U.S. immigration policy and the wages of undocumented Mexican immigrants
Peter B. Brownell — Departments of Sociology & Demography, University of California at Berkeley
Introduction
Table 2: Regression of Wages on Selected Predictors
A number of theorists (Castells 1975, Burawoy 1976,
Meillassoux 1981) have argued that immigrants amount to a
subsidy to receiving countries in that the costs of “reproducing”
immigrant workers are externalized. Here reproduction means
actual reproduction, that is replacement, as well as the
maintenance of the worker during times of unemployment. Such
theories tend to focus on the differences between immigrants
and native-born workers. A similar distinction might be drawn
between temporary migrants (who pay replacement costs in
their home country) and settled migrants (who pay such costs in
the host country).
In the case of Mexican immigrants to the U.S., the cost of living
is higher in the receiving country than the sending country. This
means that by paying part of their costs of reproduction in
Mexico, temporary migrants pay less than their settled
counterparts.
Do these savings accrue to the temporary migrants, allowing
them to purchase more than settled migrants? Or do their
employers reap the savings because they can offer lower wages?
Framed in economic language similar to Djajić’s (1989) model
of guestworker migration decision making, migrants who expect
to save or remit some of their earnings to spend in Mexico could
support a given level of consumption at a lower nominal U.S.
wage than migrants spending all their earnings in the U.S.
Hence the nominal wage at which migrating makes economic
sense is lower for temporary migrants than for those intending
to settle permanently.
Mexican Immigrant Day Laborers
and organizers in Los Angeles. Photo
by David Bacon, www.igc.org/dbacon
Data and methods
This paper draws on data from the Survey of Migration on the
Northern Border of Mexico, known by its Spanish acronym,
EMIF. The analysis here focuses on a sub-sample of migrants
returning to Mexico from the U.S. of their own free will. The
analysis is further limited to undocumented males born in
Mexico who worked at least one week on their last trip to the
United States. For more information on EMIF, see Secretaría de
Trabajo y Previsión Social (1999).
The sample used is drawn from the flow of migrants returning
to Mexico through border cities. The data include a set of
weights based on the time and place of each case, designed to
make the survey representative of the return flow. Relative to
the stock of undocumented migrants in the United States, those
who cross frequently should be over-represented in the flows
relative to those who return to Mexico less frequently. In an
effort to make the data more closely represent the U.S. stocks of
undocumented Mexican migrants, I have multiplied the survey
weights by the duration spent by each migrant in the U.S. on
this last trip.
Following Chavez (1988), I use respondents’ self report of
country of residence as my measure of temporariness or
settlement. Temporary migrants (those reporting a Mexican
residence) are assumed to spend some share of their earnings in
Mexico. Summary statistics comparing the two groups are
reported in Table 1. Table 2 shows the regression results using a
Genralized Linear Model with a logarithmic link function and a
Gamma family.
