The Effect of Immigration Quotas on Wages, the
Great Black Migration, and Industry Development∗
Bin Xie†
March, 2017
Abstract
This paper uses a natural experiment to identify the effects of immigration on the manufacturing industry and the Great Black Migration in the US between 1920 and 1930.
The immigration quota system established in 1921 severely restricted immigrants from
southern and eastern Europe while imposing modest restrictions on western and northern
Europe hence US regions that were historically major destinations of eastern and southern European immigrants experienced a greater exogenous decline in immigrant supply. I
estimate the number of “lost" immigrants to each state/county owing to the quotas as the
instrumental variable for the change in the regional foreign-born share. I find that the negative immigrant supply shock to a region significantly increases the local manufacturing
wage level and induces a greater inflow of the black population. I also find that manufacturers facing a greater decline in immigrant supply have a slower growth in firm size and
electrification rate but a greater increase in horsepower and valued added per wage worker.
JEL Code: J61 K37 N32 O14
Keywords: Immigration quota, Great Black Migration, Industry development
∗
I am indebted to Jennifer Hunt for her continued guidance and support. I am grateful to Ira Gang and Anne
Piehl for insightful discussions that greatly improved this paper. I am also thank Zhutong Gu, John Landon-Lane,
Ethan Jiang, Reka Juhasz, Carolyn Moehling, Hillel Rapoport, Scott Rozelle, Shuyang Yang, and participants in
the RGS/RWI Workshop on the Economics of Migration, Rutgers Empirical Microeconomic Workshop, Stanford
REAP seminar, EEA Annual Conference, CES North American Conference for valuable comments. I thank Ryan
Womack for help with the data. All remaining errors are my own.
†
Rutgers University and IZA (email: [email protected])
1
1
Introduction
International migration has attracted consistent attention from economists to study its economic impact. Studies show that immigrants have substantial economic gains to migration
(Abramitzky, Boustan, and Eriksson, 2012; Gibson, et. al., 2010; 2015) and benefit the receiving economy as well by expanding the economic capacity and increasing the national income.
Immigration can nonetheless have a distributional effect on the receiving economy that produces losers and winners - if new immigrants dampen the wages, native workers and existing immigrants are likely to suffer welfare loss while firms gain additional surplus.1 Hence,
whether immigrants lower the wage level and the living standard of the current population
is a major concern of natives throughout the history and it significantly affects natives’ attitude towards immigrants and the immigration policy formation (Goldin, 1994; Timmer and
Williamson, 1998). As a consequence, the impact of immigration on the labor market of the
receiving country has long been a central focus of economic studies of immigration.
On the other hand, economists have not settled the long-standing debate though abundant
studies have been done.2 The main difficulty is rooted in the endogeneity problem - because
immigrants tend to move to prospering areas with better economic opportunities, it is likely
to observe a spurious positive relationship between the regional immigrant penetration (measured as the immigrant share of the population) and the wage level/growth that contaminates
identification and causes biased estimates. Moreover, the adjustment mechanisms in response
to immigrant supply shocks are yet under-explored, including factor mobilities, capital adjustment as well as technology shifts/progress, which can attenuate or aggravate the wage impact
of immigration.
The primary contribution of this study is to identify the causal effects of immigration on
wages, the internal migration of the black population, and technology advances in the manufacturing sector using a natural experiment due to the US immigration policy shift in the
1920s. I exploit the exogenous variation of the change in immigrant supply across US regions
because of the peculiar design of the immigration quota system in 1920s to address the en1
This argument can be illustrated by a simple diagram of a downward-sloping labor demand and inelastic
labor supply curve with immigrants shifting the supply curve rightward.
2
Economists manage to reach consensus to some extent in the recently released report of the National
Academies of Sciences, Engineering, and Medicine (2016) (Later referred as “National Academies Report"). Although economics agree upon the weak wage effect, the validity of instruments is still under debate.
2
dogeneity issue. Accordingly, I develop a novel instrumental variable for the change in the
regional immigrant share to identify the effects of immigration. This study is also the first to
quantitatively evaluate the impact of the immigration quotas on the US local labor markets
and industry development.3
After embracing open immigration for more than a hundred years, the United States gradually closed its gate to immigrants at the dawn of the 20th century. In fact, the legislative
efforts to restrict immigration surged early in the middle of the Age of Mass Migration (18501914) in the form of barring minor ethnic groups, which included the Chinese Exclusion Act of
1882 and the Gentlemen Agreement of 1907 against Japanese immigrants. The climax of the
immigration restriction policy was the establishment of a stringent country-based immigration quota system in 1921 (subsequently set permanent in 1924). While excluding immigrants
from almost all Asian countries, the quota system set an annual allotment for every European
country that admits a maximum number of immigrants of the nationality every year.4 The
system reduced the total annual immigrant inflow to less than one-third of its pre-WWI level.
The great contraction of the immigration caused the share of foreign-born5 population in the
US to decline substantially from 13.2% in 1920 to 11.6% in 1930.
The design of the quota system consciously discriminated against eastern and southern European immigrants by setting meagerly low annual limits to southern and eastern European
countries while it assigned generous quotas to western and northern European countries. The
consequence of the discrimination was the exogenous variation in the change in the foreignborn share in US regions (state or county), given immigrants’ heterogeneous preferences of
locations within the US6 . For regions that had been major destinations of southern and eastern European immigrants, the quota system significantly reduced their immigrant supply and
the foreign-born share was more likely to decrease exogenously. Meanwhile regions that historically received mainly western and eastern European immigrants experienced weaker adverse
immigrant supply shocks. Regions that were not historically favored by immigrants also experienced weaker shocks by the quotas because no substantial number of immigrants came either
3
Two economic studies investigate the impacts of the quota system at a micro level on the skill composition of
immigrants (Massey, 2016) and on return migration (Greenwood and Ward, 2015).
4
African, Australia, and New Zealand were also subject to the quotas and their numbers were trivial compared
to Europe.
5
This study concerns only first-generation immigrants who are born in foreign countries so “foreign-born" is
equivalent to “immigrant" in this paper and the two terms are used interchangeably.
6
See Online Appendix II Figure B2 from Abramitzky and Boustan (2016).
3
before or after the quotas, which was the case for the regions in the Deep South7 . In addition,
regions with immigrants predominantly from non-quota countries (for instance, Mexico) were
less affected by the quota system since their immigrant supply was unconstrained, including
many counties in the south of California, Arizona, New Mexico and Texas8 .
I construct an instrumental variable for the foreign-born share change by estimating the
number of “lost" immigrants to each US state/county due to the quota system. I first project
the “no-quota" counterfactual immigrant inflow by source country and compute the difference
between the counterfactual yearly inflow and the annual quota as the number of “lost" immigrants who had been excluded by the quota system between 1920 and 1930. I then apportion
those “lost" immigrants to US state/county based on their historical settlement patterns by
origin to each state/county and aggregate them to obtain the total number of “lost" immigrants
to each US state/county (normalized by the region’s population) as a measure of the regional
policy shock. Since it is a measure of flow (more precisely, the loss of flow), I use it as the
instrumental variable for the change in regional foreign-born share (change in stock) between
1920 and 19309 to identify the causal effect of immigrants on the labor market.
The common approach to identify the effect of immigrants is to use the geographic variation of immigrant share with the instrument developed by Card (2001).10 Card (2001) redistributes the total immigrant stock in US by origin to each US region based on their historical
settlements to construct a Bartik-type shift-share instrument. It argues that the imputed immigrant stock is determined by the supply side factors (“push" from the source country) and
ethnic links to their predecessors (family ties, network, ethnic goods, etc.) so it should be orthogonal to local economic conditions. Card’s IV identification nonetheless is vulnerable to the
argument that the total immigrant inflow at country level could be “pulled" by a booming regional labor market of the receiving economy. In addition, Card’s instrument uses the change
in immigrant stock instead of the actual inflow and it is subject to the impact of return mi7
For instance, South Carolina has 0.66% of its labor force who are immigrants in 1920 and 0.50% in 1930.
Some counties in Texas were nonetheless affected by the quotas because of their non-trivial Austrian and
German immigrant shares.
9
The main reason not to extend the time span of the study beyond 1930 is the Great Depression that destroyed
employment opportunities in US and greatly discouraged immigration. Immigration was so low in the 1930s that
the quotas were not exhausted of many countries, which makes it intractable to identify the effect of quotas.
10
Another approach is to exploit the exogenous immigrant labor supply as a natural experiment, for instance,
Card (1990) studying the effect of Mariel Boatlift on Miami’s labor market. These studies include but are not
limited to Hunt (1992), Carrington and deLima (1994), Angrist and Kugler (2003), and Foged and Peri (2013).
8
4
gration that is highly endogenous11 . The identification strategy in this paper improves on the
Card’s instrument by introducing a policy shift that exogenously affects immigrant inflows.
I also contribute to the literature of understanding the labor market impact of immigration
in the US history when inter-regional factor mobility, labor market institutions, and production methods are distinct from today. The labor market effect of immigration in the modern US
economy circa 1970 has been more thoroughly studied (Friedberg and Hunt, 1995; Lewis and
Peri, 2015; National Academies, 2016) while the impact has been less scrutinized in the context
of the US economic history using modern quantitative methods. Ferrie (1996) finds a limited
effect of immigration on natives’ wages in the antebellum period 1850-1860. Two studies focusing on periods closer to mine are Goldin (1994) and Hatton and Williamson (1995), both
of which find significant negative impacts on wages. Goldin (1994) uses a panel of city-level
wages by occupation during 1890-1913 and corrects biases by controlling for city fixed effects.
Hatton and Williamson (1995) conduct the analysis using time series data. My study enriches
the understanding of the topic in the historical context with more comprehensive data and a
better identification strategy.
This paper is a policy evaluation of the historical immigration quota system that study not
only the effects of immigrants on wages, but also on the natives’ internal labor migration, the
technology adoption and skill intensity of the manufacturing sector. The history can provide insights for the contemporary policy discourse concerning the immigration system nevertheless
the impacts of the immigration quota system has not been thoroughly examined. The paper
establishes a clear causal relationship between the immigration restriction and the massive
South-to-North migration of the black population, usually termed as “the Great Black Migration", which improves on the analysis of Collins (1997). I also examine the impact of immigration on the technology changes of the manufacturing sector. There have been relatively few
studies on how the production responses to the immigrant labor supply shock. Lewis (2011)
finds that in modern US economy (1980s-90s) manufacturing plants in areas with a greater
unskilled labor supply adopt less machinery. I study the production technology changes in response to the immigrant supply in the historical context that contributes to the understanding
of its impact on the US industry development.
11
Especially in the Age of Mass Migration, immigrants were called “bird of passage", whose return migration
rate was high and sensitive to business cycles (Jerome, 1926).
5
To conduct the analysis, I compile manufacturing and demographic statistics from the
Twelfth (1900), Fourteenth (1920), and Fifteenth (1930) Censuses of United States. The countylevel statistics has largely been digitized by Professor Michael Haines and published on ICPSR
(Haines, 2010) and I also digitize the state-level manufacturing data by industry from the historical manuscripts of 1919 and 1929 Census of Manufactures for supplementary analyses. I
collect the annual immigrant inflow by source country from Annual Report of CommissionerGeneral of Immigration to the Secretary of Labor of various years and International Migration:
Volume I Statistics (Ferenczi and Wilcox, 1929). The key explanatory variable, foreign-born
share in each US state/county, is calculated from the full-count microdata of 1920 Census and
1930 Census released by IPUMS (Ruggles et al., 2016) which bears no measurement error so
the estimation is not subject to the attenuation bias (Aydemir and Borjas, 2011) that usually
arises when the sample is split into small cells (e.g. counties).
The IV estimates show that the decline in immigrant supply significantly lifts the local
manufacturing wage level that a 1-percentage-point decline in the foreign-born labor force
share in a county increases the wage level on average by about 2%.12 I find that a greater
decline in the foreign-born share causes a stronger inflow of the black population that a 1percentage-point decline in the foreign-born share causes the black population share to increase by about 0.5 percentage point.13 Regarding the industry development of the manufacturing sector, manufacturers in areas have a smaller firm size and lower electrification rate but
they have higher horsepower per worker, value added per wage worker, and skill to unskilled
labor ratio.
The rest of the paper is organized as follows. Section 2 recounts the establishment of the
US immigration quota system in 1920s and the consequential structural change of the immigrant composition. Section 3 describes the data source and the summary statistics. Section 4
illustrates the empirical framework and the construction of the instrumental variable. Section
5 presents the empirical results. Section 6 shows the results of robustness checks. Section 7
12
Compared to the wage effect identified in studies of modern US economy that is around 0 or weakly negative
(Friedberg and Hunt, 1995; National Academies Report, 2016), the negative effect identified in this paper is considerably stronger. National Academies Report, pp.183, Table 5-2 is a meta-analysis of the wage effect identified
in previous studies. The wage effect in my study translated into a comparable term to the table is around 10%
and the strongest effect in the table is 1.7%.
