Immigration Quotas and Immigrant Skill Composition: Evidence from the Frontier, 1910-1940
Catherine Massey
Department of Economics
University of Colorado, Boulder
Spring 2011
I. Introduction
Just as the Western Frontier was nearing its close in 1890, Alaska began to take its place
as America‟s Last Frontier. Although early speculators imagined taming Alaska would be similar
to the western frontier, the environmental hardships the territory presented ultimately shattered
this perception. Alaska is region of extremes. Spanning an area of 586,400 square miles of land,
Alaska is four times the size of California. Thanks to Alaska, one-sixth of America is situated
north of the Arctic Circle. Two-thirds of the state is underlain by continuous permafrost. The
eastern-most tip and western-most tip would span from San Francisco to Atlanta if laid over the
contiguous states. As a result of northern location of the state, the climate is extreme. In
Fairbanks, for instance, the temperature has been recorded both at 96 and -62 degrees Fahrenheit.
The Aleutian chain spans 6,821 square miles and contains 57 active volcanoes. One of its
islands, Unalaska, averages 250 days of rain per year. The northernmost part of the state
experiences 65 days of complete darkness a year.
This paper aims to shed light on the characteristics of those who moved to Alaska prior to
the development of the territory. Most migrants were drawn to Alaska by the burgeoning gold
industry. Other prospectors followed to provide lodging, banking and other amenities. These
early Alaskans gained a reputation for being exceptionally resilient. Newspapers were quick to
praise the heroism of Alaskans who were in constant battle with their natural environment.
Despite romanticized stories of adventure and instant fortune, it was not uncommon for
prospectors to freeze to death, starve, drown or commit suicide (Berton, 1958). Frostbite, scurvy
and tuberculosis also posed common threats. This paper answers two questions about migration
to frontier regions. First, in light of the many dangers life in Alaska presented, what type of
people chose to migrate to Alaska in the early 1900s? Second, did the evolution of national
immigration policies during the 1920s affect the skill composition of migrants to the Last
Frontier and the contiguous US?
2
There exists a vast literature on the economics of migration. Immigration research
addresses both international1 and internal migration.2 The most heavily discussed topics include
who migrates, why they migrate, spatial patterns of migration, timing of migration and the
consequences of migration (Greenwood, Mueser, Plane and Schlottman, 1991). Several topics
have been analyzed regarding who migrates. Sex-composition (Donato and Tyree, 1986;
Greenwood, 2008; Gabaccia, 1996; Greenwood and McDowell, 1999; Houstoun, Kramer and
Barrett, 1984; Tyree and Donato, 1986) the age of migrants (Greenwood, 2007; Hatton and
Williamson, 1998; Schaafsma and Sweetman, 2001) and skill-composition (see Borjas 1999 for a
survey) are among the most heavily discussed.
Greenwood (2008) identifies three reasons why the sex-composition of migrants is
important. First, because females are more likely to move with family members instead of
reasons motivated by their own economic reasons, sex-composition can greatly affect the
industrial composition of migrants. Second, female migrants simultaneously decrease the
potential for population growth in their sending countries while increasing the potential for
growth of second-generation migrants in the receiving country. Third, the social costs associated
with female versus male migrants differ, particularly because women have higher life
expectancies and are less likely to engage in crime. Greenwood (2008) finds, for migration at the
turn of the century, that birth rates, industrial composition of employment opportunities and
family composition play an important role in determining the sex composition of migrants. His
work emphasizes how age and sex-composition are closely related, primarily because the age of
migration greatly affects fertility (particularly for females) and the years of productive work.
Age of migration affects assimilation, the years migrants contribute to the receiving
country‟s economy and is directly related to skill, experience and education attainment
(Greenwood, 2007). Several studies confirm that the propensity to migrate peaks in the early to
mid-twenties and subsequently decrease with age (Greenwood, 2007; Morgan and Robb, 1981;
1
2
See Borjas (1999) for an overview.
See Greenwood (1975) for a survey of the internal migration literature.
3
Gallaway, 1969). This trend supports estimates of a negative relationship between age at
migration and earnings in the host country (Friedberg, 1993). Shaafsma and Sweetman (2001)
attribute this relationship to lower returns on foreign labor-market experience and foreign
education attainment.
By far, skills are the most extensively studied characteristic of migrants. Theoretically,
immigration represents an outward shift in supply that results in decreased wages in the receiving
country (Borjas, Freeman and Katz, 1997; Borjas, 2003). Although empirical evidence of wage
effects is mixed (see Card, 2001), the possible labor-market effects highlight the need to
understand the skills of incoming migrants. Migrants are positively (negatively) selected when
they are drawn from the top (low) end of the source country‟s skill distribution. Borjas (1987,
1991) predicts that if there is greater wage inequality (equality) in the destination country than in
the source country that migrants will be positively (negatively) selected. Again, empirical
evidence is mixed about the effectiveness of Borjas‟ model, but all agree that migrant skills and
other characteristics have economic implications (Abramitzky, Boustan, and Eriksson, 2010).
Studying immigration to frontier regions is important because the characteristics of
migrants have consequences for the economic growth of frontier regions and America as a
whole. The types of resources (human capital, physical capital and monetary resources) that
migrants brought with them all had potentially large effects on development. There are relatively
few studies of western migration during the nineteenth century. These studies match households
across censuses and found that migrants to the Western frontier were more likely to be poor and
illiterate, and were from rural, landless households exhibiting high fertility rates (Steckel, 1989;
Ferrie, 1997; Oberly, 1989; Steward, 2006; and Schaefer, 1985). However, these studies hardly
broach alien migration to the western frontier and completely ignore migration to Alaska all
together.
Because immigration was largely unrestricted in the beginning of the 1900s, the first half
of the decade offers a unique opportunity to study the economic consequences of early attempts
4
to restrict migration. The 1921 Emergency Immigration Act was the first American immigration
policy that numerically restricted entry to the US. This act created quotas for each nationality in
order to reduce migration flows from “undesirable” countries. Although aimed towards
controlling the racial composition of immigrants, the policy may have affected other
characteristics of migrants, particularly their skill level. The quotas became more stringent with
passage of the Immigration Act of 1924 and remained in place until passage of the Immigration
and Naturalization Act of 1964. To my knowledge, this is the only existing paper that
empirically tests the effect these quotas have on migrant quality.
Hand-collected ship records from Alaskan ports are utilized to study the characteristics of
migrants and the effect of quota policies. These records contain detailed descriptions of aliens
arriving in Alaska ports and railways from 1910 to 1940. First, a descriptive approach is taken
towards analyzing the characteristics of aliens that migrated to Alaska. Immigrants who migrated
to the contiguous US, or the lower 48, are used as a point of comparison. Second, the
implementation of the 1921 Emergency Immigration Act provides variation in immigrant skill
which is utilized in a differences-in-differences approach to estimate how the skill of incoming
migrants changed as a result of the policy. Because they are not restricted by the quota,
Canadian-born and Canadian citizens provide a natural comparison group for European migrants.
