Gardner Ron Goeken J. David Hacker Diana Magnuson David Beck Ryden

ÂŠ 1995 Social History Research Laboratory Department of History University of Minnesota

We would appreciate a copy of any research papers using this sample . Correspondence should be directed to : Steven Ruggles University of Minnesota Department of History Social History Research Laboratory 614 Social Sciences Building 267 19th Avenue South Minneapolis, Minnesota 55455 ruggles@atlas .socsci .umn .edu

1850 Public Use Microdata Sample User's Guide and Technical Documentation

User's Guide Page 3

TABLE OF CONTENTS

User's Guide Acknowledgments . . . . . . . . . . . . . . . . . .. . . . . Introduction . . . . . . . . . . . . . .

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. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

The Integrated Public Use Microdata Series (IPUMS) . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Sampling Units . . . . . . . . . . . . . . . . . . Sample Design . . . . . . . . . .

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Data Entry and Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . .

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Quality of Source Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . .

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10 13

. ................ . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . : . . . . . . . 13 ..... Composition and Family Interrelationships . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . .. . . . . . . . . . . . . . . . . . 14 Household Data Format . . . . . . . . .. . . . . . . . . .

Geographic Coding . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . .. . . . . Occupational Coding . . . . . . . . . . . . . . .. . . . Institutions

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References to User's Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The 1850 Census: Historical Overview and Instructions to Marshals Historical Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . : : . . . . . . . . . . . . . Instructions to Marshals . . . . . . . . . . . . . . . . . . . . . .

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Circular to Marshals . . . . . . . . . . . .. . . .. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Variable Descriptions Variables on Household Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . Variables on Person Record . . .

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Index to Codebook Household Record Layout and Index . . . . . . . . . . . .

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. 52

Person Record Layout and Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ,. . . . . . . . . . . . . . . . . . . . . . . . . 53

Codebook Household Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Person Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Appendix Appendix : County Codes by State . . . . . . . . . .

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4 Guide

User's Page

Guide and Public Technical Use Microdata Documentation Sample

User's

1850

1850 Public Use Microdata Sample

User's Guide

User's Guide and Technical Documentation

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USER'S GUIDE

ACKNOWLEDGMENTS

Principal funding for this project is National Science Foundation grant SES-9210903. The Graduate School and the College of Liberal Arts of the University of Minnesota provided additional funds. Data entry was carried out by Justine Denny, Susan Krajac, Dianne Star, and `Linda Thompson . Data editing and verification, the construction of data dictionaries, and most other tasks associated with the project were accomplished by Lisa Dillon and Matthew Mulcahy. Todd Gardner and Steven Ruggles developed the software for this project.

Diana Magnuson, William Block, J.

David Hacker, and Dave Ryden were primarily responsible for the codebook. The staffs of the Integrated Public Use. Microdata Series project, the 1880 Public Use Microdata Sample project, and the 1920 Public Use Microdata Sample-Bill Block, Todd Gardner, Ron Goeken, 3 . David Hacker, Patricia Kelly Hall, Sandra Jahn, Daniel Kallgren, Chad Ronnander, Dave Ryden, Beth Salerno, Matthew Sobek, and JoAnn Winkels-have''provided additional help' . Russell Menard and Steven Ruggles are the principal investigators. Miriam King's advice and assistance have been invaluable . We are grateful for the advice and cooperation of the creators of the previous historical public use microdata samples, especially Steven Graham and Halliman Winsborough

INTRODUCTION Individual-level public use files have proven to be an indispensable resource for social scientists, since they allow researchers to make tabulations tailored to their specific research questions.

Without individual-level data, some of the most basic questions about changing social

structure are unanswerable because of the: incompatibility of published data for different census years . In addition, public use microdata samples have allowed researchers to move beyond simple tabular analysis and apply increasingly sophisticated multivariate techniques . These data have dramatically increased the power of quantitative social science research . The Census Bureau has produced public use microdata samples as a byproduct of the .S . decennial enumeration in each census year since 1960 (U Bureau of the Census 1972, 1973, 1982, 1989) . In recognition of the value of census microdata files, historical public use microdata samples have been created for the censuses of 1880, 1900, 1910, 1940, and 1950 (Ruggles and Menard 1994 ; Graham 1980 ; U.S

Bureau of the Census 1984a, 1984b; Strong et al . 1989) .

Although most of

these files became available only recently ; they have already led to an outpouring'of new research on the nature of long-term social change . As each new sample is created, the value of the other census files is enhanced as they become increasingly useful for cohort 'analysis and studies of social change . The 1850 Public Use Microdata Sample extends theseries backward . A sample of the '1850 census will provide a baseline for understanding the processes that transformed American society

1850

Use Mierodata Sample User's Guide and Technical Documentation

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Public

over the past 150 years . In several crucial respects, it was the first "modern" census conducted by the United States and one of the first carried out by any nation in the world. The United States, of course, enumerated its population every ten years since its founding in 1790 . Before 1850, however, the unit of enumeration was the household and the census taker simply grouped residents by sex and race in broad age categories, making it impossible to investigate the characteristics of : individuals or to infer relationships among household members with any degree of confidence . The 1850 census continued to use dwellings and households as enumeration units, but within those units it listed the free inhabitants by name and reported the age, sex, color, place of birth, occupation, literacy, and other information for each individual (Anderson, 1988, 32-57; Wright and Hunt, 1900, 39-50) .' A complete list of census questions appears below, in the section "Circular to Marshals ."

Slaves were

listed individually by owner in a separate schedule . Their names were not recorded and the :schedule covered only age, sex, color and whether the slave was deaf and dumb, blind,' insane, or idiotic. Given the limited questions and the impossibility of inferring family relationships or marital status among slaves, we decided to ; confine the,,1850 Public Use Microdata Sample to free inhabitants'., although we hope to add the slave population at a later date . :Despite the high quality of the 1850 enumeration, the published tabulations from that census are of limited value .

Many of the most interesting variables collected were never tabulated . Among

the variables that were tabulated, there was little attempt at cross-classification other than by race, sex, broad age group, and locality .

These weaknesses of the published data for 1850 enhance the

value of the public use microdata sample .

THE INTEGRATED PUBLIC USE MICRODATA SERIES The 1850 sample is designed to be compatible with the Integrated Public Use Microdata Series (IPUMS). The IPUMS is a project funded by from the National Science Foundation (SES9118299)

to make the existing national census samples for 1880, 1900, 1910, 1920, 1940 ; 1950 ;

1960, 1970, 1980, and 1990 more useable and accessible . Because these samples were created at different times by different investigators, they have incompatible documentation and a wide variety of record layouts and coding schemes. series .

These differences among the samples inhibit their use as a time

We are now converting the existing public use microdata samples into a single coherent form.

This involves planning and design of record layouts, coding schemes, and constructed variables that maximize comparability and minimize information loss ; software development for reformatting,

recoding, constructing new variables; consistency checking, and allocating missing and inconsistent data ; data processing of approximately, 65 million records; and preparation of an integrated set of documentation for the entire series of datasets, including a general user's guide, a volume of procedural histories, and a volume on technical characteristics and error estimation .

A beta-test

version of the MUMS is now available, an intermediate version will be made available in June of 1995, and the final version will be complete by the summer of 1996 . directly compatible with the June 1995 version of the IPUMS .

The codes in this document are

For a more detailed discussion of the

creation of the IPUMS, the 1880, and 1850 Public Use Microdata Samples, see the special issue of

Historical Methods,

Winter, 1995 .

1850 Public Use

Microdata :Sample

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User's Guide and Technical Documentation SAMPLING UNITS

All individuals in the 1850 census were assigned to a "family," a term that the census defined more broadly than it does today.

A family was an individual or group of individuals who

jointly occupied a dwelling` place or part of a dwelling place.

Census instructions defined dwelling

places as any occupied structure; they included both wigwams and tenement houses . The term family was defined as either (1) one person living separately in a house, or a part of

house, and

providing for him or herself; or (2) several' persons living together in a house, or in part of a house, upon one common means of support, and' separately from others' in similar circumstances . Thus, a widow living alone and separately providing for herself, or 200 individuals living together and provided for by hotel,

jail,

a common

head, were each considered to be one family . The resident inmates of a

garrison, hospital, asylum, or other similar institutions

were

also considered

one

family .

The analytic power of the public use microdata samples derives in large measure from their hierarchical organization : they are simultaneously samples of households (or families) and of This complex

individuals, and within households the relationships among individuals are known.

structure allows the creation of an almost limitless number of variables . Sampling units, however;' have varied among the samples. The public use microdata samples for the censuses of 1940 through 1980 are samples of households, those for 1900 and 1910 are samples of "families," and the public use microdata'sample for 1880 is a sample of dwellings. The 1850 Public Use Microdata Sample follows the precedent set by the 1880 sample : it is also a sample of dwellings. This sample strategy adds another level of hierarchy compared to a sample of families . There are several advantages to sampling at the level of dwellings .

First, there

are defmitional differences between the nineteenth century family and the household and group quarters of the post-1940 period .

Families were distinguished by a common means of support and

separate residence; although the exact definition of a household has varied, households and group quarters :generally have required either complete cooking facilities or a separate entrance .

It is likely,

therefore, that some households or group quarters under current census definitions were considered to be two or more families in the late nineteenth century. The nineteenth-century definition of dwelling, on the other hand, is clearly broader than the current definition of household or group quarters . potential

By providing information at for consistent comparisons :

the level of both dwellings and families, we maximize the

As well as maximizing comparability, sampling by dwellings provides additional information that would not otherwise be available.

The sample indicates that over 10 percent of the total

population resided in multi-family dwellings . The high frequency of such living arrangements makes them worthy of study in their own right. Multi-household dwellings can be identified by means of the

may

be useful to

incorporate some variables constructed from the characteristics of the dwelling as a

For

variable NUMHH. Even if analysis is carried out at the level of the family, it

in the same in nineteenth-century

whole .

example, analysis of surnames allows identification of kin who resided

dwelling but in

different families,

cities .

pattern that seems to have been common

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1850 Public Guide and

Use Microdata Sample Technical Documentation

The chief liability of sampling by dwellings instead of families is that it reduces the of independent observations in the file .

number

Since census microdata files are cluster samples (ordinarily

clustered by household), standard errors depend on both the number of clusters and on the homogeneity of variables within clusters . Calculation of standard: errors for samples of this type is quite complicated (U .S . Bureau of the Census 1972 ; Kish 1965) . In the worst case, with perfect homogeneity within clusters, the standard errors for variables would be inversely proportional to the square root of the number of clusters rather than the number of individuals.

Even for variables that

are not very homogeneous within clusters, such as age, there is some loss of precision when the total number of clusters is reduced. However, the increase of error, is : small. The, public use microdata samples for 1880, 1900, ;and 191,0 substituted the modern census term "household" for the, contemporary, term "family."

To avoid confusion; this documentation

follows that precedent .

SAMPLE DESIGN The manuscript census of the free population in 1850 consists of about 560,000 enumeration pages with 42 persons per page . These records are contained on 9'76 reels of microfilm. Each reel contains the census pages for several enumeration districts. Our sampling strategy involved randomly generating one sample point, on average, for every hundred persons in the census . To ensure that dwellings had equal probability of being included in the sample regardless- of their size, they were only entered if the randomly generated sample point fell on theline containing the first person in the dwelling . When the sample point fell on any other dwelling member, the dwelling was skipped . For example, if the sample point fell within a dwelling with 5 members, there was only a 1 in 5 chance that the dwelling was included in the sample,: but if it was taken, all five members were entered. Under this procedure each dwelling, family, and individual in the population had a l in 100 probability of inclusion. We modified this procedure for persons residing in institutions and large group quarters . The previous public use samples incorporated a variety of sampling strategies for handling such cases .

In general, members of large units were sampled on an individual basis, simply by treating

each member as if they lived on their own one-person household. This procedure increased the efficiency of the sample by raising the number of observations while maintaining representativeness . Unfortunately, the criteria for designating units to be sampled on an individual basis have varied, making the samples incompatible for some appl cations.

In the 1980 Public Use Microdata

Sample, all unitswith 9 or more members unrelated to the householder were classified as group quarters and members of group quarters were sampled on an individual basis (U .S . Bureau of the Census 1982).

For the public

use microdata samples of the period 1940-1970, the procedure was

similar, except that units with 5 or more secondary individuals or secondary family members were classified as group quarters and sampled individually (U .S . Bureau of the Census 1972, 1984x,

1850 Public Use Microdata Sample User's Guide and Technical Documentation 1984b) .

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In the 1910 sample up to 20 members of a family could be unrelated to the head before the

members were sampled at the individual level (Strong 1988).

The higher threshold for individual-

level sampling in 1910 allows detailed study of the small boarding houses that were characteristic of the period ; once again, however, there is a tradeoff between sampling error and the richness of the data .

In the case of the 1900 data file, all boarders, lodgers, and the institutionalized were sampled

as individuals or as secondary families, a strategy that maximizes precision at considerable cost in terms of lost information (Graham 1980). For example, the 1900 system makes it impossible to create a precise analog of the group quarters concept used in recent census years. To ensure defi nitional comparability of the 1850 sample with all existing public use microdata samples, the number of persons allowed before sampling the unit at the individual level had to be at least as large as in the 1910 sample. We followed the precedent established by the 1880 Public Use Microdata Sample and expanded the threshold to 31, which allows study of many boarding houses as intact units.

The following set of inclusion rules assured compatibility with the

sample designs of the previous public .. use microdata samples, while at the same time enriching the data . These rules result in equal probabilities of inclusion, regardless of dwelling size, family size, or the number of coresident relatives .

:1 .

If the dwelling contains 30 or fewer residents:

accept the entire dwelling if the sample point falls on the first listed individual in the dwelling .

reject the entire dwelling if the sample point falls on any other dwelling resident .

If the dwelling contains 31 or more residents and the family contains 30 or fewer persons :

a) accept the entire family if the sample point falls on the family head ; also enter data on overall dwelling size and the number of families in the dwelling . b) reject the entire family if the sample point falls on any other family member .

3. If the dwelling contains 31 or more residents and the family contains 31 or more persons and the sample point falls within any group of related persons within the family (in 1960 census usage, within a primary or secondary family):

accept the group of related persons if the sample point falls on the first listed individual within the related group; also enter data on overall dwelling size, family size, and the number of families in the dwelling .

reject the entire related group if the sample point falls on any other member of the related group.

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4 . If the dwelling contains 31 or more residents and the family contains 31 or more persons and the sample point falls on an individual with no relatives in the family : a)

accept the individual ;

also enter data on overall dwelling size, family size, and the number

of families in the dwelling . These sampling rules may seem complex, but their implementation is straightforward . but a few percent of the cases fell under the first rule .

All

The second rule comes into play mostly in the

case of large tenement houses of the Eastern cities . The third and fourth rules apply for institutions, military barracks, hotels, dormitories, and the like . In some cases, we were unable to determine the breaks between dwellings because the marshal failed to provide dwelling numbers .

We then

sampled at the level of the family, using rules 2 through 4. Most of these cases were probably single-family dwellings; but their dwelling size was coded as missing .

The variable SAMPUNIT

indicates which sampling rule was employed for each case, distinguishing those cases in which sampling was carried out at the family level because of missing dwelling numbers .

DATA ENTRY AND QUALITY CONTROL Data Entry.

Data entry was carried out directly from the microfilm at personal computers.

The

computer program presented the data-entry operator with a facsimile of the census form . The operator read the page and line number of each sample point from a listing prepared for each reel of film, and moved the microfilm to the appropriate position . If the sample point was valid according to the rules set out in the previous section, the operator entered the case . We transcribed all the information on the census form verbatim, including name, street address, and all the other locational information that appeared at the top of the census form .

Some occupations and most birthplaces were

entered as abbreviations . The data-entry software performed a variety of interactive logical checks for internal consistency. For example, the only values that could be entered for the sex variable were M, F, % (missing), ! (illegible) and 0 (invalid entry) . Consistency Checking Program.

Beyond the edit checks carried out at the time of data entry, we

adopted several other procedures to ensure quality control and estimate error rates .

As soon as the

data entry for each microfilm reel was complete, research assistants ran a consistency checking program that carried out more sophisticated checks than the data-entry software . generated a file of problems requiring examination by research staff.

The program also

For example, the program

flagged impossible age and occupation combinations (e .g ., a five year old identified with an occupation), which often was resolved by checking the individual's age and then changing the occupation code by hand .

Or, to give another instance, since most families consisted of a single

race, the program flagged mixed-race families . Again, research staff made any appropriate changes. Research assistants re-examined every case with a failed edit check on the original microfilm unless the operator specifically noted that it was an enumeration error.

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The consistency checking program also produced a list of newly encountered valid data values for each field. These lists were distributed to the research assistant in charge of each data dictionary, who updated the dictionaries on a weekly basis. This means that each new value was classified numerically shortly after it was encountered.

For the complex classifications-such as

occupation and birthplace-each value was classified independently by two research assistants and any discrepancies were resolved in conference . Verification . To estimate transcription error rates we have carried out verification on 10 percent of the microfilm reels, randomly selected from all reels entered.

Verification was always carried out by

a different data-entry operator from the one who originally entered the data .

Once a reel was entered

twice, a research assistant ran a verification program to locate discrepancies between the original and the re-entered data file .

All such discrepancies were located on the original microfilm to determine

which file was correct.

Verification served two purposes . First, it helped us to identify data quality problems associated with particular variables and data-entry operators. Second, the verified reels allowed us to estimate transcription error rates for each variable in the sample as a whole. an extremely low overall transcription error rate (see Table 1) .

This process revealed

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User's Guide and Technical Documentation

Table 1. Transcription Error Rates from Verification

Field

Percentage of Error

city

1 .90

County

0.83

State

0.00

Line number

0.03

Dwelling number

1 .49

Dwelling size

0.65

Number of families

0.74

Family sequence number

0.00

Family size

1 .10

Institution

0 .68

Family number

1 .02

Race

0 .05

Sex

0.19

Age

1 .02

Married within year

0 .06

Occupation

0.55

School

0.38

Literacy

0.27

Disability

0.01

Birthplace

1 .30

Blind

0.00

Deaf

0.00

Idiotic

0.00

Insane

0.00

Pauper

0.00

Crime

0.01

Year

0.01

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QUALITY OF SOURCE DATA An important source of error in the 1850 Public Use Microdata Sample was underenumeration in the original manuscript census .

For the period before 1950, estimates of

underenumeration are problematic because of the weakness of vital statistics and the lack of post enumeration surveys.

Francis Walker and Carroll Wright, census directors in the late-nineteenth

century, both claimed that net underenumeration was under one percent around the turn of the century (U .S . Bureau of the Census, 1916 : 16), but such a figure cannot be believed . Coale and Zelnick (1963) and Coale and Rives (1973) have estimated net undercount for blacks and whites in the period 1880-1950 by the birth-reconstruction method .

They concluded that the 1880 census

undercounted blacks and whites by 6.5 percent. It is likely that underenumeration was more severe than this in 1850 . More recently, Social Science History published several articles using various methods (comparisons of federal census manuscripts with genealogies, commercially prepared maps, city directories, poll and tax records, and state census manuscripts) to estimate underenumeration in the 1850 to 1880 federal censuses for specific places .

Peter Knights (1991) was the only author to

address the 1850 census . He concludes that gross underenumeration in Boston was approximately 11 to 14 percent by comparing the census to other lists of Bostonians resident in the city at census time . Knights' figures should probably be interpreted as a maximum as it includes some people who were on the census but whom he failed to match up with city lists. More important, he demonstrates that underenumeration was a selective process: the foreign-born, unmarried young adults, those with lowstatus occupations, and residents in areas of rapid growth were most likely to be missed by the census takers . Historians have frequently expressed concern about underreporting in the census (e .g . Sharpless and Shortridge, 1975) . In comparison with alternative cross-sectional sources, however, the census is impressive . We can be reasonably confident that the response rate was about 90 percent or better in all census years for which we have public use microdata samples, a figure that compares favorably with the best of recent social surveys .

For-the nineteenth century, no alternative data

source even comes close to the census in terms of coverage .

DATA FORMAT Following conventional public use microdata sample practice, our preliminary file has a column-format hierarchical structure.

The file format is similar to that of previous public use

microdata samples. Variables common to a household as a whole are contained on a household record .

The household record is followed by a separate person record for each member of the

household, giving their individual characteristics. We have not created a separate record type for the dwelling ; data pertaining to the dwelling as a whole was repeated on each family record within multifamily dwellings. The layout of each record is given on pages 52-53 .

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With the exception of names and streets, numeric codes have been substituted for all alphabetic fields .

For users who want access to the alphabetic variables, an archive file is available

on request.

HOUSEHOLD COMPOSITION AND FAMILY INTERRELATIONSHIPS The study of household and family composition has proven one of the most fruitful applications of the public use microdata samples. Because the samples include information on entire households, they allow researchers to construct measures of family and household membership and family interrelationships tailored to their specific research questions.

This section

describes the tools provided in the 1850 sample for analysis of household composition and family interrelationships .

Definitions and Data Structure. As noted in the section on sample design, the 1850 sample is a sample of dwellings rather than a sample of households .

Dwellings were defined as "a separate

inhabited tenement, containing one or more families under one roof .

Where several tenements are

in one block, with walls, either of brick or wood to divide them, having separate entrances, they are to be numbered as separate houses ; but where not so divided, they are to be numbered as one house ."

Within dwellings, the Census Office identified families, a term that then encompassed the

modern census concepts of both household and group quarters .

"By the term family is meant,

either one person living separately in a house, or part of a house, and providing for him or herself, or several persons living together in a house, upon one common means of support, and separately from others in similar circumstances .

A widow living alone and separately providing for herself,

or 200 individuals living together and provided for by a common head, should be numbered together as one family ."

This definition is roughly comparable to the criteria used to distinguish

separate units-households or group quarters-in recent census years .

To avoid confusion, this documentation generally uses the term household to refer to the nineteenth-century census category "family. "

Following current Census Bureau practice,

institutions and other large units sampled at the individual level are termed group quarters, and families are considered to be related groups within households .

The unit actually sampled under the 1850 rules can be a dwelling containing one or more households, a household, a related group residing in group quarters, an individual in group quarters, or an unidentified fragment of a larger unit (see sampling rules) .

In each case, the unit

taken is identified by the variable SAMPUNIT . Like the other public use microdata samples, the 1850 Public Use Microdata Sample is organized hierarchically .

Each case consists of a household record pertaining to the unit as a

whole followed by a series of person records describing the characteristics of each individual in the household .

Multi-household dwellings appear as a series of consecutive households, and can

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be identified as members of a single dwelling because they share the same dwelling number (DWNUM) . The number of households in the dwelling is given by NUMHH.

Note that under

the 1850 sampling rules, multi-household dwellings with more than 30 residents were sampled at the household level rather than the dwelling level. Comparability of Sample Units in the Public Use Microdata Sa=le Files .

There have been some

changes in the definitions of basic units of enumeration since 1850, but the effects of these changes are probably modest .

More important are the differences among various public use microdata

samples in procedures for sampling group quarters . In all the :public use microdata samples,' large "units such as institutions and boarding houses are sampled at the individual level .

For example,

instead of sampling 1 in 100 prisons, public use microdata samples ordinarily sample 1 in 100 inmates of prisons .

Units sampled at the individual level are called group quarters .

The public use microdata samples of 1940 through 1970 had a very broad definition of group quarters : they included all units with five or more members unrelated to the head .

Thus,

for example, all members of a unit containing a primary family and five servants would be considered group quarters and sampled at the individual level.

The 1850' sample represents the

opposite extreme: up to 30 unrelated persons could coreside before the unit was considered group quarters and sampled at the individual level.

To compare the 1850 sample with recent census

years, use the GQ variable to identify households that would have been'classified as group quarters in the 1940 through 1970 Public Use Microdata Samples. Imputed Family Relationships . Explicit information on the relationship of each individual to the head of household was not gathered until 1880, but the 1850 census contains sufficient information' to impute most family relationships reliably . The 1850 census instructions to marshals specified that within each household, "the names are to be written beginning with the father and mother ; or, if either, or both, be dead, begin with some other ostensible head of the family ; to be followed, as far as practicable, with the name of the oldest child residing at home, then the next oldest, and so on to the youngest, then the other inmates; lodgers and boarders, laborers, domestics, and servants ."

addition to sequence in the household, the 1850 census provides other valuable clues to family relationship : surname, age, sex, occupation, and birthplace . The imputed relationship variable (IMPREL) is based on analysis of all these variables .

Two different strategies were used to impute relationship .

In the most clear-cut cases, we a

relied on a set of logical rules. Where there was any ambiguity, however, we turned to

probabilistic "hot deck" imputation procedure, similar to the procedures used by the Census Bureau to allocate missing and inconsistent information. Seventy-five percent of cases were assigned by the following logical rules .

The first

individual in each household was assigned as head . If the first person was male, the second was female, both were over age 20, both shared the same surname, and the woman was no more than 15 years younger or 20 years older than the head, she was assigned a relationship of wife . In 1880, when relationship was explicitly given, over 99 percent of such women were, in fact, wives of the head . Persons listed immediately after the head and wife were assigned as children of the head if

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they shared the same surname and were between 16 and 50 years younger than the head and between 15 and 45 years younger than the wife. Individuals following the first child were assigned as additional children if they met the same conditions and were listed in descending age order, with no more than 15 years separating adjacent children .

