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Federal Reserve Bank of Chicago

Remittance Behavior among New U.S.
Immigrants
Katherine Meckel

REVISED
December 5, 2008
WP 2008-19

Remittance Behavior among New U.S. Immigrants

Katherine Meckel
Federal Reserve Bank of Chicago
November 2008

I wish to thank Daniel Aaronson, Gadi Barlevy, Lisa Barrow, Alex Bartik,
Mariacristina De Nardi, Jennifer Martin, Anna Paulson, Diane Schanzenbach, Edward
Zhong, an anonymous reader, and seminar participants at the Federal Reserve Bank of
Chicago for comments on previous drafts. This paper would not have been possible
without the guidance of Joseph Altonji. All errors in fact or interpretation are my own.
The opinions in this paper do not reflect those of the Federal Reserve Bank of Chicago or
the Federal Reserve System. The author can be contacted at kmeckel@frbchi.org.

Abstract
I analyze remittance behavior among new legal immigrants in the US using the
2003 New Immigrant Survey (NIS), a nationally representative survey of immigrants
admitted to legal permanent residency in 2003. I use the NIS to address data limitations
common to empirical remittance studies, such as low sample sizes, missing information
on the donor or recipient and the absence of data which includes immigrants from many
countries. Looking first at the distribution of remittances, I find that it is skewed to the
right, with a small number of immigrants sending very large amounts. I then analyze the
determinants of remittances among new immigrants and estimate remittance-income
elasticities. From this analysis, I find evidence that the motivations to remit are not
purely altruistic and may include the desire to invest in the home country. I then discuss
how future work will re-examine this investment motivation and its relationship to return
migration by incorporating later waves of the 2003 NIS to form panel data. Finally, I
find that large country differentials in remittance behavior are only partially explained by
observable characteristics of the donor, recipient and origin country.

1
I. Introduction
Remittances from the US—transfers sent by immigrants living within the US to
friends and family in their home country— currently play an increasingly important role
in the global economy and therefore deserve high priority on a research agenda. Recent
estimates show that approximately $96 billion, or about one-third of total international
remittances, was sent from the US in 2006.1 This trend is partly caused by a recent surge
in immigration which made the years 2000-2007 the highest 7 year period of immigration
and immigration growth in US history.2 In addition, the countries currently sending the
most immigrants are largely those whose emigrants have a high propensity to remit. For
example, 60% of the immigrants arriving in the US between 2000 and 2007 came from
countries in Latin America or the Caribbean3 and roughly 75% of immigrants from these
countries reported sending remittances on a regular basis in a survey conducted in 2006.4
Other recent top sending countries include India, China and the Philippines, and we will
see that new immigrants from these countries also have a high propensity to remit.
Much of the current remittance literature analyzes the effect of these transfers on
the economic development and wealth re-distribution within the home country, (e.g.
Chami et al. (2003), Cox and Ureta (2003), Funkhouser (1992), Lowell and De la Garza
(2000), Stark and Taylor (1986), Woodruff and Zenteno (2001), and Yang (2004)), as
international remittances often constitute a significant portion of GDP for recipient
countries. In 2006, for example, remittance flows made up over 10% of GDP for Haiti,
Jamaica, El Salvador, Honduras, and the Philippines; remittances to developing countries

1

Source: World Bank
Camorata (2007)
3
Camorata (2007)
4
Bendixen (2006)
2

2
typically exceed official development assistance, are similar in magnitude to foreign
direct investment, and are more stable than either of these other flows.5
The decision to remit is often modeled with the altruism model of interfamilial
transfers (Becker, 1974), in which immigrants are motivated to remit by an interest in
their relatives’ well-being, transferring more to poorer relatives. Most empirical
remittance studies, however, have found evidence that self-interest may at least partially
motivate the remittance decision (e.g. Hoddinott, (1994), Lucas and Stark (1985), Ilahi
and Jafarey (1999), Osili (2006)). Examples of possible self-interest motivations in the
remittance decision include the desire for a larger inheritance or for relatives to visit or
call.
Empirical studies of the remittance decision are often impeded by data limitations.
Common problems include small sample sizes, missing information on the donor or
recipient and the absence of data on remittance behavior over time or across origin
country. Among the contributions of this paper are that it uses a large and nationally
representative sample of new US immigrants to create a comprehensive new look at the
decision to remit. I also use detailed income history available in the NIS to estimate
permanent income and remittance-income elasticities. With country fixed effects, I am
able to control for country unobservables, such as immigrant self-selection, in my
analysis.
I find evidence against altruism as the sole motive for remitting among new
immigrants and show that remittances may be used for investment purposes in the home
country. I also find that remittance likelihood rises with time in the US and intent to
return and that married women are least likely to be remitters. I find that remittance
5

Bernanke (2004)

3
behavior varies greatly by country of nationality, and that these differences are only
partially explained by observable characteristics of the immigrant, recipient family, and
country. To conclude, I discuss how future work will extend this analysis by
encompassing later waves of the 2003 NIS, allowing me to observe return migration and
life-cycle movements between remittances and income.
II. Literature Review
After Becker (1974) proposed the pure altruism model of interfamilial transfers,
several empirical transfer studies (Altonji et al. (1997), Bernheim et al. (1985), Cox
(1987)) showed that family members do not redistribute income to the extent implied by
this model. Instead, the results indicated that they are at least partly motivated by self
interest. Empirical remittance work has also found evidence that self-interest plays a role
in the decision to remit (Hoddinott (1994), Lucas and Stark (1985), Ilahi and Jafarey
(1999), Osili (2006)). Transfer theories which combine motives of altruism and self
interest are sometimes called theories of “enlightened self-interest.”
Below I briefly lay out the comparative statics of the model of pure altruism,
which provides simple testable hypotheses. I then discuss various models of self interest.
In my empirical analysis, I will first test the main hypothesis of the pure altruism model.
If I find evidence against this hypothesis, I will be able to conclude that self-interest
motives are at least partially involved in the decision to remit.

Let R denote remittance level, R+ denote the set of positive remittances and Yimm and Yrec
be the permanent incomes of the remittance-sending immigrant and the recipient in the

4
home country.6 This altruism framework assumes that immigrants are smoothing agents
who make remittance decisions using information about changes in permanent income,
rather than transitory income shocks.7
Under the altruism hypothesis, the level (conditional on a remittance being sent) and the
probability that a remittance is sent rise with the immigrant’s income, or:
∂R+/∂Yimm >0

(1)

and
∂Pr(R>0)/∂Yimm >0,

(2)

while the level (conditional on a remittance being sent) and likelihood of remittances fall
with recipients’ income, or:
∂R+/∂Yrec <0

(3)

and
∂Pr(R>0)/∂Yrec<0

(4)

The intuition here is simple. The altruistic immigrant remits in order to decrease
income inequality within the family network, so an immigrant with higher income
relative to that of the recipient income gives more. Some altruism models include as
parameters the “strength” of altruism between the donor and recipient, which reflects that
the donor may care more about closing an income gap with close relatives, such as
parents, than with distant relatives.

