Norms Formation: The Gold Rush and Women’s Roles
Sandra Aguilar-Gomez∗ Anja Benshaul-Tolonen†
November 14, 2018
Abstract
Does the mining-driven scarcity of women affect gender norms? Do gender norms
persist over time? We explore the Gold Rush in Western United States in the late
19th-century as a natural experiment to answer these questions. We use a geographic
difference-in-difference methodology, exploiting the location and discovery of the gold
deposits and its influence on sex ratios, to understand short term and persistent changes
in women’s labor market participation and marriage market opportunities. Gold min-
ing, through the oversupply of marriageable men with income, increased (decreased)
marriage rates among women (men). Women married older men with higher prestige
occupations. In parallel, the Gold Rush created a market based service sector econ-
omy, potentially catering to men with money but poor marriage prospects. Using all
subsequent censuses up until 1940, we show that the effects persist over time.
Keywords: Extractive industries, Sex Ratio, Marriage Markets, Labor Markets, Gen-
der Relations, Persistence of Norms.
JEL Codes: O13, J16, J12
∗School of International and Public Affairs, Columbia University.†Corresponding author. Department of Economics, Barnard College, Columbia University. Email: atolo-
[email protected]. Preliminary draft. Please do not cite or circulate without author permission. We aregrateful for comments from Rodrigo Soares, Maria Micaela Sviatschi and Raquel Fernandez, and conferenceparticipants at the AEA/ASSA 2018, 2nd IZA workshop: Family and Gender Economics (2018), IPWSD2018 at Columbia University, Nano-development conference at NYU (2018), and seminar participants atColumbia University (2017, 2018) and University of Oxford (2018). Beatriz Gomez Belmont provided greatdata collection assistance, and Natalia Shalaby provided research assistance.
1 Introduction
The San Francisco population in 1860 counted 12 men for every woman. The gender im-
balance in the population did not disappear completely until several decades later. Most
commonly, the reason for the sex imbalance was gold mining, which initially attracted pre-
dominantly male migrants. The Gold Rush however created a vibrant economy with cash rich
miners looking for services such as housekeeping, lodging, washing, cooking, and company—
services usually provided by wives. Historic accounts from surviving correspondences and
diaries suggest that early female migrants who often traveled with their husbands started as
miners, but quickly turned to the service sector as the work in the mining sector intensified,
and services became more lucrative (Levy, 1990). Soon women from around the country, and
even all the way from Europe, were arriving in the San Francisco bay to gain from the income
opportunities that cash rich gold miners were generating. The stories told by women in the
Gold Rush indicate that an entrepreneurial woman could earn more than the miners she was
cooking, washing or sewing for (Levy, 1990, Taniguchi, 2000). However, the selection bias in
these stories may be significant. The stories may fail to be representative of the experiences
that most women lived in the Gold Rush as they are predominantly from written accounts.
For instance, in 1850 almost all of the literate women were white, and their rate of illiteracy
was 6% compared to 56% among black women.
The context raises several important questions. How does a male-dominated industry
affect women’s roles? How does extreme scarcity of women affect marriage markets? And
do these short term economic and demographic factors affect gender norms in the long
run? We explore the expansion of gold mining in California, Nevada, Oregon and Arizona
to understand how marriage markets and gender norms are affected by the male-intensive
industries and the relative scarcity of women, in the short and medium term. We use
a geographic difference-in-difference methodology, exploiting the location of gold mining
sites. We match mining records to recently released historic census data from 1860-1940,
for California, Arizona, Oregon and Nevada, and delve deeper into what extent the new
1
economic and cultural gender norms—female labor force participation, women’s participation
in prestige occupations, outsourcing of household work, and marriage formation–persist in
the medium and long term in these states.
In the short run, gold mining sites, or even gold deposits, are not orthogonal to the
population structure. High intensity migration patterns were a direct response to the Gold
Rush, giving it the name as people “rushed in”. We allow for selective migration to the
mining areas as a mechanism in the short term, acknowledging that the population structure
is a direct outcome of the presence of gold mining. The skewness in the observed historic
sex-ratio is due to migration patterns that were differential by gender. Thus, a limitation
to the short-term analysis is that we cannot separate short-term changes in gender norms
spurred by the economic changes from those caused by selective migration of people with
certain norms.
Nevertheless, in the persistence analysis (1860-1940) we explore if the social norms are
sticky, long after the initial Gold Rush. We assume that the gold mining in the late 19th
century only influences 20th century economic outcomes through the historic, economic and
demographic structures it created, controlling for environmental factors and contemporary
mining. The analysis relates to a larger literature on the persistence of norms (Couttenier
et al., 2017; Grosjean and Brooks, 2017). Significant persistence in gender norms across
generations has been shown in previous empirical literature (Fernandez et al, 2004; Fernandez
and Fogli, 2009; Grosjean and Khattar, 2018). Moreover, the paper relates to literature on
economic growth, structural transformation and gender norms. Adam Smith noted already
a century before the Gold Rush, that the economic specialization has effects on women’s
status (Dimand et al, 2004), and Alesina et al. (2011; 2013) also showed that technological
innovations change gender norms if the change the comparative advantage structure of an
economy. The expansion of women’s economic rights in the U.S. also depended on economic
growth, as it increased the opportunity cost of women’s lack of participation (Geddes and
Lueck, 2002).
2
For the 1880s, we find significant differences across mining and non-mining counties.
First, we note that the sex ratio was higher in counties with more mining. Second, women
were more likely housewives or working in the service sector in mining counties. Third, both
mining and a high sex ratio increased the likelihood of a woman being married, in particular
to an older man with a higher prestige occupation. In parallel, men were less likely to be
married.
We explore if these effects persist in the medium term. We find strong indications for a
persistence of effects. Women in 1940 living in historic mining areas had higher marriage
rates (and so did men), and were on average less likely to work. However, women who did
work were more likely working in services and housekeeping in line with the results from
1880. A historically high sex ratio is associated with a higher average salary for women in
the medium term.
Subsequently, to understand and document the evolution of the norms over time, we track
the effects over time using all existing censuses from 1860-1940, and we find that women are
significantly less likely to work, more likely married and have fewer children in all censuses
following the Gold Rush. Furthermore, this repeated cross section analysis shows how the
effects dilute over time but persist over a certain threshold; there is no complete convergence
between mining and non-mining counties even 90 years after the first discoveries of mines.
The paper makes four contributions. First, there is a vast literature on the relationship
between sex ratio and women’s economic opportunities. For instance, Qian (2008), links
changes in relative earning opportunities in agriculture in China to survival rates for girls.
She finds that in presence of son-preference, changes in women’s economic worth improve
welfare outcomes for girls. We analyze the role of imbalances in the sex ratio in a context
where it is due to a selective migration to a male-oriented industry. This distinguishes our
study from most of the studies on the effects of a skewed sex ratio, where the latter is due
to a cultural preference for boys, but adds to a small but growing literature.
Second, our study speaks to the role of the service sector for the advancement of women
3
in the economy, and the difference between market-based and home-based production of
services. The expansion of the service sector has contributed to narrowing the gender wage
gaps because of women’s competitive advantage in service production (Ngai and Petrongolo,
2014). We explore the development of this sector in the historic context, and it’s continuing
effect on women’s situation.
Third, mining and other extractive industries are still, today, one of the largest drivers
of economic growth in developing countries. This has raised concerns regarding gender
equality as mining remains a male-dominated sector. This paper contributes to the small but
burgeoning literature on the effects of extractive industries on women and gender inequality
(Aragon et al, 2018; Benshaul-Tolonen, 2018; Kotsadam and Tolonen, 2016; Maurer and
Potlogea, 2017; and Wilson, 20121), a question that has received relatively little focus.
Lastly, the paper contributes to the literature on persistence and change of cultural norms,
and gender norms in particular (for example Alesina et al, 2011 and 2013; Geddes et al,
2012; Grosjean and Khattar (2018); Fernandez and Fogli, 2009; Giuliano and Nunn, 2017).
In Section 2 we provide background on sex ratios, gender norms and the Gold Rush. We
describe the data in Section 3, and the empirical strategy in Section 4. Section 5 discusses
the results, Section 6 discusses the robustness of the results and Section 7 concludes.
2 Background
In this section we discuss relevant literature regarding the sex ratio and gender relations,
history of mining in Western United States, and the link between mining and gender.
2.1 Sex ratio and gender norms
It is an empirical question how male to female sex ratios in the population is linked to
economic development and women’s status. In principle, higher sex ratios (defined as the
1See Benshaul-Tolonen and Baum, 2018, for an extensive literature review on the gender effects ofextractive industries.
4
number of men to women) could impact women’s standing in either of two ways: (1) higher
relative scarcity could give them more bargaining power in the marriage and labor markets,
or (2) make women a political minority with less access to economic and social opportunities,
and lead to social norms that keep women secluded. The historic accounts from the Gold
Rush obtained from letters and journals point toward the first hypothesis, contradicting the
results from the existing empirical literature (beyond the mining sector) that finds a negative
correlation between sex ratio and women’s empowerment. However, much of this literature
focuses on sex ratios that stem from cultural preferences for boys, such as in China and
India that may exacerbate the second effect. There is a vast literature on the relationship
between sex ratio and women’s economic opportunities (Clark (2000); Duflo (2003); Duflo
(2012); Qian (2008); Alesina et al. (2015)). Jayachandran (2015) reviews how profitability of
investments in women, which is related to the degree of patrilocal tradition within a society,
is correlated with the sex ratio.
One paper that is close to ours is Grosjean and Khattar (2018). They use the sending of
convicts to Australia as a natural experiment leading to variation in the sex ratio as convicts
were more likely male. They explore the effect of this non-natural sex ratio on women’s
status in society in the long run and short run. Higher sex ratio was historically associated
with higher marriage rates of women—which makes sense as they were in shorter supply—
and were less likely to work. Interestingly, the areas where men outnumbered women still,
today, have more conservative attitudes toward women; women earn lower wages, and work
in lower prestige occupations. One mediating channel could be that of the increasing return
to seclusion of women as a safety measure in a population dominated by convicted male
criminals. On a similar note, Baranov et al. (2018) exploit the same natural experiment to
analyze current norms about masculinity, and they find that in areas that were heavily male-
biased in the past more Australians recently voted against same-sex marriage, consistent with
more traditional masculinity norms.
An interesting historic example related to our study context is that of frontier culture in
5
the US, as migrants moved from the East to the West. Bazzi et al. (2017) show that frontier
communities between 1790-1890 had skewed sex ratios, and higher rates of individualism.
They use child names as a variable measuring individualism, where more infrequent names
is a proxy for individuality. They also show that there is persistence of frontier culture:
frontier communities today exhibit less taste for redistribution and regulation.
Asymmetries in imprisonment between men and women, can also lead to a skewed sex
ratio in certain age groups. Abramiztky et al. (2011) explore a negative shock in the number
of men due to warfare. Postwar, there were more upward socially mobile marriages for men
in areas with higher World War II mortality rates. In fact, in such areas men were less
likely to marry women of lower social classes. Instead, they married younger and more well
off brides, and men were overall more likely to marry. In parallel, women were less likely
to marry. The lack of marriageable men also affected fertility and divorce: out-of-wedlock
births increased, divorce rates decreased, and the spousal age gap decreased.
In the contemporary context, Charles and Luoh (2010) find that higher male imprison-
ment rates lower the likelihood that women marry and modestly reduce the quality of their
spouses when they do marry. They show that the gains from marriage shift from women
and toward men when men are scarce. Similarly, a recent example from Mexico (Conover et
al, 2015), explores how the lack of men due to the migration of Mexican men to the United
States, affects women who stay behind. Lower male-female sex ratio—plausibly driven by
exogenous shocks stemming from US labor demand—leads to higher school attainment of
women, more employment and lowered fertility. The authors also find that the prestige of
women increases - they are more likely found in white collar jobs and traditionally male
dominated sectors.
Thus, the sex ratio seems to be negatively correlated with women’s labor force participa-
tion. When women dominate men in numbers, women are more often found in prestigious,
white collar jobs (Conover et al, 2015), and when women are scarce, or in areas where women
were scarce historically, women are more likely working in low prestige occupations and earn
6
lower wages (Grosjean and Khattar, 2018). On the other hand, when men are in low sup-
ply, men’s marriages are more upward mobile while marriage rates among women are lower
(Abramiztky et al. 2011), and in areas where women are scarce, they tend to have higher
marriage rates in the long run (Grosjean and Khattar, 2018).