In 1880 over 99 percent of persons meeting these

conditions were actually listed as children of the head . All remaining cases--including younger potential wives and children out of age order-were assigned according to a hot deck procedure.

We identified nineteen key characteristics available in

the 1850 sample that were strong predictors of family relationship in the 1880 Public Use Microdata Sample : 1.

Surname;

surname of preceding individual ;

sequence in household;

age;

5 . difference between age and head's or head's wife's age; 6.

difference between age and age of preceding individual ;

7 . presence and location of probable own spouse (defined as an adjacent opposite sex person over age' 16 with the same surname and an appropriate age interval) ; 8.

number of probable siblings (defined as same-surname persons within fifteen years of age who

number of probable parents (defined as same-surname persons more than fifteen years older) ;

were not probable spouses) ;

10 .

number of probable children (defined as same-surname persons more than fifteen years

11 .

imputed relationship to head of immediately preceding person ;

12 .

occupation;

younger) ;

13 . head's occupation ; 14 .

migration status (defined as non-migrant in household with non-migrant head ; migrant in household with non-migrant head ; non-migrant in household with migrant head ; migrant in household with a head who migrated from the same' -place; or migrant in a household with a head who migrated from a different place) ;

15 . household type (distinguishing couple headed, single male headed and single female headed) ; 16 . urban residence ; 17 .

farm residence;

18 .

state of residence ;

19 .

region of residence. Once each case from 1850 was classified according to these nineteen characteristics, we

searched the 1880 Public Use Microdata Sample to find the most geographically proximate individual who shared all nineteen characteristics . That individual was designated as the "donor" and his or her relationship was assigned to the 1850 case . In some cases, no perfect donor could be found. We then searched the 1880 file for the best match. Each of the nineteen characteristics was weighted according to its power to predict family

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relationships correctly in 1880, as determined by regression analysis .

The case with the greatest sum

of weights was selected as the donor. Note that we did not include race as a predictor, as is customary in such allocation procedures .

In the context of the other nineteen variables, race was an insignificant predictor of

relationship to head .

Moreover, it is likely that the entire population of blacks in 1880 differed

significantly from the free black population of 1850, which might make race an unreliable predictor in our hot deck procedure. To test the imputation procedure, we applied it first to the 1880 Public Use Microdata Sample, imposing a rule that prevented individuals from donating a relationship to themselves or to any other member of their household. We then applied the procedure to the 1910 Public Use Microdata Sample, to see if thirty years of change in household composition between donors and recipients of imputed relationships would introduce unacceptable biases . Both tests yielded satisfactory results .

In 1880, the imputation procedure yields an exactly correct family relationship in

95 percent of cases and an approximate match (correctly classifying head, wife, child, and others) in 97 .5 percent of cases.

The procedure worked nearly as well when imputing relationships in 1910,

using donors from 1880:

94 .5 percent of relationships were exactly correct, and 97 .6 percent were

approximately correct. Just as important, the method is unbiased ; as shown in Table 2, it yields the correct distribution of family relationships in both census years . Table 2. Distribution of Imputed and Observed Family Relationships, 1880 and 1910 (Percents)

Relationship

1880 Observed

1880 Imputed

1910 Observed

1910 Imputed

Head

20 .7

22 .7

Wife

16 .8

Child

18 .0

50 .8

50 .9

45 .9

- 0.3

0.3

0.8

0.9

0 .9

Child-in-law

0.3

22 .7 18 .1 46 .1 -

0.4

Parent

0.8

Parent-in-law

0.5

0.6

0.7

0 .8

Sibling

1 .2

1 .4

1 .3

Sibling-in-law

0.6

0.7

0.8

Grandchild

1 .3

0.8

Other relative

1 .2

1 .1

1 .3

1 .1

Non-relative

5.8

5.7

6.8

6 .9

1 .1

Given that the 1910 imputed relationships are just as unbiased and reliable as the 1880 ones, it is reasonable to expect that the imputed relationships for 1850 are also reliable and effectively unbiased .

Nevertheless, as with any imputed variable, users should exercise reasonable caution.

particular, users should be aware that any differentials in household structure between population subgroups (e .g ., by class, race, or ethnicity) are likely to be slightly understated because of random error in the imputation . For further . discussion, see the IPUMS technical supplement (forthcoming) .

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1850 Public Use Microdata Sample User's Guide and Technical Documentation

Family Interrelationships . The 1850 Public Use Microdata Sample is simultaneously a sample of households and of individuals . This hierarchical structure is one of the greatest strengths of the census file .

By combining the characteristics of several individuals within a household,

researchers can create a broad range of new variables about family and household composition and the characteristics of family members .

For example, we can analyze fertility by attaching children

to their mothers, and we can address the family economy by simultaneously measuring the age, sex, and occupation of all family members. One of our goals has been to develop a consistent, versatile, and reliable set of tools to make it easy for researchers to construct family variables using standard statistical packages . The 1850 sample includes a variable on the imputed relationship of each household member to the head of household.

This variable (IMPREL) provides the basic measure of family

relationships, but it is not sufficient to identify all family relationships and it is often inconvenient as a tool for constructing new family variables.

Consider the following household:

Table 3. Example of Fancily Relationships

Relationship

Surname

Age

Sex

Marital Status

MULCAHY

HEAD

MULCAHY

DAUGHTER

RYDEN

SON-IN-LAW

RYDEN

DAUGHTER

RYDEN

GNDCHILD

HACKER HACKER

BOARDER

The relationship variable is sufficient to establish that the two daughters are both children of the household head, but to identify the other family interrelationships we must look to their other characteristics .

We can infer that the son-in-law is married to the second daughter rather

than the first one because they share the same surname and are both listed as married; for analogous reasons, we know that the grandchild is probably the child of the second daughter listed .

It is also safe to assume that the two boarders are married to one another, because they are

both married, they share the same surname, they are both adults and close to the same age, and they are listed adjacently . To allow users to identify relationships among spouses, parents, and children without forcing them to use multiple variables and complicated logic, the 1850 census file includes a set of pointers called SPLOC, MOMLOC, and POPLOC . These pointers identify the location within the household of each individual's own spouse, mother, and father, respectively . these variables .

Table 4 illustrates

PERNUM is the sequence number of each individual within the' household .

SPLOC shows the sequence number of each individual's own spouse ; for example, since the sonin-law is married to the second daughter who is in the fourth position, his SPLOC is 04 .

Persons

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without a spouse are assigned a SPLOC of 00 .

MOMLOC and POPLOC show the sequence

numbers of own mothers and own fathers ; for example, the mother and father of the grandchild are in positions 04 and 03, respectively .

Table 4. Example of Family Relationships

Surname

Relationship

PERNUM

SPLOC

MOMLOC

POPLOC

MULCAHY

HEAD

MULCAHY

DAUGHTER

RYDEN

SON-IN-LAW

RYDEN

DAUGHTER

RYDEN

GNDCHILD

HACKER

BOARDER

HACKER

BOARDER

00 00

SPLOC, MOMLOC and POPLOC can be used to identify conjugal units, to attach characteristics of spouses or parents, to develop specialized own-child measures, or as building blocks for more elaborate measures of family composition.

In most cases, users will be able to

manipulate these variables to construct their own measures within a statistical package and will not be forced to resort to higher-level programming .

For example, users frequently need to attach the characteristics of immediate family members .

The following SPSS-X command file uses SPLOC to attach spouse's occupation to the

record of each married person .

SERIAL is a unique identifier for each household, constructed by

combining DWSEQ and HHSEQ.

First we obtain an active file with serial number (SERIAL),

occupation (OCC1950) and spouse location (SPLOC) .

SPLOC is renamed as PERNUM, and

OCC1950 is renamed as spouse's occupation (SPOCC) . PERNUM, and match it back to the original file .

We then sort the file by SERIAL and

Because the PERNUM we are matching was

originally SPLOC, we are actually matching spousal occupations . GET FILE =' PUMS 1850 . SY S' /KEEP SERIAL OCC 1950 SPLOC /RENAME (PERNUM=SPLOC)(SPOCC=OCCUP) SORT CASES BY SERIAL, PERNUM MATCH FILES TABLE=* /FILE ='IPUMS.SYS' /BY SERIAL,PERNUM SAVE OUTFILE='PUMS2 .SYS' FINISH It is virtually as easy to use MOMLOC and POPLOC to attach characteristics of own children .

The following SPSS-X command file uses similar logic together with the AGGREGATE

command to count the number of own children under ten years old for each woman.

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GET FILE ='PUMS1850 .SYS' /KEEP SERIAL MOMLOC AGE /RENAME (PERNUM=MOMLOC) SELECT IF (AGE LT 10 AND PERNUM GT 0) SORT CASES BY SERIAL, PERNUM AGGREGATE OUTFILE= * /BREAK SERIAL PERNUM /CHLT10 = N MATCH FILES TABLE=* /FILE='IPUMS .SYS' /BY SERIAL,PERNUM IF (MISSING(CHLT10)) CHLT10=0 SAVE OUTFILE='PUMS3 .SYS' FINISH Most family classification schemes are built immediate kin.

from information on the presence of

The basic Census Bureau classifications focuses

the presence of spouses and

children of the household head ; the Laslett (1972) scheme widely used by historians is based on a count of "conjugal family units" consisting of parents and children or married couples.

SPLOC,

MOMLOC, and POPLOC make it relatively simple to construct such classifications . Family historians are increasingly moving from household-level schemes of family classification toward individual-level measures of family structure .

For example, instead of

measuring the proportion of households headed by a single female parent, we might assess the proportion of women who were single parents or the proportion of children residing with mothers only .

Such individual-level analyses offer a variety of advantages that have been detailed

elsewhere (King and Preston 1990 ; Ruggles 1987, 1994a, 1994b) .

The individual-level pointer

variables are especially well suited to creation of these kinds of measures . In addition to SPLOC, MOMLOC and POPLOC, the 1850 sample provides a variety of other constructed variables to aid researchers in creating new family variables. These are described in Table 5 and illustrated in Table 6 .

NCHILD, ELDCH, and YNGCH are based on all

own children ; NCHLT5 is a count of own children under five, excluding identifiable stepchildren and adopted children (see discussion below) .

ELDCH and YNGCH receive a value of 99 if no

own children are present, and 98 if a child is present but age is missing or illegible .

Table 5. List of Variables on Family Interrelationships

PERSEQ REL NFAM

Sequence number of person within household Relationship of person to household head Number of household members related to person

NCHILD

Number of own children in household

NCHLT5

Number of own children under age five in household

ELDCH

Age of eldest own child in household

YNGCH

Age of youngest own child in household

SPLOC

Location of own spouse within household

MOMLOC

Location of own mother within household

POPLOC

Location of own father within household

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Table 6. Example of Family Relationships

Surname

Relationship

NFAM

NCHILD

NCHLT5

ELDCH

YNGCH

MULCAHY

HEAD

MULCAHY

DAUGHTER

RYDEN

SON-IN-LAW

RYDEN

DAUGHTER

RYDEN

GNDCHILD

HACKER

BOARDER

HACKER

BOARDER

Procedures for Linking Parents and Children . is usually straightforward .

1 1

Assigning links between parents and their children

In about 97 percent of cases, the imputed variable on family

relationships (IMPREL) is sufficient to establish parent-child links .

For example, if an individual

-is listed as a child of the household head, his or her parents should always be listed as the household head or wife of head, and there is little ambiguity because each household has one head and no more than one wife .

Similarly, the parents of persons listed as the household head or a

sibling of the head are always listed as mother or father of the head, and each household contains no more than one person listed as mother and no more than one listed as father . Parentage is almost as clear-cut for persons listed as wife or sibling-in-law, since households ordinarily do not include multiple mothers-in-law or fathers-in-law .

For persons who have family relationships other than head, wife, child, sibling, or siblingin-law, the relationship information does not identify parental relationships with as much precision. For example, we know that the parent of a person listed as grandchild of the head should be listed as a child or a child-in-law, but because a family may contain multiple persons listed as child or child-in-law, the relationships do not unambiguously identify parentage .

Even if there is only one

child present, there is still room for error, since a grandchild could be the offspring of an absent child .

In some cases-such as secondary families consisting of boarders-the relationship codes

may provide no information for linking parents and children . Whenever the family relationship codes are unclear, we must turn to other information to identify parent-child relationships .

The 1850 census contains four additional pieces of information

that can be used to clarify ambiguities : age, marital status, surname and the order in which individuals are listed in the census .

Thus, for example, if a household contains a daughter

followed immediately by a grandchild who is twenty years younger than the daughter, we may reasonably infer a maternal relationship even if other daughters are present. We attempted to make a system of rules that would be compatible with public use microdata samples from other census years .

We therefore begin by establishing all parental

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relationships that can plausibly be identified using only information available in all census years : relationship, age, sex, marital status, and sequence in the household listing. This is carried out by means of three logical rules, described below.

For 1850, we then use an additional rule to add

parental relationships that can only be identified by using information on surname similarity . Additionally, Rule 7 identifies those cases where a child was linked as a step child. be established through more than one rule, the lower-numbered rule was used .

If a link could

The rule used in

any particular case is identified in the variables MOMRULE and POPRULE.

Rule 1 .

Unambiguous relationships a) If the relationship of an individual to the household head is son, daughter, or child, then establish parental links to persons with an imputed relationship of head or wife, or b) if the relationship of an individual to the household head is head, brother, or sister, then establish parental links to persons with an imputed relationship of mother or father, or c) if the relationship of an individual to the household head is wife, brother-in-law, or sister-in-law, then establish parental links to persons with an imputed relationship of mother-in-law or father-in-law.

Rule 2. Grandchildren If the relationship of individual to household head is imputed as grandson, granddaughter, or grandchild, then establish parental link to the most proximate child and/or child-inlaw with the same surname and a plausible age difference .

Plausible age differences

are- defined as 12-54 years for women, and 15-74 years for men.

If there is more than

one eligible parent, choose the most proximate. Rule 3 . All other relatives and non-relatives (using household position) Link relatives and non-relatives not mentioned above to any preceding person with the appropriate relationship and a plausible age difference as defined in rule 2, as long as there are no intervening persons other than children or spouses of the potential parent . Links between relatives and non-relatives are prohibited .

Rule 4. All other relatives and non-relatives (using surname) Same as rule 3, except that surname similarity is substituted for the requirement that there are no intervening persons between the parent and child .

If more than one eligible

parent is found, the most proximate is linked .

Rule 7. Spouse of linked parent If one parent is linked and they have a spouse present, their spouse is linked as a stepparent .

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We performed two basic checks for inconsistency of the family links. were linked but they were not married to each other, we unlinked the father .

First, if two parents Second, if both

partners in a married couple were linked to the same parent we chose the best parental link based on detailed relationship code, surname, and proximity within the household . Users should be aware that the logical rules used to create MOMLOC and POPLOC link parents to adopted children and stepchildren as well as to biological children .

This may be

appropriate for the study of topics such as the family economy, but for some topics-such as fertility analysis-adopted children and stepchildren should be eliminated whenever possible . Many stepchildren and adopted children can never be identified, but users who want to eliminate non-biological maternal links should eliminate cases in which the age difference between mothers and children fall outside the range 15 to 49 .

The variable CHLT5 excludes identifiable

stepchildren and adopted children, since it is mainly intended for fertility analysis . Procedures for Linking_Spouses .

Spousal links are much easier than parental links.

Most

households have only one married couple, and where more than one married couple is present proximity is a reliable indicator of who goes with whom . In all census years where marriages are explicit, married couples are listed adjacently in about 99 percent of cases, and the few exceptions can almost all be resolved through relationship codes .

Potential spouses must be at least 16 years

old and no more than 17 years older than their husbands ; husbands must be at least 18 years old and no more than 28 years older than their wives .

First, each eligible male is examined and linked

to any eligible adjacent subsequent female ; then, each eligible female is examined and linked to any eligible adjacent subsequent male .

If there remain any non-adjacent couples after these first

two passes they are linked provided that the wife is no more than five years older than the husband and the husband is no more than fifteen years older than the wife .

If there is more than one

potential non-adjacent spouse, the most proximate is chosen . Accuracy of the Linking Procedures .

To test the reliability of the parental and spousal links based

on imputed family relationships, we tested them against links based on explicit relationships in 1880 and 1910 .

In 1880, the MOMLOC based on imputed relationships was identical to

MOMLOC based on observed relationships in 97 .1 percent of cases; the comparable figures for POPLOC and SPLOC are 97 .5. percent and 98 .8 percent, respectively .

In 1910, the imputed links

were identical to the links. based on observed relationships in 97 .4 percent of cases for MOMLOC, 97 .5 percent for POPLOC, and 98 .9 percent for SPLOC . accuracy is greater, approaching 99 percent . most applications .

For parental links to young children,

Therefore the links may be considered reliable for

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GEOGRAPHIC CODING

State and County Residence.

The 1850 Census provided three basic pieces of geographic

information; state of residence, county of residence, and civil division . We ran data quality checks on the state and county information.

Because each microfilm reel contained individuals from a single

state, mistakes were easily corrected for state of residence. For the county data check, we used a list of counties which included specific reel numbers to verify the correlation between the county name and reel number . In cases where there was an inconsistency between reel number and county of residence, we consulted the microfilmed manuscript and corrected what usually was a data-entry error. In those rare instances where the county line was left blank by the marshal, we used additional information, usually the county entry for the previous and following page, to enter the correct county .

Both states and counties were entered as they appeared on the schedules and later recoded to

conform to the ICPSR state and county codes. Population of Incorporated Places .

The 1850 Public Use Microdata Sample includes variables which

identify the top 98 cities by name and all incorporated municipalities by population size . All incorporated municipalities were assigned their respective population figures, in hundreds, in the city population variable (CITYPOP). All unincorporated places were coded as zero . In addition, the city variable (CITY) indicates residence in one of the largest 98 cities in 1850 .

Users should note that the

Philadelphia population figure does not include the populations of Kensington, Moyamensing, Northern Liberties, Southwark, and Spring Garden, which were not annexed to Philadelphia until 1854 . Each of these cities is large enough to be ranked individually in the top twenty-five urban areas, and we have kept their populations separate . Users should also note Philadelphia is coded to reflect its population as an aggregate of these lesser divisions although they each maintain their distinct population totals . The 1850 census returns were not especially clear in distinguishing incorporated municipalities .

Indeed, according to the director of the Census Office in 1870, the 1850 returns were

"exceedingly defective and inaccurate" (U .S . Census Office, 1872, p. xlvi .) . Most states had incomplete returns for certain counties or Census marshals did not differentiate between various subdivisions within counties .

We therefore used the 1870 published materials, as well as the 1850

returns, to more accurately designate incorporated places . For example, in Knox County, Illinois, there were several subdivisions noted, but none were listed as an incorporated place. The 1870 returns, however, indicated that Knoxville was incorporated in 1850 . These cases were assigned population totals as incorporated places based on their 1850 population totals . In cases where an incorporated village or small town shared the same name as the township where it was located (Sycamore Town in Sycamore Township in De Kalb County, Illinois, for example) it was impossible to distinguish one from the other at the recoding stage unless the marshal supplied the designator "township" or "village of."

We dealt with this difficulty by going back to the

manuscript schedules to see if it was possible to distinguish between the village and the township .

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New England states and Southern states presented specific problems in designating incorporated places . Few places in New England were incorporated in 1850 . In these states, the town was typically the smallest form of government organization. equivalent to "township" in most other parts of the country) . all residents of New England states . dealing with Southern cities .

("Town" in New England was the

As a result, town population is used for

The poor quality of census returns was particularly troubling in

The 1850 published materials note that "the returns for Southern States,

except Arkansas, do not show any complete system of subdivision of counties" (U .S . Census Office, 1853, p. 1015) .

Southern cities were listed instead in a separate table, but the tables noted only those

cities and towns that Census officials could "ascertain from the schedules," and hence are not a complete listing of all incorporated places . Augusta, Georgia, for example, was enumerated with a population of 11,753 in a local census in 1852, but officials in 1850 could not determine the boundaries of the municipality in the national census, and the city was not assigned a population total in the published returns.

Assigning total city populations in the South was also complicated by slavery.

Slaves were

enumerated in a separate schedule in 1850, but the slave population was included in the published returns and we have included the figures in assigning population totals for Southern cities .

Two

problems with the slave population, however, should be noted . First, in some cases, slaves were not recorded at all. Yorkville, South Carolina, for example, has no slave population; the figures therefore reflect the white and free black populations.

Second, slave population figures were

sometimes listed in the published returns, but they were not included in the aggregate population totals for the municipality in question. The returns for Charlottesville, Virginia, for instance, list 840 slaves in the city, but this number was not added to the white and free black population totals . According to the published returns, because towns were not separated on the slave schedules, "the numbers in the slave columns . . . were collected from the returns with great labor, and include often, unavoidably, slaves owned, but not living, in the town .

The aggregates, being uncertain, are

omitted" (U .S . Census Office, 1853, p. 258) . We have not omitted the figures. For cities where any slave populations were listed, we added them to the total population of the city . In general, anyone using data concerning southern cities should consult the published materials to differentiate the accuracy of the numbers in question . Place of Birth . Marshals were instructed to obtain information on the country of birth for each individual . These data can be used to study migration by individuals . In almost every case the birthplace of the individual was recorded by the marshals . Birthplaces are coded according to the IPUMS system to ensure compatibility across all census years . Enumeration instructions for 1850 were very general, requiring only country, or U .S . state or territory of origin .

While interpretation of the place of birth variable is generally straightforward,

two complications should be mentioned.

Users should be aware that some persons born in the

Southwest, which later became part of the United States, often listed their place of birth as Mexico . In addition, the code "Indian Territory" refers specifically to the eastern half of Oklahoma .

Other

Native American birthplaces were enumerated either as a specific tribe, the Choctaw Nation for example, or as Native American.

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OCCUPATIONAL CODING

The 1850 census was the first to ask respondents their occupation . Unlike later censuses, it offered few instructions to enumerators regarding occupations. Marshals were simply instructed to note the occupation of each white and free black male over fifteen years of age, recording "the specific profession, occupation or trade which the said person is known and reputed to follow in the place where he resides ."

Clergymen were to be distinguished by denomination .

Individuals

performing more than one job were to be identified only by their primary occupation .

occupations were not reported.

Women's

The limited instructions provided by the Census Office produced inconsistent and often vague responses. Marshals sometimes listed an industry without an occupation (e .g ., "Cotton mill"), or an occupation without an industry (e .g ., "Molder"). ("dead of cholera"),

In addition, other information such as health

place of residence ("lives in a cave") or relationship status ("widow of Israel B

Sheldon") was sometimes entered in the occupation field. Occupations were most frequently difficult to identify when they had been misspelled or partially spelled ("filen" ; "mider") . Because of these irregularities, classifying and coding occupations proved challenging .

Our method of coding occupations consisted of three main stages :

data entry, sorting, and

coding . In the data-entry stage, operators recorded most occupations exactly as they were reported by the census marshals, including spelling errors or abbreviations. Some common occupations, such as servants, were abbreviated.

These titles were then copied into a separate file and sorted

alphabetically . Finally, each title was assigned a numeric code according to the 1880 and 1950 Census classification systems.

880 Occupational Classification : the 1850 census .

The Census Office did not prepare a true classification scheme for

Instead, it provided an alphabetical list of the number of men performing 324

occupations, arranging the occupations roughly by industry . Since the 1850 census lacked a practical classification of its own, we coded occupations according to the 1880 census classification .

addition, each response was assigned a code in the 1950 census occupational classification . In many cases, coding an occupation according to the standards employed by the 1880 Census Office in tabulating returns was difficult. No detailed instructions remain and many of the enumerated responses are vague or incomplete . consistent coding .

Consequently, we devised a number of procedures to ensure

These procedures were intended to replicate those used in classifying occupations

in the 1880 Public Use Microdata Sample .

Four general rules covered many of the difficult coding

problems . Rule 1 .

In cases where more than one occupation was listed we coded according to the first

occupation . However, when the first occupation was a non-occupational response (e .g ., "keeping house") and the second gave an actual occupation, we coded according to the second occupation . Rule 2.

When the response listed both an occupation and an industry, we gave preference to the

industry over the occupation if that industry was explicitly noted in the- 1880, classification . The

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rationale for this procedure is the "industrial" classification system used by the 1880 Census Office which placed greater importance on locating persons within sectors of the economy than in relating their specific tasks . Thus, for example, the response "Blacksmith on railroad" was coded as "Employee on railroad" rather than as "Blacksmith."

Rule 3 .

If the occupation response gave only a place of employment or an industry within the

manufacturing sector (e .g ., "Iron mill"), then we coded the occupation at the employee level.

the response contained only a type of store (e .g ., "Dry goods store" or "Grocery"), we coded the man as a trader and dealer in that line of trade.

If the response referred to a "Shop," it was coded

among the manufacturing occupations ; if it referred to a "Store," it was coded within trade and transportation .

Rule 4 .

In our previous work classifying occupations for the 1880 Public Use Microdata Sample,

comparisons with the published 1880 returns sometimes revealed large discrepancies .

These

differences suggested that certain responses be reclassified to approximate as closely as possible the 1880 Census Office procedures . We used this information to classify 1850 occupations as well .

This procedure was particularly helpful in dealing with agricultural laborers and clerks .

_ 1880 Occupation Modifications.

