partner search and the american marriage market, 1880 ... · partner search and the american...

Partner search and the American marriage market, 1880 – 1930

Tomas Cvrcek

December 2009

[preliminary work: please do not cite without permission from author]

Abstract: Both anecdotal and statistical evidence suggests that American marriage grew more unstable at the beginning of the 20th century. I investigate the causes behind this concurrent increase in marriage rate and in marital disruption rate. Economic theory from Becker (1993) suggests that causes of increased breakups occur either as a result of increased search costs (more bad matches in successive marriage cohorts) or a decrease in the value of marriage relative to outside options. I argue that although the value of singlehood increased in the early 20th century (particularly for working single women) the expected benefit of marriage increased even faster, leading more couples to tie the knot. At the same time, the early age at marriage meant that spouses married with less detailed knowledge about each other, leading to a greater probability of a later disruption.

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1. Introduction

The onset of the 20th century was a time of sweeping transformation of American marriage. The

1890s and early 1900s saw the reversal of many demographic and social trends: (i) after almost a

century of increase, the age at marriage peaked around 1900, commencing a sixty-year decline

(Haines, 1996); (ii) age difference between spouses also peaked around the same time, since the

movements in age at marriage were more pronounced for men than for women (Ferrie and Rolf, 2008);

(iii) as a corollary of the turnaround in age at marriage, reduced marital fertility replaced postponement

of marriage as the primary cause of decline in total fertility (Sanderson, 1979: Table 2); (iv) labor

supply of married women started to shift out of the household and the family farm or business into the

outside (“formal”) labor market (Goldin, 1990).

A newly added piece to this picture of transformation is (v) the steep increase in marital disruptions

– especially those which took the form of desertion or separation and never became official divorces in

court – as estimated by Cvrcek (2009). Not only was there an increase in the overall rate but the gap

between disruptions and divorces widened, indicating a declining reliance on legal procedure for

ending marriage among Americans. Divorce statistics therefore understate the extent of marital

instability; the disruption rate shows that the true level was up to twice as high and that it increased

significantly after the turn of the century. What were the causes behind the increase in disruptions?

How does this fit together with the broader picture of a transforming American family?

Most couples promise each other at the altar to stay together “till death do us part”, suggesting that

marriage is entered into with a long-term (even lifetime) horizon in mind. This applies even to

marriages that eventually prove short-lived. The concurrent increase in both the marriage rate and the

disruption rate therefore poses several questions: if one should argue that the marriage rate increased

because gains from marriage increased (relative to singlehood), then why did the marriages break up in

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greater numbers? Did the expected gains fail to materialize after the wedding? If so, why were early

20th-century couples so myopic as not to foresee this?

The period was a time of expanding opportunities for women – both single and married – which

introduced greater flexibility into how a household may operate.1 This probably increased the expected

gains from marriage on average given that utility is non-decreasing in the size of the choice set. Such

anincrease could account for the rise in marriage rate, provided the gains from marriage grew faster

than the value of being single. At the same time, however, the new options also increased the variance

around the higher expectation, and probably introduced more uncertainty into the search of a lifetime

partner. I argue that the declining age at marriage after 1900 represents a decline in the amount of

intensive search undertaken; i.e., men and women were getting married with less in-depth knowledge

about each other which was the direct cause of the increased variance in marital outcomes. Thus, a

greater proportion of matches produced outcomes that for one of the parties fell below the threat point,

the value of outside option, leading them to leave the marriage.

An alternative hypothesis (not explored here) would posit that the concurrent increase in both

marriage rate and the divorce rate was due to the decline in transition costs between the marital states.

It is not clear how much these costs changed in the period in question2, nor is it clear how important

they were in couples’ decision-making. After all, these costs are paid once and up-front and they must

be compared with the expected stream of lifetime benefits that would accrue from getting married or

from leaving an unhappy marriage.

1 For example, the employment during singlehood allowed even poor women, who otherwise would have no dowry, to accumulate at least some meager savings before setting up their own household after marriage and starting a family. The slowly growing married women’s employment brought about the possibility of two adult earners in a family.2 It may be argued that the costs of marital disruption actually increased in the first two decades of the 20th century and only later declined. See section 3.

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2. Historical overview of marriage and marital disruption

A wide array of evidence points to the 1890s and early 1900s as to a period of change in American

marriage. Figure 1 shows that a gradual post-Civil War decline in marriage rate was reversed right

before the turn of the century and the rate increased, by early 1920s, in fits and starts above 80

marriages per 1000 eligible women.3 Similar pattern is visible in other statistics, such as the indirect

median age at marriage which peaked, according to Fitch and Ruggles (2000: Table 4.1), in 1900 at

26.0 for white men and at 22.1 for white women, and declined for both thereafter. The changing

marriage behavior had a strong cohort component: lifetime-singlehood rate was the highest for the

birth cohort born around 1870 (who would be getting married in the 1890s and early 1900s), reaching

12% for men and 10% for women (Haines, 1996: Figure 3). Both preceding and subsequent

generations had lower rates of lifetime non-marriage. The beginning of the 20th century was therefore a

time of a renewed interest in marriage, setting a trend that would last until the 1960s (albeit with some

variation during the years of the Great Depression and the Second World War).

In Cvrcek (2008), I argued that the trend reversals in marriage behavior and fertility were a result

of increased bargaining power of single women. Industrial development and technological change

drew a growing proportion of single women into the labor force, increasing their threat points in

negotiation with potential suitors over the specifics of marriage. While at first the higher demands

made these working single women less desirable as potential partners (relative to non-working single

women), as their ranks swelled, the men eventually (around 1900) acquiesced and accepted the new

redistribution of gains from marriage. However, the increase in marital disruptions (i.e. all divorces,

desertions and separations combined) after 1900 may suggest that this new redistribution, agreed prior

to marriage, may have been difficult to enforce ex-post. The disruption rate (also presented in Figure 1)

picked up speed around 1900 and increased much faster than the divorce rate.

3 The marriage rate is very similar when calculated per 1000 eligible men.

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The gap, opening up between total disruptions and divorces after 1900, was made up of an

increasing number of unilateral desertions and mutually-agreed separations. In cases of separation and

desertion, a marriage ends ‘in real terms’, with the family ceasing to function as a social and economic

unit, but not in legal terms. Since most such non-legal breakups left no paper trail, it has long been

considered practically impossible to estimate their historical incidence (Plateris, 1973: 15; Price-

Bonham and Balswick, 1980: 966; Igra, 2007: 75). In Cvrcek (2009), their extent is estimated from an

integrated public-use microsample of the 1900, 1910 and 1950 censuses (Ruggles et al., 2008), from

data on the size of individual marriage cohorts (Jacobson, 1959) and from mortality statistics (Haines,

1998; Carter et al., 2006). Figure 2 uses the estimate to provide a cohort measure of the extent of

marital instability, giving the percentage of each marriage cohort that was disrupted during the cohort’s

lifetime. The growing gap between divorces and disruptions after 1890 shows that divorces alone

could not provide a complete picture of marital instability.