2.2 The Gold Rush in Western United States
The Gold Rush caused a significant demographic change in California. In 1848 there were
around 165,000 people living in the territory of California, the majority of whom were Native-
Americans. Shortly after the word had spread that John Marshall had found gold, the world
rushed in. California attempted to create a population census in 1850, which, while arguably
flawed, showed a sex ratio of 12.2 men per 1 woman. In the second attempt at performing
a complete census in 1852, the ratio was 7.2 men per 1 woman (Hurtado, 1999).
Women did, however, start arriving shortly after the onset of the rush. By 1860, the
ratio had decreased to 2.4 men per 1 woman, but for the older groups the sex ratio was far
from equalized. Due to increasing immigration of families and single women, and as births
increased, the sex ratio continued to decrease. Many men, especially older men, faced almost
no chances of ever marrying (Hurtado, 1999). By 1880, the sex ratio had decreased to range
between 1 and 4 men per women at the county level, and was highly correlated with the
mining intensity (See Figure 2). In this paper we will exploit that correlation to explore the
impact of skewed sex ratios on the marriage markets.
The demand for services traditionally performed by wives, and the shortage of women
to marry, led to higher demand for market based services—at a high price tag (Hurtado,
1999; Levy, 1990; Taniguchi, 2000). This, it is argued, led to more female entrepreneurship.
Many anecdotes tell stories of married female entrepreneurs who were at economic success
operating schools and boarding houses (Hurtado, 1999), some reported incomes at times
surpassing those of male gold miners. As one woman expresses it: “I have made about
$18,000 worth of pies. I bake about 1,200 pies per month and clear $200” (Levy, 1990). We
7
are, to our knowledge, the first ones to test these historic accounts using census data.
2.3 Women and the mining industry
There is a burgeoning field exploring the subnational effects of extractive industries on social
development. The majority of papers explore (i) effects on conflict and crime (e.g. Berman et
al., 2017; Axbard et al., 2016; Couttenier et al, 2017), or (ii) poverty and social development,
such as Aragon and Rud (2013) and Aragon and Rud (2015). A subfield within this topic
focuses in particular on the effects of women and gender inequality, such as Aragon et al
(2018), Benshaul-Tolonen (2018), Corno and de Walque, (2012), Kotsadam and Tolonen
(2016), Kearney and Wilson (2017), Maurer and Potlogea (2017), and Wilson (2012). The
literature on extractive industries and gender has recently been summarized in a review
paper (Benshaul-Tolonen and Baum, 2018).
The main findings from this literature address how mining (acting through gender seg-
regation on the labor market) influences men’s and women’s access to employment, and
the type of employment. While five studies confirm that mining generates employment for
women (Benshaul-Tolonen, 2018; Kotsadam and Tolonen, 2016; Kearney and Wilson 2017;
Maurer and Potlogea, 2017; and Wilson, 2012), the jobs are mostly in indirectly stimulated
sectors such as the service sector. Two of the papers also explore effects on women’s labor
force participation upon mine closure: Kotsadam and Tolonen (2016) find that in mining
communities in Sub-Saharan Africa, women reduce labor market participation when the
local mines close down, indicating that women are affected by the booms and busts that
the industry is generally associated with. Similarly, Aragon et al (2018) using data from
the 1970’s find that women working in manufacturing or services in coal mining areas are
replaced by men once the coal mining jobs disappear.
One paper that is similar to our paper is by Maurer and Potlogea (2017). Exploring US
data from the Southern states during 1900-1940, they find that discovery of oil had a zero
net effect on women’s participation in labor markets. They argue that this is because of an
8
increase in male wages following the oil discoveries, which leads to substitution of male labor
for cheaper female labor, thus offsetting the negative effect that higher male wages have
on women within the household. In particular, women start working in the non-tradable
sector which cannot be geographically displaced following a positive wage shock. Our results
confirm an increase in service sector employment, also in line with Kotsadam and Tolonen
(2016).
More contemporary evidence from the US comes from Kearney and Wilson (2017) who
explore the increase in recent fracking activities in selected US states and its effects on mar-
riage markets and fertility. They find that while marriage rates do not change substantially,
fertility rates are higher. This could potentially highlight a shift toward higher acceptance
of out-of-wedlock fertility, which may not be the case in the historic context of the US, or in
many developing countries today.
Based on the literature, we develop a conceptual framework (see Figure 1). We hypoth-
esize that a mining shock will affect sex ratios through rising male wages stimulating male
migration. The skewed sex ratio will in turn affect women’s marriage markets and labor
markets. In addition, the mining shock directly affects women’s labor markets as it stimu-
lates the tertiary economy. These two factors will subsequently affect gender norms, which
we explore if they persist in the long run. The long run analysis will be conditional upon
contemporaneous sex ratios and the presence of mining industry.
3 Data
While the Gold Rush started in 1849, the main early census data that we use is from 1880.
There are two main reasons for this. First, the mining county borders solidified over time.
Appendix Figure 9 illustrates how state and county borders developed over time, and how by
the 1880, the borders were largely set. Thus, using the 1880 data, we can compare counties
over time. Second, the counties started off large and were split over time. Therefore, we
9
Figure 1: Conceptual framework
believe that performing a local level analysis on large but sparsely populated areas will
significantly underestimate the treatment effects. In the persistence analysis, we use all
available censuses from 1860 to 1940.2
3.1 Census data 1880
We use census data from 1860 - 1940 to measure the effect of gold mining and a skewed sex
ratio on women’s labor markets and marriage markets. Table 1 shows descriptive statistics
in 1880 for the four states that were part of the Gold Rush: Arizona, California, Nevada and
Oregon. 35% of people recorded in the census were women, and the average age was 34.5.
Our sample does not include individuals below age 16. The sample was largely rural (34%
urban) and almost 1 in 2 individuals lived in a county with mining.
The summary statistics illustrate stark gender segregation on the labor and different
marriage market outcomes. Employment was almost universal among men, but merely 14%
of women stated having an occupation. Marriage rates are 20% higher among women than
among men, and divorcees (excluding those who remarried) are rare at 0.4-0.7% of the pop-
2Some census years, such as 1890, are not available due to environmental catastrophes that destroyedsome of the records.
10
ulation. The spousal age gap between men and women is on average 11 years. Appendix
Tables 17 and 18 show the 25 most common occupations for men and women in mining and
non-mining counties. In mining counties, the most common occupation is mine operatives
and mine laborers, whereas in non-mining counties the most common occupation is farmer.
These occupational categories are the original categories provided in the census, and not the
composite measures that we use in the main analysis. The occupational variables presented
in Table 1 shows the mean values for the composite occupational measures housewife (in-
cluded recorded and imputed housewives), and service and laborers (which contains several
occupations that relate to the service sector and unskilled laborers). Housewife is someone
who takes care of their own household, in contrast to a housekeeper who works for pay
elsewhere3.
3.1.1 Siegel prestige score
Prestige scores are a metric developed and used by sociologists to understand relative so-
cial class. We use the Siegel prestige score as provided by the IPUMS census data, that
measures subjective occupation prestige. The score is based on surveys undertaken by the
National Opinion Research Center in the 1960s, and retroactively applied to earlier census
data (IPUMS, 2018). We use the prestige score for the individual, as well as the prestige
gap between spouses. Note that a housewife has a prestige score of zero, which is why for
robustness, we will exclude women who are housewives (as it might not accurately reflect a
low social status). Appendix Table 19 shows the prestige score for selected occupations that
were common in the 1880 census.
3According to the IPUMS Codebook for 1880: “The term “housekeeper” will be reserved for suchpersons as receive distinct wages or salary for the service. Women keeping house for their own families orfor themselves, without any other gainful occupation, will be entered as “keeping house.” Grown daughtersassisting them will be reported without occupation.” (p. 28, Codebook 1880).
11
Table 1: Summary statistics census data 1880
mean sd min max
Female 0.346 0.476 0 1Age 34.521 12.653 16 69Urban 0.339 0.473 0 1Live in mining county 0.423 0.494 0 1
OccupationWorking 0.655 0.475 0 1Working (female) 0.137 0.344 0 1Working (male) 0.929 0.256 0 1Student (female) 0.024 0.154 0 1Student (male) 0.012 0.111 0 1Housewife 0.546 0.497 0 1Service & laborers 0.076 0.266 0 1Miner (female) 0 0.019 0 1Miner (male) 0.107 0.31 0 1Teacher (female) 0.015 0.121 0 1Teacher (male) 0.004 0.065 0 1Occupational income score 13.956 13.046 0 80
Marriage outcomesMarried (male) 0.406 0.491 0 1Married (female) 0.646 0.478 0 1Divorced (female) 0.007 0.085 0 1Divorced (male) 0.004 0.064 0 1Spouse age gap 10.832 12.652 -48 53Spouse prestige gap 20.205 15.501 -80 80
Observations 757,541
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3.2 Census data 1940
For the medium-term effects analysis, we use the 1940 census data for the same states
(Arizona, California, Nevada and Oregon). For 1940, the census includes more variables of
interest that allow us to better understand labor and marriage markets, such as income and
age at first marriage. We use a random 20% of the sample for the analysis. Table 2 shows
that almost 50% of the census count in 1940 was made up of women. The ages of people
in our sample ranges from 16 to 69. The population is significantly more urban in 1940
compared to 1880. In 1880, a mere 34% were living in urban areas, while in 1940, 67% did.
In addition, half of the sample lived in counties that had experienced mining by 1880.
We note that stark differences in labor force participation persisted in 1940: 28% of
women worked versus 84% of men. Women were somewhat more likely to be married: 67%
compared with 62% for men. This could be reflecting that women got married at a younger
age than men.
3.3 Mining data
We use data on gold mines from the United States Geological Survey (USGS), in the Mineral
Resources Data System (MRDS), a collection of reports describing metallic and nonmetallic
mineral resources throughout the world. The data includes deposit name, geographic co-
ordinates, commodities, deposit description, geologic characteristics, production, reserves,
resources, and references. The records of historical sites contained in this dataset come from
local mineral resources bureaus, historical maps, and other investigations. We considered a
record in the MRDS a gold mine if gold is listed as one of the three main minerals extracted
from that site. We obtained the historical county boundaries from the Minnesota Population
Center. National Historical Geographic Information System (NHGIS).
13
Table 2: Summary statistics census data 1940
mean sd min. max observationsFemale 0.486 0.5 0 1 6279448Age 38.793 14.408 16 69 6279448Urban 0.673 0.469 0 1 6279508Live in mining county (1880) 0.553 0.497 0 1 6279448
OccupationWorking 0.566 0.496 0 1 6279509Working (female) 0.281 0.449 0 1 3051288Working (male) 0.835 0.371 0 1 3228160Service & laborers 0.37 0.483 0 1 6279509Housewife 0.001 0.033 0 1 6279509Occupational income score 20.251 19.982 0 81.5 6279448
Marriage outcomesMarried (female) 0.668 0.471 0 1 3051288Married (men) 0.622 0.485 0 1 3228160Divorced (female) 0.04 0.195 0 1 3051288Divorced (men) 0.031 0.173 0 1 3228160Spouse age gap 4.193 6.916 -53 53 1866733Spouse prestige gap -0.768 18.789 -81.5 81.5 429704Note: Full sample for census 1940
14
3.4 Geographic controls
Following Grosjean et al. (2018), Couttenier et al. (2017) and Alesina et al. (2013) we use
a set of geographic controls to rule out the endogeneity of a settler’s location choice. In all
our specifications, we control flexibly for geographic characteristics by using a set of strictly
exogenous geographic controls: latitude, longitude, average temperature, average precipita-
tion, rivers and distance to the capital of the state. Mean temperature and precipitation are
used as measures of agricultural suitability, which may influence the share of the population
that works in mining (Alesina et al. (2013) and Grosjean & Khattar (2015)). The other
time-invariant geographic factors that could influence the placement of mining, as well as
the location of other industries.
In the robustness section, we also use the date of first political organization as a measure
of institutional maturity or state development, under the assumption that women, because
of their role as primary caregivers and providers of public goods would be less likely to
do paid work in places with less developed institutions. In places with an non-existent or
weak system of social security, women often substitute for these services with care work and
subsistence agriculture (Benerıa et al., 2015). But precisely because of these conditions,
women might be less likely to migrate to places with less institutional development, which
speaks to the literature on frontier communities (Bazzi et al., 2017). Furthermore, Davis et al.
