Smooth(er) Landing? The Dynamic Role of Networks in the

Location and Occupational Choice of Immigrants∗

Jeanne Lafortune † José Tessada‡

February 23, 2011

Abstract

How do networks influence the location and occupation decisions of immigrants? If so,is this influence long-lasting? This paper addresses these questions by analyzing immigrantflows to the United States between 1900 and 1930. We compare the distributions of immigrantsboth by intended and actual state of residence to counterfactual distributions constructed byallocating the national-level flows using network proxies. The distribution of immigrants byintended state of residence is most closely approximated by a distribution that allocates themwhere migrants of their own ethnicity, irrespective of their occupation, had settled. Mean-while, the actual distribution of immigrants is better approximated by the location of previousimmigrants of the same ethnicity and occupation, but only for the first 5 years of a migrant’sstay. These results are consistent with migrants using networks as a transitory mechanismwhile they learn about their new labor markets and not consistent with alternatives.

JEL Codes: F22, J61, N31

∗We thank Carol Graham, Sabrina Pabilonia, Peter Temin, Madeline Zavodny, and seminar participants at Yale Uni-versity, American University, Clemson University, PUC-Chile, University of British Columbia, UCL, the VIII Workshopof the Regional Integration Network (RIN) of LACEA, the Population Association of America 2010 Annual Meetings,and the Maryland Population Research Center for their comments, and particularly Judy Hellerstein for insightfuldiscussions and detailed comments on this project. We wish to acknowledge Carolina Gonzalez-Velosa for excellentresearch assistance, Gérard Lafortune for careful data entry, and Eileen Gallagher for editorial assistance. The usualdisclaimer applies.

†University of Maryland and Pontificia Universidad Católica de Chile. Corresponding author. Phone: (56-2)354-7101, Fax: (56-2) 553-2377. Email: lafortune@econ.umd.edu.

‡Pontificia Universidad Católica de Chile. Email: jtessada@gmail.com.

It is the most natural thing in the world for an immigrant to want to settle where there arenumbers of others of his immediate kind.–Henry Pratt Fairchild, 1925

1 Introduction

The fact that immigrants of a particular country or ethnic group tend to locate in simi-lar regions once in their new adoptive country has been observed for a long time. The anti-immigration discourses of the early 20th century used this pattern to argue against (mostlyEuropean) immigration over that period. A similar pattern has been observed in the most recentwave of migration to the United States, this time coming mostly from Mexico and Central Amer-ica. This pattern of concentration has not only been used in policy discussion: various studieshave used it as a way to generate (plausibly exogenous) variation in the number of migrants to aparticular location (see for example Altonji and Card 1991, Card 2001, and more recently Cortés2008 and Peri and Sparber 2009, among many others). Munshi (2003) argues that the size ofthe ethnic network (in his case, at the level of the village of origin) increases the probability thata new migrant will find employment in the destination, suggesting that this clustering may belinked to the capacity of immigrants to help each other find work through referrals.1 Patel andVella (2007) emphasize that referrals, through the networks, may help explain the occupationalconcentration of different ethnicities in different cities that are observed in the current data.

However, the dynamic process through which networks may be providing labor market as-sistance has not received significant attention previously. Do migrants select their destination bymatching their set of skills to that of the main occupations within their network? How does therole of networks appear to change as the migrant spends more time in her adopted country andlearns about labor markets in the United States? Is the reliance of migrants on their network forlabor market purposes permanent or transitory? This paper attempts to answer these questionsby first setting up a simple theoretical framework and then empirically evaluating some of theimplications using the location and occupation choices of immigrants implicit in the gross andnet immigration flows to the United States between 1900 and 1930 by ethnicity, occupation andstate. The data allow us to measure concentrations across states and occupations, measured bothat arrival to the United States and after immigrants have spent time in the country.

Overview. This study first sets up a simple location-occupation decision framework to motivateour empirical results. We assume that migrants who have just arrived in their new country areunable to find employment unless they are being referred by another individual. Once they have

1As a matter of fact, part of the literature on labor market networks has used immigrants and/or ethnic groupsas a study group to analyze the role of networks in labor markets, focusing in the way information is shared amongthem.

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acquired better knowledge of local labor markets and/or have obtained credentials that can beused to search for jobs without a direct referral they may become capable of finding employmentin the sector of their choice. Given this structure, immigrants who are deciding where to settlemay pick a location which provides a combination of two factors: a network of individuals whocan provide them referral/employment for the first period, and a labor market for the set of skillsthey possess, as it may become valuable later on.

This simple framework has some clear implications for the location and occupation patternsamong immigrants, both in the first years after their arrival and once they have spent a longerperiod in their destination. If ethnic networks only provide ethnic-specific amenities or consump-tion goods, then location choices should be related to the strength of pure ethnic networks andthe overall attractiveness of a state for one’s skills but should have little or no relation to the oc-cupations of the members of these networks beyond some ethnic-specific skill set.2 Furthermore,these patterns should not change as the migrant spends more time in the destination country.Similarly, if frictions or other features of the destination’s labor market force immigrants to relypermanently on their network for job seeking we would observe that their location decisionswould be driven by the presence of individuals of the same ethnicity and occupation and thatthis relationship will not change over time. In contrast, if individuals assimilate in the local labormarket, then we should observe that the occupations in which immigrants are employed in thefirst period after arrival are highly correlated with the most important occupations in their ethnicgroup in that particular destination. In addition, and contrary to the other two alternatives, thiseffect should change over time. If these jobs represent mostly a transition or a stepping stone,this relation should decrease as immigrants spend more time in the United States, consistentwith networks providing a smoother landing rather than a permanent “crutch”. Finally, ourhypothesized channel also suggests that, based on the skills immigrants possess before their ar-rival to the United States, we would not observe that migrants ex-ante select destinations wheretheir ethnic networks often practice their occupation. This is particularly the case when migrantsanticipate their reliance on the network to be short-lived and the loss associated with the occupa-tional switch is relatively small, thus reducing the benefit from choosing the best match in termsof occupation/skills set and opting for a larger concentration of compatriots.

We explore these implications using two sources of data that cover the period of the largewave of migration to the United States between 1900 and 1930, a period that spans part of theGreat Migration era and the years after World War I when major immigration reforms wereenacted. At that time, when immigrants arrived to the United States (and when they boardedthe ship), they were asked a series of questions including their ethnicity, the state where theyintended to live and their occupation in their country of origin. Their answers were tabulated and

2For example, Italians are more likely to work in pizza restaurants because they have specific knowledge aboutthe tasks (i.e. they have the skills), but that is not a reflection of the role of networks neither does it depends on thespecific destination.

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presented in the annual reports of the Commissioner of Immigration from 1899 to 1930, a datasource previously unexplored to the best of our knowledge.3 As they settled in the United States,they were also surveyed during the decennial Censuses where they reported the year in whichthey first arrived, their country of birth, the state where they actually live and the occupation theyare currently performing. We match the cohorts of immigrants from these two data sources byyear of immigration and ethnicity to form a panel at the cohort-level.

Using these sources, we study whether the patterns of location and occupation choice implicitin the gross and net flows of immigration are compatible with the hypothesis that ethnic networksprovide temporary support in the labor market. To do this, we allocate the national flow ofimmigrants by ethnicity and occupation for every year of our analysis in three different ways,which proxy for functions networks may serve. All proxies are based on shares constructed usingthe stock of previous immigrants, measured in 1900 for this study, as is relatively standard inthis literature (see for example Altonji and Card 1991).4 First, we assume that all newly arrivedmigrants will locate in states where past migrants of their own ethnic group (regardless of theiroccupations) had settled. This would capture the role of ethnic networks as providing ethnic-specific benefits unrelated to labor markets. Second, we allocate the flow of migrants accordingto the geographic distribution of past migrants who shared the same occupation (regardless oftheir ethnicities). This should capture the labor market attractiveness of a location. Finally, weconstruct a third distribution where all current migrants are allocated using the distribution ofprevious migrants who shared both their ethnicity and occupation, which should correspond tothe role of labor market networks within ethnic groups. We simultaneously compare the fit ofthese three counterfactual distributions to the actual distributions of immigrants at two points:ex-ante, that is at their arrival to the United States (using their reported occupations in theircountry of origin and their intended state of residence) and once they have settled, using theiractual occupations and states of residence.5

For example, let us imagine that a state in 1900 had 10 percent of all Italians, 20 percent ofall bakers and 30 percent of all Italian bakers in the United States but also only 5 percent of allcarpenters and 2 percent of all Italian carpenters. Further assume that 100 more Italian bakersand 100 more Italian carpenters arrive to the United States in 1915 compared to 1910 and that inthe 1920 Census, 150 more Italian bakers and 50 more Italian carpenters declare to have arrivedto the United States in 1915 compared to 1910. The empirical exercise performed in this paper

3See section 3 for a detailed description of the original data contained in the annual Reports of the Commissionerof Immigration and the processed data used in this paper.

4We argue that our measure of past stocks represents a measure of a network and try to construct it in a way asto diminish the potential reflection problem, as emphasized by Manski (1993). We also control for a large set of fixedeffects to capture other confounding factors.

5One could be worried about the potential collinearity between these three proxies. In order to verify that theresults are not corrupted by this problem, specifications where the ethnic share was built excluding individuals of thesame occupation and the occupation share was built excluding individuals of the same ethnicity were also run andthe results are qualitatively and quantitatively very similar.

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measures to which of the three proposed counterfactual is the actual allocation of migrants bystate, both ex-ante and ex-post, closest to.6 If individuals select their ex-ante location based onlyon ethnic networks, we would expect that 10 more Italian bakers and carpenters would declareto be heading to this state at their arrival to the United States. Were we to see 20 bakers but only5 carpenters, labor market factors would appear to be determining ex-ante choices. Similarly, forex-post location decisions, were we to observe 45 more Italian bakers but only 1 Italian carpentersin the flow of immigrants measured in the decennial Census who arrived in 1915 compared to1910, the role of ethnic-occupation networks would appear particularly influential.

As a first step to this analysis we perform a simpler exercise where we allocate newly arrivedmigrants using only two different proxies: based on the location of past migrants of the sameethnicity and of migrants of the same occupation. We find that individuals’ choice of intendedstate of residence, which were reported at their arrival to the United States, appears to be mostlyrelated to the existence of a network of individuals from the same ethnic group in their selectedstate of residence although there appears to be some role, albeit smaller, for decisions to bedriven by the existence of a labor market suited for the skills of the new migrant. However, theactual location of migrants appears to be much more influenced by the past prevalence of theiroccupation in the local labor market. This appears to be robust across various ways to computethe two network proxies.

We then make full use of the three proxy variables we described and try to separate morecarefully the effects of ethnic networks and labor market factors. In this case, our results showthat ex-ante decisions are particularly driven by the presence of individuals of the immigrant’sethnic group irrespective of their occupations. In contrast, ex-post decisions, as observed in theyears shortly after arrival, are strongly influenced by the presence of individuals of one’s ownethnic group who also appear to have the same set of skills as the migrant. These results arequalitatively similar whether we attempt to match the changes in the flow of immigrants overtime in each state by ethnicity, by occupation or by the combination of both. Results are alsorobust to alternative definitions and sample selections.

The difference between the factors influencing the distribution of immigrants observed beforeand after their arrival to the US are consistent with the hypothesized channel presented above:immigrants select their destination by looking for “strong” ethnic networks and, to a lesser de-gree, a good match with their skill set, and then finding temporary employment in the sameoccupations that are most prevalent within their specific ethnic network. These results also im-ply that immigrants do not appear to select their intended location looking for the places withan ethnic network where the most popular occupation is most similar to their own set of skills.

We explore further implications of this hypothesis. First, in our data, we can also measurehow the relative influence of these three factors, namely ethnic network, ethnic-occupation spe-

6We perform the exercise for all states, ethnicities and occupations simultaneously within the limits of the data asexplained in Section 3.

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cific network, and occupation-specific labor markets, change as immigrants spend more time inthe United States. When we separate our regressions according to the time since their reportedarrival to the United States, we observe that the difference between the ex-ante and ex-post dis-tribution is more marked for immigrants recently arrived to the United States than for thosewho have been residing for longer periods. Moreover, the pattern highlighted above is especiallyvisible for immigrants with low occupational scores, which is logical because these individualswould have the least to lose by changing their occupation at arrival.7 Moreover, the pattern weobserve is most marked for occupations that produce “tradeable” goods, which again is logicalas occupations producing non-traded goods would have fewer incentives to provide employmentto individuals in the same occupation since this might be more costly to them in terms of wages.8

Finally, we also find that the results are strongest for non-English speaking groups, which is con-sistent with poor language proficiency reducing opportunities to find employment outside one’sethnic network upon arrival to the United States.9

Two other alternative hypotheses are also explored. First, the difference between ex-anteand ex-post distributions could be driven by selective return migration through which migrantswithout the support of their networks return to their country of origin. This appears to beunlikely as the difference between ex-ante and ex-post decisions is similar across ethnic groupswith high and low rates of return migration. Second, the pattern we highlight could also bedriven by immigrants relocating within the United States at their arrival if their networks cannotprovide them with the support needed. The evidence we present, although it does not rule outinternal migration playing a role, does not seem to support it as the main or sole mechanismthrough which this pattern arises. First, for internal migration to be the only factor explainingour results, it would have to be that immigrants migrate internally at their arrival to the UnitedStates and then relocate again after a few years, in order to match the decreasing relevance ofthe ethnic-occupation specific network. Second, we obtain a very poor match if we allocate theflow of immigrants by their occupation in their country of origin using our proxies to try toexplain the actual distribution of immigrants. If immigrants were just changing locations (andnot occupation), such an exercise should offer similar results as the ones discussed above; the factthat it does not would seem to indicate that immigrants do change their occupations in responseto the presence of networks.

Overall, we believe the evidence is consistent with ethnic networks playing a role upon arrival

7Using occupations at origin to predict occupation distribution of immigrants at destination has been used in theliterature. For example, Friedberg (2001) uses this approach in her study of Russian immigration to Israel. Our resultsimply that such an strategy will be more successful for more highly skilled immigrants, especially if used to predictoccupations long after arrival to the host country.

8Alternatively, it could be costlier or harder for previous immigrants working in non-tradeable occupations toprovide employment if their total output is determined by local demand for the services and goods. This is true evenif wages are equalized across states for equivalent workers.

9Related results have been observed in the context of the recent immigration wave to the United States. Periand Sparber (2009) show that differences in communication skills are related to the sorting of native and immigrantworkers into different occupations.

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and during the first years following migration, leading migrants to adopt new occupations to fa-cilitate the transition process, but also with them being able to revert to their original occupationsonce they have learned about local labor market opportunities in subsequent years.