Preliminary results suggest that the quotas affected migrants destined for Alaska differently from
those moving to the contiguous US. Implementation of the quota lead to an increase in skill of
about 28.8-43.4 percent points for lower-48 migrants, but resulted in a decrease in skill for
migrants destined for Alaska.
II. European Settlement of Alaska
Migration into Alaska resulted from exploitation of its natural resource base. Although
known most for its prevalent gold and oil reserves, Alaska is also rich in fur, fish, timber, coal,
iron and copper. Alaska was first “discovered” by a Russian navy crew in 1741. The captain,
Danish navigator Vitus Bering, conducted enough voyages in the 18th century to establish
5
Russia‟s eventual claim to Alaska. When Russian crews brought back sea otter fur, a fur trade
was instantly created. In order to manage the fur trade, the Russian American Company was
created. The Russian American Company developed the first permanent settlement in Alaska in
1784 on the islands of Three Saints Bay. However, Russia never permanently colonized Alaska
and eventually negotiated its sale to America in 1867. Gold was discovered in Alaska in the
1870s, with the first large gold strike in Juneau in 1880. The Klondike gold rush began in 1896
and several gold discoveries continued to be made throughout the early 1900s (Berton, 1958).
In addition to gold, coal was mined since the late 1850s and in 1911, copper mines were
beginning to open. In the mid-1910s, the Alaska Railroad bill was passed by Congress to fund a
railroad connecting Seward to Fairbanks. Building the railway created demand for construction
workers and railroad gang laborers. There was also a period of time when efforts were made to
promote agriculture in Alaska. Although the government had funded agricultural experiments
throughout the late 1800s and early 1900s, the largest attempt to encourage Alaska‟s agricultural
industry was that made through the Matanuska Project. As part of the New Deal programs, the
Matanuska Project sent 203 families to settle Matanuska Valley and farm. The project was a
complete failure and no attempts towards large scale agricultural production have been made
since (Willis, 2010).
From the time of purchase until 1884, Alaska was under the jurisdiction of the
Department of Alaska. Until the department became the District of Alaska in 1884, control of the
Department of Alaska was passed between the Army, Treasury and Navy (Jones, 2010). Alaska
became a territory in 1912 and a state in 1959.
III. Early American Immigration Policy
Early American immigration policy is characterized by the inherent conflict between core
American values of freedom, equality, and individualism and the rationale that restricting certain
types of migrants is necessary to preserve American values (Shanks, 2001). Restrictionist
attitudes have risen periodically since the creation of the nation and, ironically, even by its own
6
founding fathers. Not long after Benjamin Franklin constructed the Declaration of Independence
accusing King George of inhibiting the naturalization of foreigners and discouraging migration
to America, he wrote “those who come hither are generally the most stupid of their own
nation…. Not being used to liberty, they know not how to make a modest use of it”.3 Despite
early proponents of restrictionism, it would be a century and a half after America‟s inception for
its immigration policy to reflect nationalistic sentiments.
Until 1876, US immigration legislation was at the discretion of each individual state
(Shanks, 2001). States had full power over who was allowed entry, the head tax levied and the
process to become citizens. Many states promoted immigration to draw in homesteaders, who
provided infrastructure and funds for the local treasury and helped settle the seemingly endless
West. However, devastation in Europe (such as the Irish famine and German Revolution), along
with Californian gold rushes encouraged unprecedented numbers of migrants to enter the US in
the latter half of the 19th century (Daniels, 2004). As the closing of the frontier became
imminent, restrictionist sentiments reemerged as fear increased that American wages and
standard of living would be compromised by influxes of labor from Europe and China (Shanks,
2001).
Once control over immigration was centralized, debates began about what steps Congress
should take to preserve the composition of America‟s existing labor force. The earliest Federal
immigration policies attempted to regulate immigrant quality by mandating the exclusion of
prostitutes, convicts, political offenders, lunatics, idiots, polygamists, those infected with
tuberculosis and contract laborers. The first federal immigration policy explicitly aimed towards
restricting entry based on race was the Chinese Exclusion Act of 1882. This act restricted
migration of any Chinese migrant who could not prove she was a highly skilled worker.
European migration, however, remained unrestricted until 1917.
3
Benjamin Franklin letter to Peter Collinson, May 1753, in Edith Abott, ed., Historical Aspects of the Immigration
Problem: Select Documents (Chicago: University of Chicago Press, 1926), 415.
7
Before the turn of the century, European migration generally consisted of individuals
from Western Europe.4 These migrants constituted what is considered the “old migration.”
Beginning in the late 19th century, the number of “new” migrants from Eastern and Southern
Europe began to exceed the number of “old” migrants. From 1881 to 1910, the percentage of
„new‟ migrants increased from twenty to almost eighty percent of all incoming migrants
(Shanks, 2001, pg 71). This change in sending countries spurred concern about the quality of
immigrants and led to the creation of the United States Immigration Commission in 1907. The
US Immigration Commission, also known as the Dillingham Commission, created statistical
reports of the characteristics of aliens currently residing in the US. The commission concluded
that “old” migrants were educated entrepreneurs capable of assimilating into US culture, while
“new” migrants were determined to be poor and illiterate. The Dillingham Commission was later
used to cement fears spurred by World War I that America needed to be protected from
“inferior” races and cheap labor (Daniels, 2004).
Using the results of the commission as evidence of the threat of “new” migration for the
American workforce, the Immigration Act of 1917 was made into law. The 1917 act reinforced
all previous laws attempting to control migrant quality and added a literacy test. Although a
literacy test may appear as a method to control the education and skill of incoming migrants, the
motivation for the law was racial in nature. Dillingham explained that in writing the 1917 law
“[they] took the nations from which this immigration came so largely, the eastern and southern
nations of Europe, and ascertained what the literacy percentage was among the people of those
nations. We saw at once that if we adopted the educational test, it would substantially decrease
the volume of that stream by thirty percent”.5 The first law to numerically restrict migration
flows was the 1921 Emergency Immigration Act.
IV. The Quota Acts
4
Specifically, these “old” migrants are from Ireland, the United Kingdom, France, Germany, Norway, Sweden,
Denmark, Netherlands, Switzerland, Belgium and Luxemburg.
5
Willim P Dillingham. Congressional Record v53 pt 4 (64c/1s), 25 Feb 1916, 12773
8
“If Moses attempted to come in, all his prescience and God given prophecy would
avail him nothing if there had already preceded him from the Nile to America
eighteen Egyptians.”