13
The evidence of natives’ responsive migration in modern US is still mixed: Card (2000), Card and DiNardo
(2000), Kritz and Gurak (2001), and Peri (2007) either find no relationship between immigrants’ entry and natives’
exit or find that they both move to same prospering areas. On the contrary, Borjas (2006) finds natives migrate
out of areas that immigrants migrate in.
6
makes the concluding remarks.
2
Historical Background
2.1
Immigration Quota System
Since its discovery, the New World had been a strong magnet that attracted migrants who
tried to break the shackles of poverty and sought prosperity. At the onset of the century, the
United States witnessed waves of new immigrants with more than 1 million flooding into US
annually at peak years (Figure 1). The term “new immigrants" originated from the fact that
immigrants in this period disproportionately came from the south and east of Europe that had
not been traditional sending countries. Impoverished and repressed in their home countries,
they fled to the US to seek jobs and chase the dream of “rags to riches". The immigration
was temporarily halted by the outbreak of World War I but it quickly resumed at a fast pace,
provided the prevalent social upheavals in Europe and the dissolution of the great empires.
In response to the numerous new immigrants flooding into the US, anti-immigration sentiments grew rapidly among both the public and the administration asserting the racial inferiority of new immigrants themselves and their negative influence on the country. In 1903
President Theodore Roosevelt stated the principle of US immigration policy in an address to
the US Congress: “We can not have too much immigration of the right kind, and we should
have none at all of the wrong kind. The need is to devise some system by which undesirable
immigrants shall be kept out entirely, while desirable immigrants are properly distributed
throughout the country."14 in which apparently the new immigrants from southern and eastern Europe were categorized as the “wrong kind" and “undesirable immigrants". The United
States Immigration Commission, known as the Dillingham Commission after its chairman
Republican Senator William Dillingham, was convened in 1907 to investigate the social and
economic consequences of immigrants from southern and eastern Europe. After four years of
work, the Dillingham Commission released a 41-volume report in 1911, concluding that southern and eastern European immigrants were unable to assimilate into the American society
and were detrimental to the US culturally and economically. The report advocated legislation
14
Congress Record 38:3 cited in Legislative History of American Immigration Policy: 1798-1965 by Hutchinson
(1981)
7
to restrict immigration. As the overture, the Immigration Act of 1917 (known as the Literacy
Act) was passed that required adult immigrants to take a literacy test and excluded immigrants from the “Asiatic Barred Zone"15 . Although the Literacy Act made little selection based
on ethnics or origins except for Asians, it selected immigrants in terms of “quality", which
intended to exclude more new immigrants because of their low literacy rate. Nevertheless,
the Literacy Act was proven ineffective, a fact backed up by the significant surge of European
immigration in 192116 following the temporarily suppressed inflows between 1918 and 1920
shown in Figure 1.
The support for more restrictive immigration policy led to the passage of the Emergency
Quota Act of 1921. The Act set the annual numerical limit to the immigrants admissible from
each country17 in Europe, Africa, and Oceania to be “...3 per centum of the number of foreignborn persons of such nationality reside in the United States as determined by the United
States Census of 1910" (US Department of Labor, 1922). Besides China and all countries in
the “Asiatic Barred Zone" who were already excluded by the Chinese Exclusion Act of 1884 and
the Literacy Act of 1917, the Quota Act of 1921 assigned quotas based on the same rule to a
few Asian countries, including Palestine, Syria, Cypruz (Cyprus), Hedjaz (Hejaz), Iraq, Persia
and Rhodes (Japan was self-excluded under the Gentlemen’s Agreement of 1907). The quota
of a country, if unfilled, could neither be rolled over to the next year nor filled by immigrants
from other quota countries. On the other hand, Canada, Mexico and South American countries
were exempt from the quotas. In the Emergency Quota Act of 1921 immigrants from quota
countries could be admitted after a full year of residence in the non-quota countries but in
1922 the requirement of length of residence was raised to 5 years.
The quotas were effectively enforced right after the passage of the Emergency Quota Act
of 1921. The total quota assigned to Europe was 357,803 by the Quota Act of 1921. The quotas of most European countries immediately became binding and the total inflow from Europe
dropped by half in the first year of enforcement18 . The maximum number of Immigrants ad15
It also required medical examinations and bans the entry of undesirable immigrants such as people who are
mentally or physically defective, criminals, alcoholics, prostitutes, polygamists, anarchists and so on.
16
When discussing the immigration of a specific year, this study refers to the fiscal year (FY), for instance
FY1921 as July.1 1920-June.30 1921.
17
The origin of an immigrant is defined in terms of nationality.
18
The total quota during the FY 1921-1922 was actually not fully exhausted mainly due to a significant number
of vacancies in the slots of Germany (28% filled, 19,053 used/68,059 total) and United Kingdom (55.2% filled,
42,670 used/77,342 total), who were both given generous quotas beyond the magnitude of their pre-WWI inflows.
8
missible each month was 20% of the total annual quota and the quotas assigned to many countries were so deficient to fulfill the potential immigration that they were quickly exhausted in
the first five months of a fiscal year.
The Emergency Quota Act of 1921 expired the next year and it was extended for two years.
The subsequent Immigration Act of 1924 made the quota system permanent and further tightened the regulation by setting the quotas as 2% of the foreign-born population from each country of the 1890 Census.19 Due to the lower proportion (2% instead of 3%) and the earlier
population base (1890 Census instead of 1910 Census), the total quota assigned to Europe was
reduced to 164,667. The Emergency Quota Act of 1921 and the Immigration Act of 1924 together marked the establishment of the immigration quota system that have operated for more
than 40 years until repealed by the the Immigration and Nationality Act of 1965. As shown in
Figure 1, European immigration to the US was greatly reduced under the quota system.
2.2
Structural Change of Immigration Under Quotas
The immigration quota system had an impact more than affecting the total magnitude of the
immigration. It fundamentally altered the composition of subsequent immigrant flows to the
US in terms of origins. The quotas were designed proportional to the Census population base
and they by nature limited immigrants from southern and eastern Europe more severely. Eastern and southern Europeans constituted the majority of the immigrant inflows in the pre-war
period but they had not built up a significant share in the foreign-born population in the US
yet when the quotas were imposed. The adoption of 1890 Census as the population base further tightened the grip on the inflows of southern and eastern European immigrants since the
majority of them arrived after 1890.20
As a quantitative measure of how much the immigrant flow from each source country was
affected by the quota, Table 1 shows the ratio of the annual quota to the average annual
immigrant inflow between 1900 and 1914 by sending country. Eastern and southern European
countries had been the primary sending regions in Europe during the first decade of the 20th
19
Although less mentioned, the second provision of the Act states the 2% quota of 1890 Census will be replaced
by a total annual quota of 150,000 in proportion to the inhabitants of the same national origin in 1920 Census,
effective from July 1, 1927. It was postponed and actually became effective in 1929.
20
Online Appendix Table A2 list the quotas assigned to countries according to the Quota Act of 1921 and the
Quota Act of 1924.
9
century and their quotas assigned by the Emergency Quota Act of 1921 were between 15%-34%
of the average pre-war annual inflows, except for Russian Empire (76%) and Romania (159%).
In contrast, western and northern European countries were granted quotas that are above 70%
of the past annual inflow, except for Belgium (33%). The German Empire and Switzerland were
especially favored with quotas that are 236% and 110% of the pre-war annual inflows. The
Quota Act of 1924 further cut down the quotas of eastern and southern European countries to
below 10% of the pre-war annual flows. The quota assigned to Romania was reduced from 158%
to 13% of the pre-war annual inflow. The most drastic case was Greece which had an annual
quota of 100 head counts assigned by the Quota Act of 1924 vis-a-vis an average annual inflow
of 8,507. On the other hand, although western and northern European countries experienced
the quota reduction as well, they were still assigned relatively generous quotas by the Quota
Act of 1924, mostly above 30% of the pre-war annual inflows.
2.3
Regional Variation of the Policy Shock
The design of the quota system furnishes a unique opportunity to identify the effect of immigration on the US labor market. The quotas effectively changed the ethnic structure of the
immigrant inflows and kept most new immigrants out of the US as the designer of the quota
system desired. The share of immigrants from eastern and southern European countries declined from more than 75% of the total European immigration in the pre-war period to less
than 40% (Online Appendix Figure B2). Although the quota system was a national policy, the
structural change of the immigration composition affected US regions differently due to two
distinctive features of US immigrants.
First, the distributions of immigrants from various countries showed a great disparity in
the locational preferences of residence and the level of concentration. Table 2 show the top 3
states and counties preferred by immigrants of each country in 1900 in terms of the residence.
The percentages are the shares of immigrants living in a state/county from a country in the
total immigrants in US from the same country. For instance, column 1 row 1 indicates 22.8% of
all immigrants from Austria-Hungary lived in New York state in 1900. As the table indicates,
eastern European immigrants exhibited a high level of concentration in the northeast industrialized area in 1900 and a considerably high share lived in New York City. Among eastern
10
Europeans, Polish had the most dispersed pattern of residence and there are 20% in Pennsylvania state, 18% in New York state, and 18% in Illinois state. The most concentrated group
is Romanian immigrants, more than 70% of whom lived in the state of New York and 64%
of whom lived in New York City in 1900. Immigrants from southern Europe resembled eastern Europeans who also preferred living in the northeast, despite that a substantial amount
of Portuguese and Spanish resided in California and Florida. Western and northern European immigrants were more evenly distributed across the US. Although they seemed to have
a greater tendency to live in New York City, it was much less emphasized compared to eastern
and southern Europeans and the higher shares were mainly due to the population size effect New York City is a huge city with a lot of immigrants living in. Interestingly, Scandinavians
had a strong preference for Minnesota and Wisconsin. It is worth noting that Southern states
and counties almost never show up in the table (except for Florida and Orleans, Louisiana)
and they were not major destinations of any immigrant group at the time except for Spanish.
The Mountain states also did not have a significant stock of immigrants in 1900.
Second, US immigrants tend to reside where old immigrants from the same country live,
as several studies show (Bartel, 1989; Dunlevy and Gemery, 1977; Gallaway, et. al., 1974;
Lafortune and Tessada, 2012). The strong tendency of newly comers to reside in established
ethnic enclaves have explanations including family reunion and the advantages of ethnic good
provision, social network that reduces the job search cost.21
Given the heterogeneous historical settlement patterns of immigrants and the serial correlation of immigrant inflows, areas as major recipients of immigrants from high-restricted
countries (countries that were more constraint by the quota, such as Italy, Greece) experienced
a stronger policy shock and a greater reduction of immigrant inflows. States and counties that
received more immigrants from countries less affected by the quota (such as Sweden) experienced a weaker policy shock and smaller reduction of immigrant inflows. Areas that used to
receive few immigrants (many counties in Southern states) in the pre-war period were also
less affected and experienced a weak policy shock. I exploit this variation and quantify the
exogenous policy shock to the immigrant supply of a region as the IV for the change in regional
foreign-born share.
21
As discussed above, this feature is used by Card (2001) to construct the shift-share instrument variable.
11
3
Data
3.1
Data Sources
The data used in this study is compiled from several sources. The lack of individual wage
data of a national coverage in this period poses a challenge to the study22 . I use manufacturing statistics by county from the 1919 and 1929 Censuses of Manufactures, as parts of the
Fourteenth (1920) and Fifteenth (1930) Censuses. The county-level manufacturing statistics
records the total wage bills paid to wage workers by the manufacturers of a county and the
average number of wage workers hired in the Census year. Accordingly I compute the average
yearly manufacturing wage in the county as a measure of the regional wage level. Counties
with missing manufacturing wage data in either 1920 or 1930 are drop and county boundaries
are adjusted to obtain a consistent panel of counties over time. At county level, the average
manufacturing wage is the only indicator of local wage level. For the supplementary analysis, I
also digitize the industry-by-state-level manufacturing data which contain both wages of wage
workers and salaries of salary workers of each industry in each state.
To obtain more detailed wage and salary information and indicators of manufacturing industry development, I also hand collect the industry-by-state level statistics from the 1919
and 1929 Census of Manufactures. I digitize statistics of number of establishments, total wage
bills, total salary bills, number of wage workers, number of salaried workers, total horsepower
of power equipments, electric horsepower, and value added of total products of each industry
in each state from the Census of Manufactures.
To obtain the foreign-born share in the labor force in each county/state, as well as other regional demographic characteristics including the African-American share, gender composition,
and age composition of the labor force, I use the new-released Integrated Public Use Microdata
Series (IPUMS) full-count (100%) samples of the 1920 and 1930 Census of Population (Ruggles et. al., 2016). The 100% samples yield no measurement errors in computing regional
shares and completely eliminate the attenuation bias (Aydemir and Borjas, 2011) that occurs
when the sample size in each cell is not large enough (especially in the county-level analysis).
I keep all individuals aged between 16-65, who are in the labor force and not self-employed
and aggregate to state/county level to construct the variables (urbanization rate%, African22
The Bureau of Census started collecting information of individual income as late as 1940.