Table 1: Summary Statistics
Variable
Hourly Wage
Age
Married
U.S. Duration (months)
Number of U.S. trips
# U.S. trip >10 (dummy)
U.S. family or friends
Documented
family/friends
Job search help
Length of U.S. job
(months)
Education
No school (ref)
Some Primary
Completed Primary
Some Secondary
Completed Secondary
Some High School
Completed High
Higher Education
Signed Contract
Occupation
Services/Retail (ref)
Professional/
Managerial
Agricultural Workers
Manufacturing
Industrial Operators
Unskilled
Manufacturing
Drivers/Heavy
Equipment
Clerical
Traveling
Sales/Services
Domestic Services
Survey Phase 2 (ref)
Survey Phase 3
Survey Time (days)
Settled Immigrants
Mean SD Min Max
7.19 2.92 1.75 31.25
28.28 8.0
15
70
73.2%
0
1
32.59 31.3 0.08
90
3.58 3.65
1
11
15.9%
0
1
61.8%
0
1
48.6%
0
1
15.0%
31.90 45.2
0
0.26
1
324
Temporary Immigrants
Mean SD Min Max
5.26 4.23 0.25 200
27.38 8.23
15
74
53.8%
0
1
11.80 11.3 0.03
68
2.93 2.92
1
11
7.3%
0
1
76.7%
0
1
67.7%
0
1
40.6%
7.88 8.03
0
0.23
1
84
1.5%
10.4%
43.2%
4.7%
22.0%
2.8%
10.2%
5.2%
15.6%
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
5.5%
26.9%
28.3%
5.9%
23.0%
4.9%
3.2%
2.4%
11.5%
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
5.9%
3.9%
0
0
1
1
18.9%
0.6%
0
0
1
1
7.4%
63.1%
2.6%
4.4%
0
0
0
0
1
1
1
1
29.8%
20.2%
3.0%
15.6%
0
0
0
0
1
1
1
1
1.6%
0
1
3.7%
0
1
3.1%
0.9%
0
0
1
1
0.6%
1.2%
0
0
1
1
0
0
0
4
1
1
1
938
0
0
0
0
1
1
1
938
7.3%
42.9%
57.9%
437.9 257
6.3%
33.2%
66.8%
546.4 270.2
Model 1

SE
-0.1617* 0.0603
Model 2

SE
-0.0637 0.0478
0.0123
0.0002
0.0423
Independent Variables
Temporary
Temporary*Married
Age
0.0290*
Age Squared
-0.0004*
Married
0.0198
Education (ref= none)
Some Primary
0.0170
Completed Primary
0.1427†
Some Secondary
0.0499
Completed Secondary
0.0564
Some High School
0.0505
Completed high School
0.1340†
Higher Education
0.3814*
U.S. Social & Human Capital
Duration of U.S. trip (months)
0.0057*
Number of U.S. trips
0.0011
Number of U.S. trips = 10+
0.1310
Family or friends in U.S.
-0.0190
Documented Family or friends
0.0528
Job Search help from family or
0.0433
friends
Length of U.S. job (months)
-0.0034
Length of U.S. job squared
2.0E-05*
Workplace Characteristics
Signed Contract
Occupation (ref=
services/retail)
Professional/Managerial
Agricultural Workers
Manufacturing
Industrial Operators
Unskilled Manufacturing
Drivers and Heavy Equipment Operators
Clerical
Traveling Sales/Services
Domestic Services
0.0246* 0.0086
-0.0003* 0.0001
0.0067 0.0340
Model 3

SE
-0.0787 0.0826
-0.1310 0.0969
0.0298* 0.0124
-0.0004* 0.0002
0.1293 0.0891
Model 4

SE
0.0440 0.0836
-0.1669† 0.0873
0.0258* 0.0086
-0.0004* 0.0001
0.1451† 0.0824
0.0660
0.0737
0.0680
0.0668
0.0830
0.0730
0.1531
-0.0158
0.0818
0.0028
-0.0074
-0.0562
0.0874
0.1400
0.0568
0.0597
0.0627
0.0583
0.0933
0.0680
0.1201
0.0212
0.1418†
0.0570
0.0604
0.0568
0.1325†
0.3684*
0.0648
0.0730
0.0672
0.0658
0.0811
0.0719
0.1497
-0.0137
0.0798
0.0072
-0.0057
-0.0577
0.0839
0.1221
0.0557
0.0590
0.0625
0.0573
0.0906
0.0647
0.1179
0.0014
0.0112
0.1203
0.0716
0.0492
0.0607
0.0063*
-0.0021
0.1549
-0.0512
0.0407
0.0694
0.0013
0.0097
0.0953
0.0615
0.0419
0.0567
0.0053*
0.0008
0.1406
-0.0232
0.0626
0.0445
0.0015
0.0111
0.1192
0.0714
0.0505
0.0609
0.0059*
-0.0022
0.1616†
-0.0561
0.0530
0.0715
0.0014
0.0097
0.0965
0.0612
0.0419
0.0568
0.0021
8.2E-06
-0.0049* 0.0020
2.7E-05* 8.0E-06
-0.0032 0.0022
1.9E-05* 8.5E-06
-0.0046* 0.0021
2.5E-05* 8.2E-06
0.0887*
0.0493
0.0965†
0.0497
1.0501*
-0.0972*
0.1083*
-0.0850
0.0006
-0.0245
0.3070
0.0228
-0.0023
0.3332
0.0512
0.0489
0.0879
0.0496
0.0729
0.1992
0.1575
0.0525
1.0419*
-0.1028*
