Savings and Settlement:
Evidence from Mexican Migrants
Anna Paulson†
Northwestern University
Audrey Singer
Carnegie Endowment for International Peace
April, 2000
Abstract
This paper uses variation in the probability that Mexican migrants in the U.S. will return and work in
Mexico to test the predictions of the permanent income model for savings. The higher the probability of
returning and working in Mexico, the more temporary is the increase in wages that the migrant experiences
by crossing the border. Migrants with a high probability of returning and working in Mexico should save
more of their U.S. earnings than their counterparts who are less likely to return and work in Mexico. Using
data collected by the Mexican Migration Project (Massey et. al.) from approximately 1,900 migrant
households in thirty-four communities in western Mexico between 1987 and 1994, the paper estimates how
long migrants with different characteristics will stay in the United States. These estimates are used to
create measures of the probability of returning and working in Mexico, which are then used to study
migrant savings rates in a setting which allows for the probability of returning to Mexico to be
endogenously determined. We find that a higher probability of return is associated with a significantly
lower savings rate. However, the interaction between the probability of returning and migrant income
makes savings rates significantly higher. This suggests that for lower income migrants, satisfying basic
consumption needs takes precedence over taking advantage of temporarily high wages in the U.S. through
savings. When income is sufficiently high, the predictions of the model appear to hold.
We are grateful to Angus Deaton, Katherine Donato, Deborah Lucas, Doug Massey,
Andrew Morrison, Christina Paxson, Mitchell Petersen, Sergio Rebelo and Seth Sanders
for helpful comments. Some of the research presented here was conducted while Anna
Paulson was a National Fellow at the Hoover Institution. She is grateful for their support.
Una Okonkwo provided excellent research assistance. Remaining errors are our own.
†
Address correspondence to: Anna Paulson, Kellogg Graduate School of Management, Northwestern
University, 2001 Sheridan Road, Evanston, IL 60208, (voice) 847-467-3322, (fax) 847-491-5719, (email)
[email protected]
1
Savings and Settlement:
Evidence from Mexican Migrants
Abstract
This paper uses variation in the probability that Mexican migrants in the U.S. will return and work in
Mexico to test the predictions of the permanent income model for savings. The higher the probability of
returning and working in Mexico, the more temporary is the increase in wages that the migrant experiences
by crossing the border. Migrants with a high probability of returning and working in Mexico should save
more of their U.S. earnings than their counterparts who are less likely to return and work in Mexico. Using
data collected by the Mexican Migration Project (Massey et. al.) from approximately 1,900 migrant
households in thirty-four communities in western Mexico between 1987 and 1994, the paper estimates how
long migrants with different characteristics will stay in the United States. These estimates are used to
create measures of the probability of returning and working in Mexico, which are then used to study
migrant savings rates in a setting which allows for the probability of returning to Mexico to be
endogenously determined. We find that a higher probability of return is associated with a significantly
lower savings rate. However, the interaction between the probability of returning and migrant income
makes savings rates significantly higher. This suggests that for lower income migrants, satisfying basic
consumption needs takes precedence over taking advantage of temporarily high wages in the U.S. through
savings. When income is sufficiently high, the predictions of the model appear to hold.
1.
Introduction
Whether Mexican migrants come to the United States for one agricultural season
or relocate in the United States permanently, their U.S. wages are likely to far exceed
what they could earn in Mexico. For example in 1986, the U.S. minimum wage of $4.25
per hour was eight times higher than the corresponding minimum wage in Mexico.1
Variation in how long Mexican migrants intend to stay in the United States provides an
opportunity to test implications of models which predict different savings responses to
permanent versus temporary changes in earnings.2 In this paper, we examine whether
temporary Mexican migrants save more than their more settled counterparts.
General models of the inter-temporal allocation of consumption and leisure
predict that people smooth expenditures by saving more when their incomes are
1
Source: www.tradecompass.com/library/doc/nafta/8502.htm.
The migrants we study vary considerably in how long they stay in the U.S. Twenty-five percent of trips
last less than five months, fifty percent of trips are between five and twenty-two months, and the remaining
2
2
temporarily high. Similarly, people who face a temporary upsurge in wages will choose
to work more hours. Under this hypothesis, Mexican migrants who are working in the
United States will vary in how much they save and in how many hours they work
depending on how likely they are to return to Mexico and work there. The probability of
returning and working in Mexico can be thought of as a way to characterize the
permanence of the change in wage associated with moving from Mexico to the United
States.
Migrants who are very settled in the U.S. face a slim probability of returning to
work in Mexico. We can think of their earnings in the U.S. as a permanent change in
earnings, from what they earned in Mexico. In contrast, the U.S. earnings of a less
settled migrant more closely resemble a temporary increase in earnings, since the less
settled migrant is likely to return and work in Mexico. Under the predictions of many
models of inter-temporal choice, less settled migrants should respond to U.S. labor
market conditions by working more hours and saving more than their counterparts who
are more settled in the United States.
Following Galor and Stark (1990 and 1991), we consider a two-period setting
where there is some probability that a migrant will return to Mexico in the second time
period. In the first period, when the migrant is in the United States, savings and hours
worked are affected by the probability of returning to Mexico in the second time period.
The higher the probability of returning to Mexico, and earning lower wages there, the
more hours the migrant will work in the United States during the first period. Similarly,
savings during the first period are higher the higher the probability of returning to
Mexico. The Galor and Stark studies envision comparing the savings and hours worked
behavior of migrants and natives. We depart from that conception and compare the
savings behavior among migrants who differ in how likely they are to return and work in
Mexico.
While the focus of this paper is on testing the implications of the permanent
income hypothesis, an understanding of migrant savings behavior is central to developing
trips last longer than twenty-two months. This suggests that there is also substantial variation in how long
3
effective immigration policy. Migrant savings patterns have a direct bearing on decisions
to migrate, decisions to return, patterns of assimilation and the impact of migration on the
sending and receiving communities. The behavior of Mexican immigrants is of particular
interest since they make up a large and growing part of the U.S. population. By 2005,
approximately 16% of the U.S. population will be of Mexican origin (del Pinal and
Singer 1997). Since the migrants who are studied in this paper were interviewed in
Mexico, they are not necessarily representative of Mexican immigrants in the U.S. more
generally.
However, the sample we study does include a substantial group of
undocumented migrants (61%) who are likely to be underrepresented in other data
sources.
This study uses data collected by the Mexican Migration Project (MMP, Massey
et. al.) from approximately 1,900 migrant households in thirty-four communities in
western Mexico between 1987 and 1994. We use a parametric duration model to predict
how long migrants with different characteristics will stay in the United States.3 From
these predictions, we create measures of the probability of returning and working in
Mexico. We then study how the probability measures affect migrant savings rates.
While estimates of the length of stay and the probability of return are of interest in their
own right, the primary aim of this paper is to establish how savings behavior responds to
temporary changes in income as captured by the probability of return.
Prior studies on whether or not savings and consumption behavior in the United
States conforms to the predictions of the permanent income hypothesis yield mixed
results. Using aggregate time-series data, Flavin (1981) and Campbell and Mankiw
(1989) strongly reject the predictions of the permanent income hypothesis.