The causes behind the increased marital instability were unclear to contemporaries. Yet, although

they did not have any aggregate statistics on the incidence of desertions and separations, the

proliferation of contemporary literature on the topic, penned by social workers, illustrates the growing

awareness of the changing marital landscape (Brandt, 1972; Smith, 1901). They usually cast the causes

of marital disruption in terms of behavioral problems of the husbands (such as alcoholism, short

temper or vagrancy). Modern demographic literature, on the other hand, looks more closely at the

economic, social and demographic factors behind divorce and disruptions. Most studies show that the

risk of marital disruption declines with age at marriage, education, income, duration of marriage and

presence of children in the family (though not necessarily their number); and increases with premarital

childbearing, order of marriage and age gap between spouses (Becker et al., 1977; Rodrigues, Hall and

Fincham, 2006; Faust and McKibben, 1999). Premarital cohabitation is also associated with higher

incidence of subsequent marital break-up although expert opinion is split as to whether this is due to

selection or causation. There is some evidence that high ratio of wife’s to husband’s earnings is more

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conducive to marital disruption but Rodrigues et al. (2006: 90) claim that “the findings in this area

have been mixed”.

By way of motivation, Table 1 presents a simple regression of cohort and period disruption rates on

the marriage rate, the cyclical component of GDP per capita, a measure of urbanization, the singulate

mean age at marriage (SMAM) of husbands and the SMAM difference between wives and husbands in

the year of marriage of each cohort.4 The purpose of the regression is to establish certain broad

correlations.5 Cohort disruption rate is positively correlated with the marriage rate and for every extra

marriage per 1000 marriageable women, the cohort disruption rate increases by about 0.73 percentage

points. This result makes the marriage rate the most powerful explanatory variable in this regression

specification. When marriage rate increases, the resulting marriage cohort contains greater proportion

of ‘bad matches’ which eventually break up, increasing the disruption rate. In contrast to that, the

cyclical component of GDP does not have any meaningful effect; moreover, the coefficient is clearly

statistically insignificant. However, this is because the marriage rate and GDP per capita are highly

correlated – so it is reasonable to hypothesize that the state of the economy affects who gets married

and who does not.6 Through this – indirectly – the business cycle influences the marital stability of

various cohorts. The rate of urbanization has a strong and positive effect on the cohort disruption rate

and the two specifications show that the effect is not dependent on the choice of a threshold of

‘urbanity’. The urbanization variable clearly captures many of the modernizing trends then in

operation: industrialization, migration, population growth in general as well as growth of urban

population in particular. The signs are what one would expect for the two demographic variables, with

lower age at marriage (for which SMAM is a proxy) resulting in slightly lower disruption rate and

4 Singulate mean age at marriage (SMAM) is a demographic concept similar to life expectancy: they both measure the expected time to a life event. But while life expectancy measures years of life remaining to death, SMAM measures years of life remaining to marriage. Both are calculated using cross-sectional patterns (of death and marriage, respectively) and applying them to a cohort across the cohort’s hypothetical lifetime.5 Given that the dependent variable is by definition constrained to fall between 0 and 100, a truncated regression was also estimated but the coefficient or standard errors are not substantially different from those presented in Table 1.6 This is also born out by column (v) which shows that the marriage rate responds strongly to the business cycle.

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higher gap between husbands and wives leading to a higher rate. However, of these two variables only

the age gap has an effect of any practical importance and both coefficients are estimated with large

standard errors. The period disruption rate (in columns (iii) and (iv)) is somewhat sensitive to the

business cycle and the positive coefficient implies that when marriages disrupt, they do so more under

auspicious economic conditions, i.e. in times of boom. Partly, this is probably due to the effect of the

economy on the utility from marriage but to some extent, this coefficient is capturing the effect of

economic fluctuations on early marriages which affects the cohort rate and with which the period rate

is highly correlated.7

I interpret these broad correlations as preliminary evidence that the future success or failure of a

marriage is to a considerable degree determined on the marriage market. Although the period

disruption rate varies from year to year, suggesting ample room for specific period effects, most of this

variation comes from disruption of young marriages from recent marriage cohorts which is closely

correlated with the marriage rate, i.e. with the situation on the marriage market at the time when a

marriage cohort is formed. Period effects (i.e. a shock to all marriages across all durations in one

particular point in time) are likely secondary to the cohort effect of the conditions on the marriage

market. In plainer words, finding the right lifetime partner seems more important for the success of

marriage than being able to make ends meet in any particular year of marriage.

3. Theoretical considerations

If both marriage formation and marriage disruption had their root in the changing dynamics of the

marriage market, what can be said about this market operates in general? Becker et al. (1977)

distinguish between extensive and intensive search costs, corresponding to how difficult it is to meet

new people and how hard it is to get to know them well. Extensive search costs will be high if it is

difficult to encounter the opposite sex (for example, due to high segregation by gender). They will also

7 Conditional on divorcing at some point in life, 50% of couples separate (i.e. stop living with each other) within the first five years of marriage, hence the high correlation.

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be high if a person looks for a spouse with a particular trait that is rare in a population, such as finding

a high-earner in a poor neighborhood. In such instances, a person will be willing to accept a match

with a suitor farther away from the “optimal” trait, entering a marriage with a lower expected benefit.

The extensive search cost therefore affects the mean (the expectation) of gains from marriage.

Intensive search costs, on the other hand, affect the variance of potential marriage outcome (as well

as the mean). Even a partner who is currently a high earner may not turn out to be such in the future.

For example, marrying a medical student may carry the prospect of potentially high earnings but, of

course, the actual outcome will depend on how good or bad a doctor the medical student turns out to be

eventually. While it may be easy to meet and become acquainted many medical students in a brief

period of time (low extensive search costs), it will likely be harder to establish how well remunerated

they will eventually be (high intensive search costs). Perhaps even more relevant for marriage is

physical attraction: finding a person with attractive features is a matter of extensive search because

physical features (such as face, height and bodily constitution) are relatively easy to observe. However,

finding out whether a couple is mutually sexually compatible is not immediately observable and is a

matter of intensive search. The costs of such search (along both the extensive and the intensive margin)

change through time, for example as clothing becomes more revealing and the costs associated with

premarital sex (risk of pregnancy, STDs or loss of reputation etc.) decline.

The reason why both extensive and intensive search is important is that the quality of a match is an

‘input’ in the overall utility from marriage. This in turn determines how valuable a marriage is relative

to the outside options. There are of course other factors that affect how welfare inside and outside

marriage compares, such as the presence and number of children in a family (and other such examples

of what Becker (1993) calls marital specific capital) or the situation on the remarriage market and

opportunities for financial independence outside marriage (such as ease of finding employment for

housewives) etc. which affect the outside options of each party in marriage. Changing outside options

obviously affect even surviving marriages because they act as threat points in intra-marital bargaining

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(Stevenson and Wolfers, 2006) but as long as the sum of outside options of the two spouses is lower

than the overall gain generated inside marriage, there exists an allocation of the gains such that both

spouses find it preferable to stay married.8 Otherwise, the marriage breaks down.