Related Literature. The results presented in this paper contribute to our understanding of therole networks have in the decisions of migrants and in immigration in general. First, the studiesexploiting local variation at the city level to study the impact of immigration (see for example(Card 2001; Card and Lewis 2005; Cortés 2008; Cortés and Tessada 2011; Peri and Sparber 2009)among several others) have used distributions of past waves of immigrants to generate variationin the flow of immigrants to different areas; because of the existence of ethnic networks, thisvariation is argued to be unrelated to contemporaneous local labor market conditions. Ourresults suggest that while the ex-post location of immigrants may be more endogenous to locallabor market conditions than the ex-ante one, this occurs also because immigrants change theiroccupation in response to the relative advantage of their networks and not only because theyre-optimize their location decision based on current labor market conditions. Our results alsoimply that occupation distributions among immigrants might be more persistent because of theethnic-occupation network component, but that this effect subsides as immigrants spend moretime in the host country.

Second, our results also shed some light on the dynamics of the location and occupationchoice of immigrants. Bauer, Epstein, and Gang (2005) explore the role of networks on locationchoices of migrants but does so statically. The dynamic effect of networks, in particular in thecontext of immigration, is still a fertile area for research. Theoretical models of such dynamicshave been offered (see for example Calvo-Armengol and Jackson 2004, 2007), but the availableempirical evidence has focused on the resulting segregation and workplace concentration. Thereare some papers though that have explored dynamic effects of networks in different contexts;Munshi (2009) argues that networks may be able to break occupation-based traps and presentsevidence from the Mumbai diamond industry supporting his hypothesis. In another recent pa-per, Beaman (2009) exploits the allocation process of refugees resettled in the United States tostudy these effects on employment and other labor market outcomes. She finds evidence thatethnic networks influence the access to local labor markets of newly arrived refugees, and thatthe effect depends on the “age” of the network: while the size of the recently arrived cohortsof refugees negatively affects the outcomes of the current arrivals, the size of older cohorts (twoor more years before the current refugees) improves their outcomes. Our contribution is com-plimentary to Beaman (2009) as we argue that the role of networks itself changes as a migrantspends more time in the United States, thus looking at the dynamics as cohorts “age” rather thanthe differential effect of the cohorts already present at the moment of arrival. Our focus is alsoon the occupational and geographical distribution rather than on more immediate labor marketoutcomes. Finally, Aslund (2005) look at the initial and subsequent location choices of migrants

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in Sweden and finds little variation over time in the factors influencing the decisions, in contrastto our findings.

Third, this paper is also related to the literature that studies the performance of immigrants inthe United States around the turn of the 20th century. Our results complement existing evidenceabout earnings and other labor market outcomes by linking the location and occupation choiceof immigrants to the strength of ethnic networks and labor market factors. In particular, ourestimates on how the ethnic-occupation network becomes less relevant as the immigrants spendmore time in the United States are consistent with the evidence presented in Minns (2000), whofinds evidence of positive wage growth within cohorts of immigrants arriving during the sameperiod we study. Furthermore, Minns (2000) also reports that immigrants showed high mobilityinto well-paid occupations which could be because migrants return to the occupation they wereperforming before their arrival.10

Finally, our results improve our understanding of the occupational concentration of immi-grants. Patel and Vella (2007) build on previous evidence on the role of ethnic networks, andnetworks in general, in the labor market performance of immigrants to study the occupationalchoices of immigrants from the same country in different cities. They start from the observationthat in many cities immigrants from particular countries have developed specific occupationalniches.11 Using data from the 1980, 1990 and 2000 Census in the United States, they find thatnew immigrants are more likely to choose the same occupation previous immigrants from thesame country have chosen, and that those who choose these occupations perceive a benefit intheir earnings too.12 Similar evidence is provided in the case of China by Chen, Jin, and Yue(2010). However, all these studies are constrained to look at occupations once the individual hasmigrated and thus cannot distinguish whether this pattern is driven by individuals of similarskills selecting the same location or by immigrants selecting the same occupation once they havemigrated.

Layout. The remainder of this paper is organized as follows. Section 2 sketches a framework tohelp us better understand the factors that could influence the network dynamics of immigration.Section 3 describes the data and section 4 explains the empirical methodology and the resultsare presented in section 5. Section 6 then explores what could be the reason behind the patternidentified in the previous section. Finally, in the last section we summarize the results and offersome conclusions.

10See also Hatton and Williamson (2005); O’Rourke and Williamson (1999) for a more comprehensive view ofglobalization, in general, and migration, in particular, during this period.

11See also Federman, Harrington, and Krynski (2006).12Munshi and Wilson (2008) study the connection between ethnic networks when the American Midwest was first

settled and occupational choice today.

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2 Motivating Theory

Assume a migrant from ethnic group j and skills o, i.e. she worked in occupation o in hercountry of origin and thus has that exact set of skills, has just arrived to location s. The immigrantlives for two periods and discounts the future using a discount factor β. We assume migrantsare risk-neutral, although risk-averse behavior does not change the main implications of ourframework.

The labor market for immigrants at destination s functions as follows. The wage (wsqo) offeredto individuals in occupation q with skills o in location s is a function of the match between theoccupation and the skills, reaching a maximum when q = o. Furthermore, we assume that thewage is higher for occupations that are in high demand in s, which will be reflected by a higherpast concentration of individuals in that occupation Nsq. We also assume that frictions in the labormarket are more important for immigrants than for natives (or immigrants that have stayed inthe country for one period already), and that these frictions make it difficult for an immigrantto find employment unless they have some referral from their own network. In particular, let’sassume that the probability for a migrant of ethnicity j of finding employment in occupation qin state s upon arrival is given by P

(Njsq

), where Njsq denotes a measure of the relative strength

of the migrant’s ethnic network in occupation q in state s, such that ∑q Pjqst = 1 for a given (j, s)pair.13 This dependence captures the fact that job referrals and opportunities are more likely tocome in occupations that are already more popular within the immigrant’s ethnic community.

As the migrant lives longer in her new environment, her knowledge of the labor market in-creases and her capacity to find employment given her skills may become less and less dependenton her network. If such learning occurs, which we will denote by the parameter α = 1, she willbe able to pick the occupation in her state which will give her the highest income, most probablythe one corresponding to her own skills. If there is no learning (α = 0), then she will remain inthe occupation she obtained in the first period.

The migrant’s utility for a given s is

U(j, s, o) = γ(Njs) + ∑q

P(Njqs)

(wsqo + β

[α(max

q′wsq′o) + (1− α)wsqo

])(1)

where γ(·) represents benefits of having individuals of one’s ethnicity which are unrelated tolabor markets, such as having restaurants, temples, etc.

The migrant’s location decision s∗ can then be written as

s∗ = arg maxs

U(j, s, o).

13In our empirical work we will approximate it with the stock of migrants of one’s ethnicity and occupation in thatstate.

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From this simple framework, the following general conclusions can be drawn. First, if therole of networks in the labor market is limited (P′(Njqs) ≈ 0), we should observe that migrantswould locate where they have fellow countrymen (regardless of their occupation and becauseof the non-labor market benefits of ethnic networks) and where there is demand for their skills(captured by the stock of past migrants in the same occupation). The same would be true if wewere looking at the location of immigrants based on their actual occupation q.14

Secondly, if α = 0, migrants would elect a location where they have a large number of theircountrymen exercising their own occupation as this will be their only way of working with theirskills from their own country in the United States. Once more, this conclusion should be thesame whether we consider the location choice of migrants based on their skills o or their actualoccupation once they have migrated q. If γ(·) is large enough, pure ethnic networks will alsoplay a role in the location decisions. Furthermore, neither of these two channels would changeover time and thus the patterns would be similar regardless of the time spent by the immigrantat her destination.

However, if α > 0 and P′(Njqs) > 0, a different location pattern could emerge. Conditional onthe migrant’s initial skill set o and ethnicity j, the probability that a given state will be selectedwill depend on the size of the ethnic network in that state, the wage that one could earn in theoccupations that are already taken by individuals of the network with one’s skills, and the gen-eral attractiveness of that state to migrants with similar skills sets (given the anticipation that inthe second period, such employment will become available).15 Conditional on the migrant’s firstoccupation in the United States q̃, more migrants will be found (in the first period) where indi-viduals of one’s ethnic network who perform that occupation were already settled, i.e. the newmigrants’ occupation distribution will be influenced by the occupations that are more popularamong previous migrants from the same ethnicity, this it what we call the ethnic-occupation spe-cific effect. Without that network, a migrant would be unable to find employment in that sector.More migrants would also be found in locations where this occupation q̃ is well rewarded as themigrant would have been unlikely to select a state where the wage in the first period would be,in expectations, low.

Finally, conditional on the migrant’s occupation in the second period (which will be either oor q̃), migrants should be found in states where the wages for that occupation are highest.

Assuming that the overall popularity of a location for past migrants in a given occupationgroup is an acceptable proxy for the wage that a migrant can earn in that occupation, this sim-ple model would suggest that we would observe the following pattern if the mechanism we’vedescribed is at play:

1. Based on their ex-ante skills, we would find that migrants select locations where many mi-14In our data we can study the distribution of immigrants across states by occupation and the distribution of

immigrants across states by ethnicity.15If the agents are too optimistic about the chances of obtaining a job in their own occupation this will imply a bias

towards picking states based on the demand for their own occupation.

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grants of their ethnic network (which provides with help, information and assistance duringshortly after arrival), and of their skills have previously located (which we take as a proxy ofthe demand for their set of skills).

2. In the first period after arrival, however, we would find that migrants would be located instates where many migrants of their ethnic network (and less importantly, other previousimmigrants) also share their current occupations.

3. In the second period, however, we would find that migrants would be located in states wheremigrants in the past shared their current occupations.

4. This pattern should be more marked for groups where α will be large or where the loss inhuman capital associated with switching occupations may not be large.

These elements will thus be the key insights we will be looking for in the empirical sectionsthat follow.

3 Data Description

In order to study the dynamic role of networks in the location and occupational choices ofimmigrants to the United States in the framework presented in the previous section, we need tomeasure the immigrants’ “skill set” at entry and their subsequent occupational choices as wellas their location choices over time. Without detailed individual data, we collect data on grossflows constructed with information recorded at entry or arrival to the United States, and netflows recorded ex-post. All this information is provided by the combination of two main datasources: the United States Census for the ex-post data and the Report of the Commissioner ofImmigration (henceforth RCI) for the information at entry to the United Sates. Data from theUnited States Census is taken both from the Public Use Micro Sample (PUMS), as compiled byRuggles et al. (2010), and from the original published summary tables, which contain data forthe first year of each decade.16

3.1 Administrative Data from the Commissioner of Immigration

The RCI was published annually from 1899 to 1932 (except for 1931) and presented sum-mary tables constructed using micro data from the questionnaires each immigrant coming to theUnited States had to answer.17 For each year, immigrants are classified according to their eth-nicity, self-reported occupation in the country of departure and their intended state of residence;

16The summary tables include tabulations based on the full sample of each Decennial Census.17We have tried locating these tables for years 1932-1940 without success. Furthermore, while part of the micro-data

may be available at the National Archives or a similar location in paper format, it would have been prohibitivelyexpensive and time consuming to enter the information it contains. Hopefully, this information will be available inthe future.

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this information was originally taken from the individual data each of them had to report whenboarding at origin and/or when arriving to the United States.18 Using the tables presented in theRCI, we can create an annual series of gross entry flows for each ethnic group according to theirintended state of residence, for each ethnic group according to their occupation in the country ofdeparture, and for each intended state of residence according to their occupation in the countryof departure. The three-way categorization, i.e., flows by ethnicity and occupation for each state,is not published in the RCI. Overall, we have data available for all states and territories in theUnited States for all years between 1899 and 1930 and for 1932, with a total of 75 occupationcategories, grouped in 3 major categories, and 42 ethnic groups. All the information taken fromthese reports is labeled as “administrative data” in the rest of the paper and was digitized for thepurpose of this paper.19

3.2 Census Data

The Decennial Census collects data every 10 years, and includes data on country of birthand year of first arrival in the United States (until 1930). From the 1900, 1910, 1920 and 1930Census, we obtain information on country of birth, labor market status, occupation and state ofresidence for all those who are surveyed. Our main sources for the Decennial Census data arethe 1% samples, plus the 5% sample for 1900, publicly available through the Public Use MicroSamples.20 Variables measured from any of these two sources are referred to as “Census data”or “IPUMS data” in later sections.

We define an immigrant as anyone who reports being born outside the United States ac-cording to the value recorded in the birthplace variable in the Census. Using this definition ofimmigrant, for each Census we compute the stock of immigrants in each state and group themaccording to their occupation and country of birth. Also, with the information on year of firstarrival to the US, we are able to create net flows by country of birth and actual state of residenceand occupation. In order to improve accuracy in the calculation of shares and to avoid small-cellmeasurement problems, whenever possible we use the publicly available summary tables whichwere constructed using the original data set of the 1900 Decennial Census.21

We want to emphasize one important difference between the administrative data and theCensus, and that is the timing of the information with respect to the moment of arrival to the

18It is our understanding that there was little incentive for a migrant to misreport their answers as this had nobearing on their acceptance to the United States, except in the case where their answers when boarding the vessel andat the port of entry were different. It is only in the early 1920s that entry restrictions affecting most of the potentialimmigrants were effectively imposed in the United States.

19To the best of our knowledge the disaggregated data contained in the RCI has not been used before at this levelof detail.

20Available online at http://usa.ipums.org/usa/.21The 1890 Decennial Census micro samples are not available yet. While the summary tables are available, there are

significant differences between the classification of occupations and countries of birth used in them and those used inthe samples for subsequent years.

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United States. In the administrative data the information on occupations is collected beforearrival, “ex-ante”, while that in the Census reflects the occupations after agents have interactedupon and after arrival, hence we label it “ex-post”. This is also true for the state distribution.Furthermore, while the Census data measures immigrants who have settled in the United States(or have at least stayed until the Census date), the administrative data measures entry flows andthus could differ from the Census measures if there is return or internal migration.22

3.3 Matching both Data Sources

The RCI and the Census use different classifications for occupations and for the origin of theimmigrants. The RCI uses ethnic groups as classification, but the Census data records birthplace.Also, while the large occupation groups are relatively similar, specific occupations are groupedin different ways and some specific categories are used only by one of the two sources.

The Census data in IPUMS provides two different occupations codes, the 1900 (for 1900and 1910) and the 1950 Census occupation codes (for all years). Administrative data comesin a different classification; with four main groups –professional, skilled, unskilled, and no-occupation, being divided into very detailed lists of subgroups (for each of the first three groups).We match groups of administrative data occupations to groups from the Census classifications.We match the administrative data to the IPUMS samples using the 1950 Census occupationcodes to preserve comparability across years. When matching to the 1900 Census publishedtables, however, occupations were paired using the 1900 Census codes because this was the onlyclassification available. The exact groups are in Table A-1 together with the corresponding Censuscodes. The appendix table also shows our classification of occupations as large or small, definedas being above or below the median size for all immigrant flow over the period.