001 Agricultural Laborers .

The nineteenth-century Census Office regularly complained

about the confusion of agricultural and common laborers, specifically, the undercount of farm laborers . We suspect that the Census Office often inferred agricultural laborer status from the characteristics of the household or locality .

Occupational classification in the Public Use Microdata

Sample was carried out using the occupation field in isolation from other characteristics . After initial classification, we recoded men in "Laborers (not specified)" into "Agricultural Laborers" when they resided in a household headed by a farmer .

023, 065 Clerks . Comparisons of 1880 Public Use Microdata Sample data with the published 1880 tabulations revealed that the Census-Office regularly interpreted the--response "Clerk" to mean "Clerks in stores," rather than "Clerks and copyists (not otherwise described) ." clerks accordingly in 1850 .

We coded

The residual category "Clerks and copyists (not otherwise described)"

now contains men returned as clerks who worked in a specified setting not described in other clerk categories .

Residual Categories . We coded more men within the "other" groupings than did the 1880 tabulators . We had no guidance as to whom the 1880 Census Office put in these categories . Some of the distinct groups and general rules of classification we followed are :

089- Porters and Laborers in Stores and Warehouses . Includes the numerically significant group of stevedores and longshoremen. Anyone reported as "Works in [some type of store]" was also classified here .

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172- Employees in Manufacturing Establishments (not specified) . Men reporting a manufacturing occupation that suggested employee status but did not include reference to a mill or factory (e .g ., "Works in lamp shop," and "Pressman") .

204- Mill and Factory Operatives (not specified) . Men whose title suggested employee or operative status while also mentioning a mill or factory workplace.

Some of the titles include

"Mill hand," "In pencil factory," and "Steam mill ." 210- Officials of Manufacturing and Mining Companies. Includes the following terms in the title in combination with some reference to manufacturing : keeps, owner, proprietor, manager, running, superintendent, president, treasurer.

265- Others in Manufacturing, Mechanical, and Mining Industries . Titles that suggest manufacturing occupations but that give no intimation of the man's status (employee, owner, etc.) .

Included here are many men described simply as "makers" of certain items not specified

among the other occupational categories . 266- Employed, Occupation Unspecified. A category we added for the Public Use Microdata Sample .

Men coded here gave a response that clearly indicated they were employed, but there

was no way to determine even in which economic sector to place them .

Such men are gainfully

employed . We differentiated among the non-occupational responses we encountered in the data and coded them into a number of categories above the numeric range of legitimate occupational responses (301-310). Users interested only in gainfully employed men should exclude these responses . Many of the non-occupational responses describe the condition of adolescents and the elderly. We grouped the responses to maximize their usefulness to researchers .

1950 Occupational Classification .

We coded occupations into the 1950 Census Bureau occupational

classification in addition to the 1880 scheme .

The 1950 classification was carried out in - a similar

manner to the 1880 coding (steps 1 and 3 detailed above) . In coding into the 195(3 system we did not favor industry as we did for 1880 . The procedure for 1950 coding also differed because we did not have published Census Office statistics against which to compare our figures.

Classification was

simplified greatly by a published 1950 Index of Occupations and Industries which the Bureau used for its own tabulations . The vast majority of 1880 occupations were contained in this index, which supplied the appropriate 1950 code for particular job titles, sometimes providing different codes for the same occupational title where the industry differed .

The status of certain occupations may have

changed since 1880 with respect to the particular occupational grouping in which it belongs-(e .g ., "Craftsmen" or "Operative"), but we adhered strictly to the letter and logic of the 1950 Index . leave it to the individual researcher to resolve such issues . Some occupations proved difficult to code because of ambiguity, lack of the necessary industry information, or because the particular occupation disappeared-or the title fell out of usage-between 1850 and 1950 . If no appropriate category suggested itself, we classified the

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occupation within one of the residual categories such as "Operatives and kindred workers (not elsewhere classified) ."

The following occupations proved problematic or contain subgroups that bear

pointing out:

300- Agents (not elsewhere classified). If the title suggested a man was an agent in retail, as opposed to wholesale or manufacturing, then he was coded in "Salesmen and sales clerks (not elsewhere classified)."

564-Painters, Construction, and Maintenance.

There are two categories of painters in 1950,

the other being "Painters, except construction or maintenance" (670) . We used the construction category as the default code . Men listed as "Painter" or "House painter" were coded in construction painting .

594- Craftsmen and Kindred Workers (not elsewhere classified).

This includes men returned

as coopers, brewers, and wagonwrights, among others . 625- Bus Drivers . Includes bus, coach and stage drivers.

A man returned as a "Coachman"

was coded in "Private household workers (not elsewhere classified)" (720). 682- Taxicab drivers and chauffeurs .

Includes carriage and hack drivers.

683- Truck and Tractor Drivers . Includes cartmen, expressmen, and men listed only as "driver."

We also classified "teamsters" here, rather than coding them in the 1950 category,

"Teamsters ." index .

This was the only point where we consciously broke from the 1950 occupational

Our rationale was that teamsters in 1950 were an insignificant and marginal occupation

classified in the larger grouping "Laborers, Except Farm and Mine ."

Teamsters in 1880 were a

mainstream occupation performing the function of 1950 truck drivers. With other occupations we did not let the mechanization or change of method or setting change their classification . 690- Operatives and Kindred Workers (not elsewhere classified) .

A large residual category

containing harnessmakers, tanners, wagon makers, cigar makers, and men reported as "working" in a mill or ship .

970- Laborers (not elsewhere classified) .

Subject to the same logical recoding as the 1880

laborer category whereby men in households headed by a farmer were recoded as "Farm laborers, wage workers."

The laborer category contains men identified as "Hostler .

975- Employed, Occupation Unspecified-A category we added for the Public Use Microdata Sample .

Men coded here gave a response that clearly indicated they were employed, but there

was no way to determine even in which economic sector to place them. Such men were gainfully employed .

Non-occupational responses were grouped into categories and given codes above the range of legitimate 1950 occupational responses (981-990) .

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INSTITUTIONS In 1850, census marshals were instructed to supply the names of all public institutions (see marshal instructions below) . In addition to the classification of institution type, we created a classification of institution funding to distinguish, where possible, among public, private, federal, state, and local sources of financial support .

Both variables were coded to be compatible with the

MUMS variables Group Quarters Type and Group Quarters Funding.

Often the type of institution was evident from the name, as in "Cleveland City Hospital" or "St. Vincent's Orphan Asylum ."

In other cases, however, it was necessary to examine the

characteristics of individuals in the facilities or those who were in charge . Identifying the source of funding was more problematic . The name of the institution was crucial in assigning a value. If the marshal did not supply an institutional name, the sample point went unspecified unless it was a correctional institution or workhouse (coded government unspecified), a military unit (federal government), or religious institution. Facilities with names which clearly identified them as public were given the appropriate government code . However, an institution with the name of a state or locale but no government qualifier was not placed in a publicly funded category .

A code of " 1 " was

assigned to facilities with religious names, such as "House of the Good Shepherd or St . Mary's Hospital," unless there were overriding considerations .

For example, "St. Louis City Hospital" was

assigned a public code because precedence was given to the government qualifier.

Reliance on the

name of the institution to determine type of funding does present ambiguities . Many institutions have been assigned an indeterminate funding code because their sources of funding were unclear. The coding system simply offers users an initial ordering of institutions . The institutions were divided into seven broad categories as follows:

100-199 Penal Institutions 200-299 Insane Asylums and Mental Institutions 300-399 Homes for the Dependent 500-599 Military Institutions 600-699 Schools 700-799 Group Quarters 800-899 Other Codes for the funding variable GQFUND are as follows: 11 . Federal-level Public Funding 13 . State-level Public Funding 16 . City/County-level Public Funding 17 . Public Funding, unspecified 21 . Private Enterprise 24 . Religious 99 . Not Classifiable

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REFERENCES TO USER'S GUIDE Anderson, Margo J. (1988) The American Census: A Social History .

New Haven: Yale University

Press. Coale, Ansley J . and Melvin Zelnik (1963) New Estimates of Fertility and Population in the United

States .

Princeton, NJ :

Princeton University Press .

Coale, Ansley J. and N . W. Rives (1973) "A statistical reconstruction of the black population of the United States 1880-1970: Estimates of true numbers by age and sex, birth rates, and total fertility ."

Population Index 39 :3-36. Easterlin, Richard A., George Alter, and Gretchen Condran (1978) "Farm and farm families in old and new areas :

The northern states` in 1860 ." In Family and Population in Nineteenth Century

America, ed . Tamara Hareven and Maris Vinovskis. Princeton: Princeton University Press: 22-84. 1900 Public Use Sample: User's Handbook.

Graham, Stephen N. (1980)

Seattle : Center for

Demography and Ecology, University of Washington . Ruggles, Steven and Russell R. Menard (1990) "A Public Use Sample of the 1880 Census of Population ." Historical Methods 23 : 104-115 . Ruggles and Menard, et al. (1993)

1880 Public Use Sample User's Guide .

Minneapolis: Social

History Research Laboratory, University of Minnesota.

Strong, Michael A., Samuel H . Preston,

Ann R. Miller, Mark Hereward, Harold R. Lentzner,

Jeffrey R. Seaman, Henry C. Williams (1989)

Population .

User's Guide : Public Use Sample, 1910 Census of

Philadelphia : Population Studies Center, University of Pennsylvania .

U .S . Bureau of the Census (1916) Special Reports of the 12th Census .

Supplementary Analysis and

Derivative Tables . Washington, D .C . : U .S . Government Printing Office . (1972) Public Use Samples of Basic Records From the 1970 Census : Description and Technical Documentation . Washington, D.C . : U.S . Government Printing Office . (1973) Technical Documentation for the 1960 Public Use Sample . Washington, D . C . : U. S. Government Printing Office .

(1982) Public Use Samples of Basic Records From the 1980 Census : Description and Technical Documentation .

Washington, D.C . :

U.S . Government Printing Office .

User's Guide

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Page 32

(1854)

Census of Population, 1850. The Statistics of the Population of the United States .

Washington, D .C . : U.S . Government Printing Office . Statistical View of the United States: Compendium . Washington, D.C . :

(1854)

U .S .

Government Printing Office .

(1872)

Census

Washington, D . C . :

(1984a)

of Population,

1870: The Statistics of the Population of the United States .

U .S . Government Printing Office .

Census of Population, 1940: Public Use Sample Technical Documentation .

Washington, D.C . :

U .S . Government Printing Office .

(1984b) Census of Population, 1950 : Public Use Sample Technical Documentation . Washington, D.C . :

---- (1989)

U.S . Government Printing Office .

Census of Population and Housing.

Washington, D. C. :

Tabulation and Publication Program.

U.S . Government Printing Office .

Wright, C.D ., and William C. Hunt (1900)

The History and Growth of the United States Census .

Washington, D.C . : U.S . Government Printing Office .

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THE 1850 CENSUS : HISTORICAL OVERVIEW AND ENUMERATOR INSTRUCTIONS

HISTORICAL OVERVIEW The Population Schedule . Throughout the history of the United States census, the population schedule has been marked by seasons of reform (i .e ., 1850, 1880, 1940, and 1960) followed by periods of quiescence or retreat. Particularly significant was the change between 1840 and 1850, marking the end of one era of census-taking and the beginning of another persisting to today . From 1790 through 1840, the household was the enumeration unit .

Questions asked how

many people with a given set of characteristics (e .g ., white, male, and over 16) were found in a household. The enumerator supplied the appropriate tallies in separate columns devoted to each specified set of characteristics.

Thus, if the population were simultaneously broken down into two

sexes, two races, and three age periods, twelve columns would be needed to record all the requisite information.

As census users demanded coverage of more subjects and more detailed classifications,

this household-based schedule format became increasingly awkward.

With eighty total columns, the

1840 population schedule demonstrated the practical limits to this enumeration style . The unwieldy nature of the 1840 population schedule led to numerous inaccuracies in the enumeration, which prompted Congress to consider changing the population schedule .

In response to

complaints from statisticians both in and out of Congress about the accuracy of the 1840 count, Congress appointed a Census Board to investigate the form and content of the census . This was the first time in the sixty-year history of the census that decisions surrounding the decennial census were put in the hands of men specifically appointed to wrestle with census issues . Beginning in 1850, individuals rather than households were the enumeration unit, with the same questions put to every person separately .

Thus, data on sex, race, and age could be collected

via only three columns, with no predetermined limit placed on the number of race and age subcategories . This change was revolutionary in its implications .

More substantive areas could be

covered without sacrificing legibility ; answers could be recorded using detailed classifications (e .g ., ages 0 to 100 plus); aggregate results could be reported in tables cross-tabulating any two variables; and the burden of tabulating the returns shifted to a large clerical staff at the central Census Office . Enumeration Procedures .

The 1850 decennial census of population was conducted under the

supervision of 45 marshals of the judicial districts of the United States and 3,231 assistant marshals . Marshals had the power to appoint as many assistants as they deemed necessary to carry out the enumeration. The characteristics of the assistants were to be those of "assiduous industry, active intelligence, pure integrity, great facility and accuracy of computation" and an "intimate knowledge of the division allotted to them ." The establishment of the Census Board and the creation of the Department of Interior in 1850 dramatically improved some aspects of census administration .

First, marshals and their appointed

assistants received written enumeration instructions in addition to circulars explaining each inquiry . Second, personal correspondence with the office of the Secretary of the Interior provided marshals

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Page 3 4 with a minimum of training and oversight.

The actual procedure of canvassing the population,

however, remained virtually unchanged from the first census .

Conclusion . document .

Despite these administrative and procedural difficulties, the 1850 census is a remarkable This was an era in which bureaucratic and administrative organization was nascent,

training and oversight of enumerators minimal, communication slow and difficult at best, and processing and tabulation rudimentary . Yet the Census Office organized a clerical staff of 160; prepared schedules and numerous forms; disbursed information and payments to marshals and their assistants ; published over 2,100 pages of reports; and hand tabulated census data on 23 .2 million individuals . Sources Consulted:

Margo J. Anderson, The American Census: A Social History (Yale

University Press, 1988) . 2. J .D .B . DeBow, Compendium of the Seventh Census

(GPO,

1854) . 3 .

Patricia Cline Cohen, A Calculating People : The Spread ofNumeracy in Early America (University of Chicago Press, 1982) .

4. W. Stull Holt, The Bureau of the Census: Its History, Activities and

Organization (Brookings Institution; 1929) . Growth of the U.S . Census (GPO, 1900) .

Carroll Wright and William C . Hunt, History and

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INSTRUCTIONS TO MARSHALS AND ASSISTANT MARSHALS, CENSUS OF 1850 .

To the Marshals : Having appointed your assistants, and received a duplicate of the oath of office taken by each, pursuant to your instructions from this Department, of the 25th ultimo, you will proceed immediately to the further execution of your duty, as defined by the act. It is an important service, looked to in its results with much interest, and it is expected that you will use every effort to discharge it with promptness, efficiency, and exactness . You will be immediately furnished, by express, with a portfolio for each of your assistants,

and a sufficient number of blanks for each to commence work . The necessary additional blanks will be, in like manner, furnished you as soon as practicable. As 160 names may be entered on one sheet of population returns, and as three copies altogether are required, it follows that, for 160 names, 3 sheets of schedule No . 1 will be needed ; and that for a district of 20,000 free inhabitants, 375 sheets would be required .

To the number,

however, which is required, on an accurate calculation, an addition of 25 per cent should be made to cover possible errors, losses, etc. ; so that for a population of 20,000 in any one district, there should be sent 470 sheets of population blanks, or schedule No . 1 . You will, accordingly, estimate the number of free persons in each assistant's district, and -calculate thereon the number of this schedule (No. 1) which will be required ; and you will apportion the other schedules according to the character of the district, whether it be agricultural, planting, mining, manufacturing, or mercantile . The portfolios and schedules are to be transmitted by you to your assistants by mail, pursuant to the seventeenth section of the act, unless a more eligible mode can be resorted to, without expense . No . 2.

Of schedule No . 2, Slave Inhabitants, the same number will be required for a slave

population of 20,000 that would be required for the same number of free persons, as each sheet will include the same number of slaves that schedule No . 1 will of free population . No . 3.

No less than four copies of schedule No . 3 should be sent to each assistant, the fourth

copy being sent to provide for loss or accident; and cases will not very frequently occur, except in populous districts, where more than that number will be necessary. No . 4.

Of the Agricultural schedule, you can be that only judge of what number will be

requisite for a particular subdivision.

Four sheets of schedule No . 4 should be sent for every eighty

farm or plantation owners or occupiers. No . 5 .

Of schedule No . 5, Statistics of Industry, there should be sent to the assistants about

four sheets for each thirty manufacturers in his district ; or forty, provided that manufactories are generally on a small scale.

The statistics relating to four blacksmiths would not require more room

than those relating to one woolen or cotton factory. No . 6.

Of schedule No . 6, Social Statistics, it is presumed that four sheets will be sufficient

for most assistants, except in cities ; and even there, unless that social statistics for a whole city should be taken

one individual .

If more than three copies of any schedule be required in a subdivision, six will be needed, as

there must be three copies of every variety of statistics taken. You should use much care in the distribution of the blanks, in order that the supply be not unnecessarily exhausted.

Having furnished your assistants with the blanks and instructions, you will direct them to

inform you when they commence the enumeration of the district assigned, and at least once in every

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two weeks, where mail facilities exist, they should be required to inform you of the progress made in the work .

Failing to get such information from any assistant, it will be your duty to make inquiries

concerning the district, so as to be assured that the assistant is at work, and to take those efficient steps which the law provides, to remedy any evil or inattention which may exist.

You have, at any

time, for cause sufficient, the power to cancel the appointment of an assistant, and to appoint another for the district ; and it is your, duty to do so whenever the public interest suffers from, the neglect or incompetency of any assistant. 2.

By the seventh section of the above act, it is made your duty "to keep an accurate record

of the name, and area in square miles, of each subdivision, and of each assistant within your The object of this proviso is to determine the rate of payment to be made to the assistants .

district ."

It is supposed and believed that in all States the areas of the different subdivisions may be pretty accurately known.

It should be ascertained with complete exactness when the means exist for doing

so . Where the reputed or estimated area is upon data not entirely reliable, this fact should be stated . In the new States, where the county and town divisions are made by parallel lines, little difficulty can occur, and in the older States the gazetteers usually contain the required information; but, as they can not always be relied on, and counties have undergone change of character, the information should be obtained from the county surveyor, or clerk, or othei reliable source ; and you should require each assistant to furnish you with a certificate, under the hand of some reliable person, of the number of square miles in his district . You should consider this as one of your first duties, so that, if possible, it may be made known to the assistant, soon after his appointment, the area of his district, and thus prevent the occurrence of any subsequent dispute. You should arrange a book, in some convenient method, by which you can easily refer to the description of the district, the number of square miles therein, and the name of each assistant, and the state of the work in each subdivision .

Postmasters should be notified concerning the provision in the seventeenth section of the act,

which authorizes you and your assistants to frank all census packages and letters. 3.

By the fifth section it is also made your duty " carefully to examine the returns of each

assistant," to see whether the work has been executed in a lawful manner . You should carefully examine the returns, to see that every part of the district embraced has been visited, and all the required information obtained, and the schedules filled up according to the instructions . 4.

By the fifth section it is provided, that you shall transmit, forthwith, "one set of the

returns to the census office . " This set should be transmitted without any delay, and in convenient sized packages .

You should keep an accurate account of returns forwarded to the census office, and

of the date at which they were mailed ; and if the receipt of them is not acknowledged in due course of mail, you should write and inquire whether they have been received .

You are required, by the

same section, to transmit the other copy thereof to the office of the secretary of the State, or Territory, to which your district belongs . 5.

You and your assistants are requested to obtain, if practicable, and forward to the census

office, copies of local printed reports of towns, counties, and States, relating to the expenditures, to

schools, pauperism, crime, insanity, and other local matters which are required to be investigated by the schedules.

6. You should instruct your assistants, upon the receipt of the instructions and blanks, to

commence immediately to discharge their duty, and use all exertions to have them performed during

the earlier portion of the time allotted for the work, and not procrastinate, in the expectation of being able to prosecute the work during the latter portion of the period .

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When such procrastination occurs, or other causes (which might by timely caution be avoided) operate to defeat the consummation of the duty, neither you nor your assistants will be entitled to compensation, but render yourselves liable to penalty. To the Assistant Marshals : 1.

The assistant marshal, having been duly commissioned, will be provided with a portfolio,

to be furnished with the schedules, of sufficient size to contain several sheets of the same without folding, that may be easily opened, and used for writing on, if necessary; and he should furnish himself with ink, blotting paper, and pens .

Strings should be attached to the portfolio, to prevent the

loss of any of its contents . 2.

He is to approach every family and individual from whom he solicits information with

civil and conciliatory manners, and adapt himself, as far as practicable, to the circumstances of each, to secure confidence and good will, as a means of obtaining the desired information with accuracy and dispatch .

3 . If any person, to whom application is made for information should refuse to give it, or

should designedly give false information, the assistant should inform him of the responsibility he thereby incurs, and that he renders himself liable to a penalty, according to the fifteenth section of the act of Congress . 4.

The act provides that "the assistant marshals shall make the enumeration by actual

inquiry at every dwelling house, or by personal inquiry of the head of every family, and not otherwise. " This requirement must be strictly observed . 5.

As soon as the schedules are filled up, and the information in relation to each family is

obtained according to the instructions, the assistant should read over, and exhibit to the parties from whom he received the same, the record of the information obtained, and correct or supply any error or omission . 6.

The object of this rule is to prevent mistakes, and secure accuracy .

Each assistant is to complete the enumeration with as little delay as possible, after

commencing it, and should inform the marshal, at least once in two weeks, of the progress he is making in his district . 7. On each page of the population and agricultural schedules is to be inserted the date when such page was commenced, although it may not have been completely filled up until the following day.

When the whole enumeration in his district shall have been completed, two complete copies of

all the pages are to be made . These are to be carefully read over, and each compared to see that it is correct and agrees with the original . 8. Each assistant is to sign his name on each page of the schedule, and certify, and make oath or affirmation, at the end of each set of returns, that they were made according to his oath and instruction, to the best of his knowledge and belief . Two of the sets are to be forwarded to the marshal of his district, and one filed with the clerk of the court for preservation with the county records; in proof of the fling of which he must procure, and forward to his marshal, the certificate of the clerk of the county . Discretion as to what schedules will be needed by each assistant is lodged with the marshal, and is at all times to be used .

In the free States schedule No . 2 will be omitted .

For the guidance of assistants, each will be furnished with a set of schedules filled up in the manner contemplated by the act of Congress and these instructions .

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CIRCULAR TO MARSHALS, ETC. - CENSUS OF 1850 To the United States Marshals and Assistants : Information has been received at this office that in some cases unnecessary exposure has been made by the assistant marshals with reference to the business and pursuits, and other facts relating to individuals, merely to gratify curiosity, or the facts applied to the private use or pecuniary advantage of the assistant, to the injury of others . Such a use of the returns was neither contemplated by the act itself nor justified by the intentions and designs of those who enacted the law. No individual employed under sanction of the Government to obtain these facts has a right to promulgate or expose them without authority. Although designed ultimately for the use of the people at large, the Department reserves to itself the privilege of examining into the correctness of the returns, and arranging them in proper form for publication by Congress before any other use shall be made thereof; and all marshals and assistants are expected to consider the facts intrusted to them as if obtained exclusively for the use of the Government, and not to be used in any way to the gratification of curiosity, the exposure of any man's business or pursuits, or for the private emolument of the marshal or assistants, who, while employed in this service, act as the agents of the Government in the most confidential capacity . When . your original copies are filed with the clerks of the courts and secretary of your state, they will be under the control of those officers and subject to the usual regulations of the respective offices, and you can enjoy the same access to them which can be had by every citizen. To the publication of the mere aggregate number of persons in your district there can be no objection.

EXPLANATION OF SCHEDULE NO . l-FREE INHABITANTS. This schedule is to be filled up in the following manner : Insert in the heading the name of number of the district, town, or city of the county or parish, and of the state, and the day of the month upon which the enumeration was taken:

This is to be attested

on each page of each set, by the signature of the assistant. The several columns are to be filled as follows : 1 . Under heading 1, entitled "Dwelling houses numbered in the order of visitation," insert the number of dwelling houses occupied by free inhabitants, _as they are visited.

The first house visited

to be numbered 1 ; the second visited, 2 ; the third one visited, 3 ; and so on to the last house visited in

the subdivision . By a dwelling house is meant a separate inhabited tenement, containing one or more families under one roof. Where several tenements are in one block, with walls either of brick or wood to divide them, having separate entrances, they are each to be numbered as separate houses ;

but where not so divided, they are to be numbered as one house.

If a house is used partly for a store, shop, or for other purposes, and partly for a dwelling house,

it is to be numbered as a dwelling house. Hotels, poorhouses, garrisons, hospitals, asylums, jails, penitentiaries, and other similar institutions, are each to be numbered as a dwelling house; where the house is of a public nature, as above, write perpendicularly under the number, in said column, the name or description, as "hotel," "poorhouse," etc. 2.

Under heading 2; entitled "Families numbered in the order of visitation," insert the number of

the families of free persons as they are visited. The first family visited by the assistant marshal is to be numbered 1 ; the second one visited, 2; and so on to the last one visited in his district . By the term family is meant, either one person living separately in a house, or a part of a house, and providing for him or herself, or several persons living together in a house, or in part of a house, upon one common means of support, and separately from others in similar circumstances .