Existing theory on intra-marital bargaining and marital discord, as expounded in Becker (1993),

Becker et al. (1977), McElroy and Horney (1981) and Pollak and Lundberg (1993), attribute marital

disruption to one of three causes: 1. an increase in search costs which introduces more uncertainty into

finding a spouse and produces more bad matches among the newly married 2. an improvement in

outside options of married people which increases their incentives to leave their marriage, and 3.

(which is a flip side of 2.) the gains from marriage decline for one of the parties. Note that all the

socio-economic and demographic factors mentioned in previous section can be easily recast in terms of

these three underlying causes. For example, wide age gap or large disparity in education between

spouses are a sign of a suboptimal spouse match while high earnings potential of the wife implies her

good outside options which make leaving an unsatisfactory union relatively easier and more appealing.

How do these theoretical considerations bear on the question at hand? In the early 20th century,

both marriage and disruption were on the rise. The increase in marriage rate suggests that the expected

gains from marriage relative to alternatives must have increased. Such a development is possible if the

extensive search costs fall and men and women are better able to find a partner who is close to their

“ideal”. Alternatively, a change in technology may make it easier to generate gains from marriage,

increasing the returns on investment in marital-specific capital.

The increase in disruption rate is consistent with an increase in intensive search costs which

increased the variance of marital outcomes, causing a greater proportion of them falling below the

threshold posed by the outside option of divorce or desertion. Given that a major component of the

intensive search cost is time (or more precisely the duration of a relationship before marriage), some

8 Of course, this argument relies on the assumption of zero (or low) bargaining costs, the empirical validity of which is open to question.

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evidence of the increased intensive search cost comes from the fact that the age at marriage declined

after 1900. Koller (1951: Table 2) shows that the number of suitors seriously considered by women as

potential husbands did not change significantly between the 1890s and the 1940s, with 80-90% of

women across all generations in his sample claiming to have seriously considered no more than two

men. Moreover, in each of the three generations he studies, the women’s first date with their future

husband occurred at about the same age (19). If age at marriage was declining at the same time, this

implies that less time was spent on dating, i.e. on the intensive search which is consistent with the

hypothesized increase in the cost of such search.9

In short, the hypothesis of falling extensive search costs, rising expected gains from marriage, and

increasing intensive search costs can readily explain the stylized historical facts of increasing marriage

rate, decreasing age at marriage and increasing disruption rate.

These trends could also be explained in terms of declining transition costs into and out of marriage.

If both the switch from singlehood into marriage and from marriage into marital disruption became

cheaper, then simple economic theory will imply that more of both should take place, ceteris paribus.10

The transition into marriage involves not just the costs of wedding but also the costs associated with

setting up a new household and the change in daily routine. The costs associated with the onset of

childbearing may also be included in this category since for many young couples the beginning of

married life coincided with starting a family. Although a precise measurement is difficult, it is possible

to infer that such transition costs may have declined, considering the concurrent decline in the rate of

lifetime singlehood among the cohorts born after 1870.11 The transition out of marriage involves the

9 A decline in age at marriage may also reflect a greater frequency of dating: if that was the case, then of course, short duration from first date to marriage would not represent a decline in intensive search. However, with the exception of a (small) increase in pre-marital sex, I am not aware of any evidence that the dating practice of the early century grew any more intensive. Moreover, some aspects of a prospective partner’s personality, such as one’s susceptibility to alcoholism, may just require time, regardless of how often the partners date. 10 In fact, the two may be seen as complements: if it becomes cheaper to ‘undo’ an unhappy marriage then more marriages may take place because the risk of a lifetime of unhappiness is now lower.11 See section 2 and Haines (1996) for estimates of the rates of lifetime singlehood. However, even at its highest, this rate reached about 12%, meaning that even in the least pro-marriage cohort, about 88% of men and women still tied the knot.

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costs of divorce or desertion (or separation). Desertion was very ‘cheap’ technically and with the

ongoing advances in transportation technology was getting cheaper by the day (Carter et al. 2006: Df

955). But it involved abandoning one’s children and all property.12 Given that the long term trend was

one of rising living standards and rising real wage (even for unskilled labor), one may argue that

abandoning everything by desertion was becoming more and more costly, ceteris paribus.13 Moreover,

if a deserter wished to remarry, he or she also faced the possible prosecution for bigamy.14 Unlike

desertion, divorce did not entail forsaking all of one’s possessions but it was more costly in terms of

money, time and reputation. These costs probably increased during the late 19th and early 20th century,

as the predominant trend was one towards restricting divorce laws (Amato and Irving, 2006: 46). It

was only after the First World War that this push for strictness gave way to gradual and slow

liberalization. The available anecdotal evidence on changes in transition costs thus points in the

opposite direction than the theoretical framework behind this alternative explanation would require.

In addition, the transition-costs explanation requires not only that the transition costs be falling but

also that the decline be sufficiently large to actually matter – which is not a foregone conclusion,

especially for transition into marriage. Perhaps if there were binding credit constraints, the high

transition costs may exert considerable influence but given that 90% of all men and women get married

at some point in time (usually before they were 35) and that a large portion of the transition costs is

non-monetary (e.g. loss of leisure, loss of comfort etc.) such constraints must have been of only limited

importance.

Another alternative could be that successive marriage cohorts experienced ever greater negative

shocks to their gains from marriage (or positive to their outside options) and therefore disrupted in

12 It is perhaps no coincidence that this form of ending a marriage was more prevalent among the poorer strata, so much so that it was often referred to as “poor man’s divorce” (Eubank, 1916).13 It is true, however, that some deserters cashed in as many assets as they could before deserting. Porter Benson (2007: 55) cites a case from the interwar period where a deserting husband “sold bedding, clothing and anything that could bring any money – and left sixty cents on the table”.14 On the other hand, Brockelbank (1969) argues that since family law is in the purview of states, crossing the state line provided a deserter with a considerable degree of protection against potential legal troubles because each state guarded its autonomy in such matters with great jealousy.

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greater numbers although it is unclear how such persistent shocks would be consistent with the rising

marriage rate. Presumably, sooner or later, men and women would incorporate in their expectations of

marital gains the greater probability of a negative shock and adjust their marital behavior accordingly.

Which mechanism was at work, whether the search-cost or the transition-cost, is not a priori a

question of clear-cut either-or. It is possible that both had some influence although for reasons

explained above, the transition-costs explanation is perhaps less likely of the two. The following

empirical analysis therefore concentrates on the search-costs explanation.