The Commissioner of Immigration classified immigrants by their “ethnicity” rather than theircountry of origin.23 In order to reach a matching set of ethnicities, we grouped both countriesof birth and ethnicities from the administrative data. Our final classification includes 28 “ethnic-ities” used in the regression.24 Most of the pairings are fairly intuitive but we needed to makea few adjustments in order to represent the definitions used by both data sets. For example, theRCI classified all Blacks as Africans. However, over the period studied, most Black immigrantsare from the Caribbean and not from Africa. This explains why they are paired with West Indiansrather than with Africans. Similarly, Jewish immigrants were classified as “Hebrews”. We allo-cate Jewish immigrants by their country of birth using the available information from the RCI,which presents a table with the distribution of individuals by their ethnicity and the country oflast residence, although this pairing is definitely a gross approximation. We also collated our

22Mortality rates is another reason why these two flows could differ.23For some years, a table highlights the distribution of individuals by their ethnicity and the country of last residence

but does not indicate the intended state of residence nor the occupations by countries. We use the information in thesetables to allocate certain ethnic groups to different countries as explained later in this section.

24Those groups are described in Appendix Table A-2.

12

various “ethnicities” by groups to present more easily understandable summary statistics and torun some of the regressions separately for different ethnic groups.25

Finally, we also classify each ethnic group by whether its flow was above or below the me-dian over this period and classified as “large” those that were above the median. Similarly, wecomputed the difference between the national flow as measured in the administrative data andin the Census data and classify as those that have a difference in percentage term larger than themedian as high return migration ethnicities. Most of those correspond closely to the ones iden-tified in the existing literature as having large circular migration flows (Hatton and Williamson2005).

3.4 Stylized Facts and Summary Statistics

The late 19th and early 20th centuries were periods of massive migration, with the U.S. andother countries in the New World receiving significant inflows. For example at the turn of the 20thcentury around 15% of the United States population was foreign born, a number that remainedvery high until 1930.26 Annual gross immigration flows of more than a million people per yearare observed between 1900 and 1914, a trend that was broken with the onset of the war inEurope. After the war, a significant recovery in the flows is observed but they decline again afterthe introduction of changes in the immigration: the 1917 Immigration Act, the 1921 EmergencyQuota Act and the 1924 Johnson-Reid Act. By the end of the 1920 decade and the beginningof the Great Depression era the gross immigration flows are small. While we can consider thelimits on migration before 1917 to be almost non-existent, they increased progressively over thisperiod.27 These restrictions particularly limited the entry of “unskilled” workers and favoredfamily reunification. Furthermore, while a majority of the immigration over this period wasEuropean (or from countries with European ties such as Canada and Australia), an increase inthe number of Hispanics migrants was observed after 1920. One important dimension in whichimmigrants from different countries differed was in the return and seasonality patterns; whilefor some groups return migration and seasonal migration was not particularly prevalent, othergroups had important return rates, which were close to 30% for Spaniards and Italians between1890 and 1914 (see O’Rourke and Williamson 1999).

Geographic clustering is definitely visible in this period: we find in both data sets, annualimmigrations reports and decennial Census, an extremely high level of geographical clusteringby ethnic group. States popular among immigrants today were also popular in the early 20th

25Lafortune (2010) presents a similar classification of countries into groups using Census data from the same period.26As a reference, using data from the 2000 Census micro sample we calculate that the fraction of foreign born in the

United States lies around 11%; according to the 2007 and 2008 American Community Survey the same fraction liesslightly above 12%.

27See O’Rourke and Williamson (1999), Hatton and Williamson (2005) and the references therein for a more detaileddescription of the patterns of immigration for this period, including data for other countries besides the United States,and of the possible causes behind the protectionist backslash. Also see Bandiera, Rasul, and Viarengo (2010) for ananalysis of the validity of the immigration and emigration data for this period based on flows recorded at Ellis Island.

13

century: New York, Texas, California, etc. In Table 1 we present the top 3 states and the percent-age going to the 10 most popular states for each ethnic group included in the previous figures.We can observe that for all groups in all periods at least 3 out of 4 immigrants went to one of thetop 10 destinations of the group; furthermore, it is also evident that there is a significant degreeof persistence in the top 3 destinations in the Census and in the administrative data althoughthere is no perfect coincidence between the top 3 intended states and the top 3 actual states ofresidence for each of the ethnic groups. Furthermore, this appears to be fairly constant over time:the geographical concentration of the stock of immigrants in 1900 generally reflects that of theex-ante decisions made by subsequent migrants as well as their ex-post decisions. Geographicalclustering appears to be larger in ex-ante than ex-post decisions, suggesting that more individualsturn to less traditional destinations once they set foot in their country of adoption or that thosewho eventually decide to stay in the United States are not necessarily those who picked the mostpopular destinations. However, this difference is also decreasing with time as the last migrationcohorts of this period become more and more concentrated ex-post in the United States.28

We also document something that has often gone unnoticed in some previous studies which isthat there is also a very important occupational clustering by ethnicity. Table 2 shows the patternof occupational choice according to the declared occupation in the United States and accordingto the occupation in the country of origin. We observe there is also high concentration, with the10 most popular occupations for each ethnic accounting for 65% or more of the total membersof each group. While some occupations are particularly important for all immigrants (such aslaborers and farm laborers), there is also evidence of ethnic-specific clustering on different skills.This specialization in a given skill set appears also to be relatively constant over time. Oncemore, ex-ante occupational clustering is larger than ex-post but the difference remains more orless constant throughout the period. When we compare the Census and the administrative datawe can observe that in this case we see that unskilled occupations such as laborers, farm laborersand servants are among the most popular ones in both data sets. We also observe that for mostof the groups there is a certain level of agreement between the top three categories in each ofthe data sets, we also see that certain occupations are more popular ex-post than ex-ante, e.g.,miners, hotel keepers, manufacturers, etc.

Finally, the United States tend to demand different occupations in different states, leading togeographical clustering among individuals of a given occupation. This concentration is slightlyless marked than the two previous ones as the top ten states by occupation in 1900 capturedaround 70 per cent of all workers in that occupation, but it is still noticeable.

28This change could be related to the modifications to the immigration laws introduced after the end of World WarI which not only reduced immigration overall but also change the composition towards skilled workers and familyreunification.

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4 Empirical strategy

We explore whether we can find any evidence of the empirical regularities mentioned insection 2 by attempting to contrast the “fit” of counterfactual distributions built using differentmeasures of networks to the actual ex-ante and ex-post distribution of immigrants. We areconstrained in our empirical strategy by the fact that we do not have access to micro-level datawith the individual records of migrants entering the US during this period. However, we are,as we explained in section 3, capable of constructing flows by ethnicity (j), occupation (o), state(s) and time (t), which we will denote as njost, measured at the moment of arrival to and aftersettling in the United States.

We will approximate the (relative) strength of networks in the same way traditionally used inthe immigration literature, i.e. using the distribution by states of a given group of immigrants.However, given that the stock and the flow of immigrants at a given period of time could bedetermined by other factors such as specific demand shocks, we will include the stock of immi-grants with given characteristics who are residents in a specific location in a determined periodin the past as proxies of the strength of networks.29 Specifically, we measure our networks mostlyin 1900, and include in the regressions only the immigration flows from 1905 to 1930 to avoidtoo much proximity between our flow measure and the shares employed in the prediction. Thisargument is further strengthened by the fact that immigration had significantly dampened be-tween 1890 and 1900 and thus that our measure of stocks probably reflects the location choices ofimmigrants who arrived mostly before 1890.30 Finally, our interest will lie more in the compari-son of various proxies for networks than in the significance or magnitude of a given coefficient.If all proxies suffer from the same bias, the comparison would still be valid.

In order to control for other factors such as a particular match between an ethnicity and astate (because of a particular endowment of natural resources for example), we include a numberof fixed effects to attempt to capture this and other elements.

The framework presented in section 2 suggests an empirical regression of the following type:

njost = αAjosnjot + βBjosnjot + γCjosnjot + µjos + νjot + ωjst + ηost + ε jost (2)

where n represents the immigrant flow and the shares A, B and C represent different ways ofallocating the national flow njot to different states, related to distinct types of networks.

29We recognize that this could be a biased measure of networks if past immigrants simply responded to long-lastingdemand shocks to which current migrants are also responding at their moment of arrival.

30To the best of our knowledge there is no available digitalized sample from the 1890 US Census.

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Specifically, we define

Ajos ≡(

Njs

Nj

)(3a)

Bjos ≡(

Nos

No

)(3b)

Cjos ≡(

Njos

Njo

)(3c)

where N represents the stock of immigrants with certain characteristics already in the UnitedStates. Thus, Ajos refers to the share of individuals from one’s ethnic group who elected to livein state s in the past. The second share, Bjos refers to the geographical distribution of immigrantswho share one’s occupation. Finally, Cjos represents the share of individuals from the sameethnicity and the same occupation who lived in a particular state in the past. We will refer to Aas measuring an ethnic effect, to B as a labor/occupational effect and to C as an ethnic-specificoccupational component. 31

The regression equation includes fixed effects for all triple interactions between ethnicity,occupation, state and time. This allows to control for all possible confounding effects that areaffecting the immigration rates of a particular sub-group or the overall effects in a particularstate. We have used fewer fixed effects and the results are similar. Also, the standard errorsare clustered by ethnicity-occupation-state cells although very similar results were obtained withmuch more aggressive clustering.

Intuitively, this regression attempts to test whether a state has a differential growth in aparticular occupation between two different ethnic groups compared to a different state for oneof the three following reasons. Is it because that state was popular for a particular ethnic groupand that ethnic group has a growth in the number of people of that occupation coming fromthat ethnic group? Or because that state was popular for a particular occupation and there’snow an increase among that ethnic group of that occupation? Or because that state was popularfor individuals of that ethnic group and that occupation and there’s a growth in the number ofindividuals that satisfy both categories? Each one of the three components defined in equation(3) attempt to capture each one of these reasons separately, thus enabling us to compare themwith the actual observed distributions.

The theoretical framework presented in section 2 would suggest that when s represents theintended state of residence and o the skills with which an individual arrives in the United States,we should obtain estimates of α and β to be positive and significant while γ should not be. Onthe other hand, when s represents the actual state of residence and o the occupation in which an

31One could be worried about the potential collinearity between these three proxies. In order to verify that theresults are not corrupted by this problem, specifications where the ethnic share was built excluding individuals of thesame occupation and the occupation share was built excluding individuals of the same ethnicity were also run andthe results are qualitatively and quantitatively very similar.

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immigrant is working in the United States, one would expect to find the parameter γ to be largeand significant with little importance of either α or β, and that the magnitude of the estimatedcoefficient should be smaller when we consider the case of immigrants that have spent more timein the United States.

Unfortunately, njost is only measured ex-post in our data, that is where s represents the actualstate of residence and o one’s occupation in the United States. However, because the flows nost

and njst are observed both ex-ante and ex-post, it will be useful to estimate a simpler version ofequation (2) by summing over all ethnicities/occupations in a given cell. Denoting the variableover which one is actually summing by k′ and the other by k, where k, k′ = j, o, one obtains

nkst = α ∑k′

Ajosnjot + β ∑k′

Bjosnjot + γ ∑k′

Cjosnjot + µks + νkt + ωst + εkst (4)

where the left-hand side variable nkst represents the flow of immigrants arriving in period t,either intending on residing or living in state s, depending on whether we use the administrativeor the census data, respectively, and from ethnicity (when k = j) or occupation (when k = o).

Regression equation (4) again contrasts the strength of the three different networks and it doesso by comparing the correlation between the predicted distributions across states of individualsof characteristic k to the actual distribution observed in the data. Exactly as before the variablesA, B and C are our measures of the strength of the pure ethnic, the occupation/labor market,and the ethnic-occupation specific networks, respectively.

In this case, all the double-interactions between characteristic k, time t and state s are includedand standard errors are clustered at the ks level. We will estimate these equations using onlycells where k = j and where k = o separately as well as jointly. In the latter case, the estimationconstrains the parameters α, β and γ to be the same but allows for a more efficient estimation asthe parameters ωst are common to both sets of regressions and thus estimated using more datapoints.

One limit to the last regression equation is that the network measure C can only be obtainedfrom one data source (IPUMS) because we require information on the distribution according tothe jos cells over time. To verify that this is not driving our results, another simplification can bemade and the following equation is estimated:

nkst = α ∑k′

(Ajos

)njot + β ∑

k′

(Bjos

)njot + µks + νkt + ωst + εkst (5)

once more for k = j, o. In this regression equation, we simply exclude the role of ethnic-occupation network. The impact of these will be bundled up with that of ethnic effects (inAjos) or occupational factors(in Bjos). While the results obtained here are less informative, theygive us an opportunity of verifying the robustness of our results to the use of different measuresof networks.

17

The above regression includes a full set of fixed effects to control for any ethnic/occupation-state, ethnic/occupation-time and state-time confounding factors. However, we also used asmaller set of fixed effects and found similar results. The standard errors are clustered by ksbut similar results were obtained when clustering by state only. All regressions are un-weighted.

Table 3 presents the summary statistics for all variables used in the analysis, for each of theaggregations that will be employed in the empirical sections. Two elements are particularly im-portant to notice from this table. First, there is substantial amount of variation in both the actualand predicted flows to exploit in our analysis. Secondly, the mean and standard deviations ofour predicted flows measures are all roughly similar which implies that we can simply comparethe coefficients to understand the relative strength of the match from each of the counterfactualdistributions.

5 Results

5.1 Ethnic vs. Labor networks

We first begin our empirical exploration by contrasting the role of two general types of net-works, ethnic and labor markets, as presented in equation (5). This strategy, although less infor-mative in terms of the factors driving the location and occupational choices of migrants, has theadvantage of allowing us to compare the results across a variety of alternative proxies for therelative strength of networks.

The results of this strategy are presented in Table 4 when estimating the flow of immigrantsselecting a particular state and of a given ethnic group (in Panel A) and occupation (in PanelB). These tables include all individuals who report an occupation but very similar results wereobtained when all individuals, including those reporting no occupation, were incorporated inthe sample.32 Similarly, in these regressions a period t corresponds to a 5-year period, with nooverlap between two consecutive periods; we also explored the same specification using annualvariation and obtained similar results.

In the first three columns, we attempt to explain the ex-ante location choices (as measured inthe administrative data) while the last three use as the left-hand side variable ex-post decisionsfrom IPUMS. In the first three columns, the measures of predicted flows are constructed byallocating the national flow (nkt) from the administrative data using the two types of shares asdetailed in equation (5). The last three columns repeat the same exercise but this time allocatingthe national flow as measured from the IPUMS. Finally, columns also differ by how the networkmeasures are computed. In columns (1) and (4) we report results using the average share ofindividuals with a characteristic k or k′ who declare state s as their intended state of residencefor the first 6 years of the administrative data, that is from 1899 to 1904. In columns (2) and

32Results that also include individuals without an occupation in the sample are available upon request from theauthors.