- Rufus Hardy, US Representative 1907-1923
The 1921 Emergency Immigration Act placed a ceiling of 355,825 on the number of
immigrants allowed into America during a given fiscal year. The numerical restriction took the
form of a quota in which the number of aliens of each nationality allowed entry was restricted to
three percent of the number of foreign-born persons of such nationality resident in the US as
determined by the 1910 census. Nationality was determined by birthplace for the purposes of the
quota. Under the quota, only 158,248 immigrants were allowed entry from countries other than
Northwestern Europe and the Americas. Those born in Canada, Mexico and South America were
exempt from the quota as were any immigrants who had spent a continuous year residing in any
of these countries. Each month, a port could only admit twenty percent of their allotment of the
quota. As a result, the quotas for countries in Southwestern and Eastern Europe were generally
filled by October. For any immigrant ultimately debarred, the shipping companies were
responsible for taking that person back to his point of departure.6 This resulted in a race amongst
ships to make it to port at the beginning of each month. The 1921 Emergency Immigration Act
remained in effect until replaced by a permanent version in 1924.
The 1921 quota is responsible for decreasing the number of migrants entering America
from 805,228 the year before to less than 350,000 between 1921 and 1922. Although the
immigration numbers were cut by more than half by the 1921 quota, restrictionists wanted tighter
limitations on immigration. The Immigration Act of 1924 replaced the 1921 quota act and
limited the ceiling on allowed migrants to 150,000. Though similar in flavor to the 1921 quota
act, the 1924 immigration act was different in several keys ways. First, the quota was limited to
two percent of the population in the 1890 census. This change was specifically intended to limit
6
At this time, I am unaware of whether ships preselected migrants prior to departure to America.
9
“new” migration further as there were significantly fewer “new” migrants living in America in
1890 than in 1910. Second, immigrants were required to obtain a visa from an American
consulate and provide two copies of their photograph upon entry. Lastly, immigrants were
required to reside two consecutive years in a contiguous foreign country (Canada or Mexico) in
order to be exempt from the quota (as opposed to one year under the 1921 act).
There are several questions about the implementation of the quotas that are of interest.
One criticism of the quota laws was that they did not moderate immigrant quality. Instead the
first immigrants to reach America and satisfied the restrictions placed by the 1917 Immigration
Act were allowed entry, with no regard given to education, skill or English speaking capability.
This paper examines who chooses to migrate, characteristics of debarred migrants and whether
the quotas had an effect on the skill composition of incoming migrants.
V. Data
This paper utilizes hand-collected data from ship records documenting alien arrivals at
Alaskan ports from 1910 to 1940. Over 2,190 observations were collected of male, immigrant
aliens arriving in Alaska during this period. Of these 2,190 individuals, 1,042 listed Alaska as
their final destination. Although men are the focus of this paper, 377 women also collected, 77
percent of which list a lower-48 state as their final destination. A larger percentage of the women
going to Alaska are single compared to the lower-48 destined women. The majority of women
list their occupation as “none” or “wife.”
Ship records represent the best opportunity to study immigration to Alaska. One could
use census data to study migration to Alaska in the early twentieth century. However, the IPUMs
population census sample for 1920 only contains 241 Alaskans, of whom only 87 are foreign
born, and the 1930 IPUMS sample contains no Alaskan observations. Although it would be
possible to consult the actual manuscript censuses for these years, they would not offer insights
into the characteristics of migrants at the time of migration and it would be impossible to
determine which migrants were exempt from the quotas due to residence in Canada. Unlike
10
analyses that utilize census data, ship records are not unduly influenced by the choices the
migrant has made since entry. Along with demographic data, ship records contain information on
occupation and current monetary assets recorded at the time of entry. If one simply looked at
census data, those who are highly skilled yet could only find low-skilled work in America are
simply categorized as low-skilled workers. Furthermore, early immigration records indicate and
report the characteristics of debarred migrants.
At time of entry, aliens were required to report their occupation, age, marital status,
whether they had fifty dollars on hand, citizenry, race, literacy test results, last permanent
residence, address of nearest relative, head-tax status, whether they had been in the US before,
whether they were joining anyone, their height, complexion, eye color and hair color. The forms
also allowed migrants to indicate whether they intended to migrate permanently or temporarily.
For this analysis, any migrant indicating a stay of two to twelve months was categorized as a
temporary migrant. Any migrant indicating a stay of a year or more is considered permanent.
The only questions on the arrival forms inquiring about education status were whether or not the
migrant could read and write. Migrants were able to take the reading and writing test in
whichever language they chose. The literacy test typically consisted of reading several phrases
from the constitution and writing a couple sentences (Goldin, 1994).
Occupation of each migrant was recorded in full text at time of entry. These occupation
scores were then matched to occupations listed in the 1920 census. Using occupation scores from
the 1920 census, I am able to continuously rank occupations for use in my analysis. The 1920
occupation scores represent the median incomes, in hundreds of dollars, and are derived from the
1950 census.7 This measure does not allow for measuring differences in skill within each
occupation. It could be the case that only the best or worst workers in each occupation are
migrating, but there is no way to capture this possibility in the data.
7
Minns (2000) states that income compression of American wages during the forties may result in understating
differences in occupational incomes if the census occupational scores are used. If this is the case, I would expect that
this would bias my results towards finding no effect of the quotas. I plan to correct these income scores to better
reflect actual incomes from 1910-1940.
11
Over the time span of 1910 to 1940, the questions asked and the care given to reporting
each question on the arrival forms vary. For instance, until 1924 the exact question inquiring
about cash assets read “Whether in possession of $50, and if less, how much?” At most ports,
from 1910 to 1924, the actual amount of cash shown was written down. At other ports, directions
were followed more meticulously and a simple yes or dollar amount under fifty was recorded.
After 1924, the form was changed to simply ask the amount of money shown. Consequently,
using actual quantity of money shown into the empirical analysis results in some observations
being dropped. To make a compatible measure of money, I created a dummy variable that is
equal to one if you are in possession of fifty dollars or more, equal to zero otherwise. Similar
corrections are made for other variables that change across form types. Furthermore, although all
existing records from 1910-1940 were collected, it is likely that what is available today does not
comprise the entire universe of aliens arriving in Alaska. For instance, according to the Reports
of the Department of Labor in 1919, the number of aliens arriving in Alaska was 259 in 1918 and
327 in 1919. I only have 93 observations for 1918 and 37 observations for 1919 in my handcollected sample.
