Closing Heaven’s Door: Evidence from the 1920s U.S.

Immigration Quota Acts∗

Philipp Ager Casper Worm Hansen

Abstract

The introduction of immigration quotas in the 1920s fundamentally changed U.S. immigration

policy. We develop an empirical model that captures the missing immigrants of this policy change

to estimate the economic consequences of immigration restrictions for the U.S. economy. The

implementation of the quota system led to long-lasting declines in the foreign-born share and

population growth in areas with more missing immigrants per capita. This effect was largely

driven by the policy-restricted supply of immigrants from quota-affected nationalities and reduced

fertility. Our main finding is that while native white workers faced sizable earnings losses, black

workers benefited from the quota system and improved their relative economic status within the

more affected areas. We also find that productivity in the manufacturing sector declined as a result

of the new immigration policy.

Keywords: Immigration, population dynamics, local labor markets, racial wage gap, productivity growth

JEL Codes: J31, J61, N31, O15

∗Philipp Ager, University of Southern Denmark; phag@sam.sdu.dk. Casper Worm Hansen, University of Copenhagen;

casper.worm.hansen@econ.ku.dk. We thank Leah Boustan, Brian Cadena, Nathan Nunn, and Marianne Wanamaker for

detailed comments and suggestions, as well as Martha Bailey, Hoyt Bleakley, Michael Clemens, Carl-Johan Dalgaard,

Dave Donaldson, James Fenske, Nicola Gennaioli, Paola Giuliano, Walker Hanlon, Stephan Heblich, Peter Sandholt

Jensen, Jeanne Lafortune, Ethan Lewis, Bob Margo, Petra Moser, Paul Rhode, Daniel Sturm, Jose Tessada, Zach Ward,

Jeff Williamson, Asger Moll Wingender, Niko Wolf and seminar participants at the AEA Meetings 2018 in Philadelphia,

the University of Copenhagen, Humboldt University, the Second Workshop on Immigration, Health, and Well-Being

in Oxford, and the Economic Demography Workshop in Denver for valuable feedback. We gratefully acknowledge

financial support from the Danish Research Council grant No. DFF – 4182-00043. This paper was previously entitled

“National Immigration Quotas and Local Economic Growth” (University of Copenhagen Discussion Papers Department

of Economics No.16-11, November, 2016).

1 Introduction

The issue of how immigration restrictions influence labor market conditions and demographic out-

comes is highly policy relevant. Recent events such as the European migrant crises or the discussion

about increased border security in the United States have further stimulated the ongoing debate about

how rich countries should respond to higher immigration. In general, labor economists have investi-

gated the effect of immigration on a range of economic outcomes, such as native wages, employment,

and productivity,1 but, despite its importance, still relatively little is known about the economic effects

of immigration restrictions, especially at the sub-national level.

This paper contributes to the understanding of how immigration restrictions have influenced the

U.S. economy from a historical perspective. The debate on imposing immigration restrictions to

prevent entry of certain ethnic groups into the United States is not a new one (Hutchinson, 1981;

Higham, 2002). In the 1920s, the United States changed its open door policy for European immigrants

by introducing immigration quotas based on national origins (King, 2000). While approximately 30

million immigrants from Europe arrived in the United States between 1850 and 1914 (Abramitzky

et al., 2014), the implementation of the quota system curtailed European immigration to the United

States from 4.5 million between 1910 and 1914, to less than 800,000 between 1925 and 1929 (U.S.

Department of Commerce, 1931, Table 99). This fundamental change in immigration policy in the

1920s is unprecedented in U.S. history and potentially can be used to evaluate the causal effects of

immigration restrictions on the U.S. economy (Abramitzky and Boustan, forthcoming).

We exploit this regime change in U.S. immigration policy—the passage of the Emergency Quota

Act of 1921 and the Immigration Act of 1924, which abruptly ended the era of unrestricted immi-

gration from Europe to the United States (Goldin, 1994)—to study the economic consequences of

immigration restrictions for the U.S. economy during the period 1900-1940.2 The implementation

of the quota system provides two sources of variation which we exploit in our empirical analysis.

First, the quota system restricted immigration from some nations more than from others. This allows

us to calculate the missing immigrants due to the quota system for each nationality by comparing

the expected number of immigrants under free migration to the corresponding quota numbers. The

missing immigrants are assigned to each local area (county or city) in the United States based on

their nationality share in 1920. To measure the local effect of immigration this assignment rule builds

1See, for example, Card (1990, 2001, 2009), Borjas (2003, 2006, 2014), Peri (2012), Lewis and Peri (2015), Fogedand Peri (2015), Cadena and Kovak (2016), and Dustmann et al. (2016).

2We refer to Section 2 for a detailed description of the immigration quotas.

1

on an established observation in the migration literature that newly arriving immigrants tend to settle

in areas where previous immigrants of the same nationality live (Bartel, 1989; Card, 2001; Mun-

shi, 2003). It provides us with an immigration rate of missing immigrants which varies across the

United States, and we refer to it as quota exposure. Our estimation strategy combines the quota ex-

posure with the second source of variation arising from the timing of the policy change that permits

a before-and-after quota comparison of the outcomes of interest. With the two sources of variation

at hand, we can estimate the effects of a quota supply-driven decrease in immigration due to miss-

ing migrants which, due to variation in the 1920 nationality shares, is unequally distributed across

local economies.3 We implement our estimation strategy using three different samples: U.S. Census

county level data (1900–1940); a repeated cross-section of U.S. Census microdata (1900–1940); and

hand-collected data from the U.S. Census of Manufactures at the city level (1909–1929).

Our empirical analysis provides a rigorous quantitative assessment of the economic consequences

of the quota system for the U.S. economy focusing on local economies. The first section of the

empirical analysis establishes that the quota system led to a long-lasting relative decline in the foreign-

born share, and lower population growth. Our results reveal that the foreign-born share reduces by

1.6 percentage points, and the 10-year population growth rate declines by 6.7 percentage points in

counties with one additional missing immigrant per-100-inhabitants-per-year.4 A simple accounting

exercise demonstrates that the direct effect of missing immigrants due to the quota system explains

about 68 percent of the total population growth effect. One additional factor that contributed to the

decline in population growth was that women of childbearing age had fewer children after the quota

system was in place. The corresponding decline in marriage rates in the more affected counties

suggests that young women adjusted fertility to changing economic conditions.

In the second section of the empirical analysis we study the effect of the quota system on local

labor market outcomes. The regime change in U.S. immigration policy had a sizable effect on the

earnings of native workers.5 Our results reveal that native workers living in counties that were more

exposed to the quota system were pushed into lower-wage occupations. This effect, however, differs

substantially by gender and race. For men, the implementation of the quota system led to substantial

3While this strategy has some similarities to the more traditional shift-share instrumental variable approach (Bartik,1991), we focus on isolating a specific policy-driven variation in the immigration flow to the United States (see Section3.2 for more details).

4One additional missing immigrant per-100-inhabitants-per-year amounts to 10 percent (out of the total population)fewer immigrants arriving over a decade.

5Due to the lack of individual wage data before 1940, we use occupation and industry-based earnings scores in theempirical analysis to evaluate the effect of the immigration quota acts on the earnings of native workers. We refer toSection 3.1 for further details.

2

earnings losses for white workers, while black workers benefited from it. Our estimates imply that

white workers living in counties with one additional missing immigrant per-100-inhabitants-per-year

experienced a reduction in earnings of about 2.4 percent, while black workers, on average, increased

their earnings by about 2.3 percent each decade from 1920 to 1940. Black female workers also expe-

rienced significant gains in the more quota affected counties, while white women were not affected at

all. Consistent with this finding, we show that after the quota restrictions were in place native workers

mostly filled up the low-skilled jobs of the missing immigrants (which meant some economic gains

for black workers, who were, on average, at the bottom of the earnings distribution at that time, and

losses for white workers). After the quota system was implemented, young white adults (age 15-21)

in the more affected counties also stayed longer in school compared to their black peers, suggesting

local labor market conditions in these counties improved for black workers, at least in relative terms.

In order to evaluate to what extent the implementation of the quota system reduced the black-

white earnings gap, we additionally exploit the within-county-level variation of the quota exposure

by race. We find a stronger decline of the black-white earnings gap in the more affected counties. For

the average level of quota exposure (i.e., 0.69 missing immigrants per 100 inhabitants in a county per

year), the earnings of black-male workers increased up to 3.1 percent, relative to white-male workers

in the post-quota period (1920-1940). This suggests that in comparison to black workers, European

immigrant labor at that time had a lower elasticity of substitution to white native workers.6 Our

finding that the quota acts increased the relative economic status of black workers in more affected

areas before World War II also contributes to the debate on the evolution of black-white income

differences throughout U.S. history (e.g., Myrdal, 1944; Smith, 1984; Smith and Welch, 1989; Margo

2016).7 Overall, we find that the relative earnings of black workers increased modestly after the

introduction of the quota system which is consistent with the fact of some, albeit weaker, black-white

income convergence, compared to after 1940 (Margo, 2016).

The last section of the empirical analysis evaluates whether labor productivity in the manufactur-

ing sector changed after the quota system was in place. Our estimates reveal that the quota system led

to a decline in labor productivity in manufacturing at the city level. Between 1919 and 1929, every

6The insight that there are “winners and losers from immigration” relates to a recent paper by Borjas (forthcoming),who reassesses the wage impact of the Mariel boatlift and shows that the increased number of low-skilled immigrantsfrom Cuba lowered the wage of native low-skilled groups.

7For a linked sample of black men, Collins and Wanamaker (2014) show that the Great Migration contributed substan-tially to the decline in the black-white earnings gap before World War II. Other studies primarily focus on the evolution ofblack economic progress during the period 1940–1970 (e.g., Donohue and Heckman, 1991; Malony, 1994; Margo, 1995;Collins, 2000; Boustan, 2009; Carruthers and Wanamaker, 2017).

3

additional missing immigrant in the urban population (per-100-inhabitants-per-year) decreased value

added-per-worker and wages-per-worker by 4.6 percent and 3.5 percent, respectively. The productiv-

ity decline is not driven by changes in the capital intensity or the capital-output ratio of manufactures

in the more quota affected cities, as we find no robust evidence that firms made adjustments along

these margins in response to the implementation of the quota acts.8 Our interpretation is that agglom-

eration externalities, and the degree of substitutability between native and immigrant workers in the

manufacturing sector, could be the driving forces behind this result.

The results of this paper advance the understanding of the economic consequences of immigration

restrictions for the U.S. economy. Clemens et al. (forthcoming) is one of the relatively few studies

that investigates the labor market effects of a change in U.S. immigration policy—the exclusion of

the so-called bracero workers on December 31, 1964. Using a differences-in-differences approach,

Clemens et al. show that despite half a million seasonally employed farm workers from Mexico be-

ing excluded from the labor force, domestic farm laborers did not experience a rise in real wages or

employment. They argue that this null finding is the result of employers adopting less labor-intensive

technologies and changes in crop production. Chen (2015) investigates how the Chinese Exclusion

Act of 1882 affected the occupational standing of Chinese immigrants, and finds that the average oc-

cupational income score of Chinese compared to Japanese immigrants significantly declined after the

Exclusion Act of 1882. Greenwood and Ward (2015), Massey (2016), and Ward (2017) examine how

the U.S. quota laws during the 1920s changed migration behavior. Greenwood and Ward (2015) show

that emigration rates declined significantly after the introduction of immigration quotas, especially

from unskilled occupations and farming, which Ward (2017) argues is driven by a lower rate of un-

planned return migration during the 1920s. While Massey (2016) examines how the enactment of the

Emergency Immigration Act of 1921 affected migrant selection, and finds that the average skill level

of immigrants increased after the Emergency Immigration Act.9 While our paper complements these

studies, our focus is a different one. Because we are interested in the broader economic implications

of immigration restrictions for local economies, we show that the implementation of the quota system

resulted in more affected areas to substantial population and labor productivity declines, and earnings

8Within the agricultural sector, Lew and Cater (2018) show for a restrictive set of counties/census divisions locatedin the Northern Great Plains along the US-Canadian border that the more restrictive U.S. immigration policy during the1920s increased American farmers adoption of tractors compared to Canadian farmers. For a more general discussionabout the historical evolution of capital-skill complementarity in the United States we refer the reader to Goldin and Katz(1998), Atack et al. (2004), Katz and Margo (2014), and Lafortune et al. (2015).

9More information on classical immigration topics such as immigrant selection and assimilation can be found in thesurveys of Borjas (1994), Kerr and Kerr (2011), Abramitzky and Boustan (forthcoming), and Hatton and Ward (2018) forexample.

4

losses for native-born workers relative to less affected areas.10

Our analysis of the 1920s immigration quota acts contributes to the immigration debate in the

United States from a historical perspective (e.g., Borjas, 1994, 1999; Card, 1990, 2005; Cortes,

2008; Saiz, 2003). Many American economic historians have evaluated how immigration affected

the U.S. economy during the 19th and early 20th centuries, although ours is the first study to eval-

uate the broader economic impacts of the quota system for the U.S. economy with a focus on local

economies.11 We provide estimates of well-identified effects of immigration restrictions on popu-

lation, labor productivity, and earnings of native workers from this unprecedented change in U.S.

immigration policy. In particular, our estimation approach exploits only within-county or within-city

variation of the data to study trends in outcomes (related to quota exposure) before and after the imple-

mentation of the quota system, which can be used to evaluate whether the policy change is plausibly

exogenous. Our findings are robust to a number of sensitivity checks; most importantly, we demon-

strate that our results are not driven by shocks to immigration due to World War I and the Literacy

Act of 1917.

This research also complements empirical studies that have investigated the long-term impact of

(historical) immigration flows on local economies in the United States.12 A recent county-level study

by Sequeira et al. (2017) finds positive long-term effects of immigration from Europe to the United

States during the Age of Mass Migration.13 The historical inflow of European migrants still benefits

counties today in terms of productivity gains in the agricultural and manufacturing sector, increased

urbanization, and higher rates of patenting during the Age of Mass Migration.14 Peri (2012) uses

state-level panel data to analyze the long-run effects of immigration on employment and productivity

10Since the first circulation of (an older version) of this paper other researchers have also started to evaluate the impactof the quota system on several socio-economic and political outcomes (Abramitzky and Boustan, 2017; Tabellini 2017;Xie, 2017; Doran and Yoon, 2018). These related studies investigate how the 1920s immigration restrictions affectednatives’ decision to migrate and their investment in human capital (Abramitzky and Boustan, 2017), political outcomesand public spending (Tabellini, 2017), manufacturing wages and black migration (Xie, 2017) and inventions (Doran andYoon, 2018).