4. Measures of the marriage market

In my empirical analysis I use 1% samples of the 1880, 1900, 1910 and 1920 censuses (Ruggles et

al., 2008). Of all the characteristics that people match on when looking for potential spouses, only the

most obvious are likely to be found in any dataset: race, age, broad level of education, ethnicity. The

more subtle aspects of a match such as opinions about parenting, handling of finances or sexual

compatibility are often hard to gauge even for the partners themselves and even after several years of

relationship, so any information about these from any relevant-sized sample (especially one dating

back to early 1900s) is plainly unavailable. And so while extensive search costs could potentially be

estimated (see below), the best an empirical analysis can hope for in terms of the intensive search costs

is to simply look at how much intensive search was actually undertaken. Ceteris paribus, if the

intensive search became more costly, individuals would search less and some might be discouraged

from marriage altogether. I therefore use a person’s age as the variable that captures the changes in

intensive search: if less of the intensive search was undertaken, then it should show up in lower age at

marriage, other things held equal.

In measuring the extensive search costs, I assume that men and women match on several readily

observable characteristics: race, nativity, literacy (the only available indicator of education until the

1940 census) and age. For the first three, I assume people wish to match perfectly: e.g. a black man

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wants a black women, a literate woman seeks a literate man, etc. With age, men usually desire younger

wives and women older husbands and both genders seek partners over a range of ages. So, for a man of

certain race, nativity, literacy and age, I define his marriage market to consist of women who are of the

same race, nativity and literacy, not older than him and no more than ten years his juniors; and of men

who are similar to him in that they compete for the same women that he is searching for. Similarly, for

a woman of a certain race, nativity, literacy and age, the marriage market consists of men who are like

her in the first three aspects, not younger and no more than ten years older than her; and of other

women who seek to match with such men. This way, each person’s marriage market includes both the

competitors and the potential spouses. Only rarely, however, does one’s search for a partner

encompass the whole country; more realistically, the marriage market is relatively local and so I limit

it to the size of a county. The assumptions about how well men and women wish to match reflect

actual historical experience. Tables 2-4 show that a vast majority of American couples matched on the

three characteristics of race, nativity and literacy. Moreover, in 75-80% of existing marriages in 1900,

it holds that 0 ≤ agehusband – agewife ≤ 10.15

To characterize each person’s marriage market, I construct three variables: trait, sex ratio and slfp.

The variable trait is an indicator of the rarity of a given trait: it is computed as the proportion of all

suitable matches in the adult population of the county. The greater the value of trait, the greater are the

chances of encountering a suitable match in daily intercourse, all else equal. The concept behind this

variable is taken directly from Becker et al. (1977) who argue that matching on a rarer trait constitutes

a higher extensive search cost. Mathematically,

for women Pop

Mtrait i

i

10

0 ; for men Pop

Wtrait i

i

10

0

15 The age at marriage declined after 1900 for both men and women but the decline was faster for men. Therefore, the post-1900 marriage cohorts showed ever greater degree of age homogamy and so the 1900 statistic can be viewed as a lower bound.

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where Mi represents the number of suitable men in the county who are i years older than the relevant

woman; W-i represents the number of suitable women who are i years younger than the relevant man,

and Pop stands for the total adult population of the county. Tables 5 and 6 provide a summary of how

this variable changed between 1880 and 1930. The mean is relatively stable across decades although

for both men and women, it declines between 1880 and 1900 and increases afterwards. At the same

time, the standard deviation around the mean declined from 1880 to 1930, suggesting that individuals

faced increasingly more similar marriage markets across the five decades. Table 6 shows that this

compressing trends was not an effect of changing age composition. The median woman aged 22 lived

in a county where acceptable marriage partners of the same race, literacy, nativity and age (22 – 32

years) represented about 8.9 – 9.5% of the total county adult population.16 At the upper extreme, the

proportion could reach one seventh (the 90th percentile in 1880 and 1900), at the opposite end of the

distribution, such men made up less than 2% of the county’s population. For men, the ratio is slightly

higher (median ranges from 0.118 to 0.105). In any given year, the trait is strictly decreasing in age for

women; it increases for men until about age 26 and then starts to decline.17 Across decades, the

variable falls for men, particularly at the upper tail of the distribution. As Table 5 indicated, in Table 6,

too, the distribution of women’s (and to some extent men’s) trait is compressing through time, with

lower percentiles increasing, higher percentiles falling and the median staying relatively stable.

Of course, as a measure of rarity, trait is only one component of the extensive search cost. The

mere presence of potential suitable matches in a population is not the same as being able to meet them.

For example, in sparsely populated areas or in areas that lack the necessary infrastructure that

facilitates such encounters (e.g. entertainment venues or outlets with personal ads) the same value of

trait will be associated with higher extensive search costs and lower probability of being married than

16 In Tables 6 and 7, I chose the age 22 for women and 26 for men because this was roughly the age when about 50% of women and men would be single and 50% already have a marital experience.17 I assume that both men and women can enter the marriage market no sooner than at age sixteen. That is the reason why, for men, the trait increases up to age 26: for younger men, some women who could be their potential matches in terms of the acceptable age gap are still too young to be married.

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in densely settled areas. Thus, extensive search costs can decline in two ways: either the values of trait

increase from year to year (which, as Tables 5 and 6 suggest, was not universally the case) or the

improving infrastructure makes meeting potential mates easier at any given level of trait. In the latter

case, a decline in extensive search costs would imply a higher probability of getting married across the

board, i.e. at any level of trait.

The remaining variables are designed to capture various aspects of the relative bargaining power of

men and women on the marriage market. The variable sex ratio measures the intensity of competition.

It is defined as a ratio of potential spouses over potential competitors. For example, a woman of certain

traits, aged 22, competes for men aged 22-32. But for men who are at the lower end of this range, she

must compete against women who are of her age and younger than herself while for men who are at

the upper end of the range, her competition will come from women who are her age and older than her.

The sex ratio reflects these shifting overlaps, being defined as

10

010

0

01910

10

9801

1

10910

0

......

...... i

jji

i

W

M

WWWW

M

WWWW

M

WWWW

Msexratio

where again Wi and W-i are women (of the same traits in the same county) who are i years older and

younger, respectively, than the woman for whom the ratio is being computed. By analogy, for men the

sex ratio is computed as

10

010

0

01910

10

10910

0

......

... i

jij

i

M

W

MMMM

W

MMMM

Wsexratio .

By this construction, the ratio captures local variation in the size of birth cohorts and it can realistically

reflect the different situation of, for example, two women who are otherwise the same but are five

years apart in age and who may for that reason face very different marriage markets and command

very different bargaining power with respect to potential suitors – even though they seem to compete

over a pool of men that overlaps to a great degree. Note that the variable is defined as a ratio of the

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numbers of opposite sex to the numbers of one’s own sex. Thus, both men and women consider higher

ratios to be more favourable.