12
American%, female%, age 16-24%, age 25-40%, age 41-65%) and match to the state/county
level manufacturing wage data. The urbanization rate is computed as the urban population
divided by the total population of a region. The African-American share is the percentage of
African-Americans in the total labor force. The female share is the percentage of female in
the total labor force. The age 16-24/25-40/41-65 share is the percentage of labor force aged
16-25/25-40/41-65 in the total labor force.
To construct the instrumental variable, I collect the annual immigrant inflow by the country of origin and the quotas from the Annual Reports of Commissioner-General of Immigration
to the Secretary of Labor of various years (1892-1929) and International Migrations: Volume
I Statistics by Imre Ferenczi and Walter Wilcox (1929). For more detailed description of the
data, readers can check the Online Appendix.
3.2
Descriptive Statistics
The summary statistics of all US counties between 1920 and 1930 are presented in Table 3.
The sample includes a total 2331 counties in 48 states (without Hawaii and Alaska) and District of Columbia. The South sample includes 992 counties in 14 southern states and the North
(technically, non-South23 ) sample is 1339 counties other than the South. The manufacturing
wage is calculated as the total wage bills paid to wage workers in the manufacturing sector
divided by the average number of wage workers employed in the census year. The manufacturing salary is calculated as the total salary bills paid to salaried workers divided by the
number of salaried officers and staff employed in the census year. The manufacturing wage
and salary are deflated to the 1920 level using the national CPI index. The manufacturing
industry characteristics include firm size (average employment size per establishment), horsepower per worker, and the electrification rate (the share of electric power of total horsepower
in the manufacturing sector), which are indicators of the production scale, technology level,
and skill intensity of the manufacturing sector. The firm size is calculated as the total number
of workers divided by the number of establishments. The horsepower/value added per wage
worker is the total horsepower/total value added of products divided by the number of wage
workers. The salaried worker to wage worker ratio is number of salaried workers divided by
23
For convenience, I use the term “North" to indicate any area outside of the southern economy, although strictly
it also includes the western and mountain states.
13
the number of wage workers. The electrification rate is computed as the percentage of electric
power in the total horsepower used in the manufacturing. The manufacturing industry composition is the employment share of each 2-digit industry in the manufacturing sector. The labor
force structure shows the share of each category (immigrant, black, female, literate, urban,
agriculture, manufacturing) in the total labor force aggregated from the IPUMS microdata.
[ Table 3 Here ]
The summary statistics shows that between 1920 and 1930 the real average manufacturing
wages grow substantially in the North by 20% but stagnates in the South. In contrast, the
manufacturing salary have significant increased in both the North and South. Thanks to
the immigration restriction, the foreign-born share of the labor force declines from 19.3% to
16.4% and the majority of the decline comes from the North since the South features little
immigrants in this period (3%). We observe a substantial growth of the black labor force share
in the North 3.3% to 4.1% and decline in the South from 32.2% to 29.5% as a result of the
Great Black Migration. Female labor force participation has increased in this period and the
literacy rate also increases, especially in the South. The growth of urban population share is
accompanied by the shrinkage of the agriculture sector. The South witnesses an expansion of
the manufacturing sector while the manufacturing sector share decreases slightly in the North
from 16.9% to 15.7%.
In this decade, the average firm size, the horsepower per wage worker, the value added per
wage worker, and the electrification rate in the manufacturing sector all grow substantially in
both the South and the North. But the industry composition shows that the manufacturing
sector in the South is dominated by low-value-added, low-skilled industries such as textile
(19.4% to 27.6%) and forest products (29.6% to 26.2%) and the growth of high value-added
industries is slow except for the petroleum industry. The Northern manufacturing sector have
significantly greater shares of more skilled industries such as printing and publishing and
metal industries.
4
Empirical Framework
Before getting into the formal construction of the instrumental variable, I first present the
conventional framework to estimate the local effect of immigration on wages and discuss the
14
endogeneity problem.
4.1
Identification Problem
First consider a simple cross section to evaluate the effect of the immigration on wages:
Ys = α
I Ms
+ β X s + εs
LF s
(1)
I Ms
is the foreign-born share of the labor
LF s
force in region s. Note that in the regression I use the foreign-born share of the total labor force,
where Ys is the log average wage of the region s.
rather than the foreign-born share of the population because the former better characterizes
the labor supply shock of immigrants. X i is a set of controls of region s. εs is the random error
term.
The endogeneity problem arises because immigrants are likely to be attracted to where
wages are higher and the regional characteristics correlated to higher wage are unobservable.
In this case, OLS estimates erroneously show a spurious positive correlation of the foreign-born
share and wages in cross section.
At the presence of repeated cross sections in two (or more) periods, one way to alleviate the
endogeneity issue is to do first-difference:
Yst − Yst−1 = α(
I M st I M st−1
−
) + β( X st − X st−1 ) + εst − εst−1
LF st LF st−1
(2)
Although the time-invariant component is canceled out in the error term, the first-difference
estimator could still be biased if the immigrant share is correlated to the time-variant unobservables in the region (for instance, a booming labor market). Here I develop a new instrumental variable that measures the intensity of the policy shock to each state/county that caused
the exogenous decline in the regional immigrant share.
4.2
Instrumental Variable
To measure the policy shock to each state/county, I estimate the immigrants “lost" to each
state/county due to the immigration quotas, i.e. those who could have come to each state/county
but were excluded. I first project the “no-quota" counterfactual immigrant inflows, assuming
15
no immigration quotas had been imposed in the 1920s. During the Age of Mass Migration,
the immigration from a country to US represents a general pattern of 3 phases - “growthsaturation-regression". It is first observed among Sweden emigrants (Runblom and Norman,
1976) and later generalized by Massey (1988). To simply illustrate, the long-term pattern
of immigrant inflow shows the form of a trajectory (Hatton and Williamson 1998) and the
quota system was established at the downswing of the cycle of traditional immigrants and the
upswing of the cycle of new immigrants (See Figure 2). The preferred forecasting method is
the 3-order polynomial (cubic) curve-fitting to estimate the possible non-linear trend of the
immigrant flow. Although the cubic curve-fitting is crude in capturing short-term fluctuations
and cycles, it is less of a problem here because the number of lost immigrants in the decade
will be aggregated and the year-to-year fluctuation will be smoothed out anyway.
Using the cubic curve-fitting, I estimate the immigrants by source country using inflows
between 1879-1914 and project the no-quota counterfactuals starting from 1915 to 1930. I
discard the immigrant inflows during the World War I and post-war years before the quota
(1915-1921) because the warfare caused the serious interruption of immigration to the US.
Including these years of abnormally low level of immigration in estimation causes the prediction biased downwards and attenuates the difference of predicted inflows between southern &
eastern Europe and northern & western Europe (including Scandinavia). In this sense, the
counterfactuals can be considered as “no-quota and no-WWI" immigrant inflows. Nonetheless, the structural break of immigration flow caused by the WWI, if any, is exogenous. Its
impact on immigration either plays the same role as the quotas if they dictate the immigration to the same direction or can be canceled out by the quotas if they go opposite. So not
taking the WWI into consideration should not undermine the effectiveness of the instrument.
Nonetheless, only using pre-1914 immigrant inflows in cubic curve-fitting in some cases yields
an accelerating growth of immigration that rockets to an unrealistic level. The immigrant inflow in 1921, a year before the quotas are enforced, appears to have recovered to a “normal"
level. Based on the consideration that the immigrant inflow of 1921 is indicative of the level
of post-WWI immigration without quota, I include the 1921 inflow together with 1879-1914
in the cubic curve-fitting estimation to serve as an “anchor" of the trend of the counterfactual
inflow. Practically I regress the immigrant inflow by country on time and time squared from
16
1879-1914:
I t = α0 + α1 t + α2 t2 + α3 t3 + ε
The immigrant inflow from 1922 to 1929 is projected as:
Î t = α̂0 + α̂1 t + α̂2 t2 + α̂3 t3
The projected immigrant inflows as the 3-order polynomial of the time variable are plotted by country in Figures A1-A10, together with the actual inflows and annual quota. When
including the 1921 inflow, the cubic curve-fitting simulates an stylized declining trend in the
immigrant inflows of most eastern and southern European countries (except for Romania, Portugal, and Spain). In the cases of Romania, Spain, and Portugal, the trends are lifted due to
a spike of inflow in 192124 . The trends among western and northern European countries are
more diverse. The Swedish immigrant inflow has a downward trend as Runblom and Norman
(1976) shows that it is in the phase of decline. On the other hand, other western and northern
Europe countries show modestly growing immigration. Incorporating the inflow of 1921 in the
curve-fitting makes a substantial difference in several countries.
In spite of the cubic curve-fitting, I also implement several other techniques based on
different implicit assumptions of the trend and cyclicality of the immigrant flow, including
quadratic curve-fitting (2-order polynomial), 10-year moving average of the pre-war flow, fixed
level equivalent to 1921 level, Holt-Winters exponential smoothing to test the sensitivity of
the instrumental variable to the construction of the counterfactuals. The detailed description
of the techniques and the graphs of projected inflows vs. actual inflows using all forecasting
techniques are presented in the Online Appendix. The results are discussed in the section of
robustness checks.
In the comparison of the projected inflow and the actual inflow by country, a pattern
emerges that eastern and southern European countries experienced a great reduction of immigrant inflows due to the quotas, indicated by the gap between the counterfactual inflow and the
actual inflow. It means that a large number of immigrants from eastern and southern Europe
are lost because of the implementation of the quotas. On the other hand, the differences be24
Spanish and Portuguese immigrant inflow maintain at a high level even during the WWI possibly due to the
geographic advantage that grants better access to the trans-Atlantic passage.
17
tween the counterfactual and the actual are much more nuanced among western and northern
European countries.
As a result, the quota system causes a large number of lost immigrants from eastern and
southern Europe and a relatively small number from western and northern Europe between
1920 to 1930. The total number of lost immigrants (L i ) in this decade are calculated as the
sum of the counterfactual inflow Î it minus the quota Q it of each source country i during 1922192925 :
Li =
1929
X
max(( Î it − Q it ), 0)
t=1922
To evaluate the policy shock at regional level, the aggregated lost immigrants are allocated to each state/county based on the historical distribution of the foreign-born population
by source country in 1900 US Census. Specifically, suppose I M is is the number of immigrants
from source country i who live in region s in 1900, the share of immigrants from country i in
each state/county s of the total number of immigrants from the same country i living in the US
P
(I Mi ≡
I M is ) can be calculated:
s∈ S
I M is
P is = P
I M is
s∈ S
With the distribution ( I × S ) matrix of immigrants, the lost immigrants from each source country assigned to each state/county s (denoted as P s ) is26

·
∆ I 1 ∆ I 2 ... ∆ I I −1

P11
¸

 ..
∆I I 
 .

P I1
Ls ≡
· · · P1S 
¸
 ·
.. 
..
= L 1 L 2 ... L S −1 L S
.
. 


· · · P IS
X
P is × ∆ I i
i∈ I
Scaled by the population of state/county s in 1920 to account for the regional variation of
Ls
the population size, the regional policy shock is eventually constructed as
. I use it
POP s1920
as the instrumental variable for the change in a state/county’s foreign-born share in the first
stage regression.
25
In the case that the counterfactual inflow is lower than the quota, I set the number of lost immigrants to zero
assuming no negative loss (gain) of immigrants due to the quota.
26
Though the idea of the instrument is different, the construction of the instrument resembles the construction
of Card’s instrument.
18
The lost immigrants as a share of the total population in 1920 at state level and county level
are depicted in Figure 3 and 4 as heat maps in comparison to the actual change in foreign-born
share between 1920 and 1930. Darker colors indicate more “lost" immigrants (normalized)
in Figure 3a and 4a and greater declines in the foreign-born share in Figure 3b and 4b. For
instance, many counties in New York, Illinois and Massachusetts have more “lost" immigrants
for they historically had more immigrants from high-restricted sending countries. Most areas
in the South are minimally impacted for their low shares of immigrants except for several
counties in Texas and Florida. It is notable that the maps of “lost" immigrants and actual
foreign-born change have a very comparable pattern, in particular at county level, indicating
a high correlation of the foreign-born share change and the IV.
4.3
Regression Specification
With the instrumental variable, the analysis implements the 2SLS regression using the following specification:
In the first stage, the change in the foreign-born share in state/county s is regressed on the
instrument, “lost" immigrant share in the 1920 population in state/county s and the covariates
Ls
:
X,
POP s1920
I Ms
Ls
∆
= c+γ
+ λ∆ X s + θs
LF s
POP s1920
The sign of γ̂ is expected to be negative since a stronger regional policy shock (a larger number
of immigrants lost to a region due to the quotas proportional to the region’s population) is likely
to cause a greater decline in the region’s foreign-born share.
The second stage is a first difference regression between 1920-1930 weighted by the regional employment size:
∆Ys = α∆
I Ms
+ β1 ∆ X s + ωs
LF s
The regressions are mainly conducted at county level and state level but mainly focus on
the county-level analysis given the much larger sample size. Besides it avoids the possibility
of idiosyncratic trend of some states that may drive the results.