11See, for example, Abramitzky et al. (2012, 2013, 2014, 2016); Bandiera et al. (2013); Collins (1997); Dunlevyand Hutchinson (1999); Hatton and Williamson (1998); Ferrie (1999); Greenwood (2008); Kim (2007); Timmer andWilliamson (1998). For further details and references we refer to the surveys on immigration in American history ofAbramitzky and Boustan (forthcoming), Carter and Sutch (1997), and Hatton and Williamson (1998).

12A growing literature in cultural economics investigates the long-term consequences of immigration for the U.S.economy (e.g., Fernandez and Fogli, 2009), in particular showing that the cultural composition of US counties matters foreconomic development (e.g., Ager and Brueckner, 2013, 2018; Burchardi et al., 2016.)

13Likewise, Rodriguez-Pose and von Berlepsch (2014, 2015) also find that historical settlement patterns of migrantsstill matter for local economic development in the United States today.

14Further evidence on immigrants’ positive impact on science and innovation inthe United States is from Akcigit et al.(2017), Moser et al. (2014), and Hunt and Gauthier-Loiselle (2010).

5

for the period 1960-2006. Peri finds no evidence that immigrants crowed out employment, however

he reports significant productivity gains from the net inflows of immigrants for the receiving states.

Our paper adds to the findings of this literature by providing evidence that the immigration restric-

tions established during the 1920s led—in the more affected areas—to a significant decline of labor

productivity in the manufacturing sector, and to sizable earnings losses for native (white) workers.

2 Historical Background

Opposition to immigration has a long history in the United States (Hutchinson, 1981; Higham, 2002).

The rise of the Know-Nothing party—an anti-immigration movement—during the 1850s as a response

to increased Catholic immigration from Germany and Ireland is such an example. The first laws that

restricted immigration to the United States at a larger scale were the Page Act of 1875 and the Chinese

Exclusion Act of 1882. While the Page Act of 1875 forbade indentured labor from “China, Japan,

or any Oriental countries”, immigration of alien convicts, and women for the purpose of prostitution,

the Exclusion Act of 1882 barred immigration from China to the United States in general. European

immigration remained virtually unrestricted until the passage of the Immigration Act of 1917 (also

referred to as the Literacy Act).15 The 1917 act required from immigrants over sixteen years of

age to pass a literacy test by reading 30-40 lines in any language of their choice. It further banned

immigration from the so-called Asiatic Barred Zone, which included most parts of Asia and the Pacific

Islands.16 The official statistics document that the total number of annual immigrants admitted to

the United States dropped from circa 300,000 in the years just preceding the Immigration Act of

1917 act to around 100,000 immediately afterwards. Yet, the 1917 act effectively failed to reduce

immigration from Europe to the United States on a larger scale, because literacy rates in Europe were

rising rapidly during this time period. Already in 1920 annual immigration rose above 400,000 and

exceeded 800,000 in 1921 (U.S. Department of Commerce, 1924, Table 65).

The first immigration policy effectively restricting the number of immigrants from Europe to the

15Various interest groups, such as the American Federation of Labor, were behind the passage of the Immigration Actof 1917. Goldin (1994) argues that some labor unions and native-born rural Americans were against free immigration andnearly succeeded in implementing immigration restrictions in the 1890s. However, what changed from 1890 to 1917 andbecame decisive for the vote in favor of the 1917 act was the position on free immigration among the older immigrationgroups living predominantly in the Midwestern areas, and rural-natives living in the American South, who initially had amore liberal view on immigration. We refer the reader to Hutchinson (1981), Goldin (1994), and Higham (2002) for moredetails on the political economy behind the passage of the Immigration Act of 1917.

16The Asiatic Barred Zone did not include Japan because the Gentlemen’s Agreement of 1907—an informal agreementbetween the governments of the United States and Japan—already limited immigration of Japanese workers to the UnitedStates.

6

United States was the Emergency Quota Act of 1921.17 This law restricted the annual number of

aliens of any nationality to 3 percent of the number of foreign-born persons of such nationality listed

in the U.S. Census of 1910. The implementation of nationality quotas reduced the total number of

immigrants from 805,228 in 1921 to 309,556 in 1922 (U.S. Department of Commerce, 1929, Ta-

ble 100).18 Exempted from the quota system were immigrants born in Canada, Mexico, and South

America (Massey, 2016). The 1921 act asymmetrically affected immigration from different Euro-

pean regions: Immigration from the southeastern part of Europe was now severely restricted, while

immigration from the northwestern part of Europe was still considered desirable (King, 2000). For

example, the number of Italian immigrants was reduced from 222,260 in 1921 to the 1922-quota

number of 42,057, while the number of Swedish immigrants in 1921 was 9,171 and the 1922-quota

number was 20,042 (U.S. Department of Commerce, 1924, Tables 71, 79).

The Immigration Act of 1924 (also known as the Johnson-Reed Act) replaced the Emergency

Quota Act and made the quota system permanent (King, 2000). The 1924 act involved two signif-

icant changes: First, the ceiling was reduced from 3 percent to 2 percent of the foreign-born stock.

Second, the modified formula for the national origins quotas was based on the foreign-born stock of

1890 instead of 1910, which meant that the annual quota number was reduced from 357,083 in 1924

to 164,667 in 1925 (U.S. Department of Commerce, 1929, Table 111). This change almost com-

pletely prevented immigration from Southern and Eastern Europe. For example, the annual quota for

Russia dropped from 24,405 to 2,248 immigrants (U.S. Department of Commerce, 1929, Table 111).

Further, the 1924 act completely banned immigration from Asia. Until July 1, 1927, the nationality

quotas were based on the 1890 Census. After July 1, 1927, the annual quota was fixed to a total of

150,000 immigrants, and the 1924 act was substituted for a national origins plan, which based the

quota allocation by country on the national origins of the white population in the United States in the

1920 Census (King 2000, pp.204-212).19 The national-origins-based quota system went into effect,

with some delay, on July 1, 1929, and remained in place, apart from some minor modifications, until

1965 when it was replaced by the Immigration and Nationality Act.

17There were no serious considerations in the United States Congress to implement quotas or any other blanket restric-tions to restrict immigration from Europe to the United States before 1920 (Goldin, 1994).

18Total immigration was limited to 357,000 per annum (King, 2000, p.200).19The 1927 immigration quotas by country are depicted in King (2000, Table 7.1). On top of limiting immigration from

southeastern European countries, the national origins system shifted the quotas towards immigration from Britain whilereducing quotas from other northwestern European countries, such as Germany, Ireland, or Sweden.

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3 Data and Estimation Strategy

3.1 Data

Our empirical analysis evaluates how the foreign-born share, population, individual outcomes, such

as fertility and native earnings, and manufacturing activity responded to the introduction of the quota

acts in the 1920s. The U.S. Census collected county-level data on demographic, economic, and so-

cial variables for every decade since 1790, including information on population and the number of

foreign-born. These data are retrieved from the ICPSR 2896 data file for the sample period 1900–

1940 (Haines, 2010).20 The essential ingredients for constructing the quota-exposure measure are the

1920 county/city-level population data and the number of foreign-born by nationality based on the

full-count U.S. Census microdata from IPUMS (Ruggles et al., 2017), data on annual immigration

(1899-1930), and the annual quota numbers by nationality (1922–1930).21 Annual immigration data

are reported in Willcox (1929, Table III, pp.389-393) for the years 1899–1924. This time series is

extended for the years 1925-1930 using the annual immigration data from the Statistical Abstract of

the United States (U.S. Department of Commerce, 1929, Table, 106; 1931, Table 99). Data on the

annual quota number are collected from the Statistical Abstract of the United States (U.S. Department

of Commerce, 1924, Table, 79; 1931 Table 104). We refer to Section 3.2 for further details how the

quota measure is constructed.

The individual data consist of a repeated cross-section of individuals based on the 1-percent na-

tional random IPUMS samples of the population for the Censuses 1900 to 1940. We employ a sample

of 15- to 65-year-old workers to study how the quota acts affected the earnings of natives, and a sample

of women aged 15-49, to study fertility behavior and marriage. One limitation of the individual data

is that there is no information on individual income before 1940. Since individual earnings are not

reported, economic historians rely on so-called “earning scores” based on occupations or industries to

study, e.g, the returns to migration in the United States before 1940 (Abramitzky et al., 2012; Collins

and Wanamaker, 2014). We follow this literature and use an occupation-based earnings score in our

empirical analysis. This measure is based on the annual earnings data reported in Lebergott (1964,

Table A-18), which are available for a broad category of industries over the period 1900-1960.22

20More information about the data set, such as scope of study, data collection, and data source can be found at http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02896.

21The immigration data correspond to the fiscal year, which ended on June, 30. For example, the immigration year of1922 refers to immigration inflows between July 1, 1921 and June 30, 1922.

22These broader industry categories are agriculture; manufacturing; mining (incl. subcategories); construction; trans-portation (incl. subcategories); communication and utilities (incl. subcategories); trade; service (incl. subcategories);

8

We assign the annual earnings for a given census year (1900, . . . , 1940) and industry category to

any individual reporting an industry code in IPUMS (“ind1950”), which corresponds to the broader

industry classification.23 Since the Lebergott data refer to annual earnings of full time employees,

we adjust the earnings of workers by race, gender, and region of residence (South/non-South) for

each industry category based on their actual earnings in the 1940 Census as described in Collins and

Wanamaker (2014, Appendix A2). The adjustment factor in 1940, Adfactorirgl;1940, is obtained by

dividing the earnings, Earningsirgl, of race r, gender g, in region l for every industry, i, in 1940 by

the average earnings in the 1940 Census in that industry, Earningsi.24 For every worker in our sam-

ple, we multiply the “Lebergott earnings score” with the 1940 adjustment factor, Adfactorirgl;1940, to

obtain a race, gender, and location specific earnings score by industry. As in Collins and Wanamaker

(2014, p.246), we also modify the agricultural earnings reported in Lebergott to account for differ-

ences between hired farm labor and farm operators’ income.25 The Lebergott earnings score varies

across occupations but, compared to the more frequently used IPUMS occupation score (“occscore”),

it also captures variation within occupations over time.26 Our results are robust to using alternative

occupation-based earnings scores (see the Appendix and Section 4.2 for further details). The micro-

data are then merged with the quota-exposure measure.

For the city-level analysis we digitized data from the Census of Manufactures for the years 1909,

1914, 1919, 1925, and 1929. The manufacturing outcome variables are value added-per-worker and

wages-per-worker to measure labor productivity, and horsepower-per-worker and horsepower-per-

output to proxy for capital intensity and the capital-output ratio, respectively.27 The data constraints

resulted in the creation of a balanced panel of 246 cities with more than 10,000 inhabitants in 1909.

Table 1 reports the summary statistics.

government (incl. subcategories); and finance, insurance, and real estate.23For example, individuals reporting “ind1950” codes 306-499 in IPUMS work in manufacturing and obtain the cor-

responding manufacturing earnings value from Lebergott (1964). We base the assignment on the “ind1950” code fromIPUMS, as it is considered to be comparable across different Census years.

24The average 1940 earnings in industry i are based on 15-65-year-old workers reporting an occupation in that industry.25Farm operators’ net income is retrieved from the Major Statistical Series of the USDA, which contains information

about farm operators’ net income for the years 1910-1956 (U.S. Department of Agriculture 1957; Volume 3, Table 17).There is no information on farm operators’ income for the Census year 1900. We obtain the 1900 value of farm operators’income by multiplying the ratio of farm operators’ income to earnings of hired labor in agriculture in 1910 with theearnings of hired labor in agriculture in 1900 reported in Lebergott (1964).

26The Lebergott earnings score is denoted in constant prices using 1900 as the reference year. We usedhttps://www.measuringworth.com/uscompare/ to convert the Lebergott earnings score into constant prices.

27Note that information on horsepower for the 1925 Census of Manufacturers is not available. We use a linear interpo-lation to obtain the 1925 values.

9

3.2 Estimation Strategy

The variable of interest, Quota exposurec, captures the extent to which a given area (e.g., a county or

city) was affected by the quota system. We construct Quota exposurec as:

Quota exposurec =100

Pc,1920

N∑n=1

max(Mn,22−30 −Qn,22−30, 0

) FBnc,1920

FBn,1920

. (1)

The variable Mn,22−30 denotes the predicted average number of immigrants of nationality n that were

supposed to arrive per year during the post-quota period 1922–1930 if the quota system had not been

in place. This prediction is based on the following regression model: Mnt = β1 ln t + β2 (ln t)2 +

εit,28 where Mnt refers to the actual immigration of nationality n and year t for the period 1900–

1914 reported in Willcox (1929, Table III, pp. 389-393). Since immigration to the Untied States

was significantly interrupted by World War I, the predictions are only based on the pre-WWI annual

immigration flows (see, e.g., the dashed lines in Figure 1).29 We use the estimated coefficients β1

and β2 from the prediction model to obtain the counterfactual immigration flows for nationality n for

the years 1922–1930. The variable Qn denotes the average quota number for nationality n per year

during the period 1922–1930. We interpret the difference between Mn,22−30−Qn,22−30 as the average

number of missing immigrants of nationality n per year due to the quota system. We set the number

of missing immigrants for any nationality n to zero if the expression Mn,22−30 − Qn,22−30 < 0. This

is always the case for countries without legal quotas (Qn,22−30 ≡ ∞), such as Mexico and Canada.

28The regression model allows for potential non-linearities in the immigration flows. Alternative functional forms thatalso capture such non-linearities yield similar results (not reported). We further set the prediction for nationality n for anyyear t to zero if Mnt < 0.