6

For a more detailed theoretical discussion of these comparative statics (in the context of inter vivos
transfers), see Cox (1987) pp 6-11
7
This also applies to most of the self-interest motives described below. The one notable exception is the
insurance motive, in which remittances are used to help credit-constrained recipients smooth over transitory
shocks.

5
Many self-interest theories have been put forth as alternatives to the pure altruism
motive described above. In the “exchange” model of self-interest (Cox, 1987), transfers
are made as payments for non-market services provided by the transfer recipient. An
immigrant might send a remittance home in “exchange” for services such as childcare,
taking care of property in the home country, or time spent with the immigrant in the form
of visits. The key difference between the hypotheses of the altruism and exchange models
is that, under the exchange model, remittances can rise with recipient income (i.e.
wealthier relatives receive more):
∂R+/∂Yrec > 0

(5)

This would happen if the immigrant’s demand for services is relatively inelastic. The
relationships between remittance likelihood and recipient income or immigrant income
are the same under the exchange and altruism models.
Other self-interest models define remittances as part of a contract between the
immigrant and family members in the home country. Family in the home country may
decide to send the immigrant abroad so that remittances can act as a form of “insurance”
(Stark, 1985) for risky assets (in Stark’s paper, cattle during a drought). Alternatively,
remittances may be repayment for the family’s investments in the immigrant’s migration
process. The family network might invest in an education for the immigrant if income
returns to education are greater in the host country than in the home country, in which
case remittances (which rise with the sender’s income) become a return on the family’s
investment (Ilahi and Jafarey, 1999). Additionally, it has been shown that remittances
may be payments made by the immigrant to bid for a larger inheritance (Hoddinott, 1994)
or a way to signal reliability, enhance reputation, or finance investments in the home

6
country (Osili, 2006). Empirical work by Robert Suro (2003) of the Pew Hispanic
Center on Latino remitters in the US indicates that some remittances are sent to Latin
America and the Caribbean in response to political or economic instability or natural
disasters, such as Hurricane Mitch in Honduras.
Identifying the motivations behind the remittance decision remains an unfinished
task but one that is important to understanding resource sharing among immigrants and
their families. Analyses of remittance behavior can help policymakers understand how
shocks to immigrant income due to, for example, a lay-off or need-related government
assistance, affect remittance flows and the resources of family members in the home
country.

III. Data
The New Immigrant Survey 2003
The 2003 NIS8 is a nationally representative sample of newly admitted legal
permanent residents (i.e. “green card” holders, or LPRs) based on electronic
administrative records compiled by the US government.9 I use the Adult sample (defined
as those older than 18), which consists of 8,573 observations and includes data on a wide
range of topics, including education, language skills, labor force activity, health and
health insurance, earnings and wealth, home ownership, migration history and
remittances. These immigrants may have just arrived in the US or may have been living
8

Jasso, Guillermina, Douglas S. Massey, Mark R. Rosenzweig and James P. Smith. “The New Immigrant
Survey 2003 Round 1 (NIS-2003-1) Public Release Data.” March 2006. Retrieved August 2008. Funded by
NIH HD33843, NSF, USCIS, ASPE & Pew. The data collection was performed by professional survey
staff from the National Opinion Research Center (NORC). More information about the survey and research
team, as well as documentation and data can be found here: http://nis.princeton.edu.
9 Specifically, the records of immigrants admitted to LPR status were compiled by the INS (Immigration
and Naturalization Service) and are now compiled by its successor agencies, the USCIS (US Citizenship
and Immigration Services) and the OIS (Office of Immigration Statistics).

7
in the US for some time on a temporary nonimmigrant visa or with undocumented
status.10
The sample consists of immigrants (respondents) admitted to LPR status from
May-November 2003. Surveys were conducted as soon as possible after admission.
Immigrants in the sampling frame were contacted using the address to which they had
requested the green card be mailed. The 2003 sample represents the first interview of the
first full cohort (a pilot was conducted in 1996), as the NIS will be a multi-cohort
longitudinal study.11 A second interview of the 2003 cohort was conducted in 2007 and
data from this interview will be released in late 2009.
The sampling frame for the 2003 NIS is based on visa types. Admission to LPR
status is granted to immigrants who meet certain eligibility criteria (such as spouses of a
US citizen and certain types of workers or refugees) and these immigrants are called
“principals.” The US government then also grants immigrant visas to spouses and minor
children “accompanying, or following to join” the principals. The sampling frame for the
2003 NIS consists of “principals” and accompanying spouses and is based on four strata:
10

Here is an unweighted tabulation of the adult sample by visa type with average years since first arrival in
the US to live
Avg Yrs Since
Categories of Visas in 2003 NIS
Sample
Freq
Percent
Arrival
Other
Spouse of US Citizen

786

9

3.2

1,428

17

5.5

Spouse of LPR

209

2

7.6

Parent of US Citizen

995

12

4.8

Child of US Citizen

283

3

2.7
1.4

Family Fourth Preference

533

6

Employment Preferences

1,673

20

5.2

Diversity

1,451

17

1.6

Refugee, Asylees, Parolees

554

6

6.5

Legalization

661

8

15.4

8,573

100

Total
11

For more information on the sample construction, see Jasso et al (forthcoming) and
http://nis.princeton.edu/overview.html

8
spouses of US citizens (undersampled), employment principals (oversampled), diversity12
principals (oversampled) and other immigrants. The NIS provides are sample weights to
account for sample design. The sample response rate is 68.6%.13
As described above, the survey forms a very thorough review of the immigrant’s
life before and after migration. Occupation and earnings data, for example, is available at
four different stages of the immigrant’s life—around age 16, immediately before
migration, immediately after migration and at the time of the interview. Much of the
survey was also given to available spouses living in the same household as the
respondent, yielding detailed information on 4,915 spouses. In order to deal with
language barriers, interviews were conducted in respondents' preferred languages.
I compared the NIS sample to the 2003 US foreign born population using the
American Community Survey (ACS) and found several differences. 14 The weighted NIS
sample is 56% women and 74% are married or living with a partner, whereas 50% of the
ACS sample is female and 63% are married. 15 The ACS sample of foreign born is also
older and arrived in the US earlier. The mean age in the NIS data for men and women is
39, whereas it is 42 for men and 44 women in the ACS. Immigrants in the NIS arrived an
average (median) of 6 (3) years ago whereas immigrants in the ACS sample arrived an
average (median) of 21(18) years ago. While the sending countries between the two are
12

Diversity principals are winners of an annual green card lottery which is open to people from countries
with low rates of immigration to the United States.
13
Independent analysis on the 2003 NIS by Hersch (2008) concludes that nonresponse is does not appear to
be more pronounced in some groups in the 2003 NIS than others.
14
I used an approximately 1 in 236 ACS sample, found here: http://usa.ipums.org. This sample includes
undocumented immigrants.
15
Data compiled by the Department of Homeland Security also shows that 2003 LPR recipients are
younger and more likely to be female and married than the native population of the US. See Reytina
(2005). This is because the largest proportion of LPR visas go to spouses of US citizens, and the majority
of these visas go to women (as shown in the NIS data). In addition, the majority of immigrants receiving
green cards as spouses of LPRs, parents of US Citizens and through employment are women in the NIS
data.

