Perfecting DataHISTORICAL METHODS, April–June 2010, Volume 43, Number 2Copyright C© Taylor & Francis Group, LLC

Decennial Life Tables for the WhitePopulation of the United States,

1790–1900J. DAVID HACKER

Department of HistoryBinghamton University

State University of New York

Abstract. In this article, the author constructs new life tables forthe white population of the United States in each decade between1790 and 1900. Drawing from several recent studies, he suggestsbest estimates of life expectancy at age 20 for each decade. Theseestimates are fitted to new standards derived from the 1900–1902rural and 1900–1902 overall death registration area life tables usinga two-parameter logit model with fixed slope. The resulting de-cennial life tables more accurately represent sex- and age-specificmortality rates while capturing known mortality trends.

Keywords: demographic history, demography, life table, mortality,nineteenth century, United States

Life tables summarize the effects of age-specific mor-tality rates on a real or synthetic cohort. In addition totheir descriptive value, life tables are an indispensable

tool for demographers, with many applications in the studyof mortality, fertility, migration, and population growth. Lifetables are often used in conjunction with indirect estimationmethods for the study of populations covered by a censusbut lacking a reliable vital registration system, such as manypopulations in developing countries and populations in thepast. Life tables, for example, can be used to estimate vi-tal rates from census age distributions and are required inown-child fertility analysis (see United Nations 1983, for adescription of commonly-used indirect methods).

Demographic historians of the nineteenth-century UnitedStates depend heavily on life tables and indirect estimationmethods. Although the federal government conducted a cen-sus every ten years, it did not implement a vital registrationsystem until the start of the twentieth century (the systemwas not complete until 1933).1 As a result, the timing andcontours of the demographic transition in the United States

Address correspondence to J. David Hacker, Binghamton Univer-sity, Department of History, PO Box 6000, Binghamton, NY 13902-6000, USA. E-mail: hacker@binghamton.edu

are less precisely known than that in nations such as Englandand Australia, which had comprehensive birth and death reg-istration by the mid–nineteenth century (Jones 1971; Woods2000). Despite this limitation, demographic historians havebeen able to estimate annual and age-specific birth rates, netmigration rates, and cohort trends in life cycle experiencesas far back as the early nineteenth century using census data,life tables, and indirect methods (Yasuba 1962; Coale andZelnik 1963; Kuznets 1965; Uhlenberg 1969; McClellandand Zeckhauser 1982; Tolnay, Graham, and Guest 1982;Ferrie 1996; Hacker n.d.)

Unfortunately, the results of these studies depend on asmall number of life tables, which suffer from limited geo-graphic coverage, limited temporal coverage, and a variety ofsource-based problems. The earliest life tables rely heavilyon data from Massachusetts, a small state in the Northeastcharacterized by much higher levels of urbanization, indus-trialization, and immigration and much lower levels of nup-tiality and fertility than the nation as a whole. Given the highshort-term variability in mortality rates that was characteris-tic of the nineteenth-century United States, it is also unclearwhether life tables based on a single year of data can be usedto represent mortality in a year other than the one for whichit was constructed. The failure of existing life tables to cap-ture suspected long-term trends in mortality is perhaps theirmost significant limitation. With just a handful of life tablescovering the entire nineteenth century, researchers have beenforced to make crude assumptions about long-term mortal-ity trends to conduct their analyses. As discussed in moredetail below, recent research indicates that earlier assump-tions of long-term mortality decline are in error. Mortalityincreased significantly in the mid-nineteenth-century UnitedStates before beginning its long-term decline.

This article constructs new life tables for the white pop-ulation of the United States in each decade between 1790and 1900. The first part of the article reviews research on

45

46 HISTORICAL METHODS

the level and trend of nineteenth-century mortality. Draw-ing from several recent studies, it suggests best estimates ofmale life expectancy at age 20 for each decade. The secondpart of the article investigates sex differentials in mortal-ity and suggests best estimates of female life expectancy atage 20 for each decade. The third part of the article reviewsresearch on the age pattern of male and female mortality.The results indicate that age-specific mortality rates in thenineteenth century did not match the two most frequentlycited standards: the west model of the Princeton regionalmodel (Coale, Demeny, and Vaughan 1983) life tables or the1900–1902 Death Registration Area (DRA) life tables forthe United States. It concludes, however, that the life tablesconstructed for the rural part of the 1900–1902 DRA likelyapproximate the age pattern of nineteenth-century mortal-ity. Finally, the fourth part of the article fits the decennialestimates of life expectancy at age 20 to new standards de-rived from 1900–1902 rural and overall life tables using atwo-parameter logit model with fixed slope. The resultingdecennial life tables, it is argued, more accurately representsex- and age-specific mortality rates while capturing knownmortality trends.

The Level and Trend in Nineteenth-Century Mortality

Table 1 shows life expectancy and infant mortality esti-mates from selected United States life tables in the periodbetween 1789 and 1902 by year of publication (for a morecomplete listing, including life table summaries for selectedcities, see Haines 1998). The tables were constructed froma wide variety of sources, including local bills of mortality,state and national death registration data, census data, familygenealogies, and biographical data on special populationssuch as legislators and college graduates. Table 1 also showsthe sex mortality differential at age 20, defined as the femalelife expectancy at age 20 minus male life expectancy atage 20.

Edward Wigglesworth (1793) constructed the first UnitedStates life table using Bills of Mortality for 35 New Eng-land towns in the late eighteenth century. Ezekiel B. Elliott(1858), John S. Billings (1885), Samuel W. Abbott (1898),and James W. Glover (1921) relied on death registration datain Massachusetts—the first state to implement a death regis-tration system—to calculate life tables for selected years inthe late nineteenth century.2 Levi Meech (1898) constructedthe first national life table. The lack of national death regis-tration data forced Meech to rely on an indirect approach. Heestimated cohort declines from the 1830–60 federal censuses,made adjustments from immigration data to account for thelack of a closed population and used retrospective mortalitydata published by the 1860 census to establish the age patternof death (1898, 255–59). The creation of a national death reg-istration area (DRA) in 1900 greatly facilitated the creationof life tables. Glover’s (1921) 1900–1902 DRA life tables re-lied on registration data from the 10 states and the District ofColumbia that comprised the nation’s original DRA. These

tables have been widely used by researchers to represent thelevel and age pattern of mortality in the United States at theturn of the twentieth century.

Two studies conducted in the mid-twentieth century havebeen widely cited as representative of nineteenth-centurymortality. Abram J. Jaffe and William I. Lourie Jr. (1942)relied on death registration data collected by 44 New Eng-land towns, several midsized cities, and a few larger citiesto construct life tables for the period 1826–35. The resultsindicated large differentials in life expectancy between ruralareas and large urban centers, with life expectancy at birthalmost 15 years higher in the selected towns than in the largecities of Boston, New York, and Philadelphia. Paul H. Jacob-son’s (1957) 1849–50 life tables were based on retrospectivemortality data collected by the 1850 census. Jacobson con-fined his analysis to data collected for Massachusetts andMaryland, reasoning that an arithmetic mean of their age-specific death rates would approximate those for the nationas a whole.

The life table estimates in table 1 are sorted by year of pub-lication to emphasize our relatively recent knowledge aboutnineteenth-century mortality. Researchers requiring life ta-ble data prior to the late 1970s were limited to a hand-ful of tables, which led to great uncertainty about mortal-ity trends. Inferring mortality trends in the early nineteenthcentury from existing life tables was especially problem-atic. Warren S. Thompson and Pascal K. Whelpton (1933,230–31) calculated a slow decline in the crude death ratefrom 27.8 per 1,000 in the late eighteenth century to 21.4 per1,000 in 1855 by interpolating between the Wigglesworth(1793) and Elliott (1858) life tables. Reviewing the morerecent evidence available to them in the late 1950s, ConradTaeuber and Irene B. Taeuber (1958, 269) found no con-clusive evidence of mortality decline in the first half of thenineteenth century. Yasukichi Yasuba (1962, chap. 3) sawevidence of mortality increase in the few decades preced-ing 1860 associated with increasing urbanization and declin-ing sanitary conditions. Richard Easterlin (1977), in con-trast, argued that increasing per capita income more thanoffset the negative impact of urbanization and cited life ex-pectancy estimates from the Wigglesworth (1793) and Ja-cobson (1957) life tables as evidence of significant mortalitydecline.

Most early observers agreed that the latter half of the cen-tury was characterized by substantial mortality decline; al-though opinions differed about the date of its onset. Taeuberand Taeuber (1958, 269) thought the evidence suggested an“almost continuous” decline in mortality beginning about1850. To conduct their classic study of long-term trends inwhite birth rates, Ansley Coale and Melvin Zelnik (1963)assumed a linear decline in mortality between Jacobson’s(1957) 1849–50 life tables and the 1900–1902 DRA life ta-bles. In separate analyses based on Simon Kuznets’s (1965)census-survival estimates of crude death rates, however, Ed-ward Meeker (1972) dated mortality decline after 1880, whenthe public health and sanitation movement became more

April–June 2010, Volume 43, Number 2 47

TA

BL

E1.

Lif

eTa

bles

Est

imat

esof

Infa

ntM

orta

lity

Rat

e(I

MR

)an

dL

ife

Exp

ecta

ncy

(e)

atSe

lect

edA

ges,

Uni

ted

Stat

es,1

798–

1901

Mal

eFe

mal

eD

ate

ofIn

vest

igat

or(s

)pu

blic

atio

nPo

pula

tion

Peri

odIM

Re 0

e 20

IMR

e 0e 2

0SM

D20

Wig

gles

wor

th17

93Se

lect

edM

assa

chus

etts

tow

ns17

8936

.5a

36.5

a—

Elli

ott

1858

166

Mas

sach

uset

tsto

wns

1855

0.15

5b

39.8

b39

.9b

0.15

5b

39.8

b39

.9b

Bill

ings

1886

Mas

sach

uset

ts18

78–8

20.

181

41.7

42.2

0.15

343

.542

.80.

6B

illin

gs18

86N

ewJe

rsey

1879

–80

0.15

245

.643

.30.

131

48.0

44.5

1.2

Mee

ch18

98U

.S.W

hite

s18

30–6

00.

162

41.0

40.9

0.13

442

.941

.40.

5A

bbot

t18

99M

assa

chus

etts

1893

–97

0.17

244

.141

.20.

147

46.6

42.8

1.6

Glo

ver

1921

Mas

sach

uset

ts18

900.

168

42.5

40.7

0.14

844

.542

.01.

3D

RA

stat

es19

00–0

20.

133

48.1

42.2

0.11

150

.943

.71.

6D

RA

rura

lare

as19

00–0

20.

109

54.0

45.9

0.09

055

.446

.00.

1Ja

ffe

and

Lou

rie

1942

44N

ewE

ngla

ndto

wns

1826

–35

42.9

b42

.9b

Sale

m,M

A,N

ewH

aven

,CT

1826

–36

37.8

b37

.8b

Bos

ton,

New

Yor

kC

ity,P

hila

delp

hia

1826

–37

28.0

b28

.0b

Est

imat

edU

nite

dSt

ates

41.7

b41

.7b

Jaco

bson

1957

Mas

sach

uset

tsan

dM

aryl

and

whi

tes

1849

–50

0.16

140

.440

.10.

131

43.0

41.7

1.6

Vin

ovsk

is19

72M

assa

chus

etts

1859

–61

46.4

44.0

47.3

43.0

−1.0

Kun

ze19

79G

enea

logi

es18

00–1

4—

44.7

—43

.4−1

.318

15–2

9—

44.1

—43

.3−0

.818

30–3

4—

44.4

—47

.83.

418

35–3

9—

44.6

—42

.3−2

.318

40–4

4—

43.5

—41

.7−1

.818

45–4

9—

42.5

—40

.7−1

.818

50–5

4—

42.2

—40

.1−2

.118

55–5

9—

40.8

—40

.5−0

.318

60–6

4—

40.5

—40

.2−0

.318

65–6

9—

40.1

—40

.10.

018

70–7

4—

42.6

—40

.0−2

.618

75–7

9—

43.7

—41

.5−2

.218

80–8

4—

44.4

—41

.8−2

.618

85–8

9—

43.8

—42

.2−1

.6Po

pe19

92Fa

mily

His

tori

es17

80–9

9—

47.4

—45

.6−1

.818

00–0

9—

46.4

—47

.91.

518

10–1

9—

44.6

—44

.4−0

.218

20–2

9—

43.3

—44

.91.

618

30–3

9—

44.6

—44

.60.

018

40–4

9—

41.5

—37

.1−4

.418

50–5

9—

40.8

—39

.5−1

.318

60–6

9—

41.2

—42

.21.

018

70–7

9—

44.3

—42

.2−2

.118

80–8

9—

45.8

—42

.9−2

.9(C

onti

nued

onne

xtpa

ge)

48 HISTORICAL METHODS

TA

BL

E1.

Lif

eTa

bles

Est

imat

esof

Infa

ntM

orta

lity

Rat

e(I

MR

)an

dL

ife

Exp

ecta

ncy

(e)

atSe

lect

edA

ges,

Uni

ted

Stat

es,1

798–

1901

(Con

tinue

d)

Mal

eFe

mal

eD

ate

ofIn

vest

igat

or(s

)pu

blic

atio

nPo

pula

tion

Peri

odIM

Re 0

e 20

IMR

e 0e 2

0SM

D20

Kas

akof

fan

dA

dam

s19

95N

ewE

ngla

ndfa

mili

es17

50–5

9c48

.1(s

ettle

dbe

fore

1650

)17

60–6

9c48

.317

70–7

9c43

.217

80–8

9c44

.517

90–9

9c43

.618

00–0

9c42

.018

10–1

9c40

.718

20–2

9c40

.618

30–3

9c39

.0Fe

rrie

1996

Nat

ive-

born

whi

tes

(wei

ghte

d)18

5045

.4U

rban

(wei

ghte

d)18

5038

.0R

ural

(wei

ghte

d)18

5047

.6Fo

reig

n-bo

rnw

hite

s(w

eigh

ted)

1850

35.7

Urb

an(w

eigh

ted)

1850

30.6

Rur

al(w

eigh

ted)

1850

45.3

Hac

ker

1996

Yal

egr

adua

tes

1790

–182

940

.1L

evy

1996

Mar

ylan

dle

gisl

ator

s17

50–1

764c

34.7

d

Sout

hC

arol

ina

legi

slat

ors

1750

–176

4c31

.7d

Hai

nes

1998

U.S

.whi

tepo

pula

tion

(U.S

.mod

el)

1850

0.22

838

.438

.80.

206

40.6

40.2

1.4

U.S

.whi

tepo

pula

tion

(Wes

tmod

el)

1850

0.19

538

.837

.50.

155

43.5

40.8

3.3

U.S

.whi

tepo

pula

tion

(U.S

.mod

el)

1860

0.18

843

.241

.00.

175

44.1

41.7

0.7

U.S

.whi

tepo

pula

tion

(Wes

tmod

el)

1860

0.16

543

.039

.60.

139

46.2

42.1

2.5

U.S

.whi

tepo

pula

tion

(U.S

.mod

el)

1870

0.18

544

.141

.70.

166

46.4

43.3

1.5

U.S

.whi

tepo

pula

tion

(Wes

tmod

el)

1870

0.15

644

.440

.20.

126

48.5

43.2

3.0

U.S

.whi

tepo

pula

tion

(U.S

.mod

el)

1880

0.21

440

.440

.00.

215

40.6

40.9

0.9

U.S

.whi

tepo

pula

tion

(Wes

tmod

el)

1880

0.18

040

.938

.50.

154

43.8

40.9

2.4

U.S

.whi

tepo

pula

tion

(U.S

.mod

el)

1890

0.15

746

.041

.70.

145

47.4

42.8

1.1

U.S

.whi

tepo

pula

tion

(Wes

tmod

el)

1890

0.14

845

.640

.80.

124

48.9

43.4

2.6

U.S

.whi

tepo

pula

tion

(U.S

.mod

el)

1900

0.12

848

.542

.00.

112

50.7

43.5

1.5

U.S

.whi

tepo

pula

tion

(Wes

tmod

el)

1900

0.13

547

.841

.70.

109

51.7

44.6

2.9

Hac

kere

n.d.

Prin

ceto

ngr

adua

tes

1790

–181

936

.2

Not

e.SM

D=

sex

mor

talit

ydi

ffer

entia

lata

ge20

,defi

ned

asth

efe

mal

elif

eex

pect

ancy

atag

e20

min

usm

ale

life

expe

ctan

cyat

age

20;D

RA

=de

ath

regi

stra

tion

area

.a B

oth

sexe

sco

mbi

ned.

Vin

ovsk

isre

vise

des

timat

e.b B

oth

sexe

sco

mbi

ned.

c Bir

thco

hort

s.d e 2

0es

timat

edfr

ome 2

5(M

aryl

and

legi

slat

ors=

32.2

;Sou

thC

arol

ina

legi

slat

ors=

29.6

).e E

stim

ate

mad

eby

auth

orfr

ombi

ogra

phic

alin

form

atio

npu

blis

hed

inM

cLac

hlan

1976

;Har

riso

n,M

cLac

hlan

,and

Woo

dwar

d19

80;H

arri

son

1981

;Woo

dwar

dan

dC

rave

n19

91;a

ndL

oone

yan

dW

oodw

ard

1991

.

April–June 2010, Volume 43, Number 2 49

effective, whereas Robert Higgs (1973) observed a declinein rural areas from the 1870s.

Beginning in the 1970s, new research considerably clari-fied our understanding of nineteenth-century mortality. Muchof the new research was critical of earlier studies. In a seriesof articles, Maris Vinovskis (1971, 1972, 1978) evaluated theWigglesworth (1793), Jaffe and Lourie (1942), Elliott (1858),and Jacobson (1957) life tables, all of which relied on datafrom Massachusetts. Although the Wigglesworth life tablesuggested a reasonable estimate of life expectancy at birth,Vinovskis (1971, 589) observed that Wigglesworth (1793)lacked adequate data on the age distribution of the towns,which required adjustments amounting to “little more thanintelligent guessing.” Vinovskis also noted that the townscovered by the Wigglesworth life table were not representa-tive of other New England towns in important characteristics,including their relative affluence and degree of urbanization,making it difficult to evaluate the table’s representativeness.Vinovskis (1972, 204–5) faulted Jaffe and Lourie (1942) forrelying on data from many small towns with under-registereddeaths, thus overestimating the significance of the rural-urbandifferential in mortality and understating the overall level ofmorality. Elliott (1858), Vinovskis (1972, 208–10) argued,erred in the opposite direction. To avoid including placeswith deficient record keeping, Elliott eliminated towns witha crude death rate of less than 16 per 1,000. In doing so, how-ever, Elliott likely removed towns whose true death rate waslower than 16 per 1,000 and thus overstated the true levelof mortality. Vinovskis (ibid.) also contended that Elliott’sreliance on just one year of mortality data was problematic,given the era’s high short-term variability in death rates. Fi-nally, Vinovskis demonstrated that Jacobson (1957) failed toconsider contemporary critiques of the 1850 census of mor-

tality, which noted that deaths were unevenly registered, andfailed to consider that the census was taken during a choleraepidemic, resulting in a likely overestimation of mortality de-spite the under-registration of deaths. Given these critiques,it is no surprise that Vinovskis (1978) strongly cautionedagainst inferring mortality trends from the Wigglesworth(1793), Jaffe and Lourie (1942), Elliott (1858), and Jacobson(1957) life tables. Drawing from bills of mortality and stateregistration reports, Vinovskis concluded that there was littletrend in Massachusetts mortality during the first half of thecentury.

Meech’s (1898) life table also received an extensive cri-tique. In a detailed reconstruction and analysis, Michael R.Haines and Roger C. Avery (1980) noted that Meech wasforced to make a number of assumptions to construct hislife table, including the questionable assumptions that theunderenumeration of deaths in the census and the requiredadjustment of gross to net migration were independent ofage. As a result, Haines and Avery concluded that the Meechlife table likely underestimated infant mortality and overesti-mated early childhood mortality; although it gave reasonableresults overall.

Finally, a number of researchers have cautioned againstinferring national mortality patterns from life tablesconstructed for Massachusetts and the 1900–1902 DRA(Easterlin 1977, 133; Condran and Crimmins 1979, 1;Preston and Haines 1991, 49–50; Haines and Preston 1997).Although these tables were based on relatively well-reporteddeath registration data3 and are thus reasonably accurate de-scriptions of the level of mortality and sex- and age-specificmortality patterns in those areas—they are unlikely to berepresentative of the national population. Table 2 comparesthe population of Massachusetts, the 1900–1902 DRA,

TABLE 2. Comparison of Selected Characteristics of Massachusetts and the Original Death Registration Area (DRA) Stateswith the United States as a Whole, 1850 and 1900

1850 1900

U.S. Massachusetts U.S. DRA DRA—“rural” Massachusetts

Total population 100.0 5.3 100.0 26.2 12.0 3.7Percentage urban 16.9 51.6 38.8 60.1 13.2 67.9Percentage foreign born 11.3 18.0 13.8 22.6 14.0 29.7Labor forcePercentage in agriculture 50.5 22.8 35.5 17.6 39.0 5.2Females age 20–49Average number of own children in household 2.29 1.46 1.87 1.49 1.63 1.31

Notes. The original death registration states of 1900 consisted of the six New England states (Connecticut, Maine, Massachusetts, New Hampshire,Rhode Island, and Vermont), Indiana, Michigan, New Jersey, New York, and the District of Columbia. Rural areas of the DRA were initially definedas places with less than 8,000 inhabitants. Later census definitions changed the urban/rural threshold to places of 2,500 inhabitants.Source: 1850 and 1900 IPUMS samples from Steven Ruggles, Matthew Sobek, J. Trent Alexander, Catherine A. Fitch, Ronald Goeken, Patricia Kellyet al., Integrated Public Use Microdata Series: Version 4.0 (Minneapolis, MN: Minnesota Population Center, 2009) [producer and distributor].