29One complication using the Willcox data is that Table III only reports the number of immigrants from Czechoslovakia,Finland, Poland, and Yugoslavia since 1920 (these countries either obtained their independence (Poland, Finland) orwere newly formed states (Czechoslovakia, Yugoslavia) during or right after World War I). Willcox, however, reportsdetailed tables of sending countries (e.g., from Germany, Austria-Hungary, Russia, and Turkey) that contain migrants fromthese newly established countries before 1920. The number of immigrants from Poland for the years 1899-1919 consistsof Polish immigrants from Austria-Hungary (see Table XXIIIa and XXIIIb, pp.483-484); Germany (see Table XXVII,p.486); and from Russia (see Table XXXII, p.488). The number of Finnish immigrants was included together with Russia(see Table XXXII, p.488); the number of immigrants from Czechoslovakia were included together with Austria-Hungary(see Table XXIIIa and XXIIIb, pp.483-484), and the number of immigrants from Yugoslavia were included together withAustria-Hungary (see Table XXIIIa and XXIIIb, pp.483-484) and Turkey (see Table XXXV, p.489). With this informationat hand we constructed separate time-series for those four countries for the years 1899-1919 and deducted the numbersfrom the sending countries that contained these numbers originally. For Hungary separate immigration numbers existsince 1905. For the years 1899-1904, 75% of Germans, 20% Hebrew, and all Magyar and Romanian immigrants fromAustria-Hungary were assigned to Hungary based on the racial distribution reported in Tables XXIIIa and XXIIIb for1910, the first year that separates immigrants from Austria and Hungary by “principle races”. For 1905–1909, the numberof Polish, Czechoslovakian, and Yugoslavian immigrants were deducted from the totals of Austria and Hungary accordingto the racial distribution of these two countries in 1910.

10

Equation (1) distributes these missing immigrants locally according to the share of immigrants

of nationality n living in county c prior to the quota system in 1920 (i.e., FBnc,1920/FBn,1920).30

We assign the missing immigrants to the nationality shares in 1920 for two reasons: First, the U.S.

government assigned separate quota numbers for the newly established European countries such as

Czechoslovakia, Finland, Poland, and Yugoslavia. The 1920 Census is the first where nationality

shares for those countries can be constructed systematically (see also footnote 29). Second, the 1920

Census is the last before the introduction of the quota system and thus a natural reference point.31

Our assignment rule draws on a well-established fact in the migration literature that newly arriving

immigrants tend to settle in areas where previous immigrants of the same nationality live, and that

settlement patterns of immigrants varied by nationalities across the United States during the Age of

Mass Migration (e.g., Ager and Brueckner, 2013). The basic intuition is that we would expect more

missing immigrants after the introduction of the quota acts in the 1920s in areas with larger pre-

existing immigrant communities of affected nationalities. Summing over all foreign nationalities and

multiplying by the factor 100Pc,1920

provides the total number of missing immigrants in the 1920s per-100-

inhabitants-per-year in area c. Conceptually, Quota exposurec can be interpreted as the annual number

of missing immigrants during the 1920s per-100-inhabitants in a given area c due to the introduction

of the quota system. It captures the regional effects of a fundamental change in national immigration

policy and is, for example, conceptually similar to recent work in the trade literature which analyzes

the effects of trade liberalization on local economies (Kovak, 2013; Dix-Carneiro and Kovak, 2017).

Figure 1 illustrates our procedure of predicting national immigration flows (i.e., Mn,22−30) for

the following four sending countries: Russia, Italy, Ireland, and Sweden. The predicted immigration

flows are the black dashed lines. For example, Panel A shows that the number of predicted Russian

immigrants in 1923 is around 137,000, while under the Immigration Act of 1921, the annual Russian

quota number was close to 30,000 (solid red line), which implies that during 1923 circa 117,000

Russian immigrants were missing because of the quota system. These missing Russian immigrants

are subsequently allocated across counties in the United States according to FBnc,1920/FBn,1920 in

equation (1). Figure 1 also shows that while the quota system resulted in missing Italian immigrants

30We construct the share of immigrants by nationality based on information from the IPUMS variable “BPL” for thefollowing birthplaces outside the United States: U.S. outlying areas/territories including Hawaii, Canada, Mexico, Centraland Latin America, Caribbean, Denmark, Finland, Norway, Sweden, United Kingdom, Belgium, France, Netherlands,Switzerland, Albania, Greece, Italy, Portugal, Spain, Austria, Bulgaria, Czechoslovakia, Germany, Hungary, Poland,Romania, Yugoslavia, Russian Empire, Asia, Africa, and Oceania.

31Similar results are obtained if we use nationality shares and county population in 1910 instead of 1920, albeit theestimated coefficients are, as expected, somewhat smaller (see Appendix A for further details).

11

(Panel B), the predicted immigration flows for Irish and Swedish immigrants were lower than their

quota numbers (Panels C and D), implying no missing immigrant of these nationalities.32

Figure 2 shows the total number of missing immigrants for the post-quota period 1922–1930 by

year. We see that the annual number of missing immigrants under the Immigration Act of 1921 is

approximately 720,000, while the Immigration Act of 1924 elevates this number to about 860,000.

Based on the total US population in 1920, this gives an annual average of 0.8 missing immigrants per-

100-inhabitants-per-year or, alternatively, 8 percent fewer immigrants (over the total U.S. population)

during a decade compared to a counterfactual scenario of free immigration.

Panel A of Figure 3 visualizes Quota exposurec at the county level and reveals that the well-known

immigrant clusters in the Northeast, the Great Lakes region, and the West Coast are generally more

affected by the quota system compared to counties located in the American South and the southern

parts of the Midwest. Since our baseline empirical specifications exploit only county-level variation

within the same state, Panel B of Figure 3 displays Quota exposurec conditional on state fixed effects

and reveals significant variation in quota exposure within states as well.

Appendix A discusses how sensitive our main findings are to modifications of the treatment mea-

sure. At first we compare our estimates to a more crude specification of quota treatment that resembles

what Clemens et al. (forthcoming) used to evaluate the effect of the bracero exclusion. The following

expression Quota Nationalitiesc =∑N

n=1 q1920nc illustrates the construction of this measure which is

then interacted with an indicator for the post-quota period. The fraction of the quota affected popula-

tion in 1920 for a given area c is∑N

n=1 q1920nc ≡ (

∑Nn=1 FBnc,1920× In)/Popc,1920, where the indicator

In is one if nationality n is subject to the 1920s quota acts and zero otherwise. This implies that

any nationality subject to the 1920s quota acts is being treated independently of their actual quota

number. For example, pre-existing Swedish and German communities are assumed to be treated

just like Russian or Italian communities, even if their corresponding quota number would exceed the

predicted inflow of migrants for the years 1922-1930 (i.e., there are no missing migrants from Ger-

many and Sweden). Since Quota exposurec in equation (1) explicitly exploits this additional variation

across quota affected nationalities, we consider it as the preferred treatment measure for the empirical

analysis. Second, it is worthwhile to note that while the baseline quota-exposure variable is based

on predicting immigration by nationality, we obtain qualitatively similar results when replacing the

predicted immigration inflow with the average annual number of immigrants from 1910–1914 by na-

tionality, or following the approach of Greenwood and Ward (2015) which captures the “bite” of the

32The prediction for Swedish migrants is set to zero for the years it would yield to negative values.

12

quota system (see Appendix A for further details).33 Third, we show in Appendix A that our results

are robust to controlling for the direct effect of the most important non-quota affected nationalities

(Canadians and Mexicans).

The quota-based “experiment” we have mind is the following: Firms located in areas A and B

plan to employ X workers in period t based on past immigration flows. In period t immigration to

area A is unexpectedly reduced due to the implementation of the quota system, while immigration

to area B remains unregulated. Now, we can observe how labor market outcomes change in area A

as firms readjust to the new policy relative to the labor market outcomes in area B where firms do

not need to make such readjustments.34 More formally, our econometric model follows a differences-

in-differences (DiD) approach to identify the effects of the quota system. The variable of interest,

Quota exposurec, measures the differential impact of the quota system in terms of the immigration

rate of missing immigrants during the 1920s across counties (or cities).35 More quota-affected areas

are expected to have more missing immigrants per capita relative to areas less affected by the quota

system (compared to the counterfactual of free immigration to the United States).

Our DiD estimation strategy resembles the more classical shift-share instrumental-variable ap-

proach (e.g., Bartik, 1991) in the sense that we rely on local past settlement locations of the affected

immigrant groups and aggregate shocks to migration flows. Since the quota system induced a sudden

shift of immigration inflows at the national level which are plausibly exogenous to local economies,

it increases the general validity of our and the more classical shift-share instrumental-variable ap-

proach (Jaeger et al., 2018). However, one crucial difference between the two approaches is that our

estimation strategy properly isolates the policy-driven variation in the aggregate flow of immigrants

to the United States from the implementation of the quota system. Because the classical shift-share

approach would exploit all immigration shocks (domestic and abroad) that occurred over the period

1900-1940 it is therefore not well-suited to cleanly identify the effects of the quota system which is

the main purpose of this study.36

33Data on the actual number of pre-WWI immigrants admitted over the period 1910–1914 are collected from theStatistical Abstract of the United States (U.S. Department of Commerce, 1931, Tables 99).

34We implicitly assume no spillovers between the two areas. However, in reality, the exclusion of immigrants to areaone could increase the number of immigrants coming to area two. The empirical implication of this issue is that weare only able to estimate the relative effects of the quota system (i.e., the effect between treatment and control areas).In Appendix A we also report results that take such potential spillovers into account in case Mexican and Canadianimmigration patterns changed in response to the implementation of the quota system.

35The concept of “missing immigrant” is just our way of interpreting the cross-sectional treatment measure in theDiD strategy, where the actual time variation comes from comparing outcomes before and after 1920. Conceptually, ourapproach is similar to the DiD strategies applied in Bleakley (2007) or Hornbeck and Naidu (2014).

36See, for example, the discussion on empirical approaches to identify the causal effects of immigration on local

13

For the county-level analysis, we estimate the following event-study model for the period 1900-

1940:

yct =1940∑j=1900

αj Quota exposurec × Ijt + X′ctΓ + λc + µt + φst + εct, (2)

where yct is either the foreign-born share or (log) population in county c in year t, Quota exposurec

is interacted with a full set of time fixed effects (Ijt ), where the omitted time period of comparison is

1920. This implies that α1930 quantifies the effect of the quota system on the outcome in 1930 relative

to 1920 independently from its effect in 1940, for example. In order to take into account potential

mean reversion due to initial level differences, the control vector X′ct includes (log) county population

sizes in 1910 interacted with a full set of time fixed effects, as well as controls for the (potential)

immigration shocks caused by World War I and the Literacy Act of 1917 (see Appendix A for more

details). All of our specifications control for county-fixed effects (λc) and time-fixed effects (µt). The

baseline specification also includes state-by-time-fixed effects (φst), such that the variation used to

identify the effects of the missing migrants is based on the comparison of outcomes between more

and less (or non-) quota affected counties within the same state over time. We cluster the error term,

εct, at the county level.

While our analysis does not provide any instrumental variable estimates, one could think of equa-

tion (2) as a corresponding “first-stage regression” when the foreign-born share is the outcome vari-

able. If Quota exposurec is really capturing variation of missing immigrants, one would expect to see

a break in the relationship between the foreign-born share and Quota exposurec after the implemen-

tation of the quota system. The effect on population is more likely to be muted or amplified by other

demographic changes such as fertility adjustments and in- and out-migration that were set in motion

after the quota system was in place. If the direct effect is not fully offset, one should see a negative

effect of Quota exposurec on population growth after the introduction of the quota system. We also

estimate different variants of equation (2), which are explained in detail in Section 4.

4 Empirical Results

4.1 Population Dynamics

As a first step, we provide some prima facie evidence that more affected areas experienced a (relative)

decline in the overall inflow of immigrants. We derived a retrospective measure on the number of

economies in Lewis and Peri (2015).

14

immigrants at the county-by-year level based on immigrants’ year of arrival and their current place

of residence from the IPUMS full-count census data in 1920 and 1930. This information is then used

to construct a location-based measure of immigration flows aggregated by quota exposure for every

year from 1900 to 1930.37

Figure 4 graphs the (spline of the) annual average inflow of immigrants by Quota exposurec. We

group counties such that if their quota-exposure value is above the sample median and below sample

median, they are assigned to the treatment (solid line) and control group (dashed line), respectively.

The reported average inflow of immigrants in Panel A is conditional on county fixed effects, while

in Panel B we also control for World War I and the Literacy Act of 1917.38 Before World War I,

counties in the so-called “treatment group” received on average more immigrants, but the trend in

the flows is relatively similar to counties in the “control group”.39 After the outbreak of World War

I, both treatment and control counties experienced significant declines in immigrant inflows, but the

average decline seems to be somewhat stronger among counties in the treatment group. There is an

immediate reversion in immigration inflows in the aftermath of World War I close to pre-war levels.

Following the Immigration Act of 1924, the average inflow of immigrants into treatment counties

reduces significantly compared to the control counties. This reveals that the more affected areas, in

fact, experienced a relative decline in immigration after the implementation of the quota acts compared

to the years before the outbreak of World War I. Panel B demonstrates that this conclusion is robust

to controlling for the possible confounding effects of the immigration shocks caused by World War I

and the Immigration Act of 1917. In the empirical analysis we always control for these two additional

immigration shocks.

Figure 5 depicts the event-study estimates of equation (2), controlling for county and state-by-time

fixed effects, as well as the “missing immigrants” due to World War I, a control for the Literacy Act

37The use of the variable YRIMMIG from IPUMS for this purpose is not without problems. One main concern is “yearheaping”, since some immigrants might not exactly remember the date of arrival at the time of the Census enumerationand just use the nearest round number as year of arrival (e.g., 1900, 1910, . . . ). Another concern is that the immigrationflows are downward biased due to death and emigration before the year of enumeration, since we measure year of arrivalretrospectively (i.e., in 1920 and 1930). We have compared the year of arrival measure with the Willcox (1929, Table III)immigration time series, which is based on the admission of immigrant aliens by year and country of last residence for aselected group of nationalities, to check whether measurement issues might confound our findings. While the two timeseries move very much in the same direction, the Willcox immigration flows are larger than the immigration flows derivedfrom IPUMS, which is expected, as the latter measure reflects immigrants who stayed in the United States and did not dieor return to their home country before the Census enumeration (results available upon request).