Using
household data many researchers, including Hall and Mishkin (1982), Zeldes (1989),
Carroll and Summers (1991) and Cochrane (1991), find evidence of excess sensitivity to
transitory income shocks and liquidity constraints for low income individuals. Liquidity
they intend to stay in the U.S.
3
More precisely, we estimate how long it takes for migrants to appear in the data set as working in Mexico
from the first time they are observed in the U.S. What is important from the perspective of the permanent
income hypothesis is not the probability of returning to Mexico, but the probability of returning and
4
constraints may be very important for the sample of Mexican migrants that we study,
since their incomes are quite low. More recently, Attanasio and Weber (1995) find
evidence in favor of the permanent income hypothesis in consumption once multiple
commodities, aggregation, demographic variables and labor supply factors have been
accounted for.
Since the individuals that we study divide their lives between a very developed
economy and a developing one, the results of studies of savings behavior in developing
countries are relevant for this study as well. Paxson (1992) uses rainfall data to identify
the transitory component of income and finds that Thai households save more from
transitory than permanent shocks to income.
Deaton (1990) also uses rainfall to
instrument for the transitory component of income and finds some evidence in favor of
the permanent income hypothesis among households in the Cote d’Ivoire. However, he
concludes that consumption is excessively sensitive to transitory income.
Like these studies, we attempt to directly characterize the permanence of income.
While we do not break income into its transitory and permanent components, variation in
the probability of returning and working in Mexico is used to characterize the
permanence of U.S. earnings. The probability that a migrant will return and work in
Mexico is of course unobservable.
This variable is constructed by estimating a
parametric duration model that relates migrant characteristics to the probability of
returning and working in Mexico.
We characterize the permanence of U.S. wages based on migrant decisions about
how to divide their working life between the U.S. and Mexico, rather than on variation in
some exogenous force like rainfall.
Therefore, we need to be concerned with the
possibility that the same forces that determine how long the migrant stays in the U.S. are
also important determinants of savings. For example, in some models of migration,
migrants return to their place of origin once they have saved some particular amount. We
address this concern by using instrumental variables.
The key instrument for the
predicted probability that the migrant will return and work in Mexico as a function of his
working there. Migrants may return to Mexico often (and many do), however if they only work in the U.S.
5
most recently observed characteristics is the probability that the migrant will return to
Mexico as a function of his characteristics at the beginning of his stay in the U.S.
Savings rates are regressed on a number of explanatory variables including the
predicted probability that the migrant will return and work in Mexico in the next 12
months.
The procedure makes a virtue of the fact that the data include detailed
information about each trip to the U.S. but savings information only for the migrants’
most recent trip to the U.S. Different samples of migrants are used in the savings
regressions and in the estimates of the current and past probability that the migrant will
return and work in Mexico. The parameters that are used to predict the current and past
probability of return are based on estimates of the hazard that a migrant will return to
Mexico and work there for the sample of migrants with more than one trip to the U.S.
These parameters are used in combination with some of the characteristics of the
migrants in second sample to create estimates of the probability of returning and working
in Mexico in the next 12 months. The sample that is used in the savings regressions is
made up of migrants whose most recent trip to the U.S. is also their first trip to the U.S.
The errors from the duration estimates will be uncorrelated with errors in the savings
regression since the two samples do not overlap.
The rest of the paper is organized as follows. In the next section, we discuss the
sociological literature on migrant types and settlement and link these concepts to the
probability of returning and working in Mexico. Section 3 describes the data. The
strategy for estimating the probability of return and savings rates is described and
implemented in Section 4. Section 5 concludes.
2.
Migrant Types, Settlement and the Probability of Return
While the theoretical literature on the inter-temporal allocation of consumption
suggests thinking of settlement as a continuum related to the probability of returning and
working in Mexico, sociological and ethnographic research has focused on trying to
explain why some migrants settle in the United States and why others return to Mexico.
then their U.S. wages still represent a permanent change from wages in Mexico.
6
Many authors define migrant types as though they were synonymous with settlement
patterns. Other authors focus on describing migration patterns and do not draw clear
connections between migration and settlement behavior. Massey et. al.’s (1987)
description of migrant types is convenient for our purposes since it is based on data
collected from four communities which formed the starting point for the data collection
activities in the 34 communities that we study. We use this analysis of migration types to
help to determine what variables are important to include in the duration estimates.
Massey et. al. describe three types of Mexican migration.
The first type is recurrent migration.
These migrants usually establish a
relationship with an employer in the United States, either in agriculture or other seasonal
work such as construction, and travel between the two countries, maintaining a residence
in Mexico and earning most of their income in the United States (Massey et al. 1987).
For recurrent migrants, working in the United States substitutes for the lack of economic
opportunities in origin areas (Lindstrom 1996). These migrants, the majority of whom
are married men, typically make many trips of relatively short duration over their
lifetimes, leaving behind family members in Mexico.
Life in the United States is
dominated by work, and migrants have limited social contacts outside of work. The U.S.
wages of these migrants can be thought of as relatively permanent, since their working
life is concentrated in the U.S. This suggests that the savings and hours worked of
recurrent migrants should be low relative to migrants who are more likely to return and
work in Mexico.
A second type of migration is that of target earners or strategic migrants. These
migrants make less frequent trips to the United States but their trips are comparatively
long in order to work and accumulate savings, usually a lump sum for a specific
expenditure (Lindstrom 1996).
When target earners return home, they generally
reincorporate themselves into their local economy where they are also likely to invest
their savings (Massey et al. 1987). Men also dominate this type of migration. In the U.S.,
groups of workers often live together, and migrants have limited social relationships
outside of work. The U.S. wages of target earners can be thought of as temporary
7
increase in wages, since these migrants are likely to return and work in Mexico. We
expect target earners to save more and work longer hours than recurrent migrants.
A third variety of Mexican migration, which Massey et. al (1987) call settled
migration, is when migrants stay more or less permanently in the United States. This
type of migration is often a product of a long process of living and working in the United
States. These migrants are generally more integrated into social life in the United States
and family members are more likely to be living together. Family migration and the
migration of women and children is a function of the maturity of the migration history of
a community and is facilitated by long-term development of social networks; as networks
expand, and migrants' own experience and comfort in the United States rises, wives and
children are united with husbands and fathers (Durand and Massey 1992). Compared to
recurrent migrants and target earners, we would expect "settled" migrants to save the
least and work the fewest hours, since they are the least likely to return and work (for
lower wages) in Mexico.
While many studies use duration of stay to proxy for settlement, the following
example from Rouse (1992) suggests that settlement is more complex than duration
alone. Migrants from Aguililla, Michoacán who were living in the Redwood City,
California area continued to own property in Mexico. They also often used accumulated
savings from work in the U.S. to buy land and houses in Mexico. In addition, they made
monetary contributions to enterprises back home that were run by relatives. Social and
economic ties to Mexico increased while the migrants were in the U.S., in spite of the
fact that many of these migrants owned homes in the U.S. and intended to stay in the
United States permanently.
Recent literature confronts these intricacies and criticizes the view that settlement
is bipolar, where migrants shift their social and work ties from one country to another.