VI. Methodology
The first goal of this paper is to analyze who choose to migrate to Frontier regions. This
part of the analysis is descriptive in nature and accomplished through comparison of migrants
destined for Alaska with migrants destined for the contiguous United States. A linear probability
model is also utilized to uncover which characteristics predict whether an alien went to Alaska
versus the contiguous US. The second half of the paper examines whether immigration policies
aimed towards controlling the race of incoming migrants had any consequences for the
characteristics of migrants to Alaska. To explore this, a differences-in-differences specification is
utilized.
Conceptual Framework
12
There are several reasons to believe that the 1921 quota law would change the
characteristics of those who chose to migrate. The implementation of the quota represents a
significant change in the cost of migrating. After the quota came into effect in June of 1921,
potential immigrants would have to invest time and resources obtaining evidence of their place
of birth. They also risked being debarred if the quota was filled for the month. The Immigration
Act of 1924 further increased immigration costs by requiring visas issued at American
Consulates.
After the law change, as a result of increasing migration costs, it follows that only
migrants whose benefit of migrating outweighs the new cost will choose to (Borjas, 1987). Three
scenarios arise from this. First, if it was the case that only those with more wealth could afford
the increase in migration costs, we would expect to see an increase in the amount of money
shown by migrants. If wealthier migrants tend to be higher skilled migrants, then the quota
would have resulted in an influx of higher skilled individuals. Second, if there is an increase in
costs of migration, we may observe individuals choosing to migrate at older ages as a result of
postponing migration in order to save more money. If older individuals tend to be higher skilled,
the quota would also result in an increase in the skill of migrants. Conversely, the quota may also
encourage an influx of lower-skilled migrants. The adoption of the quota may have created a
time constraint that encouraged those that want to migrate to do so as quickly as possible.
Because the quota was potentially filled by May of each year, time constraints could have
created incentives for lower-earning individuals, in the process of saving, to migrate before
accumulating adequate assets. If lower-earning immigrants are choosing to move with fewer
assets as a result of the policy, there could be a bulge of less-skilled immigrants entering the U.S.
after 1921.
Empirical Framework
This paper utilizes the implementation of the quota law as a source of variation in
migrant skill. The structure of the law creates a natural comparison group for migrants regulated
13
by the quota – Canadians. Under the 1921 and 1924 quota acts, those born in Canada are exempt
from the law. Migrants that had resided in Canada for one year or more were also exempt.
Because the British Nationality and Status of Aliens Act 1914 required aliens to have lived in
Canada for 5 year prior to naturalization, I can categorize all migrants with Canadian citizenship
as being unrestricted by the quota. The existence of a clear control group and the before/after
periods created by the implementation of the quota act in 1921 suggest that a differences-indifferences specification is appropriate for this analysis.
In the simplest specification, I regress the natural log of each individual‟s occupation
score on the relevant treatment dummies as well as age, age squared and fixed effects. The
specification is given by:
The dependent variable is the log of the occupational score of each individual migrant, i,
arriving at time t. The reported occupations on the ship records were matched with occupation
scores used in the 1920 census. These scores are based off of median incomes, in hundreds of
dollars, for each occupation as reported in the 1950 census and can be thought of as a means to
continuously rank occupations.
The coefficient on
is the DD result of the quota. It tells us how much more
occupational scores of those regulated by the policy changed relative to the comparison group
after the policy change. Age and age squared are included to model that skill is increasing in age
at a decreasing rate. I include birthplace fixed effects,
, to control for unobservables that may
cause individuals from some countries to have higher occupational scores than others. I also
include year,
, and month fixed effects,
. Year fixed effects control for macro shocks that
are common to both the treated and untreated groups. Month fixed effects are included to control
14
for the seasonality of certain occupations and migration patterns. Identification of this
specification requires
.
As noted, there are several variables that were asked in different ways or were unreadable
on certain forms. As a result, the number of observations in the regressions is highly dependent
on which variables are included. In my preferred specification, I add several more controls for
personal characteristics and fixed effects for nationality,
, and last residence,
:
Identification of this specification requires
. This specification adds to the previous regression a vector of individual controls, X. This
vector includes height, money shown, literacy, marital status, whether they are permanent
migrants, whether they have been to the US before, whether they have a ticket to their final
destination and whether they are joining anyone. I include height as a proxy for cognitive and
non-cognitive ability (Schick and Steckel, 2010). Given controls for age and country of birth,
height is likely positively correlated with occupational score.
VII. Who Migrated to the Last Frontier?
Descriptive Statistics
Because birthplace is the mechanism for implementation of the quotas, I begin my
analysis by reporting birthplaces and the percentage of migrants from each source country. I
include immigration rates for the portion of my sample migrating to the contiguous US, as well
as the percentage of all foreign born migrants living in the US in the 1920 and 1930 censuses to
offer points of comparison. The percentage of migrants born in Russia is higher for those
destined for Alaska. Table 1 indicates that nine percent of migrants destined for Alaska were
born in Russia, compared to only four percent for those going to the contiguous US. Not
surprising given the geographic proximity, a significant percentage of migrants in my sample are
15
Canadian born. However, the percentage of Canadian-born migrants moving to the contiguous
US, twenty-four percent, is much larger than the percentage destined for Alaska, fourteen
percent. Nineteen percent of migrants destined for Alaska are of Scandinavian origins compared
to twenty-two percent of migrants destined for the lower 48. These percentages are twice that of
Scandinavian-born migrants living in the US in 1920 and 1930. One of the highest rates by
birthplace for Alaskan migrants is that for Montenegro. Fourteen percent of Alaskan migrants
were born in Montenegro compared to four percent of lower-48 migrants and a mere .03 percent
and .01 percent in the 1920 and 1930 censuses, respectively. Another interesting rate is that of
German-born migrants. Compared to foreign born individuals in the 1920 and 1930 census, there
are significantly fewer German-born migrants arriving at Alaskan ports. Interestingly, of the
races most restricted by the quota policies after 1921 (Italy, Montenegro, Greece, Serbia), a
higher percentage of these restricted migrants are choosing to migrate to Alaska than the lower48.
Table 2 reports averages of several migrant characteristics arriving in Alaska. One
striking characteristic immediately apparent is the high average age. Migrants are typically
young (Greenwood, 2007). Using the 1920 and 1930 censuses, I calculated the average age at
time of migration to be 20.68 and 20.06, respectively. However, the migrants from Alaskan
borders and ports are typically in their late thirties before the quota was implemented and their
mid-forties after. Although I do not have a complete explanation for why these migrants are
older, I know that the older average age is partly due to the group of European-born migrants
who resided in Canada before migrating to Alaska.8 Table 2 reveals that a higher percentage of
Alaska-destined migrants are single compared to migrants destined for the lower forty-eight.
They also have significantly less cash on hand and are a lot less likely to be meeting someone.