38Appendix A explains the construction of the controls for World War I and the Literacy Act of 1917 in detail.39There is a steep decline in the average inflow of immigrants from 1900 to the following years, which is not visible in

the splines reported here. However, one could regard this as evidence of “year heaping”. This decline, however, is similarbetween treatment and control counties, suggesting that this type of measurement error is less of a concern in our case.

15

of 1917, and log county population size in 1910 interacted with time fixed effects. Panel A reveals

that the share of foreign-born residents in counties more exposed to the quota system increased prior

to quota system, which is consistent with the evidence in Figure 4. This upward trend in the foreign-

born share is completely reversed after the quota system is in place. For example, a one-unit increase

in Quota exposurec (or equivalently, having one immigrant less per-100-inhabitants-per-year in a

county) translates into a decline in the foreign-born share of 2.9 percentage points by 1930 and 4.3

percentage points by 1940.40 Panel B of Figure 5 shows how the missing immigrants influenced the

county-population growth path during the sample period. Counties more exposed to the quota system

experienced more rapid population growth in the pre-quota period, while there is a clear indication of

a trend break in the post-quota period, suggesting that the quota system reduced population growth.

Table 2 reports the DiD estimates for the foreign-born share (columns 1-3) and population growth

(columns 4-6). The estimation equation is (2) and the method of estimation is least squares. Compared

to the event-study, Quota exposurec is now interacted with an indicator variable which is one in the

post-quota period and zero otherwise. Column 1 controls for county- and time-fixed effects, column

2 adds controls for the immigration shocks coming from World War I and the Literacy Act, and log

county population in 1910 (interacted with time-fixed effects), and column 3 replaces the time-fixed

effects with state-by-time-fixed effects. We regard column 3 as our baseline specification. Columns

4-6 follow the same sequence, but we additionally control for county-specific linear trends, which

give us the effect of the quota system on population growth since the outcome is log population.

The DiD estimates for the foreign-born share are all negative and statistically significant at the

1-percent level, confirming the insights from the event-study (Panel A of Figure 5) that the quota

system reduced the foreign-born share substantially.41 For our baseline specification (column 3), a

one-unit increase in Quota exposurec, (or equivalently having one less immigrant per-100-inhabitants-

per-year in a county), led to a 1.6 percentage points decline in the share of foreign-born residents in

the post-treatment period. The coefficient can be interpreted as a lower-bound estimate, since Panel

A of Figure 5 revealed a positive pre-quota trend in the foreign-born share which would bias the

DiD estimate towards zero. Including county-specific linear trends to the foreign-born specification

of column 3 increases the DiD estimate, αDiD to −0.054 with a standard error of 0.004. Thus, the

county-specific linear trends eliminate pre-quota trends in the foreign-born share and, although we

40One additional missing immigrant per-100-inhabitants-per-year in a county translates into 10 percent fewer immi-grants arriving over each decade from 1920 to 1940. The average quota exposure is 0.25 missing immigrants per-100-inhabitants-per-year and one standard deviation is equal to 0.43 (see Table 1).

41Comparing columns 1-3, it is worth noting that the magnitude becomes smaller as we add further controls.

16

do not include these trends in our baseline specification for the labor market outcomes (in Section

4.2), our findings are robust to adding such controls. County-specific linear trends are included in

columns 4-6 where log population is the outcome variable, which is equivalent to studying how the

quota system influenced population growth. Results show a negative and significant effect of Quota

exposurec on population growth. The coefficients are rather stable and our baseline estimate, reported

in column 6, shows that one additional missing immigrant per-100-inhabitants-per-year lowered the

10-year population growth rate by 6.7 percentage points.

In the following, we outline a simple accounting exercise to evaluate how much of the negative

population growth effect is directly attributable to the missing immigrants. This exercise is based on

the so-called fundamental demographic equation:

Pct+1 = Pct +Bct −Dct + Ict −Xct, (3)

where Pct+1 is population size at time t + 1 in county c, Bct is the number of live births, Dct is

the number of deaths, Ict is the number of immigrants (and in-migrants), and Xct is the number of

emigrants (and out-migrants). Equation (3) can be rearranged such that:

pct+1 = bct − dct + ict − xct, (4)

where pt+1 ≡ (Pct+1 − Pct) /Pct denotes population growth, bct ≡ Bct/Pct the crude birth rate, dct ≡Dct/Pct the crude death rate, ict ≡ Ict/Pct the immigration rate, and xct ≡ Xct/Pct the emigration

rate. We now assume that all variables in equation (4) are functions of the quota system (Qt):

pct+1(Qct) = bct(Qct)− dct(Qct) + ict(Qct)− xct(Qct). (5)

Differentiating this equation with respect to the quota system (Qt) yields the following expression:

∂pct+1

∂Qct

=∂bct∂Qct

− ∂dct∂Qt

+∂ict∂Qt

− ∂xct∂Qt

, (6)

where the arguments have been omitted for simplicity. Given that Quota exposurec in itself is an

immigration rate (of missing immigrants), the estimate in column 6 of Table 2 directly reveals how

much of the negative population growth effect can be directly explained by the missing immigrants

(i.e., ∂ict/∂Qt = α). However, two minor adjustments are needed: First, the immigration rate is

denoted per 100 inhabitants, so we first need to multiply the coefficients by 100. Second, population

growth is measured over a 10-year period, while the immigration rate is measured annually, implying

17

that the coefficients also should be divided by 10. Taking these two adjustments together results in

αDiD = −0.68. According to equation (6), had the estimated coefficient αDiD been equal to unity,

the effect would be fully accounted for by the missing immigrants. Our estimate suggests that about

68% of the negative population growth effect can be accounted for by missing migrants. Taking

uncertainty into account (the 95% confidence interval is [−1.09;−0.027]) reveals that most of the

reduction in population growth is directly attributable to reduced immigration.

Next, we present results that reveal a negative effect of the quota system on fertility (i.e., ∂bct/∂Qt <

0), which also contributes to the overall negative population growth effect. Table 3 provides evidence

that the implementation of the quota system substantially reduced fertility and led to a decline in mar-

riage rates. The estimating equation is (2) and the method of estimation is least squares. Compared

to the data on the foreign-born share and population, marital status and fertility are measured at the

individual level and connected to Quota exposurec via the county of residence of an individual at

the time of the census enumeration. As in Table 2, Quota exposurec is interacted with an indicator

which is one in the post-quota period and zero otherwise. Besides the baseline controls of equation

2 (time-fixed effects, state-by-time-fixed effects, county-fixed effects, 1910 (log) county population

interacted by time, the missing immigrants from World War I, and the exposure to the Literacy Act of

1917), the individual regressions also include controls for the place of residence (rural/urban), race,

sex, and fixed effects for age and birthplace.

The first three columns of Table 3 summarize the effects on fertility for the period 1900–1940.

Fertility is measured by the number of own children under age 5 in the household. The sample

includes 15- to 49-year-old white and black women, such that the fertility variable corresponds to the

general fertility rate measured over a five-year period.42 Column 1 reports a negative and statistically

significant effect of quota exposure on fertility at the 1-percent level.43 The magnitude of the estimated

coefficient implies that for one additional missing immigrant per-100-inhabitants-per-year in a county,

the general fertility rate declines by 0.04. This number can be compared to the aggregate decline in

the general fertility rate from the pre- to the post-quota period of 0.12, suggesting an important role of

the quota system for the fertility transition over the sample period. Columns 2 and 3 of Table 3 show

that the negative effect on fertility is not exclusively driven by women born in the United States (and

both parents born in the United States) or first- and second-generation immigrant women.

One channel consistent with the observed fertility decline is that marriage-market conditions wors-

42This fertility measure has, for example, previously been used by Bleakley and Lange (2009).43Results remain unchanged if we include women of all other races to the sample (not reported).

18

ened after the quota system was in place. The effects on marriage rates presented in column 4 of Table

3 support this hypothesis. The dependent variable is a dummy variable whether a young women (age

15-35) was married at the time of the census enumeration. Column 4 shows that after the implemen-

tation of the quota system there was a statistically significant decline of marriage rates in the more

affected counties. The point estimate implies that having in a county, one less immigrant per-100-

inhabitants-per-year, reduced the likelihood of sampling a married woman by 1.4 percentage points

in the post-treatment period. This finding is consistent with the notion that changing economic con-

ditions in the more affected counties reduced the likelihood of women getting married after the quota

system was in place. Figure 6 depicts event-study estimates for the specifications corresponding to

columns 1 and 4 of Table 3 which confirm the main results of this subsection.44

The remaining two columns present separate results for young women born in the United States

(and both parents born in the United States) and first-and second-generation immigrant women. While

U.S.-born women were less likely to marry in the more quota affected counties, we find no effect

for first- and second- generation immigrants. This result is somewhat surprising as one might have

expected that the quota system would mainly worsen the local marriage-market conditions among

first- and second-generation immigrants (e.g., Angrist, 2002; Lafortune, 2013). On the other hand,

Abramitzky et al. (2016) find that during this time period, 39 percent first-generation immigrants

(arriving to the U.S. unmarried) married outside their own nationality, suggesting that marriage market

conditions for natives also worsened. Consistent with our marriage rates results, we show in the next

subsection that after the implementation of the quota system, economic conditions for native workers

deteriorated in the more affected counties.

4.2 Labor Market Outcomes of Native Workers

Table 4 examines how the quota-policy-driven reduction of immigrant labor affected native earnings

(we refer to Appendix B for a theoretical motivation of the empirical results in this section). We use

the Lebergott earnings score to proxy the economic status of native workers. The dependent variable

is measured at the individual level and relates to Quota exposurec via the county of residence of the

individual. The estimating equation is (2) and the method of estimation is least squares. As in Tables 2

and 3, Quota exposurec is interacted with an indicator taking on the value one in the post-quota period,

and zero otherwise. The specifications of Table 4 include, besides the baseline controls of equation

44Panels A and B reveal no evidence of pre-quota trend differences (indirectly supporting our main identifying assump-tion), while after the introduction of the quota system there are immediate declines in marriage and fertility.

19

(2), controls for marital status, place of residence (rural/urban), race, sex, literacy, and fixed effects

for age and birthplace. The sample spans the period 1900–1940 and is restricted to 15-to 65-year-old

U.S.-born workers reporting an occupation in the Census.45

Table 4 summarizes the results for the Lebergott earnings score. As described in Section 3.1,

this measure varies across broad industry categories and over time. Overall, native workers experi-

enced a substantial decline in their earnings after the implementation of the quota system in more

affected counties, but this effect varies substantially by gender and race. The estimate reported in

column 1 indicates that native workers living in a county with one additional missing immigrants per-

100-inhabitants-per-year experienced a 2.1-percent decline in earnings in the post-treatment period.

Alternatively, a one standard deviation increase in quota exposure, which is equivalent to having 0.78

fewer immigrants per 100 inhabitants arriving per year, translates into a 1.6-percent decrease in native

earnings. In column 2 in order to understand the importance of population sorting as a response to

the shock, we exclude workers born outside the current state of residence. For example, one could

argue that part of the baseline estimate is driven by in-migration of lower-skilled native workers from

less quota-exposed counties in order to substitute for the missing immigrant labor in the more quota-

exposed counties. The result presented in column 2 suggests that interstate migration is not driving

our finding, since the coefficient of interest remains statistically significant at the 1-percent level and

similar in magnitude. Column 3 also shows that interstate migrants in more affected counties experi-

enced earning losses relative to interstate migrants in less affected counties. The estimates reported in

columns 2 and 3 are also relatively similar in magnitude. Still, we need to acknowledge that with the

data at hand we cannot rule out whether native workers relocated within the same state as a response to

the implementation of the quota system. We refer the reader to Appendix C for more results on (black)

in-migration which are consistent with the findings of Collins (1997) that European immigration de-

layed the Great Migration before World War I. The estimates presented in columns 4 and 5 reveal that

the negative effect on native earnings varies substantially by gender. Only male workers experienced

significant earnings losses after the implementation of the quota system in more affected counties,

which seems plausible since European immigration was strongly male biased (Angrist, 2002).

The remaining four columns summarize results by gender and race. For these specifications, we

add a triple-interaction term, Quota exposurec×Ipostt ×blacki, to estimating equation (2), where blacki

is an indicator variable whether an individual i is listed as black in the census. These specifications

45The sample includes only native white and black workers, who contain 99 percent of all native workers during thesample period.

20

further include race-by-time and race-by-county fixed effects to absorb any race-specific trends and

location-specific effects by race that occurred between 1900 and 1940. For native male workers, the

DiD estimates reported in columns 6 and 7 reveal heterogeneous effects by race which are striking.

Both Quota exposurec and the triple-interaction term, turn out to be statistically significant at the 1-

percent level but with opposite signs. The shortage of immigrant labor substantially decreased the

earnings of white males in counties with more missing immigrants per 100 inhabitants, whereas the

DiD estimate for black males is αall + αblack = 0.023, with a p-value of 0.117 (and αall + αblack =

0.044, with a p-value of 0.007 when omitting state-by-time fixed effects), which suggests that for

one additional missing immigrants per-100-inhabitants-per-year, the earnings of black male workers

increased between 2.3 to 4.4 percent in the post-treatment period.

Both earning losses of white workers and the economic gains of black workers contributed to a

narrowing of the black-white earnings gap in the more affected counties over the time period 1900-

1940. This can be seen more directly in column 7, which only exploits the within-county variation

of the data (i.e., compared to column 6 this specification also includes county-by-time fixed effects).