18

(5) we use the published Census summary tables to compute the number of individuals from agiven ethnicity or from a given occupation residing in each state in 1900. This has the advantageof offering large sample sizes and reducing the probability of small sample bias. On the otherhand, it also assumes that immigrants who are active in a given set of occupations are locatingin the same states as their natives counterpart, because we do not have summary tables withemployment levels by state and birthplace. Finally, Columns (3) and (6) compute the share ofimmigrants living in a particular state from a given ethnic group or a given occupation in the1900 Census Public Use Micro-Sample (IPUMS).

The first three columns of Table 4 indicate that when trying to explain the ex-ante “intended”location decisions of migrants, the presence of individuals of their own ethnic group is particu-larly important. When we attempt to match the distribution of immigrants by ethnicity, we findthat the past location of immigrants by occupation also contribute to increase the match withthe actual distribution, although the magnitudes of the coefficient on the ethnic component arealways larger than those on the occupational element.

On the other hand, when we explore the factors behind the ex-post location decisions ofindividuals in the IPUMS, we find quite a distinct pattern, as observed in the last three columnsof Table 4. In this case, the importance of having individuals of one’s occupation who previouslylocated in that state becomes much more important and that of ethnic networks is reduced inboth panels.

These results would thus suggest that when one attempts to explain why a particular statenow has a larger set of individuals with a given set of skills claiming to be heading there, the factthat this state was particularly attractive to individuals with those skills in the past and that moreof them are now landing in the United States is of little relevance. Instead, our results suggestthat it is because a larger number of individuals with those particular sets of skills are comingfrom ethnic groups that had a large fraction of their previous migrants already living in thatgiven state by 1900.33 On the other hand, the increase in the number of immigrants performinga particular occupation in a particular state ex-post appears to be driven by the fact that moreimmigrants from that occupation are now within the United States and this particular state wasattractive to that occupation in the past. We find little evidence that this is linked to the fact thatthis state was attractive to a given ethnic group in the past and that this ethnic group now hasmore individuals reporting working as that occupation after their arrival.

The results appear to be fairly robust across the shares as measured from the IPUMS or theCensus tables, even if the occupational distributions from the Census table include all individualsin the United States, natives or immigrants. The counterfactual distributions built with sharesfrom the administrative data appear to be more weakly related to the actual flows than the ones

33Notice that our fixed effects allows us to remove the variation that comes from permanent characteristics thatcreate an intrinsic match between states and occupations or ethnicities. For example, we rule out that our results aredriven by a state having during all this period a dominant industry that offers a perfect match for a country of originthat happens to have the same dominant industry.

19

built from Census (table or micro-sample) data. This is comforting because it indicates thatimmigrants were less likely to follow the location decisions of recent migrants (as measured byflows) than the ones of established migrants (as measured by stock). This would suggest that ourresults are unlikely to be driven by contemporary shocks since in that case, one would expect theflows to have a higher predictive power than the stocks. Furthermore, this is consistent with theempirical results of Beaman (2009).

We also confirmed that the effect we find is not simply driven by similar patterns amongimmigrants, regardless of their ethnicity. First, all regressions include fixed effects for the in-teraction of state and time, thus capturing all characteristics specific to a particular location at agiven moment. A more stringent robustness test is provide in Table 5, where we add as additionalregressors, the counterfactual distribution one would obtain by allocating each period’s flow us-ing similar ethnicities. Specifically, we include all ethnicities within a broader ethnic group asdefined in Table A-2 except one’s own. For example, Irish immigrants are allocated using the ge-ographical distribution from 1900 IPUMS of other British Isles, Australia and Canada. In almostno case are these variables entering significantly in the regression equation and in all cases, themain results remain extremely similar. The results are fairly similar across samples and suggestthat it is the location choices of one’s particular ethnicity which influences locations decisionsand not that of fairly close ethnic groups.

5.2 More Disaggregated Measures of Networks

Having shown in the previous section that the results are more or less robust to the way thenetwork measures are constructed, we now turn to the estimation of the impact of our threedistinct measures of networks: ethnic, occupational and ethnic-specific occupational networks.In this case, all shares will be computed from the IPUMS as this is the only source that allows usto do such a distinction.

Table 6 presents our baseline results for four distinct samples. The first three panels corre-spond to the results of estimating equation (4) for the location choice of individuals by ethnicity(when k = j), by occupation (when k = o) and jointly (when k = j, o). Finally, panel D shows theresults of estimating equation (2). This table presents once more the regressions where ex-antedecisions are explored in the first two columns and those about ex-post decisions are in the fol-lowing two. Since the flow by ethnicity, occupation and state is only available in the IPUMS data,the last panel only includes results about ex-post location decisions.

The results from column (1) are remarkably similar across samples and all emphasize that thekey factor driving the ex-ante location decisions of individuals lies in the presence of individualsin 1900 that were of the same ethnicity than the newly arrived migrants. Allocating newly arrivedmigrants based on the past distribution of their occupation appears to only contribute to matchthe actual distribution by ethnicity, but not by occupation or in the joint estimation. Finally, in allregressions, the ethnic-specific occupational factor is small and insignificant.

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Our theoretical framework suggests that while an individual would pick ex-ante a locationbased on the presence of an ethnic network, that location would be particularly attractive toher when the occupations within this network are more similar to hers. This is explored incolumn (2) where an interaction term is added which captures the absolute difference betweenthe average occupational score of the network and that of the occupation of the migrant. As isexpected, this is negative in all specifications and significantly so in the first panel, suggestingthat while individuals select ex-ante locations based on the presence of a network of individualsof their ethnicity, they are particularly likely to pick one where the occupations of that networkare similar to theirs (although not necessarily identical).

The pattern is strikingly different in columns (3) and (4) where the ex-post distributionsare explored. The role of the ethnic factors disappears in this case and turns negative in allregressions. The counterfactual distributions based on the past location choices of immigrantsof one’s ethnicity are thus not related to the actual distribution, conditional on the other twocounterfactual distributions we constructed. The results presented in this table emphasize thatthe ex-post distributions appear to be much more closely correlated with our predicted flowsbased on occupational factors and more specifically, on the past location choices of immigrantssharing one’s ethnicity and one’s occupation.

Taken together, these results suggest that an increase in the number of individuals that intendto reside in a particular state, compared to the average for all states, is due mostly to the fact thatthis state was attractive in the past to ethnicities which are now receiving larger national flows.At the same, the results also imply that if we observe an increase in the number of migrantsactually practicing a given occupation in a particular state, compared to the average for all states,this is due mostly because that state in the past was popular among individuals who practicedthat occupation and who were of ethnicities that are currently experiencing larger flows. Themagnitudes of the coefficients should be interpreted as follows. Taking the example of Italianbakers, these results would suggest that a state which had received 10 percent more immigrantsof that ethnic group (irrespective of their occupation) in the past would attract 12 percent moreimmigrants of those newly arrived immigrants ex-ante. However, it is in a state where one found,in 1900, 10 percent more Italian bakers that one would eventually find 6-11 percent more of theseimmigrants ex-post, i.e. after they have settled in the United States.

Robustness Checks. We explore the robustness of these results in various ways. We first es-timate these regressions for each of our nine broader ethnic categories separately. While theresults are fairly noisy, they do not indicate that one ethnic group is driving the results morethan another. Table 7 further explores whether the results depend on the sample over whichthese estimates are obtained. One may first be worried that our results may be sensitive to prob-lems related to small cells. Columns (1) and (4) thus restrict the sample to groups whose nationalflows over the period were above the median. In Panel A, this corresponds to ethnicities with

21

large inflows, in Panel B, to occupations with large inflows, and in the last panel, to ethnicitiesand occupations that were observed in large numbers (ex-post). The results from these regres-sions are extremely similar to the ones presented in the previous table, suggesting that our mainempirical conclusions are not driven by small cells. Although not presented, we find that forsmall ethnicities, occupational groups are more relevant in driving the location choices and forrare occupations, ethnic networks appear to matter more. This is logical if one needs a networkof a significant size to be able to derive any benefits from it or if the returns to a network areincreasing with its size.

One could also be worried that after the introduction of limits on immigration, migrantsmay have had more of an incentive to provide false information to the immigration officer atarrival, thus contaminating our ex-ante measures. Furthermore, migrants arriving after the 1921Act were more skilled and more likely to come for the purpose of family reunification, thuspotentially explaining the importance of ethnic networks in that group. Columns (2) and (4)verify whether these concerns are founded by limiting the sample to immigrants arriving before1920, that is before any immigration restrictions were imposed. Once more, these results areextremely similar to those presented in Table 6.

Finally, the state of New York was the largest recipient of immigrants and, given that ourestimating equation is in levels and not in a logarithmic form, one could be worried that thisparticular state is driving our results. The results presented in columns (3) and (6) find littleindication of this: excluding this particular state has very limited impact on the overall results.

6 Exploring the difference between ex-ante and ex-post decisions

The previous section explored the factors influencing ex-ante and ex-post location decisionsof immigrants to the United States in the first three decades of the twentieth century. We findstriking differences between the factors that are determining location decisions at their arrivalin the United States and once they have settled. In the first case, ethnic networks and labormarket effects appear to drive the ex-ante location decisions of immigrants. Ex-post, however,we find a very important role to be played by the presence of individuals of the same ethnicityand occupation, which we interpret as evidence of the role played by ethnic networks in theincorporation of immigrants to the local labor markets.

In the theoretical framework presented in section 2 we suggest that this result is compatiblewith newly arrived immigrants taking on the same occupations of individuals in their ethnicnetworks at their arrival because they are more likely to receive job offers, or to obtain referralsfor positions, through them. In this sense, networks among immigrants become a tool to smooththe transition into the new labor market.

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6.1 Differences Across Occupations and Ethnicities

If our hypothesis that networks are the reason behind this pattern is correct, then, as weexplained in our motivating framework, we should be able to find some additional evidenceconsistent with it. For example, networks are more likely to play an important role among indi-viduals who arrive in the United States with low levels of skills.34 A doctor is much less likely tochange his occupation than a general laborer, for example. Furthermore, higher-skilled individ-uals may only offer a low chance of referrals in the same area of specialization. This hypothesisis explored in the first two columns of Table 8 where we divide the sample between occupationsthat had an occupational score (based on the average wage in 1950) above or below the median,which we take to be a proxy for the skill level of the occupation. On the one hand, for highlypaid occupations, ex-ante decisions are very closely related to the presence of an ethnic networkbut the ex-post decisions are driven by pure labor market considerations.35 On the other hand,individuals of lower paid occupations display the reliance on one’s ethnic-specific occupationalnetwork as presented above. Although not shown here, very similar results were obtained ifone divides the occupations based on the RCI classification of occupations into “professionals”,“skilled” and “unskilled”. In that case, it is for individuals with occupations identified as “un-skilled” that the pattern shown above is particularly marked, with both skilled tradesmen and,particularly, professionals relying ex-post more heavily on labor market considerations.

Columns (3) and (4) of Table 8 explore another distinction between occupations. We at-tempted to classify occupations based on whether their main product is a tradable or a non-tradable (see Table A-1 for which occupations were placed into each group). This is naturallya very gross matching as some occupations may produce a variety of goods, some which aretraded and some that are not, depending on the industry in which they perform. Neverthe-less, we would expect that the use of one’s ethnic network as a transition tool may be muchmore likely to occur in the case of occupations where the output is traded, as the wage of theseindividuals would be less likely to be affected by the number of individuals performing thatoccupation in a given location. Referrals, for these individuals, would thus be less “costly”. Col-umn (3) clearly shows that the pattern we identified above is particularly visible for occupationsproducing traded products. Column (4) show that ex-post decisions of individuals working in“non-traded” sectors are more influenced by labor market concerns than by the presence of in-dividuals sharing both one’s ethnicity and occupation. Somewhat surprisingly, however, ex-antedecisions among that group appear to be somewhat correlated with the presence of an ethnic-specific occupational network. The difference in the coefficients between the two columns issignificant at 10% level in Panel B and at 1% in Panel A.

34Hellerstein, McInerney, and Neumark (2008) use matched employer-employee data and find that networks gen-erated by residential proximity are relatively more important for minorities and less-skilled workers, particularlyHispanics.

35The coefficients of interest are statistically different for both groups at 5% for Panel A (although not for anycoefficient separately) and at 10% for Panel B.

23

Furthermore, learning about local labor markets might be particularly important for individ-uals who do not speak the same language as US natives. The first columns of 9 explore thisissue by separating ethnic groups between those who spoke English (British Isles, Canada andAustralia) and those who did not. The first group has limited variation that can be exploitedand the results are much noisier in that case. Nevertheless, it seems that the pattern we iden-tified in the overall sample is most closely related to that of non-English speaking individuals.English-speaking ethnicities do rely on the ethnic-specific occupational networks but they appearto do so consistently both at arrival and once settled, which is what would be expected if thereis no learning involved in their adaptation process. The hypothesis of equal coefficients for bothgroups is not rejected in the ex-ante data but clearly rejected in the ex-post regressions.

6.2 Time since arrival

Our framework does suggest that if the lack of credentials or verifiable experience was oneof the reasons why ethnic networks were important, then ethnic-specific occupational networksshould become less relevant (i.e. reduce their correlation with the observed location and occupa-tion choices) as immigrants stay for a longer time in their new country. Thus for a given cohort ofimmigrants the estimated effect of the ethnic-specific occupation network should be decreasingin the time since arrival to the United States.

The last two columns of Tables 8 and 9 explore this issue. The difference between ex-anteand ex-post decisions is particularly marked for individuals who have just arrived in the UnitedStates. It is for that group that we observe a very strong preference for electing a state of in-tended residence based on ethnicity and labor market considerations but a very strong preferencefor eventually residing in a state where individuals who share one’s occupation and ethnicitycan be found. For more established immigrants, we find a different pattern where all factorsappear to determine almost equally ex-ante decisions but ex-post decisions depend much moreon factors related to labor markets, although not particularly the ones related to one’s ethnicity.An extremely similar pattern (if only maybe more marked) can be found in the correspondingcolumns of Table 8. The fact that the ex-ante decisions appear to be driven by similar factors is re-assuring, suggesting that this is not because these two groups were somehow different at entry.36

Furthermore, we have repeated this exercise adding data for the groups with even longer staysand found similar results to the ones we present; the effect of the ethnic-occupation network isdiminished as the migrants spend more time in the United States, while the other two, the pureethnic and the labor market effects, are strengthened.37

36The hypothesis of equal coefficients cannot be rejected for either sets of parameters because of the noisiness ofsome results. However, the test statistic is much larger for ex-post regressions than ex-ante ones.

37Results are available upon request.

24

6.3 Alternative Channels

Two other factors could explain the difference between ex-ante and ex-post decisions. First, itmay be that individuals without a group of past migrants sharing their ethnicity and occupationare more likely to leave the United States. As we discussed before, this is a period where returnmigration was a fairly important phenomena and thus could explain the patterns we observe.Since our measure of ex-ante flows include all immigrant entries into the United States but ourex-post measure involves only those who eventually stay, the difference in our results couldbe driven by selective return migration. In order to evaluate this alternative hypothesis, thefirst two columns of Table 9 estimates the same regressions but dividing the sample betweenethnicities with high and low levels of return migration. We define a group as high returnmigration if the national (gross) flow of immigrants over the entire period of analysis obtainedfrom the administrative data is, in percentage term, above the median compared to the net flowobtained from the Census data.38 The results presented in Table 9 do not, however, suggestthat the location choices of these two groups are extremely different. In both cases, the ex-antedistribution appears to be best approximated when the national flow is allocated according tothe presence of individuals of one’s ethnic group while the ex-post distribution appears to bebest approximated when using the ethnic-specific occupational networks.39 This does not seemto suggest that the selective return migration can explain the patterns we have highlighted above.