The dependent variable ∆Ys in the first part of the empirical analysis is ∆ ln(wage)s , the
change in the log average manufacturing wage in state/county s between 1920 and 1930. Later
19
when analyzing the impacts of immigrants on the black migration and the industry development, the outcomes of interest are the black labor force share (various groups of black), firm
I Ms
is the change of the foreignsize, horsepower per worker, and value added per worker. ∆
LF s
born share in the labor force in state/county s between 1920 and 1930. I M s represents the total
number of immigrants living in state/county s and LF s is the total labor force in state/county
s.
∆ X s are first-differenced control variables in state/county s between 1920 and 1930. I
control for the urbanization rate to account for the higher living standards in urbanized areas.
I also control for firm size and electrification rate to account for the effect of different production
methods on wages27 . The percentage of female, percentage of workers aged 16-24, percentage
of workers aged 41-65 in the labor force are controlled for to take into account the effect of the
gender and age compositions on wages. The data to construct the variables is described in the
next section. I further add the dummy of being part of the Southern economy28 , considering
that the South and the rest of the country were highly segmented in this period (Rosenbloom,
1996). The postbellum South lagged behind the rest of the US to a great extent (Wright,
1987). The manufacturing wage level was significantly lower than in the northern and western
industrialized area and it was a less welcomed destination of European immigrants in general.
Failing to take the systematic non-South/South difference in terms of both wage level and
immigrant penetration into account may lead to biased estimates.
5
5.1
5.1.1
Empirical Results
Manufacturing Wages
County-level First Difference WLS and 2SLS
Table 4 shows the estimates of first difference WLS in columns1-5 and 2SLS in columns 6-10
at the county level29 . The regressions are weighted by the total employment in the manufac27
Devine (1983) shows that electrification is closely associated with the reorganization of factories and new
production methods. Gray (2015) shows that electrification significantly changed the task contents of workers.
28
The South includes 16 states in South Atlantic and South Central regions, which are Virginia, Alabama,
Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Texas, Kentucky, Oklahoma,
Tennessee, West Virginia. Although included in the South Atlantic region, Maryland and Delaware are typically
not considered part of the southern economy.
29
The cross section WLS results are presented in the Online Appendix.
20
turing sector of the state/county to account for the size of the regional manufacturing sector30 .
Column 1 show the estimates of the basic specification with foreign-born share as the only
explanatory variable. From column 2 urban population share, female share, and age structure
of the labor force are added. From column 3 the southern economy dummy is added. Column 4
and 5 further add industry characteristics and the industry composition. Columns 6-10 show
the 2SLS estimates of specifications in the same order.
[ Table 4 Here ]
In columns 1-5, the estimates of WLS regressions show a slightly negative and statistically
insignificant effect of immigrants on the wage level and the effect further declines in magnitude after adding controls of the labor force demographic structure, southern dummy. The
female labor force share is negatively associated with the regional wage level and the southern
economy shows a significantly lower wage growth.
In Table 4 column 1 shows the first difference approach corrects the time-invariant component in the error term that is associated with the foreign-born share. The coefficient in column
2 becomes significantly negative after controlling for a set of control variables. When columns
3-4 control for the dummy of Southern state, the coefficient of the foreign-born share becomes
less negative and insignificantly different from zero, which indicates that the negative coefficient in column 2 is primarily driven by the South/North difference.
Columns 6-10 show that 2SLS estimates with the foreign-born share instrumented by the
lost immigrant share. The first stage results are presented in the lower panel. The F-statistics
and the statistically significant (most at 1% level) coefficient of the “lost" immigrants indicate the instrumental variable is asymptotically valid with a high correlation with the actual
change in the foreign-born share. The negative coefficient is consistent with the intuition that
more immigrants “lost" to a region should cause a greater decline in the immigrant stock of the
region. After the foreign-born share is instrumented, the coefficient shows a very significant
and negative effect of the foreign-born share on wage ranging from -4.5 to -2.1. The “raw" effect
is about -4.5 and it declines to -3.5 after controlling for the urbanization rate and demographic
structure of the labor force in column 7.
After adding the South dummy in column 8, the coefficient of the foreign-born decreases to
30
For 1920 and 1930 cross section regressions, the weight is the region’s total number of wage workers in the
corresponding year, denoted as W orkers i,1920 and W orkers i,1930 . For the first difference regressions, the weight
is 1/(1/W orkers i,1920 + 1/W orkers i,1930 ).
21
-2.5 and the coefficient of the South dummy is -0.131 and statistically significant. It shows that
about one-third of the effect observed in column 7 is due to the gap in wage growth between
the South and North, which cannot be attributed to the difference in the foreign-born share
given the vastly different economic structures of the two segmented regions.
In columns 9 and 10, I add the industry characteristics and industry composition to reflect
the effect of different skill intensities, production technology and industry compositions of the
manufacturing sector on the regional wage level. Industries with advanced production technology employ greater proportions of skilled workers and offer higher wages as Goldin and
Katz (2007) finds that industries with a higher electrification rate/horsepower per worker hire
greater shares of high school graduates. The shifts of the manufacturing industry composition also have a non-negligible effect on the regional average wage due to the difference in the
skill/technology content of the industries31 . The dominant industries in the South are textile
and forest products with low value-added that remain a significant share of the southern economy in this decade. The inclusion of the industry composition in the regressions reduces the
coefficient to about -2.1. On the other hand, the industry shift can also be viewed as a partial effect of immigrants as Heckscher-Ohlin model indicates32 . Depending on whether taking
industry shifts into account, a 1-percentage-point decline in the foreign-born share of the labor force significantly increases the local average manufacturing wage by 2.1%-2.5% at county
level.
5.1.2
Alternative Forecasting Techniques
One concern of the identification strategy is whether the estimation is sensitive to the forecasting technique chosen to construct the IV. In Appendix Table A1, I show the 2SLS results
using different forecasting techniques at county level. The corresponding forecasted inflows
are plotted and presented in the Online Appendix. I also estimate the coefficients using Card’s
instrument. Table A1 shows that different forecasting techniques yield close 2SLS estimates
at county level of the wage effect clustered around 2%. It shows that the results are very robust to the choice of the forecasting technique at county-level. In addition, it is notable that
31
The cross section OLS results in the Online Appendix show that there are substantial inter-industry wage
differentials which significantly affect the regional wage level.
32
A multi-sector economy could shift industries of different factor intensities to absorb the change of relative
supplies of factors and keep the relative prices constant within the “cone of diversification".
22
Card’s instrument is relatively weak in the context here and produces fluctuating and insignificant coefficients. The explanation is likely that European immigrant inflows are minimal in
magnitude due to the restriction and the unrestricted inflows of Mexicans are highly driven
by the economic opportunities, which causes the mechanism (ethnic-enclave effect) behind the
Card’s instrument to fail and produces only a weak correlation. The IV I use does not include
unrestricted immigrants which circumvent this problem and performs better than Card’s instrument.
5.1.3
Industry-by-State-Level Analysis
The county-level manufacturing data contain only the average wage of wage workers so I supplement the analysis with industry-by-state-level regressions with more detailed information.
The industry-by-state-level statistics from the Censuses of Manufacture have the average wage
of wage workers and average salary of salaried officers/staff of each industry in each state. I
conduct analysis in the specification as follows:
∆Yis = α∆
I Ms
+ β1 ∆ X s + β2 ∆ Z is + D i + ε is
LF s
Each observation is an industry i of a state s. ∆Yis is the average wage or salary of inI Ms
dustry i in state s. ∆
and ∆ X s are the foreign-born labor force share of state s and state
LF s
characteristics. ∆ Z is are covariates of the industry i ’s characteristics in state s (firm size;
electrification rate; horsepower per wage worker/staff). D i is the industry dummy to capture
industry-specific time trend. ε is is the error term. In 2SLS regressions the foreign-born share
is instrumented by the “lost" immigrant IV constructed at state level.
The 2SLS results are presented in the Appendix Table A2. The results confirm that immigrants have a strong and negative effect on the wages of wage workers. The additional results
of the effect on salaries show that immigrants also negatively affect the salaries of salaried
workers.
23
5.1.4
Discussion
The wage impact is exceptionally strong compared to what has been identified in the studies of
the modern US economy, most of which are less than -1.33 For historical studies, Goldin (1994)
finds a similar wage effect in US cities in the pre-quota period34 . If we assume that immigrants
on average ($450) earn 25% less than natives ($600) at the time based on the statistics from the
Dillingham Commission report, the corresponding composition effect is around -0.26%, which
makes the wage effect slightly lower but still very strong.
The Local Average Treatment Effect (LATE) of the instrumental variable indicates the estimated wage impact is that of those local labor markets where the foreign-born share declines
due to the loss of southern and eastern Europeans caused by the policy. Southern and eastern
European immigrants have a greater wage disadvantage35 to natives than immigrants from
western and northern Europe, thus the composition effect could be more severe. If southern
and eastern European immigrants have a 50% disadvantage in earning to natives, the composition effect is -0.56%, which helps explain part of the effect but the pure wage effect remains
strong.36
5.2
The Great Black Migration
In this section I examine the effect of immigrants on the migration of the black population.
The decade 1920-1930 witnesses the first wave of the Great Migration of the black population
who leave the South and settle in the northern urbanized areas. Figure 5 shows the change in
the black labor force share in all states/counties between 1920 and 1930 where blue indicates
the increase of the black share in the area and red indicates the decline. Most Southern states
have a significant decline in the black share while the North experiences a substantial gain
of the black population. Looking at the county level we observe there is also substantial migration within the southern states, typically from rural counties to urbanized counties and the
33
The studies of modern US that identify the wage impact of a similar size are Altonji and Card (1989) and
Llull (2015), both of whom find -1.2.
34
A caveat should be made that because the limitation of the wage data I cannot distinguish between the
natives’ wages and immigrants’ wages. Thereby the effect of immigrants on wages in both my study and Goldin
(1994) is exaggerated by the composition effect. The composition effect is that If immigrants have lower wages
than natives, the regional wage level could be lower where the share of immigrants is higher, solely because the
immigrants drag down the average.
35
Serbian immigrants have an average earning as low as $212.
36
A table of wage disadvantage and corresponding composition effect is furnished in the Online Appendix
24
tips of Florida and Texas. The timing of the first wave of the Great Migration coincides with
the downturn of immigration from Europe and scholars have attempted to establish a causal
relationship between the two, termed as the "immigrant-as-deterrent" hypothesis (Thomas,
1954). Collins (1997) use state level and city level data in northern states from 1870-1950 and
concludes that black workers seek job opportunities where there are less competitions from
immigrants. I revisit this hypothesis by furnishing a more detailed dataset at county level and
a refined identification strategy37 to explore a causal relationship between the immigration
quota system and the Great Black Migration. With the more comprehensive data I am able
to explore several dimensions of the responsive migration of the black population to provide a
fuller picture.
I estimate the effect of immigrants on the change in the black labor force share and the
results of WLS and 2SLS estimates are presented in Table 5. Columns 1-5 present the WLS
results and columns 6-10 present the 2SLS results at county level. In Panel.A the dependent
variable is the black labor force share and in Panel.B the dependent variable is the labor force
share of the black population born in the South only to study the effect on the migration of
black only from the South.
[ Table 5 Here ]
In Panel.A, the WLS estimates show negative and statistically significant coefficients of 0.11 to -0.15 in columns 1-3. When splitting the sample into North and South, the coefficient of
the North sample is -0.11 and -0.44 of the South sample. The comparison to the 2SLS estimates
show that the WLS estimates are biased upward because both immigrants and black migrants
tend to cluster in areas with booming labor markets, especially in the North sample. The
coefficients from 2SLS estimates are around -0.51 to -0.57. The interpretation of the coefficient
is that a 1-percentage-point decline in the foreign-born share of a county increases the black
share of the labor force by about 0.51 to 0.57 percentage point. It is noteworthy that the WLS
estimates of the South sample show little bias as the North sample, implying that immigrants
residing in the South were potentially less mobile.
Focusing on the responsive migration of the black population born in the South, Panel.B
shows that the southern black population also responses to the decline in immigrants in the
37
Collins (1997) uses the foreign-born population share in the state as the instrumental variable of the number
of foreign migrants, which is still subject to the endogeneity problem.
25
labor market but the coefficient was reduced by about -0.2. It indicates that about half of the
black migration in response to the immigrant decline is from the migration of the blacks who
are born outside of the southern states and move to pursue job openings after immigrants are
restricted to enter.
5.2.1
The Effects on Different Black Groups
Figure 6-7 show the estimated effects of immigrants on different black groups defined in terms
of literacy, occupation, gender, and birth cohorts, with 95% and 90% confidence intervals. The
results show that the literate but unskilled blacks most significantly response to the immigrants’ decline and migrate, either to the North or within the South. The black women are
even more, although not significantly likely to migrate than black men. The effects on different black cohorts in Figure 7 show that only young black cohorts (1890-1894; 1895-1899;
1900-1904) significantly move to areas with significant immigrant declines and the results are
consistent across different groups.