Conceptually, this specification compares the earnings score between black and white workers living

within the same county who are equally exposed to the quota system. The estimated coefficient on

the triple-interaction term, Quota exposurec × Ipostt ×blacki, is positive and statistically significant at

the 1-percent level (note, that the main effect is absorbed by the county-by-time fixed effects). The

point estimate implies that for the average level of quota exposure (i.e., 0.69 missing immigrants per-

100-inhabitants-per-year; see Table 1), the earnings score of black males increased by 3.1 percent

relative to white males over the post-quota period. Since the quota system reduced the number of

immigrants by 8 percent of the total U.S. population each decade (see Figure 2), this would suggest

that the introduction of the quota system increased relative earnings of black male workers by 1.8

to 3.5 percent over the post-treatment period (based on the estimates of column 6). For comparison,

estimates by Smith (1984) show that black-white male income ratios based on occupation status (for

ages 20-64) increased by almost 7 percent from 1900 to 1940, although according to Smith’s estimates

most of the convergence occurred before 1920.46 Our results also resonate with Margo (2016), who

finds some steady black-white income convergence before 1940, based on revised black-white income

46One important difference is that Smith (1984) uses national average race-specific income-occupation weights basedon the 1970 federal Census to evaluate changes in the black-white income ratio for the years 1890 to 1980, while weadjust the Lebergott data for race, location, and gender based on information about the worker’s actual earnings reportedin the 1940 Census (see Section 3.1 for the details). Further limitations of Smith’s study are highlighted in Margo (2016,pp.309-311) and refer, for example, to the problem of using national based weights for our sample period which likely ledSmith to underestimate the relative black-white income convergence before World War II.

21

estimates for the pre-World War II period.

Columns 8 and 9 summarize the results for native female workers by race. In column 8, Quota

exposurec is statistically insignificant and literally zero indicating no earnings losses for white females

in the more quota affected counties, whereas the triple-interaction term turns out to be positive and

statistically significant at the 1-percent level. The point estimate for black females is αall + αblack =

0.036, with a p-value of 0.002 (and αall + αblack = 0.026, with a p-value of 0.001 when omitting

state-by-time-fixed effects), suggesting that for every missing immigrants per-100-inhabitants-per-

year in a county, the earnings of black female workers increased between 2.6 to 3.6 percent in the

post-treatment period (relative to the pre-treatment period).

The black-white earnings gap in the more affected counties also narrowed for female workers,

which can be observed when we only exploit the within-county variation of the data in column 9. The

coefficient of the triple-interaction term is positive and statistically significant at the 1-percent level

implying that for the average level of quota exposure, the earnings score of black females increased

by 1.9 percent relative to white females over the post-quota period. Finally, the event-study estimates

of Figure 7, which correspond to columns 1 and 7 of Table 4, confirm the presented results. There

are no significant pre-quota trends, while the sign of DiD estimates materializes after 1920, when the

quota system was in place. We further obtain similar insights when using the occupational income

score or the 1940 earnings score as an alternative outcome variable (we refer to Appendix D for more

details).

The results of Table 4 suggest that the quota system pushed the average native white worker

into lower paid occupations, suggesting some degree of complementarity between native white and

immigrant labor. Black workers, on the other hand, were to a large extent substitutes for (unskilled)

immigrant workers and filled some of the vacant jobs due to the reduced supply of immigrant labor

in the more quota-affected counties, which is consistent with Collins (1997). We also find that in

the more affected counties young white adults (age 15-21), compared to their black peers, were more

likely to stay longer in school, suggesting that labor market conditions for black workers in these

counties improved (in relative terms) after the quota system was implemented. The point estimate for

young white adults is αall = .0156, with a p-value of 0.04 while the effect for their black peers is

αall + αblack = −.0187, with a p-value of 0.055.

The effect on native earnings presented in Table 4 is also consistent with the pattern that we

observe when looking at some specific occupations of native workers after the quota system was in

place. Using the 1920 full count census, we can identify the most common unskilled and skilled

22

occupations where immigrants of the most affected nationalities (above average of missing migrants)

worked before the quota system was introduced.47 The dependent variable is an indicator variable

that is one, if a native worker reports to work in what we classified in 1920 as the most common

unskilled or skilled occupations of immigrants from the most affected nationalities. The specifications

presented in Table 5 include the same set of controls as in column 1 of Table 4, columns 3-4 of Table

5 further include race-by-time and race-by-county fixed effects. Our estimates reveal that after the

implementation of the quota system, native workers in the more affected counties were more likely to

work in jobs that we classified as the most common low skilled occupations of the missing immigrants

of the most affected nationalities. For black workers who were at the bottom of the conditional

earnings distribution in the pre-quota period (1900-1920), this meant some improvements of their

economic status suggesting that the fundamental change in U.S. immigration policy contributed to a

black-white income convergence in the more affected counties over the 1900-1940 period.48

Appendix Table 7 provides further evidence on the degree of complementarity between native and

immigrant workers. While missing migrants from new sending countries (such as Italy or Russia)

reduced the earnings of native workers, they benefited from missing migrants from old sending coun-

tries (such as Great Britain). This result indicates that immigrants from old sending countries with

occupational skills similar to native workers were much closer substitutes in the production process.

Even across new sending countries, our estimates reveal substantial variation in the degree of com-

plementary between native and immigrant workers. For example, Finns, presumably because of their

higher literacy levels, were much closer substitutes to native workers than Russian immigrants.

4.3 Estimating the Effect of the Quota System on the Manufacturing Sector

In this subsection, we evaluate the economic effects of the quota system on labor productivity and

capital intensity in the manufacturing sector at the city level. The dependent variables are (log) value

added-per-worker and (log) wages-per-worker in order to measure labor productivity, and horsepower-

per-worker and horsepower-per-output to proxy for capital intensity and the capital-output ratio, re-

47The most common low skilled occupations of this foreign-born group were mine operatives and laborers (7.97%), op-erative and kindred workers (13.12%), and laborers (20.76%). The most common high skilled occupations were managers,officials, and proprietors (8.61%) and salesmen and sales clerks (3.51%). The numbers in parenthesis refer to the occu-pation distribution of nationalities with above average missing migrants and are based on the 1920 full count census fromIPUMS. Occupations refer to IPUMS code OCC1950 and are listed at https://usa.ipums.org/usa-action/variables/OCC1950#codes_section.

48The conditional earnings distribution in the pre-quota period shows that white native workers were at the top, immi-grants in the middle, and black workers at the bottom of the earnings distribution (available upon request).

23

spectively. Quota exposurec is now calculated at the city level as outlined in Section 3.2. We have

a balanced sample of 246 cities for the years 1909, 1914, 1919, 1925, and 1929, where the average

population size in 1910 was around 113,000. The city-level regressions control for city-fixed effects,

state-by-time-fixed effects, (log) city population size interacted with time-fixed effects, and the im-

migration shocks related to WWI and the Literacy Act of 1917. The method of estimation is least

squares. Standard errors account for arbitrary heteroskedasticity and are clustered at the city level.

Figure 8 displays the event-study estimates for the four outcomes and Table 6 presents the cor-

responding DiD estimates. Panels A and B of Figure 8 reveal that labor productivity was increasing

during the pre-quota period in cities more exposed to the quota system, which indicates that the DiD

estimates reported in Table 6 are possibly biased towards zero (those could be interpreted as lower

bounds of the actual productivity losses). The point estimates displayed in Panel A imply that a one

mean increase in Quota exposurec (equivalently to 1.4 more missing immigrants per-100-inhabitants-

per-year) decreased labor productivity in 1925 by 11.1 percent and in 1929 by 10.4 percent.49 Panel B

reveals productivity losses of 3.5 percent by 1925 and 4.6 percent by 1929, for a one additional miss-

ing immigrant per-100-inhabitants-per-year. Overall, our estimates suggest that the implementation

of the quota system amounted in more affected cities to substantial productivity losses in the manu-

facturing sector. Panels C and D of Figure 8 summarize the results on whether the implementation of

the quota system led firms to adjust their capital intensity or the capital-output ratio. We use machine

horsepower-per-worker as proxy for capital intensity and machine horsepower-per-output as a proxy

for the capital-output ratio. In contrast to the productivity results, the event studies of Panels C and

D show no clear pre-quota trend which would bias the DiD estimates towards zero. The estimates

indicate that, if anything, manufacturing establishments in the more affected cities decreased capital

intensity after the quota system was in place, but these results are not very precisely estimated. There

is also no effect on the capital-output ratio. Overall, our findings indicate that manufacturers in more

affected cities did not adjust their capital intensity, nor the capital-output ratio relative to less affected

cities as response to the quota system.

49The average quota exposure in the city sample is significantly higher than in the county sample (see also Table 1).This is not unexpected as immigrants tend to settle in more populous areas, and the average city-population size (in 1910)was around 110,000 inhabitants, whereas the average county-population size (in 1910) was close to 30,000 inhabitants.

24

5 Conclusion

The 1920s were marked by a fundamental change in U.S. immigration policy. The passage of the

Emergency Quota Act of 1921 and the Immigration Act of 1924 ended the era of unrestricted immi-

gration from Europe to the United States. While economic historians have investigated the political

economy of immigration restrictions in the United States (Goldin, 1994; Timmer and Williamson,

1996; 1998) and explored how the quota acts affected migrant selection and return migration (Green-

wood and Ward, 2015; Massey 2016), a rigorous quantitative assessment of the broader economic

impacts of these immigration restrictions for the U.S. economy focusing on local economies has been

missing so far. This paper aims to fill this gap in the literature. The passage of the quota acts in the

1920s implied that some nationalities—mainly from Europe—were to a different extent affected by

these laws, while for other nationalities, such as Canadians or Mexicans, immigration to the United

States still remained open without any notable restrictions. Our empirical analysis exploited this

policy-driven variation along with the spatial distribution of different pre-existing nationality net-

works across the United States to evaluate how key drivers of economic growth, such as population

and labor productivity, responded to the missing immigrants due to the implementation of the quota

system.

We found that the implementation of the quota system had a negative effect on population growth.

More affected counties experienced a long-lasting decline in population growth relative to less af-

fected counties. We demonstrated that this decline is mainly due to the asymmetric effect of reduced

immigration inflows from quota-affected nationalities across counties. Women also reduced fertility

in the more affected counties, which reinforced the negative effect the quota system had on population

growth. In the more affected cities, the manufacturing sector experienced a substantial decline in la-

bor productivity growth. Since immigrants predominantly settled in urban areas and thereby increased

the density of economic activity (e.g., Ciccone and Hall, 1996), the decline in labor productivity in

the more affected cities might be a result of agglomeration externalities due to the shrinking of the

manufacturing sector, or because firms did not adjust their capital intensity after the introduction of

the quota system.

The shutdown of large-scale immigration from Europe to the United States during the 1920s also

significantly reduced the earnings of an average native worker. This overall negative effect of the

missing immigrants on native earnings turned out to differ substantially by race and gender. After the

implementation of the quota system, white male workers experienced sizable losses in earnings in the

25

more affected counties. This finding could indicate that those workers, and immigrant labor, were to

some extent complements in the production process at that time. On the other hand, black workers

(males and females), who were closer substitutes to (unskilled) immigrant labor, benefited from the

immigration restrictions and increased their relative economic status in the more affected counties

during the post-quota period. Our finding suggests that the quota system, which had a differential im-

pact on the supply of immigrant labor across counties, might have triggered some black-white income

convergence before the 1940s. Overall, we conclude that the fundamental change in immigration

policy during the 1920s generated winners and losers, but probably not the ones the policymakers at

that time had hoped for. Clearly, whether native workers benefited or lost from this policy change

depended on how much they stood in competition with immigrant workers.

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Figure 1: Missing immigrants by nationality

Notes: This figure shows actual immigration, predicted immigration, and the quota numbers from the

countries: Russia, Italy, Ireland, and Sweden. We refer the reader to Section 3.2 for further details.

Figure 2: Total missing quota immigrants

Notes: This figure shows the (calculated) total number of missing immigrants due to the quota system by

year. We refer the reader to Section 3.2 for further details.

Figure 3: Quota exposure

Notes: This figure shows baseline quota exposure (at the county level). Panel A without controls, and Panel

B with state fixed effects. We refer the reader to Section 3.2 for further details.

Figure 4: Total inflow of immigrants by median quota exposure

Notes: This figure shows (the splines of) the county average of the log number of immigrants by median

quota exposure. The solid/dashed line shows the (log) average immigrants for counties above/below median

quota exposure. We refer the reader to Section 4.1 for further details.

Figure 5: Event-study estimates for foreign-born share and log population

Notes: This figure reports event-study estimates for the foreign-born share (Panel A) and log population

(Panel B). The omitted year of comparison is 1920. We refer the reader to Section 4.1 and Table 2 for further

details

Figure 6: Event-study estimates for marriage and fertility

Notes: This figure reports event-study estimates for marriage (Panel A) and fertility (Panel B). The omitted

year of comparison is 1920. We refer the reader to Section 4.1 and Table 3 for further details.

Figure 7: Event-study estimates for log Lebergott Earnings Score

Notes: This figure reports event-study estimates for the (log) Lebergott Earnings Score. The omitted year

of comparison is 1920. We refer the reader to Section 4.2 and Table 4 for further details.

Figure 8: Event-study estimates for the manufacturing sector (city level)

This figure reports event-study estimates for the manufacturing sector at the city level. The omitted year of

comparison is 1920. We refer the reader to Section 4.3 and Table 6 for further details.

(1) (2) (3) (4) (5)N mean sd min max

County levelShare of foreign-born 14,060 0.071 0.092 0 0.655Ln population 14,060 9.800 1.043 1.386 15.220Quota exposure 14,060 0.249 0.435 0 5.193

Individual levelNumber of children below age 5 1,368,322 0.368 0.708 0 7== 1 if married 937,527 0.530 0.499 0 1Ln Lebergott earnings score 1,569,620 6.004 0.655 3.548 7.058==1 if works in most common low skilled occ for immigrants 1,612,045 0.176 0.381 0 1==1 if works in most common high skilled occ for immigrants 1,612,045 0.102 0.303 0 1==1 if works in most common agricultural occ for immigrants 1,612,045 0.150 0.357 0 1

City levelln value added per worker 1,258 7.698 0.529 5.123 12.46ln wages per worker 1,260 6.798 0.441 3.891 11.38ln horsepower per worker 1,260 0.990 0.543 -1.547 5.549ln horsepower per output 1,260 -7.527 0.542 -10.62 -4.997Quota exposure 1,260 1.412 1.170 0.0730 6.924

Table 1: Summary statistics

Notes: See Section 3.1 for further details. Quota exposure is defined in equation (1) of Section 3.2.

