Hilton. Hu 1996 maintains that increased welfare participation among immigrants is concentrated among the elderly. He finds that new immigrants arriving after age fifty-five are
more likely to enter assistance programs than similar natives and younger immigrants. Much work has been done on migration within the United States. Examples include
Greenwood 1975, Graves and Linneman 1979, Graves and Knapp 1988, and Dresher 1994. In these models migration depends upon the public and private economic cost and
benefits associated with relocation. More recently Conway and Houtenville 1998, 1998b, 1998c find that the elderly population is attracted to states whose tax and expenditure
policies treat them favorably. Blank 1988 inspects the movement patterns of low income female headed households. She finds that these households are more likely to leave low
welfare payment and low wage areas. Gramlich and Laren 1984 find that households in general who are moving are more likely to locate in a state that has higher AFDC benefits.
Dunlevy 1991 researches the immigrant population. He shows that the new immigrant population in a state is positively related to the state’s immigrant stock. He does not consider
welfare generosity. Bartel 1989 supports Dunlevy’s results by finding that the stock of similarly born immigrants is the major incentive in immigrant location decisions.
Buckley 1996 investigates the determinants of location for new U.S. immigrants according to their admission category and state of intended residence, using a panel for the years 1985 to
1991. His data does not allow for disaggregation of the dependent variable according to nativity or place of birth. Therefore, he uses the percent of the state’s population that is foreign born,
regardless of nativity, as an independent variable. The results suggest that there is significant welfare motivation in immigrant location choices. In addition, his results imply that less gener-
osity in welfare payments will not only reduce total immigration, but raise the skill level of the immigrant inflow since employment-based immigrants are less responsive to welfare.
Zavodny 1997, 1998 examines the determinants of immigrant locations in a panel for fiscal years 1982 and 1992. The dependent variable is the total number of immigrants from
individual countries to individual states. She does not consider differences across admission categories. According to Zavodny 1997, the presence of immigrants from the same country
of birth is the determining factor in location choices and that welfare generosity plays no part. She concludes that Buckley’s 1996 welfare motivation results are biased upwards because
he uses the total foreign-born population and not the population according to country of birth as an independent variable. In a more recent study, Borjas 1999 finds that immigrants do
cluster in states with higher welfare payments. In fact, he finds that the distribution of immigrants among the states is statistically different from the distribution of relocating
natives. Furthermore, welfare generosity appears to be a good predictor of this distribution.
2. A new model of immigrant location choice
This new model of immigrant location choice has two important advantages over Buckley 1996 and Zavodny 1997. First, the data in this specification are more complete and
include more detailed characteristics for each immigrant. With these data the welfare motivation in location decisions found by Buckley 1996 and the nativity incentive found
by Zavondy 1997 can be tested simultaneously. Second, unlike the ordinary least squares
49 M. E. Dodson III International Review of Law and Economics 21 2001 47– 67
method of estimation used by both of the previous authors, this model employs the more appropriate technique of Tobit estimation.
This model like those of Buckley 1996 and Zavodny 1997, maintains that an immigrant will choose the utility maximizing location. Eq. 1 states that the probability of individual
i choosing the location k in period t conditional on his location j in period t - 1 is equal to the probability that location k in period t is the utility maximizing choice. This conditional
probability is the basis for estimation.
Pr~k
it
j
it21
5 Pr~U
ijkt
5 MAX~U
ij1t
,U
ij2t
, . . .. . .U
ijNt
1 The use of frequencies leads to the basic model in equation 2.
I
ijk
5 a 1 X
k
b 1 N
jk
d 1 Dg 1 e
ijk
2
I is a vector of immigrants indexed by class i, source country j, and intended state of residence k. X is a matrix of state specific economic factors. N is a matrix of nativity factors.
D is a matrix of dummies that attempts to describe possible ports of entry and proxy for moving costs.
Eq. 2 is the same form of regression used by Buckley 1996 and Zavodny 1997 with the exception of an added disaggregation of the data. The equation simply states that the
number of people who found location k to be their utility maximizing choice depends upon the state and source country factors of all the possible choices. The decision to immigrate and
the location choice is not separable in this model. Rather, the choice of residence is viewed as a set of alternatives within the decision to migrate. Separating the decision to migrate from
the choice of residence would indicate that the dependent variable should be the percentage of immigrants from each country who locate in each state. Zavodny 1997 performs the
analysis with both levels and percentages and finds no difference in results.