5.2.2
Discussion
The results indicate that overall there is a substantial flow of the black population from the
South to the North (indicated by the significantly negative constant) and the black population respond to the immigration decline by migrating to areas that experienced greater policy
shocks. About half of the migration is the migration of the black out of the South and the other
half is the migration of the black who already have lived in the North. Within the South there
is also substantial black migration to more urbanized areas. The black population who migrate
responsively are young, literate, unskilled, and balanced in sex.
5.3
Industry Development
Despite labor mobility, marginal adjustments in response to the immigrant shock can take
place on the production side, such as non-Hicksian neutral technology advances (Lewis, 2013).
In the modern context, Lewis (2011) finds that producers in regions facing less immigrant labor
supply adopt more automated machines that substitutes unskilled labor relative to skilled labor. I examine whether the decline in immigrant supply affects the local industry development
26
by examine the effects of immigrants on a set of indicators of the firm organization and technology adoption, including the log form of firm size, horsepower per wage worker and value-added
per wage worker. The historical manufacturing statistics contains no direct measure of the
production technology. I use the horsepower per wage worker as an indirect, continuous measure of the technology that complements labor because Jerome (1934) suggests that a higher
horsepower per wage earner is associated with more utilization of inanimate power and automated machines, such as convey belts, to increase the marginal productivity of laborers, which
is considered an indicator of the capital-intensive and labor-saving production. I use the value
added per wage worker as an indicator of the labor productivity.
Table 6 presents the WLS estimates and 2SLS estimates. Columns 1-2 present the results
of WLS regressions and columns 3-7 present the results of 2SLS regressions. Panel.A use the
log firm size as the dependent variable. Panel.B uses the log horsepower per wage worker as
the dependent variable. Panel.C uses the log value added per wage worker as the dependent
variable.
[ Table 6 Here ]
Panel.A shows that the decline in immigrant supply significantly constrains the growth of
the firm size that a 1-percentage-point decline in foreign-born share causes the firm size to
decrease by more than 10%. On the other hand, Panel.B shows that the declined immigrant
supply increases the horsepower per wage worker significantly. After controlling for the effect
of industry shift, a 1-percentage-point decline in foreign-born share increases the horsepower
per wage worker by about 4%. In Panel.C, I show evidence that valued added per worker
increases by 1.1%-2.6% as well when the foreign-born share declines by 1 percentage point
although in columns 6-7 the effect is not statistically significant.
5.3.1
Industry-by-State-level Analysis
Table 7 show the industry-by-state level analysis with a richer set of outcomes (including the
horsepower/electric power/value added per salaried worker, electrification rate, etc.). The results of firm size (Column 1)/HP per wage worker (Column 2)/value added per wage worker
(Column 4) are consistent with the county-level analysis. Column 3 shows that however there
is neither evidence of the growth in the horsepower per wage worker in the form of electricity.
27
There is neither increased electrification rate in response to the negative immigrant supply
shock. Instead, the lessened immigrant supply results in the slowdown of the electrification
process. On the other hand, there is no significant increase in the horsepower and value added
per salaried worker in columns 5-7. In column 8 we witness a significant increase in the
salaried-worker-to-wage-worker ratio as a rough measure of the skilled-to-unskilled ratio.
[ Table 7 Here ]
5.3.2
Discussion
The results of Table 6 and 7 indicate that the decline in immigrant labor supply restricts
the expansion of employment size of manufacturing firms possibly because of the labor supply shortage or the rising labor cost. Firms in response shift towards more capital-intensive
production and tend to invest more in traditional mechanized equipments that increases the
productivity of manual labors, which is reflected by the increased horsepower and value added
per wage worker. On the other hand, the adoption of electric power is considered the advance
in production technology and firm organization in general. The stagnation of the electrification
process shows the lack of incentives of technology advances as the new production process may
require more unskilled workers (Gray, 2015).
6
6.1
6.1.1
Robustness Checks
Threats to IV Identification
Sensitivity to Projection
One concern of the instrumental variable that has been fully discussed before is whether the
validity is subject to the different forecasting techniques (depending on different assumptions)
and the corresponding projected inflows. Although the forecasting approaches yield different
projections, the results are quite robust to the choice of the forecasting method. The reasonable explanation is that the IV successfully captures the strong restriction on the eastern and
southern European immigrants and the weak restriction on the western and northern European immigrants imposed by the quotas, regardless of the forecasting methods. (Figures of
projections using alternative forecasting methods in Online Appendix)
28
6.1.2
Low-Skilled Industry Concentrated Regions
Eastern and southern European immigrants are more negatively selected than traditional
immigrants that they are relatively poorer, less skilled and educated. There is a concern that
areas with eastern and southern Europeans concentrated have a great proportion of industries
with an intensive use of unskilled workers (e.g. textiles). Although I control for industry composition in the county-level analysis and industry-specific trend in the industry-by-state-level
analysis, areas with concentrated unskilled-intensive industries may experience a common
shock that contaminates the identification and is not fully captured by the control variables.
Therefore I drop counties in the 10th decile of the “lost" immigrant/drop both 1st and 10th
decile/keep only 10th decile as the subsamples to test whether the results are driven by the
difference of unskilled-intensive industry regions and other regions. The results are fairly
stable across subsamples.(Table.C3 in Online Appendix)
6.2
Sub-Region Analysis and Region-Specific Trends
I test the robustness of the results in different subregions (excluding the South/excluding the
South and West/Only North and Midwest/Only South) and the results outside the South are
similar to the results of full sample without no statistical difference. The South is somehow
distinctively different in the effects, which worth further exploring the difference in mechanisms. One potential suspect is that the migration of black population rapidly respond to fill
in the gaps while the loss of immigrants in the South is less severe.
I also add the state-specific trend and the census region-specific trend38 in the regressions.
Adding the census region-specific trend yields similar coefficients. Adding state-specific trend
reduces the coefficient by 30% since the state-specific trend absorbs part of the wage effect from
the difference in the foreign-born share at state level. Overall the effect is not sensitive to the
different samples and adding regional-specific trends. (Table.C4-C5 in Online Appendix)
6.3
New York City and Other Major Ports
The distribution of immigrants shows that a great share of them regardless of the home country choose to live in the New York City (Table 2), New York City experiences a strong negative
38
Five census regions are: Northeast, Midwest, South Atlantic, South Central, and West.
29
shock after the quota system is operated. Given its considerable size of the manufacturing
sector in New York City, one may concern that the wage effect is driven by some idiosyncratic
shocks to the wage level in New York City. I drop the New York City and I also drop the
ports that are major gateways of immigrants (NYC, Boston, Providence, and Key West) and
the estimates are not statistical difference. (Online Appendix).
6.4
Railroad Access
Still geo-referencing the railroad access in 1920 using GIS. (TBA)
7
Concluding Remarks
This study examines the causal effect of the decline in immigrant labor supply due to the immigration restriction on the US manufacturing wages, the Great Black Migration, and the industry development of the manufacturing sector from 1920 to 1930. The passage of the Emergency
Quota Act of 1921 and the Quota Act of 1924 mark the establishment of the immigration quota
system that sets an annual quota for immigrants from every sending country. The quotas are
determined proportional to the population base of an earlier Census (1920 Census used in the
Act of 1921 and 1890 Census in the Act of 1924). As a consequence, the quotas greatly reduce
the total immigrant inflows to US and lead to a significant structural change of the immigrant
composition. Since the immigrants before the quota system are disproportionately souther and
eastern Europeans, the design of the quotas significantly curtails the majority of the inflows of
eastern and southern European immigrants. Meanwhile it imposes a mild restriction on immigrant inflows from western Europe and northern Europe. Because immigrants tend to settle in
where old cohorts of same origins used to live and the settlement patterns of immigrants from
different European regions are distinctively different, the quota system causes differentiated
policy shocks to various US regions in terms of reducing immigrant inflows to the region.
I quantify the regional policy shock by estimating the number of immigrants “lost" due to
the quota system as IV for the change in the foreign-born share in a region to identify the
effects of immigrants. The IV estimates show a statistically significant and strongly negative
effect of immigration on the local manufacturing wages during 1920-1930. Controlling for
the North/South difference, the wage effect of immigration is around -2 at county level. The
30
coefficient is interpreted as that a 1-percentage-point decline in the foreign-born share in a
county caused by the quota system increases the manufacturing wages on average by 2% at
county level. The results are not sensitive to alternate forecasting techniques to construct
the instruments and using subsamples. It is remarkably stronger than the effects found in
most modern studies. The effect of immigrants on wages I document is significantly stronger
than effects typically identified in contemporary literatures. The potential explanations of the
discrepancy can be the difference in the labor market integration or industry characteristics
including the adjustment of production technology and it worths exploring more closely in
future research.
I also find substantial evidence of migratory flows of the black population in response to
the lessened competition of immigrants, which confirms and further extends the findings of
Collins (1997). It is shown that the decline in foreign-born share in a county induces a stronger
inflow of the black population from both the South and North. A 1-percentage-point decline
in the foreign-born share causes 0.5 percentage point increase in the black labor force share,
about half of which are from the migration of the southern black population and the other half
was from the redistribution of the black population who are born in the North. The migrant
group who seek the job opportunities opened up by the absence of immigrants are constituted
by literate, young blacks who have unskilled occupations and are close substitutes of those
excluded new immigrants because of the quota system.
There is also evidence that manufacturers make adjustments to cope with the decline in
immigrant supply. The firm size increases slower in areas with a greater supply shock. Firms
tend to adopt likely more capital-intensive production methods that increases the horsepower
per wage worker and the value added per wage worker. On the other hand, they are discouraged to adopt new technology indicated by the electrification rate that is associated with a
thorough renovation of the establishment and the intensive use of unskilled workers.
To conclude, the quota system imposes a strict restriction on immigrant inflows to the US
and causes a significant decrease in the foreign-born share of the US population. It has a strong
impact in the labor market of the manufacturing sector and increases the manufacturing wage
level substantially. In the background of the Great Black Migration, it also accelerates the
migration of black population to Northern industrialized areas that experience a more signif-
31
icant decrease in immigrant labor supply. The production side adjusts as well in response to
the decline in immigrant labor supply by adopting technology more complementary to labor.
The study reaffirms the findings of previous historical studies and justifies the prevailing assertion at the time that immigrants are negatively affecting the wages. As a policy evaluation,
the study shows that the immigration regulations have substantial effects on the US manufacturing labor market in various aspects. It has favored the working class of both natives
and old immigrants by significantly lifting the wage level and “pulling" the black population
out of the lagged southern economy. The impact on the industry development is nonetheless
not monotonic - on one hand, it enhances the capital intensity of the production that increases
the labor productivity of unskilled labor, echoing Hornbeck and Naidu (2014) who find that
the agriculture production becomes more capital-intensive after the out-migration of the black
population because of the Mississippi flood; on the other hand, because of the constraint of unskilled labor supply, it appears to hinder the development of production scale and the adoption
of new production technology.
32
References
[1] R. Abramitzky, L. P. Boustan, and K. Eriksson, “Europe’s Tired, Poor, Huddled Masses:
Self-Selection and Economic Outcomes in the Age of Mass Migration,” American Economic
Review, vol. 102, no. 5, pp. 1832–1856, 2012.
[2] R. Abramitzky and L. P. Boustan, “Immigration in American History,” Journal of Economic Literature, Forthcoming.
[3] J. D. Angrist and A. D. Kugler, “Protective or Counter-productive? Labour Market Institutions and the Effect of Immigration on EU Natives,” The Economic Journal, vol. 113,
no. 488, pp. F302–F331, 2003.
[4] A. Aydemir and G. J. Borjas, “Attenuation Bias in Measuring the Wage Impact of Immigration,” Journal of Labor Economics, vol. 29, no. 1, pp. 69–112, 2011.
[5] A. P. Bartel, “Where Do the New US immigrants Live?,” Journal of Labor Economics,
pp. 371–391, 1989.
[6] G. J. Borjas, “Native Internal Migration and the Labor Market Impact of Immigration,”
Journal of Human Resources, vol. 41, no. 2, pp. 221–258, 2006.
[7] D. Card, “The Impact of the Mariel Boatlift on the Miami Labor Market,” Industrial &
Labor Relations Review, vol. 43, no. 2, pp. 245–257, 1990.
[8] D. Card, “Immigrant Inflows, Native Outflows, and the Local Labor Market Impacts of
Higher Immigration,” Journal of Labor Economics, vol. 19, no. 1, pp. 22–64, 2001.
[9] D. Card and J. DiNardo, “Do Immigrant Inflows Lead to Native Outflows?,” American
Economic Review, vol. 90, no. 2, pp. 360–367, 2000.
[10] W. J. Carrington and P. J. De Lima, “The Impact of 1970s Repatriates from Africa on the
Portuguese Labor Market,” Industrial & Labor Relations Review, vol. 49, no. 2, pp. 330–
347, 1996.