9
largely similar (the five most frequent in the NIS are Mexico (17%), India (7%), El
Salvador (6%), Philippines (5%), and China (5%)),16 the percentages of ACS immigrants
from Mexico (28%) and El Salvador (3%) are somewhat different. Finally, the foreign
born population from the ACS have higher labor income (median and mean of $23,118
and $32,882 compared to $20,280 and $30,708), which may reflect the fact that they have
had more time to assimilate in the US.

The Remittance Measure: Construction and Univariate Analysis
Under the definition currently in use by the International Monetary Fund (IMF),
remittances are international transfers of funds sent by migrant workers from the country
in which they are working to people (typically family members) in the country from
which they came.17 I used this definition to create the remittance measure used in this
analysis.
In a section of the NIS questionnaire on transfers, the most “financially
knowledgeable” of the respondent and his or her spouse are asked about money transfers
from either the respondent or the spouse to relatives and friends during the last 12
months.18 I counted a transfer to friends and relatives of the respondent as a remittance if
the recipient lives in the respondent’s country of nationality. Specifically, transfers to

16

The public use files do not release country of nationality for approximately 30% of the sample. Instead,
country of nationality is given one of 9 continents, such as “Europe and Central Asia,” “Latin America and
the Carribbean” or “African Sub-Saharan.” There are 22 country categories.
17
see International Monetary Fund (1993) p. 75
18
The survey question pertaining to transfers given to a certain relative, is the following, or a slight variant
of the following: During the last twelve months, did you or your spouse give any financial assistance (such
as gifts, transfers, bequests, or loans) to your [relative].

10
parents, spouses, siblings, children, “other relatives,” and “other friends” of the
respondent were counted.19
Some difficulty in measuring the remittances arises because about 1/5 of the
immigrants who indicated that they had sent a remittance replied that they were not sure
or refused to tell how much they had sent. This may be partially due to the question
structure which asks for an amount and then a regular frequency (biweekly, monthly,
biannually, etc.) with which this amount was sent over the past year. This does not allow
for variation in the amounts sent, and many respondents wrote in alternate frequencies
and amounts such as “Under 500 whenever she needs it,” or simply checked “do not
know,” which made it impossible to measure how much had been sent in these cases.
Finally, it is important to note that many remittance and intergenerational transfer
studies (Osili, 2006; Stark, 1985; Rosenzweig and Wolpin, 1993; Altonji et al, 1997) use
datasets which match potential transfer senders and recipients. In the NIS data, it may be
that no remittance is recorded because the immigrant does not have friends or family in
their country of origin, possibly because they have migrated themselves. I therefore
restrict the sample to immigrants who have a “close relative” (parent, a spouse, or a child
of any age)20 currently living in the country of origin. This turns out to be 73% of the
NIS sample (72%, weighted). The fact that this is a relatively large percentage probably

19

There is some difficulty ascertaining whether or not certain recipients live in the country of origin. It is
impossible to determine the current country of the respondent’s siblings as well as the “other relatives” and
“other friends,” although it is possible to tell if the friends and relatives are outside the US. In addition,
when answering how much money was transferred to young children, the respondent is not asked to
identify the children. I counted all remittances to siblings if the respondent’s parents are currently living
outside the country and all remittances to friends and relatives living outside the US. As for young
children, I recorded a remittance if any of the young children live in the country of nationality, and
assigned an amount equal to the total remittances sent to young children multiplied by the fraction of young
children living outside the US. As such, remittance frequency may be a bit overstated.
20
NIS only provides country of residence for children, spouses, and parents, as opposed to siblings, or
other relatives and friends.

11
reflects that the majority of the sample arrived in the US very recently21 and therefore
may still have strong ties to their home countries.22
Table 1 shows descriptive statistics for remittance amount and frequency in the
NIS sample as well as the restricted sample. 11.7% of the whole sample sent a
remittance over the preceding 12 months, compared with 15.4% for the restricted sample.
Among those who sent remittances, the average amount sent over the past year is
$2,90323 (median $1,200) for the whole sample and $2,950 (median $1,200) for the
restricted sample. Remittances are therefore skewed to the right, with the mean more than
double the median in both samples. However, the values higher than the median are quite
large compare with the rest of the sample. The remittances above the 75th percentile in
comprise nearly ¾ the sum of all remittances sent. Evidently, the majority of immigrants
are sending smaller amounts but some are sending very large amounts.

21

Around 2/3 of the sample arrived in the US for the first time to live within the last 5 years
Here I compare averages of important variables from the NIS sample and the restricted sample, which
shows that the samples are similar.

22

NIS Sample
Age

Restricted Sample

38

39

Female

55%

55%

Married

77%

82%

19659.51

20280.01

Average

6

6

Median

3

3

Median Labor Income
Time Since Entry

Visa
% Legalization

8%

8%

% Family

57%

60%

%Employment

10%

11%

% Mexican

15%

16%

6%

6%

% Salvadoran
23

All dollar amounts are in 2003dollars.

12
I then compared remittance frequency and amount between the NIS sample of
Latino remitters to a 2004 poll of Latino remitters in the US conducted by Bendixen and
Associates.24 I found that the average amount sent by Latino remitters in the two samples
is similar ($2,900 over the last year in the NIS vs. $2,800 on a yearly basis from the
Bendixen survey). The average frequency is not similar, however; as 61% of Latino
immigrants in the Bendixen survey remit on a yearly basis, whereas only 13% of NIS
Latinos remitted over the last year.
Estimates of Frequency and Amount by Recipient25
As can be seen in Figure 1, 9.5% of immigrants in the restricted sample sent
remittances to parents, 2.4% remitted to young children, 1.8% remitted to adult children,
1.6% remitted to siblings, 1.6% remitted to other family and <1% remitted to other
friends during the past 12 months. Figure 1 also shows average non-zero remittance
amounts by recipient. On average, immigrants in this sample sent back $2,181 to parents,
$3,499 to adult children, $3,758 to young children, and $1,921 to other relatives over the
past year.

Estimates of Frequency and Amount by Region of origin
Remittance frequency and amount sent over the preceeding 12 months differs
greatly by country of origin.26 Immigrants from African Sub-Saharan are most likely to
send in the restricted sample, at 24%, followed by those from Latin America and the
24

A powerpoint document with summary statistics from this survey can be found on the website of the
Inter-American Development Bank here
http://idbdocs.iadb.org/wsdocs/getdocument.aspx?docnum=820729
25
Data on remittance amount (as opposed to frequency) sent to siblings of the respondent and spouse is
currently missing in the NIS data files due to an error. This data will be re-released soon and the analysis
in this paper will subsequently be updated.
26
Full calculations available upon request

13
Caribbean (17%), East, South, and Pacific Asia (14%), the Middle East and North Africa
(12%), Europe and Central Asia (10%), and Other North America (9%). By country,
immigrants from Guatemala send with the highest frequency (35%), which is more than
double the sample frequency (15%), followed by those from El Salvador (32%), Nigeria
(29%), and the Dominican Republic (28%). The least frequent senders are Poland,
Korea, United Kingdom, and Canada.
Average remittance amount also shows heterogeneity by country of origin,
although median remittances by region do not vary significantly.27 Counting those
regions with over 50 remittance observations, the highest mean amount is from
immigrants from African Sub Saharan ($5,201), followed by those from East Asia, South
Asia and the Pacific ($3,447) and Latin America and the Caribbean ($2,369).