(1972) argue that “the US government territorial expansion was largely driven by population
pressure and external geopolitical forces”, as described by Coutternier et al. (2016, p.12),
who show that this variable is not correlated with the location of mineral resources.
The county’s first political organization date and its total land area are documented in the
Atlas of Historical County Boundaries. The total length of rivers come from a listing made
between 1982 and 1993 by the National Park Service, and the distance of each county to the
state’s capital from the National Bureau of Economic Research with counties information of
the Census of 2000.
15
Figure 2: Mines in 1880 and sex ratio
3.5 Summary statistics
In 1880, 1.1 million people lived in California, Arizona, Oregon or Nevada. 59.1 of the
people in this four-state relevant area lived in counties with gold mines. Our dataset from
1880 includes 757,541 people aged between 16 and 69 years old. 34% of them are women,
but the sex ratio at the county level varies between 1 and 5 men per woman (see Figure 2).
A positive correlation between mining activities and the sex ratio is visually discernible in
Figure 2, where a darker color indicates more mines or higher sex ratio.
To further the age and sex distribution, we look at the population pyramid for 1880.
(Figure 3) indicates stark discrepancies in the size of the female and male population at all
ages. Most often, women in 1880 were in the age group 20-24, while men were most often
aged 25-29, shortly followed by ages 20-24 and 30-34. There were almost than twice as many
men aged 30-34 (ca 70,000) compared to women the same age (below 40,000)
To further explore the correlation between mining and sex ratio, we look at the correlation
in a scatter plot (Figure 4) and the distribution of at the county level (Figure 7). Mining
counties with more mines have a higher sex ratio on average (Figure 4, graph A). The linear
relationship between mines and sex ratio is weaker but still significant if we limit the sample
16
020,00040,00060,00080,000 0
Women
15-19
20-24
25-29
30-34
35-3940-44
45-49
50-54
55-59
60-64
65-69
70-74
Men
20,000 40,000 60,000 80,000
Figure 3: Population pyramid for 1880 using the census data for individuals 16 and above
to only counties with any mining activities (graph B).
By 1880, the region had developed beyond mining: in mining counties, 22% of men above
the age 15 worked in the mining sector in counties with mines, while only 1% of men worked
in the mining sector in non-mining counties, as shown in Table 3.
Table 3: Mining occupation by mining county (%)
Mining countyMining occupation No Yes TotalNo 99 78 89Yes 1 22 11Total 100 100 100Source: 1880 Census
There were several stark differences between mining and non-mining counties: women in
mining counties were 8% (5 percentage points) more likely to be married, 42% less likely to
work on the service sector, 40% less likely to be housekeepers and 7.4% more likely to be
housewives. The men were 20% less likely to be married (8 percentage points), and 1% more
likely to be working. In 1850, most of the mines (90%) were located in California. While in
1880 there had been exploration in other states and an expansion of the gold region, so only
40% of the gold mines were situated in California.
17
12
34
5
0 10 20 30 40Mines per County
Male/Female Sex Ratio by County Fitted values
12
34
5
0 10 20 30 40Mines per county
Male/Female Sex Ratio at County Fitted values
Figure 4: Mining Intensity and Sex Ratio in 1880 for all counties (A) and mining countiesonly (B)
18
4 Empirical Specifications
The paper uses several different identification strategies. First, we try to understand the
relationship between the mining and sex ratio in 1880, and then the importance of the sex
ratio for women’s outcomes such as labor and marriage market participation. In the simplest
specification we explore if the presence of mining affects relevant indicators:
Yic = β0 + β1GoldCounty1880,c + αs + δr +Xi +Wc +Xi + εics (1)
where i indicates an individual observation, c the county, and s the state. The variables of
interest are Mines, a variable that takes a value of 1 if there are recorded active gold mines
in the county by 1880, and 0 otherwise. The specification includes state fixed effects αs,
and race fixed effects δr, a vector of geographic controls Wc, and a vector of individual level
controls, Xi.
Next, we add Sex Ratio that captures the sex ratio in that county in the 1880 census, and
for some robustness checks the square term of the sex ratio. There are two characteristics
that are unique to the Western US gold rush and that provide an opportunity to disentangle
the effects of mining on women’s social status. Since there was a limited sized population
living in the area before the Gold Rush, the initial migration of men generated skewed sex
ratios that prevailed for decades. Secondly, mining being a predominantly male business, we
have variation in male income across space. Hence, we will attempt to disentangle the role of
these two forces: women’s scarcity and male income. We hypothesize that, holding mining
income constant, women’s scarcity represents a source of reducing the gender imbalance
because of its effect of the bargaining power on women in both the marriage and labor
market. Secondly, holding the sex ratio constant, higher mining presence in a county would
have a negative impact on women’s status because it increases the bargaining imbalance
19
within the household through a larger income gap. We further discuss the rationale behind
including the sex ratio in the results section as well. However, it should be noted that the
sex ratio is, to some extent, an endogenous control variable that is most likely caused by the
presence of a mining industry.
Yic = β0 + β1GoldCountyc + β2 SexRatioct + αs + δr +Wc +Xi + εics (2)
The specification to measure the medium term results is slightly different. We include
contemporaneous controls to account for some persistence in sex ratio and mining over time.
Yic = β0 + β1GoldCounty1880c + β2 SexRatio1880ct + β3 SexRatio1880sqct
+ β4 SexRatio1940ct + β5 SexRatio1940sqct
+ αs + δr +Wc +Xi + εics
(3)
Because counties that had gold mining in 1880 were more likely to have gold mining in
1940, we do alternate this specification by including mining in 1940. We have chosen to not
include both as they are somewhat correlated. In particular, the measure from USGS that
we use to construct yearly county-level mining variables have poor records of closing years.
Therefore, mining in 1940 is grossly exaggerating the persistence of mining. To correct for
this, we instead use presence of mining at the county level in 1940 as captured by mining
employment. Results using this control, and using a sample split limiting our analysis to
counties with low levels of contemporaneous mining are presented in the robustness section.
Following Abadie et al. (2017) we do not cluster our standard errors, because neither the
sampling process nor the treatment assignments are clustered. The variability of mineral
20
resources does not follow a pattern that respects county or state boundaries, therefore it
does not make sense to cluster at an administrative level. A second methodological note is
that given that we use Linear Probability Models (LPM) instead of probit, we address two
important concerns usually associated with LPM: a) We checked that all the predicted values
from the models lie between 0 and 1 and b) since OLS estimation imposes heteroskedasticity
in the case of a binary response variable we use heteroskedasticity-consistent robust standard
error estimates.
4.1 Pairwise correlations
Table 4 shows the correlations between variables of interest. The indicator variable Gold
County (if a county had any gold mines prior to 1880) is positively correlated with the number
of gold mines (ρ = 0.56), and with share of population that work in mining (ρ = 0.63), and
the sex ratio in the population (ρ = 0.56), but also correlated with the average age of
the population (ρ = 0.36), and the likelihood of the individuals or their parents being born
abroad. Interestingly, we only note a small coefficient on the correlation between gold county
and year of first political organization (ρ = 0.04), meaning that political organization was
not often spurred by the mining activities. Furthermore, we note that the share of miners
in the population is correlated with the sex ratio (ρ = 0.71), an older population (ρ = 0.67),
and foreign born population (ρ = 0.54).
Table 5 shows similar pairwise correlations using the 1940 census data at the county level.
Importantly, there is a correlation between the number of mines in 1940 (ρ = 0.42) and share
of workers that are miners (ρ = 0.45), with the county producing gold prior to 1880. The
correlation between 1880 mining and share of miners in 1940 is however weaker than the
share that were reporting being miners in 1880 (see Table 4). The correlation between sex
ratio in 1880 and 1940 is ρ = 0.60, which could be because the older population in 1940,
who were young in 1880, remain highly unbalanced which could drive the much smaller sex
ratio in 1940. We encourage caution when exploring the variable number of mines in 1940,
21
Table 4: Pairwise correlations for 1880 census data
Gold Nr. gold Share Sex Mean Foreign Foreign Year of pol.county mines miners ratio age born parent org.
Gold county (1880) 1.00Nr. gold mines 0.56 1.00Share miners 0.63 0.52 1.00Sex ratio 0.56 0.31 0.71 1.00Mean age 0.36 0.47 0.67 0.17 1.00Foreign born 0.42 0.24 0.54 0.55 0.36 1.00Foreign born parent 0.37 0.23 0.47 0.46 0.37 0.99 1.00Year of pol. org. 0.04 0.02 -0.01 0.13 -0.26 -0.10 -0.13 1.00
Notes: The table shows pairwise correlations for data in 1880. Gold county is an indicator variable thattakes value =1 if the district had gold mining prior to 1880. Nr. Gold mines captures the county-levelnumber of mines. Share miners is the share of population who work as miners in 1880. Sex ratio isthe ratio men to women in the 1880 census. Mean age is county average age in census. Foreign born isshare of population that was born abroad, and Foreign born parent is the share that report having atleast one parent born abroad. Year of pol. org. is the first year of political organization by county.
as it captures to some extent cumulative number of mines since the beginning of the record
in 1849.
Table 5: Pairwise correlations for 1880 and 1940 census data
Gold Nr. gold Share Sex Sex Pop. Meancounty mines miners ratio ratio density age1880 1940 1940 1880 1940 1900 1940
Gold county (1880) 1.00Nr. gold mines in 1940 0.42 1.00Share miners in pop 0.45 0.44 1.00Sex ratio 1880 0.55 0.58 0.66 1.00Sex ratio 1940 0.36 0.11 0.53 0.60 1.00Population density 1900 -0.16 -0.07 -0.09 -0.12 -0.16 1.00Mean age -0.16 -0.20 0.04 -0.10 -0.07 0.16 1.00
Notes: The table shows pairwise correlations for data in 1940. Gold county is an indicator variable thattakes value =1 if the district had gold mining prior to 1880. Nr. gold mines captures the county-levelnumber of mines in 1940. Share miners is the share of population who work as miners in 1940. Sex ratiois the ratio men to women in the 1940 census. Mean age is county average age in census. Populationdensity is for 1900.
22
4.2 Threats to identification
4.2.1 Selective migration
The Gold Rush caused large scale migration into the Western states of the U.S. In the short
term analysis, where we use contemporary or recent gold mining within the county as the
source of variation, we cannot argue that the presence of gold causes changes to individuals’
behavior. Rather, the estimated differences between gold counties and non-gold counties
observed could largely stem from differences in selective migration. That is, individuals who
chose to settle in gold-rich counties may be different from those that settle in neighboring
counties without gold. We are not able to track individuals prior and post to the migration
decision, which is why we cannot convincingly prove that any differences observed between
these groups are due to changes in the industry composition or the sex ratio. However,
we can look at the balance between the two groups on time invariant characteristics (such
characteristics that do not change as a consequence of the gold mining for an individual).
To answer this question, we look at place of origin. Figure 5 shows the distribution
of countries of origin among the population in mining counties and non-mining counties.
Because the country of origin is not available in the 1880 census, we use the variable for
mother’s and father’s country (or U.S. state) of origin from the 1940 sample. We understand
this variable as a proxy for cultural heritage. We use the 1.4 million observations available
to estimate the representation of the ethnicity (mother’s and father’s birthplace) of the
population and plot the difference in representation for each of the 139 categories between
mining and non-mining counties (according to the 1880 definition). The distribution is
largely centered around zero, meaning that the origin of the population is fairly homogeneous
in terms of origin. We take this as indicative supportive of the argument that people who
settled in mining versus non-mining districts are fairly comparable. This analysis, however,
does not fully overcome the issue of selective migration because (i) other cultural factors that
vary within an ethnic group may have differed across the groups, such as their education,
23
Figure 5: 1940 difference in parents’ country and city of origin between mining districts andnon-mining districts for 1.4m observations
attitudes toward women, and skills (ii) the variable is measured only in 1940, and thus
includes migrant groups that arrived after the gold rush as well.
4.3 Persistence in industrial composition
We exploit variation in gold mining across space during the second half of the 19th-century.
However, places that had early gold mining might be more likely to have gold mining in later
periods too. This could cause issues for the persistence analysis as it would make it more
complicated to determine if the mediating channel is the historic or the contemporary mining.
There are 131 industry categories recorded in the 1940 census, only four had differences in
population shares that are statistically significant in 1940. Table 6 shows the distribution
of these four skewed industries across mining counties and non-mining counties. No other
industry had a statistically significant difference between mining and non-mining counties.
Predictably, the four sectors that show differences are related to metal and mineral activities.