50 HISTORICAL METHODS

and the overall United States in 1850 and 1900 using datafrom the 1850 and 1900 Integrated Public Use MicrodataSeries (IPUMS) samples (Ruggles, Sobek, Alexander, Fitch,Goeken, Kelly et al. 2009). Massachusetts was much moreurban than the rest of the nation, had a proportionatelylarger and more rapidly growing foreign-born populationand had a much lower proportion of its labor force engagedin agriculture (the state was one of the first to industrializein the early nineteenth century). Moreover, Massachusettsenjoyed one of the best public health systems in the nationand was the leading state in the employment of women inthe labor force and in the fertility transition. Massachusettswomen age 20–49, for example, had an average of just1.5 co-residing own-children in 1850 and 1.3 in 1900,suggesting fertility rates approximately one-third lower thanthat of the nation as a whole. Thus, although Massachusettshas the best available mortality data for the nineteenthcentury, its level, trend, and age pattern of mortality areunlikely to be representative of the United States as a whole.

Table 2 also indicates that the population of the 1900–1902DRA was not representative of the nation. The initial DRAincluded the six New England states, New York, NewJersey, Michigan, Indiana, and the District of Columbia. Al-though the 1900–1902 DRA was much larger than the stateof Massachusetts—representing about 26.2 percent of thenation’s population in 1900 compared with only 3.7 percentfor Massachusetts—it varied from the rest of the nation insimilar, if less dramatic, ways. The DRA was more urbanthan the United States as a whole and its population includeda higher proportion of foreign-born residents and a lowerproportion of agricultural workers. Women in the DRA hadan average of 20 percent fewer co-residing children in thehousehold than women in the nation as a whole. It is note-worthy, however, that differences between the rural parts ofthe 1900–1902 DRA and the rest of the nation were lessextreme. Rural parts of the DRA included about the sameproportion of foreign-born residents and workers engaged inagriculture. Fertility rates in rural areas of the DRA weremuch closer to the national average.

Fortunately, just as confidence in the representativenessand accuracy of existing life tables was falling, new researchsignificantly enhanced our understanding of nineteenth-century mortality trends. Beginning with Michael Haines’sanalysis of the United States censuses of mortality (1979) andKent Kunze’s (1979) and Robert W. Fogel’s (1986) demo-graphic analyses of family genealogies, life expectancy es-timates have accumulated for each decade of the nineteenthcentury. Clayne Pope’s (1992) study of family histories isperhaps the most significant contribution for the first half ofthe century while Haines’s (1998) construction of life tablesfor the white, black, and overall populations is the most im-portant work for the last half of the century; although impor-tant research has also been published by Richard S. Meindland Alan C. Swedlund (1977); Gretchen A. Condran andEileen Crimmins (1979, 1980); Crimmins (1980); Daniel

Scott Smith (1982, 2003); Condran and Rose A. Cheney(1982); Cheney (1984); Stephen Kunitz (1984); Condran(1987); Richard Steckel (1988); Barbara J. Logue (1991);Eric Leif Davin (1993); Alice Kasakoff and John Adams(1995, 2000); Joseph Ferrie (1996, 2003); Antonio McDanieland Carlos Grushka (1995); J. David Hacker (1997); John E.Murray (1997, 2000); Chulhee Lee (1997, 2003); Susan I.Hautaniemi, Alan C. Swedlund, and Douglas L. Anderton(1999); Anderton and Susan Hautaniemi Leonard (2004);and Jeffrey K. Beemer, Anderton, and Hautaniemi Leonard(2005).

Several of the newer studies—including those by Haines(1979, 1998), Ferrie (1996, 2003), and Condran and Crim-mins (1979, 1980)—have relied on retrospective mortalitydata collected by the Census Office/Bureau of the Censusbetween 1850 and 1900. Beginning In 1850, census mar-shals were instructed to record the name of every person inthe household who died in the year prior to the census, aswell as the person’s age, sex, race, marital status, occupa-tion, and cause of death. The collected data were tabulatedand published in separate mortality volumes. These tabulateddata appear to be tailor-made for the construction of life ta-bles: the number of deaths at each age and sex can be used asthe numerator in the calculation of age-specific death rateswhile the denominator for the midyear population in each agegroup can be obtained (with some adjustment for populationgrowth in the preceding year) from the regular census enu-meration. Census officials, however, immediately discernedthat the mortality data were underreported by approximately40 percent. Life tables could only be constructed by makinglarge (and ultimately unknowable) adjustments to the num-ber of deaths reported at each age (see, e.g., Elliott 1874’s“approximate” life table for the 1870 population). Differen-tial mortality could be examined only by assuming no dif-ferentials in undercounts. Census officials clearly believed,however, that the undercount varied by region, urban/ruralresidence, and between long and recently settled states. J.D. B. De Bow (1855, 8), superintendent of the 1850 census,contended that state differentials in death rates “show not somuch in favor of or against the health of either, as they do,in all probability, a more or less perfect report of the mar-shals. Thus it is impossible to believe Mississippi a healthierState than Rhode Island.” Despite this disappointment, andthe urging of some census officials to drop the expensiveundertaking, the mortality information was deemed usefulenough to continue its collection and publication through the1900 census. More questions were added and, beginning in1880, the information was supplemented with death recordsfrom states with available registration data (Condran andCrimmins 1979).

Retrospective mortality data were undercounted for sev-eral reasons. Most obviously, solitary households left no onebehind to report the death to an enumerator. The death of ahousehold member of a larger family, especially the house-hold head, often led to the dissolution of the household.

April–June 2010, Volume 43, Number 2 51

Respondent error also led to undercounting. Deaths of in-fants and the elderly were underreported, and deaths occur-ring 6–12 months prior to the census enumeration were lesslikely to be reported than deaths occurring 0–6 months priorto the count (Condran and Crimmins 1979; Ferrie 2003). Inan early comparison of death reporting between the 1880census of mortality and the early death registration states ofMassachusetts and New Jersey, J. S. Billings (1885, xlii) ob-served that “the proportion of deaths omitted in the enumera-tors’ returns increases in a tolerably regular manner as we goback in time from the date of enumeration.” Billings calcu-lated that census undercounting of deaths in the 1880 censusincreased from about 17 percent of all registered deaths 0–6months prior to the census to 30 percent of deaths registered6–12 months prior.

Despite severe underenumeration, researchers have madecreative use of the mortality censuses. By matching deathsregistered in the DRA to deaths registered by the mortalitycensuses, Condran and Crimmins (1980, 188–90) were ableto estimate undercounts in both sources and make a more ac-curate comparison of urban and rural mortality. Ferrie (2003)used surviving original manuscript returns from the 1850 and1860 mortality census to link decedents to their household oforigin and was thus able to investigate mortality differentialsby age, occupation, wealth, nativity, migration status, andhousehold size. The use of linked microdata allowed Ferrie(1996) to make another important innovation: by relying onlyon deaths reported in the six months prior to the census, hewas able to significantly reduce respondent recall error andconstruct adult life expectancy estimates for white males byregion, urban/rural residence, and nativity. The results sug-gest a substantial advantage in life expectancy at age 20 forwhite males living in rural areas and for native-born males.

Haines (1998) has made the most significant attempt touse the mortality censuses to construct life tables. He beganby observing that the underreporting of deaths for individ-uals age 5–9, 10–14, and 15–19 appeared to be small. Byfitting age-specific mortality rates for these age groups tomodel life tables, Haines was able to avoid relying on agegroups experiencing substantial underreporting of deaths andto construct life tables for the white, black, and total popu-lations by sex for each census year between 1850 and 1900.These tables are clearly superior to their predecessors and amajor step forward in our understanding of late nineteenth-century mortality. Despite some concern about regional andtemporal differences in undercounting, mortality data werecollected for the entire nation. Thus, with the exception ofMeech’s (1898) 1830–60 life table, Haines’s tables can beconsidered the only nationally representative life tables forthe nineteenth-century United States. The availability of lifetables every 10 years between 1850 and 1900 also filledmany of the gaps between existing life tables. Contrary tomost prior assumptions, Haines’s life tables indicated thatmortality did not begin its secular decline until relatively latein the century. Life expectancy at birth was variable with-

out trend between 1850 and 1880—ranging between 38.3and 44.0 years for both sexes combined. Between 1880 and1900, however, life expectancy at birth increased from 39.4to 47.8 years (U.S. model, both sexes combined).

Researchers relying on the Haines (1998) life tables needto be aware of a few potential problems with their interpreta-tion and use. First, as Haines noted, the life tables representmortality conditions only in the year preceding each decen-nial census and thus may not be representative of the periodor decade in which they nominally represent. Haines’s 1850life table, for example, like Jacobson’s 1850 life table, mayoverstate mortality because of the 1849 cholera epidemic.Interpolating between Haines’s life tables for the intercensalperiods between 1850 and 1880 suggests that individuals liv-ing in the 1860s enjoyed the period’s lowest mortality. Theopposite is likely true. During the 1860s the United Statessuffered four years of the Civil War, a major and prolongeddepression in the postwar South, and, in 1867, another majorepidemic of cholera. The war alone is believed to have re-sulted in the death of approximately 8 percent of white menaged 13 to 43 in 1860 (Vinovskis 1989). Finally, users ofthe Haines life tables should also be aware that the shapeof age-specific mortality rates are strongly influenced by theHaines’s choice of models: model “west” of the Princetonregional model life tables and a “U. S model” derived fromthe 1900–1902 DRA life table. As discussed below, there isevidence that these models fail to accurately describe the ageprofile of mortality in the nineteenth-century United States,particularly for women in their childbearing years. Despitethese qualifications, Haines’s life tables are a major point ofreference for the latter half of the nineteenth century.4

The only studies of life expectancy prior to 1850 ap-proaching the geographic coverage of the Haines life tablesare genealogical-based estimates of adult life expectancy byKunze (1979), Fogel (1986), and Pope (1992), and meanage at death estimates by Kasakoff and Adams (1995). Be-cause genealogies observe individuals from birth to death,cohort life expectancies are easily calculated. Period esti-mates can also be made by observing deaths and years ofexposure over a given interval, typically a decade. Decenniallife expectancy estimates thus reflect mortality over the en-tire decade, not just a single year. Because individuals arefollowed over time and space, genealogical data allow theapplication of event-history methods and more sophisticatedanalyses. Kasakoff and Adams (2000), for example, wereable to examine the impact of migration on subsequent mor-tality. There are several drawbacks to the use of genealogicaldata for estimating mortality, however, including substantialunderreporting of infant and childhood deaths (thus limitingestimates to adult life expectancy), underreporting of femaledeaths, a bias toward larger and longer-lived families, lack ofcoverage of the nation’s black and foreign-born populations,small sample sizes for early birth cohorts, a bias toward mar-ried individuals who reproduce, and a bias toward familiesoriginating in the Northeast and living in the North. Kasakoff

52 HISTORICAL METHODS

38

40

42

44

46

48

50

1900189018801870186018501840183018201810180017901780

Period

Life

exp

ecta

ncy

at a

ge 2

0

Kunze

Pope

Haines (U.S. model)

Kasakoff and Adams

FIGURE 1. Male life expectancy at age 20.

and Adams’s data set, for example, was drawn from ninepublished genealogies of families whose ancestors settledin seventeenth-century New England. Although nineteenth-century descendents of the nine families can be found in allparts the nation, they were primarily located in the nation’snorthern census regions. Kunze’s (1979) and Pope’s (1992)data sets were drawn to be more representative of the re-gional distribution of the United States population. Althoughnot perfectly representative, the geographic coverage of bothsamples is reasonably representative of the overall popula-tion.

Figure 1 plots estimates of white male life expectancy atage 20 by Kunze (1979), Pope (1992), and Haines (1998),and mean age at death estimates for white males known tosurvive to age 20 by Kasakoff and Adams (1995).5 Fourobservations can be made. First, the three genealogical stud-ies report very high adult male life expectancies in the lateeighteenth and early nineteenth centuries; if the estimatesare correct, adult life expectancies in the United States atthe turn of the nineteenth century were the highest in theworld and were not again exceeded in the United States un-til circa 1920, approximately four decades after the onset ofsecular mortality decline. Second, life expectancy estimatesby Haines (1998) are about three years lower, on average,than those reported in the genealogical studies in the decadesin which they overlap and can be reliably compared. Third,although there is much variation in each study’s sources,methods, and results, it is nonetheless clear from figure 1that the genealogical-based studies support Haines’s con-

tention that mortality did not begin its secular decline untillate in the century. Finally, all three genealogy-based studiessuggest a significant increase in mortality in the antebellumera, especially in the three decades between 1830 and 1860.White male life expectancy at age 20 was approximately sixyears lower at mid-century than it was in the late eighteenthcentury.

If correct, a substantial mid-century increase in mortalityrepresents a paradox; based on an assessment of the expectedimpact of urbanization, public health, and economic growth,Easterlin (1977) had hypothesized a substantial mortality de-cline before 1880. Although urbanization increased duringthe period, facilitating the spread of infectious disease andhigher mortality, Easterlin noted that the percentage of theUnited States population living in urban areas remained mod-est until late in the century. The urban population, for exam-ple, was just 28.2 percent in 1880.6 Given an expected 10-yearurban-rural differential in life expectancy—an approximatedifferential suggested by several studies—and assuming anegligible role of public health before 1880, Easterlin esti-mated that urbanization between 1800 and 1880 reduced lifeexpectancy at birth 2.1 years, all else being equal. The nega-tive effect of urbanization, however, was more than compen-sated for by increases in the standard of living. Real nationalincome per capita increased dramatically in the period be-fore 1880, leading to significant improvements in diet andhousing.7 By assuming a theoretical relationship betweenlife expectancy and per capita income suggested by cross-sectional national data for the twentieth century (Preston

April–June 2010, Volume 43, Number 2 53

1975), Easterlin estimated that growth in real income in theperiod 1800–80 should have increased life expectancy by14 years. Together with the negative impact of urbanization,Easterlin’s model suggested that life expectancy at birth in-creased 11.9 years between 1800 and 1880.

Although a reasonable theoretical argument for decliningmortality, Easterlin conceded serious doubts in estimates ofnational income in the period before 1840, the appropri-ateness of using the relationship between income and lifeexpectancy in the twentieth century to infer the relationshipa century earlier, the possibility that public health worsenedbetween 1800 and 1880, and the need for more empiricalresearch. Given these doubts, new estimates documentinga mid-nineteenth-century mortality increase cannot be dis-missed on theoretical grounds. Moreover, indirect supportfor an “antebellum paradox” of increasing mortality dur-ing a period of strong economic growth is provided by newresearch on the anthropometric history of the nineteenth-century United States. Fogel (1986, 464–67) first called at-tention to the positive long-run correlation between cohortlife expectancy at age 10 and the final achieved heights ofwhite men. Both series decline in the early to mid–nineteenthcentury and increase late in the century. Accumulating ev-idence from other sources confirms a substantial decline inmale height for cohorts born in the mid–nineteenth century.Dora L. Costa and Richard H. Steckel (1997, 72), for ex-ample, documented a decline in stature among native-bornwhite males from a mean of 173.5 centimeters in the 1830birth cohort to 169.1 in the 1890 cohort, followed by a sub-stantial and sustained increase in heights for cohorts born inthe twentieth century. While identification of the causes ofthe decline has been difficult—hypotheses include deterio-rating diets, a worsening disease environment, the negativeimpact of early industrialization and urbanization, increas-ing rates of internal migration, and rising inequality—all re-searchers have agreed that heights declined significantly. In arecent investigation of the link between antebellum mortality,heights, and net nutrition, Michael R. Haines, Lee A. Craig,and Thomas Weiss (2003) have pointed to the importance ofan increasing nationalization and internationalization of thedisease environment. Regardless of the ultimate causes, thepositive correlation between stature and life expectancy isadditional evidence that the decline of life expectancy in themid-nineteenth century reported by recent studies reflects areal increase in mortality.

There are ample reasons to remain skeptical of the overalllevel of life expectancy reported by the genealogical studiesand the size of the suggested decline, however. Genealog-ical records suffer from two types of bias: a selection biasincurred by selecting data from demographically success-ful, native-born families, and a censoring bias incurred byexcluding individuals without complete birth and death in-formation from the analysis. Although these biases act inopposite directions—selection bias causes life expectancyestimates to be biased upwards while the censoring bias typ-

ically imparts a downwards bias—it is unlikely that theycounteract each other perfectly and consistently.8

Adult life expectancy estimates based on genealogicalsources tend to be much higher than estimates based on othertypes of sources, suggesting that selection bias dominates.Between 1785 and 1814, graduates of Yale College—an eliteNew England population with nearly complete, high-qualitydemographic data—had a life expectancy at age 20 of 40.4years; Kunze’s (1979) and Pope’s (1992) genealogical es-timates for the same period are much higher, in the mid-to-upper 40s (Hacker 1996, 121). Adult life expectancies ofother elite colonial populations were even lower than thatenjoyed by Yale graduates and were especially low in thecolonial South. Life expectancy at age 20 was 36.2 years formen graduating from Princeton College between 1709 and1819; 34.7 years for Maryland legislators born between 1750and 1764; and 31.7 years for South Carolina legislators born1750–64 (Levy 1996; Hacker 1996). Even if we assume nosignificant socioeconomic status differentials in adult mor-tality, these studies suggest that genealogical sources over-estimate male life expectancy at age 20 at the turn of thenineteenth century by 5–10 years or more. Daniel S. Levy(1996) indicates that lower life expectancy in the colonialSouth was rapidly disappearing by the late eighteenth cen-tury, however, suggesting that the overstatement of male lifeexpectancy by genealogical sources was on the lower side ofthat range, perhaps six years in the last decade of the century.

The tendency of genealogical estimates to overstate adultmale life expectancy appears to have been lower in the mid-and late nineteenth century. In the two periods where they canbe compared—1850–60 and 1870–90—Kunze’s (1979) andPopes’ (1992) combined estimates of male life expectancyat age 20 are 2.73 years higher, on average, than Haines’s(1998) estimates.9 Male life expectancy estimates derivedwith two-census methods suggest a similar differential.Table 3 shows the results of applying the Samuel Preston andNeil Bennett’s (1983) two-census method to the native-bornwhite population enumerated in the 1850 and 1860 IPUMScensuses. The method assumes the population is closed tomigration, a reasonable assumption for the native-born pop-ulation of the nineteenth-century United States. Although theresults may be biased by differential undercounting and theaccuracy of age reporting in the two censuses, the resultinglife table suggests that genealogical estimates overstate malelife expectancy at age 20 in the 1850s by about 3.5 years. Un-fortunately, substantial underenumeration of the 1870 census(see Anderson 1988, 78–82; Steckel 1991) limits comparisonto the decade 1850–60.

The lower tendency of genealogical sources to overstatelife expectancy in the mid- and late nineteenth century maybe the result of greater migration censoring in the genealogi-cal data. The opening of the trans-Appalachian west with theTreaty of Paris in 1783, the defeat of the Pan-Indian alliancein 1793, land reforms in the early nineteenth century, andthe transportation revolution of the 1830s likely increased

54 HISTORICAL METHODS

TA

BL

E3.

App

licat

ion

ofth

eP

rest

on-B

enne

ttC

ensu

s-B

ased

Met

hod

toth

eN

ativ

e-B

orn

Whi

teP

opul

atio

nby

Sex:

1850

–60

Star

tof

age

inte

rval

(x)

Popu

latio

non

June

1,18

50Po

pula

tion

onJu

ne1,

1860

Ave

age

popu

latio

nIn

terc

ensa

lgr

owth

rate

5r x

Cum

ulat

edgr

owth

rate

Stat

iona

rypo

pula

tion

inte

rval

5L

x

Stat

iona

rypo

pula

tion

abov

eag

ex,

Tx

Num

ber

surv

ivin

gto

age

xin

stat

iona

rypo

pula

tion

Est

imat

edlif

eex

pect

ancy

atag

ex,

e x

Mal

es0

1,42

3,46

22,

053,

500

1,73

8,48

10.

0366

——

——

—5

1,31

6,43

61,

698,

039

1,50

7,23

80.

0254

0.06

358

1,60

6,18

7—

——

101,

147,

038

1,44

6,00

51,

296,

522

0.02

310.

1850

31,

560,

040

14,08

0,17

731

6,62

344

.515

956,

661

1,23

3,98

41,

095,

323

0.02

540.