Also, the importance of the pure labor markets do appear to be more important for groupsthat have low levels of return migration, particularly for the ex-ante distribution, which is con-sistent with our framework. When individuals are forecasting staying in their new location for along period of time, the quality of the labor market for their skills becomes more crucial.

Secondly, it may also be that individuals first locate based on general considerations butthen relocate within the United States to a location where they have someone from their ethnicgroup who also share their occupation. The difference in the pattern observed would then notbe driven by the fact that immigrants change their occupation in response to the composition oftheir networks but rather that they re-optimize their location decision to bring themselves closerto a network that can provide them with referrals in their given occupation, for example. Toexplore this, the ideal data would have included information regarding internal migration, as theone currently compiled by the Census. Unfortunately, for this period, no question on whetheran individual has moved recently is available. However, the results presented above suggest thatthe pattern we observe is particularly strong for individuals recently arrived to the United States.If internal migration is an important explanation between the phenomena we just described,we would expect that individuals who have been in the United States for a longer time period

38This definition is in agreement with Bandiera, Rasul, and Viarengo’s (2010) assessment of the emigration patternsfor this period. However, we also conducted the same analysis using the emigration flows from the administrativedata to classify the ethnicities into the high and low return groups, and we obtained similar results.

39The equality of coefficients can be rejected in Panel A because of the difference in the second and third coefficient;however, we cannot reject equality of the coefficients in Panel B.

25

would have had more time to re-optimize and thus their ex-post location decisions may respondto different factors. Thus, internal migration would only explain this pattern if individuals, attheir arrival to the United States, moved temporarily to a location where their ethnic-specificoccupational network is more important to then return to one where their skills are more valuedlater on.

Finally, we can further explore the role of internal migration by exploring the differencesbetween IPUMS and administrative data. If immigrants were mostly changing their state of resi-dence but not their occupation in response to the presence of networks, the results of regressionswhere we attempt to predict the ex-post distribution by allocating the ex-ante national flows andvice versa should be identical. This is because the national flow njot should be the same whenmeasured in the administrative data than in the Census data if individuals do not change theiroccupation upon arrival. Thus, the same pattern should be observed when estimating equations(4) or (5) whether or not the right-hand side variable is built using administrative or Census data.The result of this exercise is presented in Table 10. Each column here represents one of our sam-ple. In the first panel, the left-hand side variable is measured ex-ante from the administrative databut the predicted flows are built using the national flows from the IPUMS dataset. The reverse istrue in Panel B. The results of that table are very different from those presented in Table 6. In thefirst panel, the results are fairly noisy but those using the sample of occupation-state-time cellspresented in Column (2) suggest that contrary to our previous results, the counterfactual distri-bution built from allocating the immigrants by their actual occupation in the US according to thepast location of immigrants who shared the same occupation has significant predictive powerwhen trying to identify ex-ante location decisions, something that was absent of the previousresults. Similarly, in Panel B, the importance of ethnic-specific occupational factors are almostalways absent and the ethnic component becomes much more significant. This is not consistentwith a situation where individuals simply relocate after their arrival to the United States.

7 Conclusions

We have thus presented, through various methods, evidence regarding the role played bynetworks and labor market characteristics in the determination of the location and occupationchoices of immigrants in the United States in the first three decades of the twentieth century. Wehave shown empirical evidence consistent with the hypothesis that the presence of individuals ofone’s ethnicity, but not necessarily in the same occupation, is strongly influential in the intendedlocation reported by an immigrant at her arrival in the United States. However, the actual locationand occupation choices appear to be driven much more by considerations linked to the labormarkets, in particular to the presence of individuals of one’s ethnicity who also share one’soccupation, a measure that we call ethnic-specific occupation network. We have also shownsuggestive evidence that the reason behind this change in behavior lies in the fact that unskilled

26

immigrants are likely to change their occupation once they arrive into the United States to benefitfrom the labor market connections of their ethnic networks.

The findings in this paper help us understand the role networks play in the location choiceof immigrants. While most studies have looked separately at location or occupation choices,our study combines both decisions and seems to suggest important interactions between the twochoices. It also emphasizes the dynamic aspect of the process as immigrants spend more time intheir destination country, a fact that has not been carefully documented before. It even seems toindicate that ethnic networks are particularly useful as a smoothing mechanism for newly arrivedimmigrants and are not something that migrants need to permanently rely upon.

These conclusions might also be relevant in shaping optimal immigration policy. If immi-grants of different skill levels select their location and occupation in their new country usingnetworks differently, this has implications for the impact that these immigrants can have on na-tive and previous migrant workers. Understanding the mechanics of the effects and its connec-tion to immigrant geographical and occupational concentration can thus shed light on potentialmeasures aimed at improving immigrants incorporation into labor markets and to benefit nativepopulation.

While our specific data allows us to look at a historical period, our results might be relevantfor today’s immigration debate. The immigration wave of the early 20th century resembles thatof today in some important aspects: migrants were less skilled than natives (or at least wereperceived to be less skilled), they were perceived to be culturally different than the remainder ofthe population, and their arrival generated controversy over their potential adverse effects. Moreimportantly for our results and analysis, immigrants today also share ethnic and occupationalnetworks as defined in this paper. Whether the patterns we have highlighted here are also presentin the current migration wave is a subject of future research.

References

Altonji, Joseph G. and David Card. 1991. “The Effects of Immigration on the Labor MarketOutcomes of Less-Skilled Natives.” In Immigration, Trade and Labor, edited by John M. Abowdand Richard B. Freeman. Chicago, IL: University of Chicago Press.

Aslund, O. 2005. “Now and Forever? Initial and Subsequent Location Choices of Immigrants.”Regional Science and Urban Economics 35:141–165.

Bandiera, Oriana, Imran Rasul, and Martina Viarengo. 2010. “The Making of Moden America:Migratory Flows in the Age of Mass Migration.” URL http://www.homepages.ucl.ac.uk/~uctpimr/research/ellis_accounting.pdf. Mimeo, Harvard University, LSE and UCL.

Bauer, T., G. S. Epstein, and I. N. Gang. 2005. “Enclaves, Language, and the Location Choice ofMigrants.” Journal of Population Economics 18:649–662.

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Beaman, Lori. 2009. “Social Networks and the Dynamics of Labor Market Outcomes: Evidencefrom Refugees Resettled in the U.S.” URL http://faculty.wcas.northwestern.edu/~lab823/beaman_ethnicnetworks.pdf. Mimeo, Northwestern University.

Calvo-Armengol, Antoni and Matthew O. Jackson. 2004. “The Effects of Social Networks onEmployment and Inequality.” American Economic Review 94 (3):426–454.

———. 2007. “Networks in labor markets: Wage and employment dynamics and inequal-ity.” Journal of Economic Theory 132 (1):27–46. URL http://ideas.repec.org/a/eee/jetheo/v132y2007i1p27-46.html.

Card, David. 2001. “Immigrant Inflows, Native Outflows, and the Local Labor Market Impactsof Higher Immigration.” Journal of Labor Economics 19:22–64.

Card, David and Ethan Lewis. 2005. “The Diffusion of Mexican Immigrants During the 1990s:Explanations and Impacts.” NBER Working Paper 11552, National Bureau of Economic Re-search.

Chen, Yuyu, Ginger Jin, and Yang Yue. 2010. “Peer Migration in China.” Mimeo, University ofMaryland–College Park.

Cortés, Patricia. 2008. “The Effect of Low-Skilled Immigration on U.S. Prices: Evidence from CPIData.” Journal of Political Economy 116 (3):381–422. URL http://www.journals.uchicago.edu/doi/abs/10.1086/589756.

Cortés, Patricia and José A. Tessada. 2011. “Low-Skilled Immigration and the Labor Supply ofHighly Skilled Women.” American Economic Journal: Applied Economics.

Fairchild, Henry Pratt. 1925. Immigration: a world movement and its American significance. Macmil-lan Co., rev. edition ed.

Federman, May, David Harrington, and Kathy Krynski. 2006. “Vietnamese Manicurists: Displac-ing Natives or Finding New Nails to Polish?” Industrial and Labor Relations Review 59 (2):302–318.

Friedberg, Rachel M. 2001. “The Impact Of Mass Migration On The Israeli Labor Market.”Quarterly Journal of Economics 116 (4):1373–1408.

Hatton, Timothy J. and Jeffrey G. Williamson. 2005. Global Migration and the World Economy: TwoCenturies of Policy and Performance. Cambridge: MA: MIT Press.

Hellerstein, Judith, Melissa McInerney, and David Neumark. 2008. “Measuring the Importanceof Labor Market Networks.” Mimeo, University of Maryland.

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Lafortune, Jeanne. 2010. “Making Yourself Attractive: Pre-Marital Investments and the Returnsto Education in the Marriage Market.” URL http://sites.google.com/site/jeannelafortune/research. Mimeo, University of Maryland.

Manski, Charles F. 1993. “Identification of Endogenous Social Effects: The Reflection Problem.”Review of Economic Studies 60 (3):531–542. URL http://www.jstor.org/stable/2298123.

Minns, Chris. 2000. “Income, Cohort Effects, and Occupational Mobility: A New Look at Immi-gration to the United States at the Turn of the 20th Century.” Explorations in Economic History37:326–350.

Munshi, Kaivan. 2003. “Networks in the Modern Economy: Mexican Immigrants in the US LaborMarket.” Quarterly Journal of Economics 118 (2):549–599.

———. 2009. “Strength in Numbers: Networks as a Solution to Occupational Traps.” Mimeo,Brown University.

Munshi, Kaivan and Nicholas Wilson. 2008. “Identity, Parochial Institutions, and Career Deci-sions: Linking the Past to the Present in the American Midwest.” URL http://www.econ.brown.edu/fac/Kaivan_Munshi/midwest7.pdf. Mimeo, Brown University.

O’Rourke, Kevin H. and Jeffrey G. Williamson. 1999. Globalization and History: The Evolution of aNineteenth-Century Atlantic Economy. Cambridge: MA: MIT Press.

Patel, Krishna and Francis Vella. 2007. “Immigrant Networks and Their Implications for Occu-pational Choice and Wages.” Discussion Paper 3217, IZA.

Peri, Giovanni and Chad Sparber. 2009. “Task Specialization, Immigration, and Wages.” AmericanEconomic Journal: Applied Economics 1 (3):135–69. URL http://ideas.repec.org/a/aea/aejapp/v1y2009i3p135-69.html.

Ruggles, Steven, J. Trent Alexander, Katie Genadek, Matthew B. Schroeder, and Matthew Sobek.2010. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis,MN: Minnesota Population Center.

29

Tabl

e1.

Spat

iald

istr

ibut

ion

ofim

mig

rant

sby

ethn

icgr

oup,

over

tim

e,C

ensu

san

dad

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sted

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ks19

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1901

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1921

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1st

2nd

3rd

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101s

t2n

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Brit

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NY

MA

PA75

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78.1

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MA

NY

78.4

MA

MI

NY

90.6

Fren

chM

AM

IN

Y75

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IN

Y76

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AM

IN

Y83

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Euro

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86.3

MA

PAN

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APA

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87.5

PAN

JN

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TXA

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93.9

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CA

TX89

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77.1

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80.0

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89.7

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77.9

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PA84

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88.7

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77.0

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78.0

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84.9

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95.1

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94.8

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84.9

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80.4

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80.1

Rus

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PAIL

93.1

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PAIL

90.6

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90.9

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88.1

NY

PAO

H83

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87.5

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untr

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88.9

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85.8

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83.9

30

Tabl

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nof

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grou

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data

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ta

Brit

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Labo

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Serv

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68.1

Labo

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Cle

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69.4

Labo

rers

Serv

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Cle

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72.3

Fren

chLa

bore

rsTe

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wrk

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Farm

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75.9

Labo

rers

Farm

labo

rers

Serv

ants

73.0

Labo

rers

Serv

ants

Farm

labo

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68.9

Sout

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Labo

rers

Min

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rs82

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bore

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72.7

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ts84

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bore

rsFa

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s90

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Labo

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navi

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Farm

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Labo

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Serv

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77.4

Labo

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labo

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79.6

Labo

rers

Serv

ants

Cle

rks

70.7

Rus

sian

san

dot

hers

Labo

rers

Tailo

rsM

anuf

actu

rers

82.8

Labo

rers

Min

ers

Tailo

rs81

.2La

bore

rsM

erch

ants

Man

ufac

ture

rs72

.3O

ther

Euro

pean

sLa

bore

rsFa

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iner

s83

.0La

bore

rsM

iner

sSe

rvan

ts85

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bore

rsSe

rvan

tsH

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keep

ers

73.0

Oth

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untr

ies

Labo

rers

Farm

labo

rers

Oth

erun

skill

ed94

.3La

bore

rsFa

rmla

bore

rsSe

rvan

ts89

.3Fa

rmla

bore

rsH

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keep

ers

Oth

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skill

ed89

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Pane

lB

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mad

min

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ativ

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ta

Brit

ish

ance

stry

Serv

ants

Labo

rers

Cle

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75.3

Serv

ants

Labo

rers

Cle

rks

72.3

Fren

chLa

bore

rsSe

rvan

tsFa

rmla

bore

rs71

.9La

bore

rsSe

rvan

tsFa

rmer

s75

.6So

uth

Euro

pean

sLa

bore

rsFa

rmla

bore

rsSe

rvan

ts91

.7La

bore

rsSe

rvan

tsFa

rmla

bore

rs84

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ispa

nics

Labo

rers

Tobb

aco

wke

rsSe

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ts82

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bore

rsSe

rvan

tsC

lerk

s89

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erm

anic

Serv

ants

Labo

rers

Farm

labo

rers

75.3

Serv

ants

Farm

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Cle

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72.8

Scan

dina

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bore

rsSe

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bore

rs91

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tsFa

rmer

sFa

rmla

bore

rs80

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ussi

ans

and

othe

rsLa

bore

rsFa

rmla

bore

rsSe

rvan

ts90

.3Se

rvan

tsLa

bore

rsTa

ilors

78.1

Oth

erEu

rope

ans

Labo

rers

Farm

labo

rers

Serv

ants

95.7

Labo

rers

Serv

ants

Farm

labo

rers

87.5

Oth

erco

untr

ies

Farm

labo

rers

Labo

rers

Mer

chan

ts88

.2M

erch

ants

Labo

rers

Serv

ants

77.1

31

Table 3. Summary Statistics

Variable Mean Standard deviation

Data set by ethnicity-state-period (N=7140)