Rouse (1992) and others (Hondagneu-Sotelo (1994), Glick Schiller, Basch and BlandSzanton (1992), Goldring (1992), Smith (1992)) argue that new kinds of communities
have been created that span both sides of the U.S.-Mexican border. In their view,
"transnational migrants" do not necessarily disconnect from their Mexican community
8
and supplant themselves wholly in a new U.S. community. Rather, migrants maintain
familial, social, economic and sometimes, political ties that encompass both communities
(Rouse 1992).
These insights from the sociological literature which treat settlement as a
complicated process, rather than as a dichotomous variable, are largely consistent with
the approach we take in this paper. By estimating the probability that a migrant returns to
work in Mexico, we effectively allow for a continuum of settlement. On the other hand,
the sociological literature also suggests that while savings is affected by settlement,
settlement is in turn affected by savings and the success of migrant investments in social,
human and physical capital in Mexico and in the U.S. We investigate whether or not
migrant’s savings behavior is related to the probability of return in a way that is
consistent with the predictions of the permanent income hypothesis. The empirical work
takes into account the possibility that the probability of return is endogenously
determined. However, in the model we assume that the probability that a particular
migrant returns and works in Mexico is exogenous.
Specifically, we adapt Galor and Stark’s (1990) model to the case of Mexican
migration to the United States. Consider a two-period setting where individuals begin the
first period in the United States and face some probability of returning to Mexico in the
second period. The migrant’s utility is a function of first and second period consumption:
U(c1 , c2 ) = u(c1 ) + δ u(c2 )
[1]
where δ is the discount factor. Utility is assumed to be strictly concave. In the first
period, when the migrant is in the U.S., the migrant inelastically supplies a single unit of
labor and receives wU.S. Wages in the U.S. are assumed to be higher than wages in
Mexico. First period consumption is given by:
c1 = wU .S . − s
[2]
In the second period, the migrant also inelastically supplies their labor in either Mexico
or the U.S. For simplicity, wages are assumed to be constant. Migrants face some
positive probability α of returning to Mexico in the second period, so second period
consumption is given by:
9
ì wU .S . + (1 + r ) s with probability 1 - α
c2 = í
wMEX + (1 + r ) s with probability α
[3]
and wU.S. > wMEX. Migrants will choose savings to maximize lifetime utility, so optimal
savings is given by:
s * = argmax{u(wU .S . − s ) + δ [α u( wMEX + (1 + r ) s ) + (1 − α ) u( wU .S . + (1 + r ) s ]}
[4]
The optimal level of first period savings is unique and satisfies the following first order
condition:
(1 + r )δ[αu' ( w MEX + (1 + r ) s*) + (1 − α )u' ( wU .S . + (1 + r ) s*)] − u' ( wU .S . − s) = 0
[5]
It follows from the strict concavity of utility and the fact that wU.S. > wMEX that ds*/dα >
0.4 The higher the probability of returning to work in Mexico in the second period, the
higher first period savings will be. The model’s predictions can also be stated in terms of
saving rates, since savings rates are simply the amount of savings divided by the fixed
U.S. wage rate. The higher the probability of returning and working in Mexico, the
higher U.S. savings rates will be. This is the proposition that is tested in Section 4.
If the migrant chooses how many hours to supply to the labor market in each
period, a similar expression for hours work can be derived (see Galor and Stark 1991 for
details). Hours worked in the U.S. will increase the higher the probability of returning to
work in Mexico in the second period, provided that utility is strictly concave,
consumption and leisure are both normal goods and second period labor supply is a nondecreasing function of the wage rate.
While the theory predicts that less settled migrants will take advantage of their
temporarily higher earnings in the U.S. by working long hours, there are many factors
that may confound this. Some jobs do not allow the employee to choose how many hours
to work. For example, teams of workers often have to agree to work the same hours.
Adverse weather conditions may prevent agricultural employees from working any hours
4
Using the implicit function theorem, it follows from the first order condition, the strict concavity of the
utility function and the assumption that wU.S. > wMEX that:
(1 + r )δ [u ' ( wU .S . + (1 + r ) s*) − u ' ( wMEX + (1 + r ) s*)]
ds*
=
> 0.
d α u ' ' ( wU .S . − s*) + (1 + r ) 2 δ [αu ' ' ( wMEX + (1 + r ) s*) − (1 − α )u ' ' ( wU .S . + (1 + r ) s*)]
10
at all. Similarly the workweek of many employees is dictated by the structure of the firm
or industry. Migrant occupations and the degree to which they are free to choose how
many hours to work are both likely to vary by how settled they are. We do not attempt to
formally test the prediction that temporary migrants will work longer hours than more
permanent migrants. Instead we compare various measures of hours worked per week
vary for two groups of migrants, one that is very settled in the U.S. and another group
that is likely to be more temporary.
3.
The Data
The data we use in this paper are from the Mexican Migration Project (MMP).
The entire data consist of simple random samples of about 200 households from each of
34 communities located in Western Mexico. The sample communities are drawn from
five states in Western Mexico: Guanajuato, Jalisco, Michoacán, Nayarit and Zacatecas.
These states have historically provided the majority of migrants to the United States
(Jones 1988).
The interviews took place during the winter months of 1987-94.
Interviewing during the Christmas holidays maximizes the likelihood of finding migrants
spending time with their families in Mexico.
The MMP data include complete migration histories for the household head.
More detailed data, including wages, hours worked per week, savings, remittance and
some expenditure information, is also recorded for each head of household's most recent
trip to the United States. In our analysis, we exclude migrants whose last trip to the
United States took place before 1965, when the Bracero program for agricultural workers
ended. The sample we analyze consists of 1,857 male, migrant heads of household who
were interviewed in Mexico. We also provide summary information for 409 male,
migrant heads of household who were interviewed in the U.S.
The data collected in Mexico were supplemented with non-random samples of
migrants located in the United States. Most of these interviews were conducted during
the summer months subsequent to each winter's survey.
Using snowball-sampling
methods, a U.S. sample was compiled and approximately 10-20 households from each of
11
the 34 communities were interviewed. Massey and Parrado (1994) argue that the
Mexican community samples represent conditions in the core sending region at the time
of the survey, and that the U.S. supplements portray conditions during the same time
period.
Mexican communities were not selected to specifically find U.S.-bound
migrants, rather they were chosen to represent a broad cross-section of the Western
Mexican migrant-sending region. For more information on data collection see Massey
and Parrado (1994) and Massey and Singer (1995).
We provide summary information for both the random sample interviewed in
Mexico and the non-random U.S. sample in Tables 1, 2 and 3. The migrants that were
interviewed in the U.S. provide an interesting contrast to the Mexico group. This
juxtaposition allows us to directly compare magnitudes of the key variables for the two
groups of migrants who vary substantially in how long they have been living and working
in the U.S. and in how likely they are to return and work in Mexico. The formal analysis
is restricted to the random sample of migrants who were interviewed in Mexico.
The U.S. migrants are much more likely to fit the description of “settled”
migrants compared to their counterparts who were interviewed in Mexico. For example,
they are more likely to have legal documentation than the Mexico sample (64% v. 31%).