3064

71,

488,

128

12,52

0,13

730

4,81

741

.120

830,

860

1,05

5,63

294

3,24

60.

0239

0.42

987

1,44

9,81

811

,03

2,00

829

3,79

537

.625

654,

370

855,

794

755,

082

0.02

680.

5567

11,

317,

558

9,58

2,19

127

6,73

834

.630

548,

139

678,

327

613,

233

0.02

130.

6769

71,

206,

791

8,26

4,63

225

2,43

532

.735

452,

270

584,

639

518,

455

0.02

570.

7943

31,

147,

321

7,05

7,84

223

5,41

130

.040

372,

137

471,

681

421,

909

0.02

370.

9176

71,

056,

228

5,91

0,52

122

0,35

526

.845

310,

999

400,

900

355,

950

0.02

541.

0403

11,

007,

371

4,85

4,29

320

6,36

023

.550

256,

448

332,

500

294,

474

0.02

601.

1686

194

7,47

83,

846,

923

195,

485

19.7

5516

5,10

222

5,94

019

5,52

10.

0313

1.31

185

725,

977

2,89

9,44

416

7,34

617

.360

146,

113

194,

447

170,

280

0.02

861.

4616

073

4,39

22,

173,

467

146,

037

14.9

6593

,57

312

1,78

510

7,67

90 .

0263

1.59

881

532,

703

1,43

9,07

512

6,71

011

.470

61,01

977

,37

869

,19

90.

0237

1.72

397

387,

976

906,

372

92,06

89.

875

35,36

446

,19

440

,77

90.

0267

1.85

003

259,

355

518,

395

64,73

38.

080

20,51

524

,69

622

,60

60.

0185

1.96

310

160,

982

259,

040

42,03

46.

285

+10

,91

313

,79

812

,35

60.

0234

2.07

147

98,05

998

,05

9—

—

Fem

ales

01,

383,

318

2,02

1,27

91,

702,

299

0.03

79—

——

——

51,

266,

758

1,67

4,05

81,

470,

408

0.02

790.

0696

41,

576,

457

——

—10

1,10

6,85

61,

377,

428

1,24

2,14

20.

0219

0.19

391

1,50

7,94

313

,61

0,91

630

8,44

044

.115

968,

287

1,26

0,28

11,

114,

284

0.02

630.

3143

71,

525,

903

12,10

2,97

330

3,38

539

.920

812,

808

1,07

0,75

094

1,77

90.

0275

0.44

906

1,47

5,61

410

,57

7,07

130

0 ,15

235

.225

634,

318

791,

899

713,

109

0.02

220.

5733

31,

265,

175

9,10

1,45

727

4,07

933

.230

505,

521

638,

730

572,

126

0.02

340.

6871

81,

137,

446

7,83

6,28

224

0,26

232

.635

418,

015

531,

753

474,

884

0.02

400.

8057

21,

062,

940

6,69

8,83

522

0,03

930

.440

349,

205

447,

572

398,

389

0.02

480.

9278

31,

007,

534

5,63

5,89

520

7,04

727

.245

288,

765

355,

900

322 ,

333

0.02

091.

0420

491

3,81

44,

628,

361

192,

135

24.1

5023

8,86

830

9,83

127

4,35

00.

0260

1.15

924

874,

487

3,71

4,54

717

8,83

020

.855

161,

663

213,

946

187,

805

0.02

801.

2942

168

5,12

92,

840,

060

155,

962

18.2

6013

7,92

419

0,05

516

3,99

00.

0320

1.44

429

695,

124

2,15

4,93

113

8,02

515

.665

91,75

011

6,07

210

3,91

10.

0235

1.58

311

506,

056

1,45

9,80

712

0,11

812

.270

63,44

882

,47

672

,96

20.

0262

1.70

737

402,

343

953,

752

90,84

010

.575

36,27

548

,48

242

,37

90.

0290

1.84

534

268,

267

551,

409

67,06

18.

280

20,10

424

,19

822

,15

10.

0185

1.96

410

157,

903

283,

142

42,61

76.

685

+15

,15

416

,39

515

,77

50.

0079

2.07

183

125,

239

125,

239

——

Sour

ce:1

850–

60IP

UM

Ssa

mpl

esfr

omR

uggl

esbe

fore

the

etal

.Ste

ven

Rug

gles

,Mat

thew

Sobe

k,J.

Tre

ntA

lexa

nder

,Cat

heri

neA

.Fitc

h,R

onal

dG

oeke

n,Pa

tric

iaK

elly

etal

.,In

tegr

ated

Pub

lic

Use

Mic

roda

taSe

ries

:Ve

rsio

n4.

0(M

inne

apol

is,M

N:M

inne

sota

Popu

latio

nC

ente

r,20

09)

[pro

duce

ran

ddi

stri

buto

r].

April–June 2010, Volume 43, Number 2 55

the level and typical distance of internal migration. In theseven decades between 1790 and 1860 the area of the UnitedStates increased from 891,364 to 3,021,295 square miles andthe number of states from 16 to 33, with the greatest in-creases between 1840 and 1860 (Anderson 1988, 241, 246).The mean center of population moved further west in thetwo decades between 1840 and 1860—135.4 miles—than inany other comparable period in United States history (U.S.Bureau of the Census 1921, 34). Although we cannot be sureof the size and timing of the effect—Kunze (1979) and Pope(1992) do not report the percentage of their study popula-tions with missing death dates by decade—migrants are morelikely to be lost from observation. Without adequate attemptsto adjust the population at risk, an increase in the percentageof right-censored cases would bias life expectancy estimatesdownwards, all else being equal.10

Selection bias may also have been less important in thenineteenth century than in the eighteenth century. If selectionbias is a function of the propensity of a long-lived ancestor toproduce a large number of descendents—thus increasing theodds of producing a future genealogist—the life expectancyof earlier birth cohorts is more critical to the subsequent num-ber of descendants than that of later, larger cohorts, where wecan expect more heterogeneity. Put another way, the chancesthat a couple will produce any descendents beyond a fewgenerations is low if their mortality or the mortality of theirchildren and grandchildren is high. If mortality is low inthe first few generations, however, the chances are very highthat there will be thousands of descendants (and many po-tential genealogists) regardless of the level of mortality insubsequent generations (for a general discussion of these is-sues with regards to Chinese demographic history see Zhao2001).

Despite concerns about selection and censoring biases,it is clear from recent studies that mortality increased sig-nificantly after 1830 and remained relatively high until the1870s, at which point it began its long and sustained de-cline. Although genealogical-based estimates of male lifeexpectancy are biased upwards, especially in the eighteenthand early nineteenth centuries, they represent our best sourcefor decennial trends in life expectancy between 1790 and1890. With care, the estimates can be combined and adjustedto construct a reasonable series of adult life expectancies.

Table 4 attempts such a series by averaging the Kunze(1979) and Pope (1992) estimates of male life expectancyin each decade and adjusting the combined estimates by acorrection factor suggested by comparisons with other stud-ies. The second column shows the average Kunze and Popeestimate of male life expectancy at age 20 for each decadebetween 1790 and 1890.11 The third column shows a sug-gested correction factor for each of these decades: –6 yearsin the late eighteenth century (suggested by comparisons withthe graduates of Yale College and other special populations)and –2.73 years in the mid- to late nineteenth century (sug-gested by comparison to Haines’s [1998] life tables). The

TABLE 4. Suggested Best Estimates for Male LifeExpectancy at Age 20 (e20)

Period

Male e20 fromgenealogical-based

studies

Suggestedcorrection

factorAdjustedmale e20

1790–99 47.4 6.0 41.41800–09 45.8 –5.5 40.31810–19 44.6 –4.9 39.71820–29 44.1 –4.4 39.71830–39 43.8 –3.8 39.91840–49 42.6 –3.3 39.31850–59 41.2 –2.7 38.41860–69 40.8 –2.7 38.01870–79 43.7 –2.7 41.01880–89 45.0 –2.7 42.21890–99 n.a. n.a. 43.2 a

aInterpolated from the 1880–89 adjusted estimate and a weightedaverage of the 1900–1902 DRA and rural DRA life tables.Sources: Kent Kunze, The Effects of Age Composition and Changesin Vital Rates on Nineteenth-Century Population Estimates from NewData (Salt Lake City, UT: Department of Economics, Universityof Utah, 1979); and Clayne L. Pope, “Adult Mortality in Americabefore 1900: A View from Family Histories,” in Strategic Factors inNineteenth Century American Economic History: A Volume to HonorRobert W. Fogel, ed. Claudia Goldin and Hugh Rockoff, 267–96(Chicago: University of Chicago Press).

correction factor is interpolated between the 1790s and the1850s, corresponding to suspected trends in regional migra-tion. The adjusted male life expectancy estimates are shownin the fourth column. Because Pope’s and Kunze’s genealog-ical estimates for adult life expectancy end with the 1880–89decade, the suggested male estimate for the period 1890–99was obtained by interpolating between the 1880–89 estimateand an estimate obtained from the 1900–1902 overall andrural DRA life tables, weighted to reflect the national levelof urbanization. (The 1900–1902 DRA life tables and theirweighting to reflect national levels of urbanization is subse-quently described in more detail.)

Correction factors for the early part of the century areclearly larger and more speculative than those in the secondhalf of the nineteenth century. Indirect evidence suggests thatthey are approximately correct, however. Given the age struc-ture of the population reported in the United States censusof 1800, the adjusted estimates in the fourth column implya crude birth rate for the white population of 51.5 birthsper 1,000 inhabitants. The unadjusted estimate, however,would imply a crude birth rate of 45.6 per 1,000, whereasa 2.73-year adjustment would imply a birth rate of 47.7 per1,000. Contemporary observers and twentieth-century de-mographers have agreed that the birth rate at the turn of thenineteenth century was between 50 and 57 per 1,000, stronglysuggesting that the six-year adjustment is justified (Grabil,

56 HISTORICAL METHODS

Kiser, and Whelpton 1958, 5; McClelland and Zeckhauser1982, 71).12 Although based in part on trends in internalmigration and the known impact of migration censoring onmortality estimates, and in part on the observed bias in thegenealogical-based estimates of life expectancy compared toother sources, the linear interpolation of the adjustment factorbetween the 1790s and 1850s is also speculative. As a result,life tables constructed from these estimates will have a largermargin of error than life tables constructed from estimatesfor the latter part of the century.

The adjusted estimates shown in the fourth column suggestthat male life expectancy at age 20 declined approximatelythree years between 1790–99 and 1850–59. Male life ex-pectancy continued to decline in the 1860s, due largely to theimpact of the Civil War. Thereafter, life expectancy beganits long-term, sustained increase. It is unlikely that mortalitywas under significant human control until circa 1880, how-ever. The adjusted series suggests that male life expectancy atage 20 did not exceed its level in the late eighteenth centuryuntil the 1880s.

The suggested series indicates a more moderate declinein antebellum life expectancy than the six-year decline sug-gested by the unadjusted genealogical estimates. The de-cline is still large, however, and remains a puzzling aspect ofnineteenth-century United States demographic history. Thesuggested revisions shown here do not negate scholars’ char-acterization of the decline as an “antebellum paradox” or theneed for more research on the causes of declining health andlongevity during a period of rapid economic growth.

Sex Differentials in Adult Life Expectancy

Estimating female life expectancy at age 20 using ge-nealogical records is a major challenge. Because women ap-pear less often in public records and change their surname atmarriage, they disappear from observation more frequentlythan men. And because genealogical records do not recordwhen right-censored individuals exit observation, female es-timates of life expectancy are based on fewer cases and sub-ject to more censoring biases than male estimates.13

Difficulties determining when women entered and exitedobservation and small sample sizes in each decade likelyexplain the highly variable sex differentials in adult life ex-pectancy reported by Kunze (1979) and Pope (1992) (seetable 1). Pope reported that women experienced a 1.6-yearadvantage in life expectancy at age 20 in the 1820s and a4.4-year disadvantage in the 1840s. Kunze reported that fe-males had a 3.4-year advantage in the period 1830–34 anda 2.3-year disadvantage in 1835–39. Such rapid shifts in sexdifferentials in life expectancy are likely spurious and relatedto poor data quality.

Unfortunately, there are few studies of eighteenth- andearly nineteenth-century female life expectancy that canbe used to evaluate potential biases in Pope’s (1992) andKunze’s (1979) estimates. Female life expectancy estimates

derived using other sources and methods (e.g., estimates fromcommunity-based reconstruction studies) are also based onincomplete data and subject to substantial selection and cen-soring biases (see Hacker 1997, for a summary of existingstudies and discussion of potential biases). Life expectancyof women married to Yale graduates at age 20, for exam-ple, was five years lower in the late eighteenth than theestimates reported by Kunze (1979) and Pope (1992). Al-though the difference is approximately equal to the differ-ence observed between the genealogical estimates and thelife expectancy of Yale graduates, more than one-in-fourYale wives had an unknown date of death, rendering anassessment of bias in the genealogical estimates uncertain.Given different assumptions about the mortality experiencesof women with a missing death date, the life expectancy ofYale wives at age 20 may have been one year higher or lower(Hacker 1996, 83, 98). Much higher proportions of missingdata and margins of error characterize other late eighteenth-and early nineteenth-century estimates of female lifeexpectancy.

For the late nineteenth century, Kunze’s (1979) and Pope’s(1992) estimates of the female life expectancy can be com-pared with Haines’s (1998) estimates. The comparison indi-cates that Kunze’s (1979) and Pope’s (1992) combined esti-mates for white females at age 20 are slightly lower (–0.36years) than Haines’s estimates in the years in which they canbe reliably compared. This is in sharp contrast to the com-parison with Haines’s estimate for white males, in which thegenealogical-based estimates were substantially higher (2.73years at age 20). Given the high proportion of missing deathrecords for women in genealogies, the difficulties determin-ing when women entered and existed the at-risk population,and the highly variable sex differentials in life expectancy re-ported by Kunze and Pope, it is tempting to conclude that thisdiscrepancy is due entirely to bias in estimating female lifeexpectancy from genealogical data. Some portion of the dif-ference in the male and female comparisons with Haines’slife table estimates may be caused by Haines’s choice ofa model life table system, however. This possibility is ex-plored in the subsequent section examining age patterns innineteenth-century mortality.

Regardless of the ultimate cause, poor data quality, incon-sistent results, and the lack of an independent assessment ofpotential bias strongly suggests that determination of the levelof and trend in female life expectancy is best inferred frommale estimates. This section discusses sex differentials innineteenth-century life expectancy, suggests a best estimatefor the differential at age 20 in each decade and calculates theresulting series of female life expectancy from the adjustedmale estimates shown in table 4. The sex differential is as-sumed to be constant before 1860, after which fertility andmortality decline are assumed to have contributed to morerapid female gains in life expectancy relative to male gains(see Preston 1976, chap. 6, for a discussion of the impact ofmortality decline on sex differentials in mortality). Estimates

April–June 2010, Volume 43, Number 2 57

are made separately for the 1860s to account for excess malemortality during the Civil War.

The best estimate of the sex differential in life expectancyfor the period before 1860 and the best estimate for eachdecade after 1870 are not obvious from existing studiesof nineteenth-century U.S. mortality. Kunze’s (1979) andPope’s (1992) estimates suggest a male advantage in life ex-pectancy at age 20 while Haines’s life tables suggest a femaleadvantage. On average, the combined Pope and Kunze esti-mates of male and female life expectancies at age 20 indicatea 0.9-year male advantage before 1860. For census years1850 and 1860, Haines’s U.S. model life tables suggest anaverage female advantage in life expectancy at age 20 of 1.1years (Haines’s life tables based on Princeton model westlife tables indicate a 2.9-year female advantage).

These contrasting results persist in the postwar era.Kunze’s (1979) and Pope’s (1992) results indicate that malesenjoyed a 2.4-year advantage, on average, in the 1870s and1880s while Haines’s U.S. model life tables indicate a 1.3-year female advantage (2.7 years using model West). At thebeginning of the twentieth century, the 1900–1902 DRA lifetable shows a 1.6-year female advantage in life expectancyat age 20, which is in close agreement with Haines’s U.S.model. The close agreement is not surprising, of course;Haines’s U.S. model life tables are based on the age pattern ofmortality in the 1900–1902 DRA. The life table constructedfor the rural parts of the 1900–1902 DRA, however, showsa female advantage in life expectancy at age 20 of just 0.1years, closer to the implied sex differential in the combinedPope and Kunze estimates.

The different sex differentials in adult life expectancy ob-served in the overall and rural DRA life tables hint thatmales may have enjoyed higher adult life expectancies inthe more rural past. Such a conclusion is supported by thedemographic literature on nineteenth-century European pop-ulations.14 A recent comparative study of mortality in ruralvillages in eighteenth- and nineteenth-century Europe andAsia (the Eurasia Population and Family History Project), forexample, reports lower female lower life expectancy at age25 in three of the four European study areas. Sex differentialsin life expectancy at age 25 was –2.3-years for Sart, Belgium(a 2.3-year female disadvantage relative to males); –1.0 yearsfor Casaluidi, Italy; –2.8 years for Madregolo, Italy; and 0.7years for Scanian parishes in Sweden, for an unweighted av-erage of –1.4 years (Campbell, Lee, and Bengtsson 2004, 66).Lower female life expectancy at age 25 resulted from a re-markably consistent pattern of higher female mortality duringprime childbearing ages across study populations, suggest-ing that maternal mortality and maternal depletion played alarge role in the consistent pattern (Alter, Manfredini, andNystedt 2004). The pattern is characteristic of mortality innational populations with life expectancy below 45 and sug-gestive of higher female mortality from pulmonary tubercu-losis, other infectious diseases, and maternal causes (Preston1976, 91).

Some evidence suggests that females in rural areas ofnineteenth-century Europe suffered higher rates of infec-tious disease relative to males than females in urban ar-eas. Dominique Tabutin and Michel Willems, for example,cite evidence that excess female mortality and susceptibilityto respiratory diseases such as tuberculosis were more pro-nounced in rural areas (cited in Alter, Manfredini, and Nyst-edt 2004). Excess female mortality extended over a greaterrange of ages and was much higher in England’s 63 “healthydistricts”—mostly rural districts with crude death rates be-low 17 per 1,000—than in the 1838–54 English Life Table(Woods 2000, 187). According to Shelia Ryan Johansson,a probable reason for the higher incidence of tuberculosisamong females and higher rates of female mortality in ruralareas of Victorian England was lower nutritional status. Agri-cultural societies in the past, she observed, routinely discrimi-nated against females by reserving most food and the vast ma-jority of meat for husbands and sons. Industrialization and theability of women to participate in the paid labor force eventu-ally ended this nutritional discrimination (Johansson 1977).Higher fertility is another possible reason for higher femalemortality in rural areas. Although maternal mortality rateswere low relative to mortality rates from tuberculosis—mostnineteenth-century estimates suggest that maternal mortal-ity averaged between 5 and 10 maternal deaths per 1,000live births (Kippen 2005)—higher rates of nuptiality andmartial fertility in rural areas increased the cumulative riskof maternal mortality. Perhaps more importantly, pregnancyand lactation imposed greater nutritional demands on womenand reduced cell-mediated immunities, increasing the risk ofcontracting tuberculosis and other opportunistic infections.15

Unfortunately, with the exception of Kunze’s (1979) andPope’s (1992) studies, estimates of sex differentials in life ex-pectancy for the nineteenth-century United States are basedon highly urban, low-fertility populations such as Mas-sachusetts in the late nineteenth century, the 1900–1902DRA, or, like Haines’s (1998) life tables, are derived frommodels based on these populations. The 1850–60 Preston-Bennett life table (table 3), however, avoids this urban, low-fertility bias by relying on the national native-born whitefemale population in the 1850 and 1860 IPUMS samples.The results suggest sex differentials in life expectancy simi-lar to Kunze’s (1979) and Pope’s (1992) genealogical-basedestimates. At age 15, the sex differential in life expectancywas –1.2 years, rising to –2.3 years at age 20. The maleadvantage in life expectancy lasted until age 35. Thereafter,females enjoyed a slight advantage in expected remainingyears of life.