[11] W. J. Collins, “When the Tide Turned: Immigration and the Delay of the Great Black
Migration,” Journal of Economic History, vol. 57, no. 03, pp. 607–632, 1997.
33
[12] W. D. Devine, “From Shafts to Wires: Historical Perspective on Electrification,” Journal
of Economic History, vol. 43, no. 02, pp. 347–372, 1983.
[13] J. A. Dunlevy and H. A. Gemery, “The Role of Migrant Stock and Lagged Migration in
the Settlement Patterns of Nineteenth Century Immigrants,” Review of Economics and
Statistics, pp. 137–144, 1977.
[14] I. Ferenczi and W. F. Willcox, International Migrations, Volume I: Statistics. New York:
National Bureau of Economic Research, 1929.
[15] J. P. Ferrie, “The Impact of Immigration on Natives in the Antebellum US Labor Market,
1850-60,” Manuscript, 1996.
[16] M. Foged and G. Peri, “Immigrants’ Effect on Native Workers: New Analysis on Longitudinal Data,” IZA Working Paper, 2015.
[17] R. M. Friedberg and J. Hunt, “The Impact of Immigrants on Host Country Wages, Employment and Growth,” Journal of Economic Perspectives, vol. 9, no. 2, pp. 23–44, 1995.
[18] L. E. Gallaway, R. K. Vedder, and V. Shukla, “The Distribution of the Immigrant Population in the United States: an Economic Analysis,” Explorations in Economic History,
vol. 11, no. 3, p. 213, 1974.
[19] J. Gibson, D. J. McKenzie, H. Rohorua, and S. Stillman, “The Long-Term Impacts of International Migration: Evidence from a Lottery,” World Bank Policy Research Working
Paper, no. 7495, 2015.
[20] C. Goldin, “The Political Economy of Immigration Restriction in the United States, 1890
to 1921,” in The Regulated Economy: A Historical Approach to Political Economy, pp. 223–
258, University of Chicago Press, 1994.
[21] C. D. Goldin and L. F. Katz, The Race between Education and Technology. Harvard University Press, 2009.
[22] R. Gray, “Taking Technology to Task: The Skill Content of Technological Change in Early
Twentieth Century United States,” Explorations in Economic History, vol. 50, no. 3,
pp. 351–367, 2013.
34
[23] M. J. Greenwood and Z. Ward, “Immigration Quotas, World War I, and Emigrant Flows
from the United States in the Early 20th Century,” Explorations in Economic History,
vol. 55, pp. 76–96, 2015.
[24] M. Haines, Historical, Demographic, Economic, and Social Data: The United States, 17902002. Inter-University Consortium for Political and Social Research (ICPSR), 2010.
[25] T. J. Hatton and J. G. Williamson, “The Impact of Immigration on American Labor Markets Prior to the Quotas,” NBER Working Paper, 1995.
[26] T. J. Hatton and J. G. Williamson, The Age of Mass Migration: Causes and Economic
Impact. Oxford University Press on Demand, 1998.
[27] R. Hornbeck and S. Naidu, “When the Levee Breaks: Black Migration and Economic
Development in the American South,” American Economic Review, vol. 104, no. 3, pp. 963–
990, 2014.
[28] J. Hunt, “The Impact of the 1962 Repatriates from Algeria on the French Labor Market,”
Industrial & Labor Relations Review, vol. 45, no. 3, pp. 556–572, 1992.
[29] E. P. Hutchinson, Legislative History of American Immigration Policy:
1798-1965.
Philadelphia: University of Pennsylvania Press, 1981.
[30] H. Jerome, Migration and Business Cycles. National Bureau of Economic Research, 1926.
[31] H. Jerome, Mechanization in Industry. National Bureau of Economic Research, 1934.
[32] M. M. Kritz and D. T. Gurak, “The Impact of Immigration on the Internal Migration of
Natives and Immigrants,” Demography, vol. 38, no. 1, pp. 133–145, 2001.
[33] J. Lafortune and J. Tessada, “Smooth (er) Landing? the Dynamic Role of Networks in the
Location and Occupational Choice of Immigrants,” Manuscript, 2010.
[34] E. Lewis, “Immigration, Skill Mix, and Capital Skill Complementarity,” The Quarterly
Journal of Economics, vol. 126, no. 2, pp. 1029–1069, 2011.
[35] E. Lewis, “Immigration and Production Technology,” Annual Review of Economics, vol. 5,
pp. 165–191, 2013.
35
[36] E. Lewis and G. Peri, “Immigration and the Economy of Cities and Regions,” Handbook of
Regional and Urban Economics, vol. 5, 2015.
[37] J. Llull, “The Effect of Immigration on Wages: Exploiting Exogenous Variation at the
National Level,” Barcelona, Spain, GSE Working Paper, vol. 783, 2015.
[38] D. S. Massey, “Economic Development and International Migration in Comparative Perspective,” The Population and Development Review, pp. 383–413, 1988.
[39] C. G. Massey, “Immigration Quotas and Immigrant Selection,” Explorations in Economic
History, vol. 60, pp. 21–40, 2016.
[40] D. McKenzie, S. Stillman, and J. Gibson, “How Important Is Selection? Experimental vs.
Non-Experimental Measures of the Income Gains from Migration,” Journal of the European Economic Association, vol. 8, no. 4, pp. 913–945, 2010.
[41] National Academies of Sciences, Engineering, and Medicine, The Economic and Fiscal
Consequences of Immigration. The National Academies Press, 2016.
[42] G. Peri, “Immigrants’ Complementarities and Native Wages: Evidence from California,”
NBER Working Paper, 2007.
[43] J. L. Rosenbloom, “Was There A National Labor Market at the End of the Nineteenth
Century? New Evidence on Earnings in Manufacturing,” Journal of Economic History,
vol. 56, no. 03, pp. 626–656, 1996.
[44] S. Ruggles, K. Genadek, R. Goeken, J. Grover, and M. Sobek, Integrated Public Use Microdata Series: Version 6.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2016.
[45] H. Runblom and H. Norman, From Sweden to America: a History of the Migration. University of Minnesota Press Minneapolis, 1976.
[46] B. Thomas, Migration and Economic Growth. New York: Cambridge University Press,
1954.
36
[47] A. S. Timmer and J. G. Williams, “Immigration Policy Prior to the 1930s: Labor Markets,
Policy Interactions, and Globalization Backlash,” Population and Development Review,
pp. 739–771, 1998.
[48] United States. Bureau of the Census, Census of Manufactures: 1919. Washington: US
Government Printing Office, 1920.
[49] United States. Bureau of the Census, Census of Manufactures: 1929. Washington: US
Government Printing Office, 1930.
[50] United States. Department of Labor, Annual Report of the Commissioner-General of Immigration to the Secretary of Labor. Washington: US Government Printing Office, 18951930.
[51] G. Wright, “The Economic Revolution in the American South,” Journal of Economic Perspectives, vol. 1, no. 1, pp. 161–178, 1987.
37
Figures and Tables
Figure 1: US Annual Immigrant Inflow: World and Europe
The red dashed line shows the annual total (gross) immigration to US. The navy solid line shows the annual
European immigration to US. The black dash-dot line shows the annual total quota assigned under the quota
system.
38
Table 1: Annual Immigrant Inflow 1900-1909 and Quotas by Country
(1)
Pre-WWI inflow
(2)
1921
Quota
(3)
1924
Quota
(4)
(5)
(2)/(1)
(3)/(1)
Eastern Europe
Austria-Hungary
Poland
Romania
Russian Empire
Turkey
154,964
175,723
4,678
52,579
22,734
27,446
25,827
7,419
39,794
7,672
4,961
5,982
603
2,843
971
17.7%
14.7%
158.6%
75.7%
33.7%
2.8%
3.4%
12.9%
5.4%
4.3%
Southern Europe
Greece
Italy
Portugal
Spain
8,507
202,353
7,804
3,563
3,294
42,057
2,520
912
100
3,825
503
131
17.8%
20.8%
32.3%
25.6%
0.5%
2.0%
6.4%
3.7%
Western Europe
Belgium
France
German Empire
Netherlands
Switzerland
4,724
7,449
28,980
5,214
3,423
1,563
5,729
67,699
3,607
3,752
512
3,954
51,327
1,648
2,081
33.1%
76.9%
236.2%
69.2%
109.6%
10.8%
53.1%
177.9%
31.6%
60.8%
Northern Europe
British Isles
Denmark
Norway
Sweden
84,026
6,313
15,974
22,244
77,342
5,619
12,202
20,042
62,574
2,789
6,453
9,561
92.0%
90.2%
76.4%
90.1%
74.5%
44.2%
40.4%
43.0%
Note: Column 1 is the average annual immigrant inflow between 1900-1914 from each European country. Column 2 is the quotas assigned to each European country by the Emergency
Quota Act of 1921. Column 3 is the quotas assigned to each European country by the Quota
Act of 1924. Column 4 is the 1921 quota in column 2 divided by the average annual immigrant inflow in column 1. Column 4 is the 1924 quota in column 3 divided by the average
annual immigrant inflow in column 1. Some countries are combined to provide a consistent
account of immigrant inflow over time and the criteria are described in the Data Appendix.
Source: Annual Report of Commissioner-General of Immigration, Various Years.
39
Table 2: Top 3 States and Counties of Immigrants’ Residence by Origin in 1900
State
County
40
1st
%
2nd
%
3rd
%
1st
%
2nd
Eastern Europe
Austria-Hungary
Poland
Romania
Russian Empire
Turkey
NY
PA
NY
NY
MA
22.8
19.8
70.1
34.8
29.1
PA
NY
PA
PA
NY
20.4
18.1
8.4
10.7
19.3
IL
IL
MN
MA
CA
11.0
17.6
3.2
6.6
6.5
New York, NY
Cook, IL
New York, NY
New York, NY
New York, NY
18.2
16.3
63.5
27.2
18.2
Cook, IL
New York, NY
Philadelphia, PA
Philadelphia, PA
Worcester, MA
Southern Europe
Greece
Italy
Portugal
Spain
MA
NY
MA
NY
21.3
37.6
36.2
22.2
NY
PA
CA
FL
18.2
13.8
32.5
14.9
IL
NJ
RI
CA
18.1
8.6
6.9
12.3
Cook, IL
New York, NY
Bristol, MA
Hillsborough, FL
17.5
21.5
25.4
13.7
Western Europe
Belgium
France
German Empire
Netherlands
Switzerland
WI
NY
NY
MI
NY
14.8
19.1
18.0
28.9
11.8
IL
CA
IL
IL
OH
14.7
11.7
12.5
20.9
10.4
PA
PA
WI
NJ
CA
13.8
8.8
9.1
9.8
9.5
Brown, WI
New York, NY
Cook, IL
Cook, IL
New York, NY
Northern Europe
British Isles
Denmark&Norway
Sweden
PA
MN
MN
15.4
24.6
20.1
NY
WI
IL
15.1
15.8
17.3
MA
IL
NY
9.3
9.3
7.4
New York, NY
Cook, IL
Cook, IL
%
3rd
%
9.4
5.5
6.9
6.0
11.8
Cuyahoga, OH
Erie, NY
Kings, NY
Kings, NY
Suffolk, MA
5.1
5.2
6.2
5.4
8.9
Middlesex, MA
Kings, NY
Alameda, CA
Orleans, LA
15.5
7.7
7.3
6.5
Suffolk, MA
Philadelphia, PA
Middlesex, MA
San Francisco, CA
3.2
3.7
4.0
3.3
7.8
10.6
7.1
18.7
5.1
Cook, IL
San Francisco, CA
New York, NY
Kent, MI
Cook, IL
4.3
4.8
7.1
12.7
3.1
Allegheny, PA
Orleans, LA
Kings, NY
Passaic, NJ
St Louis, MO
3.7
4.3
3.8
6.9
2.4
4.1
6.9
9.2
Philadelphia, PA
Hennepin, MN
Hennepin, MN
4.0
2.8
3.7
Cook, IL
Kings, NY
Kings, NY
3.9
2.2
2.6
Note: The table shows the top three states and counties with highest proportions of immigrants from each sending country. The proportion is calculated as
the number of immigrants from a country living in the state/county divided by the total number of immigrants from the same country in the US. For instance,
column 1 row 1 indicates 22.8% of immigrants from Austria-Hungary live in New York state in 1900.
Source: Census of Population, 1900
Figure 2: Diagram - Migration Cycle and Quota
41
Figure 3: “Lost Immigrants"/1920 Population and Change in Foreign-born Share (1)
(a) Regional policy shock - “Lost immigrants"/1920 population by state
(b) Actual change in foreign-born share between 1920-1930 by state
In (a) darker blue states are states with a stronger policy shock (a greater proportion of “lost" immigrants between
1920-1930 to the total population in 1920). In (b) darker green states are states with a greater decline in foreignborn share of the labor force.
42
Figure 4: “Lost Immigrants"/1920 Population and Change in Foreign-born Share (2)
(a) Regional policy shock - “Lost immigrants"/1920 population by county
(b) Actual change in foreign-born share between 1920-1930 by county
In (a) darker blue counties are counties with a stronger policy shock (a greater proportion of “lost" immigrants
between 1920-1930 to the total population in 1920). In (b) darker green counties are counties with a greater
decline in foreign-born share of the labor force.