Fifty percent of the U.S. sample has a green card and 42% of these green card holders
received them via the amnesty provisions of the 1986 Immigration Reform and Control
Act (IRCA). Migrants interviewed in the U.S. are also much more likely to be family
migrants. Seventy-six percent of these migrants were accompanied by their wives in the
U.S. compared with only 11% of the Mexico group. The median migrant in the U.S.
sample was accompanied by 2 children versus 0 for the migrants interviewed in Mexico.
It is important to note that these differences are not due to differences in marital status or
in family size across the two groups. Eighty-two percent of the Mexico sample and 70%
of the U.S. sample are married, and the median migrant interviewed in Mexico has 3
children under 18 compared with a median of 1 child under 18 for the U.S. sample.
Migrants who were interviewed in the U.S. are also much more likely to speak and/or
understand some English (96% v. 47%).
12
The reported use of U.S. services by the migrants who were interviewed in the
U.S. reinforces the idea that they are much more settled in the U.S. than the Mexico
sample. Fifty-six percent of U.S. migrants have at some point enrolled their children in
U.S. public schools compared to 9% of the migrants interviewed in Mexico. The U.S.
sampled migrants are also more likely to have reported ever being the recipient of
unemployment insurance, food stamps or welfare. The U.S. sampled migrants are more
likely to report ever having used these services partly because they have had much more
opportunity to do so: the median cumulative time they have spent in the U.S. is 150
months, compared with 26 months for the migrants interviewed in Mexico.
In summary, the migrants who were interviewed in the U.S. appear to be very
settled in the U.S. As a group they are less likely to return and work in Mexico compared
with the migrants who were interviewed in Mexico. This means that their U.S. wages
represent a more permanent increase in wages compared with the wage change for the
Mexico sample. The model suggests that these differences in the permanence of the
wage change for the two groups will mean that the migrants interviewed in Mexico will
work longer hours and save more than the U.S. sample.
Hours Worked
The median number of hours worked per week varies from 40 hours per week for
the U.S. sampled group to 45 hours per week for the group interviewed in Mexico (see
Table 2). Forty-five percent of U.S. sample report working more than 40 hours a week,
compared with 53% of the migrants interviewed in Mexico.5 This can also be seen in
Figure 1 which presents histograms of hours for the migrants interviewed in the U.S. and
Mexico separately. Migrants interviewed in Mexico are the least likely to be working 40
hours and the most likely to work more than 55 hours. The U.S. sampled migrants are
the most likely to report working 40 hours a week and the least likely to report working
more than 55 hours. The difference in hours worked across the two groups cannot be
5
Calculations from the 1982 – 1989 March Current Population Surveys (CPS) suggest about 30% of U.S.
employees work more than 40 hours a week.
13
explained by differences in occupation. Among farm workers, 42% of the U.S. sampled
migrants report working more than 40 hours a week, compared with 59% of the migrants
interviewed in Mexico. These differences are consistent with the predictions of the
permanent income hypothesis. Since the Mexico sample is more likely to return and
work in Mexico, their U.S. wages represent a fairly temporary increase in earnings.
These migrants respond by increasing the number of hours that they work while they are
in the U.S. in order to take advantage of the temporary upswing in wages. In contrast, the
U.S. wages of the migrants interviewed in the U.S. represent a more permanent increase
in wages and therefore these migrants work fewer hours than the Mexico sample.
Savings
Table 3 summarizes the savings data as well as the information that is used to
calculate savings rates.
The answers to several survey questions could be used to
calculate savings rates. For their most recent trip to the U.S., migrants were asked to
report their average monthly remittances, their average monthly savings and the amount
of savings that they brought back to Mexico with them. Migrants were also asked to
report what the cash returned to Mexico, in the form of remittances and savings, was
spent on. Up to five answers were recorded for remittances and savings, respectively.
Savings and remittances could be used for: construction or repair of house, payment of
debts, purchase of consumer goods, purchase of a house or lot, purchase of farmland,
recreation, family health and maintenance, purchase of motor vehicle, start a business,
purchase tools, didn’t spend anything, and other. While the data record up to five uses of
savings and remittances, they do not report how much of the total amount was allocated
to each use.
The measure of savings that we choose to analyze is the average monthly amount
that the migrant reported saving during his most recent trip to the U.S. We considered
and rejected two other possible measures of savings: savings plus remittances and a
restricted version of savings which treated savings as zero if any part of it was used for
family health and maintenance or recreation. Although both savings and remittances
14
arguably represent foregone consumption from the point of view of the migrant, the
typical migrant in the Mexican sample is married and has a spouse and two children who
live in Mexico. From the point of the view of the family (the migrant and his spouse and
children), remittances may be used to finance current consumption in Mexico. In our
judgement, the family is the appropriate context in which to measure savings, so we
excluded remittances from our savings measure. We also concluded that restricting
savings to only those amounts that were not used to finance family health and
maintenance and recreation was inappropriate.
Even if the migrant’s savings was
eventually used for this purpose, it still represents deferred consumption for the family
until the migrant brings it back to Mexico either personally or in the form of remittances.
We were also concerned that categorizing savings based on ex post reports of its use was
inappropriate. For example, the migrant may have planned on using savings to purchase
a house, but circumstances may have caused the family to use these funds for family
health and maintenance instead.6 In any case, the results of the analysis are qualitatively
the same regardless of the savings measure we choose.
Most studies of savings use a residual approach to measuring savings. Savings is
measured as what is left over from income after expenditures have been accounted for.
This allows the researcher to include as savings categories of expenditure – repayment of
debts, for example – that the respondent might not consider savings.
The questions
about savings asked in the MMP surveys rely on the respondent’s subjective definition of
savings. This may have some empirical advantages because this measure of savings will
not inherit measurement error from expenditure and income the way residual measures of
savings do. On the other hand, it also means that it is difficult to compare the savings
levels and rates calculated from the MMP data to those found in other studies. However,
it is useful to compare the savings of the Mexican sample to the savings of the more
settled U.S. sample.
6
An additional concern was the fact that the data provide no information on how much of savings was
allocated to each use. Therefore we would have to count savings as zero if any part of it was used for
family health and maintenance or recreation.
15
Savings and remittances are described in Table 3. About one-half of the sample
report doing some monthly saving. The percentage of the sample that reports doing some
monthly saving is slightly smaller for the Mexico sample (48%) compared to the U.S.
sample (55%). The average monthly savings is also higher for the U.S. sample, $254.84
(1983 dollars) compared to $166.58 for the Mexico sample. Of course the U.S. sampled
migrants also have substantially higher incomes than the group interviewed in Mexico.
The average income of the U.S. sampled migrants is 71% higher than that of the Mexico
sample.
The analysis focuses on analyzing savings rates rather than levels. Savings rates
are calculated by dividing monthly savings by the sum of monthly expenditures on food,
rent, remittances and saving.