Together, the results from eighteenth- and nineteenth-century European populations and the results indicated by the1850–60 Preston-Bennett life tables for native-born whitessuggest that the overall average 0.9-year male advantage inlife expectancy at age 20 reported by Kunze and Pope forthe period 1780–1859 was approximately correct.16 As indi-cated by the 1900–1902 DRA life tables, however, a female

58 HISTORICAL METHODS

TABLE 5. Suggested Best Estimates for Female LifeExpectancy at Age 20

PeriodAdjustedmale e20

Suggested sexdifferential

(female-male)Suggestedfemale e20

1790–99 41.4 –0.9 40.51800–09 40.3 –0.9 39.41810–19 39.7 –0.9 38.81820–29 39.7 –0.9 38.81830–39 39.9 –0.9 39.01840–49 39.3 –0.9 38.41850–59 38.4 –0.9 37.51860–69 n.a. n.a. 38.9 a

1870–79 41.0 –0.6 40.41880–89 42.2 0.0 42.21890–99 43.2 0.6 43.8

aAverage of period estimates from 1850–59 and 1870–79. See text.Sources: Kent Kunze, The Effects of Age Composition and Changesin Vital Rates on Nineteenth-Century Population Estimates from NewData (Salt Lake City, UT: Department of Economics, University ofUtah, 1979); and Clayne L. Pope, “Adult Mortality in Americabefore 1900: A View from Family Histories,” in Strategic Factorsin Nineteenth Century American Economic History: A Volume toHonor Robert W. Fogel, ed. Claudia Goldin and Hugh Rockoff,267–96 (Chicago: University of Chicago Press).

advantage in life expectancy at age 20 had emerged bythe turn of the twentieth century. If the overall and rural1900–1902 life tables are weighted and combined to approx-imate the urban percentage of the national population, thefemale advantage in life expectancy at age 20 was 0.9 yearsin 1900.17

Table 5 suggests best estimates of female life expectancyat age 20 between 1780 and 1860 by assuming a fixed 0.9-year advantage in male life expectancy. As shown in the thirdcolumn, the sex mortality differential was assumed to shiftin favor of females in a linear fashion between the 0.9 fe-male disadvantage in life expectancy in the period before1870 and the 0.9-year advantage in female life expectancysuggested by the weighted 1900–1902 DRA life tables. Al-though somewhat speculative, the linear shift from a maleadvantage to a female advantage in life expectancy between1870 and 1900–1902 is consistent with known changes in sexmortality differentials accompanying mortality decline, theepidemiological transition, and fertility decline. The declinein pulmonary tuberculosis, in particular, likely led to morerapid declines in female mortality relative to male mortal-ity (Preston 1976, chap. 6). Because excess male mortalityduring the Civil War likely affected sex differentials in mor-tality, the female estimate of life expectancy in the period1860–69 was obtained by averaging the adjusted female lifeexpectancy in the 1850s and 1870s. Suggested best estimates

of female life expectancy at age 20 are shown in the fourthcolumn.

The Age Profile of Nineteenth-Century Mortality

Mortality varies with age in a consistent pattern, some-times characterized as a “U” or “J” shape, across a widerange of mortality levels. Mortality rates are very high ininfancy, drop rapidly in childhood, reach their lowest level inlate childhood and adolescence, and then begin to increasein a fairly regular manner with age. Because of this consis-tency, demographers have long sought to model mortality asa function of age and overall mortality. Among other uses, anaccurate model would make it possible to identify deviationsin empirical data from model patterns (suggestive of par-ticular conditions or poor data quality), to gain insight intoenvironmental and behavioral factors that may determine de-viations, and to construct life tables from poor data, partialdata, or even a single parameter (Preston, Heuveline, andGuillot 2001, 191–2). With an accurate model, for example,it would be possible to generate decennial life tables fromthe estimates of adult life expectancy suggested in tables 4and 5. Choice of model, however, involves some guessworkand is a potential source of substantial error.

Three basic approaches have been used to model the agepattern of mortality: mathematical approaches that repre-sent mortality as a function of age, tabular approaches thatshow expected patterns of age-specific mortality rates andother life table parameters at different mortality levels, anda combination of the first two approaches that uses a math-ematical function to relate mortality in a given populationto a tabulated standard population (Preston, Heuveline, andGuillot 2001, 192–201). Early attempts to describe the re-lationship between mortality and age with a single mortal-ity function were unsuccessful (see Woods 2000, 170–90for a discussion of nineteenth-century attempts to spec-ify the “laws of vitality”). For a variety of reasons, in-cluding changes in behaviors and in the leading causesof death (e.g., smoking and cancer), the age pattern ofmortality varies enough across time and space that a sim-ple mathematical model is not practical. An attempt byLarry Heligman and John H. Pollard (1980), for example,required a complex equation with eight parameters to modelthe age profile of mortality from infancy to old age.

The second approach to modeling age patterns of mortalityhas been the publication of model life table systems—setsof “model” life tables at different levels of morality. Themost popular set of model life tables, the Princeton regionalmodels, were published by Princeton demographers Ans-ley J. Coale and Paul Demeny in 1966 and revised in 1983(Coale, Demeny, and Vaughan 1966). Coale and Demenyexamined empirical data from 326 historical and contempo-rary populations. From the 192 life tables deemed reliable,Coale and Demeny identified four regional patterns, whichthey used to construct four “families” of model life tables.

April–June 2010, Volume 43, Number 2 59

In the 1983 revision, male and female life tables are shownat 25 different levels of mortality, ranging from level 1 (fe-male life expectancy at birth equal to 20 years) to level 25(female life expectancy at birth equal to 80 years) for eachof the four regional patterns. Intermediate levels are easilyobtained by interpolation. The four groups closely conformto four regions of Europe, which was the primary source ofthe life tables. The north model is based largely on life tablesfrom Scandinavian countries. It is characterized by low infantmortality and low mortality at older ages. The east model isbased on life tables from Eastern Europe and is characterizedby high infant mortality. The south model is based mostly ontables from Southern Europe and is characterized by highmortality under age 5 and above age 65 and low mortalitybetween age 40 and 60. The west model is more of a resid-ual group and is based on the largest number of life tables,including tables from Western Europe, the United States,Canada, Australia, New Zealand, and Japan. Other modellife table systems—including those created by the UnitedNations—have been created for developing countries in Asia,Africa, and Latin America, where different environments andcauses of death lead to different patterns of mortality than arefound in Coale and Demeny’s European-dominated system(United Nations 1982).

For populations with poor vital registration data, the choiceof a model life table—and thus the assumed age profile ofmortality—typically requires some guesswork. Colin Newell(1988, 165) notes that the “general, but not always helpful,rule is to use a [model life table] system which is flexibleenough to let real features and irregularities through, butwhich is sufficiently robust to be unaffected by errors inthe data.” Because the age profile of mortality is largely theresult of environmental and behavioral factors—which de-termine the distribution of causes of death and the level ofmortality—most analysts try to rely on a model life tablesystem based on data from a nearby region with a similar en-vironment, behaviors, and level of mortality. U.S. historicaldemographers tend to rely on Coale, Demeny, and Vaughn’s(1983) west model, which is based in part on historical lifetables for the United States (including the 1900–1902 DRAlife table). Robert V. Wells (1992), for example, used thewest model to infer life expectancy at birth in colonial Amer-ica from adult and child survival estimates reported in vari-ous studies. Suspecting probable underenumeration of infantdeaths in the 1900–1902 DRA, Condran and Crimmins fittedmortality rates for the one to four age group to model Westlife tables in order to estimate life expectancy in urban andrural areas of the DRA (1980, 191). Where it can be com-pared to empirical data, the west model appears to be a goodfit for the total and white populations of the early twentieth-century United States (Haines 1979, 197; Preston and Haines1991, 66). Douglas Ewbank (1987), however, found that theage mortality profile of early twentieth-century black popu-lation of the United States more closely matched the UnitedNation’s far east model life table.

TABLE 6. Implied Coale and Demeny Life TableParameters when e20 = 40 years

Variable West North East South

MaleLevel 11.71 10.78 9.32 8.83e0 43.8 41.2 38.0 38.11000q0 160.6 155.6 250.1 192.6l20 71597 66756 61927 61801

FemaleLevel 9.71 8.68 8.07 7.41e0 41.8 39.2 37.7 36.01000q0 166.7 161.7 235.7 193.0l20 67829 63075 61168 58004

Sources: A. J. Coale, P. Demeny, and B. Vaughan, Regional ModelLife Tables and Stable Populations (New York: Academic Press,1983).

Depending on the application, the choice of model can beimportant. Preston and Haines (1991) found that choice ofregional model had very little impact on indirect estimatesof child mortality in the 1900 census (1991, 64–67). Es-timating infant mortality and life expectancy at birth fromlife expectancy at age 20, however, is problematic. Table 6shows implied estimates of male and female life expectancyat birth, infant mortality rates, and the proportion of the pop-ulation surviving to age 20 when male and female life ex-pectancy is 40 years using the four Princeton regional mod-els. The implied life expectancy at birth for males rangesfrom a high of 43.8 years in model West to a low of 38.0years in the east model, a difference of nearly six years.Implied male infant mortality rates vary from a low of 156per 1,000 in the north model to a high of 250 in the eastmodel. Using the west model, nearly 72 percent of the pop-ulation survived to age 20. In the east model, the percentagewas less than 62 percent. Similar differences are observ-able for the female population. These differences illustratethe large potential error that can be incurred by relying onthe wrong model to infer a complete life table from a singleparameter.

The third approach to modeling the age profile of mor-tality, developed by William Brass (1971), uses a mathe-matical function to transform a standard life table. It thusrepresents a combination of the mathematical and tabularapproaches. Brass observed that logits of the lxs from anytwo life tables are related to one another by a linear rela-tionship, making it possible to describe a set of logits in anobserved or target population using the logits from the stan-dard table and appropriate intercept and slope values. Briefly,the logit transformation of the lx column is based on theequation:

logit(1 − lx) = Yx = 0.5 Log e(1 − lx/ lx), (1)

60 HISTORICAL METHODS

in which l0 = 1.0. The logits of an observed population,YObs(x), are related to the logits of a standard population,Ys(x), by the linear equation:

logit (Obs. lx) = YObs(x) = α + β Ys(x). (2)

To fit an observed life table to a standard table, logits of theobserved lxs are plotted against the logits of the standard lifetable. A straight line is then fitted to the points (typically withsimple linear regression or weighted regression techniques),and the intercept and slope of the line, α and β, are calculated.Once α and β are calculated, fitted logits can be computedfrom the standard logits, and the anti-logits can be taken toproduce a set of fitted lxs, as shown in the equation below:

Fitted lx = 1

1 + e2YFit (x). (3)

When the intercept (α) equals 0 and the slope (β) equals 1,the standard table will be reproduced. Values of the interceptparameter greater than 0.0 will shift the level of mortalityabove the standard table and values less than 0.0 will shiftthe level of mortality below the standard table. The slopeparameter determines the “tilt” of the table. A slope valuegreater than 1.0 indicates that infant and child mortality islower relative to adult mortality than in the standard table, anda slope less than 1.0 indicates that infant and child mortalityis higher relative to adult mortality than in the standard. Itthus becomes possible to construct a family of related lifetables from a standard life table by varying the intercept andslope parameters, calculating the anti-logits of the resultingvalues, and constructing the resulting life tables.

Although Brass (1971) suggested two sets of logits touse as a standard—a general standard and an Africanstandard—any life table can be used and logits calculateddirectly from the lx column. Appropriate choice of a standardtable can preserve variations in the age profile of mortalitythat cannot be obtained by varying the slope and interceptparameters of a standard table, such as the level of older agemortality relative to mid-age mortality or the level of infantmortality relative to childhood mortality.18 To construct hisU.S. model life tables, for example, Haines (1979) relied onthe 1900–1902 DRA life table as a standard table. With thehelp of available historical life tables from Massachusetts andother United States life tables of reasonable quality, Hainesfirst estimated the impact of urbanization and time on theslope of the age mortality profile. While more urban envi-ronments increased infant and childhood mortality relative toadult mortality, the trend in the late nineteenth century wastoward relatively lower levels of infant and child mortality(ibid., 303). From this relationship Haines determined thelikely slope parameter in each census year between 1850 and1900, effectively reducing the two-parameter logit model toa one-parameter model. The final intercept parameter was

determined by fitting the age-specific death rates of childrenage 5–19 in the mortality censuses (ibid.).

Comparison of Haines’s U.S. model life tables with thelife tables constructed using the west model as a standardindicates that the U.S. model typically yields higher infantmortality rates, lower adult mortality rates, and lower lifeexpectancy estimates at birth. In 1880, for example, Haines’sU.S. model suggests an infant mortality rate of 0.214 forwhite males and a life expectancy at birth of 40.4 years. Thelife table constructed using a west model suggests an infantmortality rate of 0.180 and a life expectancy at birth of 40.9years.

Arguably, the 1900–1902 DRA life table is a more ap-propriate standard for the nineteenth-century United Statesthan a generic standard or even model West.19 As noted intable 2, however, the DRA population was much more urbanthan the overall population in 1900, had a higher propor-tion of the population foreign born, had a lower proportionengaged in agriculture and had much lower fertility. Thecontrast is even greater with the overall population in theearly and mid-nineteenth-century United States, which wasoverwhelmingly rural and had very high fertility. Althoughvariation of the slope parameter can pick up some of the sus-pected impact of urbanization and time on the suspected ageprofile of mortality in the nineteenth century, the increase inurbanization and immigration, the decline in fertility and theagricultural sector of the economy, and the onset of the pub-lic health movement and epidemiological transition in thelater part of the nineteenth century likely affected the distri-bution of causes of death and the age profile of mortality inmore complex ways. It is likely, for example, that decliningtuberculosis in the late nineteenth century had a significantimpact on the mortality of young adults relative to infantsand older adults, especially among females. Condran andCheney (1982, 105) report that the decline in mortality frompulmonary tuberculosis explained 26.8 percent of the declinein mortality in Philadelphia between 1870 and 1900 and wasoverwhelmingly important in the decline in death rates atages 20–39.

In addition to mortality decline, rapid fertility decline inthe late nineteenth century (Hacker n.d.) likely had an impacton the age-specific mortality rates of females. Although ma-ternal mortality rates were lower than typically imagined inthe qualitative literature (Schofield 1986), repeated exposureto death in childbirth in high-fertility populations increasedfemale mortality relative to male mortality during childbear-ing age.20 Pregnancy may have been a significant risk factorin contracting tuberculosis, the leading killer of nineteenth-century Americans, and other opportunistic infections. Wecan thus expect that the shape of age-specific mortality ratesfor females in the early to mid-nineteenth century varied sig-nificantly from the shape of age-specific rates for femalesin the 1900–1902 DRA, even if the slope of the age pro-file is adjusted to account for suspected higher infant and

April–June 2010, Volume 43, Number 2 61

100

1,000

10,000

7570656055504540353025201510510Age group

Pro

port

ion

dyin

g, 1

0,00

0 q x

(lo

g sc

ale)

White females 1900-02 rural DRA,e0 = 55.4

White females 1900-02 DRA,e0 = 51.1

FIGURE 2. Proportion of dying by age group, white females in 1900–1902 death registration area.

child mortality relative to adult mortality prior to the onsetof mortality decline.

Some indication of the possible bias can be seen in figure 2,which compares the age-specific mortality rates for white fe-males in the 1900–1902 rural DRA life table with white fe-males in the 1900–1902 overall DRA life table. Age-specificrates for white females in the rural DRA were noticeablylower than that for white females in the overall DRA atmost ages, reflecting the overall higher life expectancy forfemales in rural areas. Mortality rates were roughly equalat ages 10–14, 20–24, and 25–29, however, and higher forrural females at age 15–19.21 Although we cannot be sureof the causes, higher mortality in rural areas during ado-lescence and early adulthood are suggestive of higher deathrates from tuberculosis and maternal mortality (Preston 1976;Henry 1989). Females age 20–49 residing in rural areas ofthe DRA had 9.4 percent more own children in the householdthan females in the overall DRA, increasing their exposure tomaternal mortality and risk of contracting tuberculosis andother infectious diseases.

Among the four Princeton regional models, age-specificdeath rates for white males and females in the rural andoverall 1900–1902 DRA had the closest correspondence withmodel west (after age 20, males in rural areas of the DRA hada closer relationship with the north model). Relative to themodel west level corresponding to the same life expectancyat birth, however, female death rates in the 1900–1902 DRAand rural areas of the 1900–1902 DRA were much higher inpeak childbearing years. The difference, as shown in figure 3,was especially pronounced for females residing in rural areas.

With the exception of age groups between 15 and 35, there isremarkably close correspondence between west model level15.17 and the mortality of women in the rural DRA. Age-specific death rates for rural females between ages 15 and29, however, exceeded the level expected in model West byapproximately 27 percent. The greatest divergence from themodel pattern, 36 percent, was at ages 20–24. Although asimilar pattern exists for males (not shown)—higher deathrates at ages 5–34 for white males residing in rural areasof the 1900–1902 DRA relative to the corresponding modelWest level, lower rates at ages 40 and above—the differenceswere much smaller.

Similar “humps” in age-specific mortality rates for femalesbetween the approximate ages of 15 and 45 have been ob-served in other historical populations, including eighteenth-and early nineteenth-century American populations(Rutman and Rutman 1976; Logue 1991; Hacker 1996),the mostly rural eighteenth- and nineteenth-century popu-lations studied by the Eurasia project (Alter, Manfredini, andNystedt 2004), and the mid-nineteenth-century population ofEngland (Wrigley and Schofield 1981/1989, 708–9). In theirreconstruction of English population history, for example,Edward A. Wrigley and Roger S. Schofield noted that whileage-specific mortality rates of males in the third English lifetable (1838–54) corresponded well with model North of thePrinceton regional life tables, females had higher than ex-pected rates from age 10 through age 35. The deviation fromthe model pattern prompted Wrigley and Schofield to con-struct their own model, based in part of the English life tableand in part on the north model.

62 HISTORICAL METHODS

100

1,000

10,000

0 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75Age group

Pro

port

ion

dyin

g, 1

0,00

0 q x

(lo

g sc

ale)

White females 1900-02 rural DRA,e0 = 55.4

Female model West level 15.17,e0 = 55.4

Female model North level 15.17

FIGURE 3. Proportion dying by age group, white females in rural areas of the 1900–1902 death registration area compared withPrinceton model West and model North. qx = proportion dying in age interval.

Did the same distinctive hump shape during childbear-ing years that characterized age-specific mortality rates forfemales in rural areas of the 1900–1902 DRA and variousEuropean and Asian populations also characterize the overallpopulation of the nineteenth-century United States? Figure 4shows the implied proportion dying in each age group fromthe Preston-Bennett 1850–60 life table shown in table 3 andthe model West level corresponding to the equivalent lifeexpectancy at age 10. Although the age pattern of mortalitysuggested by the Preston-Bennett life table is somewhat er-ratic, the distinctive deviation in age-specific mortality ratesfrom the expected pattern is again evident. For females, theimplied proportion dying in prime childbearing age groups25–29 and 30–34 exceeded the implied proportion dying inage groups 35–39, 40–44, and 45–49. Although much lesspronounced, a hump is also evident in the age-specific mor-tality pattern for white males. The two age profiles suggestthe known age and sex profiles of tuberculosis mortality.The less pronounced hump for males may also indicate theabsence of maternal mortality or different patterns of censuscoverage errors by age. Whatever the ultimate cause, the re-sults of the Preston-Bennett life table suggest that the age-sexpattern of mortality in the nineteenth-century United Statesmore closely resembled the pattern in the rural areas of the1900–1902 DRA than the pattern in the overall DRA.

Another way of approaching the question is through exam-ination of the sex mortality ratios by age. Despite higher lifeexpectancies in the rural 1900–1902 DRA than in the over-all DRA, the ratio of male-to-female mortality was lower at

most ages in the rural DRA. The difference was especiallypronounced during childbearing age.22 White females in therural DRA experienced excess mortality relative to males be-tween age groups 15–19 and 40–44. In contrast, females inthe overall DRA experienced lower mortality than males inall age groups. Among the nineteenth-century studies report-ing lower female life expectancies in early adulthood citedabove, most show excess females mortality relative to malesin prime childbearing years. Alter, Manfredini, and Nystedt,for example, report excess female mortality from age 25 to50 in six of the seven study populations in Sweden, Belgium,Italy, China, and Japan. In the rural village of Sart, Belgium,to cite a typical example, the ratio of male-to-female prob-ability of dying in the interval 25–50 was 0.78 (2004, 334).England’s third life table (1838–54) shows excess femalemortality in all five-year age groups between age 10 and 40(Wrigley and Schofield 1981/1989, 709), although the femaledisadvantage was modest. The lowest male-to-female mor-tality ratio, 0.95, was for the 25–29 age group. Excess femalemortality was much higher in England’s “healthy districts”(Woods 2000, 187), however, echoing the similar contrast be-tween sex mortality ratios in the rural and overall 1900–1902DRAs of the United States.

Although we lack death-registration data for thenineteenth-century United States, the 1860–1900 censusesof mortality allow the construction of sex differentials byage. Condran and Crimmins’s (1979) analysis of these dataindicated that, although the mortality censuses undercountedinfant and elderly deaths, the relative undercount of males

April–June 2010, Volume 43, Number 2 63

100

1,000

10,000

Age group

Pro

port

ion

dyin

g, 1

0,00

0 q x

(lo

g sc

ale)

Native-born white females

Male model west, level 9.0

Native-born white males

Female model west level

0 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70

FIGURE 4. Proportion dying by age group, native-born whites in Preston-Bennett 1850–60 life table. qx = proportion dying inage interval.

and females varied little by age. Figure 5 shows the averagesex ratio in mortality in the 1860–80 censuses by age com-pared to the ratios in the overall 1900–1902 DRA and therural areas of the 1900–1902 DRA. Figure 5 also includesa plot of the average sex mortality ratios in Haines’s (1998)

1850–80 U.S. model life tables. Sex mortality ratios indicatedby the census data suggest a similar pattern to the 1900–1902rural DRA pattern: excess female mortality from adolescencethrough prime childbearing years and excess male mortalityat other ages. Sex mortality ratios in Haines’s life tables,

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

Age group

Rat

io m

ale

to fe

mal

e m

orta

lity

rate

1900-1902 DRA life table

Average, Haines 1850-80 life tables, U.S. model

Average, 1860-1880 U.S. mortality censuses

1900-1902 rural DRA life table

0 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 8075

FIGURE 5. Ratio of male to female probability of dying by age (qx). DRA = death registration area; qx = proportion dying inage interval.