The distribution of sex ratio across decades is reported in Tables 5 and 7. Table 5 suggests that the

mean sex ratio declined continuously for both men and women. Standard deviation increased between

1880 and 1900 and declined thereafter. Excluding the influence of a changing age composition, Table

7 shows that the median woman, aged 22, faced a ratio of about one throughout the fifty years covered

by the table. As with the trait, the variance of the distribution fell through the years. The sex ratio of

men (at age 26) was falling through the years, especially at the high tail of the distribution. Table 8

shows how trait and sex ratio vary with age and sex in the year 1900. Note that even though the two

variables move together across ages for both men and women, the cross-sectional correlations at each

age are weakly negative and overall correlation is mildly positive for men and weakly negative for

women.

Finally, the variable slfp is the single women’s labor force participation in a given county. This

variable does not vary with the race, nativity, literacy and age of women, it is designed merely as an

indicator of the local labor market opportunities of unwed women. It is a proxy for the outside option

of women, relative to marriage, and therefore another index of relative bargaining power of women. In

a sense, sex ratio captures how strong a woman’s position is relative to other women while slfp stands

in for her bargaining power relative to men (and vice versa for men).

The variables have their share of shortcomings. The variable trait ignores many aspects on which

men and women may match, such as social status and earning power. The sex ratio implicitly imposes

identical preferences on all men and women regarding the desired age difference between spouses. All

three variables ignore any migration, taking simply those individuals recorded in the census at a

particular place as being or having been participants in the local marriage market.

The variables are calculated from all men and women regardless of their current marital status. In

my analysis which seeks to get at marriage formation, I naturally concentrate on young men and

16

women who, even if they are married as of the time of census, must have married fairly recently and so

the ratios and proportions calculated even with their inclusion should to some degree reflect what the

marriage market had been when they were actually choosing their partners.

5. Regression results

I analyze the effect of extensive and intensive search on marriage formation within the framework

of a logit model where the dependent variable is 0 if a person is never married/single and 1 otherwise

(i.e. ever married). The main explanatory variables are individual age, sex ratio, trait and slfp. As

further individual characteristics I add dummy variables for race and literacy. Fixed effects for

individual states are also included. The size of township a person resides in, a categorical variable, is

also included, controlling for the effect of urban environment. The estimation is limited to women

under age 26 and men under age 31: this way the models are estimated from data on those men and

women who either still are on the marriage market or have been there recently.18

The coefficients and mean marginal effects of the relevant independent variables are reported in

Tables 9 and 10. Generally speaking, the marginal effects have the expected signs: the probability of

marriage increases in age, in sex ratio (although the marginal effect is relatively small) and in trait.

Single women’s labor force participation has a negative effect on probability of marriage which is also

understandable: if a single woman’s employment increases the option value of remaining single

(relative to being married), then the negative effect can be expected.19 The only exception is the

coefficient on slfp in the 1930 model for men but the marginal effect is relatively small and the

18 Extending the regression to men and women of all ages produces considerably different coefficients with many of the signs flipping. This is likely due to the fact that older age groups contain, for example, many widows who are classified as ever-married (and so their dependent variable is 1) but the measures of their marriage market indicate highly unfavourable sex ratio and trait values, given the differential mortality between men and women.19 The prevailing practice of a vast majority of women at that time was to quit the labor force upon marriage. The period of singlehood was therefore for many women the last time in their life (until widowhood) when they could indulge in own consumption, independent of considerations what effect such spending would have on consumption of other members in the household.

17

coefficient is not statistically significant. Literacy also has a negative effect which probably reflects the

greater opportunities outside marriage of literate singles compared to illiterate ones.

The second aspect to notice is that the coefficients are very precisely estimated. The standard errors

are low and consistently so across decades and both for men and women. The marginal effects are also

structurally quite stable from one decade to the next. This suggests that the variables are capturing

some of the underlying mechanisms of the marriage market of the time.

The marginal effects of sex ratio are generally small across decades and genders and suggest that

relatively little of the changes in marriage behavior can be attributed to this aspect of the marriage

market. Even large move in the sex ratio would not change the imputed probability of being married

by more than 1 to 2 percentage points.

The variables proxying for search (age for intensive and trait for extensive), on the other hand, do

have measurably meaningful effects. For a woman, holding all other variables constant, a move from

trait = 0.02 to trait = 0.15 (values which roughly correspond to the 10th and 90th percentiles of trait in

1900 in Table 6) increases the imputed probability of being ever married by about 9.8 percentage

points in 1880 and about 4.3 percentage points in 1910. For a man, the same exercise yields an effect

of 17.9 percentage points in 1880 and 24.2 percentage points in 1930. Therefore, trait has the expected

effect cross-sectionally: the higher the value, the higher the probability of marriage. Moreover, for men

at least, this aspect of the marriage market was making an ever bigger difference.

Figures 3 and 4 show how the association between trait and the probability of marriage changed

across decades. The purpose of the exercise is to isolate the effects of changing extensive search costs.

The probabilities depicted in the graphs were imputed holding all other variables constant: they show

how the probability of marriage would change across decades for a person of given fixed

characteristics. For both men and women, this probability increases between 1900 and 1930 for any

level of trait. For men, the shift is gradual while for women, the change occurs primarily between 1900

and 1910. This means that even though there was relatively little movement in trait across decades, the

18

extensive search costs were declining because any given level of rarity of a suitable match was

associated with higher probability of being married.

Men and women differed, however, in terms of where the fall in these search costs had its greatest

impact: the growing slope of the imputed men’s probability from 1900 to 1930 suggests that the gains

were rather modest at low values of trait but significant at the higher end of the relevant interval. For

women, the reverse was true, with the curves getting flatter from decade to decade (with the exception

of the 1920s). In other words, for women, it was getting progressively easier to get married even if

suitable matches were relatively rare in the population. For men, it was getting ever easier to get

married especially when the suitable matches were strongly represented. Overall, the estimation results

are consistent with the hypothesized decline in extensive search costs for both men and women, even

though they occur at different pace and in different portions of the trait distribution.

The intensive search costs must be inferred indirectly from the changing relationship between age

and the probability of being married. Figure 5 and 6 show the imputed probability of being married by

age, holding other variables constant. For women, the marginal effects of age in Table 9 decline first

between 1880 and 1900 and then increase until 1930 but the biggest change occurs between 1900 and

1910: the curves for 1880 and 1900 practically overlap, as do those for 1910, 1920 and 1930. The age

gradient of the probability of being married was therefore getting steeper (at the mean), meaning that

even after controlling for various (explicitly measureable) aspects on the marriage market (such as the

sex ratio and trait), women were getting married younger by about half a year on average. This is

consistent with the hypothesis that they were undertaking less intensive search because it was now

costlier. In fact, the marginal effect of age must be the main source of the increase of the imputed

probability after 1900, because both single women’s labor force participation and literacy have an

increasingly dampening effect on it.