24
Importantly, in 1940 these sectors represent a small share of the economy in terms of labor
force, since the four combined represent less than 10% of the number of people in agriculture
or construction. This reduces the plausibility of the impact on female participation being
through industrial structure and points more towards persistence in gender norms from
historic mining. Nevertheless, since historic mining counties have higher mining employment
also in 1940, in the robustness section we will control for industry composition in 1940, and
look at heterogeneity in results across counties that remain mining heavy versus those that
do not.
Table 6: Table of balance of industries in 1940
CountyNon-mining county in 1880 Mining county in 1880 Total
Industry composition 1940Metal mining 1,388 29,046 30,434Mining (non specified) 620 1,072 1,692Nonmetallic mining 359 533 892Primary nonferrous industries 594 2000 2594Total 2,961 32,651 35,612Source: 1940 Census
5 Results
5.1 Definition of mining county
Table 7 shows the correlation between the sex ratio and gold mining using three different
specifications of gold mining: (i) the county had any recorded presence of gold mining prior
to 1880, (ii) cumulative number of gold mines leading up to 1880, and (ii) the same variable,
and including the square term. The table shows that the correlation with the sex ratio is
robust across specifications, and that the magnitude of the effect using the cumulative metric
is comparable the mining dummy once scaled by the average number of gold mines across
counties.
25
Table 7: Sex ratio in 1880 and gold mines
(1) (2) (3) (4) (5)Dependent variable Sex Ratio Sex Ratio Sex Ratio Sex Ratio Sex Ratio
Gold county (1880) 41.711*** 38.495*** 38.202***(10.636) (10.130) (10.350)
Gold mines (1880) 1.151*** 2.839***(0.401) (0.824)
Gold mines square (1880) -0.027***(0.010)
Urban -38.600** -28.346 -26.618 -20.149(17.221) (26.698) (28.735) (27.500)
State fixed effects Yes Yes Yes Yes YesPopulation size No No Yes Yes YesObservations 97 97 97 97 97R-squared 0.291 0.301 0.302 0.293 0.326Notes: Controls for state fixed effects. Standard errors in parentheses. *** p<0.01, **
p<0.05, * p<0.1
5.2 Descriptive statistics for 1880
Sex ratio
Figure 6 shows the distribution in 1880 across four variables. The first graph shows the
distribution of sex ratios across mining districts and non-mining counties. Two main findings
stand out. First, the sex ratio is starkly different across mining and non-mining counties.
The sex ratio is above 100 (which would indicate parity) in virtually all non-mining counties.
The majority of non-mining counties have a sex ratio below 200, which indicates a highly
male-skewed population. However, mining counties have significantly higher sex ratios on
average. The peak of the distribution is concentrated around 200. Second, the spread is
wider for mining counties. Few mining districts had a sex ratio close to parity, but a non
trivial share had sex ratios around 300. The tail is both fatter and longer for mining counties.
The graph indicate a correlation between sex ratio and mining, although at this time period,
non-mining counties also had sex ratios higher than normal due to selective migration.
26
0.0
05.0
1.0
15.0
2kd
ensi
ty s
ex ra
tio
100 200 300 400 500x
0.5
11.
52
2.5
kden
sity
farm
hou
seho
ld
0 .2 .4 .6 .8x
0.0
5.1
.15
.2.2
5kd
ensi
ty w
omen
age
30 32 34 36 38 40x
0.1
.2.3
kden
sity
men
age
35 40 45 50x
050
100
150
200
250
kden
sity
wom
en d
ivor
ced
0 .01 .02 .03x
050
100
150
200
0 .01 .02 .03x
01
23
4kd
ensi
ty h
ouse
wiv
es
0 .2 .4 .6 .8x
05
1015
20kd
ensi
ty fe
mal
e hh
0 .05 .1 .15x
No Mines Mine county
kden
sity
men
div
orce
d
Figure 6: Distribution of sex ratio (A), share of farm households (B), women’s average age(C), men’s average age (D), share of women who are divorcees (E), share of men who aredivorcees (F), share of women who are housewives (G), and female headed households (H)in 1880 for mining counties (red) and non-mining counties (blue)
27
Housewives, divorcees and female household heads
Next we turn to explore the effect on women’s labor market participation and marriage
outcomes. Figure 6 shows that non-mining counties have a high share of women that are
housewives, and that the distribution is shifted starkly to the left for mining counties. The
distribution is wider and seemingly bimodal for the mining counties, where some have a low
share of housewives, and others a high share.
On the other hand, the distribution for divorced women is to the left for mining counties
compared with non-mining counties. Divorced takes a value of 1 if the woman was divorced
at the time of the census. This definition underestimates the importance of divorce, as
women who remarry are not reported as ever divorced. Because remarriage rates among
women may be higher in mining counties, if the relative scarcity of women leads to higher
marriage rates, we would expect a lower rate of currently divorced women in mining counties.
Unfortunately, the 1880 census data does not allow us to explore the rates of ever-divorced
women.
5.3 Correlation between sex ratio and gold mining
To understand the correlation between the sex ratio in 1880 and gold mining, we regress
sex ratio on a set of variables representing mining. In addition to the main specification
using a binary variable for gold mining presence at the county level, we use an intensity
variable capturing the number of gold mines in the historical records. We test the following
specification:
SexRatio1880icts = β0 + β1GoldMines1880,c + β2GoldMines21880,c + αs +Xi + εicts (4)
The results are presented in Table 7. We find strong and significant positive correlations
between gold county and the sex ratio in 1880. This is also true when controlling for state
28
fixed effects, urban dummy and the population size (Column 3). Column 4 uses the number
of gold mines instead of a dummy variable, and Column 5 includes the square of the number
of gold mines. An additional gold mine increases the sex ratio, although at a decreasing speed
(Column 5). Overall, we find a strong, robust and positive relationship between mining and
sex ratio at the county level.
The mean number of gold mines per county is 9 mines, and conditional on having any
mine, the mean is 14.5 per county. This means that the specification in Column 3 that uses
a gold mine dummy for any presence of mining, and state fixed effects, yields quantitatively
similar results to column 5 that uses a continuous variable of the number of gold mines, and
its square term. While in principle this makes us indifferent between specification 3 and 5,
we do believe that the exact number of mines is measured with more measurement error. If
there was a significant known gold deposit in the county, it had likely resulted in some mining
by 1880. However, some gold deposits within a county could be discovered and depleted after
1849, but before 1880. Because the records of gold mines are likely less accurate earlier (as
California had less administration and institutions), it would underestimate the gold mining
in the early discovery counties.
5.4 Labor market effects in 1880
For the analysis, we limit the sample to women between 15 and 70 years old who lived in the
gold region. Table 8 shows the result for the likelihood that a woman is working in columns
1-3. Column 1 does not include state or race fixed effects. However, adding these controls
makes the effect of mining county stronger (Column 3). Women in gold mining counties are
less likely to be working, but conditional on working, they were more likely to be working in
the service sector and as laborers. However, we find that women are marginally less likely
to be working as housekeepers, possibly indicating an increase in service jobs on the labor
market rather than linked to a specific family.
Table 8 shows the results for the specification that includes controls for the sex ratio
29
Tab
le8:
Lab
orm
arke
tsan
dm
inin
gin
1880
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Depen
dentvariable
Wor
kin
gW
orkin
gW
orkin
gSer
vic
eT
each
erH
ouse
keep
erP
rest
ige
&la
bor
ers
for
pay
scor
ePan
elA
Gol
dC
ounty
(188
0)-0
.007
***
-0.0
22**
*-0
.026
***
0.03
9***
0.00
4-0
.013
**-0
.124
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
07)
(0.0
05)
(0.0
06)
(0.2
02)
Urb
an0.
111*
**0.
107*
**0.
109*
**0.
124*
**-0
.100
***
0.06
9***
-3.9
25**
*(0
.002
)(0
.002
)(0
.002
)(0
.006
)(0
.005
)(0
.006
)(0
.190
)A
ge-0
.003
***
-0.0
03**
*-0
.003
***
0.00
3***
-0.0
02**
*-0
.003
***
0.06
9***
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
06)
Obse
rvat
ions
262,
015
262,
015
262,
015
35,9
8835
,988
35,9
8835
,988
R-s
quar
ed0.
037
0.04
00.
049
0.02
10.
048
0.01
50.
075
Pan
elB
Gol
dC
ounty
(188
0)-0
.025
***
0.01
10.
006
-0.0
020.
091
(0.0
02)
(0.0
08)
(0.0
06)
(0.0
07)
(0.2
39)
Sex
Rat
io(1
880)
-0.0
15**
0.03
20.
050*
*-0
.071
**1.
267
(0.0
07)
(0.0
33)
(0.0
21)
(0.0
30)
(0.9
85)
Sex
Rat
iosq
uar
e(1
880)
0.00
4***
0.01
0-0
.015
***
0.01
2**
-0.4
80**
(0.0
01)
(0.0
07)
(0.0
04)
(0.0
06)
(0.1
97)
Urb
an0.
109*
**0.
131*
**-0
.100
***
0.06
5***
-3.9
69**
*(0
.002
)(0
.006
)(0
.005
)(0
.006
)(0
.194
)A
ge-0
.003
***
0.00
3***
-0.0
02**
*-0
.003
***
0.06
9***
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
06)
Obse
rvat
ions
262,
015
35,9
8835
,988
35,9
8835
,988
R-s
quar
ed0.
049
0.02
40.
049
0.01
50.
076
Sta
teF
EN
oY
esY
esY
esY
esY
esY
esR
ace
FE
No
No
Yes
Yes
Yes
Yes
Yes
Geo
grap
hic
contr
ols
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Not
es:
Rob
ust
stan
dar
der
rors
inp
aren
thes
es.
***
p<
0.0
1,
**
p<
0.0
5,
*p<
0.1
.A
llre
gre
ssio
ns
are
Lin
ear
Pro
bab
ilit
yM
od
els
wit
hro
bu
stst
and
ard
erro
rs.
Cen
sus
dat
afo
rw
omen
in1880
for
Ari
zon
a,
Cali
forn
ia,
Ore
gon
an
dN
evad
a.
30
in Panel B. Panel B, Column 3 shows the impact on the probability of being in the labor
force for women, controlling for both gold mining and the sex ratio. The probability of
working is 2.5 percentage points lower in mining counties, compared to elsewhere. This is
equivalent to a 18 percent effect from the mean. The effect is similar to the result in Table
8 Column 3 in Panel A. An increase in the sex ratio has a negative statistically significant
effect on the probability of women to work, in line with the story that they reduce labor
force participation in response to good marriage prospects.
In line with the previous results, we find that women are more likely to work in the
service sector in counties with gold mining. However, when using the endogenous control of
sex ratio, the presence of gold mining is no longer significantly associated with service sector
employment (Column 4), meaning that part of the effect on the service sector participation
stemmed from the higher sex ratios. This supports the hypothesis that gold mining in
combination with high sex ratios led to the high demand for female-oriented services, as
described in the historic literature. In line with this hypothesis, higher sex ratio itself
significantly increases the likelihood that a woman works in the service sector. We do not
find that women are more likely to work in high prestige jobs, but as it will be shown in
the following sections, they do marry to men with more prestigious occupations, which is
probably related to a scarcity effect.
Table 9 illustrates that mining is positively correlated with marriage rates for women
(Columns 1-3) and that the effect is robust to the inclusion of state and race fixed effects,
and to the inclusion of the endogenous sex ratio control. We find an increase in the fertility
rate, the spousal age gap, the spousal prestige gap as well as the likelihood that a woman
is divorced. Column 5 shows the association between the age gap and the mine presence.
Column 6 uses the occupational prestige gap as the independent variable. Women in mining
counties are more likely to form unions with men resulting in larger differences in spousal
age and occupation prestige. The age and income gaps are calculated by subtracting the
age or occupational income score of the woman to that of her partner. The income score
31
Tab
le9:
Mar
riag
em
arke
tsan
dm
inin
gin
1880
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Depen
dentvariable
Mar
ried
Mar
ried
Mar
ried
Fer
tility
Age
gap
Pre
stig
ega
pD
ivor
ced
Div
orce
dSam
ple:
(Wor
kin
g)Pan
elA
Gol
dC
ounty
(188
0)0.