Rent, food and remittances are the only available
expenditure data. We decided to look at savings as a percentage of expenditure plus
savings, rather than income, for two reasons. First, expenditures on rent and food will
vary with family size (when, for example, the migrant is accompanied by family
members), while the available income measures take into account only the head of the
household’s income.7 Examining savings as a percentage of expenditure plus savings
rather than income, has the additional virtue of allowing earnings to be included in the
savings regressions without biasing the coefficient estimates because of common
measurement error in the dependent and independent variables.
The average savings rate is 19% for the Mexico sample and 18% for the U.S.
sample. Since the wage change associated with moving to the U.S. is more permanent
for the U.S. sample compared to the Mexico group, we would expect the U.S. sample to
save less than their counterparts who were interviewed in Mexico. This is clearly not the
case. In fact there is some evidence that the U.S. sample has a higher savings rate. The
median savings rate for the U.S. sample is 12% versus 8% for the Mexico sample.
Savings rates for the Mexican sample are likely to be overstated because many of them
report zero expenditures on rent and food. Thirty-six percent of the Mexico sample
reports zero spending on rent, compared with 5% of the U.S. sample. This suggests that
16
some migrants are receiving lodging from employers, friends, or relatives. Ideally, the
value of this in-kind payment would be recorded in both income and expenditures.
However, the data provide no information about in-kind payments. In any case, because
zero rent and food expenditures are so much more common among the Mexico sample,
their savings rates are likely to be artificially high relative to savings rates for the U.S.
sample.8 On the face of it, this comparison of unconditional means and medians would
appear to be evidence against the predictions of the permanent income hypothesis.
4.
Empirical Strategy and Estimates
In order to test the hypothesis that migrants will have higher U.S. savings rates
when the wage change associated with moving to the U.S. is more temporary, we first
need to characterize the permanence of the wage change. The permanence of the wage
change can be captured by the likelihood that the migrant will return and work in Mexico.
Once we have come up with a measure of the probability of returning and working in
Mexico, we then regress savings rates on the characteristics of the migrant and the
estimated probability of returning and working in Mexico.
The estimates of the
probability of return and of savings are based on the random sample of migrants who
were interviewed in Mexico. In other words, we analyze the effect of variation in the
predicted probability of returning and working in Mexico among the Mexican sample on
the savings behavior of the same group.
Because of our concern that savings and the probability of returning and working
in Mexico are jointly determined, we instrument for the current probability of returning
and working in Mexico with the past probability of returning and working in Mexico.
More specifically, we create two estimates of the probability that the migrant will return
and work in Mexico in the next 12 months. The first estimate is based on the migrant’s
characteristics at the end of his most recent stay in the U.S., or at the time of the survey
for migrants who are still in the U.S. The second estimate is based on the migrant’s
7
The available expenditure data is still far from perfect. For example, expenditures in Mexico that are not
financed from remittances will not be included.
17
characteristics at the beginning of his stay in the U.S. The second estimate is used as an
instrument for the first. It will be a valid instrument if savings is determined by the
current probability of returning and working in Mexico and if the current probability of
returning to work in Mexico is related to the probability of returning and working in
Mexico in the past. The following set of equations describes the set-up we have in mind:
st = f ( X t , pt )
pt = g ( X t , pt −1 )
where st denotes savings at time t, Xt is a vector of the migrants characteristics at time t
and pt and pt-1 represent the current and the past probability that the migrant will return
and work in Mexico, respectively.
We use one sample to estimate the parameters that are used to calculate the
probability of return and another sample to estimate savings rates. The parameters that
determine the probability of return are estimated from the sub-sample of migrants who
have made two or more trips to the U.S. Savings regressions are estimated for the subset
of migrants whose most recent trip to the U.S. was also the first time they migrated to the
U.S. The use of the two non-overlapping samples further ensures identification of the
savings regressions, which include estimated regressors.9
Estimating the Probability of Return
We use a duration model to estimate the probability of return.
By using a
parametric model for the duration of a spell, the probability of returning and working in
Mexico can be recovered. A spell is defined as the number of months from when the
migrant travels to the U.S. until they are observed working in Mexico. Since the goal of
the estimation is to come up with a probability estimate, distributional assumptions are
clearly required. We use a Weibull model to estimate spell durations. Specifically, we
assume that the probability that a migrant is observed on a spell that last for at least t
months (the survival function) is of the following form:
8
Since the savings rate is the dependent variable in the estimates presented below, measurement error in
this variable that is uncorrelated with the independent variables will not bias the coefficient estimates.
18
S (t ) = exp(− exp( xi β )t p )
[6]
where xi is a vector of the migrant’s characteristics, and β and p are parameters to be
estimated. The parameter p tells us whether the probability that a spell ends is increasing
or decreasing as the spell length increases. If p is greater than one, then the longer the
spell, the higher the probability that it ends. The opposite is true if p is less than one.
The Weibull function was chosen based on an examination of the Kaplan-Meier survivor
function for the raw data (shown in Figure 2). This figure shows the percentage of
migrants whose spell has not yet ended (who have survived) as a function of spell length.
Figure 3 shows the predicted Weibull survival function based on estimated values of p
and β and calculated at the average beginning of spell x’s.
The probability measure that is used in the savings estimates is the probability
that a migrant will show up working in Mexico during the next year, given that the
migrant’s spell has already lasted t months. The probability that a spell ends between t
and t + 12 months, conditional on the spell lasting until t is:
P(t ≤ T < t + 12 | T ≥ t ) =
S (t ) − S (t + 12)
S (t )
[7]
If we look at this expression in the limit – the probability that the migrant will show up
working in Mexico in a vanishingly small amount of time, conditional on the spell lasting
for t months – this expression becomes the hazard function or the inverse Mill’s ratio.
Table 4 presents two maximum likelihood estimates of spell duration for migrants
who were interviewed in Mexico and who have been to the U.S. at least two times. This
sample consists of 960 migrants and 2199 spells. The first estimate uses the migrant’s
characteristics at the start of the spell and the second estimate uses the migrant’s end of
spell characteristics.
The standard errors are adjusted to account for multiple
observations per migrant. The exponent of the coefficient is reported, so the parameter
estimate for years of schooling, 1.09, for example, means that for each additional year of
schooling, the spell will last 9% longer. The equation that is estimated is:
9
The results are not sensitive to using one sample to estimate the predicted probability of return and
another sample to estimate savings rates.
19
ln(T ) = −(1 / p )βx + e,
[8]
where e has the extreme value distribution and is scaled by 1/p and –1/pβ.
In the first estimate, spell duration is modeled as a function of the migrant’s age,
age squared, education, education squared, occupation, marital status and number of
children at the beginning of his stay in the U.S. This estimate also includes and indicator
variable that is equal to one if the migrant crossed the U.S. border legally. The second
estimate includes variables that describe the migrant’s age, education, occupation etc. at
the end of the spell. This estimate also includes an indicator variable that is equal to one
if the migrant was in the U.S. legally at the end of the spell. In addition, both estimates
include controls for the year the spell began and for the migrant’s community of origin.
The year controls are meant to capture variation over time in economic conditions in the
U.S. and Mexico. The community variables control for the effect of patterns of migration
that are common to all migrants from a particular community. For example, sometimes
migrants from the same community will all work in the same industry and even for the
same employer. Migrants from the same community may also make use of a common
network of contacts in finding jobs and crossing the border.