Flow from administrative data 2071.3 11806.9Flow from IPUMS 729.5 3910.9Pred. flow based on ethn. (Admin shares, admin flow) 2071.3 13511.3Pred. flow based on ethn. (Census shares, admin flow) 1998.4 10636.9Pred. flow based on ethn. (IPUMS shares, admin flow) 2071.3 10637.6Pred. flow based on ethn. (Admin shares, IPUMS flow) 729.5 5047.0Pred. flow based on ethn. (Census shares, IPUMS flow) 716.5 3913.4Pred. flow based on ethn. (IPUMS shares, IPUMS flow) 729.5 3877.3Pred. flow based on occ. (Admin shares, admin flow) 1423.1 7796.4Pred. flow based on occ. (Census shares, admin flow) 1426.2 3722.1Pred. flow based on occ. (IPUMS shares, admin flow) 1426.2 5008.0Pred. flow based on occ. (Admin shares, IPUMS flow) 717.3 4095.3Pred. flow based on occ. (Census shares, IPUMS flow) 719.4 2242.0Pred. flow based on occ. (IPUMS shares, IPUMS flow) 719.4 2908.4Pred. flow based on ethn.-occ. (admin flow) 1410.0 7139.4Pred. flow based on ethn.-occ. (IPUMS flow) 714.4 3931.5Pred. flow based on ethn.*occ. score diff. (admin flow) 17186.1 98064.1Pred. flow based on ethn.*occ. score diff. (IPUMS flow) 6166.7 31332.0

Data set by occupation-state-period(N=15810)

Flow from administrative data 643.4 6534.4Flow from IPUMS 324.9 2940.5Pred. flow based on ethn. (Admin shares, admin flow) 644.1 7422.1Pred. flow based on ethn. (Census shares, admin flow) 616.3 5377.0Pred. flow based on ethn. (IPUMS shares, admin flow) 644.1 5664.0Pred. flow based on ethn. (Admin shares, IPUMS flow) 324.9 3889.7Pred. flow based on ethn. (Census shares, IPUMS flow) 319.1 2889.4Pred. flow based on ethn. (IPUMS shares, IPUMS flow) 324.9 2911.3Pred. flow based on occ. (Admin shares, admin flow) 642.0 7107.9Pred. flow based on occ. (Census shares, admin flow) 643.4 3739.7Pred. flow based on occ. (IPUMS shares, admin flow) 643.4 4942.8Pred. flow based on occ. (Admin shares, IPUMS flow) 324.0 3788.0Pred. flow based on occ. (Census shares, IPUMS flow) 324.9 2068.7Pred. flow based on occ. (IPUMS shares, IPUMS flow) 324.9 2778.5Pred. flow based on ethn.-occ. (admin flow) 636.8 5598.7Pred. flow based on ethn.-occ. (IPUMS flow) 322.6 3126.3Pred. flow based on ethn.*occ. score difference (admin flow) 7761.5 91167.0Pred. flow based on ethn.*occ. score difference (IPUMS flow) 2784.9 23561.5

32

Variable Mean Standard deviation

Data set by ethnicity-occupation-state-period (N=442680)

Flow from IPUMS 11.6 262.3Pred. flow based on occ. (admin flow) 23.0 349.8Pred. flow based on ethn. (admin flow) 23.0 516.7Pred. flow based on ethn.-occ. (admin flow) 22.7 587.8Pred. flow based on occ. (IPUMS flow) 11.6 196.6Pred. flow based on ethn. (IPUMS flow) 11.6 255.0Pred. flow based on ethn.-occ. (IPUMS flow) 11.5 278.9Pred. flow based on ethn.*occ. score difference (admin flow) 277.2 7925.4Pred. flow based on ethn.*occ. score difference (IPUMS flow) 99.5 2142.7

33

Table 4. Explaining changes in location choices of immigrants (excluding individuals with nooccupation)

From administrative data From IPUMS dataAdmin Census IPUMS Admin Census IPUMS

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

Panel A: By ethnicity (N=7140)

Pred. flow by ethnicity 0.623*** 0.916*** 0.838*** 0.273* 0.590*** 0.539***(0.085) (0.048) (0.089) (0.108) (0.076) (0.098)

Pred. flow by occupation 0.390** 0.641*** 0.537*** 0.540*** 0.794*** 0.626**(0.130) (0.132) (0.127) (0.146) (0.229) (0.192)

R-square 0.963 0.947 0.936 0.907 0.929 0.930

Panel B: By occupation (N=15810)

Pred. flow by ethnicity 0.549*** 1.124*** 1.163*** 0.121 0.437*** 0.102(0.150) (0.170) (0.238) (0.108) (0.092) (0.104)

Pred. flow by occupation 0.344* 0.109 -0.166 0.564** 0.835*** 0.918***(0.150) (0.141) (0.226) (0.185) (0.174) (0.141)

R-square 0.969 0.937 0.930 0.931 0.944 0.960The left-hand side variable of the regressions presented in this table is the flow of immigrantsfrom a particular ethnicity/occupation electing a particular state in a particular period of migra-tion and the right-hand side variables are predicted flows as detailed in the left-hand column.Actual and predicted flows are measured from the administrative data (ex-ante) in the firstthree columns but from the IPUMS (ex-post) in the last three. In columns (1) and (4), thesepredicted flows are built using location shares from the administrative data, in columns (2) and(5), from the 1900 Census tables and in columns (3) and (6), from the 1900 IPUMS. All regres-sions include fixed effects for the double interactions of state, ethnicity/occupation and periodof immigration.Standard errors are clustered at the group-state level.*: 5% significance, **: 1% significance, ***: 0.1% significance

34

Table 5. Robustness checks: using close ethnic groups as an alternative

From administrative data From IPUMS dataAdmin Census IPUMS Admin Census IPUMS

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

Panel A: By ethnicity (N=6885)

Pred. flow by ethnicity 0.625*** 0.898*** 0.839*** 0.274* 0.577*** 0.536***(0.086) (0.057) (0.088) (0.110) (0.075) (0.097)

Pred. flow by occupation 0.378** 0.604*** 0.506*** 0.522** 0.775** 0.620**(0.133) (0.131) (0.123) (0.166) (0.253) (0.215)

Pred. flow by close eth. grp 0.015 0.078 0.025 0.035 0.059 0.018(0.008) (0.050) (0.052) (0.051) (0.040) (0.052)

R-square 0.964 0.949 0.937 0.909 0.930 0.930

Panel B: By occupation (N=15810)

Pred. flow by ethnicity 0.541*** 1.063*** 1.309*** 0.110 0.370*** 0.120(0.150) (0.163) (0.234) (0.100) (0.086) (0.092)

Pred. flow by occupation 0.324* 0.116 -0.124 0.501** 0.813*** 0.983***(0.139) (0.139) (0.227) (0.184) (0.180) (0.119)

Pred. flow by close eth. grp 0.034 0.083 -0.223* 0.122* 0.159 -0.136(0.029) (0.090) (0.099) (0.051) (0.111) (0.095)

R-square 0.969 0.937 0.933 0.933 0.946 0.962The left-hand side variable of the regressions presented in this table is the flow of immigrantsfrom a particular ethnicity (Panels A) or a particular occupation (Panels C) electing a particularstate in a particular period of migration and the right-hand side variables are predicted flows asdetailed by the list. Actual and predicted flows are measured from the administrative data (ex-ante) in the first three columns but from the IPUMS (ex-post) in the last three. In columns (1)and (4), predicted flows are built using location shares from the administrative data, in columns(2) and (5), from the 1900 Census tables and in columns (3) and (6), from the 1900 IPUMS. Allregressions include fixed effects for the double interactions of state, ethnicity/occupation andperiod of immigration.Standard errors are clustered at the group-state level.*: 5% significance, **: 1% significance, ***: 0.1% significance

35

Table 6. Explaining changes in location choices of immigrants, networks more finely defined

From administrative data From IPUMS data

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

Panel A: By ethnicity (N=7140)Pred. flow by ethnicity 1.226*** 2.541*** -0.609** -0.782**

(0.174) (0.381) (0.231) (0.275)Pred. flow by ethnicity*occ. score diff. -0.097*** 0.023

(0.028) (0.014)Pred. flow by occupation 0.613*** 0.615*** 0.275*** 0.278***

(0.165) (0.152) (0.072) (0.072)Pred. flow by ethn.-occupation -0.225 -0.264 1.346*** 1.338***

(0.148) (0.139) (0.240) (0.239)R-square 0.929 0.938 0.953 0.953

Panel B: By occupation (N=15810)Pred. flow by ethnicity 1.193*** 1.409*** -0.182 -0.196

(0.245) (0.217) (0.103) (0.125)Pred. flow by ethnicity*occ. score diff. -0.012 0.002

(0.014) (0.006)Pred. flow by occupation -0.072 -0.076 0.561*** 0.561***

(0.155) (0.155) (0.099) (0.100)Pred. flow by ethn.-occupation -0.104 -0.143 0.536*** 0.636***

(0.120) (0.103) (0.115) (0.116)R-square 0.937 0.939 0.966 0.966

Panel C: Joint estimation (N=22950)Pred. flow by ethnicity 1.249*** 1.682*** -0.236 -0.315

(0.210) (0.270) (0.145) (0.193)Pred. flow by ethnicity*occ. score diff. -0.026 0.011

(0.018) (0.008)Pred. flow by occupation 0.136 0.129 0.323*** 0.323***

(0.118) (0.116) (0.061) (0.062)Pred. flow by ethn.-occupation -0.179 -0.249* 0.839*** 0.836***

(0.139) (0.103) (0.126) (0.127)R-square 0.924 0.927 0.954 0.954

Panel D: By ethnicity and occupation (N=442680)Pred. flow by ethnicity -0.212 -0.200

(0.116) (0.125)Pred. flow by ethnicity*occ. score diff. -0.002

(0.007)Pred. flow by occupation 0.159* 0.159*

(0.065) (0.065)Pred. flow by ethn.-occupation 0.891*** 0.891***

(0.101) (0.101)R-square 0.747 0.745The left-hand side variable of the regressions presented in this table is the flow of immigrants from a particularethnicity/occupation electing a particular state in a particular period of migration and the right hand side variablesare predicted flows constructed as detailed by the left-hand column using shares from 1900 IPUMS. Actual andpredicted flows are measured from the administrative data (first two columns) and from IPUMS (last two). Allregressions include fixed effects for the double interactions of state, group and period of immigration for the firstthree panels and for all triple interactions of state, ethnicity, occupation and period of immigration for panel D.Standard errors are clustered at the group-state level.*: 5% significance, **: 1% significance, ***: 0.1% significance

36

Table 7. Robustness checks: networks more finely defined

From administrative data From IPUMS dataLarge Bef. 1920 Excl. NY Large Bef. 1920 Excl. NY

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

Panel A: By ethnicityPred. flow by ethnicity 1.195*** 1.188*** 0.948*** -0.645** -0.484* -0.529*

(0.178) (0.206) (0.220) (0.228) (0.250) (0.246)Pred. flow by occupation 0.651*** 0.740*** 0.461*** 0.254** 0.295*** 0.290***

(0.173) (0.221) (0.139) (0.077) (0.079) (0.074)Pred. flow by ethn.-occupation -0.231 -0.236 -0.237 1.409*** 1.218*** 1.255***

(0.138) (0.181) (0.128) (0.242) (0.250) (0.255)R-square 0.938 0.939 0.846 0.958 0.965 0.927N 3570 4284 7000 3570 4284 7000

Panel B: By occupationPred. flow by ethnicity 1.185*** 1.200*** 1.525*** -0.178 -0.181 -0.032

(0.244) (0.279) (0.252) (0.102) (0.110) (0.081)Pred. flow by occupation -0.070 -0.053 -0.225 0.557*** 0.451*** 0.407***

(0.154) (0.195) (0.138) (0.099) (0.123) (0.093)Pred. flow by ethn.-occupation -0.103 -0.093 -0.209 0.535*** 0.664*** 0.627***

(0.117) (0.140) (0.112) (0.115) (0.139) (0.102)R-square 0.938 0.951 0.920 0.967 0.974 0.954N 7905 9486 15500 7905 9486 15500

Panel C: By ethnicity and occupationPred. flow by ethnicity -0.215 -0.278* -0.265*

(0.118) (0.121) (0.131)Pred. flow by occupation 0.157* 0.110 0.214***

(0.065) (0.067) (0.070)Pred. flow by ethn.-occupation 0.894*** 0.971*** 0.902***

(0.101) (0.104) (0.123)R-square 0.746 0.744 0.658N 244290 265608 434000The left-hand side variable of the regressions presented in this table is the flow of immigrants from a partic-ular ethnicity/occupation electing a particular state in a particular period of migration and the right handside variables are predicted flows constructed as detailed by the left-hand column using shares from 1900IPUMS. Actual and predicted flows are measured from the administrative data (first three columns) andfrom IPUMS (last three). All regressions include fixed effects for the double interactions of state, group andperiod of immigration for the first two panels and for all triple interactions of state, ethnicity, occupationand period of immigration for panel C.Standard errors are clustered at the group-state level.*: 5% significance, **: 1% significance, ***: 0.1% significance

37

Table 8. Exploring the differences between ex-ante and ex-post regressions, by occupation.

High occ. Low occ. Traded Non- Within Betweenscore score traded 5 years 5-10 years

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

Panel A: Flows from administrative data

Pred. flow by ethnicity 1.066** 1.186*** 1.205*** 0.428 1.239*** 1.132***(0.342) (0.243) (0.242) (0.397) (0.303) (0.242)

Pred. flow by occupation 0.130 -0.070 -0.135 0.224 -0.097 -0.102(0.194) (0.154) (0.154) (0.179) (0.206) (0.147)

Pred. flow by ethn.-occupation 0.119 -0.102 -0.101 0.405* -0.103 -0.108(0.200) (0.118) (0.110) (0.205) (0.154) (0.101)

R-square 0.922 0.938 0.933 0.983 0.905 0.954N 7905 7905 8925 6885 9486 6324

Panel B: Flows from IPUMS data

Pred. flow by ethnicity 0.165 -0.182 -0.186 0.072 -0.175 -0.186(0.146) (0.102) (0.103) (0.153) (0.121) (0.147)

Pred. flow by occupation 0.764*** 0.558*** 0.553*** 0.764*** 0.516*** 1.007**(0.213) (0.099) (0.099) (0.112) (0.122) (0.348)

Pred. flow by ethn.-occupation 0.215 0.536*** 0.542*** 0.250* 0.573*** 0.156(0.158) (0.116) (0.117) (0.126) (0.154) (0.252)

R-square 0.885 0.967 0.966 0.962 0.966 0.967N 7905 7905 8925 6885 9486 6324The left-hand side variable of the regressions presented in this table is the flow of immigrants from aparticular occupation electing a particular state in a particular period of migration and the right handside variables are predicted flows constructed as detailed by the left-hand column using shares from1900 IPUMS. Actual and predicted flows are measured from the administrative data (Panel A) and fromIPUMS (Panel B). The first 2 columns divide the sample by the occupational score of each occupation.Columns (3) and (4) compare occupations classified as traded and non-traded as detailed in Table A-1.Column (5) restricts the sample to immigrants observed in the Census within 5 years of their arrivalto the United States while the last includes individuals observed in the Census 5-10 years after theirarrival. All regressions include fixed effects for the double interactions of state, occupation and periodof immigration.Standard errors are clustered at the occupation-state level.*: 5% significance, **: 1% significance, ***: 0.1% significance

38

Table 9. Exploring the differences between ex-ante and ex-post regressions, by ethnicity.