64 HISTORICAL METHODS

however, more closely conform to the 1900–1902 overallDRA. Although Haines’s tables indicate modest excess fe-male mortality in childhood and approximately equal sex ra-tios during prime childbearing years 20–34, the age pattern ofsex mortality ratios is much closer to the overall 1900–1902DRA pattern than to the rural DRA pattern. Haines’s tablesalso suggest a lower sex differential in mortality in infancythan either the 1900–1902 overall or 1900–1902 rural DRAlife tables.

Figures 4 and 5 strongly suggest that the 1900–1902 ru-ral DRA life table is more representative of the shape ofmortality in the nineteenth-century United States than theoverall DRA life table. Age-specific mortality rates impliedby the Preston-Bennett 1850–60 life table and sex mortal-ity ratios by age in the 1860–80 censuses of mortality moreclosely conform to the pattern in the 1900–1902 rural DRAlife table than the overall DRA life table (which was itself acloser match than model West). The correspondence shouldnot be surprising: like the rural DRA life table, the populationof the nineteenth-century United States was less urban, wasmore agricultural and had higher fertility than the populationof the 1900–1902 DRA and populations used in the construc-tion of model West. Although we cannot be certain of the trueshape of age-specific mortality rates in the nineteenth cen-tury, the available evidence indicates that any model used toconstruct nineteenth-century life tables, especially life tablesfor the earlier part of the century, should draw more heav-ily from the 1900–1902 rural DRA life table than from theoverall DRA life table.

New Decennial Life Tables, 1790–1910

Two life tables constructed by Glover (1921) for the1900–1902 DRA are essential for this project: (1) the lifetable for the white population residing in the 10 DRA statesand the District of Columbia, and (2) the life table for thewhite population in the rural areas of the DRA. When thelife tables were published in 1921, the Census Bureau’s def-inition of “urban” was considered cities of 8,000 or moreinhabitants. All other places were considered rural. The Cen-sus Bureau subsequently redefined urban as places of 2,500or more inhabitants. So although nominally nonurban, the1900–1902 “rural” DRA life table is based in part on a pop-ulation residing in the modern definition of an urban area,albeit modest towns and cities of 2,500 to 8,000 inhabitants.

As shown in table 2, the population living in the 1900–1902DRA was predominately urban: over 60 percent lived in themodern definition of an urban area. Over 13 percent of thepopulation in the rural areas of the DRA also lived in anurban area. The DRA covered 26.2 percent of the national1900 population; the rural parts of the DRA only 12.0 percent.

What can be inferred about the level and pattern of nationalmortality in 1900–1902 given that nearly three-quarters ofthe population lived in states that were not part of the DRA?Although we could assume that the larger, more inclusive

life table for the overall DRA is more representative of thenational population, we know that urbanization, industrial-ization, nativity, and fertility in the DRA were not repre-sentative of the national population and likely affected theshape, level, and sex differential in mortality. A better choicemight be the 1900–1902 rural DRA life table. Although asubset of the overall DRA, the rural population was morerepresentative of the national population in terms of fertility,nativity, and occupation structure. Unsurprisingly, however,urbanization was higher in the nation as a whole than in therural areas of the DRA and was likely the most importantfactor influencing mortality.

The simplest and most defensible inference is to com-bine the overall and rural DRA life tables, using appropriateweights to produce a life table reflecting the rate of urbaniza-tion in the nation as a whole. If we assume that that nationalpopulation in 1901 was 40.2 percent urban (an interpolationof the Census Bureau’s estimate of urbanization in the na-tion as a whole in 1900 and 1910), it is a simple matter tocalculate the weight needed for each DRA life table and tocombine the two to produce one life table representative ofthe nation’s urban population.23 Relative to the overall DRAlife table, the resulting combined life table would increaseestimates of white life expectancy at age 20 by 1.5 years forwhite males and 0.9 years for white females. Sex differen-tials in life expectancy at age 20 would fall from a 1.6-yearfemale advantage in the overall DRA life table to a 0.9-yearfemale advantage in the combined table.

The combined table could in turn be used as a model forearlier years: logits of the table’s lx values could be taken andnew life tables generated by varying the slope and interceptshown in equation 2 to construct a predicted set of logits,calculating the lx values by taking the anti-logits using equa-tion 3, and constructing a new life table from the predictedlx values.

There are several problems in such an approach. Most ob-viously, urbanization was increasing rapidly in the decadesbefore the 1900 census. By design, the combined 1900–1902life table is representative of urbanization in the 1900–1902national population; nineteenth-century populations were farmore rural. Haines’s (1979) method is one possible wayaround this problem. Drawing on his analysis of availablelate nineteenth-century city and state life tables, Haines ob-served that the slope of age-specific mortality rates variedacross time and by level of urbanization in a predictable way.Haines was thus able to set the slope of his model as neededto fit the period and level of urbanization.

Although a useful innovation, Haines’s method cannot beapplied uncritically to decades early in the nineteenth cen-tury. Most of the change observed in the slope of mortalitylikely reflected the impact of public health initiatives between1880 and 1900 in the nation’s largest cities, particularly ef-forts to clean water and milk supplies.24 The net result wasfalling infant and early childhood mortality relative to adultmortality in large urban areas, despite rapidly increasing

April–June 2010, Volume 43, Number 2 65

urbanization. Because most small cities made only mod-est attempts at public health initiatives before 1900 (Duffy1990, chap. 12), it is much less certain if infant and child-hood mortality fell relative to adult mortality for the na-tion as a whole between 1850 and 1900, which Haines’smodel predicts. Indeed, as Haines (1979, 300–301) noted,the Princeton west model suggests the opposite. Betweenlevels 9 and 13—equivalent to an increase in female lifeexpectancy from 40 to 50 years and roughly spanning the in-crease in life expectancy in the late nineteenth-century UnitedStates—the west model suggests that infant and childhoodmortality should increase relative to adult mortality. Only atmortality levels above level 13 does infant and childhoodmortality begin to decline faster than adult mortality.

Given this uncertainty, a better approach would be to createa unique standard for each decade of the nineteenth centuryby repeating the weighting exercise of the 1900–1902 DRAand 1900–1902 rural DRA life tables described above, usingthe appropriate weights to yield a new standard life tablerepresentative of the urbanization level in each decade. Table7 shows the results of that exercise. Included in the tableare estimates of the mid-census level of urbanization in eachdecade (an average of the percentage urban in each of thebeginning and ending censuses), the corresponding propor-tional weights of the 1900–1902 overall and rural DRA lifetables used to create each standard, and the resulting logits ofthe tables’ lx values by age and sex. Before 1850, the nationallevel of urbanization was below that estimated in the rural1900–1902 DRA table. It was therefore assumed that the rural1900–1902 table represented the standard mortality patternfor all decades before to 1850. After 1850 urbanization be-gan to exceed the level of urbanization in the 1900–1902rural DRA life table, requiring increasing weight to be givento the overall DRA life table. The applied weighting of the1900–1902 overall DRA life table increased from 0.09 in the1850–59 decade to 0.51 in the 1890–99 decade.

From there it was a simple matter of varying the interceptin equation 2 and constructing a new life table to fit theestimates of adult life expectancy shown in tables 4 and 5.With one exception, the resulting life tables are shown intable 8. The exception is the 1860–69 life table for whitemales, which was modified to account for high mortalityamong males of military age during the Civil War. It wasconstructed in three steps. First, a “base” life table for the1860–69 period was constructed by using the average of the1850–59 and 1870–79 estimates of male life expectancy atage 20. Second, an estimate of excess male deaths in the1860–70 intercensal period was made by cohort using two-census survival methods.25 Finally, the excess male deathswere added to the base life table. Table 9 shows the results foreach year of the war.26 Unsurprisingly, mortality was highestin 1864, the last full year of the conflict. The estimates implya white male life expectancy at birth of 25.9 years, likelythe lowest level in U.S. history. Although based on crudeestimates, the method retains the unusual risk of early death

among young white males in the war. The resulting life tablefor the 1860s suggests a male life expectancy at age 20 of35.1 years, approximately two years lower than the adjustedKunze (1979) and Pope (1992) estimate. Although the baselife table and the number of excess male deaths could beadjusted to yield a perfect match, it is unclear which estimateto adjust. It is also possible that the genealogical samples,which are known to underrepresent individuals who do notmarry or reproduce, are biased against soldiers participatingand dying in the war. It was therefore decided to make nofurther adjustments to the life table.

Figures 6, 7, and 8 compare some of the new life ta-ble estimates with Haines’s (1998) life table estimates. Asshown in figure 6, the new life tables describe a decline inlife expectancy at birth from approximately 44 years in thelate eighteenth century to just over 40 years in the 1840s.Although the models assumed a slight male advantage inlife expectancy at age 20, higher male mortality in infancypushed female life expectancy at birth slightly above the maleestimate. Life expectancy at birth then declined another 3–4years in the 1850s to approximately 37 years. The decline islargely the result of the model’s prediction of increased in-fant mortality. Although the decline in adult life expectancybetween the 1840s and 1850s was relatively modest (1.4years), the model suggests that infant mortality rates rosefrom 215 to 247 per 1,000 for white males and from 190to 222 for white females. Life expectancy reached an evenlower level in the 1860s for white males—the result of theU.S. Civil War—but then increased rapidly with estimatesfor white females for the remainder of the century. Life ex-pectancy for white females increased more rapidly. By the1890s, white females enjoyed about a two-year advantage inlife expectancy at birth.

Haines’s (1998) estimates are plotted with a marker toemphasize their limitation to individual census years. In gen-eral, Haines’s life tables document a similar pattern of lowlife expectancy at midcentury and a rapid increase late inthe century. Haines’s estimates for 1860 are relatively high,however, whereas his estimate for the 1880s is relativelylow. It is difficult to know what to make of the differences.The substantial decline in life expectancy between 1870 in1880, in particular, does not correspond with known epi-demics or the qualitative literature on the mortality declinein the United States. The decline may reflect that 1880 wasa particularly unhealthy year or be an artifact of differentialcensus enumeration. The 1870 census has been long sus-pected to have undercounted the population and may wellhave undercounted mortality as well. The 1880 census, onthe other hand, benefitted from a shift from enumeration byUnited States marshals to enumeration by trained enumera-tors, a sharp increase in the number of enumerators relativeto the population, and the supplementation of mortality datain the census with available death registration data.

Figures 7 and 8 compare the proportions dying in five-year age intervals in male and female life tables selected

66 HISTORICAL METHODS

TA

BL

E7.

Stan

dard

Lif

eTa

ble

Log

itV

alue

s,Y

s(x)

,for

Dec

enni

alL

ife

Tabl

es

Dec

ade

Age

1790

–99

1800

–09

1810

–19

1820

–29

1830

–39

1840

–49

1850

–59

1860

–69

1870

–79

1880

–89

1890

–99

Mal

es0 1

−1.0

505

−1.0

505

−1.0

505

−1.0

505

−1.0

505

−1.0

505

−1.0

390

−1.0

254

−1.0

147

−1.0

029

−0.9

888

2−0

.947

6−0

.947

6−0

.947

6−0

.947

6−0

.947

6−0

.947

6−0

.934

4−0

.918

8−0

.906

6−0

.893

2−0

.877

23

−0.9

081

−0.9

081

− 0.9

081

−0.9

081

−0.9

081

−0.9

081

−0.8

941

−0.8

775

−0.8

646

−0.8

504

−0.8

334

4−0

.884

0−0

.884

0−0

.884

0−0

.884

0−0

.884

0−0

.884

0−0

.869

4−0

.852

3−0

.838

9−0

.824

3−0

.806

85

−0.8

656

−0.8

656

−0.8

656

−0.8

656

−0.8

656

−0.8

656

−0.8

508

−0.8

334

−0.8

198

−0.8

049

−0.7

871

10−0

.814

4−0

.814

4−0

.814

4−0

.814

4−0

.814

4−0

.814

4−0

.799

2−0

.781

3−0

.767

3−0

.752

1−0

.733

815

−0.7

775

−0.7

775

−0.7

775

−0.7

775

−0.7

775

−0.7

775

−0.7

629

−0. 7

457

−0.7

322

−0.7

175

−0.6

999

20−0

.722

5−0

.722

5−0

.722

5−0

.722

5−0

.722

5−0

.722

5−0

.708

8−0

.692

6−0

.679

9−0

.666

0−0

.649

425

−0.6

538

−0.6

538

−0.6

538

−0.6

538

−0.6

538

−0.6

538

−0.6

403

−0.6

246

−0.6

121

−0.5

985

−0.5

822

30−0

.588

7−0

.588

7−0

.588

7−0

.588

7−0

.588

7−0

.588

7−0

.575

0−0

.558

9−0

.546

3−0

.532

4−0

.515

835

−0.5

285

−0.5

285

−0.5

285

−0.5

285

−0.5

285

−0.5

285

−0.5

137

−0.4

964

−0.4

827

−0.4

678

−0.4

499

40−0

.467

3−0

.467

3−0

.467

3−0

.467

3−0

.467

3−0

.467

3−0

.451

2−0

.432

2−0

.417

3−0

.401

1−0

.381

645

−0.4

024

−0.4

024

−0.4

024

−0.4

024

−0.4

024

−0.4

024

−0.3

850

−0.3

645

−0.3

485

−0.3

310

−0.3

100

50−0

.329

6−0

.329

6−0

.329

6−0

.329

6−0

.329

6−0

.329

6−0

.310

9−0

.288

9−0

.271

8−0

.253

0−0

.230

555

−0.2

458

−0.2

458

−0.2

458

−0.2

458

−0.2

458

−0.2

458

−0.2

258

−0.2

023

−0.1

839

−0.1

638

−0.1

398

60−0

.137

5−0

.137

5−0

.137

5−0

.137

5−0

.137

5−0

.137

5−0

.116

3−0

.091

4−0

.071

8−0

.050

5−0

.024

965

0.00

190.

0019

0.00

190.

0019

0.00

190.

0019

0.02

370.

0494

0.06

960.

0917

0.11

8270

0.18

970.

1897

0.18

970.

1897

0.18

970.

1897

0.21

140.

2370

0.25

720.

2794

0.30

6275

0.43

290.

4329

0.43

290.

4329

0.43

290.

4329

0.45

410.

4792

0.49

910.

5211

0.54

77

(Con

tinu

edon

next

page

)

April–June 2010, Volume 43, Number 2 67

TA

BL

E7.

Stan

dard

Lif

eTa

ble

Log

itV

alue

s,Y

s(x)

,for

Dec

enni

alL

ife

Tabl

es(C

ontin

ued)

Dec

ade

Age

1790

–99

1800

–09

1810

–19

1820

–29

1830

–39

1840

–49

1850

–59

1860

–69

1870

–79

1880

–89

1890

–99

Fem

ales

0 1−1

.158

1−1

.158

1−1

.158

1−1

.158

1−1

.158

1−1

.158

1−1

.146

5−1

.132

7−1

.121

9−1

.110

1−1

.095

92

−1.0

471

−1.0

471

−1.0

471

−1.0

471

−1.0

471

−1.0

471

−1.0

336

−1.0

177

−1.0

053

−0.9

917

−0.9

755

3−1

.000

4−1

.000

4−1

.000

4−1

.000

4−1

.000

4−1

.000

4−0

.986

4−0

.969

9−0

.957

0−0

.942

9−0

.926

04

−0.9

726

−0.9

726

−0.9

726

−0.9

726

−0.9

726

−0.9

726

−0.9

582

−0.9

412

−0.9

279

−0.9

134

−0.8

961

5−0

.951

2−0

.951

2−0

.951

2−0

.951

2−0

.951

2−0

.951

2−0

.936

5−0

.919

3−0

.905

8−0

.891

1−0

.873

610

−0.8

944

−0.8

944

−0.8

944

−0.8

944

−0.8

944

−0.8

944

−0.8

794

−0.8

619

−0.8

482

−0.8

333

−0.8

154

15−0

.853

2−0

.853

2−0

.853

2−0

.853

2−0

.853

2−0

.853

2−0

.839

1−0

.822

5−0

.809

5−0

.795

3−0

.778

320

−0.7

867

−0.7

867

−0.7

867

−0.7

867

−0.7

867

−0.7

867

−0.7

742

−0.7

593

−0.7

477

−0.7

349

−0.7

196

25−0

.703

7−0

.703

7−0

.703

7−0

.703

7−0

.703

7−0

.703

7−0

.692

7−0

.679

6−0

.669

3−0

.658

0−0

.644

430

−0.6

241

−0.6

241

−0.6

241

−0.6

241

−0.6

241

−0.6

241

−0.6

138

−0.6

017

−0.5

921

−0.5

815

−0.5

687

35−0

.551

4−0

.551

4−0

.551

4−0

.551

4−0

.551

4−0

.551

4−0

.541

1−0

.528

9−0

.519

3−0

.508

7−0

.495

940

−0.4

842

−0.4

842

−0.4

842

−0.4

842

−0.4

842

−0.4

842

−0.4

735

−0.4

608

−0.4

507

−0.4

397

−0.4

264

45−0

.415

9−0

.415

9−0

.415

9−0

.415

9−0

.415

9−0

.415

9−0

.404

7−0

.391

4−0

.381

0−0

.369

4−0

.355

550

−0.3

448

−0.3

448

−0.3

448

−0.3

448

−0.3

448

−0.3

448

−0.3

328

−0.3

187

−0.3

075

−0.2

952

−0.2

804

55−0

.261

2−0

.261

2−0

.261

2−0

.261

2−0

.261

2−0

.261

2−0

.248

3−0

.233

0−0

.221

0−0

.207

8−0

.191

860

−0.1

566

−0.1

566

−0.1

566

−0.1

566

−0.1

566

−0.1

566

−0.1

426

−0.1

261

−0.1

130

−0.0

986

−0.0

813

65−0

.025

9−0

.025

9−0

.025

9−0

.025

9−0

.025

9−0

.025

9−0

.011

10.

0064

0.02

030.

0355

0.05

3970

0.14

490.

1449

0.14

490.

1449

0.14

490.

1449

0.16

050.

1791

0.19

380.

2100

0.22

9575

0.37

630.

3763

0.37

630.

3763

0.37

630.

3763

0.39

200.

4107

0.42

560.

4421

0.46

2080

+0.

6887

0.68

870.

6887

0.68

870.

6887

0.68

870.

7043

0.72

300.

7379

0.75

440.

7746

%ur

ban

5.6

6.7

7.2

8.0

9.8

13.0

17.5

22.7

26.9

31.6

37.3

1901

DR

Aw

eigh

t0.

000.

000.

000.

000.

000.

000.

090.

200.

290.

390.

5119

01ru

ralD

RA

wei

ght

1.00

1.00

1.00

1.00

1.00

1.00

0.91

0.80

0.71

0.61

0.49

68 HISTORICAL METHODS

TA

BL

E8.

New

Lif

eTa

bles

for

the

Whi

teP

opul

atio

nof

the

Uni

ted

Stat

es,1

780–

1900

Whi

tem

ales

,179

0–99

Whi

tefe

mal

es,1

790–

99

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

1797

1000

0017

969

8796

144

0906

144

.10

0.16

0310

0000

1603

089

580

4418

888

44.2

10.

0394

8203

132

3580

122

4321

100

52.7

10.

0383

8397

032

1982

070

4329

308

51.6

20.

0171

7879

613

4978

081

4240

978

53.8

20.

0185

8075

114

9179

960

4247

237

52.6

30.

0110

7744

785

477

003

4162

897

53.8

30.

0117

7926

092

978

777

4167

277

52.6

40.

0087

7659

366

676

246

4085

894

53.3

40.

0094

7833

173

577

948

4088

500

52.2

50.

0253

7592

719

2137

4832

4009

648

52.8

50.

0263

7759

520

3938

2878

4010

552

51.7

100.

0195

7400

614

4536

6418

3634

815

49.1

100.

0205

7555

615

5237

3900

3627

674

48.0

150.

0309

7256

122

4335

7200

3268

397

45.0

150.

0356

7400

426

3736

3427

3253

774

44.0

200.

0419

7031

929

4934

4222

2911

197

41.4

200.

0492

7136

735

1034

8059

2890

347

40.5

250.

0434

6737

029

2432

9541

2566

974

38.1

250.

0526

6785

735

6733

0367

2542

289

37.5

300.

0435

6444

628

0231

5226

2237

433

34.7

300.

0529

6429

034

0431

2942

2211

921

34.4

350.

0475

6164

429

3130

0892

1922

207

31.2

350.

0532

6088

732

4229

6329

1898

979

31.2

400.

0541

5871

331

7728

5621

1621

316

27.6

400.

0584

5764

533

6827

9805

1602

650

27.8

450.

0652

5553

636

2226

8624

1335

695

24.1

450.

0653

5427

735

4326

2528

1322

845

24.4

500.

0806

5191

441

8524

9109

1067

071

20.6

500.