(1) (2) (3) (4) (5) (6)

Quota Exposure x Ipost -0.0578*** -0.0288*** -0.0161*** -0.0738*** -0.0876*** -0.0681***(0.00371) (0.00304) (0.00259) (0.0144) (0.0173) (0.0210)

ln Population size 1910 x time FE No Yes Yes No Yes YesWWI x Ipost No Yes Yes No Yes YesLiteracy Act x Ipost No Yes Yes No Yes YesCounty FE Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes YesState-by-time FE No No Yes No No YesCounty-specific linear trend No No No Yes Yes Yes

Observations 14,060 14,060 14,060 14,060 14,060 14,060R-squared 0.893 0.921 0.959 1.000 1.000 1.000

Table 2: Population dynamics -- The effect of the immigration quotas on the share of foreign-born and population

Dependent Variable

Share of foreign-born ln(population)

Notes: The table reports DiD estimates. The observations are at the county level for the period 1900 to 1940. The outcome variable is the share offoreign-born in columns (1)-(3) and ln(population) in columns (4)-(6). Quota exposure is defined in equation (1) of Section 3.2. All specificationsinclude county and time fixed effects. Additional controls are ln population size in 1910 interacted with a full set of time fixed effects, WWI, and theLiteracy Act of 1917 in columns (2)-(3) and (5)-(6), a county-specific linear trend in columns (4)-(6), and state-by-time fixed effects in columns (3) and(6). Appendix A details the construction of the controls for WWI and the Literacy Act of 1917. The total sample includes all counties for which thereexist data for all the years. Constants are not reported. Standard errors (in parentheses) account for arbitrary heteroskedasticity and are clustered atthe county level. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4) (5) (6)

Quota Exposure x Ipost -0.0406*** -0.0196*** -0.0223*** -0.0139*** -0.0124*** -0.00365(0.00656) (0.00583) (0.00822) (0.00337) (0.00476) (0.00567)

ln Population size 1910 x time FE Yes Yes Yes Yes Yes YesWWI x Ipost Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes YesIndividual controls Yes Yes Yes Yes Yes YesCounty FE Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes Yes Yes

Observations 1,368,322 967,321 318,232 937,527 677,442 200,338R-squared 0.153 0.151 0.184 0.337 0.333 0.366

Table 3: Population dynamics -- The effect of the immigration quotas on fertility and marriage

Dependent Variable

Number of children below age 5 ==1 if married

Notes: The table reports DiD estimates. The observations are at the individual level for the period 1900 to 1940. The sample spans 15-49-year-oldwomen in columns (1)-(3) when the number of children below age 5 is the outcome variable and 15-35-year-old women in columns (4)-(6) when theoutcome variable is a dummy whether being married. Quota exposure is defined in equation (1) of Section 3.2. In columns (2) and (5) the sample isrestricted to women born in the United States. (and both parents born in the United States), while in columns (3) and (6) the sample includes only first-and second-generation immigrant women. All regressions include county fixed effects, time fixed effects, state-by-time fixed effects, ln population sizein 1910 interacted with a full set of time fixed effects, and controls for WWI and the Literacy Act of 1917 (Appendix A details the construction of thecontrols for WWI and the Literacy Act of 1917). The set of individual controls includes a dummy for race (black/white), place of residence(rural/urban), age fixed effects, and birthplace fixed effects. Constants are not reported. Standard errors (in parentheses) account for arbitraryheteroskedasticity and are clustered at the county level. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Quota Exposure x Ipost -0.0208*** -0.0198*** -0.0205*** -0.0215*** 0.000299 -0.0243*** -0.00480(0.00502) (0.00531) (0.00588) (0.00598) (0.00481) (0.00601) (0.00507)

Quota Exposure x Ipost x black 0.0479*** 0.0453*** 0.0408*** 0.0275**(0.0119) (0.0103) (0.0140) (0.0125)

ln Population size 1910 x time FE Yes Yes Yes Yes Yes Yes Yes Yes YesWWI x Ipost Yes Yes Yes Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes Yes Yes Yes YesIndividual controls Yes Yes Yes Yes Yes Yes Yes Yes YesCounty FE Yes Yes Yes Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Sample AllLives in state

of birthLives outside state of birth

Men Women Men Men Women Women

Observations 1,569,620 1,114,087 455,514 1,205,563 364,037 1,205,335 1,205,975 363,812 362,945R-squared 0.642 0.659 0.583 0.572 0.754 0.578 0.591 0.765 0.783

Table 4: Immigration quotas and their impact on the earnings of native workers

Dependent Variable

ln(Lebergott Earnings Score)

Notes: The table reports DiD estimates. The observations are at the individual level for the period 1900 to 1940. The sample spans 15-65-year old native born workers. The outcomevariable is the ln Lebergott earnings score (see Section 3.1 for further details). Quota exposure is defined in equation (1) of Section 3.2. All regressions include county fixed effects,time fixed effects, state-by-time fixed effects, ln population size in 1910 interacted with a full set of time fixed effects, and controls for WWI and the Literacy Act of 1917 (AppendixA details the construction of the controls for WWI and the Literacy Act of 1917). The set of individual controls includes the following indicator variables: marital status, place ofresidence (rural/urban), gender, race (black/white), literacy. We further include birthplace and age fixed effects. Columns (6)-(9) further include quota exposure, WWI, and theLiteracy Act of 1917 each interacted with a dummy for race (black), race-by-time fixed effects, and race-by county fixed effects. County-by-time fixed effects are added to thespecifications in column (7) and (9). Constants are not reported. Standard errors (in parentheses) account for arbitrary heteroskedasticity and are clustered at the county level. ***p<0.01, ** p<0.05, * p<0.1.

==1 if works in most common low skilled occ for immigrants

==1 if works in most common high skilled occ for immigrants

==1 if works in most common low skilled occ for immigrants

==1 if works in most common high skilled occ for immigrants

(1) (2) (3) (4)

Quota Exposure x Ipost 0.0138*** -0.00410** 0.0136*** -0.00373**(0.00354) (0.00159) (0.00351) (0.00158)

Quota Exposure x Ipost x black 0.0113** 0.00326**(0.00561) (0.00153)

ln Population size 1910 x time FE Yes Yes Yes YesWWI x Ipost Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes YesIndividual controls Yes Yes Yes YesCounty FE Yes Yes Yes YesTime FE Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes

Observations 1,612,045 1,612,045 1,611,835 1,611,835R-squared 0.074 0.048 0.082 0.050

Table 5: Probablility of natives working in the most common immigrant occupationsDependent Variable

Notes: The table reports DiD estimates. The observations are at the individual level for the period 1900 to 1940. The sample spans15-65-year old native born workers. The outcome variable is an indicator variable which is one if a native worker reports to workin what we classified in 1920 as the most common unskilled (column 1) or skilled (column 2) occupations of immigrants from themost affected nationalities (see footnote 47 for further details). Quota exposure is defined in equation (1) of Section 3.2. Allregressions include county fixed effects, time fixed effects, state-by-time fixed effects, ln population size in 1910 interacted with afull set of time fixed effects, and controls for WWI and the Literacy Act of 1917 (Appendix A details the construction of the controlsfor WWI and the Literacy Act of 1917). The set of individual controls includes the following indicator variables: marital status, placeof residence (rural/urban), gender, race (black/white), literacy. We further include birthplace and age fixed effects. Columns (3)-(4) further include quota exposure, WWI, and the Literacy Act of 1917 each interacted with a dummy for race (black), race-by-timefixed effects, and race-by county fixed effects. Constants are not reported. Standard errors (in parentheses) account for arbitraryheteroskedasticity and are clustered at the county level. *** p<0.01, ** p<0.05, * p<0.1.

ln(va per worker) ln(wages per worker) ln(hp per worker) ln(hp per value added)

(1) (2) (3) (4)

Quota Exposure x Ipost -0.0452* -0.0127 -0.0315 -0.00857(0.0258) (0.00861) (0.0231) (0.0365)

ln Population size 1910 x time FE Yes Yes Yes YesWWI x Ipost Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes YesCity fixed effects Yes Yes Yes YesTime fixed effects Yes Yes Yes YesState-by-time fixed effects Yes Yes Yes Yes

Observations 1,228 1,230 1,230 1,230R-squared 0.850 0.931 0.858 0.826

Table 6: Immigration quotas and their impact on the manufacturing sector

Dependent Variable

Notes: The table reports DiD estimates. The observations are at the city level for the years 1909, 1914, 1919, 1925, and 1929.The outcome variable is ln value added per worker (column 1), ln wages per worker (column 2), horsepower per worker(columns 3) and horsepower per value added (columns 4). Quota exposure is defined as in equation (1) of Section 3.2, butcalculated at the city level. All regressions include, city fixed effects, time fixed effects, state-by-time fixed effects, and lnpopulation in 1910 interacted with a full set of time fixed effects. Linear interpolation was used to construct horsepower atthe city level for the year 1925. Constants are not reported. Standard errors (in parentheses) account for arbitraryheteroskedasticity and are clustered at the county (city) level. *** p<0.01, ** p<0.05, * p<0.1.

For Online Publication

Appendix

A. The Construction of Alternative Quota Exposure Measures and Controls for

World War I and the Literacy Act of 1917

Appendix A describes the construction of the World War I and the Literacy Act of 1917 controls and

explores how robust our results are to alternative assumptions when constructing the quota-exposure

variable. The control for the World War I immigration shock is constructed in the same spirit as Quota

exposurec:

WWIc =100

Pc,1910

N∑n=1

(Mn,15−19 −WWIn,15−19

) FBnc,1910

FBn,1910

, (7)

where Mn,15−19 is the predicted average annual number of immigrants of nationality n, supposed to

arrive during WWI (1915–1919).50 The predictions are based on the same method as outlined in

Section 3.2. WWIn,15−19 is the average number of immigrants of nationality n per year that actually

arrived to the United States during the war years. Analogously to the construction of the missing

immigrants from the quota system, Mn,15−19 − WWIn,15−19 denotes the missing immigrants from

World War I. These missing immigrants are then distributed according to the nationality share in 1910,

FBnc,1910/FBn,1910 following the same logic as outlined in Section 3.2. The total number of missing

immigrants in each area (county/city) is then scaled by the area population in 1910, 100/Pc,1910. For

the empirical analysis, we interact this cross-sectional variable with an indicator variable which is one

after 1910 and zero otherwise. While nationality shares and population are measured in 1920 when

constructing Quota exposurec, they are measured in 1910 in equation (7). This is motivated by the

timing of the two events. However, as we will show in this Appendix, similar results are obtained

when using nationality shares and population in 1910 to construct Quota exposurec instead.

With the data at hand it is not possible to construct the Literacy Act control in the spirit of

“missing-immigrants”. Instead, this measure takes on the following form:

Literacy Actc =N∑n=1

Literacyn,1915FBnc,1910

Pc,1910, (8)

50Since the immigration year ends on June, 30, the year 1915 refers to immigration from July 1, 1914 to June 30, 1915.See also footnote 20 in the main text.

44

where Literacyn,1915 is the share of people with no schooling in the age group 15-64, measured in

1915 in the home country of nationality n (Lee and Lee, 2016), and FBnc,1910/Pc,1910 is the foreign-

born share of nationality n in county c in 1910. The basic idea of this measure is that the share of

people with no schooling in the sending country is supposed to capture the share of people failing the

literacy test from this nationality when immigrating to the United States. For the empirical analysis,

we interact Literacy Actc with an indicator which equals one after 1910 and zero otherwise.

Next, we report the robustness of our main findings to including controls for the 1920 county pop-

ulation share of the two most important non-quota affect nationalities Canada and Mexico (interacted

with a set of time dummies) and using alternative methods to construct the quota-exposure variable.

The first alternative measure is already described in section 3.2 in detail and follows the spirit of the

treatment bracero exposure variable in Clemens et al. (forthcoming). This alternative measure is

constructed as:

Quota Nationalitiesc =N∑n=1

q1920nc , (9)

which is then interacted with a post-quota indicator. The fraction of the quota affected population in

1920 for a given area c is∑N

n=1 q1920nc ≡ (

∑Nn=1 FBnc,1920 × In)/Popc,1920, where indicator In is one

if nationality n is subject to the 1920s quota acts and zero otherwise.

The second alternative is constructed as:

Quota exposureA2c =

100

Pc,1920

N∑n=1

(Mn,22−30 −Qn,22−30

) FBfnc,1920 + FBs

nc,1920

FBfn,1920 + FBs

n,1920

, (10)

where FBfnc,1920 (FBs

nc,1920) is the number of first (second) generation immigrants of nationality n

living in area c in 1920, and FBfn,1920 (FBs

n,1920) is the number of first (second) generation immigrants

of nationality n living in the United States in 1920. The remaining variables are defined as in equation

(1). Compared to Quota exposurec, this measure exploits the extended local nationality network

consisting of both first- and second generation immigrants.

The third alternative replaces 1920 nationality shares and population by the ones reported in 1910:

Quota exposureA3c =

100

Pc,1910

N∑n=1

(Mn,22−30 −Qn,22−30

) FBnc,1910

FBn,1910

, (11)

where the remaining variables are define as outlined in equation (1).

45

The forth alternative is constructed as:

Quota exposureA4c =

100

Pc,1920

N∑n=1

(IF n,10−14 −Qn,22−30

) FBnc,1920

FBn,1920

, (12)

where the predicted immigrant inflow (Mn,22−30) for the period 1922-1930 is replaced by the average

immigration inflow from 1910 to 1914, IF n,1910−1914.

The final alternative follows Greenwood and Ward (2015) and is constructed as:

Quota exposureA5c =

N∑n=1

(Mn,22−30 −Qn,22−30

Mn,22−30

)FBnc,1920

Pc,1920, (13)

where 0 ≤(Mn,22−30 −Qn,22−30

)/Mn,22−30 < 1 captures the bite of the quota for nationality n.