A widow

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living alone and separately providing for herself, or 200 individuals living together and provided for by a common head, should each be numbered as one family . The resident inmates of a hotel, jail, garrison, hospital, an asylum, or other similar institution, should be reckoned as one family . 3.

Under heading 3, entitled, "The name of every person whose usual place of abode on the 1st

day of June, 1850, was in this family," insert the name of every free person in each family, of every age, including the names of those temporarily absent, as well as those that were at home on that day. The names of every member of a family who may have died since the 1st day of June is to be entered and described as if living, but the name of any person born since the lst day of June is to be omitted.

The names are to be written beginningwith the father and mother ; or if either, or both, be

dead, begin with some other ostensible head of the family ; to be followed, as far as practicable, with the name of the oldest child residing at home, then the next oldest, and so on to the youngest, then the other inmates, lodgers and borders, laborers, domestics, and servants . All landlords, jailors, superintendents of poorhouses, garrisons, hospitals, asylums, and other similar institutions, are to be considered as heads of their respective families, and the inmates under their care to be registered as members thereof, and the details concerning each designated in their proper columns. Indians not taxed are not to be enumerated in this or any other schedule . By place of abode is meant the house or usual lodging place of a person .

Anyone who is

temporarily absent on a journey, or for other purposes, without taking up his place of residence _elsewhere, and with the intention of returning again, is to be considered a member of the family which the assistant marshal is enumerating . Students in colleges, academies, or schools, when absent from the families to which they belong, are to be enumerated only as members of the family in which they usually boarded and lodged on the lst day of June . Assistant marshals are directed to make inquiry at all stores, shops, eating houses, and other similar places, and take the name and description of every person who usually slept there, provided such person is not otherwise enumerated . Inquiries are to be made at every dwelling house, or of the head of every family .

Those only

who belong to such family, and consider it their home or usual place of abode, whether present or temporarily absent on a visit, journey, or a voyage, are to be enumerated .

Persons on board of

vessels accidentally or temporarily in port , temporarily boarding for a few days at a sailors boarding or lodging house, if they belong to other places are not to be enumerated as the population of a place . The sailors and hands of a revenue cutter, which belongs to a particular port should be enumerated as of that port . lakes, rivers, and canals .

A similar rule will apply to those employed in the navigation of the

All are to be taken at their homes or usual place of abode, whether present

or absent ; and if any live on board of vessels or boats who are not so enumerated, they are to be taken as of the place where the vessel or boat is owned, licensed, or registered .

And the assistant

marshals are to make inquiry at every vessel and boat employed in the internal navigation of the United States, and enumerate those who are not taken as belonging to a family on shore; and all persons of such description in any one vessel are to be considered as belonging to one family and the vessel their place of abode . The assistants in all seaports will apply at the proper office for lists of all persons on a voyage at sea and register all citizens of the United States who have not been registered as belonging to some family . Errors necessarily occurred in the last census in enumerating those employed in navigation because no uniform rule was adopted for the whole United States .

Assistant marshals are required to

be particular in following the above directions, that similar errors may now be avoided .

The 1850 Census Enumeration Page 40

1850 Public Use Microdata Sample - User's Guide and Technical Documentation

4 . Under heading 4, entitled "Age,;" insert in figures what was the specific age of each person at his or her last birthday previous to the 1st

of June,

opposite the name of such person. If the exact

age in years can not be ascertained, insert a number which shall be the nearest approximation tot. The age, either exact or estimated, is to be inserted . If the person be a child under 1 year old, the entry is to be made by the fractional : parts of a year, thus : one month, one-twelfth; two months,, two-twelfths ; three months, three-twelfths, and so on to eleven months, eleven-twelfths . 5 . Under heading 5, entitled "Sex," insert the letter M for male and F for female; opposite the name, in all cases, as the fact may be . 6. - Under heading 6, entitled "Color," in all cases where the person is white; leave the space blank; in all cases where the person is black, insert the letter B ; if mulatto, insert M.

It is very

desirable that these particulars be carefully regarded : 7. Under heading 7, entitled "Profession, occupation,: or trade of each person over 15 years of age," insert opposite the name of each male the specific profession, occupation, or trade which the said person is known and reputed to follow in the place where he resides - as clergyman ; physician, lawyer, shoemaker, student, farmer, carpenter, laborer, tailor, boatman, sailor, or otherwise ; as the fact may be . When more convenient, the name of the article he produces may be substituted. When the individual is a clergyman, insert the initials of the denomination to which he belongs before his profession - as Meth. for Methodist, R.C . for Roman Catholic, O .S .P . for Old School Presbyterian, or, other appropriate initials, as the fact may be . When a person follows several professions or occupations the name of the principal one only is to be given. If' a person follows no particular occupation ; the space is to be filled with the word "none." 8.

Under heading 8 insert the value of real estate owned by each individual enumerated .

You

are to - obtain the value of real estate by inquiry of each individual who is supposed to own real estate, be the same located where it may, and insert the amount in dollars. No abatement of the value is to be made on account of any lien or incumbrance thereon in the nature of debt. 9.

Under heading 9, "Place of birth."

in the family .

The marshal should ask the place of birth of each person

If born in the State or Territory where they reside, insert the name or initials of the

State or Territory, or the name of the government or country if without the United States ;

The

names of the several States may be abbreviated. Where the place of birth is unknown, state "unknown : " 10 .

Under No . 10 make a mark, or dash, opposite the name of each person married during the

year previous to the 1st of June, whether male or female .: 11 .

Under heading 11 ; entitled "At school within the last year ."

The marshal should ask what

member of this family has been at school within the last year ; he is to insert a mark, thus ; (1),

opposite the names of all those, whether male or female, who have been at educational institutions within that period . -Sunday schools are not to be included . 12 .

Under heading 12, entitled "Persons over 20 years of age who cannot read and write ."

The

marshal should be careful to note all persons in each family, over 20 years of age, who can not read and write, and opposite the. name of each make a mark, thus, (1) . The spaces opposite the names of those who can read and write are to be left blank.

If the person can read and write a foreign

language, he is to be considered as able to read and write . 13 .

Heading 13, entitled "Deaf and dumb, blind, insane ; idiotic, pauper, or convict."

The

assistant marshal should ascertain if there be any person in the family deaf,: dumb, idiotic, blind, insane, or pauper . If so, who?

And insert the term "deaf and dumb," "blind," "insane, ". and

"idiotic," opposite the name of such :persons ; as the fact maybe . When persons who had been convicted of crime within the year reside in families on the lst of June; the fact should be stated, as in the other cases- of criminals; but, as the interrogatory might give offense, the assistants had better

1850 Public Use Microdata Sample User's Guide and Technical Documentation

The 1850 Census Enumeration Page 41

refer to the country record for information on this head, and not make the inquiry of any family . With the county record and his own knowledge he can seldom err. Should a poorhouse, asylum for the blind, insane or idiotic, or other charitable institution, or a

penitentiary, a jail, house of refuge, or other place of punishment, be visited by,the assistant marshal, he must number such building in its regular order, and he must write after the number, and perpendicularly in the same column (No . 1) the nature of such institution - that it is a penitentiary, jail , house of refuge, as the case may be ; and in column 13, opposite the name of each person, he must state the character of the infirmity or misfortune, in the one case, and in the other he must state the crime for which each inmate is confined, and of which such person was convicted; and in column No . 3, with the name, give the year of conviction, and fill all the columns concerning age, sex, color, etc., with as much care as in the case of other individuals.

Variable Descriptions Page 42

1850 Public Use Microdata Sample User's Guide and Technical Documentation

VARIABLE DESCRIPTIONS This section provides a brief description of the variables in the sample . Further details on most variables can be found in the detailed sections above and in the enumeration instructions which are reproduced above. The classification schemes and frequency distributions for

each variable

are

given in the codebook . Most variables are classified with numeric codes . The only exceptions are the household record variable RECTYPE (Record Type), and person record variables RECTYPE (Record Type), FNAME (First Name), and LNAME (Last Name), which use alpha characters .

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Variable Descriptions

Page 43

VARIABLES ON THE HOUSEHOLD RECORD

H1 RECTYPE - Record Type Indicates whether a record is a household record or a person record .

Household records are type H,

and person records are type P. H2-3 YEAR -1850 Census Identifier Census year identifier .

This variable gives the middle two digits of the census year for identification

within the IPUMS. H4-11 SERIAL - Household Serial Number Unique serial number for each household. H12-13 DATANUM - 1850 Data Set Number IPUMS data set number . This sample is coded 01 . H14-15 NIII - Number of Person Records in Household Total number of persons in the sampled unit (household or group quarters).

H16-19 DWSIZE - Dwelling Size Number of persons in the entire dwelling . If the entire dwelling was taken (sampling rule 1), this is based on a machine count. If only one household was taken from a large multi-household dwelling, or the case was sampled as group quarters, (rules 2 through 4), DWSIZE is based on a hand count by the data-entry operator .

A missing value indicates that dwelling numbers were omitted by the

marshal. H2O-21 NUMHH - Number of Households in Dwelling In cases sampled as entire dwellings (sampling rule 1), this is based on a machine count.

In cases

sampled as households or group quarters (sampling rules 2 through 4) NUMHH is based on a hand count by the data-entry operator .

A missing value indicates that dwelling numbers were omitted by

the marshal . H22-23 REGION - Census Region Identifies 1980 census regions .

Territories are placed in the census region that they eventually

joined .

H2425 STATEICP - State of Enumeration (ICPSR system) There are two variables showing state or territory of enumeration.

The first, STATEICP, uses the

coding scheme of the Inter-University Consortium for Political and Social Research, and allows easy . merging of the 1850 Public Use Microdata Sample with ICPSR data files.

Variable Descriptions

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Page 44

H26-27 STATEFIPS - State of Enumeration (FIPS system) Identifies state or territory of enumeration by the Federal Information Processing Standards (FIPS) code in alphabetical order by state name . H28-31 CITY - City Identifier Unique identification code for each of the ninety-eight largest cities in 1850 . H32 URBAN - Urban/Rural Status Urban places are defined as incorporated municipalities and New England townships with a population of 2,500 or more .

Users should be aware that because of the poor quality of many

returns, particularly in the South and West, many people who may have lived in urban areas are not classified as such . For further discussion of this problem, see section on "Geographic Coding" above. H33-37 CITYPOP - City Population (in hundreds) Population totals in hundreds for all incorporated places and New England towns. Persons outside of incorporated places receive a value of 0 for CITYPOP. In New England there are relatively few incorporated places ; towns usually assume the functions of municipal governments, so we assigned populations to all New England towns . (Town in New England is the equivalent of township in other parts of the country) . elsewhere .

These towns, however, are not precisely comparable to incorporated places

Persons whose residence was listed as a township outside New England were categorized

as residents of unincorporated places . For further explanation, see the section on "Geographic Coding ." H3841 COUNTY - County Code County codes based on the ICPSR system .

These allow easy linking with the ICPSR county data

tape for those who want to add county-level variables .

Some persons residing in territories had no

county and were assigned a county code of 9999 .

H42-44 SEA - State Economic Area

State Economic Areas are relatively homogeneous subdivisions of states . They consist of counties or groups of counties that had similar social and economic characteristics in 1950 . codes are available in the IPUMS from 1850 through 1950 . J . Bogue's State

Economic

Areas (Washington:

Compatible SEA

For further information consult Donald

Government Printing Office, 1951).

H45 GQ - Group Quarters Residence Residence in institution or other units with over thirty members.

This variable also identifies units

that would have been sampled as group quarters in later census years. See "Sample Design" above. H46-48 GQCODE - Group Quarters Code This classification of institutions is coded to be compatible with the MUMS classification . The first digit identifies one of eight general group quarters categories (correctional institutions, health-related, homes for dependents, etc.) and the second digit gives a more detailed breakdown. on "Institutions," above .

See the section

1850 Public Use Microdata Sample

Variable Descriptions

User's Guide and Technical Documentation

Page 45

H49-50 GQFUND - Group Quarters Funding Code Indicates the source of support or institutional affiliation where that could be determined . GQCODE, this variable follows the system developed for the IPUMS .

For more information, see

the section on "Institutions," above . H51 FARM - Farm Identifies farms as households in which any member reported their occupation as farmer . H52 SAMPUNIT - Sample Unit Identifies the sampling rule used for each case .

See the "Sample Design" section above.

Cases may

be taken as dwellings, households, related groups, or individuals. Category 1 identifies households where the entire dwelling is included in the sample ; if the household is located in a multi-household dwelling, the other households in the dwelling will appear in the same record . households within large multi-household dwellings.

Category 2 identifies

Category 3 identifies households taken in

districts where dwelling information was missing. Categories 4 and 5 identify related groups in group quarters and individuals in group quarters ; these cases should be omitted for analysis of households or household characteristics. The final SAMPUNIT category, "Fragment," denotes parts of households that were initially missed by marshals and later entered on the census forms at the end of the enumeration district . It was not feasible to locate the correct household to which these people belonged .

To ensure that each

enumerated individual had a 1-in-100 chance of inclusion in the sample, we treated these cases as if they were residents of group quarters . Thus, fragments with no obviously related adjacent kin were included only if the sample point fell on their line ; fragments consisting of a group of kin were included only if the sample line fell on the first individual in the group . H53 HHSEQ - Sequence of Household in Dwelling Sequence number of each household in the dwelling .

This variable is always equal to 1 except when

the sample includes more than one household from a dwelling (that is, for multi-household dwellings sampled under rule 1) .

H54-57 NLTMPERHH - Number of Person Records in Household Number of person records following the household record .

For cases sampled under rules 1 or 2,

this variable is equivalent to HHSIZE ; otherwise, it is a machine count of the number of person records in the sample unit taken. H58-61 REEL - Microfilm Reel Number

. ,.

Identifies the microfilm reel from which the case was taken, using the Census Bureau's numbering system for reels. H62-64 PAGENO - Microfilm Page Number These page numbers were stamped on the census pages before microfilming . This can be useful in locating cases included in the 1850 Public Use Microdata Sample on the original schedules.

Variable Descriptions Page 46

1850 Public Use Microdata Sample User's Guide and Technical Documentation

H65-66 SEQHH - Sequence in Dwelling of First Family Taken If the entire dwelling is included in the sample (sampling rule 1), SEQHH takes a value of 1 . Under sampling rules 2 through 4, SEQHH indicates the order of the household within the dwelling .

For

example, if a single household is taken from a large multi-household dwelling, SEQHH indicates whether the household is the first household listed in the dwelling, the second, and so on . H67 SIDE - Side of Enumeration Form Each enumeration page had two sides, and only the front was stamped with a page number .

This

variable indicates if the case was drawn from the front (side 1) of the form or the back (side 2) . This can be useful in locating cases included in the 1850 Public Use Microdata Sample on the original schedules .

H68-69 DWTAKE - Number of Persons Taken in Dwelling Number of person records included in the sample from the current dwelling, across all households taken in the dwelling . When the entire. dwelling is included (sampling rule 1), DWTAKE is the same as DWSIZE .

H70 ENUMDUR - Duration of enumeration Length of time, in days, that it took the enumerator to record the cases on the census page . Almost 90 percent of the census pages were completed in a day, but in some rural districts it took over a week to gather enough names to fill up a sheet.

H71-72 ENUMMO - Month of enumeration Month in 1850 that the marshal began to gather information for the census page containing the case . Enumeration began on June 1, 1850 and the results returned to the Secretary of the Interior on or before November 1, 1850 .

H73-74 ENUMD - Day of enumeration Day of the month that the marshal began to gather the information on the census page . H75-77 LINE - Line number Identifies the line number on the enumeration form of the randomly selected sample point. See section on "Sample Design" above .

H78-81 DWNUM - Dwelling number Dwelling number appears in the first column of the census form, and is our principal means of establishing where one dwelling ends and the next one begins . numbers, and these cases were assigned a DWNUM of zero .

Some marshals omitted dwelling

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Variable Descriptions Page 47

VARIABLES ON THE PERSON RECORD

PI RECTYPE - Record Type See H1 . P2-3 YEAR - 1850 Census Identifier See H2-3 . P4-11 SERIAL - Serial Number See H4-11 . P12-13 PERNUM - Sequence of Person in Household Sequence number of each person in a household.

This variable can be combined with SERIAL to

create a unique identifier for every individual in the sample . P14-15 MOMLOC - Position in Household of Own Mother A pointer to the. position of the individual's own mother within the household, expressed in terms of the mother's PERNUM . This variable is useful for the more sophisticated forms of own-child fertility analysis and for attaching parental characteristics to individuals . For further information, see the section on "Family Interrelationships" above. P16 MOMRULE - Rule for Linking Mother The specific rule used for linking the individual's own mother .

For further information, see the

section on "Family Interrelationships" above.

P17-18 POPLOC - Position in Household of Own Father A pointer to the location of the individual's own father within the household, expressed in terms of the father's PERNUM . For further information, see the section on "Family Interrelationships" above .

P19 POPRULE - Rule for Linking Father The specific rule used for linking the individual's own father . For further information, see the section on "Family Interrelationships" above . P20-21 SPLOC - Position in Household of Own Spouse A pointer to the location of the individual's own spouse within the household, expressed in terms of the spouse's PERNUM .

This variable is useful for attaching and analyzing spouse's characteristics .

For further information, see the section on "Family Interrelationships" above . P22-23 ELDCH - Age of Eldest Own Child Age of the eldest coresident own child of the individual . For further information, see the section on "Family Interrelationships" above .

Variable Descriptions Page 48

1850 Public Use Microdata Sample User's Guide and Technical Documentation

P24-25 YNGCH - Age of Youngest Own Child Age of the youngest coresident own child of the individual . For further information, see the section on "Family Interrelationships" above.

P26-27 NFAM - Number of Own Family Members in Household A count of the individual's own kin within the household.

For further information, see the section on

"Family Interrelationships" above. P28 NCHILD - Number of Own Children in Household A count of the individual's own children, with no age restrictions . For further information, see the section on "Family Interrelationships" above. P29 NCHLT5 - Number of Own Children Under Age 5 in Household A count of the individual's own children under 5 within the household: this variable is intended primarily to aid in fertility analysis . For further information, see the section on "Family Interrelationships" above.

P30-31 IMPREL - Imputed Relationship to Head Imputed relationship to head of household. For further information, see the section on "Family Interrelationships" above .

P32-34 AGE - Age of Person Expressed in Years

P35 SEX - Sex Sex of individual .

Missing or inconsistent information on sex could almost always be inferred from

information in the first name and relationship fields .

P36-38 RACE - Race Race of individual . The 1850 population schedule of free inhabitants distinguished between free Blacks and Free Mulattos . The marshal instructions placed emphasis on the careful reporting of these categories . P39 MARRINYR - Married Within Year Indicates persons married within the previous census year, from May 31, 1849 to June l , 1850 .

P40 BLIND- Blind Indicates blind individual . P41-45 BPL-Birthplace The birthplace of persons based on the IPUMS classification system . For additional information, see "Geographic Coding" above .

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Variable Descriptions Page 49

P46 SCHOOL - School Attendance in the Past Year Indicates whether person attended school within the past year .

Some marshals failed to indicate

school attendance in the SCHOOL column, but noted it in the occupation column .

P47 LIT - Literacy This variable determined whether persons 20 years old and older could not read and write.

If the

person could read and write a foreign language, they were considered as able to read and write . P48-50 OCC1880 - Occupational Classification, 1880 System Occupations of males over the age of fifteen, classified by the 1880 system .

The 1880 Census

occupational classification scheme divided the working population into 265 detailed categories . Individual responses included over 10,000 variant occupational listings, and these were allocated to the 1880 categories with the aid of a variety of occupational dictionaries and census technical papers from later years. In general, we attempted to follow the 1880 procedures for classifying occupations. We have added codes in the range 301 to 306 to capture non-occupational responses.

Two of these

categories, "Keeping house". and "Capitalist," could be considered to be occupations, but they do not fit into the 1880 system . See the section above on "Occupations," above. P51-53 OCC1950 - Occupational classification, 1950 system Occupations of males over the age of fifteen, classified by the 1950 system . The 1950 Census Bureau occupational classification system is the easiest one to approximate across different public use microdata samples .

Users who are more interested in socioeconomic status than in economic sector

will generally find the 1950 classification more useful than the 1880 classification .

See the section

above on "Occupations," above. P54-55

SCORE - Income Score

Income score based on the man's occupation under the 1950 classification system (OCC1950) . The score gives that occupation's median total income in 1950 (in hundreds of dollars) . be a general indicator of economic status . special issue of Historical Methods .

It is intended to

For more detail and discussion, see the Winter, 1995

P56-61 REALPROP- Value of real property owned This variable indicates the value of real estate owned by each individual enumerated . Marshals obtained the value of real estate by inquiry of each individual who owned real estate . P62 DEAF- Deaf and Dumb Includes persons defined as "dumb," deaf only, and dumb only .

P63 IDIOTIC Includes persons listed as "idiotic," "fool," "simple," etc . P64 INSANE Includes persons listed as "insane," "delirious," "maniacs" and "lunatics ."

Variable Descriptions Page 50

1850 Public Use Microdata Sample User's Guide and Technical Documentation

P65 PAUPER Indicates person was a "pauper."

P66-67 SURSIM - Surname Similarity Sequence number for each unique surname encountered in the dwelling .

Individuals with the same

SURSIM code within the dwelling shared the same surname. P68-69 AGEMO - Age in months The age in months of infants less than one year old on .June 1, 1850 . P70 CRIME- Crime If person is listed in a family, the variable indicates any crime they were convicted of within the past year . If the person is in an institution such as a penitentiary, the variable indicates the crime for which they were convicted.

P71-86 FNAME- First Name Person's first name in alphabetic characters including middle initial . P87-102 LNAME- Surname Person's surname in alphabetic characters .

1850 Public Use Microdata Sample

Record Layout

User's Guide and Technical Documentation

INDEX TO CODEBOOK

Page 51

Record Layout Page 52

1850, Public Use Microdata Sample User's Guide and Technical Documentation

HOUSEHOLD RECORD LAYOUT Character Location_

Variable Name

H1 H2-3

RECTYPE YEAR

H4-11 H 12-13 H14-15 H16-19 H2O-21

SERIAL DATANUM NHH DWSIZE NUMHH

H22-23 H24-25 H26-27

REGION STATEICP STATEFIPS

H28-31 H32 H33-37 H38-41

CITY URBAN CITYPOP COUNTY

H42-44 H45 H46-48 H49-50

SEA GQ GQCODE GQFUND FARM

H51 H52 H53 H54-57 H58-61 H62-64 H65-66 H67 H68-69 H70 H71-72 H73-74 H75-77 H78-81

SAMPUNIT HHSEQ NUMPERHH REEL PAGENO SEQHH SIDE DWTAKE ENUMDUR ENUMMO ENUMD LINE DWNUM

Variable Description

Codebook Pa ge

NQ,.

Record Type Identifier (H or P) . . . . . . . . . . . . . 1850 Census Identifier . . . . . . . . . . . . . . . . . . . . . . . . Household Serial Number . . . . . . . . . . . . . . . . . . . . 1850 Data Set Number . . . . . . . . . . . . . . . . . . . . . . . . Number of Person Records in Household . Dwelling Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of Households in Dwelling . . . . . . . . Census Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

.. . .. ... ... .. . .. ... ..... ... .. . .. . .. ... .. . .. ... .. . .. ... ... .. . .. ... ... .. . .. ... ... .. ... ... ... .. ... ... ..... ... .. . ..... ... ..... ... .. . .. ...

.. .. .. ..

... ... ... . ..

... .. . ... ... .. . .. ... ... .. . .. ... .... ... ........ ... ... .....

.. . ... ... .. .

54 54 54 54 . . . . . 54 . . . . . 54 . . . . . 55 . . . . . 55 . . . . . 55 . . . . . 56

State of Enumeration (ICPSR system) State of Enumeration (FIPS system) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . City Identifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban/Rural Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . City Population (in hundreds) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . County Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . State Economic Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Group Quarters Residence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Group Quarters Code . . . . Group Quarters Funding Farm . . . . . . . . . . . . . . . . . . . . . . . . Sample Unit . . . . . . . . . . . . . . .

.. ... .. ... Code . . .. . ... ..... .....

..... ..... . . . ... . . . . . . .. .. .. .. ...... ..

... ... ..: ...

.. ... .. . ..... ... . ... ... ... .....

... .. . ... ...

. . 57 . . 59 . . 59

.. . . 60 . . 60 . . . . . . . . . . . 60 . . . . . . . . . . . . . . . 61 . . . . . . . . . . . . . . . 61 ... .. ... . . . 62

Sequence of Household in Dwelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Number of Person Records in Household . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Microfilm Reel Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . 63 Microfilm Page Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Sequence in Dwelling of First Family Taken . . . . . . . . . . . . . . . . . . . . . . . . 63 Side of Enumeration Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Number of Persons Taken in Dwelling . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 63 Duration of Enumeration . . . . . . . . . . . . . Month of Enumeration . . . . . . . . . . . . . . . . . Day of Enumeration . . . . . . . . . . . . . . . . . . . Line Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dwelling Number . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 . . . . . . . . . . . . . . . . . . . . . . . . . _ . . . . . . . . . 64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 64

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Record Layout

Page 53

PERSON RECORD LAYOUT Character

Variable

Location

Name

P12-13 P14-15 P16 P17-18 P19 P20-21 P22-23 P24-25 P26-27 P28 P29

P30-31 P32-34 P35 P36-38

P39

P40 P41-45 P46 P47 P48-50

PERNUM MOMLOC MOMRULE POPLOC POPRULE SPLOC ELDCH YNGCH NFAM NCHILD NCHLTS

IMPREL AGE SEX RACE

MARRINYR BLIND BPL SCHOOL LIT OCC 1880

P51-53 P54-55 P56-61

OCC 1950 SCORE REALPROP

P63 P64 P65

DEAF IDIOTIC INSANE PAUPER

P70

CRIME

P62

P66-67 P68-69

SURSIM AGEMO

Codebook

Variable Description

Page No .