012*
**0.
018*
**0.
018*
**0.
027*
**0.
069
0.86
8***
0.00
1***
0.00
7***
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
09)
(0.0
52)
(0.0
63)
(0.0
00)
(0.0
02)
Urb
an-0
.106
***
-0.0
91**
*-0
.091
***
-0.4
68**
*-0
.993
***
7.74
0***
0.00
2***
-0.0
01(0
.002
)(0
.002
)(0
.002
)(0
.009
)(0
.053
)(0
.068
)(0
.000
)(0
.002
)A
ge0.
010*
**0.
010*
**0.
010*
**0.
048*
**-0
.655
***
0.01
1***
0.00
0***
0.00
1***
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
02)
(0.0
02)
(0.0
00)
(0.0
00)
Obse
rvat
ions
262,
015
262,
015
262,
015
262,
015
196,
789
196,
789
262,
015
35,9
88R
-squar
ed0.
080
0.08
20.
082
0.10
80.
387
0.09
70.
001
0.00
8
Pan
elB
Gol
dC
ounty
(188
0)0.
011*
**0.
067*
**0.
128*
*0.
239*
**0.
000
0.00
5**
(0.0
02)
(0.0
10)
(0.0
59)
(0.0
71)
(0.0
00)
(0.0
02)
Sex
Rat
io(1
880)
0.02
4**
-0.0
431.
188*
**2.
475*
**0.
004*
*0.
009
(0.0
10)
(0.0
42)
(0.2
45)
(0.3
25)
(0.0
02)
(0.0
10)
Sex
Rat
iosq
uar
e(1
880)
-0.0
01-0
.019
**-0
.365
***
-0.1
69**
-0.0
01-0
.002
(0.0
02)
(0.0
08)
(0.0
49)
(0.0
69)
(0.0
00)
(0.0
02)
Urb
an-0
.088
***
-0.4
79**
*-0
.989
***
7.95
7***
0.00
2***
-0.0
00(0
.002
)(0
.009
)(0
.054
)(0
.069
)(0
.000
)(0
.002
)A
ge0.
010*
**0.
047*
**-0
.656
***
0.01
2***
0.00
0***
0.00
1***
(0.0
00)
(0.0
00)
(0.0
02)
(0.0
02)
(0.0
00)
(0.0
00)
Obse
rvat
ions
262,
015
262,
015
196,
789
196,
789
262,
015
35,9
88R
-squar
ed0.
083
0.10
80.
387
0.09
90.
001
0.00
8Sta
teF
EN
oY
esY
esY
esY
esY
esY
esY
esR
ace
FE
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Geo
grap
hic
contr
ols
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Not
es:
Rob
ust
stan
dar
der
rors
inpar
enth
eses
.**
*p<
0.01
,**
p<
0.05
,*
p<
0.1.
All
regr
essi
ons
are
Lin
ear
Pro
bab
ilit
yM
odel
sw
ith
robust
stan
dar
der
rors
.C
ensu
sdat
afo
rw
omen
in18
80fo
rA
rizo
na,
Cal
ifor
nia
,O
rego
nan
dN
evad
a.
32
is calculated by IPUMS using the average wage of each occupation, so it measures whether
the person is in an occupation that generally has a high income. This variable is often used
as an occupational prestige measure4. Based on the results of Table 9, in a mining county
is associated with an increase of 8 months in the age gap, and an increase of 80 dollars of
median income (the mean income score is 1300 dollars from 1950). The effect rises to 102
dollars when considering only working women.
Panel B of Table 9 shows the main results with a horse-race of the gold mining and the
sex ratio variable. All regressions use linear probability model and control for age, race,
state, urban/rural county and have robust standard errors. Column 1 shows that being in
a mining county is associated with an increase in the probability of a woman being married
of 1.2 percentage points, also when controlling for the sex ratio. This is a small impact
with respect to the mean, but at a time when marriage was the main source of economic
stability for women, constitutes evidence of better conditions in places where women were
more scarce. The effect is very comparable to the effect in Table 9, Panel A, Column 3,
meaning that the effect that the gold rush had on women’s marriage markets went beyond
those of the sex ratio. The reduction in the coefficient size however makes sense as sex ratio
is positively correlated with marriage rates among women (and positively correlated with
gold mining). Column 4 indicates that women in mining counties have more own children
living in their household, but the sex ratio has a weak insignificant negative effect on the
number of children ever born.
The majority of women were housewives. However, it is relevant to assess living in a
country with a high share of miners because it impacted the lives of women who were in
the workforce. These women were qualitatively different from the rest. We analyzed the
impact of living in a mining county on the probability of divorce for this sub-population,
under the intuition that for working women, being able to leave an unhappy marriage is
4OCCSCORE assigns each occupation in all years a value representing the median total income (inhundreds of 1950 dollars) of all persons with that particular occupation in 1950. OCCSCORE thus providesa continuous measure of the economic rewards enjoyed by people working in each occupation existing in1950. (IPUMS USA: https://usa.ipums.org/usa-action/variables/)
33
a proxy for freedom and individual stability. The distinction between marriage status for
working and non-working women is important: for housewives, being divorced represents a
source of economic uncertainty. Results in Table 9, Panel A and B, Columns 8 show small
increases in the likelihood that women who are working are divorced. However, divorces,
despite the anecdotes of how it spread in the gold counties (Levy, 1990), remain uncommon
in our data. It should also be noted that while divorce may be more common among working
women in gold mining counties, compared to women elsewhere, we remain agnostic whereas
to whether women start working when they get divorced, or divorce as they gain employment
opportunities. However, the specific context that the gold mining boom created, with many
eligible men and service sector opportunities, did increase demand for divorce.
5.5 Medium term effects: 1940 analysis
To understand the persistence of the results in the medium term, we analyze the effect of
the presence of gold mining in 1880 on women’s outcomes in 1940. We also include controls
sex ratio in 1940, and in the robustness section controls for contemporary mining. Table
10 shows the result for 1940 using a specification with mining in 1880, controls for the
sex ratio in 1940 (Panel A), and including control for the sex ratio in 1880 (Panel B). The
results indicate that historic mining has a persistent effect on women’s outcomes also in 1940.
Women in historic mining counties, controlling for sex ratio, are less likely to be working,
and earn a lower salary. However, women in historic mining counties are more likely to work
as teachers, and as housekeepers. These results are consistent with a persistence story.
The effects of the historic sex ratio (1880) are generally in the same direction as historic
mining, although with higher magnitude. The starkest exception is salary: in 1940, women
who lived in historic high sex ratio areas earned significantly more than women who live
in areas with historic lower sex ratios (Panel B, Column 4), conditional upon working. In
addition, women in high sex ratio areas were less likely teachers (Panel B, Column 5) or as
housekeepers (Panel B, Column 7), but we find no effect on the composite measure of service
34
Tab
le10
:W
omen
lab
orm
arke
tsin
1940
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Wor
kin
gw
omen
Depen
den
tvariable
Wor
kin
gW
orkin
gW
orkin
gS
alar
yT
each
erS
ervic
eH
ouse
kee
per
Pre
stig
e&
lab
orer
sfo
rpay
scor
e
Panel
AG
old
Cou
nty
(188
0)
-0.0
11**
*-0
.012
***
-0.0
11**
*-4
9.59
7***
0.01
0***
-0.0
020.
018*
**-0
.091
(0.0
01)
(0.0
01)
(0.0
01)
(3.6
08)
(0.0
01)
(0.0
03)
(0.0
02)
(0.0
79)
Sex
rati
o(1
940
)-1
.171*
**
-1.1
27*
**-1
.093
***
-2,1
84.7
62**
*0.
668*
**-0
.819
***
0.04
7-2
7.96
4***
(0.0
73)
(0.0
74)
(0.0
74)
(242
.464
)(0
.099
)(0
.168
)(0
.121
)(5
.310
)S
exra
tio
squ
are
(1940)
0.4
60***
0.44
1***
0.42
8***
898.
265*
**-0
.245
***
0.35
9***
-0.0
0512
.872
***
(0.0
32)
(0.0
32)
(0.0
32)
(106
.477
)(0
.044
)(0
.073
)(0
.053
)(2
.324
)u
rban
0.1
18*
**
0.11
7***
0.11
8***
163.
646*
**-0
.014
***
0.12
5***
-0.0
19**
*1.
899*
**(0
.001)
(0.0
01)
(0.0
01)
(3.9
50)
(0.0
02)
(0.0
03)
(0.0
02)
(0.0
97)
Age
-0.0
03***
-0.0
03**
*-0
.003
***
1.99
3***
0.00
1***
-0.0
03**
*0.
002*
**0.
025*
**(0
.000)
(0.0
00)
(0.0
00)
(0.1
20)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
03)
Ob
serv
atio
ns
610,
756
610,
756
610,
756
166,
807
171,
578
171,
578
171,
578
171,
578
R-s
qu
ared
0.0
260.
026
0.02
80.
033
0.00
90.
043
0.04
60.
032
Panel
BG
old
Cou
nty
(188
0)
-0.0
10**
*-6
8.49
8***
0.01
1***
-0.0
030.
015*
**-0
.115
(0.0
01)
(3.9
36)
(0.0
01)
(0.0
03)
(0.0
02)
(0.0
85)
Sex
rati
o(1
880)
-0.0
21**
223.
046*
**-0
.030
**-0
.033
0.04
7**
-0.2
56(0
.010
)(3
0.41
3)(0
.014
)(0
.025
)(0
.018
)(0
.785
)S
exra
tio
squ
are
(1880
)0.
001
-44.
065*
**0.
006*
*0.
008
-0.0
06-0
.099
(0.0
02)
(6.7
53)
(0.0
03)
(0.0
06)
(0.0
04)
(0.1
78)
Sex
rati
o(1
940)
-0.9
67**
*-2
,835
.981
***
0.75
8***
-0.7
79**
*-0
.102
-32.
618*
**(0
.085
)(2
55.1
44)
(0.1
08)
(0.1
88)
(0.1
37)
(5.8
04)
Sex
rati
osq
uare
(1940
)0.
387*
**1,
123.
707*
**-0
.278
***
0.34
8***
0.03
315
.524
***
(0.0
36)
(110
.394
)(0
.047
)(0
.081
)(0
.059
)(2
.492
)O
bse
rvati
on
s56
9,16
415
7,84
316
2,17
216
2,17
216
2,17
216
2,17
2R
-squ
are
d0.
028
0.03
20.
009
0.04
20.
050
0.03
2
Sta
teF
EN
oY
esY
esY
esY
esY
esY
esY
esR
ace
FE
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Geo
grap
hic
contr
ols
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Not
es:
Rob
ust
stan
dard
erro
rsin
par
enth
eses
.**
*p<
0.01
,**
p<
0.05
,*
p<
0.1.
All
regr
essi
ons
are
Lin
ear
Pro
bab
ilit
yM
od
els
wit
hro
bu
stst
an
dar
der
rors
.U
sin
g19
40
cen
sus
20%
sam
ple
.S
iege
locc
up
atio
nal
pre
stig
esc
ore
isp
rovid
edby
IPU
MS
.
35
sector and laborers.
The negative effect of historic gold mining on women’s labor markets indicate that in the
medium term it led to several changes: (1) less overall labor force participation, (2) lower
wages overall, but higher in areas with skewed sex ratio, (3) a higher concentration to service
based, and in particular domestic service sectors. The findings indicate that a pattern of both
gold mining (creating potentially high income men), joint with high sex ratio (making women
scarce, and higher value), created a pattern of labor force participation that persisted longer
than 60 years. On the one hand, the gold rush reduced women’s participation possibly due
to increasing women’s opportunities of stepping out of the labor markets through marriage.
On the other hand, the skewed sex ratio that it generated, increased the returns to female
labor especially in sectors where women have competitive advantage and sectors that are
not easily geographically displaced.
Our analysis of the marriage markets in 1940 (Table 11) shows that women are more
likely to be married in historic mining districts (Columns 1-3), even several decades after
the mining boom. This is also true while controlling for the sex ratio in 1880 and the sex
ratio in 1940 (Panel B, Column 3). Moreover, women in historic mining districts marry
earlier and give birth to fewer children. We hypothesize that they marry earlier because
they used to be more scarce historically, which is consistent with the increase in age gap
between spouses. Women in historic gold mining areas are more likely divorced (Column 8),
and this is especially true for the population of working women (Column 9) consistent with
the patterns observed in 1880.
5.6 Labor and marriage markets for men
Table 12 shows gender differential results for marriage markets. In 1880, women in gold
districts were more likely to be married, also when controlling for the sex ratio. In parallel,
men were less likely to be married, as expected due to the shortage of women to marry.
