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-

nen@barnard.edu. 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 Please do not cite or circulate without author permission.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

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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).

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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

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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.

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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

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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