Remittances as a Percentage of Income
I created four measures of annual income: permanent labor income of the head of
household and immigrant, current labor income of the immigrant and family income
(includes income from labor as well as assets). These measures were created using
current annual earnings, annual earnings immediately post migration, and annual asset
income.28 The questions on occupation, wage and salary history in Sections B and C first
determine if the respondent is doing/has done any work for pay, and then establish a
frequency of payment. The earnings measure was created either by using annual reported
earnings or by combining hours and weeks worked per year with hourly wage. It
includes self employment earnings and earnings at a second job.
27

Counting those regions with more than 50 remittance observations, the range for median remittances is
$1,200 to $1,500.
28
Assets include additional homes and real estate, checkings and savings accounts and transportation
equipment.

14
It is important to estimate permanent income, as the use of current income in
transfer and remittance studies is problematic in two ways. First, it is assumed that
immigrants exhibit smoothing behavior and make remittance decision based on
permanent, rather than current, income. Second, current income of the donor and
recipient may be endogenous to remittance behavior and this is impossible to control for
without knowledge of past remittance behavior.
To create a measure of current labor income of the immigrant, I took the log of
current earnings of respondent. For family income, I created the sum of non-missing
respondent earnings, spouse earnings, and asset income. As for estimating permanent
labor income, I use a modification of the method used in Altonji et al. (1997). They use
panel data on salaries from a mostly native sample of the US population, and I adapt their
specification to the NIS restricted sample by adding variables to account for the process
of assimilation. There exists a large body of economic literature on the effect of
assimilation on immigrant wages in the host country, showing that wages, especially right
after the immigrant arrives (which is relevant in this sample of new LPRs), are biased
downward.29 Because I combined current earnings with those measured immediately
after arrival, it was important to account for the bias in this first wage when estimating
permanent income.
After creating panel data from the two head of household30 or immigrant earnings
observations, I ran gender-specific regressions of earnings in log form on age in cubic
29

See Borjas (1994). In the NIS data, large gains in earnings are made in the first five years since first
arrival in the US to live.
30
I defined the head of household as the respondent if male, or of the spouse if the respondent is female.
Exceptions to this rule are when the female respondent has non missing or non zero earnings and their male
spouse have a zero or missing earnings measure. In cases in which the head of household have zero
earnings, I created variables which indicate labor force status (retired, homemaker, disabled or
unemployed).

15
form, marriage dummies and number of children in the household as well as number of
years in the US and country of origin and then estimated residuals. I then predicted the
wage for a 40 year person who is married and has one child (the median in the dataset)
and arrived in the US five years ago.31 I then added a weighted average of the
individual’s two residuals from the intial regression to this. As such, this measure of
permanent income functions as a kind of time-adjusted income average.
A quick look at Table 3 shows the difference between the average permanent
income (higher) and current income (lower), which reflects the fact that roughly 2/3 of
the sample entered the US to live for the first time within the last 5 years and therefore
may have a current wage which is biased downward due to the effects of assimilation.
The median head of household annual permanent labor income is calculated to be
$32,016, with 25th percentile at $17,923 and 75th percentile at $45,813. In comparison, a
foreign born man between the ages of 38-42 who is married and has been in the country
between 5 and 10 years has median annual earnings of $32,446 in the 2003 ACS, with the
25th percentile at $18,251 and 75th percentile at $60,837.
Table 2 contains the ratio of remittances sent over the past year to head of
household permanent income, the immigrant’s current income, and GDP per capita in the
home country (using the restricted sample). The average immigrant in this sample sent
less than 5% of both measures of annual income in remittances each year. Among those
who sent remittances, the mean ratio to permanent and current income is 0.10 and 0.16,
respectively, while the medians are less than half the means, at 0.04 and 0.07
respectively. The distribution of these ratios is skewed to the right, like the remittance
31

Because it is possible that the native born spouse is the head of household (in which case he or she only
has one wage observation—current), I put in a time since entry indicator of “all years” for these people.
There were relatively few native born head of household spouses.

16
distribution, showing that while most immigrants give a low amount of their income,
some give much more, with the 90th percentile giving 0.38 of their permanent income, or
0.80 of their current income. The ratio of remittances to GDP in the home country is of
course much higher—the overall mean (median) is 0.35 (0) vs. 2.42 (0.57) in the
restricted sample vs. remitters only.

Profile of A remitter
Table 3 shows summary statistics for the estimation sample as well as for
remitters and non-remitters separately. A quick comparison shows some important
differences. Remitters in the 2003 NIS are much less likely to be female overall, but
particularly female and married, and much more likely to be male and married. This
most likely reflects differences in earning and labor force participation (31% of women
immigrants are not in the labor force compared to 5% of male immigrants). Remitters
have been in the country longer; the average remitter arrived 2 years before the average
non-remitter, which probably reflects the effects of assimilation on earnings, but may also
reflect a cohort effect.32 Additionally, immigrants who remit are more likely (16% vs.
12%) to intend to return home. In terms of income, remitters earn more than nonremitters, and are less likely to be unemployed or not in the labor force. They are much
more likely (18% vs. 11%) to hold assets in the home country.33 Finally, remitters are
more likely to have families that are poorer, both in relative (to their countrymen) and
absolute (the level of GDP) terms.
32

In particular, the difference between time of entry for remitters and non-remitters diminishes to 1.4 if
Salvadorans are excluded. Salvadorans have one of the highest propensities to remit and average amount
remitted in the sample and arrived on average 13 and at the median 14 years ago (s.d. 5 yrs). The vast
majority of Salvadorans (around 80%) received their green cards through amnesty.
33
Further analysis shows that the majority of these assets are real estate, as opposed to savings or checkings
accounts or transportation equipment.