Appendix Figure 1 shows the relationship between our baseline quota-exposure variable and the

alternatives quota exposure measures A2-A5, which are all highly correlated. The results presented in

Appendix Tables 1-4 show that overall qualitatively similar results are obtained for the foreign-born

share, population growth, marriage, fertility, the Lebergott earnings score, and the manufacturing out-

comes, when controlling for the direct effect of Mexican and Canadian immigration, using Quota Na-

tionalitiesc (except for the effect on population growth which is negative but statistically insignificant),

or Quota exposureA2c −Quota exposureA5

c . Column 1 of all tables reported in this Appendix replicates

our baseline finding for convenience and column 2 reports results where the county-population shares

of Mexicans and Canadians in 1920 interacted with a full set of time dummies are added to the

baseline estimating equation (results for the alternative quota exposure measures A1-A5 remain un-

affected when adding the county-population shares of Mexicans and Canadians in 1920 interacted by

time (available upon request)).

B. Theoretical Motivation

Appendix B presents a simple theoretical model, which is used to clarify how the introduction of

the quota system could have influenced native wages. It also motivates the earnings score estimation

equation presented in Section 4.2.

Our model assumes that the total production in a county c and year t is described by a Cobb-

Douglas production function:

Yct = ActKαctL

1−αct , with 0 < α < 1 and Kc0 given, (14)

46

where Act is total factor productivity, Kct is the total capital stock, and Lct is a CES aggregate of two

types of labor:

Lct =(η(Lhct)σ−1

σ + (1− η)(Llct)σ−1

σ

) σσ−1

, 0 < η < 1, σ > 0 and σ 6= 1. (15)

One can think of these two types of labor inputs as high skilled (Lhct) and low skilled (Llct) labor, and

σ denotes the elasticity of substitution between these two factors. For simplicity, we assume that all

high skilled workers (Lhct) are natives and all low skilled workers (Llct) are immigrant workers (i.e.,

the stock of foreign-born workers).

The next assumptions are that goods and factor markets are perfectly competitive and that no

migration of workers between counties takes place (i.e., local labor markets). In the short-run, real

wages are then given by:

whct = (1− α) ηAct

(Kct

Lct

)αL1−σ−1

σct

(Lhct)σ−1

σ−1, (16)

wlct = (1− α) (1− η)Act

(Kct

Lct

)αL1−σ−1

σct

(Llct)σ−1

σ−1. (17)

It can be shown that when the capital stock is fixed in the short run, native wages will respond to a

marginal increase in the stock of foreign-born workers in the following way:

∂whct∂Llct

= (1− α) (1− η)ηAct

(1− α− σ − 1

σ

)(Kct

Lct

)α×

L−σ−1

σct

(Lhct)σ−1

σ−1L

σσ−1−1

ct (1− η)(Llct)σ−1σ−1. (18)

One can easily verify that the effect on the native wage is ambiguous. For example, if 1 − α < σ−1σ

the effect is negative. For the case where foreign-born and native workers are perfect substitutes

(i.e., σ → ∞), the native wage rate decreases as the number of foreign-born increases because of

diminishing returns to labor. While if foreign-born and native workers are perfect complements (i.e.,

σ → 0), the effect turns out to be positive. In other words, the negative effect coming from diminishing

returns to labor can be more than offset by the complementarity between the two labor types, which

demonstrates one of our main points in the paper.

Next, we motivate the earnings score estimation equation used in Section 4.2. To this end, we

assume that capital is perfectly mobile across counties, which implies that through import/export of

47

capital, the county-level capital intensity (kt ≡ Kct/Lct), adjusts in each period to accommodate:51

k = (Act)1

1−α

(α

rt

) 11−α

, (19)

where rt is the national real interest rate, which is allowed to vary over time. Taking equation (16) in

logs and substituting condition (19) in for capital k, we obtain the following expression:

lnwhct = ln (1− α) η +1

1− αlnAct +

α

1− αlnα

rt+(

1− σ − 1

σ

)ln Lct +

(σ − 1

σ− 1

)lnLhct.

(20)

Next, we denote (20) in first-differences:

lnwhct+1 − lnwhct =1

1− α([lnAct+1 − lnAct] + α [ln rt − ln rt+1]) +

(1− (σ − 1) /σ)([

ln Lct+1 − ln Lct

]−[lnLhct+1 − lnLhct+1

]), (21)

and using ∆ lnxct ≡ lnxct+1 − lnxct and that the CES aggregate for the two labor types can be

rewritten as Lct =(η + (1− η)

(Llct/L

hct

)σ−1σ

) σσ−1

Lhct:

∆ lnwhct =1

1− α∆ lnAct +

α

1− α∆ ln rt + (1− σ

σ − 1)∆ lnFBct, (22)

where FBct ≡(η + (1− η)

(Llct/L

hct

)σ−1σ

) σσ−1

. We note that in the case that native and immigrant

workers are perfect substitutes, immigration has no (direct) effect on wages, as capital flows balance

out the negative effect coming from diminishing returns. Next, we see that ∆ lnFBct is driven by

changes in the number of foreign-born relative to native workers and, therefore, a function of the

quota system (i.e., missing flow of immigrants per capita), such that ∆ lnFBct = F (Quota expo-

surec × Ipostt ). Let’s assume for simplicity that ∆ lnFBct = Quotaexposurec × Ipostt and insert this

relationship into equation (22). This yields the following expression:

∆ lnwhct =1

1− α∆ lnAct + µt + βQuota exposurec × I

postt , (23)

where µt ≡ α1−α∆ ln rt is a time fixed effect and β ≡ (1− σ

σ−1). Since the growth rate of total factor

51One could alternatively assume no capital mobility at all and end up with similar predictions. This would, however,change the transitions dynamics. Therefore, to avoid this complication and to illustrate our point, we simply assumeperfect capital mobility across counties. Notice, we also assume that labor markets are completely local (i.e., no in andout-migration), which is also a theoretical simplification. All we need in the empirical model is that labor is not perfectlymobile.

48

productivity (∆ lnAct) could be influenced by the quota system as well, our framework generally does

not allow us to identify the elasticity of substitution (via estimating β). Instead, we end up estimating

the reduced-form effect which, according to equation (23), works either through the degree of com-

plementarity of native and immigrant labor and/or productivity. Estimation equation (23) suggests

that changes in (log) wages should be regressed on Quota exposurec × Ipostt , which captures the flow

of (missing) immigrants. However, the empirical specification used in this paper regresses the level

of (log) wages on Quota exposurec × Ipostt instead. Nevertheless, controlling for county fixed effects

makes these two approaches similar in spirit.

The main reason for this particular choice is that the earnings-score specifications use repeated

cross-sectional data at the individual level, which makes it impossible to construct ∆ lnwhct at the

individual level. In addition, if one believes that ∆ lnAct is influenced by county-specific factors

which are time-invariant and correlated with Quota exposurec × Ipostt , this equation motivates why it

could be important to control for county fixed effects, even if the outcome is in (log) changes. In our

case, this is then equivalent to adding county-specific linear trends to the estimating equation. Adding

county-specific linear trends to the estimating equation reported in column 1 of Table 4 returns an

estimate equal to −0.015 with a p-value of 0.035, which indicates that the findings on native earnings

are generally robust to this extension.

C. The Effect of the Quota System on In-migration

Appendix C summarizes how the quota system influenced (black) in-migration. Appendix Table

5 shows how the black population and the black-population share (at the county-by-time level) re-

sponded to the implementation of the quota system. The estimation equation is (2), where (for sim-

plicity) Quota exposurec is interacted with an indicator which is one in the post-quota period and zero

otherwise. For comparison, column 1 reports the DiD estimate for county population growth where

the sample has been restricted to counties that report a black population larger then zero. As in Table

2, the estimate is negative and statistically significant. For the same sample of counties, column 2

shows a positive but statistically insignificant estimate for black population growth, suggesting that

more quota-exposed counties did not experience any significant increases in black population due to,

for example, in-migration. The estimates reported in columns 1 and 2 of Appendix Table 5 would sug-

gest that the (log) black-population share increased more in counties with more missing immigrants,

which is confirmed in column 3. Importantly, this increase is not due to black in-migration into

49

more quota-exposed areas, but rather driven by a general population decline (i.e., the denominator) as

outlined in section 4.1.

Appendix Figure 2 documents whether the implementation of the quota system triggered inter-

state migration based on event-study estimates (DiD estimates available upon request). The outcome

variable is an indicator variable for whether a 15- to 65-year-old native worker lives outside the state

of birth. The estimating equation is (2), but also including a set of individual level controls (see notes

to Appendix Figure 2 for details). Panel A shows no evidence of interstate migration being system-

atically related to quota exposure. This is also the case for Panel B, where the triple-interaction term

Quota exposurec × Ipostt ×blacki is added to the estimating equation in order to capture differences in

internal population movements by race.

Overall, the evidence in Appendix C indicates that the quota system was not an important trig-

ger of internal (interstate) population movements. However, there are two important caveats to this

non-finding: First, all specifications control for immigration shocks related to World War I and the

Literacy Act of 1917, and consistent with the hypothesis that European immigration delayed the Great

Migration (Collins, 1997), the World War I shock variable is positively related to the black-county

population share and interstate migration (not reported). Second, the indicator-variable used for Ap-

pendix Figure 2 does not capture intrastate migration. In fact, exploiting the linked sample of Collins

and Wanamaker (2014) for the period 1910-1930 reveals that black migrants were more likely to opt

for places exposed to the quota system. However, this relationship is not robust to the WWI migration-

shock variable, which generally supports our findings reported in this section (not reported).

D. Alternative Occupation-based Earnings Scores

We use two alternative occupation-based earnings scores to check the robustness of the results pre-

sented in Table 4 of Section 4.2. The first measure is the so-called occupational income score (“occ-

score”) from IPUMS.52 The occupational income score captures variation in individual’s earnings if

individuals change their occupations after the quota system was in place. It is important to note that

the occupational income score only captures variation across occupations, but not within occupations

over time. We also need to assume that relative incomes of different occupations were constant over

time.52The “occscore” is based on a 1956 special report from the Census on occupational characteristics. It reports median

incomes by occupations, which reflect the relative economic standing of occupations in 1950. The IPUMS then assignedto every individual in the Census reporting an occupation (“occ1950”) the respective value of their occupation.

50

The second measure follows the spirit of the IPUMS occupation score, but we use the information

about wages and salaries of workers from the IPUMS 1940 full count sample instead. Compared to the

IPUMS occupation score, our measure has the advantage that we can construct an earnings score that

differs by gender, race, census region (Northeast, Midwest, South, and West), and occupation.53 The

“1940 earnings score” captures variation in individuals’ wage income that arose from the implemen-

tation of the quota acts in the 1920s if individuals changed their occupation or kept their occupation

but moved to a different region. However, compared to the Lebergott earnings score this measure is

time invariant and the ranking of occupations is based on the terminal year of the sample. A further

drawback is that the 1940 Census did not report any business and farm income. To overcome this is-

sue in part, we assign farm operators (occ1950 codes 100 and 123) their net income in 1940 from the

Lebergott earnings score adjusted by race, gender, and location (South/Non-South); however, we do

not have such income information on other businesses than farming. As for the occupational earnings

score, we further need to assume that the relative standing of occupations was constant over time.

Because of these limitations, we consider the Lebergott earnings score to be our preferred measure.

The results of Appendix Table 6 confirm the findings of Table 4 when using the occupational in-

come score (Panel A) and the 1940 earnings score (Panel B) as alternative outcome variable (although

the estimates for female workers by race are not statistically significant for the 1940 earnings score).

53We use the following broad occupation categories from IPUMS variable “occ1950”: professional, technical; farmers;managers, officials, and proprietors; clerical and kindred; sales workers; craftsmen; operatives; service workers (privatehouseholds); service workers (not household); farm laborers; and laborers.

51

Appendix Figure 1: Quota exposure

Notes: This figure shows the relationship between baseline quota exposure and alternative measures of quota

exposure. We refer the reader to Appendix A for further details.

Appendix Figure 2: Event-study estimates for out-of-state migrant status

Notes: This figure reports event-study estimates for the dummy variable which indicates whether the sampled

individual is born out of state. The omitted year of comparison is 1920. We refer the reader to Appendix C

for further details.

(1) (2) (3) (4) (5) (6) (7)

Panel A:

Quota Exposure x Ipost -0.0161*** -0.0148*** -0.0148*** -0.0155*** -0.0159*** -0.240***(0.00259) (0.00250) (0.00202) (0.00163) (0.00213) (0.0286)

Quota Nationalites x Ipost -0.290***(0.0174)

ln Population size 1910 x time FE Yes Yes Yes Yes Yes Yes YesShare Mex/Can 1920 x time FE No Yes No No No No NoWWI x Ipost Yes Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes Yes YesCounty FE Yes Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes Yes Yes Yes

Quota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5

Observations 14,060 14,060 14,060 14,060 14,060 14,060 14,060R-squared 0.959 0.976 0.961 0.959 0.959 0.959 0.959

Panel B:

Quota Exposure x Ipost -0.0681*** -0.0727*** -0.0599*** -0.0399*** -0.0608*** -0.848***(0.0210) (0.0210) (0.0194) (0.0135) (0.0184) (0.255)

Quota Nationalites x Ipost -0.0191(0.167)

ln Population size 1910 x time FE Yes Yes Yes Yes Yes Yes YesShare Mex/Can 1920 x time FE No Yes No No No No NoWWI x Ipost Yes Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes Yes YesCounty FE Yes Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes Yes Yes YesCounty-specific linear trend Yes Yes Yes Yes Yes Yes YesMQuota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5

Observations 14,060 14,060 14,060 14,060 14,060 14,060 14,060R-squared 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Appendix Table 1: Population dynamics -- alternative measures of quota exposure

Dependent Variable

Share of foreign born

ln(population)

Notes: The table reports DiD estimates. The observations are at the county level for the period 1900 to 1940. The outcome variable is the share of foreign born in PanelA and ln(population) in Panel B. Column (1) shows the baseline estimates of Table 2 for comparison. The set of controls included are identical to Table 2 column (3) forPanel A and Table 2 column (6) for Panel B, except for column (2), which additionally includes the 1920 county-population shares of Mexicans and Canadians interactedby time. Quota exposure in column (1) is constructed according to equation (1) of Section 3.2. Column (3) replaces quota exposure with the share of quota affectednationalities ("Quota Nationalities") as described in equation (9) of Appendix A. Columns (4)-(7) present results for different versions of quota exposure as described inequations (10)-(13) of Appendix A. Constants are not reported. Standard errors (in parentheses) account for arbitrary heteroskedasticity and are clustered at the countylevel. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4) (5) (6) (7)