0823

5073

441

7724

3230

1060

317

20.9

550.

1122

4772

953

5622

5256

8179

6217

.155

0.11

0646

558

5147

2199

2181

7087

17.5

600.

1564

4237

366

2719

5298

5927

0614

.060

0.14

8941

411

6167

1916

3659

7166

14.4

650.

2266

3574

680

9915

8483

3974

0711

.165

0.20

8735

244

7357

1578

2740

5529

11.5

700.

3119

2764

786

2211

6679

2389

258.

670

0.29

7927

887

8307

1186

6724

7703

8.9

750.

4312

1902

582

0374

616

1222

456.

475

0.41

1119

580

8050

7777

612

9036

6.6

80+

1.00

0010

822

1082

247

629

4762

94.

480

+1.

0000

1153

011

530

5126

051

260

4.4

Whi

tem

ales

,180

0–09

Whi

tefe

mal

es,1

800–

09

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

2036

1000

0020

357

8636

141

3806

141

.40

0.18

0710

0000

1807

088

254

4166

944

41.7

10.

0444

7964

335

4077

554

4051

700

50.9

10.

0430

8193

035

2379

851

4078

690

49.8

20.

0193

7610

314

6575

327

3974

146

52.2

20.

0207

7840

616

2077

547

3998

839

51.0

30.

0124

7463

892

574

157

3898

819

52.2

30.

0131

7678

610

0676

263

3921

292

51.1

40.

0098

7371

371

973

340

3824

662

51.9

40.

0105

7578

079

475

367

3845

029

50.7

50.

0283

7299

420

6535

9809

3751

323

51.4

50.

0293

7498

621

9436

9444

3769

662

50.3

100.

0218

7092

915

4535

0784

3391

514

47.8

100.

0228

7279

216

6035

9809

3400

218

46.7

150.

0344

6938

423

8434

0961

3040

730

43.8

150.

0394

7113

228

0434

8648

3040

409

42.7

200.

0464

6700

031

0932

7228

2699

769

40.3

200.

0541

6832

736

9833

2393

2691

761

39.4

250.

0478

6389

130

5431

1820

2372

541

37.1

250.

0575

6463

037

1831

3853

2359

368

36.5

300.

0477

6083

729

0129

6931

2060

721

33.9

300.

0577

6091

135

1229

5776

2045

515

33.6

350.

0519

5793

630

0728

2159

1763

791

30.4

350.

0577

5739

933

1327

8714

1749

739

30.5

400.

0588

5492

832

2926

6569

1481

631

27.0

400.

0630

5408

634

0926

1910

1471

025

27.2

450.

0704

5169

936

4224

9392

1215

062

23.5

450.

0700

5067

835

5024

4513

1209

115

23.9

500.

0865

4805

841

5722

9894

9656

7020

.150

0.08

7847

128

4139

2252

9296

4602

20.5

550.

1195

4390

052

4420

6389

7357

7616

.855

0.11

7142

989

5033

2023

6373

9310

17.2

600.

1648

3865

663

7117

7350

5293

8713

.760

0.15

6337

956

5934

1749

4753

6947

14.1

650.

2359

3228

476

1614

2382

3520

3610

.965

0.21

6932

023

6945

1427

5136

2000

11.3

700.

3206

2466

879

0810

3571

2096

548.

570

0.30

5925

078

7672

1062

0821

9249

8.7

750.

4380

1676

073

4065

449

1060

836.

375

0.41

7617

406

7268

6885

711

3041

6.5

80+

1.00

0094

2094

2040

634

4063

44.

380

+1.

0000

1013

710

137

4418

444

184

4.4

(Con

tinu

edon

next

page

)

April–June 2010, Volume 43, Number 2 69

TA

BL

E8.

New

Lif

eTa

bles

for

the

Whi

teP

opul

atio

nof

the

Uni

ted

Stat

es,1

780–

1900

(Con

tinue

d)

Whi

tem

ales

,181

0–19

Whi

tefe

mal

es,1

810–

19

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

2190

1000

0021

898

8532

839

7500

739

.80

0.19

3810

0000

1938

487

400

4015

840

40.2

10.

0476

7810

237

2175

906

3889

679

49.8

10.

0460

8061

637

0778

428

3928

440

48.7

20.

0206

7438

015

3373

568

3813

773

51.3

20.

0221

7690

816

9776

009

3850

011

50.1

30.

0133

7284

796

572

345

3740

206

51.3

30.

0140

7521

110

5174

665

3774

002

50.2

40.

0104

7188

274

971

492

3667

860

51.0

40.

0112

7416

082

973

729

3699

338

49.9

50.

0302

7113

321

4735

0295

3596

368

50.6

50.

0311

7333

122

8336

0950

3625

608

49.4

100.

0232

6898

516

0134

0925

3246

073

47.1

100.

0242

7104

917

2235

0938

3264

659

45.9

150.

0365

6738

524

6133

0770

2905

148

43.1

150.

0418

6932

728

9733

9392

2913

720

42.0

200.

0492

6492

331

9331

6635

2574

378

39.7

200.

0572

6643

037

9832

2655

2574

329

38.8

250.

0505

6173

131

1930

0856

2257

743

36.6

250.

0606

6263

237

9530

3673

2251

674

36.0

300.

0503

5861

229

4628

5694

1956

887

33.4

300.

0605

5883

735

6228

5282

1948

001

33.1

350.

0546

5566

630

3727

0737

1671

192

30.0

350.

0604

5527

533

3926

8029

1662

719

30.1

400.

0616

5262

932

4225

5041

1400

455

26.6

400.

0658

5193

634

1725

1138

1394

690

26.9

450.

0736

4938

736

3323

7853

1145

414

23.2

450.

0729

4851

935

3623

3756

1143

552

23.6

500.

0900

4575

441

1821

8475

9075

6119

.850

0.09

1144

983

4096

2146

7490

9796

20.2

550.

1237

4163

651

5019

5306

6890

8516

.655

0.12

0940

887

4942

1920

7969

5122

17.0

600.

1697

3648

661

9016

6956

4937

8013

.560

0.16

0635

945

5772

1652

9550

3043

14.0

650.

2412

3029

673

0613

3214

3268

2410

.865

0.22

1530

173

6682

1341

5833

7749

11.2

700.

3254

2299

074

8196

246

1936

108.

470

0.31

0423

490

7292

9922

320

3590

8.7

750.

4416

1550

968

4960

420

9736

36.

375

0.42

1116

199

6822

6394

110

4367

6.4

80+

1.00

0086

5986

5936

943

3694

34.

380

+1.

0000

9377

9377

4042

640

426

4.3

Whi

tem

ales

,182

0–29

Whi

tefe

mal

es,1

820–

29

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

2166

1000

0021

663

8548

639

9938

240

.00

0.19

1810

0000

1918

487

531

4038

406

40.4

10.

0472

7833

736

9476

157

3913

896

50.0

10.

0455

8081

636

8078

645

3950

875

48.9

20.

0204

7464

315

2373

836

3837

739

51.4

20.

0219

7713

716

8676

243

3872

230

50.2

30.

0131

7312

095

972

621

3763

903

51.5

30.

0138

7545

110

4574

908

3795

986

50.3

40.

0103

7216

074

571

773

3691

282

51.2

40.

0111

7440

682

473

978

3721

079

50.0

50.

0299

7141

621

3535

1740

3619

509

50.7

50.

0308

7358

322

6936

2239

3647

101

49.6

100.

0230

6928

015

9334

2420

3267

769

47.2

100.

0240

7131

317

1335

2283

3284

862

46.1

150.

0362

6768

824

5033

2314

2925

349

43.2

150.

0414

6960

028

8334

0793

2932

578

42.1

200.

0488

6523

831

8131

8236

2593

035

39.7

200.

0567

6671

737

8432

4125

2591

785

38.8

250.

0501

6205

731

1030

2509

2274

799

36.7

250.

0601

6293

337

8430

5206

2267

660

36.0

300.

0499

5894

729

4028

7384

1972

290

33.5

300.

0601

5914

935

5528

6858

1962

454

33.2

350.

0542

5600

730

3327

2451

1684

906

30.1

350.

0600

5559

433

3626

9629

1675

597

30.1

400.

0612

5297

432

4125

6766

1412

455

26.7

400.

0654

5225

834

1625

2748

1405

967

26.9

450.

0731

4973

336

3623

9576

1155

688

23.2

450.

0725

4884

135

3923

5359

1153

219

23.6

500.

0895

4609

741

2522

0175

9161

1319

.950

0.09

0645

302

4104

2162

5291

7860

20.3

550.

1231

4197

251

6519

6949

6959

3816

.655

0.12

0341

199

4956

1936

0370

1608

17.0

600.

1689

3680

762

1816

8490

4989

8913

.660

0.16

0036

242

5797

1667

1950

8005

14.0

650.

2404

3058

973

5313

4560

3304

9910

.865

0.22

0830

445

6722

1354

2034

1286

11.2

700.

3247

2323

575

4597

315

1959

388.

470

0.30

9823

723

7348

1002

4420

5866

8.7

750.

4411

1569

169

2161

150

9862

36.

375

0.42

0616

375

6887

6465

510

5622

6.5

80+

1.00

0087

6987

6937

473

3747

34.

380

+1.

0000

9487

9487

4096

640

966

4.3

(Con

tinu

edon

next

page

)

70 HISTORICAL METHODS

TA

BL

E8.

New

Lif

eTa

bles

for

the

Whi

teP

opul

atio

nof

the

Uni

ted

Stat

es,1

780–

1900

(Con

tinue

d)

Whi

tem

ales

,183

0–39

Whi

tefe

mal

es,1

830–

39

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

2119

1000

0021

188

8580

440

4912

140

.50

0.18

7810

0000

1877

987

794

4084

478

40.8

10.

0462

7881

236

3976

665

3963

317

50.3

10.

0446

8122

136

2479

083

3996

684

49.2

20.

0200

7517

315

0274

377

3886

652

51.7

20.

0214

7759

716

6276

716

3917

601

50.5

30.

0129

7367

194

773

178

3812

275

51.7

30.

0136

7593

510

3175

399

3840

884

50.6

40.

0101

7272

473

672

341

3739

097

51.4

40.

0109

7490

481

374

481

3765

485

50.3

50.

0293

7198

821

1135

4664

3666

756

50.9

50.

0303

7409

122

4336

4849

3691

004

49.8

100.

0226

6987

815

7634

5448

3312

092

47.4

100.

0236

7184

916

9435

5007

3326

154

46.3

150.

0355

6830

224

2733

5441

2966

644

43.4

150.

0407

7015

428

5534

3633

2971

147

42.4

200.

0479

6587

531

5632

1484

2631

202

39.9

200.

0558

6729

937

5432

7110

2627

514

39.0

250.

0493

6271

930

9130

5867

2309

718

36.8

250.

0592

6354

537

6130

8322

2300

404

36.2

300.

0491

5962

829

2729

0822

2003

852

33.6

300.

0592

5978

435

4129

0066

1992

083

33.3

350.

0533

5670

130

2527

5942

1713

030

30.2

350.

0592

5624

333

2927

2892

1702

017

30.3

400.

0603

5367

632

3826

0285

1437

089

26.8

400.

0645

5291

434

1525

6033

1429

124

27.0

450.

0721

5043

836

3924

3093

1176

803

23.3

450.

0716

4949

935

4423

8636

1173

091

23.7

500.

0884

4679

941

3822

3651

9337

1020

.050

0.08

9645

955

4117

2194

8393

4455

20.3

550.

1218

4266

151

9520

0318

7100

5916

.655

0.11

9241

838

4985

1967

2671

4972

17.1

600.

1675

3746

662

7517

1642

5097

4213

.660

0.15

8736

853

5847

1696

4651

8245

14.1

650.

2388

3119

174

4913

7334

3380

9910

.865

0.21

9431

005

6803

1380

2034

8600

11.2

700.

3233

2374

276

7599

525

2007

658.

570

0.30

8424

203

7464

1023

5221

0580

8.7

750.

4400

1606

870

7062

664

1012

406.

375

0.41

9516

738

7022

6613

610

8227

6.5

80+

1.00

0089

9889

9838

577

3857

74.

380

+1.

0000

9716

9716

4209

242

092

4.3

Whi

tem

ales

,184

0–49

Whi

tefe

mal

es,1

840–

49

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

2286

1000

0022

862

8468

238

7722

738

.80

0.20

2010

0000

2020

586

867

3925

388

39.3

10.

0496

7713

838

2974

879

3792

544

49.2

10.

0478

7979

538

1877

543

3838

521

48.1

20.

0215

7330

915

7372

475

3717

666

50.7

20.

0229

7597

817

4375

054

3760

978

49.5

30.

0138

7173

698

971

222

3645

190

50.8

30.

0145

7423

510

7873

674

3685

924

49.7

40.

0108

7074

776

770

348

3573

969

50.5

40.

0116

7315

784

972

716

3612

250

49.4

50.

0314

6998

021

9434

4415

3503

620

50.1

50.

0323

7230

823

3435

5705

3539

534

49.0

100.

0241

6778

616

3233

4849

3159

205

46.6

100.

0251

6997

417

5734

5476

3183

829

45.5

150.

0379

6615

425

0432

4509

2824

356

42.7

150.

0432

6821

729

4933

3710

2838

353

41.6

200.

0509

6365

032

3831

0154

2499

847

39.3

200.

0590

6526

838

5331

6705

2504

643

38.4

250.

0522

6041

231

5229

4179

2189

693

36.2

250.

0624

6141

438

3529

7485

2187

938

35.6

300.

0518

5726

029

6727

8882

1895

514

33.1

300.

0623

5758

035

8627

8935

1890

452

32.8

350.

0562

5429

330

4926

3842

1616

632

29.8

350.

0620

5399

433

5026

1597

1611

517

29.8

400.

0633

5124

432

4324

8114

1352

790

26.4

400.

0674

5064

534

1524

4685

1349

920

26.7

450.

0754

4800

136

2123

0955

1104

676

23.0

450.

0746

4722

935

2222

7341

1105

236

23.4

500.

0921

4438

140

8621

1688

8737

2119

.750

0.09

3043

707

4064

2083

7687

7894

20.1

550.

1262

4029

450

8418

8762

6620

3316

.455

0.12

3139

643

4880

1860

1766

9518

16.9

600.

1725

3521

060

7316

0870

4732

7113

.460

0.16

3134

764

5669

1596

4648

3500

13.9

650.

2442

2913

771

1512

7899

3124

0210

.765

0.22

4129

095

6521

1291

7332

3854

11.1

700.

3282

2202

272

2792

045

1845

038.

470

0.31

3022

574

7065

9520

919

4681

8.6

750.

4437

1479

665

6557

565

9245

86.

275

0.42

3115

509

6562

6114

199

472

6.4

80+

1.00

0082

3182

3134

893

3489

34.

280

+1.

0000

8947

8947

3833

138

331

4.3

(Con

tinu

edon

next

page

)

April–June 2010, Volume 43, Number 2 71

TA

BL

E8.

New

Lif

eTa

bles

for

the

Whi

teP

opul

atio

nof

the

Uni

ted

Stat

es,1

780–

1900

(Con

tinue

d)

Whi

tem

ales

,185

0–59

Whi

tefe

mal

es,1

850–

59

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

2465

1000

0024

647

8348

636

8494

136

.80

0.22

2010

0000

2219

885

572

3713

004

37.1

10.

0542

7535

340

8772

941

3601

455

47.8

10.

0532

7780

241

4175

359

3627

432

46.6

20.

0236

7126

516

7970

376

3528

514

49.5

20.

0254

7366

118

7172

669

3552

073

48.2

30.

0151

6958

710

5269

039

3458

138

49.7

30.

0161

7179

011

5871

188

3479

403

48.5

40.

0118

6853

481

068

113

3389

099

49.5

40.

0128

7063

290

570

162

3408

215

48.3

50.

0339

6772

422

9633

2882

3320

986

49.0

50.

0353

6972

824

6434

2478

3338

053

47.9

100.

0254

6542

916

6032

2992

2988

104

45.7

100.

0268

6726

318

0133

1815

2995

575

44.5

150.

0397

6376

825

3431

2505

2665

112

41.8

150.

0457

6546

329

9131

9835

2663

760

40.7

200.

0538

6123

432

9529

7932

2352

607

38.4

200.

0623

6247

138

9230

2628

2343

925

37.5

250.

0555

5793

932

1328

1661

2054

676

35.5

250.

0661

5858

038

7028

3224

2041

298

34.8

300.

0557

5472

630

5026

6003

1773

015

32.4

300.

0662

5471

036

2326

4493

1758

074

32.1

350.

0605

5167

631

2725

0561

1507

012

29.2

350.

0661

5108

733

7924

6989

1493

581

29.2

400.

0679

4854

932

9523

4505

1256

451

25.9

400.

0716

4770

834

1722

9998

1246

592

26.1

450.

0805

4525

336

4121

7165

1021

946

22.6

450.

0792

4429

135

1021

2680

1016

594

23.0

500.

0977

4161

340

6619

7899

8047

8119

.350

0.09

8440

781

4012

1938

7580

3914

19.7

550.

1326

3754

749

8017

5285

6068

8216

.255

0.12

9536

769

4762

1719

3761

0040

16.6

600.

1790

3256

758

2814

8264

4315

9713

.360

0.16

9832

006

5434

1464

4643

8102

13.7

650.

2502

2673

966

9011

6968

2833

3310

.665

0.23

1326

572

6145

1174

9929

1656

11.0

700.

3331

2004

966

7883

549

1663

658.

370

0.31

9020

427

6516

8584

617

4157

8.5

750.

4476

1337

159

8551

893

8281

66.

275

0.42

7613

911

5948

5468

688

311

6.3

80+

1.00

0073

8673

8630

923

3092

34.

280

+1.

0000

7963

7963

3362

533

625

4.2

Whi

tem

ales

,186

0–69

Whi

tefe

mal

es,1

860–

69

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

2071

1000

0020

708

8612

535

0691

735

.10

0.18

9310

0000

1893

087

696

4057

822

40.6

10.

0469

7929

237

1877

098

3420

791

43.1

10.

0467

8107

037

8378

838

3970

126

49.0

20.

0206

7557

315

5674

749

3343

693

44.2

20.

0223

7728

717

2376

374

3891

288

50.3

30.

0132

7401

798

073

508

3268

945

44.2

30.

0142

7556

510

7675

005

3814

913

50.5

40.

0103

7303

775

372

645

3195

437

43.8

40.

0113

7448

984

074

052

3739

908

50.2

50.

0295

7228

421

3335

6085

3122

792

43.2

50.

0311

7364

922

8836

2524

3665

856

49.8

100.

0216

7015

015

1334

6968

2766

706

39.4

100.

0230

7136

116

3835

2709

3303

332

46.3

150.

0812

6863

755

7232

9256

2419

738

35.3

150.

0392

6972

327

3134

1787

2950

623

42.3

200.

1049

6306

566

1329

8795

2090

482

33.1

200.

0540

6699

236

1632

5919

2608

837

38.9

250.

0999

5645

256

3926

8165

1791

687

31.7

250.

0582

6337

636

8730

7660

2282

917

36.0

300.

1041

5081

452

9124

0840

1523

522

30.0

300.

0594

5968

835

4528

9580

1975

257

33.1

350.

0927

4552

242

2221

7057

1282

682

28.2

350.

0602

5614

333

7827

2272

1685

677

30.0

400.

0831

4130

134

3219

7924

1065

625

25.8

400.

0657

5276

534

6525

5163

1413

406

26.8

450.

0819

3786

931

0318

1586

8677

0122

.945

0.07

3549

300

3626

2374

3611

5824

223

.550

0.09

4334

766

3279

1656

3068

6115

19.7

500.

0922

4567

442

0921

7849

9208

0620

.255

0.12

5131

487

3938

1475

8952

0484

16.5

550.

1225

4146

550

8119

4623

7029

5717

.060

0.16

9727

549

4675

1260

5737

2896

13.5

600.

1617

3638

458

8516

7208

5083

3414

.065

0.23

9422

874

5476

1006

7824

6839

10.8

650.

2228

3049

967

9613

5506

3411

2611

.270

0.32

2917

397

5617

7294

314

6161

8.4

700.

3101

2370

373

5110

0139

2056

208.

775

0.44

0211

780

5186

4593

673

218

6.2

750.

4205

1635

268

7664

570

1054

816.

580

+1.

0000

6594

6594

2728

227

282

4.1

80+

1.00

0094

7694

7640

910

4091

04.

3(C

onti

nued

onne

xtpa

ge)

72 HISTORICAL METHODS

TA

BL

E8.

New

Lif

eTa

bles

for

the

Whi

teP

opul

atio

nof

the

Uni

ted

Stat

es,1

780–

1900

(Con

tinue

d)

Whi

tem

ales

,187

0–79

Whi

tefe

mal

es,1

870–

79

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

1753

1000

0017

533

8825

343

9889

944

.00

0.16

1610

0000

1616

489

493

4387

395

43.9

10.

0406

8246

733

4880

491

4310

646

52.3

10.

0407

8383

634

1581

821

4297

902

51.3

20.

0180

7911

914

2278

365

4230

154

53.5

20.