English Non Low High Within BetweenEnglish return return 5 years 5-10 years

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

Panel A: Flows from administrative data

Pred. flow by ethnicity -0.424 1.222*** 1.264*** 0.943*** 1.243*** 1.203***(1.156) (0.177) (0.209) (0.182) (0.224) (0.194)

Pred. flow by occupation 0.844 0.618*** 0.710** 0.154 0.686** 0.404*(0.509) (0.170) (0.228) (0.130) (0.213) (0.187)

Pred. flow by ethn.-occupation 1.263 -0.223 -0.311 0.216 -0.240 -0.214(1.152) (0.148) (0.172) (0.175) (0.211) (0.125)

R-square 0.964 0.928 0.935 0.925 0.886 0.949N 1020 6120 3570 3570 4284 2856

Panel B: Flows from IPUMS

Pred. flow by ethnicity -0.724* -0.593* -0.688** -0.230 -0.683** -0.460(0.362) (0.244) (0.235) (0.397) (0.256) (0.436)

Pred. flow by occupation -0.317** 0.291*** 0.256*** 0.234 0.165* 0.634***(0.096) (0.073) (0.070) (0.176) (0.069) (0.142)

Pred. flow by ethn.-occupation 2.043*** 1.315*** 1.445*** 0.967* 1.503*** 0.903*(0.358) (0.253) (0.246) (0.425) (0.264) (0.450)

R-square 0.949 0.956 0.970 0.893 0.954 0.949N 1020 6120 3570 3570 4284 2856The left-hand side variable of the regressions presented in this table is the flow of immigrants from aparticular ethnicity electing a particular state in a particular period of migration and the right handside variables are predicted flows constructed as detailed by the left-hand column using shares from1900 IPUMS. Actual and predicted flows are measured from the administrative data (Panel A) and fromIPUMS (Panel B). The first 2 columns divide the sample by whether the ethnicity is English speakingor not. Columns (3) and (4) compare ethnicities by the share of return migration observed over thisentire period. Column (5) restricts the sample to immigrants observed in the Census within 5 years oftheir arrival to the United States while the last includes individuals observed in the Census 5-10 yearsafter their arrival. All regressions include fixed effects for the double interactions of state, ethnicity andperiod of immigration.Standard errors are clustered at the ethnic-state level.*: 5% significance, **: 1% significance, ***: 0.1% significance

39

Table 10. Explaining location choice using the “wrong” predicted flows

Ethnicity Occupation Joint Ethn. and Occ.

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

Panel A: Flows from administrative data

Pred. flow by ethnicity 1.786 1.579* 1.885*(1.380) (0.722) (0.770)

Pred. flow by occupation -0.015 0.780** -0.024(0.538) (0.266) (0.525)

Pred. flow by ethn.-occupation 0.435 -0.850 -0.154(1.074) (0.778) (0.656)

R-square 0.830 0.681 0.756N 7140 15810 22950

Panel B: Flows from IPUMS data

Pred. flow by ethnicity 0.205*** 0.041 0.090 0.050(0.052) (0.096) (0.088) (0.046)

Pred. flow by occupation 0.255** 0.219 0.232** 0.142***(0.083) (0.115) (0.078) (0.038)

Pred. flow by ethn.-occupation -0.004 0.113 0.086 0.049(0.049) (0.132) (0.083) (0.029)

R-square 0.758 0.772 0.763 0.111N 7140 15810 22950 442680The left-hand side variable of the regressions presented in this table is the flow of im-migrants from a particular occupation electing a particular state in a particular period ofmigration and the right hand side variables are predicted flows constructed as detailed bythe left-hand column using shares from 1900 IPUMS. Actual flows are from the admin-istrative data and the predicted flows from IPUMS in Panel A and vice versa in Panel B.Column (1) represents the regression computed on the sample by ethnicity, column (2) onthe one by occupation. Column (3) corresponds to the regressions on both ethnicity andoccupation cells are estimated simultaneously. The final column includes all ethnicity-occupation combinations as observations. All regressions include fixed effects for thedouble interactions of state, group and period of immigration, Column (4) also include alltriple interactions between state, ethnicity, occupation and period of immigration.Standard errors are clustered at the group-state level.*: 5% significance, **: 1% significance, ***: 0.1% significance

40

Tabl

eA

-1.M

atch

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ofoc

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tion

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nad

min

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ativ

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Adm

inis

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Dat

a19

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ies

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on

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PRO

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Elec

tric

ians

515

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ians

Elec

tric

ians

No

No

603

App

rent

ice

elec

tric

ians

Engi

neer

s(p

rofe

ssio

nal)

41En

gine

ers,

aero

naut

ical

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neer

s(c

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etc)

and

surv

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o42

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emic

alD

esig

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and

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ntor

s43

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gine

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46En

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47En

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met

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48En

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min

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35D

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92Su

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Offi

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42

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Yes

No

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unta

nts

Yes

No

302

Att

enda

nts,

phys

icia

n’s

and

dent

ist’s

offic

eC

lerk

san

dco

ypis

ts31

0Bo

okke

eper

sM

esse

nger

san

der

rand

and

offic

ebo

ys32

0C

ashi

ers

Sten

ogra

pher

san

dty

pew

rite

rs32

1C

olle

ctor

s,bi

llan

dac

coun

t32

5Ex

pres

sm

esse

nger

san

dra

ilway

mai

lcle

rks

335

Mai

lcar

rier

s34

0M

esse

nger

san

dof

fice

boys

341

Offi

cem

achi

neop

erat

ors

342

Ship

ping

and

rece

ivin

gcl

erks

350

Sten

ogra

pher

s,ty

pist

s,an

dse

cret

arie

s36

0Te

legr

aph

mes

seng

ers

365

Tele

grap

hop

erat

ors

370

Tele

phon

eop

erat

ors

390

Cle

rica

land

kind

red

wor

kers

(n.e

.c.)

450

Insu

ranc

eag

ents

and

brok

ers

520

Elec

trot

yper

san

dst

ereo

type

rs

Bank

ers

204

Cre

dit

men

Bank

ers

and

brok

ers

No

No

305

Bank

telle

rsO

ffici

als

ofba

nks

and

com

pani

es48

0St

ock

and

bond

sale

smen

Gar

dene

rs93

0G

arde

ners

,exc

ept

farm

,and

grou

ndsk

eepe

rsG

arde

ners

,flor

ists

,nur

sery

men

,etc

Yes

No

Gar

den

and

nurs

ery

labo

rers

Wat

chan

dcl

ock

mak

ers

534

Jew

eler

s,w

atch

mak

ers,

gold

smit

hs,a

ndsi

lver

smit

hsC

lock

and

wat

chm

aker

san

dre

pair

ers

Yes

Yes

Jew

eler

sG

old

and

silv

erw

orke

rs

Car

pent

ers

and

join

ers

510

Car

pent

ers

Car

pent

ers

and

join

ers

Yes

Yes

Ship

wri

ghts

602

App

rent

ice

carp

ente

rs

Cab

inet

mak

ers

505

Cab

inet

mak

ers

Cab

inet

mak

ers

No

Yes

Woo

dwor

kers

(not

spec

ified

)67

4Sa

wye

rsC

oope

rsN

oYe

sSa

wan

dpl

anin

gm

illem

ploy

ees

Oth

erw

ood

wor

kers

Plum

bers

574

Plum

bers

and

pipe

fitte

rsPl

umbe

rsan

dga

san

dst

eam

fitte

rsN

oN

o61

0A

ppre

ntic

epl

umbe

rsan

dpi

pefit

ters

Pain

ters

and

glaz

iers

530

Gla

zier

sPa

inte

rs,g

lazi

ers

and

varn

ishe

rsYe

sYe

s56

4Pa

inte

rs,c

onst

ruct

ion

and

mai

nten

ance

670

Pain

ters

,exc

ept

cons

truc

tion

orm

aint

enan

ce

Mas

ons

504

Bric

kmas

ons,

ston

emas

ons,

and

tile

sett

ers

Mas

ons

Yes

Yes

601

App

rent

ice

bric

klay

ers

and

mas

ons

Ston

ecut

ters

584

Ston

ecu

tter

san

dst

one

carv

ers

Mar

ble

and

ston

ecu

tter

sYe

sYe

s63

5Fi

lers

,gri

nder

s,an

dpo

lishe

rs,m

etal

Blac

ksm

iths

501

Blac

ksm

iths

Blac

ksm

iths

Yes

Yes

524

Forg

emen

and

ham

mer

men

43

Adm

inis

trat

ive

Dat

a19

50O

ccup

atio

nC

lass

ifica

tion

1900

Occ

upat

ion

Cla

ssifi

cati

onLa

rge?

Trad

ed?

Cat

egor

ies

Cod

eD

escr

ipti

on

Engi

neer

s20

3C

ondu

ctor

s,ra

ilroa

dC

ondu

ctor

s(s

team

railr

oad)

Yes

No

541

Loco

mot

ive

engi

neer

sEn

gine

ers

and

firem

en58

3St

atio

nary

engi

neer

sEn

gine

ers

and

firem

en(n

otra

ilroa

d)

Iron

and

stee

lwor

kers

503

Boile

rmak

ers

Iron

and

stee

lwor

kers

Yes

Yes

Met

alw

orke

rs53

1H

eat

trea

ters

,ann

eale

rs,t

empe

rers

Stea

mbo

iler

mak

ers

535

Job

sett

ers,

met

alSt

ove,

furn

ace

and

grat

em

aker

s56

1M

olde

rs,m

etal

Wir

ew

orke

rs58

0R

olle

rsan

dro

llha

nds,

met

alBr

ass

wor

kers

585

Stru

ctur

alm

etal

wor

kers

Oth

erm

etal

wor

kers

612

App

rent

ices

,met

alw

orki

ngtr

ades

(n.e

.c.)

642

Hea

ters

,met

al68

5W

elde

rsan

dfla

me

cutt

ers

Stok

ers

641

Furn

acem

en,s

mel

term

enan

dpo

urer

sC

harc

oal,

coke

and

lime

burn

ers

No

Yes

Mac

hini

sts

544

Mac

hini

sts

Mac

hini

sts

Yes

Yes

604

App

rent

ice

mac

hini

sts

and

tool

mak

ers

Whe

elw

righ

ts54

5M

echa

nics

and

repa

irm

en,a

irpl

ane

Whe

elw

righ

tsYe

sYe

sM

echa

nics

550

Mec

hani

csan

dre

pair

men

,aut

omob

ileM

echa

nics

(nec

)Lo

cksm

iths

551

Mec

hani

csan

dre

pair

men

,offi

cem

achi

ne55

2M

echa

nics

and

repa

irm

en,r

adio

and

tele

visi

on55

3M

echa

nics

and

repa

irm

en,r

ailr

oad

and

car

shop

554

Mec

hani

csan

dre

pair

men

(n.e

.c.)

600

App

rent

ice

auto

mec

hani

cs60

5A

ppre

ntic

em

echa

nics

,exc

ept

auto

Prin

ters

512

Com

posi

tors

and

type

sett

ers

Prin

ters

,lit

hogr

aphe

rsan

dpr

essm

enN

oYe

sTi

nner

s57

5Pr

essm

enan

dpl

ate

prin

ters

,pri

ntin

g61

3A

ppre

ntic

es,p

rint

ing

trad

esTi

npla

tean

dti

nwar

em

aker

s59

1Ti

nsm

iths

,cop

pers

mit

hs,a

ndsh

eet

met

alw

orke

rs

Hat

and

Cap

mak

ers

645

Mill

iner

sH

atan

dca

pm

aker

sN

oYe

sM

illin

ers

Mill

iner

s

Seam

stre

sses

633

Dre

ssm

aker

san

dse

amst

ress

es,e

xcep

tfa

ctor

ySe

amst

ress

esYe

sYe

sD

ress

mak

ers

Dre

ssm

aker

s

Shoe

mak

ers

582

Shoe

mak

ers

and

repa

irer

s,ex

cept

fact

ory

Boot

and

shoe

mak

ers

and

repa

irer

sYe

sYe

s

Tailo

rs59

0Ta

ilors

and

tailo

ress

esTa

ilors

and

tailo

ress

esYe

sYe

s

Text

ilew

orke

rs(n

otsp

ecifi

ed)

543

Loom

fixer

sBl

each

ery

and

dye

wor

ksop

erat

ives

Yes

Yes

634

Dye

rsC

arpe

tfa

ctor

yop

erat

ives

Cot

ton

mill

oper

ativ

esH

osie

ryan

dkn

itti

gnm

illop

erat

ives

Woo

len

mill

oper

ativ

esSh

irt,

colla

r,an

dcu

ffm

aker

s

Wea

vers

and

spin

ners

675

Spin

ners

,tex

tile

Oth

erte

xtile

mill

oper

ativ

esYe

sYe

sFu

rrie

rsan

dfu

rw

orke

rs68

4W

eave

rs,t

exti

leO

ther

text

ilew

orke

rs52

5Fu

rrie

rs

Uph

olst

erer

s59

3U

phol

ster

ers

Uph

olst

erer

sN

oYe

s

Mar

iner

s24

0O

ffice

rs,p

ilots

,pur

sers

and

engi

neer

s,sh

ipBo

atm

enan

dsa

ilors

Yes

No

44

Adm

inis

trat

ive

Dat

a19

50O

ccup

atio

nC

lass

ifica

tion

1900

Occ

upat

ion

Cla

ssifi

cati

onLa

rge?

Trad

ed?

Cat

egor

ies

Cod

eD

escr

ipti

on

623

Boat

men

,can

alm

en,a

ndlo

ckke

eper

s67

3Sa

ilors

and

deck

hand

s

Bake

rs50

0Ba

kers

Bake

rsYe

sYe

s

Butc

hers

644

Mea

tcu

tter

s,ex

cept

slau

ghte

ran

dpa

ckin

gho

use

Butc

hers

Yes

Yes

Mill

ers

555

Mill

ers,

grai

n,flo

ur,f

eed,

etc.