8
If I separate the sample by untreated (born in Canada or a Canadian citizen and not subject to the quota) and
treated group, the average age of the untreated migrants is ten years higher than that of the treated migrants (see
Table 7).
16
Another element of migrant selection, not often discussed, is that of debarred migrants.
Because most data do not include information on those that are not allowed entry, this aspect of
migration is understudied. Ship record data contain the characteristics of debarred migrants and,
often times, the reason why they were debarred. The migrants arriving at Alaskan ports are
debarred typically because they are deemed a likely public charge, they are convicts, prostitutes,
or because of the quotas. Column five in Table 2 shows that debarred migrants (from the entire
period of 1910-1940) have lower occupational scores, are less literate, less likely to speak
English, have less money and are less likely to be permanent migrants.
Table 3 breaks down the percentage of migrants in each occupation by industry. Almost
sixty percent of migrants heading to Alaska are in the mining industry. Also, a significant portion
of Alaskan migrants report that they are a laborer. A large proportion of migrants heading to the
lower 48 are also miners and laborers. However, compared to those going to Alaska, a larger
percentage of lower-forty eight migrants are employed in other trades such as food, building,
travel and farming.
Average occupational scores, shown in Table 2, are slightly lower for immigrants
destined for Alaska compared to those going to the lower-48. Alaska-destined migrants have
occupational score that range from about 22.8 before quotas to twenty-four after. Figure 1 shows
that the distribution of occupation scores for those going to Alaska has fatter tails than the
distribution of occupation scores for lower-48 migrants. Overall, Alaskan immigrants are more
concentrated in very low-skill and high-skill occupations compared to migrants going to the
lower 48. The distribution of occupational scores of employed native workers from the PUMS
census sample for 1920 is shown in Figure 1 for comparison. The distribution of occupational
scores of native-born American men is to the left of the distributions of migrants in my sample at
almost all occupation scores. Therefore, on average, the migrants in the sample appear to be
higher-skilled than native-born Americans in the US in 1920.
17
Overall, aliens migrating to Alaska between 1910 and 1940 are of Scandinavian, Russian
and Montenegrin origins. They are higher skilled than the average native-born American in the
1920 census and are older than the typical migrant. They have less cash on-hand than migrants
going the lower 48 and are less likely to be married or joining family or friends upon arrival.
They are also more likely to be employed in the mining and laborer industries
A Linear Probability Model of Migration to Alaska
Although a comparison of averages reveals a lot about the characteristics of migrants,
simple descriptives do not provide a way to control for all characteristics simultaneously. In
order to access which characteristics made a migrant more or less likely to migrate to Alaska
versus the contiguous US, I employ a linear probability model. I regress a dummy variable equal
to one if the migrant is going to Alaska, zero otherwise, on the various observed characteristics
of migrants. The coefficients from this regression are reported in Table 4.
The regression coefficients in Table 4 show that migrants going to Alaska were, all else
constant, less likely to be literate on average than those destined for the lower-48. They were also
less likely to be joining family, have fifty dollars or more, or be single than lower-48 migrants,
all else constant. Alaskan migrants are more likely to be permanent migrants and originate from
Finland, England, Italy and Montenegro. The regression results also indicate that age, height and
occupation scores were not significantly different between the Alaskan and lower-48 migrants.
VIII. DD Results
The first step in analyzing the effect of the quota on migrant characteristics is
determining whether the quota affected migrant behavior. Because only twenty percent of the
allotted quota amount could enter in any given month, if the quota actually affected behavior,
there should be bunching of migrants attempting entry at the beginning of each month. To
analyze this, I constructed histograms that show the distribution of entering migrants by the day
of the month they enter in Figure 2. In both the Alaskan destined and lower-48 destined samples,
the distribution of the entries by day of the month is somewhat uniform (see panels a and b of
18
Figure 2) in the sense that, typically, no more than eight percent of the total migrants come in on
any given day of the month. There are no clear peaks or shape to the distributions. However,
after the policy went into effect, there is a clear drop off of migrants attempting entry in the last
week of the month. Also, the percentage of migrants entering on the first two days of the month
increases from less than eight percent to sixteen percent for Alaska-bound migrants. Therefore, I
conclude that migrants were indeed changing timing of travel as a result of implementation of
the quota.
In a differences-in-difference specification, an appropriate control group of migrants
should capture what would have happened to restricted migrants had the quota not been
implemented. I compare treated and untreated characteristics before the law was implemented in
tables 5, 6 and 7. Because the control group consists of Canadians and European-born migrants
residing in Canada, these migrants serve as an excellent control group for the Europeans treated
by the policy change. Furthermore, the long run trends of occupational scores of the treated and
untreated groups are statistically the same prior to implementation of quotas. This can be seen in
Figure 3 for the migrants destined for Alaskan cities. Except for the year 1915, the percentage
difference between the occupational scores of the treated and untreated groups is not statistically
different than zero, at the ninety-fifth confidence level, from 1910 to 1921. Beginning in 1921,
the two trends begin to diverge greatly.
The results of the DD specification with log occupational score as the dependent variable
can be found in Table 8. Columns one and three give the coefficients for the specification laid
out in equation one and columns two and four lay display the coefficients from the fuller
specification of equation two. For those migrants destined for the lower forty eight, the DD
regressions suggest that, on average, occupation scores increased by 28.8% to 43.4% as a result
of the quota, all else constant. The large increase in skill resulting from the quota fits in with the
story that quotas increase costs which causes older, wealthier individuals to migrate. This
supported by regressions with age (Table 9) and money shown (Table 10) as the dependent
19
variable. Regression results suggest that migrants are postponing migration by seven to ten years
as a result of the quota and are migrating with three to eighteen percent more cash on hand.
For Alaskan migrants, the opposite is true. The regression coefficients in columns one
and two of Table 8 suggest that the skill of migrants moving to Alaska decreased by 2.8 -7% as a
result of the quota, all else equal. These coefficients are not statistically significant. This result
potentially coincides with the evidence in Table 1 that a higher percentage of the most restricted
races are choosing to migrate to Alaska versus the lower-48. In Tables 5 and 7, I show that the
migrants restricted by the quota have lower average occupational skills. Alaska may have been
more attractive to “undesirable” migrants after the quota was in place. Because these migrants
have lower average occupation scores, this could be driving the decrease in occupational scores
for those migrating to Alaska after the quota.