However, in the medium term analysis, it is unclear whether the norm for low marriage rates
36
Tab
le11
:W
omen
mar
riag
em
arke
tsin
1940
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Depen
den
tvariables
Mar
ried
Mar
ried
Marr
ied
Age
at
firs
tC
hil
dre
nW
age
Pre
stig
eD
ivorc
edD
ivorc
edm
arr
iage
ever
born
gap
gap
(cu
rren
t)(c
urr
ent)
Sample
(work
ing)
Panel
AG
old
Cou
nty
(188
0)0.
013*
**0.
017*
**
0.0
16***
-0.2
42***
-0.0
55***
-33.5
29***
0.5
42***
0.0
01**
0.0
04**
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
77)
(0.0
19)
(8.5
03)
(0.1
42)
(0.0
01)
(0.0
02)
Sex
Rat
io(1
940)
0.95
3***
0.99
2***
0.9
78***
-10.8
23**
2.6
67**
1,2
57.2
39**
8.8
61
-0.3
41***
-0.5
40***
(0.0
77)
(0.0
78)
(0.0
78)
(4.2
89)
(1.2
70)
(536.4
01)
(8.7
35)
(0.0
30)
(0.0
95)
Sex
Rat
iosq
uar
e(1
940)
-0.3
55**
*-0
.372
***
-0.3
67***
3.9
45**
-0.9
42*
-451.4
95*
-3.8
72
0.1
34***
0.2
16***
(0.0
33)
(0.0
34)
(0.0
34)
(1.8
51)
(0.5
54)
(235.3
28)
(3.8
11)
(0.0
13)
(0.0
41)
Urb
an-0
.090
***
-0.0
89**
*-0
.089***
0.4
84***
-0.0
13
53.4
80***
-0.7
72***
0.0
22***
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
85)
(0.0
21)
(9.4
29)
(0.1
62)
(0.0
01)
Age
0.00
3***
0.00
3***
0.0
03***
0.0
81***
0.0
07***
-11.9
77***
-0.0
14**
0.0
00***
0.0
02***
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
03)
(0.0
01)
(0.3
47)
(0.0
06)
(0.0
00)
(0.0
00)
Ob
serv
atio
ns
610,
609
610,
609
610,6
09
21,4
08
610,6
09
58,8
54
86,0
63
610,6
09
171,7
87
R-s
qu
ared
0.01
70.
017
0.0
18
0.0
54
0.0
00
0.0
20
0.0
04
0.0
06
0.0
09
Panel
BG
old
Cou
nty
(188
0)0.0
18***
-0.2
88***
-0.0
61***
-47.2
27***
0.7
85***
0.0
01**
0.0
03*
(0.0
01)
(0.0
88)
(0.0
21)
(9.4
28)
(0.1
55)
(0.0
01)
(0.0
02)
Sex
Rat
io(1
880)
0.0
05
-0.8
43
-0.0
43
281.7
66***
-1.7
39
-0.0
10**
0.0
00
(0.0
11)
(0.6
65)
(0.1
95)
(71.2
94)
(1.3
34)
(0.0
04)
(0.0
14)
Sex
Rat
iosq
uar
e(1
880)
0.0
01
0.1
70
0.0
19
-47.9
29***
0.3
24
0.0
02**
0.0
01
(0.0
02)
(0.1
48)
(0.0
45)
(15.4
62)
(0.3
02)
(0.0
01)
(0.0
03)
Sex
Rat
io(1
940)
0.9
43***
-10.9
31**
3.0
79**
157.1
46
13.3
35
-0.3
33***
-0.5
97***
(0.0
89)
(5.0
97)
(1.4
50)
(617.0
24)
(9.8
46)
(0.0
35)
(0.1
06)
Sex
Rat
iosq
uar
e(1
940)
-0.3
62***
4.1
57*
-1.1
18*
-65.5
98
-5.2
21
0.1
33***
0.2
38***
(0.0
38)
(2.1
46)
(0.6
20)
(266.5
28)
(4.2
11)
(0.0
15)
(0.0
46)
Ob
serv
atio
ns
569,1
71
19,9
59
569,1
71
55,4
73
80,9
66
569,1
71
162,4
67
R-s
qu
ared
0.0
16
0.0
52
0.0
00
0.0
20
0.0
03
0.0
06
0.0
09
Sta
teF
EN
oY
esY
esY
esY
esY
esY
esY
esY
esR
ace
FE
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Geo
grap
hic
contr
ols
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Not
es:
Rob
ust
stan
dar
der
rors
inp
aren
thes
es.
***
p<
0.0
1,
**
p<
0.0
5,
*p<
0.1
.A
llre
gre
ssio
ns
are
Lin
ear
Pro
bab
ilit
yM
od
els
wit
hro
bu
stst
an
dard
erro
rs.
Usi
ng
1940
cen
sus
20%
sam
ple
.A
gega
pis
the
age
gap
bet
wee
nth
ew
om
an
an
dh
ercu
rren
tsp
ouse
.P
rest
ige
gap
isth
eS
iegel
Occ
up
ati
on
al
Pre
stig
esc
ore
gap
bet
wee
nth
ep
artn
ers.
37
among men, or high marriages rates among women would prevail as the sex ratio neutralizes.
In 1940, we find that both women and men are more likely to be married in historic gold
mining areas and areas with high historic sex ratios. The gold rush and skewed sex ratio
seem to have created a culture with high value placed on marriage. In the context of the
gold rush, scarcity of women led to more conservative values, including higher importance of
marriage, but at the same time led to progressive outcomes such as lower fertility and more
hypergamy.
To further unpack the result for men, Table 13 shows the main outcome variables for men
in 1880. We confirm that men were more likely to work, especially in the mining industry
directly, in gold mining counties. They were less likely to work in services and as teachers
(in line with higher representation of women in these sectors). The average prestige score
for men is lower, most likely due to the rating the survey respondents in the 1960’s gave
miners, which was lower than farmers which was the most common occupational category
in non-mining districts (see Appendix Tables 18 and 19).
5.7 Persistence over time
To shed light on the persistence of effects across time, we use data from available censuses
and plot the main coefficients. The specification is using an interaction between gold mining
county and an indicator for female, for each census year separately. The specification con-
trols for the baseline controls, such as age, urban, state fixed effects, race fixed effects, and
geographic controls (presence of rivers, distance to capital, latitude, longitude, and mean
temperature and precipitation). Figure 7 shows that the likelihood that a woman is working
is lower in gold counties in the 1860s, and remain lower until the last census in 1940 (A). In
addition, graph (B) illustrates that women living in gold counties are more likely married
in 1880, and that this effect persists until 1940, and lastly, women in gold areas have fewer
children according to all censuses from 1880 and until 1940.
38
Table 12: Gender differences in marriage markets in 1880 and 1940
(1) (2) (3) (4)Dependent variable Married Married Married MarriedSample: Women, 1880 Men, 1880 Women, 1940 Men, 1940
Gold county (1880) 0.011*** -0.013*** 0.018*** 0.028***(0.002) (0.002) (0.001) (0.001)
Sex ratio (1880) 0.024** -0.322*** 0.027*** 0.024***(0.010) (0.006) (0.005) (0.005)
Sex ratio square (1880) -0.001 0.048*** -0.004*** -0.004***(0.002) (0.001) (0.001) (0.001)
Sex ratio (1940) 1.030*** 0.153***(0.040) (0.034)
Sex ratio square (1940) -0.401*** -0.182***(0.017) (0.014)
Urban -0.088*** 0.036*** -0.088*** 0.009***(0.002) (0.002) (0.001) (0.001)
Age 0.010*** 0.015*** 0.003*** 0.011***(0.000) (0.000) (0.000) (0.000)
Constant 0.306*** 0.283*** -0.112*** 0.095***(0.014) (0.009) (0.022) (0.019)
Observations 262,015 495,526 2,843,380 2,991,786R-squared 0.083 0.177 0.016 0.119State FE Yes Yes Yes YesRace FE Yes Yes Yes Yes
Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
39
Table 13: Labor and Marriage Market outcomes for Men in 1880
(1) (2) (3) (4) (5) (6) (7)Conditional on working
Dependent variable Working Miner Service Teacher Prestige Married DivorcedPanel AGold County (1880) 0.011*** 0.191*** -0.030*** -0.001** -0.744*** -0.073*** 0.000*
(0.001) (0.001) (0.001) (0.000) (0.041) (0.002) (0.000)Urban 0.013*** -0.058*** 0.404*** -0.002*** 3.518*** 0.059*** -0.001***
(0.001) (0.001) (0.002) (0.000) (0.045) (0.002) (0.000)Age 0.003*** 0.002*** 0.000*** -0.000*** 0.213*** 0.015*** 0.000***
(0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000)
Observations 495,526 460,463 460,463 460,463 460,463 495,526 495,526R-squared 0.023 0.144 0.170 0.002 0.132 0.168 0.003Panel BGold County (1880) 0.001 0.137*** -0.024*** 0.001* 0.107** -0.013*** 0.000
(0.001) (0.001) (0.002) (0.000) (0.047) (0.002) (0.000)Sex Ratio (1880) 0.029*** 0.260*** -0.060*** -0.004*** -5.599*** -0.322*** 0.001
(0.003) (0.005) (0.006) (0.001) (0.161) (0.006) (0.001)Sex Ratio square (1880) -0.002*** -0.036*** 0.012*** 0.000*** 0.938*** 0.048*** -0.000*
(0.001) (0.001) (0.001) (0.000) (0.030) (0.001) (0.000)
Observations 495,526 460,463 460,463 460,463 460,463 495,526 495,526R-squared 0.025 0.161 0.170 0.002 0.134 0.177 0.003State FE Yes Yes Yes Yes Yes Yes YesRace FE Yes Yes Yes Yes Yes Yes YesGeographic controls Yes Yes Yes Yes Yes Yes Yes
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All regressions are LinearProbability Models with robust standard errors. Census data for men in 1940 for Arizona, California,Oregon and Nevada. Prestige is the Siegel Occupational Prestige score.
40
-.2-.1
5-.1
-.05
0.0
5
1860 1870 1880 1900 1910 1920 1930 1940
A) Working
0.0
5.1
.15
.2
1880 1900 1910 1920 1930 1940
B) Married
0.1
.2.3
.4.5
1880 1900 1910 1920 1930 1940
C) Children
Figure 7: Coefficient plot of gold county * female for independent regressions by year, con-trolling for age, urban, race, state FE, and geographic controls. (A) is working, (B) Married,(C) Children.
41
6 Robustness
Gold mining significantly influenced the historic sex ratio. While the sex ratio was also
high in non-mining areas, there is a strong correlation between the intensity of the mining
activities and the male to female ratio. For this reason, sex ratio is an endogenous control
to gold mining. To explore further the total effect of gold mining on gender norms, we alter
the specifications in Tables 14 and 15, using sex ratios in 1880 and interaction effects. The
tables show fairly consistent coefficients and standard errors for the likelihood of working
and marriage for men and women with varying levels of fixed effects, geographic controls,
control for the year of formation of political institutions, population density in 1900, and
clustering of the standard errors at the county level. One exception is the coefficient historic
gold mining on the likelihood of married in 1940 when the standard errors are clustered at
the county level, when the coefficient is statistically insignificant.
We explore robustness of the main labor results for 1940 in Table 15. Gold mining,
unconditional on sex ratio, has a negative significant effect on the likelihood of a woman
to work. So does also the historic sex ratio. The coefficient on gold mining is stable to
the inclusion of historic sex ratio control (Column 3), but the effect size increases when
conditioning on the sex ratio in 1940.
Some counties in the states still had a mining industry mining in 1940. Using the 1940
census, we check if counties with contemporaneous mining in 1940 have different outcomes for
men and women’s labor markets (Table 16) using the 1940 mining employment as recorded
in the census. Mining employment in the 1940 census ranges from 0 up to 14%. We find that
the presence of mining employment is positively correlated with male employment (Column
1), and negatively with female employment (Column 2). Importantly, gold mining in 1880
still predicts lower labor force participation of women. These results are also confirmed
using a sample splits in Columns 3 and 4. Column 3 shows results on counties that had less
than 1% mining employment in 1940, and Column 4 counties with more than 2% mining
employment. Both coefficients for the historic gold mining are negative and significant. This
42
Tab
le14
:D
iffer
ent
spec
ifica
tion
sfo
r18
80
Work
ing
Marr
ied
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Gol
dC
ounty
(188
0)0.