0195

8042

115

6679

591

4216

081

52.4

30.

0116

7769

690

077

228

4151

790

53.4

30.

0125

7885

598

778

341

4136

491

52.5

40.

0090

7679

669

176

436

4074

562

53.1

40.

0099

7786

876

977

468

4058

149

52.1

50.

0257

7610

519

5837

5629

3998

125

52.5

50.

0272

7709

920

9938

0246

3980

681

51.6

100.

0185

7414

713

6936

7311

3622

496

48.9

100.

0197

7500

014

8037

1300

3600

435

48.0

150.

0291

7277

821

2035

8587

3255

185

44.7

150.

0337

7352

024

7536

1412

3229

135

43.9

200.

0409

7065

728

8834

6066

2896

598

41.0

200.

0468

7104

533

2734

6906

2867

723

40.4

250.

0434

6776

929

4133

1493

2550

532

37.6

250.

0512

6771

734

6532

9924

2520

817

37.2

300.

0455

6482

829

4931

6767

2219

039

34.2

300.

0530

6425

234

0831

2742

2190

893

34.1

350.

0506

6187

931

2930

1571

1902

272

30.7

350.

0544

6084

433

1029

5946

1878

151

30.9

400.

0574

5875

033

7328

5316

1600

701

27.2

400.

0598

5753

434

4127

9069

1582

205

27.5

450.

0689

5537

738

1626

7343

1315

385

23.8

450.

0677

5409

436

6326

1310

1303

136

24.1

500.

0851

5156

043

8724

6834

1048

042

20.3

500.

0856

5043

143

1724

1360

1041

825

20.7

550.

1172

4717

355

2922

2044

8012

0817

.055

0.11

5046

113

5304

2173

0780

0466

17.4

600.

1602

4164

566

7219

1542

5791

6413

.960

0.15

3140

810

6247

1884

3158

3158

14.3

650.

2284

3497

279

8915

4888

3876

2211

.165

0.21

3534

563

7379

1543

6539

4728

11.4

700.

3124

2698

384

2911

3844

2327

358.

670

0.30

0527

183

8167

1154

9924

0362

8.8

750.

4323

1855

480

2172

720

1188

916.

475

0.41

2719

016

7847

7546

212

4863

6.6

80+

1.00

0010

533

1053

346

171

4617

14.

480

+1.

0000

1116

911

169

4940

149

401

4.4

Whi

tem

ales

,188

0–89

Whi

tefe

mal

es,1

880–

89

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

1494

1000

0014

936

8999

347

0822

447

.10

0.13

1710

0000

1316

891

441

4795

214

48.0

10.

0354

8506

430

0883

290

4618

231

54.3

10.

0340

8683

229

5085

091

4703

773

54.2

20.

0158

8205

612

9681

370

4534

941

55.3

20.

0163

8388

213

6583

158

4618

682

55.1

30.

0102

8076

182

380

333

4453

571

55.1

30.

0105

8251

786

882

065

4535

524

55.0

40.

0079

7993

863

179

609

4373

238

54.7

40.

0083

8164

967

681

297

4453

458

54.5

50.

0225

7930

717

8639

2067

4293

629

54.1

50.

0228

8097

318

4840

0245

4372

161

54.0

100.

0158

7752

012

2838

4531

3901

562

50.3

100.

0162

7912

512

8239

2420

3971

916

50.2

150.

0251

7629

219

1137

6682

3517

031

46.1

150.

0276

7784

321

5238

3834

3579

496

46.0

200.

0357

7438

126

5736

5261

3140

349

42.2

200.

0389

7569

129

4137

1101

3195

662

42.2

250.

0384

7172

427

5635

1729

2775

089

38.7

250.

0431

7275

031

3635

5908

2824

561

38.8

300.

0411

6896

828

3133

7761

2423

360

35.1

300.

0455

6961

431

6534

0155

2468

652

35.5

350.

0461

6613

730

5032

3058

2085

599

31.5

350.

0473

6644

931

4332

4385

2128

497

32.0

400.

0526

6308

733

2130

7129

1762

541

27.9

400.

0525

6330

633

2030

8227

1804

112

28.5

450.

0636

5976

538

0028

9325

1455

412

24.4

450.

0602

5998

536

1029

0900

1495

886

24.9

500.

0791

5596

544

2926

8753

1166

086

20.8

500.

0770

5637

543

3827

1030

1204

985

21.4

550.

1098

5153

656

5824

3536

8973

3317

.455

0.10

4852

037

5452

2465

5393

3955

17.9

600.

1511

4587

869

3321

2057

6537

9714

.360

0.14

1146

584

6575

2164

8468

7402

14.8

650.

2176

3894

584

7617

3534

4417

4011

.365

0.20

0340

009

8013

1800

1347

0917

11.8

700.

3017

3046

991

9312

9361

2682

068.

870

0.28

6631

996

9169

1370

5729

0904

9.1

750.

4241

2127

690

2383

821

1388

456.

575

0.40

1122

827

9156

9124

315

3848

6.7

80+

1.00

0012

253

1225

355

024

5502

44.

580

+1.

0000

1367

113

671

6260

462

604

4.6

(Con

tinu

edon

next

page

)

April–June 2010, Volume 43, Number 2 73

TA

BL

E8.

New

Lif

eTa

bles

for

the

Whi

teP

opul

atio

nof

the

Uni

ted

Stat

es,1

780–

1900

(Con

tinue

d)

Whi

tem

ales

,189

0–99

Whi

tefe

mal

es,1

890–

99

Age

q xl x

d xL

xT

xe x

Age

q xl x

d xL

xT

xe x

00.

1305

1000

0013

052

9125

549

4801

349

.50

0.11

0610

0000

1105

992

812

5121

250

51.2

10.

0316

8694

827

5085

326

4856

758

55.9

10.

0292

8894

125

9987

407

5028

438

56.5

20.

0142

8419

811

9983

563

4771

433

56.7

20.

0140

8634

212

0985

701

4941

031

57.2

30.

0092

8299

976

482

602

4687

870

56.5

30.

0091

8513

277

584

730

4855

330

57.0

40.

0071

8223

558

481

931

4605

268

56.0

40.

0071

8435

860

384

044

4770

601

56.6

50.

0202

8165

116

4940

4130

4523

337

55.4

50.

0196

8375

516

4641

4661

4686

556

56.0

100.

0139

8000

111

0939

7235

4119

207

51.5

100.

0136

8211

011

1740

7756

4271

895

52.0

150.

0219

7889

217

3139

0135

3721

972

47.2

150.

0231

8099

318

7340

0281

3864

139

47.7

200.

0318

7716

224

5437

9672

3331

837

43.2

200.

0328

7912

025

9438

9113

3463

858

43.8

250.

0347

7470

725

9036

7061

2952

165

39.5

250.

0369

7652

628

2537

5565

3074

744

40.2

300.

0378

7211

727

2535

3773

2585

104

35.8

300.

0396

7370

129

2036

1203

2699

179

36.6

350.

0429

6939

229

7533

9522

2231

331

32.2

350.

0418

7078

129

5734

6511

2337

976

33.0

400.

0492

6641

732

6632

3918

1891

809

28.5

400.

0467

6782

431

6533

1204

1991

465

29.4

450.

0597

6315

137

6830

6333

1567

890

24.8

450.

0542

6465

835

0531

4527

1660

260

25.7

500.

0748

5938

244

3928

5814

1261

557

21.2

500.

0700

6115

342

8129

5060

1345

733

22.0

550.

1043

5494

357

2926

0394

9757

4417

.855

0.09

6456

871

5485

2706

4610

5067

318

.560

0.14

4149

214

7092

2283

4271

5350

14.5

600.

1311

5138

767

3924

0086

7800

2715

.265

0.20

8942

123

8801

1886

1048

7008

11.6

650.

1889

4464

884

3520

2151

5399

4112

.170

0.29

2833

321

9758

1422

1329

8398

9.0

700.

2741

3621

399

2615

6250

3377

909.

375

0.41

7223

564

9831

9324

215

6185

6.6

750.

3903

2628

710

260

1057

8518

1540

6.9

80+

1.00

0013

733

1373

362

943

6294

34.

680

+1.

0000

1602

716

027

7575

475

754

4.7

74 HISTORICAL METHODS

TABLE 9. Proportion Dying in Age Interval x to x + n, White Males

Exact age, x n 1860 1861 1862 1863 1864 1865 1866–69

0 1 0.2071 0.2071 0.2071 0.2071 0.2071 0.2071 0.20711 1 0.0469 0.0469 0.0469 0.0469 0.0469 0.0469 0.04692 1 0.0206 0.0206 0.0206 0.0206 0.0206 0.0206 0.02063 1 0.0132 0.0132 0.0132 0.0132 0.0132 0.0132 0.01324 1 0.0103 0.0103 0.0103 0.0103 0.0103 0.0103 0.01035 5 0.0295 0.0295 0.0295 0.0295 0.0295 0.0295 0.0295

10 5 0.0216 0.0216 0.0216 0.0216 0.0216 0.0216 0.021615 5 0.0339 0.0434 0.1300 0.1507 0.1682 0.1451 0.033920 5 0.0468 0.0549 0.1398 0.1821 0.2518 0.1703 0.046825 5 0.0491 0.0587 0.1440 0.1713 0.2230 0.1445 0.049130 5 0.0505 0.0588 0.1383 0.1755 0.2391 0.1617 0.050535 5 0.0555 0.0605 0.1118 0.1403 0.1927 0.1411 0.055540 5 0.0626 0.0656 0.0900 0.1069 0.1410 0.1148 0.062645 5 0.0747 0.0762 0.0829 0.0892 0.1032 0.0957 0.074750 5 0.0915 0.0955 0.0962 0.0971 0.0992 0.0990 0.091555 5 0.1251 0.1251 0.1251 0.1251 0.1251 0.1251 0.125160 5 0.1697 0.1697 0.1697 0.1697 0.1697 0.1697 0.169765 5 0.2394 0.2394 0.2394 0.2394 0.2394 0.2394 0.239470 5 0.3229 0.3229 0.3229 0.3229 0.3229 0.3229 0.322975 5 0.4402 0.4402 0.4402 0.4402 0.4402 0.4402 0.440280+ 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

from table 8 with a closely corresponding Haines life table.Figure 7 compares the 1870–79 life table for white males(e0 = 44.0) with Haines’s 1870 life table (e0 = 44.1). Ingeneral, there is close correspondence between the two age

profiles. The 1870–79 life table indicates higher mortalityrates between ages 10 and 30, but the difference is modest:age-specific mortality rates are 12 percent higher at age 20than in Haines’s table. Figure 8 compares the 1840–49 life

34.0

36.0

38.0

40.0

42.0

44.0

46.0

48.0

50.0

52.0

54.0

17901800

18101820

18301840

18501860

18701880

18901900

Year

Life

exp

ecta

ncy

at b

irth

(ye

ars)

New es�mates, white males

New es�mates, white females

Haines U.S. model, white males

Haines U.S. model, white females

FIGURE 6. Life expectancy at birth, white population of the United States, 1790–1900.

April–June 2010, Volume 43, Number 2 75

100

1,000

10,000

Age group

Pro

port

ion

dyin

g, 1

0,00

0 q x

(lo

g sc

ale)

Haines 1870 white males,U.S. model

New estimates, white males,1870-79

0 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

FIGURE 7. Proportion dying by age group, white males.

table for white females (e0 = 40.6) with Haines’s 1850 lifetable (e0 = 40.6). Again, with the exception of the part ofthe profile between adolescence and middle age, there isclose correspondence between the two age profiles. The dif-ference between the curves between ages 10 and 35, however,

is much greater. At age 20 the 1840–49 life table suggestsa mortality rate 27 percent higher than Haines’s 1850 lifetable.

Figure 8 also includes a modified plot of the proportionsdying in Haines’s 1850 female life table. The age-specific

100

1,000

10,000

Age group

Pro

port

ion

dyin

g, 1

0,00

0 q x

(lo

g sc

ale)

Haines 1850, white females,U.S. model

New estimates, white females,1840-49

Haines 1850, white females,adjusted

0 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

FIGURE 8. Proportion dying by age group, white females. qx = proportion dying in age interval.

76 HISTORICAL METHODS

death rates in the Haines table were modified by doublingcause-specific death rates attributed to pulmonary tubercu-losis and maternal mortality reported by Samuel Preston fornational populations with life expectancy at birth less than 45years (1976). The adjusted profile corresponds very closelywith the age profile of female mortality in the 1840–49 lifetable. Although speculative and seemingly large, the ad-justments correspond with what we know about changesin mortality and fertility between the mid-nineteenth andearly twentieth centuries. As discussed earlier, mortality fromtuberculosis fell rapidly in the late nineteenth century. Fer-tility among women in the 1900–1902 DRA life table wasapproximately half that of women in the 1840s. Although wecannot know the true age-specific mortality rates for whitewomen in the 1840s, it is likely that the profile differed fromthat in the 1900–1902 DRA in the way indicated.

All of the decennial tables in table 8 are based, of course,on assumptions with substantial risk of error. Much moreresearch is needed on biases in demographic estimation fromgenealogical sources. Although based in part on comparisonwith other sources and in part on the suspected impact ofmigration censoring and selection biases, the crude assump-tions about the overestimation of adult life expectancy ingenealogical-based estimates and sex differentials in adultlife expectancy made in tables 4 and 5 are sources of po-tential error. Another weakness is the required method ofinferring a complete life table from a single parameter, lifeexpectancy at age 20. Historical demographers in Europeand elsewhere have called attention to the changing relation-ship between infant, childhood, and adult mortality over thecourse of the nineteenth century (Woods 1993). It is unlikelythat the United States was an exception. Empirical researchon infant and childhood mortality in the United States issorely needed. Source material, however, remains a majorissue.

Despite these caveats, the life tables shown in table 8should prove useful for a wide variety of historical research.In addition to capturing known mortality trends not reflectedin existing life tables, they more accurately represent thelikely sex- and age-specific profile of nineteenth-centurymortality. The life tables should also prove useful as a point ofreference for subsequent studies and critiques. With any luck,nineteenth-century demographers will have more choices oflife tables with a firmer empirical base in the not too distantfuture.

NOTES

1. This work was supported in part by NIHCD grant number 1 K01-HD052617–01 and an Arthur H. Cole Grant-in-Aid Award from the Eco-nomic History Association. The author would like to thank Samuel H.Preston, Douglas Ewbank, and Michael R. Haines for helpful comments.

2. The original Death Registration Area (DRA) included only 10 statesand the District of Columbia. The system was deemed complete in 1933,when Texas was added to the system, although considerable underreportingof births and deaths continued to plague the system until the 1940s.

3. Although Massachusetts’s death registration system was implementedin 1842, it took several years for the system to become effective. By 1860,

Robert Gutman has estimated that only 8 percent of deaths were unrecorded(Vinovskis 1972, 186).

4. Condran and Crimmins’s (1980, 188–90) application of the ChandraSekar-Deming technique suggests that approximately 85 percent of deathsin rural areas and 92 percent of deaths in urban areas were registered. Infantdeaths were missed more often than deaths at other ages.

5. Other potential problems include the possibility that the deaths ofchildren ages 5–19, while more fully enumerated than deaths at other ages,were still underreported, and the possibility of a changing level of undercountfrom census to census. If underreporting was significant, the Haines (1998)life tables may overstate life expectancy. The addition of some state deathregisters in 1880 likely lowered the overall undercount and may explainsome of the sharp decline in life expectancy between the 1870 and 1880estimates.

6. Kasakoff and Adams (1995) report the average age at death by birthcohort, not period. In the figure, the cohort estimates are offset 20 years toincrease comparability.

7. The urban population is defined liberally as all individuals livingin urbanized areas and in all places of 2,500 or more residents outsideof urbanized areas. The percentage living in large cities with significantsanitation problems was much smaller. The urban population increasedfrom 6.1 percent in 1800 to 10.8 percent in 1840, 28.2 percent in 1880, and51.2 percent in 1920. By the turn of the century, when urbanization wassignificant enough to pose a major impact on national life expectancy, thepublic health movement had made significant strides in introducing cleanwater supplies, sewer systems, and other public health projects, greatlyreducing the urban-rural differential in life expectancy.

8. Considerable uncertainties surround estimates of real national incomein the early nineteenth century. Most economic historians conclude that therewas a sharp increase in real economic growth in the 1820s. According toRichard Sutch, the annual growth rate between 1800 and 1828 averagedabout 0.6 percent per year. Between 1828 and 1860 it averaged more thantwice that rate (Sutch 2006).

9. Under some conditions, censoring bias does not impart a downwardbias in life expectancy estimates. If a researcher knows when an individualdisappeared from observation and if censored individuals experienced thesame risk of death as noncensored individuals, for example, it is possible toconstruct nonbiased age-specific mortality estimates. Relative to the exten-sive rules followed by analysts of community-based reconstitution studies,however, researchers relying on genealogical data have shown little inter-est in precisely determining when the population was under observation.Neither Kunze (1979) nor Pope (1992) appeared to have included risk yearsfrom right-censored individuals in the calculation of age-specific death rates.Only individuals with known birth and death dates are included. Given theseselection criteria, censoring bias will impart a downward bias (for an ex-tended rumination on biases in early American mortality studies, see Smith1979).

10. The average of Haines’s (1998) 1850 and 1860 U.S. model census-based estimates of life expectancy at age 20 was assumed to be representativeof the 1850s, the 1870 and 1880 estimates representative of the 1870s, andthe 1880 and 1890 estimates representative of the 1880s. It was not assumedthat the average of the 1860 and 1870 estimates would be representative ofthe 1860s, however, because the census-based estimates fail to consider theimpact of the U.S. Civil War (1861–65).

11. Although genealogies are successful in tracking some family mem-bers across time and space, migrating family members are more prone tobe lost from observation. Hall and Ruggles (2004) have shown that internalmigration in the United States exhibited a “U-shaped” pattern between 1850and 2000. Almost one-in-two whites age 50–59 between 1850 and 1880were living in a state other than their birth state. This ratio dropped steadilyafter 1880, reached a low of about one-in-three in the period 1940–70 andthen increased to over four-in-ten in the 2000 census.

12. Although Kunze’s (1979, 200) sample appears to be slightly largerthan Pope’s (1992, 282) sample, Kunze does not report the number of casesused in his period estimates. The combined estimates shown in table 5 aretherefore unweighted averages, smoothed slightly in the period before 1850.

13. Estimates of the white birth rate were obtained with stable populationmethods, the published age distributions of the 1800 census, and life tablesconstructed by fitting the adjusted and unadjusted Pope (1992) and Kunze(1979) estimate of life expectancy at age 20 to the 1901 rural DRA life tableas described in the latter part of this article.

14. Males and females enter Pope’s (1992) sample as either a childof bloodline parents or as a spouse of a bloodline individual. The former

April–June 2010, Volume 43, Number 2 77

contribute risk years from birth to death while the latter contribute risk yearsfrom marriage to death. Theoretically, there should be approximately equalnumbers of men and women in the samples. According to Pope’s illustrationof a “typical” family history, however, 11 percent of men in the genealogicalsamples had a missing birth date and 43 percent a missing death date. Thepercentages for women were 16 and 59 percent, respectively (ibid., 273). Asa result, Pope’s period life expectancy estimates are based on 3,166 malesand 2,338 females with known birth and death dates (ibid., 282). Kunze(1979, 200–204) does not discuss the completeness of his demographic databy sex, but similar differences are apparent in the number of males andfemales used in his analysis. Kasakoff and Adams (1995) report only themean age at death of males.

15. Stolnitz’s (1956, 23–25) classic review of long-term mortality trendscalled explicit attention to instances of higher female mortality in the pre-vious century. Although females in Western countries between 1840 and1910 typically enjoyed lower mortality rates during infancy and older ages,higher female mortality rates from late childhood through most of the child-bearing years was common. The modern pattern of lower female mortalityat all ages did not become typical until the 1930s. Although the life ta-bles Stolnitz examined tended to favor higher female life expectancy at allages, higher male life expectancy could be found across an “appreciable”range of ages well into the twentieth century in Ireland, Italy, Austria, andBulgaria.

16. Stolnitz (1956, 23–25) reported the largest persisting female disad-vantages in life expectancy among the Irish population, which experiencedhigh fertility, low nutritional status, preferential treatment for males, andendemic tuberculosis well into the twentieth century (ibid.; Kennedy 1973).

17. The results also suggest that Haines’s (1998) life tables overstatefemale life expectancy at age 20 relative to male life expectancy. The relativeoverstatement is likely a result of Haines’s choice of model life tables—a“U.S. model” constructed from the 1900–1902 DRA and Coale, Demeny,and Vaughan’s (1966) west model. Both models are based on the mortalityexperience of more urban and lower fertility populations than the nineteenth-century population of the United States. As discussed at greater length inthe section on the age profile of nineteenth-century mortality, these modelslikely understate female mortality during childbearing years relative to otherages and overstate female life expectancy at age 20 relative to male lifeexpectancy.

18. Details on weighting and combining the 1900–1902 overall and ruralDRA life tables can be found in the section on new decennial life tables andin note 24 below.