It could also be the case that the lack of strong results for the Alaskan migrants is due to
the labor markets in Alaska in the early 1900s. Most migrants to Alaska were drawn by gold and
other mining opportunities - eighty percent of the migrants moving to Alaska were doing so to be
miners or laborers. Perhaps the incentives to migrate to Alaska cloud the increase in cost created
by the quota. Skill is only one determinant of wages. Because I am using a measure of median
wages across the US in 1950 as my measure of skill, I am likely to miss wage premiums
associated with unpleasant working conditions. If there are large compensating wage
differentials in Alaska that are drawing miners and laborers there to do unpleasant work, then my
measure of skill will not capture these differentials.9
There are several coefficients in the main specification, with occupational score as the
dependent variable, which are worth discussion. Age does not appear to have a strong positive
correlation with occupation scores. Money shown appears to have no effect on occupational
score and there is a negative relationship associated with being a permanent migrant.
9
Because most of my migrants are entering dangerous occupations with compensating wage differentials that are
likely large, future drafts of the paper will use the Historical International Standard Classification of Occupations
(HISCO) and their measures of occupation prestige for robustness. HISCO measures of occupation scores take into
account manual labor, skill and whether the job is supervisory or non-supervisory.
20
Interestingly, marital status appears to have no effect on the occupational scores of Alaskan
migrants, but does have large and positive effects on occupation scores of those moving to the
lower forty-eight.
IX. Conclusions
The majority of migrants to Alaska were miners and laborers. Alaskan migrants were less
likely to be literate, joining family, single or have fifty dollars or more than comparable migrants
destined for the contiguous US. The implementation of immigration quotas in the 1920s does
not appear to have had large effects on the skill composition of migrants destined for Alaska. If
anything, estimates suggest that the average skill of a migrant heading to Alaska decreased as a
result of quota policies. However, the results make it clear that the quotas had a significantly
different affect on the quality of migrants to Alaska versus the lower-48 migrants. Consistent
with the theory that immigration quotas are associated with higher costs and will increase the
skill of incoming migrants, regression results show that the skill of migrants to the lower-48
increased significantly after implementation of quotas.
21
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24
Figure 1: Distribution of Occupation Scores
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
-20.00
0.00
20.00
40.00
60.00
80.00
100.00
Occupation Score
Lower 48
Alaska
1920 Census
Notes: The kernel densities above use a bandwidth of eight. The 1920 Census occupational
scores are from the IPUMS sample and are of native-born men of age sixteen or greater.
25
Figure 2: Distribution of Day of Arrival
Panel A: Arrivals by Day of
Month Before 1921:
Destination Alaska
Panel B: Arrivals by Day of
Month After 1921:
Destination Alaska
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
Panel C: Arrivals by Day of
Month Before 1921:
Destination Lower 48
Panel D: Arrivals by Day of
Month After 1921:
Destination Lower 48
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
26
Figure 3: Occupation Trends for Alaska-Destined Migrants
Panel A: Log Occupational Score by Treatment Group
4
3.8
3.6
3.4
3.2
3
2.8
2.6
2.4
2.2
2
1909 1911 1913 1915 1917 1919 1921 1923 1925 1927 1929 1931 1933 1935 1937 1939 1941
Axis Title
Untreated
Treated
Panel B: Percentage Difference
2.5
2
1.5
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
1909 1911 1913 1915 1917 1919 1921 1923 1925 1927 1929 1931 1933 1935 1937 1939 1941
Year
Percentage Change
95% Confidence Interval
27
Figure 4: Occupation Trends for Lower-48-Destined Migrants
Panel A: Log Occupational Score by Treatment Group
4
3.8
3.6
3.4
3.2
3
2.8
2.6
2.4
2.2
2
1909 1911 1913 1915 1917 1919 1921 1923 1925 1927 1929 1931 1933 1935 1937 1939 1941
Axis Title
Untreated
Treated
Panel B: Percentage Difference
2
1.5
1
0.5
0
-0.5
-1
-1.5
-2
1909 1911 1913 1915 1917 1919 1921 1923 1925 1927 1929 1931 1933 1935 1937 1939 1941
Axis Title
Confidence Interval
Percentage Difference
28
Table1: Immigration Flows by Birthplace
Country of Birth
Austria
Canada
Denmark
England
Finland
France
Germany
Greece
Ireland
Italy
Japan
Yugoslavia
Montenegro
Norway
Russia
Scotland
Serbia
Sweden
Switzerland
Destination
Alaska
1910-1940
(%)
Destination Lower 48
1910-1940
(%)
1920
(%)
1930
(%)
3.30
13.98
0.79
5.28
6.73
1.06
1.19
3.17
4.49
6.2
1.06
0.4
13.85
4.35
9.37
4.22
2.11
8.58
0.66
2.52
23.8
1.56
6.01
3.37
1.2
2.88
0.72
3.61
3.37
5.29
0.48
5.41
7.57
3.61
3.25
0.84
9.38
1.32
3.68
6.57
1.59
5.58
1.1
0.78
8.31
1.7
5.9
11.99
1.25
0.41
0.03
2.62
8.71
1.8
0.19
4.42
0.89
2.68
7.53
1.45
5.39
1.03
0.76
10.44
1.73
4.31
13.71
0.66
1.56
0.01
2.6
8.14
2.41
0.07
4.37
0.85
Census
Notes: The census columns are from the IPUMS samples of the population censuses and include all
foreign-born males sixteen years of age or older.
29
Table 2: Descriptive Statistics Before and After Quota Change
Occupational Score
Age
Literacy
English
Ticket to Final Dest
Been to USA
Height
Money Shown
Permanent
Single
Married
Meeting No One
Meeting Family
Destination Alaska
Before June 1921
After June 1921
22.82
23.99
(5.44)
(6.82)
35.37
45.04
(10.23)
(14.76)
0.91
0.99
(0.29)
(0.08)
0.85
0.93
(0.36)
(0.26)
0.85
0.92
(0.36)
(0.27)
0.93
0.92
(0.26)
(0.27)
68.91
68.03
(2.75)
(2.70)
427.19
394.98
(661.61)
(344.75)
0.94
0.43
(0.24)
(0.50)
0.78
0.79
(0.41)
(0.41)
0.20
0.18
(0.40)
(0.39)
0.71
0.79
(0.45)
(0.41)
0.17
0.08
(0.37)
(0.27)
Destination Lower 48
Before June 1921
After June 1921
23.13
25.18
(8.15)
(10.06)
38.70
44.84
(11.53)
(15.02)
0.98
1.00
(0.14)
(0.07)
0.90
0.97
(0.30)
(0.17)
0.86
0.91
(0.35)
(0.29)
0.95
0.91
(0.21)
(0.29)
68.43
68.56
(2.80)
(2.53)
834.79
1180.40
(1133.61)
(1921.06)
0.86
0.44
(0.34)
(0.50)
0.70
0.58
(0.46)
(0.50)
0.29
0.37
(0.46)
(0.48)
0.60
0.30
(0.49)
(0.46)
0.31
0.58
(0.46)
(0.50)
Notes: Standard Deviations in Parentheses.