17
IV. The Model
Given the discussion of theoretical models of remittance behavior above, it is clear
that it is important to use measures of the economic resources of the immigrants and
relatives to model remittance behavior. I use as measures of the economic resources of
the immigrant: income, employment status, education and an indicator for whether the
immigrant holds assets in the home country. As for economic resources of the relatives, I
use income level of the immigrant’s family, 34 whether or not they live in a rural area,
GDP per capita in the home country,35 unemployment rate in the country of origin and
the father’s total years of education.
I also use as controls variables relating to the immigrant’s migration patterns. These
variables include years since first entry to the US to live, intent to return, number of reentries into the home country to live and country of nationality indicators. As mentioned
above, theoretical altruism models normally include a measure of the altruistic bond
between donor and recipient, in order to differentiate between strong relationships in the
family, such as parent-child, and weaker relationships, such as aunt-child. Some

34

With regards to income of the family in the home country, the immigrant is asked:
Now I’d like to ask you some questions about when you were a child. Thinking about the time
when you were 16 years old, compared with families in the country where you grew up, would you
say your family income during that time was far below average, below average, average, above
average, or far above average?
The fact that this is given for the time period when the immigrant was 16 means it is a good indicator for
the parent’s permanent income (income measure at the time of the survey would be biased downward since
most parents are past prime earning age). There is no way to measure the income of the other recipients
(such as siblings), however, so this must serve proxy for the income of other remittance recipients. In
addition, the fact that this is measured at the time the immigrant was 16 solves usual problems of
endogeneity of recipient labor supply and income in transfer models. The question pertaining to whether
or not the family lived in a rural area is also asked for the time when the immigrant was 16.
35
I include GDP in US dollars using official exchange rate (OER) and purchasing power parity (PPP)
estimates separately. Although PPP estimates are normally preferred because they give a consistent
measure of the “quality of life” between countries, it is necessary to use OER rates because the transfer
amount (and sender income) is in current US dollars. I include the unemployment rate along with the OER
help control for “quality of life” between countries.

18
variables, such as distance and time separation may weaken the strength of these bonds,
and so it is important to include them in the model.
Finally, I include various demographic controls of the immigrant and relatives,
including age, gender and marital status interacted, total number of children in the
household, and number of siblings of the immigrant.
To model the likelihood of remitting, I estimated the following specification:
Let E denote the event in which a remittance takes place, Ximm denote immigrant
characteristics and Xrel denote relatives’ characteristics.
Pr( E ) = Φ ( β 0 + β1 X imm +β 2 X rel + ε )

(6)

where Φ denotes the cumulative normal distribution function. This is the probit
model.
Modeling remittance level is trickier. For one, about 85% of the distribution is at
0 (i.e. no remittance sent) and using OLS to estimate remittance amount on the whole
sample would create coefficient estimates which are biased downward and inconsistent.36
Using the Tobit37 model creates unbiased coefficient estimates, but constrains regressors
to have the same effect on the likelihood and level and this may be unreasonable.
Hoddinott (1994) notes that distance to one’s home country, for example, may reduce the
likelihood of a remittance but not the amount.38 In addition, under any motive besides
pure altruism, remittance amount may be nondecreasing in recipient income while
remittance likelihood would be decreasing in recipient income. Finally, I want to check

36

Specifically, this is because the truncated distribution causes the mean of the estimated error term (call
this ê), to be different from 0, which violates the assumption that E(e) = 0.
37
Introduced by Tobin (1958)
38
This is particularly plausible because many remittances are sent by way of friends and relatives traveling
to the home country. In 2004, around 11% of Latino immigrants sent remittances via “people travelling”
(Bendixen, 2004).

19
the prediction of the altruism model that remittance amount, conditional on remitting, is
decreasing in recipient income (see Eqn 1), and for this, I need to model non-zero
remittances.
I model the following equation:
ln( R) = β 0 + β 1 X imm +β 2 X rel + ε | R > 0

(7)

where R is the remittance amount
If remittances are non-decreasing in recipient income, this would provide
evidence against a pure altruism motive (see Eqn. 1). If remittances are increasing in
recipient income, this could indicate an exchange motivation (see Eqn. 5).
Using OLS on the sample of remitters to estimate Eqn. (7) may create biased
coefficient estimates due to sample selection. Sample selection bias arises if there is
nonzero correlation between the error terms in the likelihood and level equations (i.e. if
there is an unobserved immigrant characteristic, such as “diligence”—Hoddinott (1994)
provides this example—which affects the level and likelihood of remitting). Using
Heckman’s (1979) two-step method, one can look for sample selection bias by estimating
the correlation and testing whether it is different from zero using a t-test.

V. Results
Estimation results for the probit are shown in Tables 4; marginal effects evaluated
at the mean of the regressors are reported. As for the level regressions, I first estimated
both the two step and maximum likelihood Heckman sample selection models and found

20
no evidence of bias due to sample selection. 39 In the two step model, the effect of the
selection term was not precisely estimated and in the maximum-likelihood model, I was
unable to reject the hypothesis that ρ = 0. I then estimated OLS coefficients on the
sample of remitters and found very little difference in the OLS and Heckman coefficients,
which also indicates a lack of sample selection bias in the OLS model.
Specifications in both tables vary by regressors included. In Table 4,
specifications (1)-(4) include as the income measure (in this order): permanent income of
the head of household, permanent income of the immigrant, current income, and family
income. Specification (5) repeats specification (1) with the addition of country of
nationality indicators and the subtraction of the GDP indicator. As for Table 5,
specifications (1)-(3) use the official exchange rate GDP, and (4)-(6) use PPP GDP.
Specifications (2) and (5) in Table 5 add time since entry. Effects on income may be
sensitive to the presence of time since entry if it picks up a cohort effect and cohorts have
different income levels. Specifications (3) and (6) in Table 5 add country indicators and
take away the GDP control.
Below I discuss the results in Tables 4 and 5 by regressor group.40

Economic Resources of the Immigrant

39

I tried two different selection equations: using the log of distance and an indicator for being from Latin
America as variables which might affect the likelihood but not level of remittances. Since remittances are
sometimes transmitted through informal channels (i.e. relatives or friends traveling to the home country),
proximity may enable the act of remitting while not affecting the amount remitted. As for the Latin
America dummy, I find that the likelihood of remitting is much higher among immigrants from countries in
Latin America, whereas there is negligible difference in amount remitted. This may be due to behavioral
differences, differences in cultural norms, or differences in immigrant self-selection.
40
The specifications shown exclude some variables listed as covariates in Section IV. In preliminary
analysis, I found that the following have negligible effects and eliminated them from the specification:
education, father’s education and number of re-entries to the home country to live.

21
For new immigrants, an increase in income means an increase in the likelihood of
remitting and the amount sent. This is consistent with the predictions of the altruism
model (see Eqns. 1,2) as well as various self-interest model, such as exchange. Table 4
shows that the effect of income on the likelihood of remitting varies a bit by
specification; a 1% increase in income increases the likelihood of remitting somewhere in
between 1.4 and 2.1 percentage points, which is about 1/10 of the sample frequency. In
addition, the effect of being unemployed or not in the labor force is negative and around
4-7 and 9-10 percentage points, respectively.
The OLS results from Table 5 show that the effect of permanent labor income is
consistently estimated at about 0.4, although this effect falls to 0.3 when country
indicators are added, meaning that a 1% increase in income increases remittance amount
by 0.3-0.4% for the average immigrant in the sample who remits. Table 7 shows
remittance-income elasticities by type of income. In comparison to permanent income
elasticity, the current income elasticity is lower (~0.1, for the immigrant as well as the
head of household).
Holding assets (virtually all of which are real estate) in the home country has a
very large, positive and significant effect on the likelihood (~0.1, or ½ the sample
frequency) and level (~10-30% increase) of remitting, conditional on remitting. This
may indicate that for some new immigrants, remittances are a means of investing in the
home country. Recent studies by Dustmann and Kirchkamp (2002) and Osili (2006)
provide evidence that remittances are used to finance investments in the country of origin
in the form of land and housing acquisition.