Sequence of Person in Household . . . . . . . . . . . . . . . . . . . . Position in Household of Own Mother . . . . . . . . . . . . . . Rule for Linking Mother . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Position in Household of Own Father . . . . . . . . . . . . . . .

. . . . . . . . . . . . : . . . . . 65 . . . . . . . . . . . . . . . . . . 66 . . . . . . . . . . . . . . . . . . 66 . . . . . . . . . . . . . . . . . . 66

Rule for Linking Father . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Position in Household of Own Spouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age of Eldest Own Child . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age of Youngest Own Child . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of Own Family Members in Household . . . . . . . . . . . . . . . Number of Own Children in Household . . . . . . . . . . . . . . . . . . . . . . . . . . Number of Own Children under 5 in Household . . . . . . . . . . . . . . .

. . . . 66 . . . . 67 . . . . 67 . . . . 68 . . . . 69 . . . . 70 . . . . 70

Imputed Relationship to Head . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age of Individual Expressed in Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . : . . . . . . . . . . . . . . . . . Race . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Married Within Year . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Blind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Birthplace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . School Attendance in the Past Year . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Occupational Classification, 1880 System . . . . . . . . . . . . . . . . Occupational Classification, 1950 System . . . . . . . . . . . . . . . . Income Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Value of Real Estate Property Owned . . . . . . . . . . . . . . . . . . . . Deaf and Dumb . . . . . . . . . . . . . . . . . . . . . Slow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pauper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surname Similarity . . . . . . . . . . . . . . . .

.. ... ..... ... .. . .. . ..... ..... ... ... ... ..... ... .. ... . .. ... .. ... ..... ... ... ... ... .. . .. ... ... ... ..

.. ..... ... . ..... ... ... .. ... ... ... ... .. . .. . ..

. . . .

70 71 73 73

73 74

76 76

87 . . . . . . . . . . . . . . . . . . . . 87 . . . . . . . . . . . . . . . . . .. 87 . . . . . . . . . . . . . . . . . . . .88

. . . . . . . . . . . . . . . . . . . . .88 . . . . . . . . . . . . . . . . . . . . 88 Age in months . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Crime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Codebook . Household Record Page 54

1850 Public Use Microdata Sample User's Guide and Technical Documentation CODEBOOK

Record Type Identifier

RECTYPE HI

37,105

100 .0

37,105

100 .0

37,105

100 .0

3-7,105

100 .0,

37,105

100 .0

37,105

100 .0

37,105

100 .0

37,105

100 .0

37,105

100 .0

37,105

100 .0

576

1 .6

2,752

7 .4

4,161

11 .2

4,713

12 .7

4,779

12 .9

4,478

12 .1

3,769

10 .2

3,127

8 .4

2,207

5 .9

1,544

4 .2

913

2 .5

Household Record Identifier

1.850 Census Identifier

YEAR

H2-3

1850 Census year identifier

Household Serial Number

SERIAL H4-11 1-37105

Household serial number

1850 Data Set Number

DATANUM

H12-13

Data set number

Number of Person Records in Household

NHH H14-15

Dwelling Size

DWSIZE H16-19

12 13

612

1 .6

346

0 .9

1850 Public Use Microdata Sample

Codebook: Household Record

User's Guide and Technical Documentation 2016

Page 55 38

0 .1

0 .2

37,105

100 .0

1 2

32,905

88 .7

682

281

0 .8

0 .0

9999

NUMHH

Missing

Number of Households in Dwelling

H2O-21

2,460

90 99

REGION

Missing

243

6 .6 1 .8

0 .7

37,105

100 .0

5,404

14 .6

10,999

29 .6

8,020

21 .6

1,493

4 .0

5,607

15 .1

3,940

10 .6

1,186

3 .2

159

0 .4

Census Region

H22-23 11

New England Division - Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut .

Middle Atlantic Division - New York, New Jersey, Pennsylvania .

East North Central Division - Ohio, Indiana,

West North Central Division - Minnesota Territory,

Illinois, Michigan, Wisconsin . Iowa, Missouri .

South Atlantic Division - Delaware, Maryland, District of Columbia, Virginia, North Carolina, South Carolina, Georgia, Florida .

East South Central Division - Kentucky, Tennessee, Alabama, Mississippi .

West South Central Division - Arkansas, Louisiana, Oklahoma, Texas .

Mountain Division -- New Mexico Territory, Utah Territory .

STATEICP

Pacific Division - Oregon Territory, California .

297

0 .8

37,105

100 .0

State of Enumeration (ICPSR)

H24-25 1

Connecticut

699

1 .9

Maine

1,034

2 .8

Massachusetts

2,154

5 .8

New Hampshire

662

1 .8

Rhode Island

281

Vermont

574

0 .8 1 .5

Codebook: Household Record

Page 56

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Delaware

149

0 .4

New Jersey

950

2 .6

New York

5,872

15 .8

Pennsylvania

4,177

11 .3

Illinois

1,547

4 .2

Indiana Michigan

1,704 725

4 .6

23 24

Ohio

3,438

9 .3

Wisconsin

606

1 .6

Iowa

332

0 .9

0 .0

1,149

3 .1

1,749

4 .7

Minnesota Territory

Missouri

Virginia

2 .0

Alabama

737

2 .0

Arkansas

276

0 .7

Florida

106

0 .3

Georgia

923

2 .5

Louisiana

626

1 .7

Mississippi

496

1 .3 2 .9

North Carolina

1,069

South Carolina

543

1 .5

Texas

284

0 .8

Kentucky

1,391

3 .7

Maryland

Tennessee

New Mexico Territory

Utah Territory

California

72 98

STATEFIPS

Oregon Territory District of Columbia

973

2 .6

1,316

3 .5

137

0 .4

0 .1

274

0 .7

0 .1

0 .3

37,105

100 .0

737

2 .0

276

0 .7

274

0 .7

State of Enumeration (FIPS)

H26-27 01

Alabama

Arkansas

California

Connecticut

Delaware

District of Columbia

Florida

Georgia

Illinois

Indiana

Iowa

Kentucky

Louisiana

Maine

Maryland

699

1 .9

149

0 .4 0 .3

95 106

0 .3

923

2 .5

1,547

4 .2

1,704

4 .6

332

0 .9

1,391

3 .7

626

1 .7

1,034

2 .8

973

2 .6

1850 Public Use Microdata Sample

Codebook: Household Record

User's Guide and Technical Documentation 25

Massachusetts

Michigan

Minnesota Territory

Mississippi

Missouri

33 34

New Hampshire

New Mexico Territory

36 37

Page 57 2,154 725

5 .8 2 .0

0 .0

496

1 .3

1,149

3 .1

662

1 .8

950

2 .6

137

0 .4

New York

5,872

15 .8

North Carolina

1,069

Ohio

3,438

2 .9

Oklahoma

Oregon Territory

1,749

New Jersey

9 .3 4 .7

0 .1

Pennsylvania

4,177

Rhode island

281

11 .3

South Carolina

543

Tennessee

Texas

Utah Territory

Vermont

Virginia

Wisconsin

CITY

0 .8 1 .5

1,316

3 .5

284

0 .8

0 .1

574 1,749

1 .5 4 .7

606

1 .6

37,105

100 .0

31,203

84 .1

0 .2

City Identifier

H28-31 0

Household not located in one of 98 largest cities

Albany, NY

Alexandria, VA

0 .0

110

Allegheny, PA

390

Auburn, NY

0 .1

430

Augusta, ME

530

Baltimore, MD

550

Bangor, ME

570

Bath, ME

810

Boston, MA

830

Bridgeport, CT

890

Buffalo, NY

930

Cambridge, MA

970

Camden, NY

0 .1 0 .0

363

1 .0

0 .1

0 .0

341

0 .9

0 .1

0 .2

0 .1

1030

Charlestown, MA

0 .0

1050 1190

Charleston, SC Chicago, IL

0 .1

1290

Cincinnati, OH

1330

Cleveland, OH

1450 1490

Columbus, OH Concord, NH

1530

Covington, KY

1210

Chicopee, MA

0 .1 0 .2

0 .1

222

0 .6

37 52 25 18

0 .1 0 .1 0 .1 0 .0

Codebook. Household Record Page 58

1850 Public Use Microdata Sample User's Guide and Technical Documentation

1610

Danvers, MA

1670

Dayton, OH

1750

Detroit, MI

1770

Dorchester, MA

1790

Dover, NH

2230

Fall River, MA

2510

Gloucester, MA

0 .1

2690

Harrisburg, PA

0 .1

2710

Hartford, CT

0 .0

2990

Indianapolis, IN

0 .1

3410

Lafayette, LA

3470

Lancaster, PA

3530

Lawrence, MA

3770 3790

0 .0

0 .1

0 .0 0 .1

0 .0

0 .1

Louisville, KY

Lowell, MA

0 .3

3810

Lynchburg, VA

3830

Lynn, MA

0 .0

0 .1

3870

Madison, IN

0 .0

3930

Manchester, NH

4050

Memphis, TN

0 .1

4170

Milwaukee, WI

0 .0

0 .1

4210

Mobile, AL

0 .1

4290

Montgomery, AL

0 .0

4410

Nantucket, MA

4451

Nashville, TN

0 .0

4470

New Albany, IN

0 .0

4490

New Bedford, MA

0 .1

4570

New Haven, CT

0 .1

4590

New London, CT

4610

New Orleans, LA

4652

Brooklyn, NY

186

0 .0

4653

New York, NY

4670

Newark, NJ

- 4730

Newburyport, MA

0 .1

4770

Newport, RI

0 .0

4850

Norfolk, VA

0 .1

4890

North Providence, RI

0 .0

4930

Norwich, CT

0 .0

5150

Oswego, NY

0 .1

5250

Paterson, NJ

0 .1

5350

Petersburg, VA

0 .1

5370

Philadelphia, PA

210

0 .6

5371

Kensington, PA

0 .3

5372

Mayamensing, PA

0 .1

5373

Northern Liberties, PA

0 .2

5374

Southwark, PA

0 .2

5375

Spring Garden, PA

101

0 .3

5410

Pittsburgh, PA

0 .2

5550

Portland, ME

0 .1

5590

Portsmouth, NH

0 .0

5630

Portsmouth, VA

0 .0

0 .2

0 .5

173

0 .5

1,027

2 .8 --

0 .2

1850 Public Use Microdata Sample

Codebook: Household Record

User's Guide and Technical Documentation

Page 59

5640

Pottsville, PA

0 .0

5670

Providence, RI

0 .2

5810

0 .1

Reading, PA

5890

Richmond, VA

0 .1

5950

Rochester, NY

0 .2

6030

Roxbury, MA

0 .1

6110

Saint Louis, MO

6170

Salem, MA

6390

Savannah, GA

214

0 .6 0 .1 0 .0

Schenectady, NY

0 .0

6570

Smithfield, RI

0 .1

6730

Springfield, MA

0 .1

6910

Syracuse, NY

6970

Taunton, MA

7090

Troy, NY

0 .2

7150

Utica, NY

0 .1

7270

Warwick, RI

0 .0

7290

Washington, DC

0 .2 ,

7291

Georgetown, DC

0 .0

7420

West Troy, NY

0 .0

7430

Wheeling, VA

0 .1

7530

Wilmington, DE

0 .1

7550

Wilmington, NC

0 .0

7610

Worcester, MA

0 .1

7690

Zanesville, OH

0 .0

7530

Wilmington, DE

6410

0 .1 .

0 .1

37,105

100 .0

28,825

77 .7

Urban/Rural Status

URBAN H32 1

Rural

Urban

Missing

8,127

21 .9

153

0 .4

37,105

100 .0

City Population (in Hundreds)

CITYPOP H33-37 0

Less than 100 or Unincorporated

100-199

25,284 55

68 .1 0 .1

200-299

0 .2

5155

51500+ Missing

1,027 153

2 .8

9999

37,105

100 .0

0 .4

Codebook: Household Record

Page 60

COUNTY

1850 Public Use Microdata Sample User's Guide and Technical Documentation

County Code (See Appendix A)

H38-41

10-5100

36,931

99 .5

9998

Illegible

0 .0

9999

Missing

173

0 .5

37,105

100 .0

37,105

100 .0

37,105

100 .0

35,020

94 .4 1 .24

SEA

State Economic Area

H42-44

0-498

Group Quarters Residence

H45 1

Not group quarters

Group quarters in 1970 definition, but not 1850

463

Institution (IPUMS classification)

473

1 .3

Other group quarters

1,126

3 .03

Fragment

0.1

37,105

100.0

Group Quarters Code

GQTYPE

H46-48 0

Not available (not Group Quarters or missing)

95 .9

35,601

111

Prison

112

Penitentiary

0 .1

121

Jail

0 .0

140

Reformatory

0.0

150

Camp or chain gang

0.0

160

House of correction, workhouse

0.0

200

Mental institutions

0 .1

311

Aged, dependent home

0 .0

313

Old soldier's home

0 .1

332

Orphan's home, asylum

0 .1

341

Children's home

0.0

351

Deaf, blind school

0.0

352

Deaf, blind, epilepsy

0.0

381

Poor house, almshouse

190 .

0 .5

392

Homes for widows ; single, fallen women

395

Home, other dependent

0 .1

0.0

0 .0

500

Military

0 .1

501

U .S . army installation, fort

0 .1

502

Navy, marine installation

0 .0

503

Navy ships

0 .0

1850 Public Use Microdata Sample

Codebook: Household Record

User's Guide and Technical Documentation 600

College dormitory

601

Military service academies

701

Page 61 35

0 .1

0 .0

Hotel

389

1 .0

702

House, lodging apartment, boarding house

254

0 .7

810

Schools, boarding schools

812

Academy, institute

0 .1

820

Hospitals

0 .1

821

Hospital, charity

0 .0

822

Infirmary

0 .0

832

Convent

833

Monastery

0 .0

835

Seminary

836

Religious commune

837 843 844

Railroad

845

0 .1

Work boarding house

0 .1

847

Ships, boats

849

Other worksites

0 .1

870

Other group quarters

GQFUND

0 .0

0 .1

Other religious

Mining

0 .0

0 .1 0 .0

37,105

100 .0

Group Quarters Funding Code

H49-50 0

Not available (not Group Quarters or missing)

35,601

95 .9

Federal support

State support

0 .2

County/City

0 .2

Other government

127

0 .3

Private

24 99

FARM

0 .1

805

2 .2

Religious

100

Not classifiable

0 .3

281

0 .8

37,105

100 .0

19,475

52 .5

Farm

H51 1

Non-Farm

Farm

17,630

47 .5

37,105

100 .0

Codebook: Household Record

Page 62

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Sample Unit

SAMPUNIT H52

35,170

94 .8

267

0 .7

0 .1

136

0 .4

1,458

3 .9

5 23

0 .0

37,105

100 .0

35,013

94 .4

1,564

4 .2

337

0 .9

4 5

115

0 .3

0 .1

0 .0

Dwelling size 30 or less

Dwelling size greater than 30

Individual in group quarters

Related group in group quarters

Primary individual, dwelling size unknown

Household, dwelling size unknown

Fragment

0 .1

Sequence of Household in Dwelling

HHSEQ H53

7 8 9

NUMPERHH

0 .0

37,105

100 .0

Number of Person Records in Household

H54-57 1

693

1 .9

3,373

9 .1

4,812

13 .0

5,212

14 .0

5,146

13 .9

4,648

12 .5

3,696

10 .0

2,861

7 .7

1,978

5 .3

1,295

3 .5

674

1 .8 1 .0

11 12 13 2016 9999

Missing

388 207

0 .6

0 .1

192

0 .5

37,105

100 .0

1850 Public Use Microdata Sample

Codebook: Household Record

User's Guide and Technical Documentation

Page 63

Microfilm Reel Number

REEL H58-61

Microfilm Page Number

PAGENO H62-64

SEQHH

Sequence in Dwelling of First Family Taken

H65-66

92 99

Missing

36,766

99 .1

0 .0

0 .2

37,105

100 .0

Side of Enumeration Form

SIDE H67

Number of Persons Taken in Dwelling

DWTAKE H68-69 1

2,067

5 .6

2,890

7 .8

4,224

11 .4

4,781

12 .9

4,828

4,519_

13 .0

10 .2

3,146

8 .5

2,219

1,549

6 .0 4 .2

919

2 .5

612

1 .6

15 37,105

ENUMDUR H70

ENUNIIVIO H71-72

12 .2

3,801

Duration of Enumeration

Month of Enumeration

0 .0 .

100 .0

Codebook: Household Record Page 64

ENUMD H73-74

Day of Enumeration

LINE H75-77

Line Number

1850 Public Use Microdata Sample User's Guide and Technical Documentation

1850 Public Use Microdata Sample

User's Guide and Technical Documentation

RECTYPE

Codebook: Person Record Page 65

Record Type Identifier

PI P

YEAR

Person Record Identifier

197,678

100 .0

197,678

100 .0

197,678

100 .0

197,678

100 .0

197,678

100 .0

197,678

100 .0

1850 Census Identifier

P2-3 85

SERIAL

1850 Census Year Identifier

Household Serial Number

P4-I1 1-37105

PERNUM

Household serial number

Sequence of Person in Household

P12-13 1

37,105

18 .8

34,890

31,278

17 .6

26,329

21,055

15 .8 13 .3 10 .7

15,944

8 .1

7 8

11,354

5 .7

4,838

2,889

1 .5

1,608

0 .8

921

0 .5

529

0 .3

11 12 13 14

7,677

326

3 .9 2 .4

0 .2

15 16

223

114

0 .1

92 72

0 .0

18 19 20 21 22 23 24 25

167

0 .1 0 .1

0 .0

Codebook: Person Record Page 66

1850 Public Use Microdata Sample User's Guide and Technical Documentation .

0 .0

04 .0 0 .0

197,678

100 .0

97,391

49 .3

7,511

87,385

MOMLOC

Position in Household of Own Mother

P14-15

3 .8 44 .2

0 .0

197,678

100 .0

94,495

47 .8

98,530

49 .8

2,079

1 .1

1,661

0 .8

273

0 .1

640

0 .3

197,678

100 .0

MOMRULE

Rule for Linking Mother

P16

POPLOC

Position in Household of Own Father

P17-18 0

102,268

51 .7

91,970

46 .5

809

0 .4

0 .0

197,678

100 .0

101,059

51 .1

93,424

47 .3

2 3

1,087

0 .5

1,276

0 .6

655

0 .3

177

0 .1

197,678

100 .0

Rule for Linking Father

POPRULE P19 0

1850 Public Use Microdata Sample

User's Guide and Technical Documentation

SPLOC

Codebook : Person Record Page 67

Position in Household of Own Spouse

P20-21 0

135,772

68 .7

29,052

14 .7

29,247

14 .8

710

0 .4

0 .0

197,678

100 .0

2,237 2,281

1 .1

1 2

2,363

1 .2

2,328

1 .2

2,414

1 .2

2,275

1 .2

2,134

2,259

1 .1

2,209

1 .1

1,955

1 .0

2,232

1 .1

1,905

1 .0

2,289

1 .2

2,058

1 .0

2,265

1,993 _

1 .1

2,257

1 .1

2,214

1 .1

2,289

1 .2 1 .0

ELDCH

Age of Eldest Own Child

P22-23

1 .2

1 .1

1 .0

2,056

2,076

1 .1

1,714

0 .9

1,658

0 .8

1,345

0 .7

1,090

0 .6

1,040

0 .5

776

0 .4

-619

0 .3

652

0 .3

436

0 .2

737

0 .4

282

0 .1

323

261

0 .2 0 .1

219

0 .1

Codebook: Person Record

Page 68

1850 Public Use Microdata Sample User's Guide and Technical Documentation

330

0 .2

205

0 .1

190

0 .1

171

0 .1

132

309

0 .2

0 .0

104

0 .1

0 .0

100

0 .1

146

0 .1

137,948

69 .8

197,678

100 .0

9,758

4 .9

10,332

5 .2

7,605

3 .8

4,119

2 .1

YNGCH

0 .0

Age of Youngest Own Child

P24-25

3,027

1 .5

2,380

1 .2

2,068

1 .0

1,732

0 .9

1,682

0 .9

1,230

0 .6

1,427

0 .7

1,018

0 .5

1,228

0 .6

1,042

0 .5

1,007

0 .5

774

0 .4

895

0 .5

752

0 .4

733

0 .4

608 673

0 .3 0 .3

505

0 .3

530

0 .3

395

0 .2

329

0 .2

13 14 15 16 17 18 19 20 21 22 23 24

392

0 .2

266

0 .1

217

0 .1

220

0 .1

192

332

0 .1 0 .2

25 26 27 28

1850 Public Use Microdata Sample

Codebook: Person Record

User's Guide and Technical Documentation

Page 69

150

0 .1

149

0 .1

140

0 .1

135

0 .1

188

0 .1

128

111

0 .1

105

0 .1

0 .0

176

0 .1

0 .0

0 .1

0 .0

137,948

69 .8

197,678

100 .0

12,949

6 .6

9,764

4 .9

16,783

8 .5

22,453

11 .4

25,901 26,347

13 .1

23,973

12 .1

NFAM

0 .0

Number of Own Family Members in Household

P26-27

13 .3

20,624

10 .4

15,678

7 .9

10,540

5 .3

6,303

3 .2

3,288

1 .7

1,664

0 .8

784

0 .4

345

0 .2

160

0 .1

0 .0

197,678

100 .0

Codebook: Person Record Page 70

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Number of Own Children in Household

NCHILD P28 0

137,948

69 .8

15,356

7 .8

11,732

3 4

9,519

5 .9 4 .8

7,621

3 .9

5,779

2 .9

4,079

2 .1

2,721

1 .4

1,601

0 .8

1,322

0 .7

197,678

100 .0

179,970

91 .0

9,676

4 .9

6,654

3 .4

1,290

0 .7

0 .0

197,678

100 .0

35,945

18 .2

Number of Own Children Under Age 5 in Household

NCHLT5 P29

Imputed Relation to Head

IMPREL P30-31 1

Head

Wife

Child

Child-in-law

Parent

Parent-in-law

Sibling

8 9

Sibling-in-law Grandchild

Other relative

Non-relative Individual in Group Quarters

29,179

14 .8

99, 821 810

50 .5 0 .4

1,682

0 .9

1,180

0 .6

4,020

2 .0

2,427

1 .2.

3,297

1 .7

4,978

2 .5

12,947

6 .5

1,392

0 .7

197,678

100 .0

1850 Public Use 1Vlicrodata Sample User's Guide and Technical Documentation

AGE P32-34

Codebook: Person Record Page 71

Age in Years

5,271

2 .7

5,819

2 .9

6,091

3 .1

5,829

2 .9

5,994

3 .0

5,805

5,727

2 .9

5,358

2 .7

8 9

5,757

2 .9

4,742

2 .4

5,492

2 .8

4,369

2 .2

5,262

2 .7

13 14

4,479

2 .3

4,887

2 .5

4,187

2 .1

2 .9

4,587

4,304

2 .2

4,644

2 .3

3,898

2 .0

4,498

2 .3

3,734

1 .9

4,224

2 .1

3,864

2 .0

3,639

1 .8

4,337

2 .2

3,471

1 .8

2,886

1 .5

3,460

1 .8

2,265

1 .1

4,768

2 .4

1,912

1 .0

2,563

1 .3

2 .3

2,216

1 .1

2,083

1 .1

3,133

1 .6

2,044

1,899

2,149

1 .1

1,572

0 .8 1 .7

39 40

3,351

41 42

1,208 1,610

43 44

1,346 1,418

2,271

1,308

1 .0 .