30
Debarred Immigrants
1910-1940
19.918
(6.445)
44.625
(10.794)
0.625
(0.5)
0.714
(0.488)
0.8889
(0.1111)
0.917
(0.289)
68.154
(2.075)
278.26
(250.65)
0.125
(0.342)
0.8125
(0.403)
0.125
(0.342)
0.5
(0.1507)
0.25
(0.452)
Table 3: Industries of Migrants
Mining
Laborers
Food
Building Trades
Machinists
Metals
Engineer
Travel
Printing and Publishing
Clothing
Farm
Brick and Stone Masons
Destination:
Alaska
Destination: Lower
48
56.7%
22.9%
3.3%
3.2%
1.6%
1.0%
0.7%
0.6%
0.5%
0.2%
0.2%
0.0%
41.7%
15.1%
10.8%
4.3%
1.3%
1.2%
2.2%
3.8%
0.5%
0.8%
1.0%
0.2%
31
Table 4: Linear Probability Model of Migrant Characteristics, Y=1 if Destined to Alaska
1910-1940
Before 1921
After 1921
Age
0.00632
0.00798
0.0221*
(0.00599)
(0.00736)
(0.0116)
Age2
-8.04e-05
-0.000117
-0.000239*
(6.95e-05)
(8.86e-05)
(0.000128)
Literate
-0.247***
-0.233***
-0.502
(0.0661)
(0.0675)
(0.447)
In USA before
-0.0641
-0.0907
0.0671
(0.0507)
(0.0590)
(0.0981)
Height
0.00235
0.00362
0.00775
(0.00494)
(0.00543)
(0.0125)
Occupation Score
0.00116
0.00127
0.00211
(0.00181)
(0.00213)
(0.00338)
Not Joining Anyone
0.0801
-0.0752
0.0992
(0.0487)
(0.0832)
(0.119)
Joining Family
-0.184***
-0.299***
-0.329***
(0.0509)
(0.0856)
(0.121)
Permanent Migrant
0.0935**
0.139***
0.0737
(0.0427)
(0.0533)
(0.0728)
Have $50 or More?
-0.194***
-0.186***
-0.487**
(0.0483)
(0.0500)
(0.233)
Single?
-0.169*
-0.245*
-0.0304
(0.0967)
(0.129)
(0.153)
Birth Place
Austria
0.850*
-0.174
1.550*
(0.483)
(0.459)
(0.914)
England
0.805*
-0.200
1.333*
(0.477)
(0.452)
(0.800)
Finland
0.957**
-0.0677
1.582*
(0.479)
(0.454)
(0.822)
Germany
0.704
-0.343
1.433*
(0.483)
(0.456)
(0.841)
Italy
0.916*
-0.109
1.548*
(0.480)
(0.454)
(0.824)
Montenegro
0.942**
-0.0526
1.273
(0.479)
(0.454)
(0.918)
September
-0.191**
-0.338***
0.354
(0.0853)
(0.111)
(0.230)
October
-0.254***
-0.384***
0.249
(0.0823)
(0.107)
(0.236)
November
-0.176**
-0.332***
0.461*
(0.0873)
(0.113)
(0.242)
Observations
1,412
1,144
268
R-Square
0.331
0.319
0.615
Robust standard errors are reported in parentheses.
32
Table 5: Descriptive Statistics by Treatment Group from 1910-1921
Treated
Control
Canadian-Born
Canadian-Citizen
Occupational Score
21.484
25.377
24.548
(0.188)
(0.595)
(0.401)
Age
32.675
43.323
41.917
(0.309)
(0.844)
(0.541)
Literacy
0.903
0.984
0.993
(0.011)
(0.009)
(0.004)
English
0.681
0.958
0.945
(0.055)
(0.029)
(0.024)
Ticket to Final Dest
0.814
0.907
0.894
(0.014)
(0.215)
(0.016)
Been to USA
0.940
0.917
0.944
(0.009)
(0.022)
(0.012)
Height
68.825
68.903
68.161
(0.103)
(0.190)
(0.153)
Money Shown
313.314
603.080
471.874
(16.841)
(82.015)
(31.331)
Permanent
0.953
0.826
0.857
(0.008)
(0.028)
(0.017)
Single
0.815
0.646
0.654
(0.014)
(0.035)
(0.023)
Married
0.175
0.339
0.334
(0.014)
(0.035)
(0.023)
Widowed
0.009
0.016
0.009
(0.003)
(0.009)
(0.005)
Meeting No One
0.641
0.653
0.680
(0.018)
(0.036)
(0.025)
Meeting Family
0.236
0.249
0.242
(0.016)
(0.033)
(0.023)
Meeting Friend
0.094
0.052
0.061
(0.011)
(0.017)
(0.013)
Standard Errors in Parenthesis.
33
Country of
Birth
Austria
Canada
Denmark
England
Finland
France
Germany
Greece
Ireland
Italy
Japan
Yugoslavia
Montenegro
Norway
Russia
Scotland
Serbia
Sweden
Switzerland
Table 6: Immigration Flows by Birthplace
Treated Group
Control
All
Canadian
Individuals
Citizen
%
%
%
0.41
0
1.51
2.61
8.65
0.82
1.37
3.16
2.06
6.87
1.65
0
19.78
8.38
13.05
1.92
2.88
13.46
1.51
1.28
34.67
0.91
8.21
1.46
0.55
3.47
0.55
5.84
1.82
6.93
0
0.91
3.28
1.28
7.3
0
5.29
0.18
34
1.96
0
1.4
12.57
2.23
0.84
5.31
0.84
8.94
2.79
10.61
0
1.4
5.03
1.96
11.17
0
8.1
0.28
Table 7: Mean Characteristics by Treatment Group and Destination, 1910-1921
Destination: Alaska
Untreated
Treated
Occupational Score
23.74
21.76
(6.42)
(4.21)
Age
39.91
31.95
(10.97)
(8.06)
English
0.97
0.65
(0.17)
(0.49)
Ticket to Final Dest.
0.85
0.82
(0.35)
(0.38)
Visit US
0.91
0.96
(0.29)
(0.20)
Height
68.68
68.83
(2.65)
(2.69)
Money Shown
561.21
382.38
(1011.46)
(462.52)
Permanent
0.88
0.96
(0.33)
(0.20)
Literacy
1.00
0.87
(0.00)
(0.33)
Single
0.73
0.80
(0.44)
(0.40)
Married
0.24
0.18
(0.43)
(0.39)
Widowed
0.03
0.02
(0.16)
(0.13)
joining none
0.82
0.65
(0.38)
(0.48)
joinging family
0.08
0.22
(0.27)
(0.42)
joining friend
0.07
0.10
(0.25)
(0.30)
joinin work
0.03
0.03
(0.16)
(0.16)
Notes: Standard deviation in parentheses.