032*
**0.
023***
0.0
23***
0.0
23**
0.0
11***
-0.1
04***
-0.0
94***
-0.0
94***
-0.0
94***
-0.0
20***
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
09)
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
20)
(0.0
02)
Gol
dC
ounty
*Fem
ale
-0.0
62**
*-0
.060***
-0.0
60***
-0.0
60**
-0.0
46***
0.1
57***
0.1
55***
0.1
55***
0.1
55***
0.0
50***
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
26)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
36)
(0.0
03)
Fem
ale
-0.7
69**
*-0
.763***
-0.7
63***
-0.7
63***
-0.6
96***
0.2
04***
0.2
01***
0.2
01***
0.2
01***
-0.2
59***
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
24)
(0.0
07)
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
29)
(0.0
11)
Sex
Rat
io(1
880)
0.0
39***
-0.3
45***
(0.0
03)
(0.0
06)
Sex
Rat
iosq
.(1
880)
-0.0
04***
0.0
51***
(0.0
01)
(0.0
01)
Sex
Rat
io(1
880)
*Fem
ale
-0.0
63***
0.4
18***
(0.0
07)
(0.0
11)
Sex
Rat
iosq
.(1
880)
*Fem
ale
0.0
10***
-0.0
60***
(0.0
02)
(0.0
02)
Ob
serv
atio
ns
757,
541
757,5
41
757,5
41
757,5
41
757,5
41
757,5
41
757,5
41
757,5
41
757,5
41
757,5
41
R-s
qu
ared
0.63
20.6
33
0.6
33
0.6
33
0.6
33
0.1
74
0.1
75
0.1
75
0.1
75
0.1
83
Sta
teF
EN
oN
oN
oN
oN
oN
oN
oN
oN
oN
oR
ace
FE
No
No
No
No
No
No
No
No
No
No
Geo
grap
hic
contr
ols
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Pol
itic
alor
g.N
oN
oY
esN
oN
oN
oN
oY
esN
oN
oP
opu
lati
ond
ensi
ty19
00N
oN
oY
esN
oN
oN
oN
oY
esN
oN
oC
lust
erS
Eat
cou
nty
leve
lN
oN
oN
oY
esN
oN
oN
oN
oY
esN
o
Not
es:
Robu
stst
and
ard
erro
rsin
par
enth
eses
.***
p<
0.0
1,
**
p<
0.0
5,
*p<
0.1
.A
llre
gre
ssio
ns
are
Lin
ear
Pro
bab
ilit
yM
od
els
wit
hro
bu
stst
an
dard
erro
rs.
Cen
sus
dat
afo
rm
enan
dw
om
enin
1880
for
Ari
zon
a,
Cali
forn
ia,
Ore
gon
an
dN
evad
a.
43
Tab
le15
:D
iffer
ent
spec
ifica
tion
sfo
r19
40
Work
ing
Marr
ied
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Gol
dC
ounty
(188
0)-0
.009
***
-0.0
08***
-0.0
08***
-0.0
08
-0.0
12***
0.0
28***
0.0
29***
0.0
29***
0.0
29
0.0
38***
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
11)
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
19)
(0.0
01)
Gol
dC
ounty
*Fem
ale
0.00
6***
0.006***
0.0
06***
0.0
06
0.0
11***
-0.0
18***
-0.0
17***
-0.0
17***
-0.0
17
-0.0
27***
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
27)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
32)
(0.0
02)
Fem
ale
-0.5
60**
*-0
.560***
-0.5
60***
-0.5
60***
-0.3
04***
0.0
58***
0.0
56***
0.0
56***
0.0
56***
-0.3
26***
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
16)
(0.0
09)
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
14)
(0.0
11)
Sex
Rat
io(1
880)
0.1
20***
-0.1
50***
(0.0
08)
(0.0
10)
Sex
Rat
iosq
.(1
880)
-0.0
20***
0.0
27***
(0.0
02)
(0.0
02)
Sex
Rat
io(1
880)
*Fem
ale
-0.2
39***
0.3
67***
(0.0
10)
(0.0
12)
Sex
Rat
iosq
.(1
880)
*Fem
ale
0.0
41***
-0.0
65***
(0.0
02)
(0.0
03)
Sex
Rat
io(1
940)
-0.0
86*
-0.0
63
-0.0
63
-0.0
63
0.0
29
1.0
94***
1.0
07***
1.0
07***
1.0
07***
0.8
07***
(0.0
44)
(0.0
45)
(0.0
45)
(0.3
38)
(0.0
53)
(0.0
51)
(0.0
52)
(0.0
52)
(0.3
60)
(0.0
59)
Sex
Rat
iosq
.(1
940)
0.02
90.0
18
0.0
18
0.0
18
-0.0
29
-0.5
11***
-0.4
72***
-0.4
72***
-0.4
72***
-0.3
94***
(0.0
19)
(0.0
19)
(0.0
19)
(0.1
51)
(0.0
22)
(0.0
22)
(0.0
23)
(0.0
23)
(0.1
50)
(0.0
25)
Ob
serv
atio
ns
1,25
5,88
71,
255,8
87
1,2
55,8
87
1,2
55,8
87
1,1
67,1
19
1,2
55,8
87
1,2
55,8
87
1,2
55,8
87
1,2
55,8
87
1,1
67,1
19
R-s
qu
ared
0.31
60.3
16
0.3
16
0.3
16
0.3
11
0.0
49
0.0
50
0.0
50
0.0
50
0.0
51
Sta
teF
EN
oN
oN
oN
oN
oN
oN
oN
oN
oN
oR
ace
FE
No
No
No
No
No
No
No
No
No
No
Geo
grap
hic
contr
ols
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Pol
itic
alor
g.N
oN
oY
esN
oN
oN
oN
oY
esN
oN
oP
opu
lati
ond
ensi
ty19
00N
oN
oY
esN
oN
oN
oN
oY
esN
oN
oC
lust
erS
Eat
cou
nty
leve
lN
oN
oN
oY
esN
oN
oN
oN
oY
esN
o
Not
es:
Rob
ust
stan
dar
der
rors
inp
aren
thes
es.
***
p<
0.0
1,
**
p<
0.0
5,
*p<
0.1
.U
sin
g1940
cen
sus
20%
sam
ple
.A
llre
gre
ssio
ns
are
Lin
ear
Pro
bab
ilit
yM
od
els
wit
hro
bu
stst
and
ard
erro
rs.
Cen
sus
dat
afo
rm
enan
dw
om
enin
1940
for
Ari
zon
a,
Cali
forn
ia,
Ore
gon
an
dN
evad
a.
44
Table 16: Robustness using sample splits in 1940
(1) (2) (3) (4) (5) (6) (7) (8)Dependent var Working Working Working Working Married Married Married Married
Gold County (1880) -0.005*** -0.006*** -0.007*** -0.013*** 0.027*** 0.008* 0.018*** 0.014***(0.001) (0.001) (0.002) (0.004) (0.002) (0.004) (0.002) (0.004)
Presence of miners 0.508*** -0.551***(0.039) (0.048)
Sex Ratio (1940) 0.656*** -0.891*** -0.802*** 0.120 0.080 -0.283 1.121*** 1.242***(0.057) (0.074) (0.099) (0.187) (0.088) (0.187) (0.104) (0.213)
Sex Ratio square (1940) -0.278*** 0.354*** 0.310*** -0.029 -0.144*** -0.036 -0.436*** -0.458***(0.025) (0.032) (0.043) (0.074) (0.038) (0.074) (0.045) (0.085)
Sample Men Women Women Women Men Men Women WomenSample split (share miners) - - < 1% > 2% < 1% > 2% < 1% > 2%Observations 645,085 610,804 503,004 64,238 520,356 77,038 503,004 64,238R-squared 0.002 0.028 0.024 0.019 0.123 0.097 0.013 0.028State FE No No No No No No No NoRace FE No No No No No No No NoGeographic controls Yes Yes Yes Yes Yes Yes Yes Yes
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Using 1940 census 20% sample. All regressions are LinearProbability Models with robust standard errors. Presence of miners includes the county-average mining employment. Sample splits in columns
3-8, split by gender and by presence of mining employment above 2% or below 1%.
rules out that the effect is due to the longevity of the gold mining industry, which continues
to recreate the conditions that lead to lower labor force participation of women. Equally, we
can rule out that the effects on the marriage market are driven by contemporaneous mining
activities. Men living in historic mining areas are more likely married in both contemporary
mining and non-mining areas (Columns 5 and 6), and so are women. We also checked
and the results are robust to using a stricter definition of non-mining county (below 0.1%
employment).
7 Discussion
Extractive industries are important generators of economic growth in many developing coun-
tries today. That role in economic development is far from new, as many large cities around
the world, Johannesburg and San Francisco included, were started as mining camps driving
inward migration movements. Yet, we have until now known little about the role of extrac-
tive industries for women’s roles in society. We explore this question in the context of the
US Gold Rush that started in California in 1849.
The discovery of gold in California in the mid 1800s had a significant effect on the social
fabric. The prospect of gold lured potential miners en masse to the previously not so densely
populated California. The men arrived first, creating a ratio between the sexes seen in few
45
places in the world. San Francisco counted a dozen men for each woman.
The gold rush would have significant effects on gender roles, through several mechanisms.
We find evidence that it created a lucrative marriage markets for women. Women in mining
counties were more likely to be married, specifically to men that were older than them and
with more prestigious occupations. Second, the gold rush changed women’s labor market
opportunities. Historic, anecdotal accounts, tell how women could make a lot of money by
feeding, clothing and serving rich male miners. The analysis show that women in mining
areas were significantly more likely to work in the service sector, conditional on working.
However, at the extensive margin women were working less, possibly due to the oversupply
of marriageable men offering economic security.
We subsequently explore whether these cultural shifts persist in the medium term, using
the 1940 US census. To account for potential persistence in the skewed sex ratio, we control
for sex ratio in 1940. We find that women, 80 years after the peak of gold mining boom, are
less likely to be working in historic gold mining areas, but more likely working in services or
as housekeepers. Moreover, in historic gold mining areas, and historic high sex ratio areas,
marriage rates among women are higher (also when controlling for the sex ratio in 1940, and
mining in 1940), illustrating a persistence of the cultural changes the gold boom brought
to the marriage markets. Importantly, we document that historic mining areas are different
in all subsequent censuses from 1880 to 1940, and we confirm that the results hold both in
areas with and without significant mining in 1940.
Moreover, our results provide nuance to the discussion of how scarcity of women affects
gender equality. One study finds that male-biased areas in Australia–due to the sending of
convicts, a largely male population–had higher marriage rates among women, and women
were less likely to work, and if working, were doing so in less prestigious occupations (Gros-
jean and Khattar, 2018). Our results are largely in agreement, although we find that women
are more likely working in the service sector. Some differences across the studies would
be expected and be due to (1) positive selection of women to gold areas, (2) the income
46
opportunities available for men, (3) the rise of the tertiary sector because of the lack of
the traditional structures providing reproductive housework. Regarding the first potential
mechanism—selective migration—according to historic accounts, women in California were
positively selected: they were more educated, more likely to be literate, and from more
affluent backgrounds compared to the population as a whole (Levy, 1990).
Regarding the second and third potential mechanisms, we hypothesize that both (i)
a positive wage shock to the male sector, and (ii) a skewed sex ratio, are necessary to
push women into market-based service sector employment. Maurer and Potlogea (2017)
who explore changes in a male-biased sector, oil, which has a less skewed sex ratio as the
extractive shocks happens in an already established population in the US South, also find
increased employment in the tertiary sector.
The results in this paper speak to a literature focusing on the persistence of cultural
norms across generations (Fernandez and Fogli, 2009), and the importance of economic
specialization in determining gender norms (Alesina et al., 2013; Qian, 2008). We show that
a historic employment and income shock have ramifications for gender norms almost 100
years after the beginning.
47
8 References
Abadie, Alberto, Susan Athey, Guido W. Imbens, and Jeffrey Wooldridge. When
Should You Adjust Standard Errors for Clustering?. No. w24003. National Bureau of
Economic Research, (2017).
Abramitzky, Ran, Adeline Delavande, and Luis Vasconcelos. “Marrying Up: The
Role of Sex Ratio in Assortative Matching.” American Economic Journal: Applied
Economics 3, no. 3 (2011): 124-57. http://www.jstor.org/stable/41288641.
Alesina, Alberto, Paola Giuliano, and Nathan Nunn. “On the origins of gender roles:
Women and the plough.” The Quarterly Journal of Economics 18.2 (2013): : 469-530.
