Michael J. Greenwood

Many, many papers and books have dealt with historical U.S. immigration from Europe. These contributions have made solid contributions and have greatly improved our understanding of why migration occurred from Europe to the Americas during the 19 th and early 20 th centuries. However, virtually all of these studies were concerned with the volume and rates of movement. Hatton and Williamson ask a much less studied question: “Who were the migrants?” “Who” could refer to numerous migrant characteristics, such as age, sex, occupations, and marital status. I decided to study the migrant characteristics that I could extract from published U.S. immigration statistics. My first historical study concerns age composition.

Most historical studies use 12 European source countries as the origins of U.S. immigration because we have data for these countries. The data are not pristine. They are characterized by many problems that must be acknowledged and dealt with in some way or other. For the 12 source countries, 3 consistent age groups are available for study annually for the period : 1. under 15, , and 3. over 40. The data indicate considerable differences in the age composition of U.S. immigration from the various countries. Note the differences across countries as well as the differences across time for a given country.

Table 1 Age Composition of U.S. immigration from 12 source countries, 1873 and 1898 ______________________________________________________________________________ ___________________________ Country Total ImmigrantsPercent aged 15-40Percent aged over Belgium43,6511, Denmark161,2524,9311, France142,81014,7981, Germany2,455,519149,67117, Ireland1,304,27577,34425, Italy834,2028,75758, Netherlands96,3923, Norway330,05416,2474, Spain14, Sweden682,76714,30312, Great Britain1,502,75989,48212, Portugal22,759241, Source: Bureau of Statistics (1903, ).

1. Younger immigrants have more years to contribute to the U.S. economy (i.e., they have longer expected working lives). 2. Younger immigrants assimilate more rapidly, in part because they learn English more quickly. 3. Because they learn English more rapidly, their labor productivity and earnings also grow more rapidly. Thus, younger individuals not only contribute to the U.S. economy over a longer time period, but also they contribute more on an annual basis. 4. Birth rates of women are highest for roughly the same ages that migration propensities are highest. Thus, age in combination with sex increases the potential population of second generation immigrants. 5. Through the marriage market, even young male migrants may influence subsequent population growth.

6. Skill is a function of age, so older immigrants tend to arrive with higher skill levels. 7. Older immigrants who do not participate in the work force may constitute a burden. 8. Very young immigrants impose costs on society that are not recovered for some years. These costs are mainly due to education.

Dorothy Swain Thomas in her 1938 discussion of “age ” differentials argues that “of all the gaps in our knowledge of the operation of age- selective migration, the most important are: 1. Our lack of precise information as to the operation of varying economic and social structure upon age selection… 2. Our lack of precise information as to the operation of distance as a factor limiting or extending the range of age-selective migration… 3.Our lack of any information at all as to the operation of upswings and downswings in economic conditions upon age-selective migration.” To this day, very little analytical work has ever been done to address these issues. Thus, I attack them in the context of historical U.S. immigration from Europe, where countries rather than communities provide the spatial dimension.

I estimate 3 models, one for the migration rate of the age group, a second for the migration rate of the over 40 age group, and a third for composition (percentage of immigrants 15 and over who were 14-40). Each models contains 3 sets of variables: 1. Differential economic opportunity, 2. Cost of migrating, and 3. Control variables.

1. Differential economic opportunity a. relative real wage, t-1 (i/US) b. relative growth of GDP (US/i) c. percent manufacturing d. percent agriculture Cost of migrating a. total migration prior 2 years b. birthrate in i c. English spoken in i d. Southern Europe e. distance from i Control variables a. population of i

The marginal migrant

Table 2 Means and Standard Deviations _______________________________________________________________________ _______________ Variable MeanStd. dev. Relative real wage, t-1 (i/US) Relative growth of GDP, avg. t-1 to t-3 (US/i) Percent manufacturing in i Percent agriculture in i Total migration prior 2 years (x10 3 ) Birthrate in i (per thousand) English spoken in i Southern Europe Distance from i to U.S. (x10 3 ) Population of i, (x10 6 ) Population of i, over 39 (x10 6 ) Percent pop. 15 and over that was (x10 2 ) Migration rate, (x10 -3 ) Migration rate, over 40 (x10 -3 ) Percent of immigrants 15 and over that was Sources: Relative (international) real wage (Williamson, 1995); relative growth of GDP (Maddison, 2003); percent manufacturing in i (Mitchell, 1992); percent agriculture in i (Mitchell); total migration prior 2 years (Bureau of Statistics, 1903); birthrate in i (Mitchell, 1992); population of i, and also by age (Mitchell, 1992); migration rate, by age (Bureau of Statistics, 1903; Mitchell, 1992). Annual data were developed by linear interpolation and, where necessary, by (backward) extrapolation.

Table 3 Emigration rates and the age composition of U.S.-bound migrants 15 to 40 and over 40, : Hausman-Taylor instrumental variable estimates Migration Rates Age Composition Variable 15-40Over Differential econ. Opportunity Relative real wage, t-1 (x 1 ) (1.249)(4.836)(2.008) Relative growth of GDP (x 1 ) (1.960)(1.120)(0.746) Percent manufacturing in i (x 1 ) (0.264)(3.710) (4.995) Percent agriculture in i (x 1 ) (2.472)(4.399)(3.068) Cost of Migrating Total migration prior 2 years (x 2 ) (5.718)(6.179)(0.410) Birthrate in i (x 2 ) (4.698)(0.587)(5.537) English spoken in i (z 2 ) (2.378)(1.601)(0.424) Southern Europe (z 1 ) (2.185)(1.835)(0.626) Distance from i (z 1 ) (1.072)(1.516)(0.167) Control variables Population (over 39) of i (x 1 ) (2.597)(2.530)(0.446) Constant (z 1 ) (0.514)(0.060)(2.521) Test for exogeneity of HT instruments

Let’s summarize the findings along the lines of the issues raised by D. Thomas. 1. We lack precise information as to the operation of communities of varying economic and social structure upon age selection. My findings: a. Older potential migrants were discouraged by relatively well- developed manufacturing sectors in source countries. b. Younger migrants tended to originate in source countries that were more agricultural. c. Older migrants tended to come from less agriculturally oriented countries.

2. We lack precise information as to the operation of distance as a factor limiting or extending the range of age-selective migration. My findings a. Older migrants tended to come from more distant countries. b. Younger migrants also tended to come from more distant countries, so composition was not much influenced by distance.

3. We lack any information at all as to the operation of upswings and downswings in economic conditions upon age-selective migration. My findings a. Both younger and older migrants responded to economic incentives, but in different ways: i. Older potential migrants were discouraged from leaving high-wage countries. ii. Younger persons responded strongly to superior job opportunities in the U.S.