The index i indicates the category of admission family sponsored, immediate relatives, employment based, refugees. The country of origin is indicated by the subscript j. There are
twenty-one countries of origin in the sample.
8
These twenty-one countries accounted for 83.9 of total immigration in fiscal year 1991 and 75.2 in fiscal year 1992. The subscript
k indicates the forty-nine possible location choices. They include all the continental states of the United States plus the District of Columbia.
Table 1 provides some descriptive evidence about the dependent variable. Immigrants legalized under IRCA are excluded from the data, since their residence is already established.
Also, children have been excluded from the sample. If children were included, the identical and independent distribution assumption, necessary for estimation would be violated, since
children locate with their parents. Annual values from this data set are calendar years not fiscal years, as reported in most INS publications.
In recent years, immigrants from Asian countries have increased dramatically. Between 1961 and 1970, Asian countries accounted for only 12.8 percent of all new immigrants. That
8
The countries are Germany, Poland, Soviet Union former, United Kingdom, China, India, Iran, Korea, Phillipines, Vietnam, Taiwan, Jamaica, Canada, El Salvador, Nicaragua, Mexico, Cuba, Dominican Republic,
Haiti, Guyana, Colombia. 50
M. E. Dodson III International Review of Law and Economics 21 2001 47– 67
Table 1 Where are the new immigrants?
State Residence Family
Sponsored Immediate
Relative Employment
Based Refugees and
Asylees Total
Favored Other
Favored Other
Favored Other
Favored Other
Favored Other
Total Country of Origin
Germany 203
116 2,056
3,630 272
379 86
43 2,617
4,168 6,785
Poland 985
3,245 1,083
2,509 211
442 849
2,475 3,128
8,671 11,799
Russia 88
62 926
791 128
32 4,646
2,174 5,788
3,059 8,847
United Kingdom 917
656 3,396
3,706 1,438
1,869 94
98 5,845
6,329 12,174
China 10,146
2,913 8,294
3,627 1,329
1,442 1,165
701 20,934
8,683 29,617
India 5,406
7,283 4,354
5,812 2,039
3,242 88
40 11,887
16,377 28,264
Iran 1,218
707 2,805
1,801 1,133
535 3,518
1,356 8,674
4,399 13,073
Korea South 4,135
3,119 3,195
4,500 1,550
1,933 91
14 8,971
9,566 18,537
Phillippines 7,490
2,588 19,601
10,108 4,093
2,741 96
60 31,280
15,497 46,777
Vietnam 9,681
9,985 8,332
6,198 29
28 4,333
1,884 22,375
18,095 40,470
Taiwan 3,630
1,766 1,944
941 2,402
1,411 76
47 8,052
4,165 12,217
Jamaica 7,328
3,041 3,511
1,669 861
297 89
19 11,789
5,026 16,815
Canada 776
603 2,395
3,606 1,091
1,643 355
253 4,617
6,105 10,722
El Salvador 3,069
974 2,342
965 2,638
1,726 1,424
569 9,473
4,234 13,707
Nicaragua 1,451
306 1,506
370 213
121 10,412
1,687 13,582
2,484 16,066
Mexico 17,219
3,167 23,861
8,190 1,626
251 1,028
184 43,734
11,792 55,526
Cuba 1,241
216 526
131 5
9 3,116
347 4,888
703 5,591
Dominican Rep. 10,557
2,789 8,373
2,654 149
106 19
2 19,098
5,551 24,649
Haiti 5,142
1,474 1,985
837 167
80 165
48 7,459
2,439 9,898
Guyana 4,756
1,009 2,036
593 562
144 1
7,355 1,746
9,101 Columbia
2,240 1,313
2,808 1,927
471 371
119 74
5,638 3,685
9,323
Total 97,678
47,332 105,329
64,565 22,407
18,802 31,770
12,075 257,184
142,774 399,958
Total as Percent of
Category 67
32 62
38 54
46 72
28 64
36 100
Total admissions in calendar year 1991 for the selected categories and countries are 399,958. Data tabulated from INS records by author.
The favored states are CA, NY, FL, and TX. Other states contain the remaining 44 continental states plus DC.
51 M.
E. Dodson
III International
Review of
Law and
Economics
21 2001
47– 67
share has increased to 37.3 between 1981 and 1990. The Asian countries represented in this sample sum to 147,618 admissions. This is over one-third of the total admissions or 36.9
percent. They include China with 29,617; South Korea with 18,537; 46,777 for the Phillip- pines; 40,470 immigrants from Vietnam; and Taiwan totaling 12,217.