43
Table 3: Summary Statistics
All
North
South
1920
1930
1920
1930
1920
1930
1158
(138)
1999
(116)
1330
(227)
2677
(205)
1193
(102)
2008
(114)
1408
(124)
2711
(185)
936
(128)
1913
(113)
922
(200)
2386
(115)
19.3
11.0
20.9
93.3
51.2
23.7
14.6
16.4
10.9
22.9
95.4
56.2
19.4
14.0
25.0
3.3
20.9
96.2
62.0
16.0
16.9
21.3
4.1
23.0
97.6
66.0
12.7
15.7
3.6
32.2
20.8
85.2
25.4
45.0
8.8
2.9
29.5
22.7
89.5
31.9
37.8
9.8
Manufacturing Industry Characteristics
Firm size
3136
(1308)
Horsepower per wage worker
3.2
(1.0)
Value added per wage worker
2753
(380)
Salaried worker to wage worker ratio
0.16
(0.04)
Electrification rate (%)
32.0
4312
(1827)
4.9
(1.3)
3607
(715)
0.15
(0.04)
53.1
3299
(1362)
3.1
(1.0)
2834
(314)
0.17
(0.04)
33.3
4511
(1912)
4.8
(1.3)
3789
(554)
0.16
(0.03)
53.7
2392
(678)
3.8
(0.9)
2246
(375)
0.11
(0.04)
25.3
3378
(980)
5.1
(1.1)
2662
(724)
0.10
(0.03)
49.7
Manufacturing Industry Structure (%)
Food and kindred products
9.35
Textiles and their products
17.02
Forest products
9.43
Paper and allied products
2.19
Printing and publishing
4.45
Chemical and allied products
3.51
Products of petroleum and coal
1.43
Rubber products
1.88
Leather products
3.95
Stone, clay, and glass products
3.27
Iron and steel and their products
9.29
Nonferrous metals
2.94
Machinery
12.32
Transportation equipment
8.17
Railroad repair shops
5.47
Miscellaneous industries
5.34
9.16
18.41
9.94
2.66
5.98
3.00
1.72
1.47
3.57
3.56
9.54
3.22
12.86
6.25
4.54
4.12
9.44
16.66
6.37
2.45
4.62
3.20
1.36
2.17
4.39
3.14
10.14
3.28
13.60
8.86
4.92
5.41
9.20
16.75
6.98
2.97
6.34
2.73
1.54
1.74
4.06
3.46
10.49
3.68
14.70
7.15
3.99
4.22
8.81
19.35
29.62
0.49
3.34
5.57
1.90
0.01
1.04
4.08
3.68
0.74
3.81
3.67
9.08
4.81
8.93
27.58
26.21
0.91
4.00
4.48
2.73
0.01
0.87
4.10
4.32
0.71
2.76
1.27
7.55
3.57
States
Counties
49
2331
35
1339
35
1339
14
992
14
992
Manufacturing wage ($/year)
Manufacturing salary ($/year)
Labor Force Structure (%)
Foreign-born
Black
Female
Literate
Urban
Agriculture
Manufacturing
49
2331
Note: The sample is 2331 counties in 48 states (excluding Hawaii and Alaska) and D.C.. The North (nonSouth) include 1339 counties in 34 states and DC. The South includes 992 counties in 14 states. “Manufacturing wage" is calculated as the total manufacturing wage bills divided by the average number of
wage earners in this year. “Manufacturing salary" is calculated as total salary bills divided by the average
number of salaried officers and employees in this year. “Firm size" is the average number of workers per
establishment. “Electrification rate" is the share of electricity used in the total horsepower in the manufacturing sector. “Manufacturing industry composition" includes the employment share of each 2-digit
industry in the manufacturing sector. Standard deviations are reported in parentheses.
44
Table 4: The Effect of Immigration on Wages: County-level First Difference 1920-1930
WLS
(1)
(2)
(3)
2SLS
(4)
(5)
(6)
DepVar: ln(wage)
Foreign-born share
Urban pop. share
Female share
(7)
(8)
(9)
(10)
IV: Cubic Curve-fitting
-0.408
(0.528)
-0.756∗
(0.400)
-0.349∗∗∗
(0.118)
-1.268∗∗∗
(0.366)
Southern economy
-0.161
(0.273)
-0.106
(0.127)
-1.031∗∗∗
(0.303)
-0.183∗∗∗
(0.030)
-0.136
(0.256)
-0.116
(0.136)
-0.968∗∗∗
(0.249)
-0.194∗∗∗
(0.027)
-0.107
(0.213)
-0.118
(0.122)
-0.999∗∗∗
(0.241)
-0.180∗∗∗
(0.023)
-4.546∗∗
(2.162)
-3.483∗∗∗
(1.204)
-0.468∗∗
(0.193)
-0.899∗∗
(0.365)
-2.537∗∗
(1.041)
-0.272
(0.182)
-0.800∗∗∗
(0.308)
-0.131∗∗∗
(0.028)
-2.527∗∗∗
(0.887)
-0.299∗
(0.170)
-0.752∗∗∗
(0.243)
-0.143∗∗∗
(0.031)
-2.074∗∗∗
(0.590)
-0.239∗
(0.131)
-0.865∗∗∗
(0.249)
-0.137∗∗∗
(0.029)
First stage
Lost immigrants
45
Cragg-Donald F-Stats
Kleibergen-Paap rk F-Stats
Age structure
Industry characteristics
Industry composition
X
X
X
X
X
X
X
-0.185∗∗
(0.071)
-0.206∗∗∗
(0.048)
-0.182∗∗∗
(0.042)
-0.183∗∗∗
(0.032)
-0.198∗∗∗
(0.027)
230.9
6.8
398.5
18.6
309.6
18.6
263.5
32.2
306.3
53.5
X
X
X
X
X
X
X
Note: Columns 1-5 show the estimates from WLS. Columns 6-10 show the 2SLS regression results using the instrument constructed from forecasts of
cubic curve-fitting. The sample is all 2,331 counties. The dependent variable is the change in the average manufacturing wage in log form between 1920
and 1930. “Southern economy" is a dummy that indicates if the county belongs to the South. Age structure includes the labor force shares of 5 age groups
(16-24; 25-34; 35-44; 45-54; 55-65 (omitted)). Industry characteristics include the log of firm size, electrification rate, and the log of horsepower per wage
worker. Industry composition includes the employment shares of 15 2-digit industries (“Misc. industries" omitted due to collinearity). All the variables
except the "Southern economy" dummy are first-differenced. The lower panel reports the estimates of the first stage and F-statistics. CraggDonald Wald F-stats and Kleibergen-Paap Wald F-stats are reported. “Lost immigrants" is the total number of lost immigrants between 1920 and 1930
scaled by the region’s population in 1920. Standard errors clustered by state are reported in parentheses.
∗
p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01.
Figure 5: The Great Black Migration
(a) Change in the labor force share of the black between 1920-1930 by state
(b) Change in the labor force share of the black between 1920-1930 by county
Red areas experience a decline in the black labor force share in the labor force between 1920-1930 and darker
red indicates a greater decline. Blue areas experience an increase in the black labor force share in the labor force
between 1920-1930 and darker blue indicates a greater increase.
46
Table 5: The Effect on Black Migration: County-level First Difference
WLS
All
(1)
2SLS
North
South
(3)
(4)
(5)
(6)
(7)
-0.152∗∗∗
(0.043)
-0.060∗∗∗
(0.018)
-0.023∗∗∗
(0.004)
-0.152∗∗∗
(0.041)
-0.053∗∗∗
(0.016)
-0.022∗∗∗
(0.004)
-0.111∗∗
(0.045)
-0.049∗∗∗
(0.016)
-0.437∗∗∗
(0.153)
-0.057∗
(0.031)
-0.523
(0.554)
X
X
X
X
X
X
X
2331
2331
1339
992
-0.373∗∗
(0.156)
-0.056∗∗
(0.027)
(2)
All
North
South
(8)
(9)
(10)
-0.573∗∗
(0.278)
-0.082∗∗∗
(0.030)
-0.014∗∗
(0.006)
-0.512∗∗
(0.202)
-0.069∗∗∗
(0.021)
-0.015∗∗∗
(0.005)
-0.453∗∗
(0.205)
-0.057∗∗
(0.023)
-0.423∗∗∗
(0.149)
-0.056∗
(0.031)
X
X
X
X
X
X
X
2331
2331
2331
1339
992
-0.197
(0.330)
-0.333∗∗
(0.147)
-0.054∗∗∗
(0.018)
-0.015∗∗∗
(0.005)
-0.296∗∗
(0.115)
-0.045∗∗∗
(0.015)
-0.015∗∗∗
(0.004)
-0.223∗
(0.129)
-0.034∗∗∗
(0.012)
-0.337∗∗
(0.156)
-0.055∗∗
(0.027)
X
X
X
X
X
X
X
2331
2331
1339
992
Panel.A - Dep Var: Labor force share of black
Foreign-born share
-0.122∗∗∗
(0.042)
Urban pop. share
Southern economy
Demographic structure
Industry composition
Observations
-0.025∗∗∗
(0.004)
2331
-0.015
(0.017)
47
Panel.B - Dep Var: Labor force share of black born in the South
Foreign-born share
-0.103∗∗∗
(0.040)
Urban pop. share
Southern economy
Demographic structure
Industry composition
Observations
-0.021∗∗∗
(0.005)
2331
-0.126∗∗∗
(0.040)
-0.043∗∗∗
(0.015)
-0.019∗∗∗
(0.004)
-0.124∗∗∗
(0.037)
-0.038∗∗∗
(0.014)
-0.019∗∗∗
(0.004)
-0.082∗∗
(0.037)
-0.031∗∗∗
(0.010)
X
X
X
X
X
X
X
2331
2331
1339
992
-0.019
(0.012)
2331
Note: Columns 1-5 report estimates from WLS regressions. Columns 6-10 report estimates from 2SLS regressions using the IV constructed from cubic curvefitting. The sample in columns 1-3 and 6-8 is total 2331 counties. The sample in columns 4 and 9 is 1339 counties of the non-Southern states. The sample in
columns 5 and 10 is 992 counties of the Southern states. Southern economy is a dummy that equal 1 if the county belongs to the South. Age structure includes
the labor force shares of 4 age groups (16-24; 25-34; 35-44; 45-54; 55-65 (omitted)). Industry composition includes the employment shares of 15 2-digit industries
(“Misc. industries" omitted due to collinearity). Standard errors clustered by state are reported for in parentheses.
∗
p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01.
Figure 6: The Effects on Various Black Groups
All Black Population
Literate
Illiterate
Skilled
Unskilled
Male
Female
48
Black Born in South
Literate
Illiterate
-1
-.5
0
.5
IV Estimates
Point Estimate
95% C.I.
90% C.I.