Ideally the duration estimates would also include some measure of the migrant’s
income in the U.S. and their earning potential in Mexico. Unfortunately, U.S. income is
only recorded for the migrant’s most recent trip to the U.S. Data on earnings in Mexico
are also not systematically reported. The impact of these important omitted variables
(and others) will be captured by the other independent variables to the extent that they are
correlated with income in the U.S. and Mexico. The coefficient on years of schooling is
likely to be particularly affected. Since we are interested in the duration model as a tool
for predicting the probability of return, rather than as an accurate description of the
impact that a particular variable will have, the potential for omitted variable bias should
not be a problem for our application.10 However, omitted variable bias does limit the
interpretability of the effect of the independent variables.
10
As a test to see how much of a problem the omitted variable bias might be, we estimated the model
presented in table 4 and an augmented model for the sample of migrants’ most recent spells in the U.S.
The augmented model also included income in the U.S. and dummy variables for whether or not the
20
The estimate that uses the migrant’s beginning of spell characteristics as
independent variables suggests that the longer a spell has been underway, the lower the
risk that it will end. The estimated shape parameter, p, is 0.81, indicating that the hazard
of ending a spell decreases with spell length. In other words, the longer the migrant stays
in the U.S., the less likely he is to leave. The estimate that uses the migrant’s end of spell
characteristics suggests that this effect is less dramatic.
In this estimate, the shape
parameter is close to one, which means that the length of the spell has little impact on the
hazard that the spell will end.
Both estimates indicate that older migrants have shorter spells, and that this effect
mitigates with age, although the effect is not significant. Migrants with more education
stay in the U.S. significantly longer, although each additional year of schooling has a
smaller effect. The number of children also has a significant impact on the length of the
spell. Each additional child under 18 makes the spell about 4% longer. Being a farm
worker does not appear to have a significant impact on how long the migrant spends in
the U.S., compared to non-farm workers.
The migrant’s legal status at the time they crossed the U.S. border seems to have a
very large impact on spell length.11 The coefficient on the dummy variable for legal
status implies that migrants who crossed the border legally stay in the U.S. 127% longer
than their undocumented counterparts. This effect can also be seen in Figure 4, which
shows the Kaplan-Meier survivor functions for migrants who crossed the border legally
and for those who did not. Fifty months after first being observed in the U.S. only 8% of
the undocumented migrants have not yet been observed working in Mexico, compared
with about 15% of the migrants who crossed the border legally. The effect is even larger
for migrants who are in the U.S. legally at the end of the spell. These migrants have
spells that are 437% longer than their undocumented counterparts.
migrant was accompanied by his wife and children. This information was recorded for each migrant’s most
recent trip to the U.S. but not for other trips. The results indicate that while the means of the two
predictions are different, the null hypothesis that the normalized predictions are the same cannot be rejected
at a 10% level of significance. The correlation between the two measures is 0.93 and is significant at a 5%
level.
11
Although it is important to remember that the coefficient estimate may be biased due to omitted
variables.
21
Both the year of the trip and the migrant’s origin community controls have an
important impact on the length of the spell. The χ2 test for the joint significance of the
year controls indicates that they are highly significant, as does the same test for the
community of origin controls.
The models of spell length that are presented in Table 4 provide us with estimates
of β and p for the beginning and for the end of the spell. These estimated parameters, β̂b,
p̂b and β̂e, p̂e (b for beginning and e for end of spell), are combined with the appropriate
x’s from the sample of migrants who have only one spell to compute the survival
function (equation [6]), and in turn the predicted probability of returning and working in
Mexico in the next 12 months (equation [7]) for each migrant’s most recent trip to the
U.S., as a function of beginning and end of spell characteristics. We use these measures
of the predicted probability of returning and working in Mexico in the next 12 months to
describe the permanence of the wage increase that the migrant experienced when he
began working in the U.S. If migrants save more when wages are temporarily high, we
would expect migrants with a higher estimated probability of returning to work in
Mexico to have higher savings rates.
Estimating Savings Rates
Table 5 presents estimates of savings rates.
In addition to the predicted
probability of returning and working in Mexico, the regressions include variables that
measure the size and composition of the migrant’s family both in Mexico and the U.S.
These variables are: the number of children in the migrant’s household less than 18 years
of age, and three indicator variables that are equal to one if the migrant is married, if the
migrant is accompanied by his wife in the U.S. and if the migrant is accompanied by his
kids in the U.S. Both regressions also include variables that measure earning ability and
proximity to retirement: age, age squared, years of schooling, years of schooling squared
and a measure of the migrant’s ability to speak and understand English.12 A variable that
12
The variable “English” is equal to zero if the migrant does not speak nor understand English, one if the
migrant does not speak but understands some English, two if the migrant does not speak but understands
English well, three if the migrant speaks and understands some English and four if the migrant speaks and
22
is equal to one if the migrant was a farm worker is also included, as is a variable which is
equal to one if the migrant was in the U.S. legally. Twenty-seven year of trip controls are
also included in an effort to account for the effect of variation in wages, interest rates and
exchange rates in the U.S. and Mexico on savings rates.
Many studies (see Deaton 1992 for a summary) suggest that savings rates are
positively related to income, so we allow for this possibility. The regressions include a
measure of monthly income that is equal to the reported hourly wage times the number of
hours worked per week multiplied by 30/7 weeks per month. Because we measure
savings rates as monthly savings divided by expenditures plus savings, we avoid
problems due to common measurement error in savings rates and income.
Because the regressions include estimated independent variables – the predicted
probability of returning and working in Mexico in the next 12 months and in some
specifications this variable multiplied by monthly income – conventional methods for
measuring standard errors are inappropriate. Instead, we report bootstrapped standard
errors based on 1,000 repetitions. Significance is based on the bias corrected 95th or 90th
percent confidence interval.
There are four estimates of savings rates in Table 5. The first two estimates use
ordinary least squares and treat the predicted probability that the migrant will return and
work in Mexico as a function of his end of spell characteristics as exogenously
determined.
The third and fourth estimates allow the probability of returning and
working in Mexico to be endogenously determined. These estimates use instrumental
variables for the estimated probability that the migrant will return and work in Mexico as
a function of his characteristics at the end of the spell. The instruments are the estimated
probability that the migrant will return and work in Mexico predicted from his
characteristics at the beginning of the spell and the other exogenous variables in the
regression.
We concentrate on describing the instrumental variable results (regressions [3]
and [4] in Table 5), because of the concern that the probability of returning to work in
understands English well. These answers are based on the respondent’s own assessment of their English
23
Mexico is endogenous and make comparisons with the OLS results where appropriate.
Estimate [3] indicates that age does not seem to have a significant effect on savings.
However, education does, although the significant portion of the effect operates through
the quadratic term. If a migrant were to go from having 4 years of schooling to 8 years of
schooling, holding other characteristics fixed, we would expect savings to decrease by
about 3%. This is an decrease of 16% compared to the average savings rate of 19%.