The same table provides some evidence about the preferences for some states among new United States immigrants. The favored states in Table 1 include immigrants who located in
California, Texas, Florida, and New York. Immigrants locating in the remaining forty-five possible destinations are found in columns labeled other. The favored states were chosen
simply to illustrate the general preference among immigrants for these states versus the remainder. Together, the favored states absorbed 257,184 immigrants, 64 percent of this
1991 sample. Only 36 percent of the immigrants in this sample located in the remaining forty-four possible states or the District of Columbia, obviously these four states are
preferred by immigrants.
Previous research has only been able to disaggregate immigration data across two major characteristics, either admission category and state of intended residence Buckley, 1996 or
country of birth and state of intended residence Zavodny, 1997. The data used here is from unpublished Immigration and Naturalization Service records provided by the National
Technical Information Service.
9
The previous research employed the same data, but was encumbered with the published forms that report only a few major characteristics.
10
Cross- tabulation of the admission category, country of origin, and state of intended residence,
available with this set, is not available in the published format. In order to attempt to settle the controversy between Buckley 1996 and Zavodny 1997 all these characteristics are
needed.
Estimation of the determinants of location choices among immigrants may also be biased without this level of disaggregation. If immigrants are attracted to locations that contain large
populations of similarly born immigrants, using the population of all foreign born immi- grants may underestimate any correlation with location decisions. In other words, the fraction
of the total foreign-born population not from the new immigrants country of birth may produce enough statistical noise to cover up any correlation. This may be the case in Buckley
1996 and displays why country of origin is important in this estimation. Zavodny 1997 recognized this possibility but could not estimate differences across admission categories.
Using total immigration without disaggregating with respect to admission categories may also be biased. If immigrants in the immediate relative category or the family-sponsored
category locate in the same state as their sponsors then the strong attraction between location choices and similarly born immigrants is to be expected. However, this fact may be biased
upwards with regard to employment-based immigrants who will most likely locate in the same states as their employer or areas where they can get the best return for their skills.
9
Immigrants Admitted into the United States, 1990-1995. Dept. of Justice, Immigration and Naturalization Service. NTIS PB97-500763.
10
Data available in the INS Statistical Yearbook are also derived from this data set. However, the published format produces limited cross-tabulation due to space restrictions. All immigrant data is derived from the U.S.
Department of State forms, Immigrant Visa and Alien Registration OF-155 and Application for Immigrant Visa and Alien Registration OF-230.
52 M. E. Dodson III International Review of Law and Economics 21 2001 47– 67
In addition to addressing the data constraint faced by Buckley 1996 and Zavodny 1997, this model uses a Tobit estimation technique. Tobit is appropriate for two reasons. First, the
dependent variable is censored.
11
The dependent variable is the number of immigrants locating in any state and therefore is nonnegative. The second reason Tobit is required, is a
massing problem. Disaggregating the data to this level reveals a number of zero observations for each of the dependent variables. The data set contains a frequency for each of the
forty-nine states stacked according to the twenty-one countries of origin. The total data set contains 1029 observations. Out of this total, there are 271, 84, 398, and 516 zero observa-
tions for the family sponsored, immediate relative, employment based, and refugee and asylee categories, respectively. The existence of these observations seriously biases the
ordinary least squares estimator.
The matrix X in equation 2 includes state economic factors. These factors include per
capita gross state product, the unemployment rate, state employment shares in the goods producing sector and agriculture, the percent of the state population living in metro areas, the
growth rate of employment between 1985 and 1990, the total population of the state, the sales tax rate for the state and the average January temperature for each state. Also, included in this
matrix is the combined maximum monthly potential benefit from AFDC and the food stamp program for a one-parent family of three persons. Table 2 provides a summary definition for
each of the independent variables and identifies each source.
These independent variables have three specific measurement objectives. They are vari- ables that measure the health of the state’s economy, the disposition or typical features of the
state and its economy, and two auxiliary control variables. The measures of economic health include per capita gross state product, the employment growth rate and the unemployment
rate. Per capita gross state product will correlate positively with location choices if immi- grants are attracted by productive, high income states. Growth in employment should
indicate an expanding opportunity set for new arrivals in each state. Positive correlation between location choice and employment opportunities will result if these opportunities are
important parts of the utility function.