22
There are several plausible “investment” scenarios, which include motives of
altruism, self-interest, or a combination. Immigrants may be investing in the home
country to maintain ties and increase stature, particularly if they intend to return, 41 or to
provide for their family members, possibly in the form of housing. Alternatively, it could
be that immigrants are sending money to pay relatives in exchange for managing the sale
of real estate they currently own in the home country, since the percentage holding assets
in the home country drops with time since entry (in the first year, 20%; in the second
16%; in the third 10%),.42 This could also reflect, however, the fact that those who
initially invested intended to return home and do so within the first couple of years.43
Future research which will incorporate later waves of the 2003 NIS to form panel data
will re-examine this investment motive by looking at the relationship between return
migration44 and remittances.

Economic Resources of Relatives in the Home Country
The effects of both GDP and family income on remittance level (shown in Table
5) provide evidence against the model of pure altruism, indicating that self-interest is at
least partially involved in the remittance decision for new immigrants. It may be that
some remittances are sent back for “self-interest” investment purposes, as described

41

This “prestige” motive is put forth in Lucas and Stark (1985). See pg 904
While, it is plausible that transfers are for mortgage payments on home country real estate, it is not likely
those payments are included in the remittance measure for two reasons. First, the interviewees are
specifically asked about mortgage payments in the previous section of the questionnaire, and, second, the
wording of the remittance question is such that the transfer is supposed to be “financial assistance” for the
recipient.
43
Osili (2006) finds evidence that less skilled immigrants (who have lower future expected income) are
more likely to invest in the home country, perhaps out of a precautionary savings motive in case they
decide to return home.
44 Return migration is a reality for many LPRs. Jasso and Rosenzweig (1982) estimate that 30% of LPRs
return over the first 10 years
42

23
above. More specifically, we can see that remittance level is nondecreasing45 in recipient
income, whereas under the altruism hypothesis, remittance level should fall with recipient
income (as shown in Eqn. 3). The effect of a 1% increase in GDP raises remittance level
by somewhere between 0.02-0.2%. In addition, immigrants with family whose income is
“Above Average” or “Far Above Average” send about 50-90% and 60-130%
(respectively) more than those whose families have “Average” incomes. Interestingly,
immigrants with family whose income is “Below Average” or “Far Below Average” also
send more than those with family whose income is “Average,” although only about 10%
and 40-50% (respectively) more. Thus, the results show a kind of U-shape relationship
between relative family income and remittance amount.
In the probit regressions shown in Table 4, both GDP and relative income of the
immigrant’s family have a negative effect (as is predicted by the altruism model in Eqn.
(4) as well as the exchange model). A 1% increase in GDP lowers remittance likelihood
by about 0.06, which is roughly 1/3 of the sample frequency. The effect on remittance
likelihood of the family having “Below Average” or “Far Below Average” income,
compared to “Average” income, is positive and significant, whereas the effect of the
family having income “Above Average” or “Far Above Average” is negative.46 The
effect of the unemployment rate in the home country and whether or not the immigrant’s
family lived in a rural area (two measures of poverty) when he or she was 16 are both
positive.

45

Recall from (5) that under the exchange model remittance level increases in recipient income under
certain conditions
46
It is possible that that family income could serve as a proxy for unobserved characteristics of the
migrant’s income process, such as skill level and would therefore bias the coefficient on migrant income. I
find no evidence of this bias when I exclude family income from the regression equations.

24
Demographics
Table 4 shows that the effect of being a married woman (as opposed to a married
man), when significant, drops the likelihood of remitting about 4-6 percentage points for
the average immigrant in the estimation sample, which ranges from about 1/5 to 1/3 of
the sample frequency. The effects of being a single man or woman on remittance
likelihood are positive (and larger for single men) with regards to being a married man,
although these effects are not significant. The coefficients from the OLS regressions
presented in Table 5 reveal a similar pattern, although they are not estimated precisely.
It is possible that the married immigrants send less to their families because of
obligations to their spouse or spouse’s relatives.47 The fact that single or married women
send less than their male counterparts may indicate that the role of the remitter is partly
defined along gender lines. Besides these effects, the effect of an extra young child in the
immigrant’s household (which indicates constraints on the immigrant’s income), an extra
sibling, or an extra year of age are very small and not precisely estimated.48

Migration Variables
Tables 4 and 5 also show the effects on remittance behavior of the migration
history and future migration plans of new immigrants. Intent to return to the home
country to live, for example, has a large, positive effect on both remittance level and
likelihood. In the probit regressions, the effect is 6 to 9 percentage points, which is
around 1/3 to ½ of the sample frequency, and in the OLS regressions (where the effect is
significant at the 15% level) intent to return raises remittance amount by about 20-34%,
47

If the majority of married immigrants’ spouses were living abroad, one might expect them to send more
on average, than single immigrants. In this sample, however, only 4% of the sample have a spouse in the
home country.
48
I tried entering various nonlinear forms of age into the equation and found no remarkable results.

25
holding all else equal.49 As mentioned above, it may be that immigrants who intend to
return send more to maintain ties or increase stature in their home country. Funkhouser
(1995) put forth an altruistic model of remittance behavior in which remittances rise with
intent to return because immigrants who want to return have stronger altruistic bonds
with their families.
Time since first entry to live in the US (entered non-linearly as five indicators)
also has a strong effect on both the likelihood and level of remittances. The effect on
likelihood is positive for the average immigrant, while the effect on remittance level is
initially positive and then falls after about year 10. While remitting frequency may
increase as the new immigrant becomes assimilated (gets a job, finds a permanent
residence, determines the best method through which to send remittances), one might
expect that as time passes it would decrease, especially if altruistic bonds weaken over
time. 50 One possible explanation is that the web of dependents in the home country
increases with time. Alternatively, this result may be evidence of immigrant selection
over time, in that if only successful immigrants eventually stay in the US, then those who
have been here longer will send back more because they are more prosperous.51
I also considered that this effect might have to do with the fact that immigrants
transferring to LPR status through legalization (amnesty) have been in the US for much

49

When time since entry is added to the equations the effects on intent to return fall, possibly because the
two are highly correlated as those who intend to return leave as time goes by, or change their minds.
50
In separate analysis, I determined that yearly earnings rise steeply with time since entry until the fifth
year, after which point the rise is much flatter. This indicates that the affects of assimilation might be more
muted after year 5. In the results in Table 4, we can see that the effect of additional years is significant and
positive at year 16. Additionally, a variant of the altruism model which incorporates the idea that altruism
weakens over time in the host country is described in Rapoport and Docquier (2005) on p. 23
51
As an additional test, I determined that time since entry is not picking up the effects of age or parental
age in a nonlinear form by adding these variables into the probit regressions. An immigrant might send
more frequently to aged parents because of increased dependency or in an effort to bid for inheritance.