1 .0

0 .6 0 .8 0 .7 0 .7 1 .1 0 .7

Codebook. Person Record

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Page 72 47

1,163

0 .6

1,338

0 .7

1,088

0 .6

2,458

1 .2

798

0 .4

1,120 789

0 .6 0 .4

51 52 53

843

0 .4

1,126

0 .6

838

0 .4

615

0 .3

684

0 .3

504

0 .3

1,348

0 .7

419

0 .2

555

0 .3

57 58 59 60 61 62 63 64

540 471

0 .3 0 .2

682

0 .3

439

0 .2

361

0 .2

351

0 .2

266

0 .1

529

0 .3

189

0 .1

261

0 .1

248

0 .1

211

0 .1

256

0 .1

161

0 .1

155

0 .1

139

0 .1

0 .0

171

0 .1

0 .0

9 7

0 .0 0 .0

0 .0

65 66

69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

1850 Public Use Microdata Sample

Codebook: Person Record

User's Guide and Technical Documentation

Page 73

98 99 100

0 .0

0 .0 0 .0

101

103

104

106 110

0 .0

0 .0 0 .0

998

Illegible

999

Missing

125

SEX

0 .0

0 .1

197,678

100 .0

101,259

51 .2

96,248

48 .7

0 .0

Sex

P35 1

Male

Female

Illegible

Missing/blank

RACE

169

0 .1

197,678

100 .0

190,126

96 .2

Race

P36-38 0

White (blank)

White

Black/Negro

Mulatto

Other race, not elsewhere classified

MARRINYR

3,202

1 .6

2,816

1 :4

1,525

0 .8

0 .0

197,678

100 .0

195,688

99 .0

Married Within Year

P39 1

No (blank)

Yes

BLIND

1,990

1 .0

197,678

100 .0

197,574

99 .9

Blind

P40 1

No (blank)

Yes

104

0 .1

197,678

100 .0

Codebook. Person Record Page 74

BPL

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Birthplace

P41-45 10100

Alabama

10200

Alaska

10500

Arkansas

10600

California

10800

Colorado

10900

Connecticut

0 .0

Delaware

3,910

2 .0

11000

967

0 .5

327

0 .2

3,254

1 .6

0 .0

680

0 .3 0 .0

11100

District of Columbia

11200

Florida

11300

Georgia

11500

Hawaii

11700

Illinois

3,843

11800

Indiana

5,787

Iowa

2 .9

11900

592

0 .3

12000

Kansas

0 .0

12100

Kentucky

8,411

12200

Louisiana

1,455

4 .3

12300

Maine

5,521

2 .8

12400

Maryland

5,363

2 .7

12500 12600

Massachusetts

9,092

Michigan

1,492

4 .6

12700

Minnesota

12800

Mississippi

1,544

0 .8

12900

Missouri

3,128

1 .6

13300

New Hampshire

3,702 4,932

1 .9

13400

New Jersey

13600

New York

13700

North Carolina

13900

Ohio

14100

Oregon

0 .0

14200

Pennsylvania

20,598

10 .4

14400

Rhode Island

1,349

0 .7

14500 14700

South Carolina

4,341

2 .2

Tennessee

8,257

4 .2

14800

Texas

422

0 .2

15000

Vermont

3,555

1 .8

15100

Virginia

11,747

5 .9

15500

Wisconsin

590

0 .3

16200

New Mexico Territory

469

0 .2

16300

Utah Territory

0 .0

16500

Indian Territory

0 .0

19900

United States

0 .0

30500

Canada

936

0 .5

30502

New Brunswick Newfoundland

115

0 .1

30503

0 .0

30504

Nova Scotia

155

0 .1

305 05

Ontario

0 .0

213 4,850

0 .1 2 .5 0 .0 1 .9

0 .7

0 .8 0 .0

2 .5

25,688

13 .0

8,008

4 .1

14,643

7 .4

1850 Public Use Microdata Sample

User's Guide and Technical Documentation 30506

30507 31000 31100 31200 31300 32000 32003 32004 32007 33000 33100 33200 33400 33500 34000 34100

34200 343 00 344 00 34700 34800 34900 35032 35040 35100 35200 35201 35202 35205 35208 35209

35220 35222 35224 35226 35300

Prince Edward Island Quebec/French Central America, not elsewhere classified Mexico Caribbean, not elsewhere classified Cuba South America, not elsewhere classified Brazil Chile Guyana Europe, not elsewhere classified Belgium France Netherlands Switzerland United Kingdom, not elsewhere classified England Scotland Wales Denmark Ireland Norway Sweden Bohemia-Moravia Poland Austria Germany, not elsewhere classified Bavaria Wurttemberg Mecklenburg Hessen Baden Saxony Prussia Hanover Hamburg Hungary

36100 36200

Greece

36300

Portugal

36400 36510 37002 37003 37100 37401 38000 39201 39901 39903 99700

Spain

Italy

Russia East Indies Southwest Asia China India Africa, not elsewhere classified Bermuda At sea Abroad Indeterminate

Codebook: Person Record Page 75 17 79 1

145 47 19 3

4 5 4 6

0.0 0.0

0 .0

0 .1 0 .0

0 .0 0 .0

0 .0 0 .0 0.0

0 .0

116

0 .1

595 132 27 2,769 694

274 21

9,660 131 35 1

32 9 5,592 43 8 1

14 23 22-

104

13 1 1 1

25 18 27 7

2 1

7 5 5

0.3 0.1 0.0 1 .4 0.4 0.1

0 .0

4.9 011 0.0

0 .0

0.0 0.0 2.8 0.0

0 .0 0 .0

0 .0 0 .0 0 .0 0 .1

0.0

0 .0 0 .0 0 .0

0 .0

0 .0 0 .0

0 .0

19 348

0 .0 0 .2

Codebook: Person Record

Page 76

998 00

Illegible

999 00

Blank

SCHOOL

1850 Public Use Microdata Sample User's Guide and Technical Documentation 45

0 .0

6,328

3 .2

197,678

100 .0

156,999

79 .4

School Attendance in the Past Year

P46 1

No, not in school

Yes, in school

LIT

40,679

20 .6

197,678

100 .0

56,303

28 .5

Literacy

P47 0

NIA, under age 21

Can read and can write

Can't read, writing unknown

130,848 10,527 197,678

OCC1880

66 .2 5 .3 100 .0

Occupational Classification, 1880 System

P48-50 AGRICULTURE 1

Agricultural laborers

Dairymen

2836

1 .2

0 .0

Farm and plantation overseers

Farmers and planters

Florists

Gardeners, nurserymen, and vinegrowers

Stock-drovers

Stock-herders

0 .0

Stock-raisers

0 .0

Turpentine farmers and laborers

0 .0

5 4

0 .0

24,124

12 .2

0 .0

PROFESSIONAL AND PERSONAL SERVICES 13

Actors

Architects

0 .0

Artists and teachers of art

Auctioneers

0 .0

Authors, lecturers, and literary persons

0 .0

Barbers and hairdressers

0 .0

Billiard and bowling saloon keeper and employees

0 .0

Boarding- and lodging-house keepers

0 .0

Chemists, assayers, and metallurgists

0 .0

Clergy

292

0 .1

Clerks and copyists

0 .0

Clerks in government offices

0 .0

Codebook: Person Record

1850 Public Use Microdata Sample

User's Guide and Technical Documentation 25

26 27 28 29 30 31

32 33 34 35

Clerks in hotels and restaurants Collectors and claim agents Dentists Designers, draughtsmen, and inventors Domestic servants Employees of charitable institutions Employees of government (not clerks) Employees of hotels and restaurants (not clerks) Engineers (civil) Hostlers Hotel keepers

36 38 39 40

Hunters, trappers, guides, and scouts

Lawyers

Journalists Laborers (not specified) Launders Livery-stable keepers Messengers

Nurses

Officers of the army and navy

Officials of government

53 54 56 57 58

12 20

Musicians (professional) and teachers of music

0.0

0.0 0.0

0.0

244 27 144

19 46 127

6,197

241

43 45

49 50 51 52

Page 77

0.1

0.0 0 .1

0 .0 0 .0 0 .1

0 .0

0 .0 4 .5 0.0 0 .1

0.0

12 168 428

0.0 0.1 0 .2

Restaurant keepers

0 .0

Sexton

0 .0

Physicians and surgeons

Showmen and employees of shows Soldiers, sailors, and marines (U .S . army and navy) Teachers and scientific persons Watchmen (private)

and detectives

Whitewashers Others in professional and personal services

74 342 27

0 .0 0 .2 0 .0

0.0

29 24

0.0

288 28

0 .0 0 .1 0 .0

0 .0'

TRADE AND TRANSPORTATION

59 60 61

63 64 65 66 69 70 71 72 73 74 75 76 77

Agents (not specified) Bankers and brokers Boatmen and watermen Bookkeepers and accountants in stores Brokers (commercial) Canal men Clerks in stores Clerks and bookkeepers in banks Clerks and bookkeepers in railroad offices Commercial travelers Draymen, hackmen, teamsters, etc . Employees in warehouses Employees of banks (not clerks)

1,014

7 2 2 384 2

0 .0 0 .5 0 .0 0.0 0.0 0.2 0.0

0.0

Employees of railroad companies

119

0 .1

Hucksters and peddlers

112

0 .1

0 .0

Employees of insurance companies (not clerks)

Milkmen

0.0

Codebook: Person Record

Page 78

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Newspaper criers and carriers

Officials and employees of express companies (not

0.0

clerks) 80

Officials and employees of street railroad companies

0 .0

Officials and employees of telegraph companies

Officials and employees of trade and transportation

0.0

companies (not specified) 84

Officials of banks

Officials of insurance companies

0.0

Officials of railroad companies

0.0

Packers

0 .0

Pilots

0.0

Porters and laborers in stores and warehouses

0.0

Sailors

702

0.4

Salesmen

0.0

Saloon keepers and bartenders

133

0.1

Shippers and freighters

Steamboat men

95 96 97

Traders and dealers (not specified)

Traders and dealers in books and stationary

0.0

Stewards

0.0

Toll-gate and bridge keepers

0.0

1,183

0.6

0.0

100

Traders and dealers in boots and shoes

101

Traders and dealers in cabinet ware

102

Traders and dealers in cigars and tobacco

103

Traders and dealers in clothing and men's furnishings

0.0

104

Traders and dealers in coal and wood

0.0

105

Traders and dealers in cotton and wool

0.0

106

Traders and dealers in crockery, china, glass, and

0.0

stoneware 107

Traders and dealers in drugs and medicines

0.0

108

Traders and dealers in dry foods, fancy foods, and

0.0

231

0 .1

notions 110

Traders and dealers in groceries

Ill

Traders and dealers in hats, caps, and furs

0.0

112

Traders and dealers in ice

0 .0

113

Traders and dealers in iron, tin, and copperware

0.0

114

Traders and dealers in junk

0 .0

115

Traders and dealers in leather, hides, and skins

0 .0

116

Traders and dealers in liquors and wines

0 .0

117

Traders and dealers in livestock

0.0

118

Traders and dealers in lumber

'0 .0

119

Traders and dealers in marble, stone, and slate

0 .0

120

Traders and dealers in music and musical instruments

0.0

122

Traders and dealers in oils, paints, and turpentine

0 .0

123

Traders and dealers in paper and paper stock

124

Traders and dealers in produce and provisions

125 127

0.0

Traders and dealers in real estate

0.0

Undertakers

0.0

128

Weighers, gaugers, and measurers

0.0

129

Others in trade and transportation

0.0

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Codebook: Person Record

Page 79

MANUFACTURING, MECHANICAL, AND MINING INDUSTRIES Agricultural implement makers

0 .0

132

Apprentices to trades

0 .0

133

Bag makers

0 .0

134

Bakers

147

0 .1

135

Basket makers

136

Blacksmiths

137

Bleachers, dyers and scourers

138 139 140

Bone and ivory workers

141

Bookbinders and finishers

142

Boot and shoe makers Bottlers and mineral-water makers

130

143 144

0 .0

965

0 .5

0 .0

Blind, door and sash makers

0 .0

Boat makers

22 4

0 .0

Box factory operatives

0 .0

1,476

0 .7

0 .0

145

Brass founders and workers

0 .0

146

Brewers and maltsters

0 .0

147

Brick and tile makers Bridge builders and contractors

0 .0

149

Britannia and japanned ware makers

0 .0

150

Broom and brush makers

0 .0

151

Builders and contractors (not specified)

152

Butchers

148

153

Button-factory operatives

154

Cabinet makers

155

Candle, soap, and tallow makers

156

Car makers

0 .0

190

0 .1

0 .0

379

0 .2

0 .0

2,056

1 .0

157

Carpenters and joiners

158

Carpet makers

159

Carriage and wagon makers

160

Charcoal and lime burners

162

Chemical-works employees

163

Cigar makers

165

Clock and watch makers and repairers

0 .0

166

Confectioners

167

Coopers

168

Copper workers

170 171 172

0 .0

310

0 .2

0 .0

404

0 .2

0 .0

Cotton-mill operatives

0 .0

Distillers and rectifiers

0 .0

Employees in manufacturing establishments (not

246

0 .1

173

specified) Engineers and firemen

121

0 .1

174

Engravers

0 .0

175

Fertilizer establishment operatives

0 .0

176

File makers, cutters, and grinders

0 .0

177

Fishermen and oystermen

126

0 .1

178

Fur workers

0 .0

179

Galloon, gimp, and tassel makers

0 .0

Codebook: Person Record

Page 80

180

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Gilders

0.0

182

Glass-works operatives

0.0

183

Glove makers

184

Gold and silver workers and jeweler

185

Gun- and lock-smiths

186

Hair cleaners, dressers, and workers

188

0.0

101

0.1

0.0

Harness and saddle makers

216

0.1

189

Hat and cap makers

112

0.1

190

Hosiery and knitting-mill operatives

191

Iron and steel works and shops operatives

192

0.0

204

0.1

Lace makers

0.0

194

Leather case and pocket-book makers

0.0

195

Leather curriers, dressers, finishers, and tanners

157

0.1

196

Lumbermen and raftsmen

0 .0

197

Machinists

229

0 .1

198

Manufacturers

199

0 .1

199

Marble and stone cutters

128

0.1

200

Masons (brick and stone)

505

0.3

201

Meat and fruit preserving establishment employees

0.0

202

Meat packers, curers, and picklers

203

Mechanics (not specified)

204

Mill and factory operatives (not specified)

205

Millers

206

Milliners, dressmakers, and seamstresses

207

Miners

0.0

193

0.1

0.0

315

0.2

0 .0

852

0.4

208

Mirror and picture-frame makers

0 .0

209

Nail makers

0 .0

210

Officials of manufacturing and mining companies

0 .0

212

Oil-well operatives and laborers

0 .0

213

Organ makers

0.0

214

Painters and varnishers

231

0.1

0.0

215

Paperhangers

216

Paper-mill operatives

217

Pattern makers

0 .0

0.0

218

Photographers

219

Pianoforte makers and tuners

0.0

220

Plasterers

0 .0

221

Plumbers and gasfitters

0.0

222

Potters

223

Printers, lithographers, and sterotypers

224

Print-works operatives

0 .0

174

0. i

0.0

225

Publishers of books, maps, and newspapers

226

Pump makers

0.0

227

Quarrymen

0.0

229

Rag pickers

0.0

230

Railroad builders and contractors

0 .0

231

Roofers and slaters

0 .0

232

Rope and cordage makers

0.0

233

Rubber factory operatives

0 .0

234

Sail and awning makers

0.0

235

Salt makers

0.0

1850 Public Use Microdata Sample

Codebook: Person Record

User's Guide and Technical Documentation 236

Saw- and planing-mill operatives

237

Sawyers

238

Page 81 6

0 .0

Scale and rule makers

242

0.0

Shingle and lath makers

243

Ship carpenters, caulkers, riggers, and smiths

244

Shirt, cuff, and collar makers

245

0.0

Silk-mill operatives

247

0.0

Stave, shook, and heading makers

0 .0

248

Steam-boiler makers

249

0 .0

Stove, furnace, and grate makers

251

Sugar makers and refiners

252

Tailors

253

Thread makers

254

Tinners and tinware makers

255

Tool and cutlery makers

256

139

567

0 .0 0 .1

0 .0 0.0

0 .3

0.0

105

0 .1

0.0

Trunk, valise, and carpet-bag makers

0 .0

257

Tobacco-factory operatives

0 .0

258

Umbrella and parasol makers

259

0 .0

Upholsterers

0 .0

260

Wheelwrights

128

261

0 .1

Wire makers and workers

262

0.0

Wood choppers

263

0 .0

Wood turners, carvers, and woodenware makers

264

0 .0

Woolen-mill operatives

265

Others in manufacturing, mechanical and mining

0 .0

218

0 .1

0 .0

industries

266

Employed, occupation unspecified NON-OCCUPATIONAL RESPONSE

302

Housewife/housework

304

0 .0

Student

305

461

0 .2

Retired

1,392 -

0 .7

306

None or pauper

307

0 .0

Sick, disabled or dead

308

0.0

Prisoners or institutional inmates

309

0 .0

Gentlemen or lady

997

0 .0

Unclassifiable

998

Illegible

999

Blank

152 13

0 .1 0.0

141,986

71 .8

197,678

100 .0

Codebook: Person Record Page 82

OCC1950

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Occupational Classification, 1950 System

P51-53 PROFESSIONAL, TECHNICAL, AND KINDRED WORKERS 0

Accountants and Auditors

0.0

Actors and actresses

0.0

Architects

0.0

Artists and art teachers

0 .0

Athletes

0.0

Authors

0.0

Chemists

0.0

290

0.1

0.0

Clergymen

College presidents and deans

Professors and instructors, mathematics

0.0

Professors and instructors, non-scientific subjects

0 .0

Professors and instructors, subject not specified

0 .0

Dancers and dancing

0 .0

Dentists

0.0

Designers

0 .0

Draftsmen

0 .0

Editors and reporters

0 .0

Engineers, civil

0 .0

Engineers, mechanical

0 .0,

Entertainers (not elsewhere classified)

0 .0

Foresters and conservationists

0 .0

Funeral directors and embalmers

0 .0

Lawyers and judges

241

0 .1

Musicians and music teachers

0.0

Miscellaneous natural scientists

Pharmacists

Photographers

Physicians and surgeons

Religious workers

Surveyors

Teachers (nec)

Professional, technical and kindred workers (nec)

1 54

0 .0 -

0 .0

0.0

428

0.2

0.0

328

0.2

0.0

24,135

12 .2

0.0

FARMERS AND FARM MANAGERS 100

Farmers (owners and tenants)

123

Farm managers MANAGERS, OFFICIALS, AND PROPRIETORS, EXCEPT FARM

201

Buyers and shippers, farm products

0 .0

203

Conductors, railroad

0.0

210

Inspectors, public administration

0.0

1850 Public Use Microdata Sample

User's Guide and Technical Documentation 230 240 250 270 290

Managers and superintendents Officers, pilots, pursers, and engineers, ship Officials and administrators (nee), public administration Postmasters Managers, officials, and proprietors (nee)

Codebook: Person Record Page 83

22 77 76

0 .0 0 .0 0 .0

0 .0

2,192

1 .1

CLERICAL AND KINDRED WORKERS

300 302

Agents (nee)

304

Baggagemen, transportation

305 310 320 321 335 340 342 360 365 380 390

Attendants, physician's and dentist's office Bank tellers Bookkeepers Cashiers Collectors, bill and account Mail carriers Messengers and office boys Shipping and receiving clerks Telegraph messengers Telegraph operators Ticket, station, and express agents Clerical and kindred workers (nee)

27 1

8 4

22 1

0.0 0.0

0.0 0.0 0 .0

0.0

0 .0

0.0

0 .0

0.0

0 .0 0 .0

8 112

0 .0 0 .1

0 .0

SALES WORKERS

410 430 450 460 470 480 490

Auctioneers Hucksters and peddlers Insurance agents and brokers Newsboys Real estate agents and brokers Stock and bond salesmen Salesmen and sales clerks (nee)

5 4

0 .0 0 .0

0 .0

1,042

0 .5

147

0.1

CRAFTSMEN, FOREMEN, AND KINDRED WORKERS 500

501 502 503 504 505

510 511 512 521 522 523 524 525

Bakers Blacksmiths Bookbinders Boilermakers Brickmasons, stonemasons, and tile setters Cabinetmakers Carpenters Cement and concrete finishers Compositors and typesetters Engravers, except photoengravers Excavating, grading, and road machinery operators

Foremen (nee)

Foreman and hammerman Furriers

965

34 20 505 380 2,185 1

163 18

24 10 2

0.5

0.0 0.0 0.3 0.2 1 .1 0.0

0.1 0.0

0.0

0.0 0.0

Codebook: Person Record Page 84

1850 Public Use Microdata Sample User's Guide and Technical Documentation

532

Inspectors, scalers, and graders, log and lumber .

533

0.0

Inspectors (nec)

534

Jewelers, watchmakers, goldsmiths, and silversmiths

0 .0

541

Locomotive engineers

118

0.1

0 .0

544

Machinists

553

Mechanics and repairmen, railroad and car shop

554

0 .0

Mechanics and repairmen (nec)

555

291

Millers, grain, flour, feed, etc.

315

0 .1

560

Millwrights

561

0 .0

Molders, metal

563

0 .0

Opticians, lens grinders and polishers

564

Painters, construction and maintenance

565

Paperhangers

570

Pattern and model makers, except paper

571

Photoengravers and lithographers

573

233

0 .1

0 .2

0.0

213

0.1

0.0

0 .0

0.0

Plasterers

0 .0

574

Plumbers and pipe fitters

0.0

575

Pressmen and plate printers, printing

580

0.0

Rollers and roll hands, metal

0.0

581

Roofers and slaters

582

Shoemakers and repairers, except factory

583

Stationary engineers

584

Stone cutters and stone carvers

590

Tailors

567

0 .3

591

Tinsmiths, coppersmiths, and sheet metal workers

112

0.1

592

Tool makers, and die makers and setters

593

0 .0

Upholsterers

594

Craftsmen and kindred workers (nec)

595

Members of the armed services

0.0

1,447

0 .7

104

0.1

112

0.1

0 .0

479

0 .2

0 .0

OPERATIVES AND KINDRED WORKERS

602

Apprentice carpenters

0.0

611

Apprentices, building trades (nec)

0 .0

614

Apprentices, other specified trades

0.0

615

Apprentices, trade not specified

0 .0

623

Boatmen, canalmen, and lock keepers

284

0 .1

624

Brakemen, railroad

` 5

0 .0

625

Bus drivers

0 .0

632

Deliverymen and routemen

633

0 .0

Dressmakers and seamstresses, except factory

634

Dyers

0 .0 "0 .0

635

Filers, grinders, and polishers, metal

0 .0

641

Furnacemen, smeltermen and pourers

642

0 .0

Heaters, metal

0 .0

643

Laundry and dry cleaning operatives

0.0

644

Meat cutters, except slaughter and packing houses

190

645

0 .1

Milliners

650

Mine operatives and laborers

661

Motormen, street, subway, and elevated railway

670

Painters, except construction/maintenance

0.0

869

0 .4

0.0

0 .0

1850 Public Use Microdata Sample

User's Guide and Technical Documentation 673

Sailors and deck hands

674

Sawyers

675

Spinners, textile

680

Stationary firemen

681

Switchmen, railroad

682

Taxicab drivers and chauffeurs

683

Truck and tractor drivers

684

Weavers, textile

685

Welders and flame cutters

690

Operative and kindred workers (nec)

700

Housekeepers, private household

710

Laundry workers, private household

Codebook: Person Record

Page 85

694 97

0 .4 0 .0

0 .0

338

0 .2

169

0 .1

1 1,833

0 .0 0 .9

0 .0

236

0 .1

0 .0

SERVICE WORKERS, EXCEPT PRIVATE HOUSEHOLD 720

Private household workers (nec)

730

Attendants, hospital and other institution

732

Attendants, recreation and amusement

740

Barbers, beauticians, and manicurists ,

750

78 .