Typically an unexpected positive sign plagues the relationship between unemployment and location decisions in similar estimations Buckley, 1996; Zavodny, 1997. There may be
several reasons for these results. The time lag on the variable may be inappropriate. The unemployment rate during the last six months could be more important than the previous
year’s unemployment rate. A more intuitive explanation could be that the unemployment rate in relation to the source country’s unemployment rate is more important. Every state in the
United States may have an unemployment rate that is so low in relation to the source country’s unemployment rate that this variable makes very little difference in the location
decision. The addition of employment growth to this model may mitigate the consequences of this suspect variable.
The second set of variables describes the typical economic or social features within a state. Included in this set are the agricultural share of total employment, the goods producing share
of total employment, the percent of the state’s population that resides within a metro area,
11
See Greene 1990, p.691. 53
M. E. Dodson III International Review of Law and Economics 21 2001 47– 67
and the total population of the state. Industry employment shares will measure work preferences of immigrants. Reason suggests that if the immigrant is trained and experienced
in a manufacturing process he will choose a state with a relatively larger share of employ- ment in the goods producing sector. The same argument applies for agriculture. These
estimates will produce a coefficient that is relative to the service producing sector, since it is left out of the model.
Casual inspection of the concentration of foreign-born individuals and their current state of residence suggests that states with large metropolitan areas are preferred by immigrants.
Zavodny 1997 finds support for this contention. She finds that the percent of a state’s population living within a metropolitan area is a strong attractor in location decisions. This
same percentage is included in this model. States with large populations may indicate an area that allows for easier assimilation or an area with more social opportunities. In order to
Table 2 Immigration model variables
Variable Description Source
Per capita gross state product for each state in 1990. REIS
Average annual non farm total employment growth between 1985-1990 for each state
REIS Annual unemployment rate in 1990 for each state
REIS Agricultural share of total employment in 1990 for
each state. REIS
Goods producing share of total employment in 1990 for each state.
REIS Percent of total population living in a metropolitan
area in 1990 for each state. U.S. Stat. Abst., 1992
Total state population in 1990 REIS
Maximum combined AFDC and food stamp benefit for a one-parent family of three in January 1990
for each state. 1990 Green Book, Comm. on Ways and Means,
U.S. House of Representatives Percent of total state population that is from each
country in the sample in 1990 1990 Census, Foreign Born Population of the
United States. Total number of immigrants admitted in fiscal years
1989 and 1990 according to their country of birth and reported state of intended residence.
INS, Statistical Yearbook, 1990 and 1991. Sales tax rate in 1990 for each state.
Significant Features of Fiscal Federalism, v. I, 1991
Average January temperature in each state. U.S. Stat. Abst., 1992
Dependent Variable Number of immigrants from country j locating in
state k, for each of the four major admission categories in calendar year 1991.
INS NTIS Countries include Germany, Poland, Soviet Union
former, United Kingdom, China, India, Iran, Korea, Phillipines, Vietnam, Taiwan, Jamaica,
Canada, El Salvador, Nicaragua, Mexico, Cuba, Dominican Republic, Haiti, Guyana, Colombia.
States include the 48 continental plus DC 54
M. E. Dodson III International Review of Law and Economics 21 2001 47– 67
account for these possibilities, the total population of the state is included as an independent variable.
The auxiliary controls are the state sales tax rates and the average January temperature within each state. Any influence due to taxes may vary across admission categories. For
example, if employment-based immigrants can expect higher wages they may choose locations with lower tax rates, holding other influences constant. On the other hand, some
immigrants may take higher tax rates as a signal for a higher provision of public goods. Since, income tax structures across states are extremely varied and property taxes differ at
the county level, the only consistent statewide tax measure left is the sales tax rate.
12
January temperatures may account for any natural amenities connected with the location choice. It is
reasonable to suggest that an immigrant will wish to locate in a state with a climate similar to that in the source country. In order to account for this possibility, January temperatures are
interacted with the source country dummies. All the state factors are lagged one year since this is the information available to the individual immigrant.