Migrants who are accompanied by their spouse have savings rates that are 13% higher
than their counterparts whose spouses are in Mexico. One possibility is that the reported
savings figures reflect the combined savings of the couple when they are in the U.S., so
that this variable captures the effect of the wife’s savings. In contrast to coming to the
U.S. with a spouse, being accompanied by a child in the U.S. significantly lowers savings
rates. Migrants whose children were in the U.S. save 9% less than migrants who were
not accompanied by children. According to these estimates, if a migrant is accompanied
by both his spouse and his children in the U.S., his savings rate should be about 4%
higher compared to an otherwise identical migrant who traveled to the U.S. on his own.13
The controls for being married and for the number of children under 18 appear to have no
effect on savings rates.
However, omitting these variables would change the
interpretation of the controls for whether the migrant’s spouse and/or children have
accompanied him to the U.S.
Farm workers have significantly higher savings rates than otherwise identical
non-farmers. Holding the other variables fixed, farm workers have savings rates that are
7% higher than those of non-farmers. This is a 37% increase above the average savings
rate of 19%. One possibility is that farm workers save more because of precautionary
motives. Their U.S. incomes are likely to be more volatile because variation in weather
and crop size will effect hours worked and potentially wages as well if migrants are paid
on a piece-rate basis.
ability.
13
The vast majority (85%) of the migrants in this sample are accompanied by neither spouses nor children.
Ten percent of the migrants are accompanied by both their wives and their children. Spouses, but no
children, accompany two percent of the migrants. A similar percentage of migrants is accompanied by
children and not wives.
24
Savings rates are significantly lower for migrants that are in the U.S. legally.
These migrants save about 30% less than their undocumented counterparts. This finding
also seems consistent with a precautionary motive for savings, since legal migrants face a
lower risk of being deported. It is also consistent with our central hypothesis – that
migrants who are more settled in the U.S. will save less – since achieving legal status
often requires migrants to have lived in the U.S. for a substantial period of time. The sign
and the significance of the coefficient on legal status depend crucially on controlling for
the endogeneity of returning and working in Mexico. In the OLS estimates, the effect of
legal status is positive and insignificant.
Savings rates are higher for migrants who speak and understand English better. A
migrant who speaks and understands English well will have a savings rate that is 8%
higher than an otherwise identical migrant who can neither speak nor understand English.
Monthly income does not significantly effect savings rate in this specification.
The probability of returning and working in Mexico has a large and significantly
negative effect on savings rates. An increase in the likelihood of returning and working
in Mexico of 10% is associated with a decrease in savings rates of 6.4%. A comparison
of the IV estimates and the OLS estimates suggests that treating the probability of return
as endogenous is very important. In the OLS estimates the coefficient on the probability
of return is positive and insignificant. However, the IV result seems to be completely
counter to the permanent income hypothesis. Instead of increasing savings rates when
income is high only temporarily, migrants appear to decrease savings rates.
The next specification explores this finding further. In deriving the implication
that savings rates will be higher the more temporary the migrant’s stay in the U.S., we
implicitly assumed that consumption could be zero. Suppose instead that individuals
must consume a certain fixed amount in order to survive. If their income exceeds this
amount, then they are free to save some of the excess.14 Below the threshold savings is
constrained to be zero. If migrants who have a very high probability of returning and
working in Mexico also have very low incomes, then they may be unable to save at all.
14
This is captured by a Stone-Geary utility function.
25
The data suggest that this may be the case. The estimated probability of returning and
working in Mexico is negatively correlated with income. The correlation coefficient is
–0.13 and is significant at a 5% level
In order to test whether migrant savings is constrained in this way, we add another
independent variable – the interaction between the predicted probability of return and
monthly income. In the instrumental variable estimate, the instrumented probability of
return is interacted with income. One can interpret a significant positive coefficient on
this variable as an indication that when income is sufficiently high, migrants save more
the more likely they are to return and work in Mexico. We find that the interaction
between the probability of return and income has a significant positive effect on savings
rates. When we add the interaction term, we also find that the direct effect of the
estimated probability of returning and working in Mexico is significantly negative. This
is exactly the opposite of the model’s predictions. The results indicate that the derivative
of savings with respect to the probability of return is equal to –0.77 +0.0001x(monthly
income). If monthly income is greater than $7,700, then the predicted derivative of
savings with respect to the probability of return is positive, otherwise it is not. Only 8 of
the 1,372 migrants have monthly incomes above this threshold. We can be 95% certain
that the true threshold lies between $13,200 (1983 dollars) and $2,200, according to the
bias corrected confidence interval and holding the coefficient on the interaction point
fixed at 0.0001. This is a large range and it is well above federal poverty thresholds. For
example, the 1983 poverty threshold was $848/month for a family of four and
$421/month for a single individual.
5.
Conclusions
The savings behavior of Mexican immigrants in the U.S. appears to be
significantly affected by how long they are likely to live and work in the U.S. However,
our findings suggest that the permanent income hypothesis does not provide an adequate
description of migrant savings behavior in general. In contrast to the permanent income
hypothesis, we find a higher probability of returning and working in Mexico is associated
26
with lower savings rates. However, the interaction between the probability of return and
migrant income makes savings rate significantly higher. When income is sufficiently
high, the predictions of the model do appear to hold and savings rates increase with the
likelihood of returning and working in Mexico. For lower income migrants, however,
satisfying basic consumption needs takes precedence over taking advantage of
temporarily high wages in the U.S. through savings. Adjusting the model to account for
this possibility would add to its predictive power, at least for the Mexican migrants
studied here.
27
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30
Table 1: Migrant Characteristics (Male Heads of Household)
Mexico Sample
Observations
1857
Documentation during most recent trip to the U.S. (%)
Green Card
11
Amnesty
4
Citizen
1
Tourist
7
Seasonal Agricultural Worker
10
Undocumented
69
% accompanied by spouse on most recent trip to the U.S.
11
% married
83
Median number of kids on most recent trip to the U.S.
0
Median number of kids < 18 years
3
% EVER Using U.S. Services
Public Schools
9
Unemployment Insurance
10
Food Stamps
4
Welfare
1
Median months spent in the U.S.
26
English Speaking and Comprehension
% who can do neither
53
% who do both well
3
Median Years of Schooling by year of most recent trip to the U.S.
before 1975
3
1975-1979
3
1980-1984
4
1985-1989
5
after 1989
6
Employment during most recent trip to U.S.