26
longer than others in the sample and also have a much higher rate of remitting.52 When I
control for visa type or exclude amnesty recipients from the regression sample, however,
the effect of time since entry in the probit regressions from Table 4 is still positive (even
past year 16), although the magnitude of this effect is greatly reduced.53 It appears that
the positive effect of time since entry on remittance frequency may be partly but not
solely due to differences in remittance behavior by visa types.
Empirical evidence on the effect on remittance behavior of time spent in the host
country is mixed. Rodriguez (1995) finds that remittances (likelihood and level) rise and
then fall with years spent abroad for Filipino immigrants. Suro (2003), on the other hand,
finds evidence of long-standing remittance flows from US immigrants to Honduras,
Guatemala and El Salvador. Likewise, Funkhouser (1995) found that remittances from
the US to close family members in El Salvador reacted positively to years spent in the US
but remittances to close family members in Nicaragua were negatively affected by time.
Lastly, in Tables 6a and 6b, we can see the country effects from regressions (5)(6) and (10)-(11) in Tables 4-5 (with regards to the baseline category: Mexico). There
are very strong country-specific effects on remittance likelihood and level. For example,

52

Here is a table which gives average years since entry and remittance frequency by visa type

Categories of Visas in 2003 NIS Sample

Freq

Percent

Legalization (Amnesty)

661

8

15.4

0.33

Refugee, Asylees, Parolees

554

6

6.5

0.22

Spouse of LPR

209

2

7.6

0.17

Other
Spouse of US Citizen

Avg. Yrs Since Entry

Remit Frequency

786

9

3.2

0.15

1,428

17

5.5

0.14

Employment Preferences

1,673

20

5.2

0.13

Diversity

1,451

17

1.6

0.13

Family Fourth Preference

533

6

1.4

0.1

Parent of US Citizen

995

12

4.8

0.07

Child of US Citizen

283

3

2.7

0.03

Total
8,573
Avg. Yrs Since Entry and Remit Frequency are weighted.

100

53

Results available on request

27
the effect of being from the Dominican Republic, El Salvador, Guatemala, Nigeria or the
Philippines is an increase of 0.1-0.2 in the likelihood of sending a remittance for the
average immigrant, which is around the magnitude of the sample frequency, and
represents some of the largest marginal effects in the regression.
Comparing 6a and 6b, it is clear, however, that country effects on remittance
likelihood and level do not necessarily have the same sign. For example, the effect on
level of being from El Salvador or Nigeria is negative. There are large, positive and
precise effects of being from Canada, China, India, the Philippines, or Poland on
remittance level. The effect of being from China (vs. Mexico), for example, increases
remittance amount by 81%.
These effects may be caused by country-specific unobservables (such as cultural
expectations) which inform remittance behavior. Funkhouser (1995) compared a sample
of Nicaraguans and Salvadorans in the US and found that striking differences in
remittance behavior between the two samples could not be explained by the observable
characteristics of the migrant or the recipient household. He concluded instead that
behavioral differences and differences in self selection of immigrants explained these
differences.

Determinants of Remittance Differentials by Country
The coefficients discussed above represent large, unexplained remittance
differentials by country, and in this section, I want to see how effective a group of
variables which capture the differences between countries will be in explaining these
differentials. I chose country-specific variables which give information about the

28
economy of the country, the quality of life of its inhabitants, and the ease of remitting to
this country. The variables are listed below.

Variable
Log GDP, 2003
GDP growth 1970-2003
Openness, 1965-1990
Infant Mortality Rate,
2003
Log(Distance) between
countries

Source
CIA World Factbook
Penn World Tables
Sachs & Warner (0 = closed, 1 = open to trade)
CIA World Factbook
Centre D'Etudes Prospectives Et D'Informations
Internationales

I use the GLS estimator outlined in Borjas (1987), Borjas and Sueyoshi (1994),
and Aaronson, Barrow and Sanders (2007) to evaluate the impact of the variables above
on the remittance behavior differentials by country, which are given by the 21
coefficients on the country indicators in regressions (1) and (2) in Tables 4 and 5 (cols.
(1) in both tables). The GLS estimator is needed to account for the fact that that the error
in the following OLS regression (where ĉj is the vector of country coefficients, Z is the
country characteristics listed above, and j indexes country) is heteroskedastic:54
ĉj = γZ + uj,

(8)

The variance of this error, uj, will be given by E(uj2) = σj2 + φ2, where σj2 is the variance
of the coefficients from regressions (1)55 and (2), and φ2 is the homoskedastic error from
the following regression:
cj = γZ + uj .

(9)

Using estimates of σj2 from estimation of (1) and (2) combined with that of uj from
estimation of (3), we can calculate φ2, which is then used to estimate (3) using GLS. The
results are shown in Tables 8 and 9.
54

In order to simplify the calculations for the GLS estimator, I re-estimated the likelihood regression as an
OLS linear probability model.
55
Estimated as an OLS linear probability model instead of a probit, as explained in the last footnote.

29
Looking at Tables 8 and 9, one can see that remittance frequency differentials are
much more effectively explained by the country-specific variables than the remittance
amount differentials. Whereas the country-specific variables explain up to 55% of the
variation in country effects from the frequency regression, they explain up to only 20% of
the variation in the country effects from amount regression. The fact that a significant
amount of the large remittance level differentials is unexplained may indicate that
country unobservables, partially determine remittance behavior.
As for the determinants of remittance differentials, Tables 8 and 9 show that
“openness” explains a large amount of the variation. This variable has a strong negative
effect, meaning that a country more open to trade is associated with a smaller country
effect on remittance behavior. A higher infant mortality rate is associated with a larger
country effect, while a bigger distance is associated with a smaller country effect. For
remittance level, higher GDP growth is associated with a larger country effect.

IV. Directions for future research
In the future, I will extend the research presented in this paper by incorporating
future waves of the 2003 NIS into my analysis. This will enable me to observe return
migration and the joint life-cycle movements between remittances and income. I will
examine the relationship between investment in the home country and return migration. I
will be able to measure more accurately the permanent income, which is affected by
assimilation in the first wave. Finally, as the NIS releases data which is currently
classified, such as more detailed country of nationality indicators, it will be possible to re-

30
examine the results found here, including the importance of country-specific effects on
remittance behavior.

V. Conclusions
Using a nationally representative and detailed survey of new US immigrants from
the 2003 NIS, I created univariate and multivariate analysis of remittance level and
likelihood. I found that the distribution of remittances and remittances as a percentage of
income is skewed to the right, with a few immigrants sending large amounts or large
proportions of their income. I found evidence against the pure altruism model of
remittance behavior, showing that, in the sample of remitters, remittances are
nondecreasing in recipient income. I also found that remittances may be used for
investments in the home country. Finally, I found that remittance behavior varies greatly
by country of nationality, and that these differences are only partially explained by
observable characteristics of the immigrant, recipient family, and country.

31
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Institute for the Study of Labor (IZA) Discussion Paper No. 1531.
Rytina, Nancy F. 2005. U.S. Legal Permanent Residents: 2004. Office of Immigration
Statistics Annual Flow Report.