Similar argument can be made for men’s marginal effect of age which also declines first and picks

up after 1900. The movement across decades is not as clear-cut as it is for women but, clearly, from

19

1900 to 1930, the curves shifted significantly such that men reached the same imputed probability of

being married about one year earlier, ceteris paribus. The increase in the probability of being married

was especially marked at the younger ages. Assuming that the age at which men and women entered

the marriage market did not change much, this suggests that less time was spent (on average) learning

about the character of prospective spouse, i.e. less intensive search.

The actual realized age at marriage, as has been mentioned, was declining from 1900 (Fitch and

Ruggles, 2000; Haines, 1996). If Fitch and Ruggles (2000: Table 4.1) argue that men’s indirect median

age at marriage declined by a year and a half between 1900 and 1930, then the present results suggest

that the increase in intensive search costs can account for about two thirds of this change. Similarly,

women’s indirect median age at marriage fell by about 0.6 years over the same time period (most of it

occurring between 1900 and 1910), which corresponds closely to the magnitude of the shift observed

in Figure 6.

Note that, according to the regression results, the rising single women’s labor force participation

acted as a powerful and ever stronger factor against marriage. It is therefore conceivable that the entry

onto the marriage market (particularly by women) was actually occurring at later ages by 1910 and

1920 then in 1900, implying that the actual duration of a pre-marital period of intensive search was in

fact getting short faster than the decline in age at marriage alone would indicate.

Literacy also played a consistently large role in determining the likelihood of tying the knot. For

women, the marginal effect of this binary variable grew more negative between 1880 and 1910 and

then fell back to the 1880 level. A marginal effect of -0.148 in 1910 implies that a literate woman of a

given set of characteristics was 25% less likely to be married than a woman of the same characteristics

but illiterate.

20

6. Conclusion

The era around the beginning of the 20th century was a time of profound change for American

women and it is not surprising that such changes found their way into marriage and courtship. With the

changing shape of the marriage market came also changes in the way marriage was entered into and

that affected marriage outcomes.

Historical evidence shows that as the 20th century progressed, marriage was becoming ever more

complicated affair. The expectations associated with one’s spouse were becoming more diverse, as

men were increasingly expected to be not only breadwinners but also competent fathers and good

companions. Women, on the other hands, saw their roles change from being almost exclusively

homemakers and mothers to slowly becoming second earners in the family. As these demands

increased, the search for a suitable spouse had to unfold along ever more not so easily observable

dimensions, making the intensive search ever more costly. At the same time, however, the statistical

evidence suggests that it was gradually getting easier to meet prospective partners and to match on the

easily observed characteristics such as race, age and general level of education.

Together, these changes led to the formation of marriage cohorts who had ever higher expectations

of gains in marriage but who also faced greater uncertainty (or variance) as to the eventual outcome.

Thus, American population was concurrently becoming increasingly eager to marry but just as strongly

eager to break up. These trends, commenced in the early decades of the 20th century, are still operating

today.

21

Reference:

Amato, Paul R. and Irving, Shelley. “Historical trends in Divorce in the United States” In: Fine, Mark A. and Harvey, John H. (eds.) Handbook of Divorce and Relationship Dissolution, Lawrence Erlbaum Associates, Publishers, Mahwah, NJ, 2006: pp. 41 – 58

Becker, Gary S., Landes, Elisabeth M. and Michael, Robert T. “An Economic Analysis of Marital Instability”, Journal of Political Economy 85 (6), 1977, pp. 1141 – 1187

Becker , Gary S. A Treatise on the Family: Enlarged Edition, Harvard University Press, 1993

Brandt, L. Five hundred and seventy-four deserters and their families: A descriptive study of their characteristics and circumstances. New York City: The Charity Organization Society, 1905. Reprint edition: New York: Arno Press, 1972

Britton, G.H. Marriage and divorce analysis of Cook County Statistics for years 1914 and 1915. Bureau of Social Service Publication no. 1, Chicago, IL: Board of County Commissioners, Cook County, IL, 1916

Brockelbank W.J. “The Family Desertion Problem across State Lines”. Annals of the American Academy of Political and Social Sciences 383 – Progress in Family Law, May 1969: pp. 23 – 33

Carter, Susan B., Gartner, Scott Sigmund, Haines, Michael R., Olmstead, Alan L., Sutch, Richard, Wright, Gavin. Historical Statistics of the United States: Millennial Edition, Cambridge: Cambridge University Press, 2006

Cvrcek, Tomas. “Mothers, Wives and Workers: The Dynamics of White Fertility, Marriage and Women’s Labor Supply in the United States, 1870 – 1930”, working paper, September 2008

Cvrcek, Tomas. “When Harry Left Sally: A New Estimate of Marital Disruption in the U.S., 1860 –1948”, Demographic Research 21 (24), November 2009: pp. 719 – 758

Eubank, Earle Edward. A Study of Family Desertion. Chicago, IL: Department of Public Welfare, 1916

Faust, Kimberly A. and McKibben, Jerome N. “Marital Dissolution: Divorce, Separation, Annulment, and Widowhood” In: Sussman, Marvin B., Steinmetz, Suzanne K. and Peterson, Gary W. Handbook of Marriage and the Family, Plenum Press, New York, 1999: pp. 475 - 500

Ferrie, Joseph P. and Rolf, Karen. “The May-December Relationship since 1850: Age Homogamy in the United States”, working paper, March 2008

Fitch, Catherine A. and Ruggles Steven. “Historical Trends in Marriage Formation: The United States, 1850 – 1990”. In: Waite, Linda J., Bachrach, C., Hindin, M., Thomson, E., Thornton A., The Ties That Bind: Perspective on Marriage and Cohabitation, Hawthorne: Aldin de Gruyter, New York, 2000: pp. 59 – 90

Goldin, Claudia. Understanding the Gender Gap: An Economic History of American Women, Oxford University Press, 1990

Haines, Michael R. “Long-Term Marriage Paterns in the United States from Colonial Times to the Present”, The History of the Family 1 (1), 1996, pp. 15 – 39

Haines, Michael R. “Estimated Life Tables for the United States, 1850 – 1910”, Historical Methods 31 (4), Fall 1998, pp. 149 – 169

Igra, Anna R. Wives without Husbands: Marriage, Desertion & Welfare in New York, 1900 – 1935. Chapel Hill, NC: University of North Carolina Press, 2007

22

Jacobson, Paul H. American marriage and Divorce, Rinehart & Co., New York, 1959

Koller, Marvin R. “Some Changes in Courtship Behavior in Three Generations of Ohio Women”, American Sociological Review 16 (3), June 1951, pp. 366 – 370.Marquis

Plateris, Alexander A. 100 Years of Marriage and Divorce Statistics: 1867 – 1967. Rockville, MD: US Department of Health, Education, And Welfare – National Center for Health Statistics, 1973

Porter Benson, Susan. Household Accounts: Working-Class Family Economies In the Interwar United States. Cornell University Press: Ithaca, 2007