0 .0

Bartenders

752

Boarding and lodging house keepers

763

Guards, watchmen, and doorkeepers

0 .0

764

Housekeepers and stewards, except private

0 .0

households 770

Janitors and sextons

771

Marshals and constables

773

Policemen and detectives

780

Porters

781

Practical nurses

782

Sheriffs and bailiffs

784

Waiters and waitresses

785

Watchmen (crossing) and bridge tenders

790

Service workers, except private household, (nec)

0 .0

FARM LABORERS AND FOREMEN 810

Farm foreman

820

Farm laborers, wage

162 162

0 .1 0 .1

LABORERS, EXCEPT FARM AND MINE 910

Fishermen and oystermen

930

Gardeners, except farm, and groundskeepers Longshoremen and stevedores

940 950 970

Lumbermen, raftsmen, and woodchoppers Laborers (nec)

126

0 .1

86 13

0 .0

81 9,005

0 .0 0 .0 4 .6

Codebook: Person Record Page 86

1850 Public Use Microdata Sample User's Guide and Technical Documentation

NON-OCCUPATIONAL RESPONSE 975

Employed, unclassifiable

980

House work, keeping house

983

At school

984

Retired

985

None, without occupation

986

Invalid, sick, disabled

987

Institutional inmate, prisoner

25 13

0 .0

991

Capitalist, gentleman

0 .0

995

Other non-occupation

132

0 .1

996

Unclassifiable

997

Occupation missing, unknown

998

Illegible

0 .0

461

0 .2 0 .0 0 .0 0 .0

0 .0

143,366

72 .5

0 .0

197,678

100 .0

144,154

72 .9

0 .0

3 236

0 .0

162

0 .1

191

0 .1\

105

0 .1

112

0 .1

24,135

12 .2

0 .0

130

0 .1

183

0 .1

132

0 .1

0 .0

10,520

5 .3

Income Score

SCORE P54-55

0 .1

0 .0

34 3,074

0 .0 1 .6

4,427

2 .2

1,805 1,036

0 .9 0 .5

26 27 28

467 377

0 .2 0 .2

911

666

0 .3 0 .0

872

0 .4

0 .0

0 .5

1850 Public Use Microdata Sample

Codebook: Person Record

User's Guide and Technical Documentation

Page 87

137

0 .1

0 .0

382

0 .2

0 .0

2,269

1 .1

0 .0

241

0 .0

428

0 .2

197,678

100 .0

177,481

89 .8

0 .0 0 .1

Value of Real Property Owned

REALPROP P56-61

0 1

0 .0

0 -

0 .0

708,000

0 .0

197,678

100 .0

197,534

99 .9

120

0 .1

0 .0

DEAF

Deaf and Dumb

P62 1

No (blank)

Yes

Dumb not deaf

Deaf not dumb

IDIOTIC

0 .0

197,678

100 .0

197,502

99 .9

Idiotic

P63 0

No (blank)

Yes

176

0 .1

197,678

100 .0

Codebook . Person Record Page 88

1850 Public Use Microdata Sample User's Guide and Technical Documentation

Insane

INSANE P64 0

No (blank)

Yes

197,538

99 .9

140

0 .1

197,678

100 .0

197,279

99 .8

399

0 .2

197,678

100 .0

170,032

86 .0

17,107

8 .7

5,092

2 .6

2,047

1 .0

1,023

0 .5

589

0 .3

0 .0

Pauper

PAUPER P65 0 1

SURSIM

No (Blank) Yes

Surname Similarity

P66-67 1

1st Surname Encountered

2 3

24 99

AGEMO

Blank

0 .0

197,678

100 .0

192,412

97 .3

651

0 .3

479

0 .2

615

0 .3

538

0 .3

469

0 .2

756

0 .4 0 .2

Age in Months

P68-69

1 2 3 4 5 6 7 8 9 10 11 12

369 455

0 .2

395

0 .2

326

0 .2

212

0 .1

0 .0

197,678

100 .0

1850 Public Use Microdata Sample

User's Guide and Technical Documentation

CRIME

Codebook: Person Record

Page 89

Convicted of Crime

P 70

FNAME

Drunk or Disorderly

Theft/Burglary/Larceny

Assault

Rape

Murder/Manslaughter

Miscellaneous

Convict/in jail

Unknown

Blank

Forename (First Name)

P71-86 LNAME P87-102

Surname (Last Name)

0 .0

0 :0 0 .0 0 .0 0 .0

0 .0

197,572

99 .9

197,678

100 .0

Appendix A: County Codes by State Page 90

1850 PUBLIC USE SAMPLE COUNTY CODES BY STATE (ICPSR System) ALABAMA-STATE CODE 41 10 AUTAUGA 30 BALDWIN 50 BARBOUR 70 BIBB ,90 BLOUNT 110 BULLOCK 13,0 BUTLER 150 CALHOUN/BENTON 190 2-10 230 250 270 290 310 330 3-SO 370 390 410 430 450 470 490 510 530 S50 570 590 610 630 650 670 710 730 750 770 790 810 830 850 870 890 910 930 950 970 990 1010

CHEROKEE CHILTON/BAKER CHOCTAW CLARKE CLAY CLEBURNE COFFEE COLBERT 'CONECUH COOSA COVINGTON CRENSHAW CULLMAN DALE DALLAS DE KALB ELMORE ESCAMBIA ETOWAH FAYETTE FRANKLIN GENEVA GREENE HALE HENRY JACKSON JEFFERSON LAMAR/SANFORD LAUDERDALE LAWRENCE LEE LIMESTONE LOWNDES MACON MADISON MARENGO MARION MARSHALL MOBILE MONROE MONTGOMERY

1850 Public Use Microdata Sample User's Guide and Technical Documentation

1030 1050 1070 1090 1110 1130 1170 1150 1190 1210 1230 1250 1270 1290 1310 1330

MORGAN/COTACO PERRY PICKENS PIKE RANDOLPH RUSSELL SHELBY ST CLARR' SUMTER TALLADEGA TALLAPOOSA TUSCALOOSA WALKER WASHINGTON WILCOX WINSTON/HANCOCK

ARKANSAS-STATE CODE 42 10 ARKANSAS 30 ASHLEY 50 BAXTER 70 BENTON 90 BOONE' 110 BRADLEY 130 CALHOUN 150 CARROLL 170 CHICOT 190 CLARK 210 CLAY 270 COLUMBIA 290 CONWAY 310 CRAIGHEAD 330 CRAWFORD 350 CRITTENDEN -370 CROSS 390 DALLAS 410 DESHA 415 DORSEY 430 DREW 450 FAULKNER 470 FRANKLIN 490 FULTON S10 GARLAND S30 GRANT 550 GREENE 570 HEMPSTEAD 590 HOT SPRING 610 HOWARD 630 INDEPENDENCE 650 I ZARD 670 JACKSON 690 JEFFERSON 710 JOHNSON 730 LAFAYETTE 750 LAWRENCE

1854 Public Use Microdata Sample User's Guide and Technical Documentation

770 790 810 830 850 870 890 910 930 950 970 990 1010 1030 1050 1070 1090 1110 1130 1150 1170 1190 1210 1250 1270 1290 1310 1330 1350 1230 1370 1390 1410 1430 1450 1470 1490

LEE LINCOLN LITTLE RIVER LOGAN LONOKE MADISON MARION MILLER MISSISSIPPI MONROE MONTGOMERY NEVADA NEWTON OUACHITA PERRY PHILLIPS PIKE POINSETT POLK POPE PRAIRIE PULASKI RANDOLPH SALINE SCOTT SEARCY SEBASTIAN SEVIER SHARP ST FRANCIS STONE UNION VAN BUREN WASHINGTON WHITE WOODRUFF YELL

CALIFORNIA-STATE CODE 71 10 ALAMEDA 30 ALPINE 50 AMADOR 70 BUTTE 90 CALAVERAS 110 COLUSA 130 CONTRA COSTA 150 DEL NORTE 170 EL DORADO 190 FRESNO 230 HUMBOLDT 270 INYO 290 KERN 330 LAKE 350 LASSEN 370 LOS ANGELES

Appendix: County Codes by State (ICPSR Coding) Page 91

410 430 450 470 490 510 530 550 570 610 630 670 690 710 730 750 770 790 810 830 850 870 890 910 930 950 970 990 1010 1030 1050 1070 1090 1110 1130 1150

MARIN MARIPOSA MENDOCINO MERCED MODOC MONO MONTEREY NAPA NEVADA PLACER PLUMAS SACRAMENTO SAN BENITO SAN BERNARDINO SAN DIEGO SAN FRANCISCO SAN JOAQUIN SAN LUIS OBISPO SAN MATEO SANTA BARBARA SANTA CLARA SANTA CRUZ SHASTA SIERRA SISKIYOU SOLANO SONOMA STANISLAUS SUTTER TEHAMA TRINITY TULARE TUOLUMNE VENTURA YOLO YUBA

CONNECTICUT-STATE CODE 01 10 FAIRFIELD 30 HARTFORD 50 LITCHFIELD 70 MIDDLESEX 90 NEWHAVEN 110 NEW LONDON 130 TOLLAND 150 WINDHAM DELAWARE-STATE CODE 11 10 KENT 30 NEW CASTLE 50 SUSSEX FLORIDA-STATE CODE 43 10 ALACHUA 30 BAKER

Appendix A: County Codes by State Page 92

70 90 130 190 230 250 310 330 370 390 470 530 570 590 630 650 670 730 750 770 790 810 830 870 890 950 1050 1070 1130 1090 1190 1210 1230 1270 1290 1310 1330

BRADFORD BREVARD/ST LUCIE CALHOUN CLAY COLUMBIA DADE DUVAL ESCAMBIA FRANKLIN GADSDEN HAMILTON HERNANDO/BENTON HILLSBOROUGH HOLMES JACKSON JEFFERSON LAFAYETTE LEON LEVY LIBERTY MADISON MANATEE MARION MONROE NASSAU ORANGE/MOSQUITO POLK PUTNAM SANTA ROSA ST JOHNS SUMTER SUWANNEE TAYLOR VOLUSIA WAKULLA WALTON WASHINGTON

GEORGIA-STATE CODE 44 10 APPLING 70 BAKER 90 BALDWIN 110 BANKS 150 BARTOW/CASS 190 BERRIEN 210 DIED 270 BROOKS 290 BRYAN 310 BULLOCH 330 BURKE 350 BUTTS 370 CALHOUN 390 CAMDEN 410 CAMPBELL 450 CARROLL

1850 Public Use Microdata Sample User's Guide and Technical Documentation

470 490 510 530 550 570 590 610 630 650 670 690 710 730 770 790 830 850 890 870 910 930 950 970 990 1010 1030 1050 1070 1110 1130 1150 1170 1190 .1210 1230 1250 1270 1290 1330 1350 1370 1390 1410 1430 1450 1470 1490 1510 1530 1550 1510 1590 1630 1670

CATOOSA CHARLTON CHATHAM CHATTAHOOCHEE CHATTOOGA CHEROKEE CLARKE CLAY CLAYTON CLINCH COBB COFFEE COLQUITT COLUMBIA COWETA CRAWFORD DADE DAWSON DE KALE DECATUR DODGE DOOLY DOUGHERTY DOUGLAS EARLY ECHOLS EFFINGHAM ELBERT EMANUEL FANNIN FAYETTE FLOYD FORSYTH FRANKLIN FULTON GILMER GLASCOCK GLYNN GORDON GREENE GWINNETT HABERSHAM HALL HANCOCK HARALSON HARRIS HART HEARD HENRY HOUSTON IRWIN JACKSON JASPER JEFFERSON JOHNSON

1850 Public Use Microdata Sample User's Guide and Technical Documentation 1690

1750 1770 1790 1810 1850 1870 1930 1950 1970 1890 1910 1990 2010 2030 2050 2070 2090 2110 2130 2150 2170 2190 2210 2230 2270 2290 2310 2330 2350 2370 2390 2410 2430 2450 2470 2490 2510 2550 2590 2610 2630 2650 2670 2690 2710 2730 2750 2810 2850 2890 2910 2930 2950 2970

JONES LAURENS LEE LIBERTY LINCOLN LOWNDES LUMPKIN MACON MADISON MARION MCDUFFIE MCINTOSH MERIWETHER MILLER MILTON MITCHELL MONROE MONTGOMERY MORGAN MURRAY MUSCOGEE NEWTON OCONEE OGLETHORPE PAULDING PICKENS PIERCE PIKE POLK PULASKI PUTNAM QUITMAN RA13UN RANDOLPH RICHMOND ROCKDALE SCHLEY SCREVEN SPALDING STEWART SUMTER TALBOT TALIAFERRO TATTNALL TAYLOR TELFAIR TERRELL THOMAS TOWNS TROUP TWIGGS UNION UPSON WALKER WALTON

Appendix : County Codes by State (ICPSR Coding) Page 93

2990 3010 3030 3050 3070 3110 3130 3150 3170 3190 3210

WARE WARREN WASHINGTON WAYNE WE13STER WHITE WHITFIELD WILCOX WILKES WILKINSON WORTH

ILLINOIS-STATE CODE 21 10 ADAMS 30 ALEXANDER 50. BOND 70 BOONE 90 BROWN 110 BUREAU 130 CALHOUN 150 CARROLL 170 CASS 190 CHAMPAIGN 210 CHRISTIAN 230 CLARK 250 CLAY 270 CLINTON 290 COLES 310 COOK 330 CRAWFORD 350 CUMBERLAND 370 DE KALB 390 DE WITT 410 DOUGLAS 430 DU PAGE 450 EDGAR 470 EDWARDS 490 EFFINGHAM 510 FAYETTE 530 FORD 550 FRANKLIN 570 FULTON 590 GALLATIN 610 GREENE 630 GRUNDY 650 HAMILTON 670 HANCOCK 690 HARDIN 710 HENDERSON 730 HENRY 750 IROQUOIS 770 JACKSON 790 JASPER 810 JEFFERSON 830 JERSEY

Appendix A: County Codes by State Page 94 850 870

890 910 930 950 990 970 1010 1030 1050 1070 1150 1170 1190 1210 1230 1250 1270 1090 1110 1130 1290 1310 1330 1350 1370 1390 1410 1430 1450 1470 1490 1510 1530 1550 1570 1590 1610 1650 1670 169'0 1710 1730 1630 1750 1770 1790 1810 1830 1850 1870 1890 1910 1930

JO DAVIESS JOHNSON KANE KANKAKEE KENDALL KNOX LA SALLE LAKE LAWRENCE LEE LIVINGSTON LOGAN MACON MACOUPIN MADISON MARION MARSHALL MASON MASSAC MCDONOUGH MCHENRY MCLEAN MENARD MERCER MONROE MONTGOMERY MORGAN MOULTRIE OGLE PEORIA PERRY PIATT PIKE POPE PULASKI PUTNAM RANDOLPH RICHLAND ROCK ISLAND SALINE SANGAMON SCHUYLER SCOTT SHELBY ST CLARR STARK STEPHENSON TAZEWELL UNION VERMILION WABASH WARREN WASHINGTON WAYNE WHITE

1850 Public Use Microdata Sample User's Guide and Technical Documentation

1950 1970 1990 2010 2030

WHITESIDE WILL WILLIAMSON WINNEBAGO WOODFORD

INDIANA-STATE CODE 22 10 ADAMS 30 ALLEN 50 BARTHOLOMEW 70 BENTON 90 BLACKFORD 110 BOONE 130 BROWN 150 CARROLL 170 CASS 190 CLARK 210 CLAY 230 CLINTON 250 CRAWFORD 270 DAVIESS 330 DE KALB 290 DEARBORN 310 DECATUR 350 DELAWARE 370 DUBOIS 390 ELKHART 410 FAYETTE 430 FLOYD 450 FOUNTAIN 470 FRANKLIN 490 FULTON 510 GIBSON 530 GRANT 550 GREENE 570 HAMILTON 590 HANCOCK 610 HARRISON 630 HENDRICKS 650 HENRY 670 HOWARD 690 HUNTINGTON 710 730 750 770 790 810 830 850 910 870 890 930

JACKSON JASPER JAY JEFFERSON JENNINGS JOHNSON KNOX KOSCIUSKO LA PORTE LAGRANGE LAKE LAWRENCE

1850 Public Use Microdata Sample

User's Guide and Technical Documentation 950

970 990 1010 1030 1050 1070 1090 1110 1130 1150 1170 1190 1210 1230 1250 1270 1290 1310 1330 1350 1370 1390 1430 1450 1470 1410 1490 1510 1530 1550 1570 1590 1610 1630 1650 1670 1690 1710 1730 1750 1770 1790 1810 1830

MADISON MARION MARSHALL MARTIN MIAMI MONROE MONTGOMERY MORGAN NEWTON NOBLE OHIO ORANGE OWEN PARKE PERRY PIKE PORTER POSEY PULASKI PUTNAM RANDOLPH RIPLEY RUSH SCOTT SHELBY SPENCER ST JOSEPH STARKE STEUBEN SULLIVAN SWITZERLAND TIPPECANOE TIPTON UNION VANDERBURGH VERMILLION VIGO WABASH WARREN WARRICK WASHINGTON WAYNE WELLS WHITE WHITLEY

IOWA-STATE CODE 31 10 ADAIR 30 ADAMS 50 ALLAMAKEE 70 APPANOOSE 90 AUDUBON 110 BENTON 130 BLACK HAWK 150 BOONE

Appendix: County Codes by State (ICPSR Coding)' Page 95

170 190 210 230 250 270 290 310 330 350 370 390 410 430 450 470 490 510 530 550 570 590 610 630 650 670 690 710 730 750 770 790 810 830 850 870 890 910 930 950 970 990 1010 1030 1050 1070 1090 1110 1130 1150 1170 1190 1210 1230 1250

BREMER BUCHANAN BUENA VISTA BUTLER CALHOUN CARROLL CASS CEDAR CERRO GORDO CHEROKEE CHICKASAW CLARKE CLAY CLAYTON CLINTON CRAWFORD DALLAS DAVIS DECATUR DELAWARE DES MOINES DICKINSON DUBUQUE EMMET FAYETTE FLOYD FRANKLIN FREMONT GREENE GRUNDY GUTHRIE HAMILTON HANCOCK HARDIN HARRISON HENRY HOWARD HUMBOLDT IDA IOWA JACKSON JASPER JEFFERSON JOHNSON JONES KEOKUK KOSSUTH LEE LINN LOUISA LUCAS LYON/BUNCOMBE MADISON MAHASKA MARION

Appendix A: County Codes by State Page 96

1270 1290 1310 1330 1350 1370 1390 1410 1430 1450 1470 1490 1510 1530 1550 1570 1590 1610 1630 1650 1670 1690 1710 1730 1750 1770 1790 1810 1830 1850 1870 1890 1910 1930 1950 1970

MARSHALL MILLS MITCHELL MONONA MONROE MONTGOMERY MUSCATINE 0 BRIEN OSCEOLA PAGE PALO ALTO PLYMOUTH POCAHONTAS POLK POTTAWATTAMIE POWESHIEK RINGGOLD SAC SCOTT SHELBY SIOUX STORY TAMA TAYLOR UNION VAN BUREN WAPELLO WARREN WASHINGTON WAYNE WEBSTER WINNEBAGO WINNESHIEK WOODBURY WORTH WRIGHT

KENTUCKY-STATE CODE 51 10 ADAIR 30 ALLEN 50 ANDERSON 70 BALLARD 90 BARREN 110 BATH 130 BELL 150 BOONE 170 BOURBON 190 BOYD 210 BOYLE 230 BRACKEN 250 BREATHITT 270 BRECKINRIDGE 290 BULLITT 310 BUTLER 330 CALDWELL

1850 Public Use Microdata Sample User's Guide and Technical Documentation

350 370 410 430 450 470 490 510 530 550 570 590 610 630 650 670 690 710 730 750 770 790 810 830 850 870 890 910 930 950 970 990 1010 1030 x050 1070 1090 1110 1130 1150 1155 1170 1210 1230 1250 1270 1290 1310 1330 1350 1370 1390 1410 1430 1510

CALLOWAY CAMPBELL CARROLL CARTER CASEY CHRISTIAN CLARK CLAY CLINTON CRITTENDEN CUMBERLAND DAVIESS EDMONSON ELLIOTT ESTILL FAYETTE FLEMING FLOYD FRANKLIN FULTON GALLATIN GARRARD GRANT GRAVES' GRAYSON GREEN GREENUP HANCOCK HARDIN HARLAN HARRISON HART HENDERSON HENRY HICKMAN HOPKINS JACKSON JEFFERSON JESSAMINE JOHNSON JOSH BELL KENTON KNOX LARUE LAUREL LAWRENCE LEE LESLIE LETCHER LEWIS LINCOLN LIVINGSTON LOGAN LYON MADISON

1850 Public Use Microdata Sample

User's Guide and Technical Documentation

1530 1550 1570 1590 1610 1450 1490 1630 1650 1670 1690 1710 1730 1750 1770 1790 1810 1830 1650 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090 2110 2130 2150 2170 2190 2210 2230 2250 2270 2290 2310 2330 2350 2370 2390

MAGOFFIN MARION MARSHALL MARTIN MASON MCCRACKEN MCLEAN MEADE MENIFEE MERCER METCALFE MONROE MONTGOMERY MORGAN MUHLENBERG NELSON NICHOLAS OHIO OLDHAM OWEN OWSLEY PENDLETON PERRY PIKE POWELL PULASKI ROBERTSON ROCKCASTLE ROWAN RUSSELL SCOTT SHELBY SIMPSON SPENCER TAYLOR TODD TRIGG TRIMBLE UNION WARREN WASHINGTON WAYNE WEBSTER WHITLEY WOLFE WOODFORD

LOUISIANA-STATE CODE 45 50 ASCENSION 70 ASSUMPTION 90 AVOYELLES 130 BIENVILLE 150 BOSSIER 170 CADDO 190 CALCASIEU

Appendix: County Codes by State (ICPSR Coding

Page 97

210 230 250 270 290 310 330 350 370 410 430 450 470 490 510 550 570 610 630 650 670 690 710 730 750 770 790 810 830 850 870 890 910 930 950 970 990 1010 1030 1050 1070 1090 1110 1130 1150 1170 1190 1210 1230 1250 1270

CALDWELL CAMERON CATAHOULA CLAIBORNE CONCORDIA DE SOTO EAST BATON ROUGE EAST CARROLL EAST FELICIANA FRANKLIN GRANT IBERIA IBERVILLE JACKSON JEFFERSON LAFAYETTE LAFOURCHE LINCOLN LIVINGSTON MADISON MOREHOUSE NATCHITOCHES ORLEANS OUACHITA PLAQUEMINES POINTE COUPEE RAPIDES RED RIVER RICHLAND SABINE ST BERNARD ST CHARLES ST HELENA ST JAMES ST JOHN THE--BAPTIST ST LANDRY ST MARTIN ST MARY ST TAMMANY TANGIPAHOA TENSAS TERREBONNE UNION VERMILLION VERNON WASHINGTON WEBSTER WEST BATON ROUGE WEST CARROLL WEST FELICIANA WINN

MAINE--STATE CODE 02 10 ANDROSCOGGIN 30 AROOSTOOK

Appendix A: County Codes by State Page 98 50

70 90 110 130 150 170 190 210 230 250 270 290 310

CUMBERLAND FRANKLIN HANCOCK KENNEBEC KNOX LINCOLN OXFORD PENOBSCOT PISCATAQUIS SAGADAHOC SOMERSET WALDO WASHINGTON YORK

MARYLAND-STATE CODE 52 10 ALLEGANY 30 ANNE ARUNDEL 50 BALTIMORE 70 CALVERT 90 CAROLINE 110 CARROLL 130 CECIL 150 CHARLES 170 DORCHESTER 190 FREDERICK 210 GARRETT 230 HARFORD 250 HOWARD 270 KENT 290 MONTGOMERY 310 PRINCE GEORGES 330 QUEEN ANNES 350 SOMERSET 370 ST MARYS 390 TALBOT 410 WASHINGTON 430 WICOMICO 450 WORCESTER MASSACHUSETTS-STATE CODE 03 10 BARNSTABLE 30 BERKSHIRE 50 BRISTOL 70 DUKES 90 ESSEX 110 FRANKLIN 130 HAMPDEN 150 HAMPSHIRE 170 MIDDLESEX 190 NANTUCKET 210 NORFOLK 230 PLYMOUTH 250 SUFFOLK 270 WORCESTER

1850 Public Use Microdata Sample User's Guide and Technical Documentation

MICHIGAN-STATE CODE 23 10 ALCONA 50 ALLEGAN 70 ALPENA 90 ANTRIM 130 BARAGA 150 BARRY 170 BAY 190 BENZIE 210 BERRIEN 230 BRANCH 250 CALHOUN 270 CASS 290 CHARLEVOIX 310 CHEBOYGAN 330 CHIPPEWA 350 CLARE 370 CLINTON 390 CRAWFORD 410 DELTA 450 EATON 470 EMMET 490 GENESEE 510 GLADWIN 550 GRAND TRAVERSE 570 GRATIOT 590 HILLSDALE 610 HOUGHTON 630 HURON 650 INGHAM 670 IONIA 690 IOSCO 730 ISABELLA 735 ISLE ROYALE 750 JACKSON 770 KALAMAZOO 790 KALKASKA 810 KENT 830 KEWEENAW 850 LAKE 870 LAPEER 890 LEELANAU 910 LENAWEE 930 LIVINGSTON 970 MACKINAC/MICHILIM 990 MACOMB 1010 MANISTEE 1015 MANITOU 1030 MARQUETTE 1050 MASON 1070 MECOSTA 1090 MENOMINEE 1110 MIDLAND 1130 MISSAUKEE

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1170 1190 1210 1230 1250 1270 1290 1310 1330 1350 1370 1390 1410 1430 1450 1510 1530 1550 1470 1490 1570. 1590 1610 1630 1650

MONROE MONTCALM MONTMORENCY MUSKEGON NEWAYGO OAKLAND OCEANA OGEMAW ONTONAGON OSCEOLA OSCODA OTSEGO OTTAWA PRESQUE ISLE ROSCOMMON SAGINAW SANILAC SCHOOLCRAFT SHIAWASSEE ST CLAIR ST JOSEPH TUSCOLA VAN BUREN WASHTENAW WAYNE WEXFORD

MINNESOTA TER .-STATE CODE 33 10 AITKIN 30 ANOKA 50 BECKER 70 BELTRAMI 90 DENTON 110 BIG STONE 130 BLUE EARTH 150 BROWN 170 CARLTON 190 CARVER 210 CASS 230 CHIPPEWA 250 CHISAGO 270 CLAY 310 COOK 330 COTTONWOOD 350 CROW WING 370 DAKOTA 390 DODGE 410 DOUGLAS 430 FARIBAULT 450 FILLMORE 470 FREEBORN 490 GOODHUE 510 GRANT 530 HENNEPIN 550 HOUSTON

Appendix: County Codes by State (ICPSR Coding) Page 99

590 610 630 650 670 690 730 750 790 810 830 890 910 850 930 950 970 990 1010 1030 1050 1090 1110 1150 1170 1190 1210 1230 1270 1290 1310 1330 1390 1410 1430 1370 1450 1470 1490 1510 1530 1550 1570 1590 1610 1630 1650 1670 1690 1710 1730

ISANTI ITASCA JACKSON KANABEC KANDIYOHI KITTSON/PEMBINA LAC QUI PARLE LAKE LE SUEUR LINCOLN LYON MARSHALL MARTIN MCLEOD MEEKER MILLE LACS MORRISON MOWER MURRAY NICOLLET NOBLES OLMSTED OTTER TAIL PINE PIPESTONE POLK POPE RAMSEY REDWOOD RENVILLE RICE ROCK SCOTT SHERBURNE SIBLEY ST LOUIS STEARNS STEELE STEVENS SWIFT TODD TRAVERSE WABASHA WADENA WASECA WASHINGTON WATONWAN WILKIN/TOOMBS WINONA WRIGHT YELLOW MEDICINE