Figure 7: The Effects on Black Birth Cohorts
-.35
-.35
-.3
-.3
-.25
-.25
IV Estimates
-.2 -.15 -.1
IV Estimates
-.2 -.15 -.1
-.05
-.05
0
0
.05
(b) Black Born in the South
.05
(a) All Black
19
00
-19
04
18
95
-18
99
18
90
-18
94
18
85
-18
89
18
80
-18
84
18
75
-18
79
18
70
-18
74
18
65
-18
69
19
00
-19
04
18
95
-18
99
18
90
-18
94
18
85
-18
Birth Cohorts
89
18
80
-18
84
18
75
-18
79
18
70
-18
74
18
65
-18
69
Birth Cohorts
-.35
-.35
-.3
-.3
-.25
-.25
IV Estimates
-.2 -.15 -.1
IV Estimates
-.2 -.15 -.1
-.05
-.05
0
0
.05
(d) Literate Black Born in the South
.05
(c) All Literate Black
19
00
-19
04
18
95
-18
99
18
90
-18
94
18
85
-18
89
18
80
-18
84
18
75
-18
79
18
70
-18
74
18
65
-18
69
19
00
-19
04
18
95
-18
99
18
90
-18
94
18
85
-18
Birth Cohorts
89
18
80
-18
84
18
75
-18
79
18
70
-18
74
18
65
-18
69
Birth Cohorts
(f) Unskilled Black Born in the South
-.35
-.35
-.3
-.3
-.25
-.25
IV Estimates
-.2 -.15 -.1
IV Estimates
-.2 -.15 -.1
-.05
-.05
0
0
.05
.05
(e) All Unskilled Black
19
00
-19
04
18
95
-18
99
18
90
-18
94
18
85
-18
89
18
80
-18
84
18
75
-18
79
18
70
-18
74
18
65
-18
69
Birth Cohorts
19
00
-19
04
18
95
-18
99
18
90
-18
94
18
85
-18
89
18
80
-18
84
18
Birth Cohorts
49
75
-18
79
18
70
-18
74
18
65
-18
69
Table 6: The Effect on Industry Development: County-level First Difference
WLS
Panel.A - Dep Var: ln(firm size)
Foreign-born share
2SLS
(1)
(2)
(3)
(4)
(5)
(6)
(7)
2.583∗∗∗
(0.659)
1.924∗∗∗
(0.497)
-0.031
(0.046)
13.421∗∗∗
(4.766)
14.089∗∗∗
(3.557)
9.537∗∗∗
(2.228)
15.583∗∗∗
(3.875)
-0.180∗∗
(0.086)
10.564∗∗∗
(2.455)
-0.143∗∗
(0.062)
X
X
X
X
X
X
X
X
X
X
-6.029∗∗∗
(1.743)
-4.068∗∗∗
(1.538)
-5.642∗∗∗
(2.064)
-0.047
(0.055)
-3.733∗∗
(1.747)
-0.047
(0.044)
X
X
X
X
X
X
X
X
X
X
-2.625∗∗
(1.127)
-2.122∗∗
(1.067)
-1.289
(1.041)
-0.161∗∗∗
(0.035)
-1.082
(0.996)
-0.145∗∗∗
(0.026)
X
X
X
X
X
X
X
X
X
X
Southern economy
X
X
X
Age structure, Female share
Urban pop. share
Industry composition
Panel.B - Dep Var: ln(horsepower per wage worker)
Foreign-born share
-2.579∗∗∗
(0.688)
50
Southern economy
-0.877∗
(0.493)
-0.084∗∗
(0.035)
-7.509∗∗∗
(1.344)
X
X
X
Age structure, Female share
Urban pop. share
Industry composition
Panel.C - Dep Var: ln(value added per wage worker)
Foreign-born share
Southern economy
Age structure, Female share
Urban pop. share
Industry composition
-1.469∗∗
(0.721)
-0.384
(0.396)
-0.154∗∗∗
(0.027)
X
X
X
-5.345∗∗
(2.193)
Note: Columns 1-2 report estimates from WLS regressions. Columns 3-7 report estimates from 2SLS regressions using the IV constructed from cubic
curve-fitting. The sample is total 2331 counties. Southern economy is a dummy that equal 1 if the county belongs to the South. Age structure includes the
labor force shares of 4 age groups (16-24; 25-34; 35-44; 45-54; 55-65 (omitted)). Industry composition includes the employment shares of 15 2-digit industries (“Misc. industries" omitted due to collinearity). Standard errors clustered by state are reported for in parentheses.
∗
p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01.
Table 7: The Effects on Wages and Salaries: Industry-by-State-Level First Difference
Dependent Variable (Columns 1-7 In Log Form)
Firm size
Horsepower
per wage
worker
Electric
power per
wage
worker
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Salaried
worker to
wage
worker
ratio
(8)
10.741∗∗
(4.339)
-7.218∗∗∗
(1.983)
4.698
(3.748)
-4.717∗∗
(2.216)
-0.321
(1.981)
9.056∗
(5.101)
2.180
(2.749)
-1.043∗∗∗
(0.300)
1.985∗
(1.172)
628
628
628
628
628
628
628
628
628
6.822∗
(3.576)
-4.059∗∗∗
(1.256)
14.707∗
(7.937)
-3.848
(2.508)
2.301
(1.586)
17.798∗∗
(7.300)
2.512
(2.662)
-1.083∗∗∗
(0.315)
2.012∗∗
(1.012)
3180
3180
3180
3180
3180
3180
3180
3180
3180
Valued
added per
wage
worker
Horsepower
per salaried
worker
Electric
power per
salaried
worker
Valued
added per
salaried
worker
Electrification
rate
(9)
Panel.A - 2-digit industry
Foreign-born share
Observations
Panel.B - 4-digit industry
51
Foreign-born share
Observations
Note: The table reports estimates from 2SLS regressions. The sample in columns 1-3 is 628 state-specific 2-digit industries and the sample in columns 4-6
is 3180 state-specific 4-digit industries. The specification is the same as Table.A2 column 2 and 5. column Standard errors clustered by state are reported in
parentheses.
∗
p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01.
Appendix
Figure A1: Actual vs. Projected Inflow
0
10
00
00
20
00
00
30
00
00
(a) Eastern Europe: Austria-Hungary
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
10
00
00
20
00
00
30
00
00
(b) Eastern Europe: Poland
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
52
1920
Quota
1930
Figure A2: Actual vs. Projected Inflow
0
20
00
0
40
00
0
60
00
0
80
00
0
10
00
00
(a) Eastern Europe: Russian Empire
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
10
00
0
20
00
0
30
00
0
40
00
0
50
00
0
(b) Eastern Europe: Romania
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
53
1920
Quota
1930
Figure A3: Actual vs. Projected Inflow
0
20
00
0
40
00
0
60
00
0
80
00
0
10
00
00
(a) Eastern Europe: Turkey
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
10
00
0
20
00
0
30
00
0
40
00
0
50
00
0
(b) Southern Europe: Greece
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
54
1920
Quota
1930
Figure A4: Actual vs. Projected Inflow
0
10
00
00
20
00
00
30
00
00
(a) Southern Europe: Italy
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
10
00
0
20
00
0
30
00
0
(b) Southern Europe: Portugal
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
55
1920
Quota
1930
Figure A5: Actual vs. Projected Inflow
0
10
00
0
20
00
0
30
00
0
40
00
0
50
00
0
(a) Southern Europe: Spain
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
56
1920
Quota
1930
Figure A6: Actual vs. Projected Inflow
0
10
00
00
20
00
00
30
00
00
(a) Northern Europe: Great Britain
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
10
00
00
20
00
00
30
00
00
(b) Northern Europe: Ireland
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
57
1920
Quota
1930
Figure A7: Actual vs. Projected Inflow
0
10
00
0
20
00
0
30
00
0
(a) Western Europe: France
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
10
00
00
20
00
00
30
00
00
(b) Western Europe: German Empire
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
58
1920
Quota
1930
Figure A8: Actual vs. Projected Inflow
0
10
00
0
20
00
0
30
00
0
(a) Western Europe: Belgium
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
10
00
0
20
00
0
30
00
0
(b) Western Europe: Switzerland
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
59
1920
Quota
1930
Figure A9: Actual vs. Projected Inflow
0
10
00
0
20
00
0
30
00
0
(a) Western Europe: Netherlands
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
20
00
0
40
00
0
60
00
0
80
00
0
10
00
00
(b) Scandinavia: Sweden
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
60
1920
Quota
1930
Figure A10: Actual vs. Projected Inflow
0
10
00
0
20
00
0
30
00
0
(a) Scandinavia: Denmark
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
1920
1930
Quota
0
10
00
0
20
00
0
30
00
0
(b) Scandinavia: Norway
1880
1890
1900
1910
Actual
3-order Polynomial Curve-fitting
61
1920
Quota
1930
Table A1: The Effect on Wages: Using Different Forecasting Techniques
(1)
IV Coefficients
(2)
(3)
-2.537∗∗
(1.041)
-2.697∗∗∗
(0.863)
-2.233∗∗
(0.904)
-2.176∗∗
(0.908)
-2.143
(1.397)
-3.238∗∗∗
(1.078)
-2.307∗∗
(0.972)
-2.488∗∗
(1.005)
-2.408∗∗
(1.004)
-2.358∗∗∗
(0.908)
-2.264
(1.755)
-2.527∗∗∗
(0.887)
-2.582∗∗∗
(0.689)
-2.049∗∗∗
(0.762)
-1.971∗∗
(0.788)
-2.075
(1.350)
-3.419∗∗∗
(0.976)
-2.094∗∗
(0.830)
-2.359∗∗∗
(0.810)
-2.197∗∗
(0.863)
-2.206∗∗∗
(0.745)
-2.553
(2.246)
-2.074∗∗∗
(0.590)
-2.056∗∗∗
(0.579)
-1.505∗∗
(0.617)
-1.415∗∗
(0.646)
-1.579∗
(0.833)
-2.716∗∗∗
(0.603)
-1.500∗∗
(0.665)
-1.797∗∗∗
(0.591)
-1.572∗∗
(0.693)
-1.627∗∗∗
(0.579)
-0.769
(1.173)
X
X
X
X
X
X
Forecasting Method
a. Cubic curve-fitting including 1921
b. Cubic curve-fitting excluding 1921
c. Quadratic curve-fitting including 1921
d. Quadratic curve-fitting excluding 1921
e. Quartic curve-fitting including 1921
f. Quartic curve-fitting excluding 1921
g. Pre-1914 10-year moving average
h. Fixed at 1921 level
i. Holt-Winters exp. smoothing
j. Holt-Winters exp. smoothing tuned
k. Card’s (2001) instrument
Table 4 Col (8)’s full covariates
Industry characteristics
Industry composition
K-P F-Stats
(4)
53.5
35.4
50.3
69.9
33.0
25.4
45.2
31.3
39.8
32.6
2.5
X
X
X
Note: This table reports the coefficients of the foreign-born share from the 2SLS estimations using IVs
constructed from the “lost immigrants" IV using various forecasting techniques and Card’s method.
Column 2 and 3 further add industry characteristics and industry composition as control variables.
Column 4 reports the Kleibergen-Paap F-statistics from the first stage in regressions of specifications
in column 3. Row a-j construct the IVs using my method of projected “lost" immigrants. Row c and
d use the forecasted inflows from the cubic curve-fitting including/excluding the immigrant inflow in
1921. Row c and d use the forecasted inflows from the quadratic curve-fitting including/excluding the
immigrant inflow in 1921. Row e and f use the forecasted inflows from the quartic curve-fitting including/excluding the immigrant inflow in 1921. Row g uses the forecasted inflows from the 10-year
moving average of pre-1914 immigrant inflow. Row f uses the forecasted inflows as a constant level
of immigrant inflow in 1921. Row i uses the forecasted inflows from the Holt-Winters exponential
smoothing. Row j uses the forecasted inflows from the Holt-Winters exponential smoothing with parameters adjusted to fit the 1921 level of immigrant inflow. Row k constructs the IV identical to Card
(2001). Standard errors clustered by state are reported in parentheses.
∗
p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01.
62
Table A2: The Effects on Wages and Salaries: Industry-by-State-Level First Difference
2-Digit Industry by State
4-Digit Industry by State
(1)
(2)
(3)
(4)
(5)
(6)
-4.583∗∗∗
(1.358)
-3.873∗∗
(1.865)
-3.528∗
(1.841)
-3.047∗∗
(1.373)
-2.266
(1.708)
-1.865
(1.717)
Female share
-0.920
(1.840)
-1.363
(1.559)
-1.386
(1.446)
1.127
(1.407)
0.309
(1.228)
0.488
(1.133)
Urban pop. share
-0.699
(0.545)
-0.279
(0.699)
-0.293
(0.660)
-0.879∗∗
(0.411)
-0.324
(0.528)
-0.285
(0.500)
-0.048
(0.061)
-0.054
(0.058)
-0.059
(0.048)
-0.064
(0.047)
Panel.A - DepVar: ln(wage)
Foreign-born share
South economy
Firm Size
-0.023
(0.016)
-0.017∗∗
(0.008)
Electrification rate
0.083∗∗
(0.034)
0.016
(0.018)
Horsepower per wage worker
0.055∗
(0.030)
0.073∗∗∗
(0.014)
Horsepower per salaried worker
0.010
(0.022)
-0.051∗∗∗
(0.013)
X
X
X
X
X
X
X
X
X
X
X
X
628
628
628
3180
3180
3180
-3.608∗∗
(1.485)
-4.349∗
(2.248)
-5.086∗∗
(2.230)
-1.748∗∗
(0.704)
-2.845∗∗
(1.358)
-3.447∗∗
(1.516)
Female share
0.774
(1.671)
1.237
(1.783)
-0.066
(1.796)
2.794∗∗∗
(1.047)
3.942∗∗∗
(1.522)
3.367∗∗
(1.547)
Urban pop. share
-0.551
(0.531)
-0.989
(0.756)
-0.924
(0.770)
-0.261
(0.253)
-1.039
(0.652)
-1.123
(0.685)
0.050
(0.067)
0.040
(0.071)
0.083
(0.058)
0.087
(0.064)
Age structure
Industry-specific trend
Observations
Panel.B - DepVar: ln(salary)
Foreign-born share
South economy
Firm size
0.025
(0.025)
0.020∗∗∗
(0.006)
Electrification rate
-0.028
(0.041)
-0.025
(0.022)
Horsepower per wage worker
-0.117∗∗
(0.045)
-0.135∗∗∗
(0.019)
Horsepower per salaried worker
0.165∗∗∗
(0.042)
0.164∗∗∗
(0.019)
Age structure
Industry-specific trend
Observations
X
X
X
X
X
X
X
X
X
X
X
X
628
628
628
3180
3180
3180
Note: The table reports estimates from 2SLS regressions. The sample in columns 1-3 is 628 state-specific
2-digit industries and the sample in columns 4-6 is 3180 state-specific 4-digit industries. Standard errors
clustered by state are reported in parentheses.
∗
p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01.
63