0.1131*
-0.0797
-0.0006
-0.0348
0.2892
0.0209
-0.0131
0.3234
0.0517
0.0495
0.0834
0.0498
0.0734
0.1914
0.1574
0.0522
Survey Time (days)
Survey Phase 3 (ref= phase 2)
4.1E-05 1.9E-04
-0.1007 0.1062
3.7E-05 1.5E-04
-0.0664 0.0896
4.9E-05 1.8E-04
-0.1065 0.1055
5.0E-05 1.5E-04
-0.0768 0.0898
Constant
1.2064*
1.2379*
1.1161*
1.1231*
0.2016
0.1656
0.2194
0.1819
* p.05; † p<.10; Standard Errors are robust Huber/White/Sandwich. Cases are weighted by survey (flow) weights
times U.S. Duration to better approximate stocks. N=1739
Results
• Without controlling for other factors, settled migrants'
hourly wages were almost 37% higher than the wages
of temporary migrants.
• Controlling for age, marital status, and migrants’
human and social capital, settled migrants’ predicted
wages were still 17.6% higher than those of temporary
migrants.
• Wage differences are not statistically significant when
controls for occupation are added, suggesting that
temporary migrants are selected into low-wage
occupations.
• Among married migrants the wage difference is only
statistically significant with controls for occupation,
predicting temporary married migrants wages are
11.6% lower than settled married migrants.
Conclusions
These finding support the theoretical conclusion that a
migrant intending to spend at least part of his foreign
earnings in his home country might be motivated to
migrate to a country like the U.S. at a nominal wage
lower than the wage which would be required to make
permanent migration an economically rational thing to
do.
Although U.S. (and European) workers may find
the concept of temporary migration somehow more
palatable, there is little evidence that it affords any real
protection from competition with migrants in general or
from any deflation of wages in increasingly immigrantconcentrated sectors. On the contrary, this analysis finds
that the employers of temporary migrants are the
beneficiaries of a system such a system. U.S. workers
can expect to benefit from programs which settle and
integrate immigrants, rather than those which keep
circulating between the U.S. and Mexico.
Literature cited
Burawoy, Michael (1976) "The Functions and Reproduction
of Migrant Labor: Comparative Material from Southern
Africa and the United States," American Journal of
Sociology 81(5): 1050-87.
Castells, Manuel. (1975) "Immigrant Workers and Class
Struggles in Advanced Capitalism: the Western
European Experience," Politics and Society 5(1): 33-66.
Chavez, Leo. (1988) “Settlers and Sojourners: The Case of
Mexicans in the United States,” Human Organization
47(2): 95-108.
Djajić, Slobodan (1989) "Migrants in a guest-worker
system: a utility maximizing approach," Journal of
Development Economics 31(2): 327-349.
Meillassoux, Claude (1981) Maidens, Meals and Money:
Capitalism and the Domestic Economy. New York:
Cambridge University Press.
Secretaría de Trabajo y Previsión Social (1999) Encuesta
sobre Migración en la Frontera Norte de México 19961997. Mexico City: Secretaría de Trabajo y Previsión
Social.
Acknowledgments
I wish to thank Jorge Santibañánez for providing the EMIF data
used here. This research was conducted with support from the
University of California Institute for Labor and Employment
and a traineeship funded by the National Institute of Child
Health and Human Development. Thanks also to Trond
Petersen, Peter Evans, Michael Burawoy, David Fairris, and
Ron Lee for their helpful comments.
For further information
Please e-mail brownell@demog.berkeley.edu. The full paper is
available at demog.berkeley.edu/~brownell/docs/Brownell_MA.pdf .