MISSISSIPPI-STATE CODE 46 10 ADAMS 30 ALCORN

Appendix A : County Codes by State

Page 100 50

70 90 110 130 150 170 190 210 230 250 270 290 310 330 370 410 430 450 470 490 510 550 570 590 610 630 670 690 710 750 770 790 810 830 850 870 890 910 930 950 970 990 1010 1030 1050 1070 1110 1130 1150 1170 1190 1210 1230 1250

AMITE ATTALA BENTON BOLIVAR CALHOUN CARROLL CHICKASAW CHOCTAW CLAIBORNE CLARKE CLAY COAHOMA COPIAH COVINGTON DE SOTO FRANKLIN GREENE GRENADA HANCOCK HARRISON HINDS HOLMES ISSAQUENA ITAWAMBA JACKSON JASPER JEFFERSON JONES KEMPER LAFAYETTE LAUDERDALE LAWRENCE LEAKE LEE LEFLORE LINCOLN LOWNDES MADISON MARION MARSHALL MONROE MONTGOMERY NESHOBA NEWTON NOXUBEE OKTIBBEHA PANOLA PERRY PIKE PONTOTOC PRENTISS QUITMAN RANKIN SCOTT SHARKEY

1850 Public Use Microdata Sample User's Guide and Technical Documentation

1270 1290 1315 1330 1350 1370 1390 1410 1430 1450 1490 1510 1530 1570 1590 1610 1630

SIMPSON SMITH SUMNER SUNFLOWER TALLAHATCHIE TATE TIPPAH TISHOMINGO TUNICA UNION WARREN WASHINGTON WAYNE WILKINSON WINSTON YALOBUSHA YAZOO

MISSOURI-STATE CODE 34 10 ADAIR 30 ANDREW 50 ATCHISON 70 AUDRAIN 90 BARRY 110 BARTON 130 BATES 150 BENTON 170 BOLLINGER 190 BOONE 210 BUCHANAN 230 BUTLER 250 CALDWELL 270 CALLAWAY 290 CAMDEN -310 CAPE GIRARDEAU .330 CARROLL 350 CARTER 370 CASS/VAN BUREN 390 CEDAR 410 CHARITON 430 CHRISTIAN 450 CLARK 470 CLAY 490 CLINTON 510 COLE 530 COOPER 550 CRAWFORD 570 DADE 590 DALLAS 610 DAVIESS 630 DE KALB 650 DENT 670 DOUGLAS 690 DUNKLIN -710 FRANKLIN

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730 750 770 790 810 830 850 870 890 910 930 950 970 990 1010 1030 1050 1070 1090 1110 1130 1150 1170 1210 1230 1250 1270 1190 1290 1310 1330 1350 1370 1390 1410 1430 1450 1470 1490 1510 1530 1550 1570 1590 1610 1630 1650 1670 1690 1710 1730 1750 1770 1790 1810

GASCONADE GENTRY GREENE GRUNDY HARRISON HENRY/RIVES HICKORY HOLT HOWARD HOWELL IRON JACKSON JASPER JEFFERSON JOHNSON KNOX LACLEDE LAFAYETTE LAWRENCE LEWIS LINCOLN LINN LIVINGSTON MACON MADISON MARIES MARION MCDONALD MERCER MILLER MISSISSIPPI MONITEAU MONROE MONTGOMERY MORGAN NEW MADRID NEWTON NODAWAY OREGON OSAGE OZARK PEMISCOT PERRY PETTIS PHELPS PIKE PLATTE POLK PULASKI PUTNAM RALLS RANDOLPH RAY REYNOLDS RIPLEY

Appendix: County Codes by State (ICPSR Coding) Page 101

1950 1970 1990 2010 2030 2050 1830 1850 1870 1890 1930 2070 2090 2110 2130 2150 2170 2190 2210 2230 2250 2270 2290

SALINE SCHUYLER SCOTLAND SCOTT SHANNON SHELBY ST CHARLES ST CLAIR ST FRANCOIS ST LOUIS STE GENEVIEVE STODDARD STONE SULLIVAN TANEY TEXAS VERNON WARREN WASHINGTON WAYNE WEBSTER WORTH WRIGHT

NEW EAMPSEIRE-STATE CODE 04 10 BELKNAP 30 CARROLL 50 CHESHIRE 70 COOS 90 GRAFTON 110 HILLSBOROUGH 130 MERRIMACK 150 ROCKINGHAM 170 STRAFFORD 190 SULLIVAN NEW JERSEY-STATE CODE 12 10 ATLANTIC 30 BERGEN 50 BURLINGTON 70 CAMDEN 90 CAPE MAY 110 CUMBERLAND 130 ESSEX 150 GLOUCESTER 170 HUDSON 190 HUNTERDON 210 MERCER 230 MIDDLESEX 250 MONMOUTH 270 MORRIS 290 OCEAN 310 PASSAIC 330 SALEM 350 SOMERSET

Appendix A: County Codes by State Page 102

370 SUSSEX 390 UNION 410 WARREN NEW MEXICO TER .- CODE 66 010 BERNALILLO 390 RIO ARRIBA 470 SAN MIGUEL 490 SANTA FE 550 TAOS 610 VALENCIA NEW YORK-STATE CODE 13 10 ALBANY 30 ALLEGANY 70 - BROOME 90 CATTARAUGUS 110 CAYUGA 130 CHAUTAUQUA 150 CHEMUNG 170 CHENANGO 190 CLINTON 210 COLUMBIA 230 CORTLAND 250 DELAWARE 270 DUTCHESS 290 ERIE 310 ESSEX 330 FRANKLIN 350 FULTON 370 GENESEE 390 GREENE 410 HAMILTON 430 HERKIMER 450 JEFFERSON 470 KINGS 490 LEWIS 510 LIVINGSTON 530 MADISON 550 MONROE 570 MONTGOMERY 610 NEW YORK 630 NIAGARA 650 ONEIDA 670 ONONDAGA 690 ONTARIO 710 ORANGE 730 ORLEANS 750 OSWEGO 770 OTSEGO 790 PUTNAM 810 QUEENS 830 RENSSELAER 850 RICHMOND 870 ROCKLAND

1850 Public Use Microdata Sample User's Guide and Technical Documentation

910 930 950 970 990 890 1010 1030 1050 1070 1090 1110 1130 1150 1170 1190 1210 1230 NORTH 10 30 50 70 90 130 150 170 190 210 230 250 270 290 -310 330 350 370 390 410 430 450 470 490 510 530 550 570 590 610 650 670 690 710 730

SARATOGA SCHENECTADY SCHOHARIE SCHUYLER SENECA ST LAWRENCE STEUBEN SUFFOLK SULLIVAN TIOGA TOMPKINS ULSTER WARREN WASHINGTON WAYNE WESTCHESTER WYOMING YATES CAROLINA-STATE CODE 47 ALAMANCE ALEXANDER ALLEGHANY ANSON ASHE BEAUFORT BERTIE BLADEN BRUNSWICK BUNCOMBE BURKE CABARRUS CALDWELL CAMDEN CARTERET CASWELL CATAWBA CHATHAM CHEROKEE CHOWAN CLAY CLEVELAND COLUMBUS CRAVEN CUMBERLAND CURRITUCK DARE DAVIDSON DAVIE DUPLIN EDGECOMBE FORSYTH FRANKLIN GASTON GATES

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750 770 790 610 830 850 870 890 910 950 970 990 1010 1030 1070 1090 1130 1150 1170 1110 1190 1210 1230 1250 1270 1290 1310 1330 1350 1370 1390 1410 1430 1450 1470 1490 1510 1530 1550 1570 1590 1610 1630 1670 1690 1710 1730 1750 1770 1790 1830 1850 1870 1890 1910

GRAHAM GRANVILLE GREENE GUILFORD HALIFAX HARNETT HAYWOOD HENDERSON HERTFORD HYDE IREDELL JACKSON JOHNSTON JONES LENOIR LINCOLN MACON MADISON MARTIN MCDOWELL MECKLENBURG MITCHELL MONTGOMERY MOORE NASH NEW HANOVER NORTHAMPTON ONSLOW ORANGE PAMLICO PASQUOTANK PENDER PERQUIMANS PERSON PITT POLK RANDOLPH RICHMOND ROBESON ROCKINGHAM ROWAN RUTHERFORD SAMPSON STANLY STOKES SURRY SWAIN TRANSYLVANIA TYRRELL UNION WAKE WARREN WASHINGTON WATAUGA WAYNE

Appendix: County Codes by State (ICPSR Coding)

Page 103

1930 1950 1970 1990

WILKES WILSON YADKIN YANCEY

OHIO-STATE CODE 24 10 ADAMS 30 ALLEN 50 ASHLAND 70 ASHTABULA 90 ATHENS 110 AUGLAIZE 130 BELMONT 150 BROWN 170 BUTLER 190 CARROLL 210 CHAMPAIGN 230 CLARK 250 CLERMONT 270 CLINTON 290 COLUMBIANA 310 COSHOCTON 330 CRAWFORD 350 CUYAHOGA 370 DARKE 390 DEFIANCE 410 DELAWARE 430 ERIE 450 FAIRFIELD 470 FAYETTE 490 FRANKLIN 510 FULTON 530 GALLIA 550 GEAUGA 570 GREENE 590 GUERNSEY 610 HAMILTON 630 HANCOCK 650 HARDIN 670 HARRISON 690 HENRY 710 HIGHLAND 730 HOCKING 750 HOLMES 770 HURON 790 JACKSON 810 JEFFERSON 830 KNOX 850 LAKE 870 LAWRENCE 890 LICKING 910 LOGAN 930 LORAIN 950 LUCAS 970 MADISON

Appendix A: County Codes by State Page 104

990 MAHONING 1010 MARION 1030 MEDINA 1050 MEIGS 1070 MERCER 1090 MIAMI 1110 MONROE 1130 MONTGOMERY 1150 MORGAN 1170 MORROW 1190 MUSKINGUM 1210 NOBLE 1230 OTTAWA 1250 PAULDING , 1270 PERRY 1290 PICKAWAY 1310 PIKE 1330 PORTAGE 1350 PREBLE 1370 PUTNAM 1390 RICHLAND 1410 ROSS 1430 SANDUSKY 1450 SCIOTO 1470 SENECA 1490 SHELBY 1510 STARK 1530 SUMMIT 1550 TRUMBULL 1570 TUSCARAWAS 1590 UNION 1610 VAN WERT 1630 VINTON 1650 WARREN 1670 .WASHINGTON 1690 WAYNE 1710 WILLIAMS 1730 WOOD 1750 WYANDOT

OREGON TER .-STATE CODE 72 10 BAKER 30 BENTON 50 CLACKAMAS 70 CLATSOP 90 COLUMBIA 110 COOS 150 CURRY 190 DOUGLAS 230 GRANT 290 JACKSON 330 JOSEPHINE 370 LAKE 390 LANE 430 LINN

1850 Public Use Microdata Sample User's Guide and Technical Documentation

470 510 530 570 590 610 650 670 710

MARION MULTNOMAH POLK TILLAMOOK UMATILLA UNION WASCO WASHINGTON YAMHILL

PENNSYLVANIA-STATE CODE 14 10 ADAMS 30 ALLEGHENY 50 ARMSTRONG 70 BEAVER 90 BEDFORD 110 BERKS 130 BLAIR 150 BRADFORD 170 BUCKS 190 BUTLER 210 CAMBRIA 230 CAMERON 250 CARBON 270 CENTRE 290 CHESTER 310 CLARION 330 CLEARFIELD 350 CLINTON 370 COLUMBIA 390 CRAWFORD 410 CUMBERLAND 430 DAUPHIN 450 DELAWARE -470 ELK 490 ERIE 510 FAYETTE 530 FOREST 550 FRANKLIN 570 FULTON 590 GREENE 610 HUNTINGDON 630 INDIANA 650 JEFFERSON 670 JUNIATA 690 LACKAWANNA 710 LANCASTER 730 LAWRENCE 750 LEBANON 770 LEHIGH 790 LUZERNE 810 LYCOMING 830 MCKEAN 850 MERCER 870 MIFFLIN

1850 Public Use Microdata Sample User's Guide and Technical Documentation

890 910 930 950 970 990 1010 1030 1050 1070 1090 1110 1130 1150 1170 1190 1210 1230 1250 1270 1290 1310 1330

MONROE MONTGOMERY MONTOUR NORTHAMPTON NORTHUMBERLAND PERRY PHILADELPHIA PIKE POTTER SCHUYLKILL SNYDER SOMERSET SULLIVAN SUSQUEHANNA TIOGA UNION VENANGO WARREN WASHINGTON WAYNE WESTMORELAND WYOMING YORK

RHODE 10 30 50 70 90

ISLAND-STATE CODE 05 BRISTOL KENT NEWPORT PROVIDENCE WASHINGTON

SOUTH 10 30 70 110 130 190 230 250 270 290 310 370 390 430 450 490 510 550 570 590 630 670 690

CAROLINA-STATE CODE 48 ABBEVILLE AIKEN ANDERSON BARNWELL BEAUFORT CHARLESTON CHESTER CHESTERFIELD CLARENDON COLLETON DARLINGTON EDGEFIELD FAIRFIELD GEORGETOWN GREENVILLE HAMPTON HORRY KERSHAW LANCASTER LAURENS LEXINGTON MARION MARLBORO

Appendix: County Codes by State (ICPSR Coding)

Page 105

710 730 750 770 790 830 850 870 890 910

NEWBERRY OCONEE ORANGEBURG PICKENS RICHLAND SPARTANBURG SUMTER UNION WILLIAMSBURG YORK

TENNESSEE-STATE CODE 54 10 ANDERSON 30 BEDFORD 50 DENTON 70 BLEDSOE 90 BLOUNT 110 BRADLEY 130 CAMPBELL 150 CANNON 170 CARROLL 190 CARTER 210 CHEATHAM 250 CLAIBORNE 270 CLAY 290 COCKE 310 COFFEE 330 CROCKETT 350 CUMBERLAND 370 DAVIDSON 410 DE KALB 390 DECATUR 430 DICKSON 450 DYER 470 FAYETTE 490 FENTRESS 510 FRANKLIN 530 GIBSON 550 GILES 570 GRAINGER 590 GREENE 610 GRUNDY 630 HAMBLEN 650 HAMILTON 670 HANCOCK 690 HARDEMAN 710 HARDIN 730 HAWKINS 750 HAYWOOD 770 HENDERSON 790 HENRY 810 HICKMAN 830 HOUSTON 850 HUMPHREYS 870 JACKSON

Appendix A: County Codes by State Page 106

875 890 910 930 950 970 990 1010 1030 1050 1110 1130 1150 1170 1190 1070 1090 1210 1230 1250 1270 1290 1310 1330 1350 1390 1410 1430 1450 1470 1490 1510 1530 1550 1570 1590 1610 1630 1650 1670 1690 1710 1730 1750 1770 1790 1810 1830 1850 1870 1890

JAMES JEFFERSON JOHNSON KNOX LAKE LAUDERDALE LAWRENCE LEWIS LINCOLN LOUDON MACON MADISON MARION MARSHALL MAURY MCMINN MCNAIRY MEIGS MONROE MONTGOMERY MOORE MORGAN OBION OVERTON PERRY POLK PUTNAM RHEA ROANE ROBERTSON RUTHERFORD SCOTT SEQUATCHIE SEVIER SHELBY SMITH STEWART SULLIVAN SUMNER TIPTON TROUSDALE UNICOI UNION VAN BUREN WARREN WASHINGTON WAYNE WEAKLEY WHITE WILLIAMSON WILSON

TEXAS-STATE CODE 49 10 ANDERSON 30 ANDREWS

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50 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390 410 450 490 510 530 550 570 590 610 630 650 670 690 710 730 750 --770 790 830 850 870 890 910 930 950 970 990 1010 1050 1070 1110 1130 1150 1230 1170 1190 1210

ANGELINA ARANSAS ARCHER ARMSTRONG ATASCOSA AUSTIN BAILEY BANDERA BASTROP BAYLOR BEE BELL BEXAR BLANCO BORDEN BOSQUE BOWIE BRAZORIA BRAZOS BRISCOE BROWN BURLESON BURNET CALDWELL CALHOUN CALLAHAN CAMERON CAMP CARSON CASS/DAVIS CASTRO CHAMBERS CHEROKEE CHILDRESS CLAY COCHRAN COLEMAN COLLIN COLLINGSWORTH COLORADO COMAL COMANCHE CONCHO COOKE CORYELL COTTLE CROCKETT CROSBY DALLAM DALLAS DAWSON DE WITT DEAF SMITH DELTA DENTON

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1250 1270 1290 1310 1330 1370 1410 1390 1415 1430 1450 1470 1490 1510 1530 1570 1590 1610 1630 1650 1670 1690 1710 1750 1770 1790 1810 1830 1850 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090 2110 2130 2150 2170 2190 2210 2230 2250 2270 2310 2330 2370 2390 2410

DICKENS DIMMIT DONLEY DUVAL EASTLAND EDWARDS EL PASO ELLIS ENCINAL ERATH FALLS FANNIN FAYETTE FISHER FLOYD FORT BEND FRANKLIN FREESTONE FRIO GAINES GALVESTON GARZA GILLESPIE GOLIAD GONZALES GRAY GRAYSON GREGG GRIMES GUADALUPE HALE HALL HAMILTON HANSFORD HARDEMAN HARDIN HARRIS HARRISON HARTLEY HASKELL HAYS HEMPHILL HENDERSON HIDALGO HILL HOCKLEY HOOD HOPKINS HOUSTON HOWARD HUNT HUTCHINSON JACK JACKSON JASPER

Appendix: County Codes by State (ICPSR Coding)

Page 107

2450 2510 2530 2550 2570 2590 2630 2650 2670 2690 2710 2750 2830 2770 2790 2810 2850 2870 2890 2910 2930 2950 2970 2990 3030 3050 3130 3150 3170 3190 3210 3230 3070 3090 3110 3250 3270 3310 3350 3370 3390 3410 3430 3450 3470 3490 3510 3530 3550 3570 3590 3610 3630 3650 3670

JEFFERSON JOHNSON JONES KARNES KAUFMAN KENDALL KENT KERR KIMBLE KING KINNEY KNOX LA SALLE LAMAR LAMB LAMPASAS LAVACA LEE LEON LIBERTY LIMESTONE LIPSCOMB LIVE OAK LLANO LUBBOCK LYNN MADISON MARION MARTIN MASON MATAGORDA MAVERICK MCCULLOCH MCLENNAN MCMULLEN MEDINA MENARD MILAM MITCHELL MONTAGUE MONTGOMERY MOORE MORRIS MOTLEY NACOGDOCHES NAVARRO NEWTON NOLAN NUECES OCHILTREE OLDHAM ORANGE PALO PINTO PANOLA PARKER

Appendix A: County Codes by State Page 108

3690 3710 3730 3750 3770 3790 3810 3870 3910 3930 3950 3970 3990 4010 4030 4050 4070 4090 4110 4150 4170 4190 42-10 4230 4250 4270 4290 4330 4370 4390 4410 4450 4470 4490 4510 4530 4550 4570 4590 4630 4670 4690 4710 4730 4770 4790 4810 4830 4850 4870 4910 4930 4970 4990 5010

PARMER PECOS POLK POTTER PRESIDIO RAINS RANDALL RED RIVER REFUGIO ROBERTS ROBERTSON ROCKWALL RUNNELS RUSK SABINE SAN AUGUSTINE SAN JACINTO SAN PATRICIO SAN SABA SCURRY SHACKELFORD SHELBY SHERMAN SMITH SOMERVELL STARR STEPHENS/BUCHANAN STONEWALL SWISHER TARRANT TAYLOR TERRY THROCKMORTON TITUS TOM GREEN TRAVIS TRINITY TYLER UPSHUR UVALDE VAN ZANDT VICTORIA WALKER WALKER WASHINGTON WEBB WHARTON WHEELER WICHITA WILBARGER WILLIAMSON WILSON WISE WOOD YOAKUM

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5030 YOUNG 5050 ZAPATA 5070 ZAVALA UTAH TER .-STATE CODE 67 110 DAVIS 210 IRON 350 SALT LAKE 390 SANPETE 490 UTAH 570 WEBER VERMONT--STATE CODE 0 6 10 ADDISON 30 BENNINGTON 50 CALEDONIA 70 CHITTENDEN 90 ESSEX 110 FRANKLIN 130 GRAND ISLE 150 LAMOILLE 170 ORANGE 190 ORLEANS 210 RUTLAND 230 WASHINGTON 250 WINDHAM 270 WINDSOR VIRGINIA-STATE CODE 40 10 ACCOMACK 30 ALBEMARLE 50 ALLEGHANY 70 AMELIA 90 AMHERST -110 APPOMATTOX __ 130 ARLINGTON/ALEXAND 150 AUGUSTA 155 BARBOUR 170 BATH 190 BEDFORD 195 BERKELEY 215 BOONE 230 BOTETOURT 235 BRAXTON 245 BROOKE 250 BRUNSWICK 290 BUCKINGHAM 295 CABELL 310 CAMPBELL 330 CAROLINE 350 CARROLL 360 CHARLES CITY 370 CHARLOTTE 410 CHESTERFIELD

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430 470 490 530 535 550 570 590 610 615 630 650 670 690 710 715 730 750 770 775 790 810 830 850 855 865 870 890 910 930 935 950 955 965 970 990 1010 1030 1050 1055 1065 1070 1090 1110 '113 0 1141 1145 1148 1150 1170 1175 1190 1195 1205 1210

CLARKE CULPEPER CUMBERLAND DINWIDDIE DODDRIDGE ELIZABETH CITY ESSEX FAIRFAX FAUQUIER FAYETTE FLOYD FLUVANNA FRANKLIN FREDERICK GILES GILMER GLOUCESTER GOOCHLAND GRAYSON GREENBRIER GREENE GREENSVILLE HALIFAX HANOVER HARDY HARRISON HENRICO HENRY HIGHLAND ISLE OF WIGHT JACKSON JAMES CITY JEFFERSON KANAWHA KING AND QUEEN KING GEORGE KING WILLIAM LANCASTER LEE LEWIS LOGAN LOUDOUN LOUISA LUNENBURG MADISON MARION MARSHALL MASON MATHEWS MECKLENBURG MERCER MIDDLESEX MONONGALIA MONROE MONTGOMERY'

Appendix : County Codes by State (ICPSR Coding)

Page 109

1215 1230 1250 1270 1275 1290 1310 1330 1350 1355 1370 1390 1410 1415 1430 1445 1450 1455 1470 1490 1530 1510 1550 1555 1565 1567 1570 1590 1595 1610 1630 1650 1670 1690 1710 1730 1750 1770 1790 1810 1830 1835 1850 1859 1870 1875 1910 1915 1930 1935 1945 1955 1965 1970 1990

MORGAN NANSEMOND NELSON NEW KENT NICHOLAS NORFOLK NORTHAMPTON NORTHUMBERLAND NOTTOWAY OHIO ORANGE PAGE PATRICK PENDLETON PITTSYLVANIA POCAHONTAS POWHATAN PRESTON PRINCE EDWARD PRINCE GEORGE PRINCE WILLIAM PRINCESS ANNE PULASKI PUTNAM RALEIGH RANDOLPH RAPPAHANNOCK RICHMOND RITCHIE ROANOKE ROCKBRIDGE ROCKINGHAM RUSSELL SCOTT SHENANDOAH SMYTH SOUTHAMPTON SPOTSYLVANIA STAFFORD SURRY SUSSEX TAYLOR TAZEWELL TYLER WARREN WARWICK WASHINGTON . WAYNE WESTMORELAND WETZEL WIRT WOOD WYOMING WYTHE YORK

Appendix A: County Codes by State Page 110

WISCONSIN--STATE CODE 25 10 ADAMS 30 ASHLAND 50 BARRON/DALLAS 70 BAYFIELD/LA POINT 90 BROWN 110 BUFFALO 130 BURNETT 150 CALUMET 170 CHIPPEWA 190 CLARK 210 COLUMBIA 230 CRAWFORD 250 DANE 270 DODGE 290 DOOR 310 DOUGLAS 330 DUNN 350 EAU CLAIRE 390 FOND DU LAC 430 GRANT 450 GREEN 470 GREEN LAKE 490 IOWA 530 JACKSON 550 JEFFERSON 570 JUNEAU 590 KENOSHA 610 KEWAUNEE 630 LA CROSSE 650 LAFAYETTE 670 LANGLADE 690 LINCOLN 710 MANITOWOC 730 MARATHON 750 MARINETTE 770 MARQUETTE 790 MILWAUKEE 810 MONROE 830 OCONTO 870 OUTAGAMIE 890 OZAUKEE 910 PEPIN 930 PIERCE 950 POLK 970 PORTAGE 990 PRICE 1010 RACINE 1030 RICHLAND 1050 ROCK 1110 SAUK 1150 SHAWANO 1170 SHEBOYGAN

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1210 1230 1270 1310 1330 1350 1370 1390 1410

ST CROIX

TAYLOR TREMPEALEAU VERNON/BAD AX WALWORTH WASHINGTON WAUKESHA WAUPACA WAUSHARA WINNEBAGO WOOD