Mill

ers

Yes

Yes

Min

ers

650

Min

eop

erat

ives

and

labo

rers

Min

ers

and

quar

rym

enYe

sYe

s

Phot

ogra

pher

s74

Phot

ogra

pher

sPh

otog

raph

ers

No

No

671

Phot

ogra

phic

proc

ess

wor

kers

Plas

tere

rs57

3Pl

aste

rers

Plas

tere

rsN

oYe

s

Book

bind

ers

502

Book

bind

ers

Book

bind

ers

No

Yes

Engr

aver

s52

1En

grav

ers,

exce

ptph

otoe

ngra

vers

Engr

aver

sN

oYe

s57

1Ph

otoe

ngra

vers

and

litho

grap

hers

Patt

ern

mak

ers

570

Patt

ern

and

mod

elm

aker

s,ex

cept

pape

rM

odel

and

patt

ern

mak

ers

No

Yes

Sadd

lers

and

harn

ess

mak

ers

511

Cem

ent

and

conc

rete

finis

hers

Har

ness

and

sadd

lem

aker

san

dre

pair

sYe

sYe

sTa

nner

san

dcu

rrie

rs51

3C

rane

men

,der

rick

men

,and

hois

tmen

Leat

her

curr

iers

and

tann

ers

Brew

ers

514

Dec

orat

ors

and

win

dow

dres

sers

Brew

ers

and

mal

ster

sC

igar

pack

ers

522

Exca

vati

ng,g

radi

ng,a

ndro

adm

achi

nery

oper

ator

sD

isti

llers

and

rect

ifier

sC

igar

ette

mak

ers

523

Fore

men

(n.e

.c.)

Toba

cco

and

ciga

rfa

ctor

yop

erat

ives

Toba

cco

wor

kers

540

Line

men

and

serv

icem

en,t

eleg

raph

,tel

epho

ne,a

ndpo

wer

Swit

chm

en,y

ardm

enan

dfla

gmen

Cig

arm

aker

s54

2Lo

com

otiv

efir

emen

Tele

grap

han

dte

leph

one

linem

enO

ther

skill

ed56

2M

otio

npi

ctur

epr

ojec

tion

ists

Dec

orat

ors

and

win

dow

dres

sers

563

Opt

icia

nsan

dle

nsgr

inde

rsan

dpo

lishe

rsW

eigh

ters

,gau

gers

,and

mea

sure

rs56

5Pa

perh

ange

rsPa

per

hang

ers

572

Pian

oan

dor

gan

tune

rsan

dre

pair

men

Roo

fers

and

slat

ers

581

Roo

fers

and

slat

ers

Bric

kan

dti

lem

aker

s59

2To

olm

aker

s,an

ddi

em

aker

san

dse

tter

sG

lass

woe

krs

594

Cra

ftsm

enan

dki

ndre

dw

orke

rs(n

.e.c

.)Po

tter

s61

1A

ppre

ntic

es,b

uild

ing

trad

es(n

.e.c

.)Bu

tter

and

chee

sem

aker

s61

4A

ppre

ntic

es,o

ther

spec

ified

trad

esC

onfe

ctio

nner

s61

5A

ppre

ntic

es,t

rade

not

spec

ified

Trun

kan

dle

athe

r-ca

sem

aker

s62

0A

sbes

tos

and

insu

lati

onw

orke

rsBo

ttle

rsan

dso

dam

aker

s62

2Bl

aste

rsan

dpo

wde

rmen

Box

mak

ers

624

Brak

emen

,rai

lroa

dBr

oom

and

brus

hm

aker

s63

0C

hain

men

,rod

men

,and

axm

en,s

urve

ying

Glo

vem

aker

s66

2O

ilers

and

grea

ser,

exce

ptau

toru

bber

fact

ory

oper

ativ

es67

2Po

wer

stat

ion

oper

ator

sTo

olan

dcu

tler

ym

aker

s68

1Sw

itch

men

,rai

lroa

d

TO

TAL

UN

SKIL

LED

Farm

labo

rers

640

Frui

t,nu

t,an

dve

geta

ble

grad

ers,

and

pack

ers

Farm

and

plan

tati

onla

bore

rsYe

sYe

s81

0Fa

rmfo

rem

enFa

rmla

bore

rs(m

embe

rsof

fam

ily)

820

Farm

labo

rers

,wag

ew

orke

rsD

airy

men

and

dair

ywom

en83

0Fa

rmla

bore

rs,u

npai

dfa

mily

wor

kers

Stoc

kra

iser

s,he

rder

san

ddr

over

s84

0Fa

rmse

rvic

ela

bore

rs,s

elf-

empl

oyed

Turp

enti

nefa

rmer

san

dla

bore

rs

Farm

ers

100

Farm

ers

(ow

ners

and

tena

nts)

Farm

ers,

plan

ters

,and

over

seer

sYe

sYe

s

45

Adm

inis

trat

ive

Dat

a19

50O

ccup

atio

nC

lass

ifica

tion

1900

Occ

upat

ion

Cla

ssifi

cati

onLa

rge?

Trad

ed?

Cat

egor

ies

Cod

eD

escr

ipti

on

123

Farm

man

ager

s

Fish

erm

en91

0Fi

sher

men

and

oyst

erm

enFi

sher

men

and

oyst

erm

enN

oYe

s

Labo

rers

690

Ope

rati

vean

dki

ndre

dw

orke

rs(n

.e.c

.)La

bore

rs(n

otsp

ecifi

ed)

Yes

Yes

920

Gar

age

labo

rers

and

car

was

hers

and

grea

sers

Labo

rers

(ste

amra

ilroa

d)95

0Lu

mbe

rmen

,raf

tsm

en,a

ndw

oodc

hopp

ers

Labo

rers

(str

eet

railw

ay)

970

Labo

rers

(n.e

.c.)

Oil

wel

land

oilw

orks

empl

oyee

s94

0Lo

ngsh

orem

enan

dst

eved

ores

Oth

erch

emic

alw

orke

rsO

ther

food

prep

arer

sO

ther

mis

cella

neou

sin

dust

ries

Serv

ants

700

Hou

seke

eper

s,pr

ivat

eho

useh

old

Hou

seke

eper

san

dst

ewar

dsYe

sN

o71

0La

undr

esss

es,p

riva

teho

useh

old

Serv

ants

720

Priv

ate

hous

ehol

dw

orke

rs(n

.e.c

.)75

3C

harw

omen

and

clea

ners

764

Hou

seke

eper

san

dst

ewar

ds,e

xcep

tpr

ivat

eho

useh

old

780

Port

ers

Age

nts

280

Purc

hasi

ngag

ents

and

buye

rs(n

.e.c

.)A

gent

sN

oN

o30

0A

gent

s(n

.e.c

.)St

atio

nag

ents

and

empl

oyee

s(s

team

railr

aod)

380

Tick

et,s

tati

on,a

ndex

pres

sag

ents

Stat

ion

agen

tsan

dem

ploy

ees

(str

eet

railr

oad)

470

Rea

lest

ate

agen

tsan

dbr

oker

s

Dra

ymen

,hac

kmen

and

team

ster

s30

4Ba

ggag

emen

,tra

nspo

rtat

ion

Dra

ymen

,hac

kmen

,tea

mst

ers

No

No

322

Dis

patc

hers

and

star

ters

,veh

icle

Bagg

agem

en62

5Bu

sdr

iver

sBr

akem

en63

1C

ondu

ctor

s,bu

san

dst

reet

railw

ayC

ondu

ctor

s(s

tree

tra

ilway

)63

2D

eliv

erym

enan

dro

utem

enD

rive

rs(s

tree

tra

ilway

)66

0M

otor

men

,min

e,fa

ctor

y,lo

ggin

gca

mp,

etc.

Mot

orm

en66

1M

otor

men

,str

eet,

subw

ay,a

ndel

evat

edra

ilway

682

Taxi

cab

driv

ers

and

chau

ffer

s68

3Tr

uck

and

trac

tor

driv

ers

960

Team

ster

s

Man

ufac

ture

rs29

0M

anag

ers,

offic

ials

,and

prop

riet

ors

(n.e

.c.)

Fore

men

and

over

seer

sN

oYe

s56

0M

illw

righ

tsM

anuf

actu

rers

and

offic

ials

Mer

chan

tsan

dde

aler

s20

0Bu

yers

and

depa

rtm

ent

head

s,st

ore

Com

mer

cial

trav

eler

sYe

sYe

s20

1Bu

yers

and

ship

pers

,far

mpr

oduc

tsH

ucks

ters

and

pedd

lers

205

Floo

rmen

and

floor

man

ager

s,st

ore

Mer

chan

tsan

dde

aler

s(e

xcep

tw

hole

sale

)40

0A

dver

tisi

ngag

ents

and

sale

smen

Mer

chan

tsan

dde

aler

s(w

hole

sale

)41

0A

ucti

onee

rsSa

lesm

enan

dsa

lesw

omen

420

Dem

onst

rato

rsA

ucti

onne

ers

430

Huc

kste

rsan

dpe

ddle

rsN

ewsp

aper

carr

iers

and

new

sboy

s46

0N

ewsb

oys

490

Sale

smen

and

sale

scl

erks

(n.e

.c.)

Hot

elke

eper

s62

1A

tten

dant

s,au

tose

rvic

ean

dpa

rkin

gBa

rten

ders

No

No

750

Bart

ende

rsBo

ardi

ngan

dlo

dgin

gho

use

keep

rs75

2Bo

ardi

ngan

dlo

dgin

gho

use

keep

ers

Hot

elke

eper

s75

4C

ooks

,exc

ept

priv

ate

hous

ehol

dR

esta

uran

tke

eper

s76

0C

ount

eran

dfo

unta

inw

orke

rsSa

loon

keep

ers

784

Wai

ters

and

wai

tres

ses

Wai

ters

Oth

erm

isce

llane

ous

230

Man

ager

san

dsu

peri

nten

dent

s,bu

ildin

gLu

mbe

rmen

and

raft

smen

Yes

No

46

Adm

inis

trat

ive

Dat

a19

50O

ccup

atio

nC

lass

ifica

tion

1900

Occ

upat

ion

Cla

ssifi

cati

onLa

rge?

Trad

ed?

Cat

egor

ies

Cod

eD

escr

ipti

on

643

Laun

dry

and

dry

clea

ning

oper

ativ

esW

ood

chop

pers

680

Stat

iona

ryfir

emen

Oth

erag

ricu

ltur

alpu

rsui

ts73

0A

tten

dant

s,ho

spit

alan

dot

her

inst

itut

ion

Jani

tors

and

sext

ons

731

Att

enda

nts,

prof

essi

onal

and

pers

onal

serv

ice

(n.e

.c.)

Laun

dere

rsan

dla

undr

esse

s73

2A

tten

dant

s,re

crea

tion

and

amus

emen

tM

idw

ives

751

Boot

blac

ksW

atch

men

,pol

icem

en,fi

rem

en,e

tc.

761

Elev

ator

oper

ator

sO

ther

dom

esti

can

dpe

rson

alse

rvic

es76

2Fi

rem

en,fi

repr

otec

tion

Hos

tler

s76

3G

uard

s,w

atch

men

,and

door

keep

ers

Live

ryst

able

keep

ers

770

Jani

tors

and

sext

ons

Pack

ers

and

ship

pers

772

Mid

wiv

esPo

rter

san

dhe

lper

s(i

nst

ores

)77

3Po

licem

enan

dde

tect

ives

Oth

erpe

rson

sin

trad

ean

dtr

ansp

orta

tion

(nec

)78

3U

sher

s,re

crea

tion

and

amus

emen

t78

5W

atch

men

(cro

ssin

g)an

dbr

idge

tend

ers

790

Serv

ice

wor

kers

,exc

ept

priv

ate

hous

ehol

d(n

.e.c

.)

TO

TAL

NO

OC

CU

PAT

ION

No

occu

pati

on98

0K

eeps

hous

e/ho

usek

eepi

ngat

hom

e/ho

usew

ife

981

Impu

ted

keep

ing

hous

e(1

850-

1900

)98

2H

elpi

ngat

hom

e/he

lps

pare

nts/

hous

ewor

k98

3A

tsc

hool

/stu

dent

984

Ret

ired

985

Une

mpl

oyed

/wit

hout

occu

pati

on98

6In

valid

/dis

able

dw

/no

occu

pati

onre

port

ed98

7In

mat

e99

1G

entl

eman

/lad

y/at

leis

ure

995

Oth

erno

n-oc

cupa

tion

alre

spon

se

47

Tabl

eA

-2.M

atch

ing

ethn

icgr

oups

and

coun

trie

s.

Ethn

icgr

oup

Ethn

icit

ies

(fro

mad

min

istr

ativ

eda

ta)

Cou

ntry

ofbi

rth

(fro

mC

ensu

s)H

igh

retu

rn?

Larg

e?

Brit

ish

ansc

estr

yEn

glis

hEn

glan

d,C

anad

a(E

nglis

h),A

ustr

alia

No

Yes

Iris

hIr

elan

dN

oYe

sSc

otch

Scot

land

No

Yes

Wel

shW

ales

No

No

Fren

chD

utch

and

Flem

mis

hBe

lgiu

m,N

ethe

rlan

dsN

oN

oFr

ench

Fran

ce,C

anad

a(F

renc

h)N

oYe

sSo

uth

Euro

pean

sIt

alia

n(N

orth

),It

alia

n(S

outh

)It

aly

Yes

Yes

Port

ugue

sePo

rtug

alYe

sN

oSp

anis

hSp

ain

Yes

No

His

pani

csM

exic

anM

exic

oN

oYe

sSp

anis

h-A

mer

ican

Cen

tral

and

Sout

hA

mer

ica

No

No

Cub

an,W

est

Indi

an,A

fric

an(B

lack

)A

llC

arri

bean

sIs

land

sYe

sN

oG

erm

ans

Ger

man

s,G

erm

anH

ebre

ws

Aus

tria

,Ger

man

y,Sw

itze

rlan

dN

oYe

sSc

andi

navi

ans

Finn

ish

Finl

and

No

No

Scan

dina

vian

Den

mar

k,N

orw

ay,S

wed

enN

oYe

sR

ussi

ans

and

othe

rsLi

thua

nian

,Rus

sian

,Rus

sian

Heb

rew

sR

ussi

aN

oYe

sPo

lish

Pola

ndYe

sYe

sR

oman

ian,

Rom

ania

nH

ebre

ws

Rom

ania

Yes

No

Oth

erEu

rope

Bohe

mia

nan

dM

orav

ian,

Rut

heni

an,S

lova

kBo

hem

ia(C

zech

oslo

vaki

a)Ye

sYe

sSe

rbia

nan

dM

onte

negr

in,C

roat

ian

and

Slov

enia

n,Bu

lgar

ia,Y

ugos

lavi

aYe

sYe

sBu

lgar

ian,

Dal

mat

ian,

Bosn

ian,

Her

zego

vini

anG

reek

Gre

ece

Yes

Yes

Mag

yar

Hun

gary

No

Yes

Oth

erC

hine

seC

hina

Yes

No

East

Indi

anIn

dia

Yes

No

Japa

nese

Japa

nN

oN

oPa

cific

Isla

nder

Paci

ficIs

land

sN

oN

oSy

rian

,Tur

kish

,Arm

enia

nTu

rkey

Yes

No

Oth

ers,

othe

rH

ebre

ws,

Kor

ean

Kor

ea,o

ther

coun

trie

sYe

sN

o

48