Price-Bonham, Sharon and Balswick, Jack O. “The Noninstitutions: Divorce, Desertion, and Remarriage,” Journal of Marriage and the Family 42 (4), November 1980: pp. 959 – 972

Riley, Glenda. Divorce: An American Tradition, Oxford University Press, Oxford and New York, 1991

Rodrigues, Amy E., Hall, Julie H. and Fincham, Frank D. “What Predicts Divorce and Relationship Dissolution?” In: Fine, Mark A. and Harvey, John H. (eds.) Handbook of Divorce and Relationship Dissolution, Lawrence Erlbaum Associates, Publishers, Mahwah, NJ, 2006: pp. 85 - 112

Ruggles, Steven, Sobek, Matthew, Alexander, Trent, Fitch, Catherine A., Goeken, Ronald, Hall, Patricia K., King, Miriam, and Ronnander, Chad. Integrated Public Use Microdata Series: Version 4.0 [Machine-readable database]. Minneapolis, MN: Minnesota Population Center [producer and distributor], 2008.

Sanderson, Warren C. “Qualitative Aspects of Marriage, Fertility and family Limitation in Nineteenth Century America: Another Application of the Coale Specification”, Demography 16 (3), Aug 1979, pp. 339 – 358

Smith, Z.D. Deserted wives and deserting husbands: a study of 234 families based on the experience of the district committees and agents of the Associated Charities of Boston. Boston: George H. Ellis, 1901

Stevenson B., Wolfers J., “Bargaining in the Shadow of the Law: Divorce Laws and family Distress”, Quarterly Journal of Economics, 2006, pp. 267 – 288

U.S. Bureau of the Census, Marriage and Divorce, 1867 – 1906, Parts I and II. Washington, DC: Government Printing Office, 1909

Zunser, C. Family desertion (Report on a study of 423 cases). The Annals of the American Academy of Political and Social Sciences 145(Part 1): 98–104. (Law and Social Welfare), 1929

23

Figure 1 - Marriage and marital disruption, 1865 - 1929

0

10

20

30

40

50

60

70

80

90

100

1865 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925

0

2

4

6

8

10

12

14

16

18

20

Marriage rate per 1000 eligible women (left-hand axis) Disruption rate per 1000 married couples (right-hand axis)Divorce rate per 1000 married couples - right-hand axis

Source: Jacobson (1959) and Cvrcek (2009).

24

Figure 2 - Cohort rates of marital disruption (%)

0

10

20

30

40

50

60

1860 1870 1880 1890 1900 1910 1920 1930 1940

Proportion ever disrupted - baseline estimate

3-year moving average of baseline estimate

Proportion ever divorced (Preston & McDonald, 1979)

Source: Cvrcek (2009).

25

Figure 3 - Imputed probability of being married given trait - MEN

0.400

0.450

0.500

0.550

0.600

0.650

0.700

0.750

0.800

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16

18801900191019201930

Note: The probability was imputed at age = 26, sex ratio = 1, slfp = 0.4, and literacy= 1 for all five decades. Values of all other variables (fixed effects) were set at zero.

Figure 4 - Imputed probability of being married given trait - WOMEN

0.500

0.520

0.540

0.560

0.580

0.600

0.620

0.640

0.660

0.680

0.700

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16

18801900191019201930

Note: The probability was imputed at age = 22, sex ratio = 1, slfp = 0.4, and literacy = 1 for all five decades. Values of all other variables (fixed effects) were set at zero.

26

Figure 5 - Imputed probability of being married by given age - MEN

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

18801900191019201930

Note: The probability was imputed at trait = 0.1, sex ratio = 1, slfp = 0.4, and literacy= 1 for all five decades. Values of all other variables (fixed effects) were set at zero.

Figure 6 - Imputed probability of being married by given age - WOMEN

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

18801900191019201930

Note: The probability was imputed at trait = 0.1, sex ratio = 1, slfp = 0.4, and literacy = 1 for all five decades. Values of all other variables (fixed effects) were set at zero.

27

Table 1 - Regression results for the proportion ever disrupted, period disruption rate and the marriage rate(i) (ii) (iii) (iv) (v)

Dependent variable:cohort rate of

disruptioncohort rate of

disruptionperiod rate of

disruptionperiod rate of

disruptionmarriage

rate

Marriage rate0.73 0.74

(0.11) (0.11)

Business cycle-0.55 -0.44 4.06 4.43 37.88(7.28) (7.45) (3.37) (3.40) (17.60)

Pro

port

ion

livin

g:

in cities with over 25,000 inhabitants

32.75 27.49 52.98(13.17) (4.83) (18.90)

in cities with over 100,000 inhabitants

44.69 37.36(17.92) (6.43)

SMAM - Males-0.23 -0.12(2.37) (2.42)

SMAM gap4.23 4.24

(4.08) (4.07)

First World War-0.06 -0.04 2.18 2.20 1.89(1.07) (1.06) (0.62) (0.61) (3.11)

Second World War0.79 0.92 -1.86 -1.89 -6.94

(2.43) (2.42) (1.49) (1.49) (10.74)

Constant-50.28 -53.77 -2.70 -2.89 21.35(56.93) (58.51) (3.93) (3.94) (20.35)

Observations 84 84 84 84 89Note: ‘Marriage rate’ is the number of marriages per 1000 eligible women, taken from Jacobson (1959). ‘Business cycle’ is the de-trended real GDP per capita from Carter et al. (2006: Ca 11). The proportions living in cities above 25,000 and 100,000 inhabitants were calculated from Carter et al. (2006: Aa699 – 712) and linearly interpolated between census years. SMAM of men and women is from Haines (1996: Table 5) and Carter et al. (2006: Ae 489 – 490). First World War and Second World War are dummy variables. Newey-West standard errors are reported in parentheses.

28

Table 2 - Race of spouses in 1900Husband

White Black Amer. Indian Total

Wife

White 85.96% 0.06% 0.03% 86.05%Black 0.02% 13.40% 0.00% 13.42%

Amer. Indian 0.04% 0.00% 0.48% 0.53%Total 86.03% 13.46% 0.51% 100.00%

Note: Pertains to marriages of 5 years or less. Source: IPUMS

Table 3 - Literacy level of spouses in 1900Husband

Illiterate Literate Total

Wife Illiterate 6.92% 4.29% 11.22%

Literate 8.91% 79.88% 88.78%Total 15.83% 84.17% 100.00%


Table 4 - Nativity of spouses in 1900Husband

Foreign born

Native born Total

Wife

Foreign born 11.06% 3.65% 14.71%

Native born 10.74% 74.55% 85.29%Total 21.80% 78.20% 100.00%


29

Table 5 - Descriptive statistics1880

Men N mean st.dev. Women N mean st.dev.age 71780 22.78 4.28 age 51073 20.43 2.85

sex ratio 71780 1.389 2.276 sex ratio 51073 2.019 2.170trait 71780 0.070 0.054 trait 51073 0.097 0.053slfp 70885 0.36 0.20 slfp 50807 0.36 0.20

literacy 71780 0.85 0.36 literacy 51073 0.84 0.371900












literacy 156432 0.97 0.18 literacy 111598 0.98 0.14Source: IPUMS 1880 - 1930, "slfp" stands for single women's labor force participation and is the proportion of single women in a given county who reported a gainful occupation in a given census.