35
Destination: Lower 48
Untreated
Treated
25.06
20.91
(8.62)
(5.51)
43.54
32.76
(11.37)
(8.57)
0.94
0.74
(0.25)
(0.44)
0.91
0.78
(0.29)
(0.42)
0.95
0.96
(0.22)
(0.20)
68.27
68.72
(2.98)
(2.66)
932.06
719.35
(1299.75)
(921.45)
0.80
0.95
(0.40)
(0.22)
1.00
0.97
(0.06)
(0.18)
0.59
0.84
(0.49)
(0.37)
0.40
0.16
(0.49)
(0.37)
0.01
0.00
(0.08)
(0.06)
0.59
0.60
(0.49)
(0.49)
0.34
0.27
(0.48)
(0.45)
0.05
0.10
(0.22)
(0.30)
0.02
0.03
(0.13)
(0.17)
Table 8: DD Results with Occupational Score as Dependent Variable
Destination Alaska
Destination: Lower 48
(1)
(2)
(3)
(4)
Before/After
0.153
0.355***
-0.350*
-0.144
(0.115)
(0.118)
(0.207)
(0.172)
Treated
-0.0256
0.217**
-0.0585
0.0230
(0.0281)
(0.102)
(0.0385)
(0.165)
After*Treat
-0.0702
-0.0283
0.288***
0.434***
(0.0585)
(0.160)
(0.105)
(0.128)
Age
0.00675*
0.0125
0.0109*
0.000460
(0.00400)
(0.00798)
(0.00565)
(0.00711)
Age2
-7.01e-05
-0.000140
-8.80e-05
9.40e-06
(4.69e-05)
(9.84e-05)
(6.40e-05)
(7.81e-05)
Height
0.00704
0.00893
(0.00444)
(0.00604)
Visit US
0.0307
0.0122
(0.0419)
(0.0655)
Literate
0.0158
0.114*
(0.0310)
(0.0638)
Money Shown
4.00e-05*
4.91e-05***
(2.14e-05)
(1.29e-05)
Ticket to Final
Dest.
-0.0733*
0.0407
(0.0380)
(0.0880)
Permanent
Migrant
-0.00506
-0.103*
(0.0546)
(0.0540)
Single
0.00198
0.146
(0.0742)
(0.115)
Married
0.00507
0.254**
(0.0763)
(0.111)
fam1
0.0176
0.101*
(0.0526)
(0.0614)
fam2
0.00788
0.160**
(0.0576)
(0.0620)
fam3
0.0499
0.130
(0.0540)
(0.161)
Birthplace FE
Yes
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Yes
Month FE
Yes
Yes
Yes
Yes
Citizen FE
Yes
Yes
Last Residence FE
Yes
Yes
Observations
744
555
802
604
R-squared
0.245
0.345
0.253
0.321
*** p<0.01, ** p<0.05, * p<0.1, Robust standard errors in parentheses. The dependent variable is log
occupational score.
36
Table 9: DD Regression with Age as the Dependent Variable
Destination Alaska
Destination: Lower 48
(1)
(2)
(3)
(4)
Before/After
-0.172
-0.0643
-0.0261
0.000809
(0.134)
(0.210)
(0.176)
(0.176)
Treated
0.126***
0.0180
0.218***
-0.0542
(0.0322)
(0.330)
(0.0329)
(0.279)
After*Treat
0.0796
0.0838
0.277***
0.206
(0.0658)
(0.141)
(0.0907)
(0.150)
Height
0.00768*
-0.00101
(0.00464)
(0.00508)
Visit US
0.129**
0.177***
(0.0571)
(0.0539)
Literate
-0.0364
-0.0282
(0.0412)
(0.0990)
Money Shown
1.43e-05
1.70e-05
(1.70e(1.17e05)
05)
Ticket to Final Dest.
0.0213
0.0126
(0.0506)
(0.0574)
Permanent Migrant
0.0275
0.0388
(0.0606)
(0.0356)
Single
0.195***
0.513***
(0.0427)
(0.164)
Married
-0.0338
-0.396**
(0.0465)
(0.164)
fam1
-0.0985
0.102
(0.0655)
(0.0968)
fam2
-0.125*
0.0925
(0.0668)
(0.0962)
fam3
-0.138**
-0.0149
(0.0677)
(0.103)
Birthplace FE
Year FE
Month FE
Citizen FE
Last Residence FE
Observations
R-squared
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
758
0.367
564
0.442
827
0.304
613
0.463
*** p<0.01, ** p<0.05, * p<0.1, Robust standard errors in parentheses. The dependent variable is log age.
37
Table 10: DD Results with Money Shown as Dependent Variable
Before/After
Treated
After*Treat
Age
Age2
Height
Visit US
Literate
Destination Alaska
(1)
(2)
-0.526
-0.181
(0.564)
(0.852)
-0.270*
-0.0191
(0.151)
(0.476)
-0.0870
-0.262
(0.367)
(0.661)
0.0283
0.0610*
(0.0205)
(0.0312)
-0.000195
-0.000635
(0.000245)
(0.000387)
0.0363*
(0.0214)
-0.198
(0.299)
0.226
(0.158)
Destination: Lower 48
(3)
(4)
-1.437**
-1.704**
(0.660)
(0.853)
-0.127
-0.895*
(0.128)
(0.476)
0.0277
0.181
(0.399)
(0.411)
0.0621***
0.0401*
(0.0194)
(0.0219)
0.000633***
0.000443*
(0.000219)
(0.000260)
0.0228
(0.0184)
0.302
(0.216)
-1.087**
(0.458)
Money Shown
Ticket to Final Dest.
-0.181
(0.256)
-0.00343
(0.259)
0.738**
(0.360)
0.823**
(0.374)
-0.0566
(0.267)
-0.0782
(0.293)
-0.127
(0.258)
Permanent Migrant
Single
Married
fam1
fam2
fam3
-0.0728
(0.236)
0.0511
(0.149)
0.00774
(0.628)
0.222
(0.635)
0.518**
(0.220)
0.576***
(0.221)
0.315
(0.283)
Birthplace FE
Yes
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Yes
Month FE
Yes
Yes
Yes
Yes
Citizen FE
Yes
Yes
Last Residence FE
Yes
Yes
Observations
645
562
751
612
R-squared
0.256
0.306
0.221
0.316
*** p<0.01, ** p<0.05, * p<0.1, Robust standard errors in parentheses. The dependent variable is log money
shown.
38