19. Four- and five-parameter models have also been proposed (see, e.g.,Ewbank, de Leon, and Stoto 1983).

20. Although Coale and Zelnik (1963, 168–69) observed a good corre-spondence between the 138 life tables that were used to construct the westmodel and the 1900–1902 DRA life table, only 36 of the 138 life tables camefrom nineteenth-century populations. The model matches the male experi-ence better than the female experience. Coale and Zelnik did not comparethe 1900–1902 rural DRA life table with the model.

21. Rebecca Kippen (2005) has noted that maternal deaths are often un-derreported in official statistics and in estimates derived from family recon-stitution studies. Her revised estimates of maternal mortality for nineteenth-century Tasmania—7 deaths per 1,000 live births—are approximately twiceas high as estimates derived from other sources. Even so, maternal mortalityremained a distant second leading cause of death among women age 29–44behind pulmonary tuberculosis.

22. It is important to remember that the 1900–1902 overall DRA in-cluded females in the rural DRA. The differences would have been greaterif we were able to compare urban females directly to rural females (for ananalysis of urban-rural mortality differentials in 1890 and 1900 see Condranand Crimmins 1980).

23. Typically, sex mortality differentials favor females at lower mortalitylevels. Sex differences in mortality between historical and modern popula-tions are the result of changes in causes of death associated with mortalitydecline. Female advantages in mortality at all ages emerged only with the de-cline of tuberculosis and other infectious diseases as leading causes of deathand their replacement with degenerative diseases. The decline of maternalmortality also played a small role (Preston 1976).

24. According to table 2, the 1900–1902 DRA life table was 60.1 per-cent urban and the 1900–1902 rural DRA life table was 13.2 percent ur-ban. If W1 is the weight needed for the overall DRA life table, W2 is theweight for the rural DRA life table, and the desired combined life tableis 40.2 percent urban, then (W1 × 60.1) + (W2 × 13.2) = 40.2. Further,

W1 + W2 = 1. Solving the second equation for W2, we get W2 = 1—W1.By substitution, the first equation becomes (W1 × 60.1) + ([1– W1] × 13.2)= 40.2. Solving for W1, we get 0.575. Substituting the result in the secondequation and solving yields 0.425 for W2.

25. Haines and Preston (1997, 77) state that the “improvement was mostrapid in large urban areas, where mortality had been the worst. The substan-tial urban mortality penalty . . . of the late nineteenth century was rapidlydisappearing by the early twentieth century. Public health improvements,better nutrition and shelter, and some advances in medical science all playeda role.”

26. Cohort differences between the male-female differential in 10-yearsurvivorship ratios in the 1860s relative to the average male-female differ-ential in 10-year survivorship ratios in the 1850s and 1870s were assumedto be because of the excess male mortality in the war. The estimate requiredfour major assumptions: (1) the native-born white population was closed tomigration; (2) changes in net census underenumeration had an equal impacton native-born white males and females; (3) foreign-born white men suf-fered rates of mortality in the war equal to the native-born white population;and (4) there were negligible civilian deaths among native-white women age15–45. For the approximately equal rates of mortality among foreign-bornand native-born men, see Lee (2003, 60). For the limited number of civil-ian casualties in the U.S. Civil War, see McPherson (1988, 619) and Neely(2007). Although the resulting estimate of approximately 713,000, excessmale deaths is larger than the 588,000 usually attributed to white men in thewar, there are many reasons to assume the 588,000 figure is too low (Hacker1999, chap. 2; Faust 2006).

27. The Union Army data set, collected by the University of ChicagoCenter for Population Economics and Brigham Young University under thedirection of Robert W. Fogel, was used to parse deaths by year and withinfive-year age groups.

REFERENCES

Abbott, S. W. 1899. A Massachusetts life table for the five years 1893–97. InThirteenth annual report of the State Board of Health of Massachusetts,797–827. Boston: Wright and Potter.

Alter, G., M. Manfredini, and P. Nystedt. 2004. Gender differences in mor-tality. In Life under pressure: Mortality and living standards in Europeand Asia, 1700–1900, ed., T. Bengtsson, C. Campbell, and J. Z. Lee,327–57. Cambridge, MA: The MIT Press.

Anderson, M. J. 1988. The American census: A social history. New Haven,CT: Yale University Press.

Anderton, D. L., and S. H. Leonard. 2004. Grammars of death: An analysisof nineteenth-century literal causes of death from the age of miasmas togerm theory. Social Science History 28:111–43.

Beemer, J. K., D. L. Anderton, and S. Hautaniemi Leonard. 2005. Sewers inthe city: A case study of individual-level mortality and public health ini-tiatives in Northampton, Massachusetts at the turn of the century. Journalof the History of Medicine and the Allied Sciences 60:42–72.

Billings, J. S. 1885. Report on the mortality and vital statistics of the UnitedStates as returned by the tenth census. U.S. Census of Population: 1880,vol. 10, part 1. Washington, DC: Government Printing Office.

———. 1886. U.S. report on the mortality and vital statistics of the UnitedStates as returned by the tenth census. U.S. Census of Population: 1880,vol. 10, part 2. Washington, DC: Government Printing Office.

Brass, W. 1971. On the scale of mortality. In Biological aspects of demog-raphy, ed. W. Brass. London: Taylor & Francis.

Campbell, C., J. Z. Lee, and T. Bengtsson. 2004. Economic stress andmortality. In Life under pressure: Mortality and living standards in Europeand Asia. ed., T. Bengtsson, C. Campbell, J. Z. Lee, 61–84.

Cheney, R. A. 1984. Seasonal aspects of infant and childhood mortal-ity: Philadelphia, 1865–1920. Journal of Interdisciplinary History 14:561–85.

Coale, A. J., P. Demeny, and B. Vaughan. 1966. Regional model life tablesand stable populations. Princeton, NJ: Princeton University Press.

Coale, A. J., and M. Zelnik. 1963. New estimates of fertility and populationin the United States: A study of annual white births from 1855 to 1960and of completeness of enumeration in the censuses from 1880 to 1960.Princeton, NJ: Princeton University Press.

78 HISTORICAL METHODS

Condran, G. A. 1987. Declining mortality in the United States in thelate nineteenth and early twentieth centuries. Annales de DemographieHistorique:119–37.

Condran, G. A., and R. A. Cheney. 1982. Mortality trends in Philadel-phia: Age- and cause-specific death rates 1870–1930. Demography 19:97–123.

Condran, G. A., and E. Crimmins. 1979. A description and evaluation of mor-tality data in the federal census: 1850–1900. Historical Methods 12:1–23.

———. 1980. Mortality differentials between rural and urban areas of statesin the northeastern United States, 1890–1900. Journal of Historical Ge-ography 6:179–202.

Costa, D. L., and R. H. Steckel. 1997. Long-term trends in health, welfare,and economic growth in the United States. In Health and Welfare dur-ing Industrialization, ed. R. H. Steckel and R. Floud, 47–89. Chicago:University of Chicago Press.

Crimmins, E. M. 1980. The completeness of 1900 mortality data collectedby registration and enumeration for rural and urban parts of states: Es-timates using the Chandra Sekar-Deming technique. Historical Methods13:163–69.

Davin, E. L. 1993. The era of the common child: Egalitarian death in ante-bellum America. Mid-America 75:134–63.

De Bow, J. D. B. 1855. Mortality statistics of the seventh census of theUnited States, 1850. Washington, DC: United States Census Office, A.O. P. Nicholson.

Duffy, J. 1990. The sanitarians: A history of American public health. Ur-bana: University of Illinois Press.

Easterlin, R. A. 1977. Population issues in American economic history:A survey and critique. In Recent developments in the study of businessand economic history: Essays in honor of Herman E. Krooss, ed. R. E.Gallman, 131–58. Greenwich, CT: JAI Press.

Elliott, E. B. 1858. On the law of human mortality that appears to obtainin Massachusetts, with tables of practical value deduced therefrom. InProceedings of the American Association for the Advancement of Science,ed. J. Lovering. 51–81. Cambridge, MA: Allen and Farnham.

———. 1874. An approximate life-table for the United States on the basisof the ninth census, 1870. In U.S. Census Office, Statistical atlas ofthe United States based on the results of the Ninth Census 1870, withcontributions from many eminent men of science and several departmentsof the government, 1–3. New York Bien.

Ewbank, D. C. 1987. History of black mortality and health before 1940.Milbank Quarterly 65:100–28.

Ewbank, D. C., J. C. G. de Leon, and M. A. Stoto. 1983. A reducible4-parameter system of model life-tables. Population Studies 37:105–27.

Faust, D. G. 2006. “Numbers on top of numbers”: Counting the Civil Wardead. Journal of Military History 70:995–1010.

Ferrie, J. P. 1996. A new sample of males linked from the public use mi-crodata sample of the 1850 U.S. federal census of population to the 1860U.S. federal census manuscript schedules. Historical Methods 29:141–56.

———. 2003. The rich and the dead: Socioeconomic status and mortality inthe United States, 1850–1860. In Health and Labor Force Participationover the Life Cycle: Evidence from the Past, ed. D. L. Costa, 11–50.Chicago: University of Chicago Press.

Fogel, R. W. 1986. Nutrition and the decline in mortality since 1700:Some preliminary findings. In Long-term Factors in American EconomicGrowth, ed. S. Engerman and R. Gallman, 439–527. Chicago: Universityof Chicago Press.

Glover, J. W. 1921. United States life tables, 1890, 1901, 1910, and1901–1910. Washington, DC: Government Printing Office.

Grabill, W. H., C. V. Kiser, and P. K. Whelpton. 1958. The fertility ofAmerican women. New York: Wiley.

Hacker, J. D. 1996. Adult mortality in early New England: Evidence fromthe graduates of Yale College, 1701–1805. Master’s diss. Minneapolis:University of Minnesota.

———. 1997. Trends and determinants of adult mortality in early NewEngland: Reconciling old and new evidence from the long eighteenthcentury. Social Science History 21:481–519.

———. 1999. The human cost of war: White population in the UnitedStates, 1850–1880. PhD. Thesis. Department of History. Minneapolis,University of Minnesota.

———. n.d. Rethinking the “early” decline of marital fertility in the UnitedStates. Demography 40:605–20.

Haines, M. R. 1979. The use of model life tables to estimate mortalityfor the United States in the late nineteenth century. Demography 16:289–312.

———. 1998. Estimated life tables for the United States, 1850–1910. His-torical Methods 31:149–69.

Haines, M. R., and R. C. Avery. 1980. The American life table of1830–1860: An evaluation. Journal of Interdisciplinary History 11:73–95.

Haines, M. R., L. A. Craig, and T. Weiss. 2003. The short and the dead:Nutrition, mortality, and the “antebellum puzzle” in the United States.Journal of Economic History 63:382–413.

Haines, M. R., and S. H. Preston. 1997. The use of the census to es-timate childhood mortality: Comparisons from the 1900 and 1910United States census public use samples. Historical Methods 30:77–97.

Hall, P. K., and S. Ruggles. 2004. Restless in the midst of their pros-perity: New evidence of the internal migration patterns of Americans,1850–1990. Journal of American History 91:829–46.

Harrison, R. A. 1981. Princetonians, 1776–1783: A biographical dictionary.Princeton, NJ: Princeton University Press.

Harrison, R. A., J. McLachlan, and R. L. Woodward. 1980. Princetonians,1769–1775: A biographical dictionary. Princeton, NJ: Princeton Univer-sity Press.

Hautaniemi, S. I., A. C. Swedlund, and D. L. Anderton. 1999. Mill townmortality: Consequences of industrial growth in two nineteenth-centuryNew England towns. Social Science History 1999:1–39.

Heligman, L., and J. H. Pollard. 1980. The age pattern of mortality. Journalof the Institute of Actuaries 107:49–80.

Henry, L. 1989. Men’s and women’s mortality in the past. Population: AnEnglish Selection 44:177–201.

Higgs, R. 1973. Mortality in rural America. Explorations in Economic His-tory 10:177–95.

Jacobson, P. H. 1957. An estimate of the expectation of life in the UnitedStates in 1850. Milbank Memorial Fund Quarterly 35:197–201.

Jaffe, A. J., and W. I. Lourie Jr. 1942. An abridged life table for the whitepopulation of the United States in 1830. Human Biology 14:352–71.

Johansson, S. R. 1977. Sex and death in Victorian England. In A wideningsphere: Changing roles of Victorian women, ed. M. Vicinus, 163–81.Bloomington: Indiana University Press.

Jones, E. F. 1971. Fertility decline in Australia and New Zealand 1861–1936.Population Index 37:301–38.

Kasakoff, A. B., and J. W. Adams. 1995. The effect of migration on ages atvital events: A critique of family reconstitution in historical demography.European Journal of Population 11:119–242.

———. 2000. The effects of migration, place, and occupation on adult mor-tality in the American North, 1740–1880. Historical Methods 33:115–30.

Kennedy, R. E., Jr. 1973. The Irish: Emigration, marriage, and fertility.Berkeley: University of California Press.

Kippen, R. 2005. Counting nineteenth-century maternal deaths: The case ofTasmania. Historical Methods 38:14–25.

Kunitz, S. J. 1984. Mortality change in America, 1620–1920. Human Biol-ogy 56:559–82.

Kunze, K. 1979. The effects of age composition and changes in vital rates onnineteenth century population estimates from new data. Salt Lake City,UT: Department of Economics, University of Utah.

Kuznets, S. 1965. Long swings in population growth and related economicvariables. In Economic growth and structure: Related essays, 328–78.New York: Norton.

Lee, C. 1997. Socioeconomic background, disease, and mortality amongUnion Army recruits: Implications for economic and demographic his-tory. Explorations in Economic History 34:27–55.

———. 2003. Prior exposure to disease and later health and mortality:Evidence from Civil War medical records. In Health and labor forceparticipation over the life cycle: Evidence from the past, ed. D. L. Costa,51–87. Chicago: University of Chicago Press.

Levy, D. S. 1996. Long-run geographic and temporal changes in mortalityin the colonial South. Paper delivered at the annual meeting of the SocialScience History Association, New Orleans, LA.

Logue, B. J. 1991. In pursuit of prosperity: Disease and death in aMassachusetts commercial port, 1660–1850. Journal of Social History25:309–43.

Looney, J. J., and R. L. Woodward. 1991. Princetonians, 1791–1794: Abiographical dictionary. Princeton, NJ: Princeton University Press.

April–June 2010, Volume 43, Number 2 79

McClelland, P. D., and R. J. Zeckhauser. 1982. Demographic dimensions ofthe new republic: American interregional migration, vital statistics, andmanumissions, 1800–1860. New York: Cambridge University Press.

McDaniel, A., and C. Grushka. 1995. Did Africans live longer in the ante-bellum United States? The sensitivity of mortality estimates of enslavedAfricans. Historical Methods 28:97–105.

McLachlan, J. 1976. Princetonians, 1748–1768: A biographical dictionary.Princeton, NJ: Princeton University Press.

McPherson, J. M. 1988. The battle cry of freedom: The Civil War era. NewYork: Oxford University Press.

Meech, L. 1898. System and tables of life insurance. New York: Spectator.Meeker, E. 1972. The improving health of the United States, 1850–1915.

Explorations in Economic History 9: 353–73.Meindl, R. S., and A. C. Swedlund. 1977. Secular trends in mortality in the

Connecticut Valley, 1700–1850. Human Biology 49:389–414.Murray, J. E. 1997. Standards of the present for people of the past: Height,

weight, and mortality among men of Amherst College, 1834–1949. Jour-nal of Economic History 57:585–606.

———. 2000. Marital protection and marital selection: Evidence froma historical-prospective sample of American men. Demography 37:511–21.

Neely, M. E., Jr. 2007. The Civil War and the limits of destruction. Cam-bridge, MA: Harvard University Press.

Newell, C. 1988. Methods and models in demography. New York: Guilford.Pope, C. L. 1992. Adult mortality in America before 1900: A view from

family histories. In Strategic Factors in Nineteenth Century AmericanEconomic History: A Volume to Honor Robert W. Fogel, ed. C. Goldinand H. Rockoff, 267–96. Chicago: University of Chicago Press.

Preston, S. H. 1975. The changing relation between mortality and level ofeconomic development. Population Studies 29:231–48.

———. 1976. Mortality patterns in national populations: With special ref-erence to recorded causes of death. New York: Academic Press.

Preston, S. H., and N. G. Bennett. 1983. A census-based method for esti-mating adult mortality. Population Studies 37:91–104.

Preston, S. H., and M. R. Haines. 1991. Fatal years: Child mortality inlate nineteenth-century America. Princeton, NJ: Princeton UniversityPress.

Preston, S. H., P. Heuveline, and M. Guillot. 2001. Demography: Measuringand modeling population processes. Malden, MA: Blackwell.

Ruggles, S., M. Sobek, J. T. Alexander, C. A. Fitch, R. Goeken, P. Kelly, et al.2009. Integrated Public Use Microdata Series: Version 4.0. Minneapolis,MN: Minnesota Population Center [producer and distributor].

Rutman, D. B., and A. H. Rutman. 1976. Of agues and fevers: Malaria inthe early Chesapeake. William and Mary Quarterly 33:31–60.

Schofield, R. S. 1986. Did the mothers really die? Three centuries of maternalmortality in “the world we have lost.” In The world we have gained:Histories of population and social structure, ed. L. Bonfield, R. Smith,and K. Wrightson, 229–60. Oxford: Blackwell.

Smith, D. S. 1979. The estimates of early American historical demographers:Two steps forward, one step back, what steps in the future? HistoricalMethods 12:24–38.

———. 1982. Differential mortality in the United States before 1900. Jour-nal of Interdisciplinary History 13: 735–59.

———. 2003. Seasoning, disease environment, and conditions of exposure:New York Union army regiments and soldiers. In Health and labor forceparticipation over the life cycle: Evidence from the past, ed. D. L. Costa,11–50. Chicago: University of Chicago Press.

Steckel, R. H. 1988. The health and mortality of women and children,1850–1860. Journal of Economic History 48: 333–45.

———. 1991. The quality of census data for historical inquiry: A researchagenda. Social Science History 15: 579–99.

Stolnitz, G. 1956. A century of international mortality trends: II. PopulationStudies 10: 17–42.

Sutch, R. 2006. National income and product. In Historical statistics of theUnited States, ed. R. Sutch and S. B. Carter, Volume 3, 3.3–3.6. NewYork: Cambridge University Press.

Taeuber, C., and I. B. Taeuber. 1958. The changing population of the UnitedStates. New York: Wiley.

Thompson, W. S., and P. K. Whelpton. 1933. Population trends in the UnitedStates. New York: McGraw-Hill.

Tolnay, S. E., S. N. Graham, and A. M. Guest. 1982. Own-child estimatesof U.S. white fertility, 1886–99. Historical Methods 15:127–38.

Uhlenberg, P. R. 1969. A study of cohort life cycles: Cohorts of native bornMassachusetts women, 1830–1920. Population Studies 23:407–20.

United Nations. 1982. Model life tables for developing countries. PopulationStudies no. 77. New York: United Nations.

———. 1983. Manual X: Indirect techniques for demographic estimation.New York: United Nations.

U.S. Bureau of the Census. 1921. Fourteenth census of the United Statestaken in the year 1920: Population 1920, number and distribution ofinhabitants, volume 1. Washington, DC: Department of Commerce, Gov-ernment Printing Office.

Vinovskis, M. A. 1971. The 1789 life table of Edward Wigglesworth. Journalof Economic History 31:570–90.

———. 1972. Mortality rates and trends in Massachusetts before 1860.Journal of Economic History 32:184–213.

———. 1978. The Jacobson life table of 1850: A critical re-examinationfrom a Massachusetts perspective. Journal of Interdisciplinary History8:703–24.

———. 1989. Have social historians lost the Civil War? Some preliminarydemographic speculations. Journal of American History 76:34–58.

Wells, R. V. 1992. The population of England’s colonies in America: OldEnglish or new Americans? Population Studies 46:85–102.

Wigglesworth, E. 1793. A table shewing the probability of the duration, thedecrement, and the expectation of life, in the states of Massachusetts andNew Hampshire, formed from sixty-two bills of mortality on the files ofthe American Academy of Arts and Sciences. In Vol. II, Part I of Memoirs,131–35. Boston: Andrews.

Woods, R. 1993. On the historical relationship between infant and adultmortality. Population Studies 47: 195–219.

———. 2000. The demography of Victorian England and Wales. Cam-bridge: Cambridge University Press.

Woodward, R. L., and W. F. Craven. 1991. Princetonians, 1784–1790:A biographical dictionary. Princeton, NJ: Princeton UniversityPress.

Wrigley, E. A., and R. S. Schofield. 1981/1989. The population history ofEngland, 1541–1871. New York: Press Syndicate of the University ofCambridge.

Yasuba, Y. 1962. Birth rates of the white population in the UnitedStates, 1800–1860: An economic study. Baltimore, MD: John HopkinsPress.

Zhao, Z. 2001. Chinese genealogies as a source for demographic re-search: A further assessment of their reliability. Population Studies 55:181–93.

Copyright of Historical Methods is the property of Taylor & Francis Ltd. and its content may not be copied or

emailed to multiple sites or posted to a listserv without the copyright holder's express written permission.

However, users may print, download, or email articles for individual use.