The matrix of nativity factors N includes the 1990 percent of the total population in state
k whose country of birth is j. This variable measures the stock of similarly born immigrants. Like Zavondy 1997 this variable will correlate positively with location choices if immi-
grants are attracted to states with communities that represent their country of origin. This percentage of the state population that is similarly born, however, may not capture the
influence of recent arrivals. The number of similarly born immigrants, regardless of admis- sion category, admitted in fiscal years 1989 and 1990 who reported state k as their intended
residence is also included in N. This flow variable is intended to test for a possible hysteresis effect. For example, Arizona may be an attractive location choice to 1991 immigrants from
Nicaragua if a Nicaraguan community just recently developed. In this case, the recent change in immigrant inflow creates a permanent incentive to locate in Arizona. In fact, this incentive
may be more powerful than that seen in the stock variable because those immigrants represented by the stock variable may have become well assimilated and less representative
of the source country over many years.
The cost of relocating or distance is also an important factor in the location decision. Zavodny 1997 uses the distance between the most populated city in the source country and
the most populated city in the host state as a proxy for these moving costs. However, this variable may not accurately measure the important part of the moving cost. The most
important measure for an immigrant, who is choosing a state residence, is the distance between the port of entry and the state of intended residence. An immigrant may choose an
Atlantic or Pacific passage based solely on the distance between the resulting port of entry and the intended destination state. In other words, the cost of moving from the source country
to the port of entry is sunk once the immigrant reaches the United States. Once arriving in the United States an immigrant can stay in the state that encompasses the port of entry or
12
Admittedly, the sales tax rate may be a poor proxy for the burden of taxes across states. I replaced the variable with per capita total state and local government tax receipts less federal transfers in order to test for
any sensitivity in the results. None of the results were altered and the tax variable remained insignificant in all regressions.
55 M. E. Dodson III International Review of Law and Economics 21 2001 47– 67
reside in another state. Furthermore, this could explain why the states which are preferred by immigrants lie next to an international border or have a coastline. This model attempts to
control for this sunk cost by incorporating the more appropriate cost of moving between the port of entry and the state of intended residence.
In order to capture these possibilities three dummy variables are included in the model. The first takes a value of one for states that maintain a border with Canada. The second
identifies states with a southern international border. In the third dummy variable states with a coastline take the value of one. In order to describe some of the most probable ports of entry
for each country the variable COST is constructed. This variable takes on the value for the coastal dummy variable for each state and country combination except for Mexico and
Canada. For these two exceptions, the variable takes on the values found in the international border dummies.
To elaborate, consider the observation for immigrants from India who locate in Nevada, in this case COST would maintain a zero observation since Nevada has no coastline. On the
other hand, COST would have a value of one for Indian immigrants who locate in Virginia. For the first case, the immigrant incurred the additional expense of locating beyond the first
available port of entry. In the second case, remaining in Virginia indicates that the cost of further travel once reaching a coastline state may have been a factor in the location choice.
A third example, is an immigrant from Mexico who locates in Nevada, the COST value would be a zero again since Nevada has no border with Mexico.
The interpretation of this dummy variable is simple. A value of one in COST implies the immigrant located in a state with a probable port of entry; the marginal cost of locating in
a more distant state was burdensome. A value of zero indicates the immigrant paid the additional cost of moving to a more distant state. Since observations with a one in this
variable identify the situation where the marginal cost was foregone it should vary positively with location choice. States that embody minimal or no marginal cost should see more
immigration. The remaining three dummy variables are also included in the specification. Each should vary positively with the dependent variable. Obviously, states that are more
accessible to immigrants should have more immigration, especially if the marginal cost of further travel is important. More important, this specification will control for immigration to
states that merely have an international border or coastline.
The variables listed above can be characterized as “pull” or host country factors. None of these would pick up any factors in the source country that encourage emigration or “push”
the immigrants out of the source country. Dummy variables for the j countries are included in order to pick up any variation in location choices that are specific to a certain country.
These dummies also proxy for some of the “pushes” associated with immigration.
After estimating the relationship, elasticities can produce an answer to the controversy raised by Buckley 1996 and Zavodny 1997. Namely, if both welfare generosity and the
presence of similarly born immigrants are significant determinants in the location choices of new U.S. immigrants, which provides the stronger incentive? Additionally, in order to find
more evidence a further disaggregation of the dependent variable may be helpful. The dependent variable can be disaggregated according to sex. Examining female immigrants
according to country of birth, admission class and destination state may provide more information concerning any possible welfare motivation in immigrant location decisions.
56 M. E. Dodson III International Review of Law and Economics 21 2001 47– 67
3. Estimation results for the full set of 1991 United States immigrants