Alesina, A., Giuliano, P., and Nunn, N. (2011). “Fertility and the Plough.” American
Economic Review, 101(3), 499-503.
Aragon, Fernando M., and Juan Pablo Rud. “Natural resources and local communities:
evidence from a Peruvian gold mine.” American Economic Journal: Economic Policy
5.2 (2013): 1-25.
Aragon, Fernando M., and Juan Pablo Rud. “Polluting industries and agricultural
productivity: Evidence from mining in Ghana.” The Economic Journal 126.597 (2015):
1980-2011.
Aragon, Fernando M., Juan Pablo Rud, and Gerhard Toews. “Resource shocks, em-
ployment, and gender: evidence from the collapse of the UK coal industry.” Labour
Economics 52 (2018): 54-67.
Axbard, Sebastian, Jonas Poulsen, and Anja Tolonen. “Extractive Industries, Produc-
tion Shocks and Criminality: Evidence from a Middle-Income Country.” (2016).
Baranov, Victoria, Ralph De Haas, and Pauline A. Grosjean. “Men. Roots and Con-
sequences of Masculinity Norms.” (2018).
48
Bazzi, S., Fiszbein, M., and Gebresilasse, M. (2017). “Frontier Culture: The Roots and
Persistence of “Rugged Individualism”” in the United States (No. w23997). National
Bureau of Economic Research.
Benerıa, L., Berik, G., & Floro, M. (2015). Gender, development and globalization:
economics as if all people mattered. Routledge.
Benshaul-Tolonen, Anja and Baum, Sarah (2018). “Structural Transformation, Natu-
ral Resources and Gender Equality.” Unpublished.
Benshaul-Tolonen, Anja. (2018). “Endogeous Gender Norms: Evidence from Africa’s
Gold Mining Industry.” Unpublished.
Berman, N., Couttenier, M., Rohner, D., and Thoenig, M. (2017). “This mine is mine!
How minerals fuel conflicts in Africa.” American Economic Review, 107(6), 1564-1610.
Boyd, Monica. (2008). “A Socioeconomic Scale for Canada: Measuring Occupational
Status from the Census.” Canadian Review of Sociology 45(1): 51-91.
Charles, Kerwin Kofi, and Ming Ching Luoh. “Male incarceration, the marriage mar-
ket, and female outcomes.” The Review of Economics and Statistics 92.3 (2010):
614-627.
Clark, Shelley. “Son preference and sex composition of children: Evidence from India.”
Demography 37.1 (2000): 95-108.
Conover, Emily, Melanie Khamis, and Sarah Pearlman. “Missing Men and Female
Labor Market Outcomes: Evidence from large-scale Mexican Migration.” Unpublished
Manuscript (2015).
Corno, Lucia, and Damien de Walque. “Mines, Migration and HIV/AIDS in Southern
Africa.” (2012).
49
Couttenier, Mathieu, Pauline Grosjean, and Marc Sangnier. (2017) “The Wild West is
Wild: The Homicide Resource Curse.” Journal of the European Economic Association
15.3: 558-585.
Davis Lance, E., A. Easterlin Richard, & N. Parker William (1972). American Eco-
nomic Growth: An Economists History of the United States. New York: Harper &
Row.
Dimand, Robert W., Evelyn L. Forget, and Chris Nyland. “Retrospectives: gender in
classical economics.” Journal of Economic Perspectives 18.1 (2004): 229-240.
Duflo, Esther. “Grandmothers and granddaughters: oldage pensions and intrahouse-
hold allocation in South Africa.” The World Bank Economic Review 17.1 (2003):
1-25.
Duflo, Esther. “Women empowerment and economic development.” Journal of Eco-
nomic Literature 50.4 (2012): 1051-79.
England, Paula. (1979). “Women and Occupational Prestige: A Case of Vacuous Sex
Equality.” Signs: Journal of Women in Culture and Society 5(2): 252-265.
Fernandez, Raquel, and Alessandra Fogli. “Culture: An empirical investigation of
beliefs, work, and fertility.” American Economic Journal: Macroeconomics 1.1 (2009):
146-77.
Fernandez, Raquel, Alessandra Fogli, and Claudia Olivetti. “Mothers and sons: Prefer-
ence formation and female labor force dynamics.” The Quarterly Journal of Economics
119.4 (2004): 1249-1299.
Geddes, Rick, and Dean Lueck. “The gains from self-ownership and the expansion of
women’s rights.” American Economic Review 92.4 (2002): 1079-1092.
50
Geddes, Rick, Dean Lueck, and Sharon Tennyson. “Human capital accumulation and
the expansion of womens economic rights.” The Journal of Law and Economics 55.4
(2012): 839-867.
Giuliano, Paola, and Nathan Nunn. “Understanding cultural persistence and change.”
No. w23617. National Bureau of Economic Research. (2017).
Grosjean, P., and Brooks, R. C. (2017). Persistent effect of sex ratios on relationship
quality and life satisfaction. Phil. Trans. R. Soc. B, 372(1729), 20160315.
Grosjean, Pauline A., and Rose Khattar. “It’s Raining Men! Hallelujah?.” (2015).
Forthcoming Review of Economic Studies.
Hauser, Robert M. and John Robert Warren. 1997. “Socioeconomic Indexes for Oc-
cupations: A Review, Update, and Critique.” Sociological Methodology 27: 177-298.
Hurtado, A. L. (1999). Sex, gender, culture, and a great event: The California gold
rush. Pacific Historical Review, 68(1), 1-19.
Jayachandran, Seema. “The roots of gender inequality in developing countries.” eco-
nomics 7.1 (2015): 63-88.
Kearney, M. S., and Wilson, R. (2017). Male Earnings, Marriageable Men, and Non-
Marital Fertility: Evidence from the Fracking Boom. Review of Economics and Statis-
tics.
Kotsadam, Andreas, and Tolonen, Anja. (2016). African mining, gender, and local
employment. World Development, 83, 325-339.
Levy, JoAnn. “They Saw the Elephant: Women in the California Gold Rush.” Uni-
versity of Oklahoma Press. (1990).
Maurer, Stephan, and Andrei Potlogea. “Male-biased Demand Shocks and Womens
Labor Force Participation: Evidence from Large Oil Field Discoveries.” (2017).
51
Ngai, Rachel, and Barbara Petrongolo. “Gender gaps and the rise of the service econ-
omy.” (2014).
Qian, Nancy. “Missing women and the price of tea in China: The effect of sex-specific
earnings on sex imbalance.” The Quarterly Journal of Economics 123.3 (2008): 1251-
1285.
Taniguchi, N. J. (2000). Weaving a different world: Women and the California Gold
Rush. California History, 79(2), 141-168.
Baranov, Victoria and De Haas, Ralph and Grosjean, Pauline A., Men. Roots and
Consequences of Masculinity Norms (2018). UNSW Business School Research Paper.
Warren, John Robert, Jennifer T. Sheridan, and Robert M. Hauser. 1998. “Choosing a
Measure of Occupational Standing: How Useful Are Composite Measures in Analyses
of Gender Inequality in Occupational Attainment?” Sociological Methods & Research
27(1): 3-76.
Wilson, N. (2012). Economic booms and risky sexual behavior: evidence from Zambian
copper mining cities. Journal of Health Economics, 31(6), 797-812.
52
Appendix Figures and Tables
Figure 8: States and counties included in the sample in green
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1840 1850 1860
1870 1880 1900
Figure 9: State and Counties in 1840, 1850, 1860, 1870, 1880, 1900
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Table 17: Top 25 most common occupations for women in 1880
Rank Mining counties Percent Non-mining counties Percent1 Housewife 57.28 Housewife 54.52 Other non-occupation 22.62 Other non-occupation 21.343 Imputed keeping house (1850-1900) 6.15 Imputed keeping house (1850-1900) 4.764 Private household workers 2.29 Private household workers 4.525 At school/student 2.22 Dressmakers and seamstresses 3.166 Dressmakers and seamstresses 1.56 At school/student 2.577 Teachers 1.35 Teachers 1.558 Housekeepers in private household 0.71 Operative and kindred workers 0.839 Attendants,professional and personal 0.7 Housekeepers in private household 0.5610 Farmers (owners and tenants) 0.54 Attendants, professional and personal 0.5111 Laundresses in private household 0.5 Managers, officials, and proprietors 0.4512 Laborers 0.45 Practical nurses 0.4313 Managers, officials, and proprietors 0.45 Laundresses in private household 0.3814 Helping at home/helps parents 0.43 Boarding and lodging house keepers 0.3815 Boarding and lodging house keepers 0.34 Cooks, except private household 0.3716 Cooks, except private household 0.31 Milliners 0.3717 Milliners 0.27 Musicians and music teachers 0.3518 Musicians and music teachers 0.21 Helping at home/helps parents 0.3319 Practical nurses 0.17 Farmers (owners and tenants) 0.3120 Farm laborers, wage workers 0.16 Tailors and tailoresses 0.2221 Service workers, except private hh 0.14 Weavers, textile” 0.1922 Operative and kindred workers 0.13 Salesmen and sales clerks 0.1623 Religious workers 0.12 Service workers, except private hh 0.1424 Waiters and waitresses 0.11 Laborers (nec) 0.1425 Mine operatives and laborers 0.1 Religious workers 0.13
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Table 18: Top 25 most common occupations for men in 1880
Rank Mining counties Percent Non-mining counties Percent1 Mine operatives, laborers 22.07 Farmers (owners and tenants) 14.552 Farmers (owners and tenants) 14.56 Laborers 13.173 Laborers 14.31 Farm laborers, wage workers 9.214 Farm laborers, wage workers 10.15 Managers, officials, and proprietors 7.125 Other non-occupation 4.89 Operative and kindred workers 6.946 Managers, officials, and proprietors 4.44 Other non-occupation 4.767 Operative and kindred workers 2.8 Carpenters 3.018 Carpenters 2.17 Salesmen and sales clerks 2.969 Cooks, except private household 2.11 Cooks, except private household 1.7710 Truck and tractor drivers 1.77 Private household workers 1.7711 Blacksmiths 1.44 Sailors and deck hands 1.6812 Salesmen and sales clerks 1.42 Fishermen and oystermen 1.6413 Lumbermen, raftsmen, woodchoppers 1.33 Laundry and dry cleaning operatives 1.6214 At school/student 0.93 Truck and tractor drivers 1.5415 Laundry and dry cleaning operatives 0.88 At school/student 1.4916 Members of the armed services 0.74 Mine operatives and laborers 1.2217 Meat cutters, except slaughter and pack 0.71 Blacksmiths 1.1618 Private household workers 0.67 Clerical and kindred workers 1.0419 Stationary engineers 0.65 Painters, construction and maintenance 0.9720 Gardeners, except farm and groundskeeper 0.63 Lumbermen, raftsmen, woodchoppers 0.9321 Craftsmen and kindred workers 0.48 Meat cutters, except slaughter and pack 0.8922 Painters, construction and maintenance 0.43 Bookkeepers 0.8723 Lawyers and judges 0.41 Hucksters and peddlers 0.8324 Physicians and surgeons 0.4 Tailor 0.7525 Teachers 0.38 Craftsmen and kindred workers 0.68
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Table 19: Siegel occupational prestige score for selected occupations
Variable Mean Std. Dev.Working (average) 31.2 12.71Teacher 59.6 0Managers, officials, and proprietors 50.3 0Farmer 40.7 0Carpenters 39.9 0Service (composite) 34.3 11.21Service workers, except private hh 17.6 0Dressmaker 31.7 0Mine operator 26.3 0Private household worker 18.9 0Laborer 17.5 0Housewife 0 0
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Table 20: Variable definitions
Variable Census Year Definition
Occupational outcomesWorking 1880/1940 =1 if man/woman is workingSalary 1940 total pre-tax salary income for the previous yearHousewife 1880/1940 =1 if woman is a housewifeService 1880/1940 =1 if man/woman works in the service sectorHousekeeper 1880/1940 =1 if man/woman is a housekeeperSiegel Prestige score 1940 Siegel occupational prestige score
Marital outcomesMarried 1940 =1 if woman is marriedAge at first marriage 1940 The woman’s age at her first marriageChildren 1880/1940 Children ever born to womanAge gap 1880/1940 Age gap between the woman and her spouseWage gap 1940 Wage gap between the woman and her spousePrestige gap 1880/1940 Prestige gap between woman and her spousePrestige gap 2 1880/1940 Prestige gap excluding housewives
58