Median Hourly Wage (1983 U.S. $) by date of most recent trip
before 1975
5.74
1975-1979
4.40
1980-1984
3.89
1985-1989
3.59
after 1989
3.44
Median Hours/Week
45
% Working > 40 Hours/Week
53
% Farm Workers Working > 40 Hours/Week
59
% Paid in Cash
23
% Paid w/ Check
77
% Paying Social Security Taxes
77
% Paying Other Federal Taxes
72
31
U.S. Sample
409
29
21
2
2
10
36
76
70
2
1
56
33
17
10
150
4
9
6
6
6
9
6
12.41
7.04
6.08
5.33
4.27
40
43
42
10
90
90
90
Table 2: Spell Characteristics
Mexico Sample
3126
1857
Observations (# of spells)
Observations (# of migrants)
Documentation at spell start (%)
Green Card
Amnesty
Citizen
Tourist
Seasonal Agricultural Worker
Undocumented
Migrant Characteristics (at spell start)
Median Age
Median Years of Schooling
% married
Median # of children < 18 years
% farm worker
Median length of prior trips to U.S. (months)
Year Spell Began (%)
before 1975
1975-1979
1980-1984
1985-1989
after 1989
Spell Number (%)
1st spell
2nd spell
3rd or higher spell
Duration of spell (months)
median
mean
standard deviation
U.S. Destination (%)
California
Texas
Illinois
Other States
32
U.S. Sample
552
409
6
1
0
7
2
81
10
0
1
10
0
77
30
4
75
2
49
7.5
22
6
43
0
19
12
26
22
21
22
9
27
25
19
24
5
49
26
25
69
20
11
12
36
57
108
118
90
56
21
5
18
78
11
6
5
Table 3: Monthly Income, Expenditure and Savings during most recent trip to the U.S., 1983 dollars
Mexico Sample
Observations
Income15
mean
(std. deviation)
median
missing observations
Rent
mean
(std. deviation)
median
missing observations
Food
mean
(std. deviation)
median
missing observations
Remittances
% remitting
mean
(std. deviation)
median
missing observations
Savings
% saving
mean
(std. deviation)
median
missing observations
Savings Rate16
mean (%)
(std. deviation)
median (%)
missing observations
15
U.S.
Sample
1857
409
1053.82
1163.09
817.93
485
1805.27
1600.24
1283.77
27
82.07
141.76
45.63
337
494.76
569.45
322.58
3
122.54
153.34
91.81
418
389.70
425.84
277.01
4
79%
225.08
304.34
153.02
241
43%
104.42
360.51
0
11
48%
136.96
296.02
0
484
57%
257.13
508.58
87.95
17
19%
25%
8%
712
18%
20%
12%
23
Monthly income is equal to wages times hours worked per week times 30/7.
The savings rate is equal to monthly savings divided by the sum of monthly expenditure on food, rent,
remittances and savings.
16
33
Table 4: Weibull Estimates of Months Until Working in Mexico, for Migrants with More than One Spell
Independent Variable
Age
Age Squared
Years of Schooling
Years of Schooling Squared
Married (= 1 if married)
# of kids < 18
Farm (= 1 if farm worker)
Legal Status (= 1 if Documented)
Shape Parameter, p
Start of Spell
Standard Error†
exp(β
β)
0.9859
0.0207
1.0002
0.0003
1.0926**
0.0341
0.9928**
0.0021
0.8646
0.0902
1.0405**
0.0201
0.9923
0.0772
2.2685**
0.2784
0.8107**
0.0143
End of Spell
Standard Error†
exp(β
β)
0.9891
0.0184
1.0003
0.0002
1.0739**
0.0261
0.9943**
0.0016
0.7642**
0.0658
1.0391**
0.0166
0.9110
0.0568
5.3706**
0.3517
0.9930**
0.0212
116.99**
77.72**
χ2(33) test for community effects
82.74**
69.41**
χ2(26) test for year effects
Log Likelihood
-3545.59
-3160.17
Wald Chi-Squared (67)
407.19
1400.23
Number of Spells
2199
2224
Number of Migrants
960
965
** significant at the 5% level. * significant at the 10% level. †Standard errors are adjusted to account for multiple observations per migrant.
34
Table 5: Savings Rates Estimates for Migrants with One Spell
[1]
w/ probability of return
OLS
Independent Variable
coefficient
Standard
Error†
Characteristics at End of Most Recent Spell
Age
-0.0089
0.0073
Age Squared
0.0001
0.0001
Years of Schooling
0.0026
0.0106
Years of Schooling Squared
0.0002
0.0007
Accompanied by Spouse
0.1129*
0.0556
Accompanied by Kids
-0.0690
0.0495
Married (= 1 if married)
-0.0230
0.0348
# of kids < 18
0.0009
0.0073
Farm (= 1 if farm worker)
0.0594**
0.0262
Most Recent Legal Status in U.S.
0.0204
0.0729
(= 1 if Documented)
English
0.0218*
0.0110
Real Monthly Income‡
-0.0166
0.0190
Probability of Return
0.0718
0.1476
Probability of Return×Income‡
Constant
0.2291
0.1833
[2]
w/ probability of return
and interaction
OLS
coefficient
Standard
Error†
[3]
w/ probability of return
IV
coefficient
Standard
Error†
[4]
w/ probability of return
and interaction
IV
coefficient
Standard
Error†
-0.0091
0.0001
0.0013
0.0002
0.1237**
-0.0713
-0.0196
0.0011
0.0523*
0.0147
0.0073
0.0001
0.0105
0.0007
0.0551
0.0496
0.0346
0.0073
0.0261
0.0728
-0.0040
0.0000
-0.0096
0.0012*
0.1338**
-0.0861*
0.0234
-0.0069
0.0692**
-0.3066**
0.0080
0.0001
0.0123
0.0008
0.0585
0.0528
0.0409
0.0082
0.0281
0.1297
-0.0043
0.0000
-0.0112
0.0013*
0.1444**
-0.0884*
0.0272
-0.0069
0.0618**
-0.3201**
0.0081
0.0001
0.0122
0.0008
0.0583
0.0532
0.0408
0.0082
0.0281
0.1313
0.0241**
-0.0543**
-0.0332
0.0883*
0.2758
0.0110
0.0231
0.1557
0.0554
0.1825
0.0193*
-0.0170
-0.6436**
0.0113
0.0195
0.2710
0.5520**
0.2206
0.0217*
-0.0571**
-0.7712**
0.0943*
0.6134**
0.0113
0.0246
0.2811
0.0618
0.2211
F test for year effects (27 years)
1.39
1.53
1.53
1.68
Adjusted R2
6.83%
7.68%
7.90%
8.75%
Number of Observations
485
485
485
485
†Bootstrap standard errors based on 1,000 replications. ‡ Income is measured as the real hourly wage×hours/week×30/7. Numbers in table are
coefficient×1,000 and standard error×1,000. ** significant at the 5% level, based on bias corrected 95% bootstrap confidence interval. * significant at
the 10% level, based on bias corrected 95% bootstrap confidence interval. In estimates [3] and [4] the probability of return at the end of the spell is
instrumented with the probability of return at the beginning of the spell and the other independent variables in the regression.
35
Mexico Sample
U.S. Sample
.75
.5
Fraction
.25
0
10 25 40 55 70 85 100
10 25 40 55 70 85 100
Distribution of Hours Worked per Week
Figure 1
Kaplan-Meier survival estimate
1.00
0.75
0.50
0.25
0.00
0
50
100
150
Spell Duration in Months
Figure 2
36
200
250
Weibull Survival Estimates, at mean of exp(b'x)
1
.75
.5
.25
0
0
50
100
150
Spell Duration in Months
200
250
Figure 3
Kaplan-Meier Survival Estimate, by legal status
1.00
0.75
0.50
0.25
legal 1
legal 0
0.00
0
50
100
150
Spell Duration in Months
Figure 4
37
200
250