Panel A:

Quota Exposure x Ipost -0.0406*** -0.0376*** -0.0472*** -0.0254*** -0.0377*** -0.643***(0.00656) (0.00635) (0.00586) (0.00366) (0.00600) (0.0771)

Quota Nationalites x Ipost -0.414***(0.0492)

ln Population size 1910 x time FE Yes Yes Yes Yes Yes Yes YesShare Mex/Can 1920 x time FE No Yes No No No No NoWWI x Ipost Yes Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes Yes YesIndividual Controls Yes Yes Yes Yes Yes Yes YesCounty FE Yes Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes Yes Yes Yes

Quota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5

Observations 1,368,322 1,361,012 1,368,322 1,368,322 1,368,488 1,368,322 1,368,322R-squared 0.153 0.153 0.153 0.153 0.153 0.153 0.153

Panel B:

Quota Exposure x Ipost -0.0139*** -0.0137*** -0.0154*** -0.00913*** -0.0125*** -0.199***(0.00337) (0.00346) (0.00333) (0.00227) (0.00287) (0.0438)

Quota Nationalites x Ipost -0.100***(0.0305)

ln Population size 1910 x time FE Yes Yes Yes Yes Yes Yes YesShare Mex/Can 1920 x time FE No Yes No No No No NoWWI x Ipost Yes Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes Yes YesCounty FE Yes Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes Yes Yes YesCounty-specific linear trend Yes Yes Yes Yes Yes Yes Yes

Quota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5

Observations 937,527 932,700 937,527 937,527 937,633 937,527 937,527R-squared 0.337 0.338 0.337 0.337 0.337 0.337 0.337

Appendix Table 2: Population dynamics -- alternative measures of quota exposure

Dependent Variable

Number of children below age 5

==1 if married

Notes: The table reports DiD estimates. The observations are at the individual level for the period 1900 to 1940. The sample spans 15-49-year-old women in Panel Awhen the number of children below age 5 is the outcome variable and 15-35-year-old women in Panel B when the outcome variable is a dummy for being married.Column (1) shows the baseline estimates of Table 3 for comparison. The set of controls included are identical to Table 3 column (1) for Panel A and Table 3 column (4)for Panel B, except for column (2), which additionally includes the 1920 county-population shares of Mexicans and Canadians interacted by time. Quota exposure incolumn (1) is constructed according to equation (1) of Section 3.2. Column (2) replaces quota exposure with the share of quota affected nationalities ("QuotaNationalities") as described in equation (9) of Appendix A. Columns (3)-(6) present results for different versions of quota exposure as described in equations (10)-(13) ofAppendix A. Constants are not reported. Standard errors (in parentheses) account for arbitrary heteroskedasticity and are clustered at the county level. *** p<0.01, **p<0.05, * p<0.1.

(1) (2) (3) (4) (5) (6) (7)

Quota Exposure x Ipost -0.0208*** -0.0182*** -0.0222*** -0.00786*** -0.0229*** -0.344***(0.00502) (0.00494) (0.00504) (0.00300) (0.00424) (0.0645)

Quota Nationalites x Ipost -0.173***(0.0450)

ln Population size 1910 x time FE Yes Yes Yes Yes Yes Yes YesShare Mex/Can 1920 x time FE No Yes No No No No NoWWI x Ipost Yes Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes Yes YesIndividual Controls Yes Yes Yes Yes Yes Yes YesCounty FE Yes Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes Yes Yes Yes

Quota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5

Observations 1,569,620 1,560,009 1,569,620 1,569,620 1,569,913 1,569,620 1,569,620R-squared 0.642 0.643 0.642 0.642 0.642 0.642 0.642

Appendix Table 3: Earnings of native workers -- alternative measures of quota exposure

Dependent Variable

ln(Lebergott Earnings Score)

Notes: The table reports DiD estimates. The observations are at the individual level for the period 1900 to 1940. The sample spans 15-65-year old native bornworkers.The outcome variable is the ln Lebergott earnings score (see Section3.1 for further details). Column (1) shows the baseline estimates of Table 4 for comparison.The set of controls included are identical to Table 4 column (1), except for column (2), which additionally includes the 1920 county-population shares of Mexicans andCanadians interacted by time. Quota exposure in column (1) is constructed according to equation (1) of Section 3.2. Column (2) replaces quota exposure with the shareof quota affected nationalities ("Quota Nationalities") as described in equation (9) of Appendix A. Columns (3)-(6) present results for different versions of quotaexposure as described in equations (10)-(13) of Appendix A. Constants are not reported. Standard errors (in parentheses) account for arbitrary heteroskedasticity andare clustered at the county level. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4) (5) (6)

Panel A:

Quota Exposure x Ipost -0.0452* -0.0584** -0.0173* -0.0428* -0.995**(0.0258) (0.0264) (0.00989) (0.0228) (0.495)

Quota Nationalites x Ipost -0.504(0.368)

Quota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4

Observations 1,228 1,228 1,228 1,228 1,228 1,228R-squared 0.850 0.851 0.849 0.849 0.850 0.850

Panel B:

Quota Exposure x Ipost -0.0127 -0.0181** -0.0106*** -0.00594 -0.267(0.00861) (0.00832) (0.00297) (0.00792) (0.188)

Quota Nationalites x Ipost -0.0762(0.151)

Quota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4

Observations 1,230 1,230 1,230 1,230 1,230 1,230R-squared 0.931 0.931 0.931 0.931 0.931 0.931

Panel C:

Quota Exposure x Ipost -0.0315 -0.0369 -0.0187* -0.0256 -0.799*(0.0231) (0.0234) (0.0109) (0.0197) (0.437)

Quota Nationalites x Ipost -0.413(0.343)

Quota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4

Observations 1,230 1,230 1,230 1,230 1,230 1,230R-squared 0.858 0.859 0.858 0.858 0.858 0.858

Panel D:

Quota Exposure x Ipost -0.00857 -0.00916 -0.00814 -0.00676 -0.406(0.0365) (0.0371) (0.0135) (0.0305) (0.698)

Quota Nationalites x Ipost -0.365(0.512)

Quota measure Baseline Baseline Alternative 1 Alternative 2 Alternative 3 Alternative 4

Observations 1,230 1,230 1,230 1,230 1,230 1,230R-squared 0.826 0.826 0.826 0.826 0.826 0.826

Controls Panel A-Dln Population size 1910 x time FE Yes Yes Yes Yes Yes YesShare Mex/Can 1920 x time FE No Yes No No No NoWWI x Ipost Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes YesCity fixed effects Yes Yes Yes Yes Yes YesTime fixed effects Yes Yes Yes Yes Yes YesState-by-time fixed effects Yes Yes Yes Yes Yes Yes

Notes: The table reports DiD estimates. The observations are at the city level for the years 1909, 1914, 1919, 1925, and 1929. The outcome variableis ln value added per worker (Panel A), ln wages per worker (Panel B), horsepower per worker (Panel C) and horsepower per value added (Panel D).Column (1) shows the baseline estimates of Table 6 for comparison, except for column (2), which additionally includes the 1920 county-populationshares of Mexicans and Canadians interacted by time. The set of controls included are identical to Table 6. Quota exposure in column (1) isconstructed according to equation (1) of Section 3.2, but calculated at the city level. Column (2) replaces quota exposure with the share of quotaaffected nationalities ("Quota Nationalities") as described in equation (9) of Appendix A. Columns (3)-(5) present results for different versions ofquota exposure as described in equations (10)-(13) of Appendix A. Constants are not reported. Standard errors (in parentheses) account forarbitrary heteroskedasticity and are clustered at the county level. *** p<0.01, ** p<0.05, * p<0.1.

Appendix Table 4: Manufacturing -- alternative measures of quota exposure

Dependent Variable

ln(value added per worker)

ln(wages per worker)

ln(horsepower per worker)

ln(horsepower per value added)

ln(population) ln(black population) ln(share black) share blacks

VARIABLES (1) (2) (3) (4)

Quota Exposure x Ipost -0.0709*** 0.0112 0.345*** 0.00638***(0.0218) (0.0686) (0.0522) (0.00143)

ln Population size 1910 x time FE Yes Yes Yes YesWWI x Ipost Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes YesCounty FE Yes Yes Yes YesTime FE Yes Yes Yes YesState-by-time FE Yes Yes Yes YesCounty-specific linear trend Yes Yes No No

Observations 13,198 13,198 13,198 14,060R-squared 1.000 0.997 0.963 0.991

Appendix Table 5: Population dynamics -- The effect of the immigration quotas on the black population

Dependent Variable

Notes: The table reports DiD estimates. The observations are at the county level for the period 1900 to 1940. The outcomevariable is ln population in column (1); ln black population in column (2); and the (ln) share of blacks in columns (3)-(4). Quotaexposure is defined in equation (1) of Section 3.2. All specifications include county and time fixed effects. Additional controlsare ln population size in 1910 interacted with a full set of time fixed effects, WWI, and the Literacy Act of 1917, state-by-timefixed effects, and a county-specific linear trend in columns (1)-(2). See Appendix C for further details. Constants are notreported. Standard errors (in parentheses) account for arbitrary heteroskedasticity and are clustered at the county level. ***p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Panel A:

Quota Exposure x Ipost -0.0159*** -0.0137*** -0.0133*** -0.0171*** -0.00981 -0.0180*** -0.0162*(0.00417) (0.00508) (0.00468) (0.00411) (0.00964) (0.00414) (0.00905)

Quota Exposure x Ipost x black 0.0381*** 0.0367*** 0.0663*** 0.0636**(0.00653) (0.00699) (0.0250) (0.0261)

Sample AllLives in state

of birthLives outside state of birth

Men Women Men Men Women Women

Observations 1,612,045 1,143,685 468,342 1,240,295 371,731 1,240,075 1,240,748 371,509 370,677R-squared 0.363 0.376 0.329 0.382 0.378 0.387 0.400 0.392 0.423

Panel B:

Quota Exposure x Ipost -0.0151*** -0.0146*** -0.0168*** -0.0188*** -0.00162 -0.0207*** -0.00658(0.00401) (0.00453) (0.00391) (0.00390) (0.00794) (0.00379) (0.00676)

Quota Exposure x Ipost x black 0.0248*** 0.0217*** 0.0104 0.00872(0.00562) (0.00551) (0.00639) (0.00700)

Sample AllLives in state

of birthLives outside state of birth

Men Women Men Men Women Women

Observations 1,611,505 1,143,377 468,109 1,239,815 371,671 1,239,595 1,240,268 371,449 370,621R-squared 0.651 0.668 0.593 0.592 0.712 0.599 0.609 0.730 0.746

Controls Panel A-Bln Population size 1910 x time FE Yes Yes Yes Yes Yes Yes Yes Yes YesWWI x Ipost Yes Yes Yes Yes Yes Yes Yes Yes YesLiteracy Act x Ipost Yes Yes Yes Yes Yes Yes Yes Yes YesIndividual controls Yes Yes Yes Yes Yes Yes Yes Yes YesCounty FE Yes Yes Yes Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes Yes Yes Yes YesState-by-time FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Appendix Table 6: Alternative occupation-based earnings scoresDependent Variable

ln(Occupational Income Score)

ln(1940 Earnings Score)

Notes: The table reports DiD estimates. The observations are at the individual level for the period 1900 to 1940. The sample spans 15-65-year old native born workers. Theoutcome variable is the ln occupational income score from IPUMS in Panel A and the ln 1940 earnings score in Panel B (see Appendix D for further details). Quota exposure isdefined in equation (1) of Section 3.2. All regressions include county fixed effects, time fixed effects, state-by-time fixed effects, ln population size in 1910 interacted with a full setof time fixed effects, and controls for WWI and the Literacy Act of 1917 (Appendix A details the construction of the controls for WWI and the Literacy Act of 1917). The set ofindividual controls includes the following indicator variables: marital status, place of residence (rural/urban), gender, race (black/white), literacy. We further include birthplaceand age fixed effects. Columns (6)-(9) further include quota exposure, WWI, and the Literacy Act of 1917 each interacted with a dummy for race (black), race-by-time fixed effects,and race-by county fixed effects. County-by-time fixed effects are added to the specifications in column (7) and (9). Constants are not reported. Standard errors (in parentheses)account for arbitrary heteroskedasticity and are clustered at the county level. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4) (5) (6)

Quota Exposureold x Ipost 0.0756*(0.0427)

Quota Exposurenew x Ipost -0.0225***(0.00518)

Quota Exposurej x Ipost 0.713*** -0.109*** -0.0295 -0.0363 0.191*(0.193) (0.0141) (0.0195) (0.0712) (0.105)

Quota Exposure-j x Ipost -0.0264*** -0.00527 -0.0190*** -0.0195** -0.0217***(0.00526) (0.00493) (0.00635) (0.00763) (0.00509)

Country j Great Britain Russia Italy Austria FinlandControls Table 4 Yes Yes Yes Yes Yes Yes

Observations 1,569,620 1,569,620 1,569,620 1,569,620 1,569,620 1,569,620R-squared 0.642 0.642 0.642 0.642 0.642 0.642

Appendix Table 7: Missing specific immigration groups and their impact on the earnings of native workers

Dependent Variable

ln(Lebergott Earnings Score)

Notes: The table reports DiD estimates. The observations are at the individual level for the period 1900 to 1940. The sample spans 15-65-yearold native born workers.The outcome variable is the ln Lebergott earnings score (see Section3.1 for further details). The set of controls includedare identical to Table 4 column (1). Quota exposure in column (1) is constructed according to equation (1) of Section 3.2, but split into old andnew immigration groups. Quota Exposureold refers to missing immigrants from Northern and Western Europe, while Quota Exposurenew refersto immigrants from Southern and Eastern Europe. The classification follows the "Dillingham Report" (U.S. Immigration Commission, Table 6).Columns (2)-(6) present results by specific immigration groups. Quota Exposurej denotes missing immigrants from country j, where j = {GreatBritain, Russia, Italy, Austria, and Finland}. Quota Exposure-j denotes missing immigrants from all other countries except j . Constants are notreported. Standard errors (in parentheses) account for arbitrary heteroskedasticity and are clustered at the county level. *** p<0.01, **p<0.05, * p<0.1.