30

Table 6 - Percentiles of distribution of trait for men and women1880 10th 25th 50th 75th 90th

Men aged 26 0.014 0.05 0.118 0.15 0.177Women aged 22 0.022 0.06 0.09 0.119 0.149

1900Men aged 26 0.016 0.057 0.114 0.142 0.168

Women aged 22 0.024 0.059 0.094 0.12 0.143

1910Men aged 26 0.011 0.044 0.107 0.134 0.16

Women aged 22 0.026 0.06 0.089 0.115 0.137

1920Men aged 26 0.015 0.054 0.105 0.127 0.152

Women aged 22 0.027 0.061 0.093 0.114 0.135

1930Men aged 26 0.017 0.083 0.113 0.129 0.15

Women aged 22 0.028 0.071 0.095 0.108 0.126Source: IPUMS

Table 7 - Percentiles of the distribution of sex ratio for men and women1880 10th 25th 50th 75th 90th

Men aged 26 0.68 1 1.35 2 5.99Women aged 22 0.65 0.8 1 1.57 3.98

1900Men aged 26 0.74 0.99 1.18 1.5 5

Women aged 22 0.71 0.89 1.02 1.33 3

1910Men aged 26 0.53 0.9 1.16 1.4 3.97

Women aged 22 0.73 0.86 1.01 1.42 3.03

1920Men aged 26 0.64 0.9 1.12 1.34 2.93

Women aged 22 0.76 0.91 1.03 1.37 3

1930Men aged 26 0.73 1 1.17 1.36 3

Women aged 22 0.76 0.87 0.98 1.17 2Source: IPUMS

31

Table 8 - Mean sex ratio and trait by sex and age, and their correlationMen Women

Age sex ratio trait Corr. sex ratio trait Corr.16 0.101 0.011 0.565 2.989 0.109 0.00517 0.221 0.022 0.115 2.166 0.106 -0.10818 0.342 0.032 0.071 1.765 0.102 -0.13419 0.470 0.042 -0.004 1.566 0.099 -0.14520 0.585 0.051 0.011 1.418 0.095 -0.14421 0.725 0.060 -0.034 1.331 0.094 -0.15122 0.842 0.069 -0.033 1.229 0.089 -0.16923 0.992 0.078 -0.060 1.184 0.087 -0.14324 1.137 0.087 -0.037 1.157 0.083 -0.12525 1.226 0.093 -0.038 1.105 0.079 -0.13526 1.467 0.102 -0.062 1.171 0.078 -0.16227 1.505 0.100 -0.092 1.161 0.076 -0.15228 1.500 0.097 -0.061 1.142 0.074 -0.12829 1.587 0.094 -0.077 1.245 0.072 -0.16130 1.396 0.088 -0.045 1.151 0.068 -0.08931 1.760 0.089 -0.112 1.395 0.070 -0.17832 1.558 0.086 -0.073 1.299 0.067 -0.15633 1.580 0.082 -0.110 1.291 0.065 -0.16934 1.628 0.077 -0.083 1.350 0.062 -0.137

Overall 0.119 -0.044Source: IPUMS

Table 9 - Results of logit model: women1880 1900 1910 1920 1930

ß (s.e.) mean m.e. ß (s.e.) mean m.e. ß (s.e.) mean m.e. ß (s.e.) mean m.e. ß (s.e.) mean m.e.

age0.425

0.07400.392

0.06930.412

0.07250.405

0.07360.416

0.0745(0.004) (0.003) (0.003) (0.003) (0.003)

sex ratio0.015

0.0030.010

0.0020.028

0.0050.018

0.0030.011

0.002(0.005) (0.004) (0.004) (0.004) (0.004)

trait3.144

0.5481.673

0.2961.436

0.2531.205

0.2192.242

0.401(0.259) (0.241) (0.244) (0.243) (0.245)

single women's LFP

-0.025-0.004

-0.246-0.044

-0.341-0.060

-0.443-0.080

-0.308-0.055

(0.071) (0.060) (0.056) (0.054) (0.054)

Literate-0.429

-0.075-0.670

-0.118-0.842

-0.148-0.620

-0.113-0.465

-0.083(0.038) (0.036) (0.038) (0.044) (0.055)

Constant-8.800 -7.626 -7.650 -7.690 -8.212(0.122) (0.100) (0.096) (0.094) (0.095)

N 50807 79747 90333 94320 111475Log L -26581.31 -42241.34 -47691.99 -51096.74 -59681.171

Pseudo-R2 0.21 0.19 0.21 0.20 0.20Note: For definition of individual variables, see section 4. Fixed effects for race, size of the city of residence and state of residence were also included (not reported). Only women aged 25 and younger were included in the regression.m.e. stands for marginal effects; s.e. for standard errors. LFP stands for labor force participation.

1

Table 10 - Results of logit model: men1880 1900 1910 1920 1930

ß (s.e.) mean m.e. ß (s.e.) mean m.e. ß (s.e.) mean m.e. ß (s.e.) mean m.e. ß (s.e.) mean m.e.

age0.372

0.05050.334

0.04740.326

0.04750.308

0.04790.333

0.0483(0.003) (0.003) (0.002) (0.002) (0.002)

sex ratio0.008

0.0010.015

0.0020.016

0.0020.013

0.0020.007

0.001(0.004) (0.004) (0.004) (0.004) (0.004)

trait5.379

0.7295.353

0.7616.008

0.8776.197

0.9657.272

1.056(0.227) (0.206) (0.188) (0.195) (0.199)

single women's LFP

-0.217-0.029

-0.097-0.014

-0.072-0.011

-0.249-0.039

0.0520.008

(0.066) (0.056) (0.050) (0.049) (0.051)

Literate-0.611

-0.083-0.506

-0.072-0.529

-0.077-0.548

-0.085-0.553

-0.080(0.034) (0.031) (0.028) (0.033) (0.039)

Constant-8.825 -8.260 -7.984 -7.467 -8.172(0.098) (0.081) (0.075) (0.073) (0.075)

N 70885 114994 134664 136013 156112Log L -29772.2 -50036.2 -60328.3 -64496.4 -69879.1

Pseudo-R2 0.32 0.28 0.28 0.27 0.31Note: For definition of individual variables, see section 4. Fixed effects for race, size of the city of residence and state of residence were also included (not reported). Only men aged 30 and younger were included in the regression.m.e. stands for marginal effects; s.e. for standard errors. LFP stands for labor force participation.

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