33
Rodriguez, Edgard R. 1995. International Return Migration and Remittances in the
Philippines. University of Toronto, Department of Economics Working Paper.
Rosenzweig, Mark R. and Kenneth I. Wolpin. 1993. Intergenerational Support and the
Life-Cycle Incomes of Young Men and Their Parents: Human Capital Investments,
Coresidence, and Intergenerational Financial Transfers. Journal of Labor Economics 11:
84-112.
Stark, Oded, J. Edward Taylor, and Shlomo Yitzhaki. 1986. Remittances and inequality.
Economic Journal 96383: 722-740.
Suro, Roberto. 2003. Remittance Senders and Receivers: Tracking the Transnational
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Tobin, James. 1958. Estimation of relationships for limited dependent variables.
Econometrica 26: 24-36.
Woodruff, Christopher M. and Rene Zenteno. 2001. Remittances and Microenterprises in
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Paper.
Yang, Dean. 2004. Remittances and Human Capital Investment: Child Schooling and
Child Labor in the Origin Households. Ann Arbor, MI: University of Michigan,
Manuscript.

34

Table 1: Remittance Frequency and Amount (Weighted)
Whole Sample
Remit in Past 12 months = 1
Mean
Nonzero Remittance Level
Mean
Std. Dev.
5%
10%
25%
50%
75%
90%
95%
Obs
1

Restricted Sample1

11.70%

15.40%

$2,903
$5,776
$100
$200
$500
$1,200
$3,000
$6,000
$10,000
905

$2,950
$5,816
$100
$200
$500
$1,200
$3,000
$6,000
$10,000
851

Refers to the sample of immigrants with a close relative in the home country

35

Figure 1: Mean Remittance Frequency and Amount by Recipient (Restricted
Sample,1 Weighted)

0.1

$4,500
$4,000
$3,500
$3,000
$2,500
$2,000
$1,500
$1,000
$500
$0

0.08
0.06
0.04
0.02
0
Parent

1

Adult Young Sibling Friend Relative
Children Children

Refers to the sample of immigrants with a close relative in the home country

Frequency
Level

36

Table 2: Remittances as a Percentage of Income
Restricted
Sample1
Remit /

HH Permanent Income2 Migrant’s Current Income3 GDP (OER)
All

Remit>0 All

Remit>0

All

Remit>0

Mean

0.01

0.1

0.03

0.16

0.35

2.42

5%

0

04

0

0

0

0.03

25%

0

0.01

0

0.03

0

0.19

50%

0

0.04

0

0.07

0

0.57

75%

0

0.11

0

0.16

0

1.7

95%

0.06

0.38

0.15

0.8

1.07

8.8

Obs.

6,029

845

2,733

541

3,873

604

1

Refers to the sample of immigrants with a close relative in the home country
Ratios over 2 were coded to 0 (around 10 observations for both)
4
The ratio is displayed as 0 because of rounding
2,3

37

Table 3: Summary Statistics
All
Restricted Sample1
Mean
Demographics
Age
38.2
Married, Female
0.45
Married, Male
0.37
Single, Female
0.1
Single, Male
0.08

SD

Remitters
Mean SD

Non-Remitters
Mean SD

12.7
0.5
0.48
0.3
0.27

37.9
0.38
0.44
0.08
0.1

10
0.48
0.5
0.28
0.3

38.2
0.47
0.36
0.1
0.08

13.2
0.5
0.48
0.3
0.26

1
4.7

1.2
4.4

1.2
4.9

1.3
3.6

1
4.6

1.2
4.5

5.6
0.24
0.22

6.7
0.43
0.42

7.3
0.17
0.19

7.4
0.37
0.39

5.3
0.26
0.23

6.5
0.44
0.42

0.36
0.4
0.33
0.28
0.8
0.33
0.6

0.15
0.2
0.18
0.12
8.5
0.16
0.28

0.36
0.4
0.39
0.33
0.7
0.37
0.83

0.16
0.2
0.12
0.08
8.6
0.12
0.2

0.36
0.4
0.32
0.27
0.8
0.22
0.6

0.9
0.9
4.9
4.8
0.37
0.4
0.33

9.6
10.3
6.8
8.8
0.13
0.1
0.18

0.9
0.9
4.7
3.7
0.34
0.3
0.39

9.6
10.2
4.6
6.6
0.17
0.22
0.11

0.9
0.9
4.9
4.9
0.38
0.41
0.32

Economic Resources of Family in HC
Income Relative to Countrymen
Far Below Average
0.1
0.29
Below Average
0.18 0.38
Average
0.53 0.5
Above Average
0.15 0.36

0.12
0.22
0.5
0.13

0.32
0.41
0.5
0.19

0.09
0.18
0.53
0.16

0.29
0.38
0.5
0.36

# Children in HH
# Siblings
Migration
Time Since 1st Entry
0 years
1-2 years

3-5 years
0.16
6-10 years
0.2
11-15 years
0.13
16+ years
0.08
Log Distance
8.6
Intent to Return
0.13
Num Re-Entries to Live
0.2
Economic Resources of Immigrant
ln Permanent Income
9.6
ln Permanent Income HH 10.2
ln Current Income
4.9
ln Family Income
6.9
Unemployed
0.17
NILF
0.2
Holds Assets in HC
0.12

38
Far Above Average
Rural
log gdp per capita, ppp
Unemployment Rate
log gdp per capita, OER
1

0.04
0.41
8.5

0.19
0.49
0.7

0.04
0.44
8.4

0.19
0.5
0.7

0.04
0.4
8.6

0.2
0.49
0.8

7.8

1.2

7.6

1

7.8

1.2

Refers to the sample of immigrants with a close relative in the home country

39

Table 4: Multivariate Analysis of Remittance Likelihood, Probit Estimates
Dependent Variable: Remit in Past 12 Months = 1, 0 otherwise
(1) Permanent (2) Permanent (3) Current,
HH Income
Immigrant
Immigrant
Income
Income
Demographics
Age
-0.001
-0.000
0.001
(0.001)
(0.001)
(0.001)
Married, Female
-0.029
-0.063
-0.044
(0.021)
(0.030)*
(0.023)*
Single, Female
0.004
0.013
0.010
(0.023)
(0.021)
(0.024)
Single, Male
0.011
0.022
0.009
(0.027)
(0.033)
(0.036)
# Children in HH 0.008
0.011
0.003
(0.006)
(0.007)
(0.009)
# Siblings
0.000
0.000
0.000
(0.002)
(0.002)
(0.002)
Migration
1-2 yrs

0.041
0.039
(0.024)
(0.037)
3-5 yrs
0.070
0.086
(0.036)*
(0.048)
6-10 yrs
0.082
0.094
(0.036)*
(0.050)
11-15 yrs
0.161
0.187
(0.040)**
(0.047)**
16+ yrs
0.196
0.166
(0.032)**
(0.046)**
Intent to Return
0.064
0.087
(0.030)*
(0.048)
Economic Resources of the Immigrant
Income
Permanent, HH
0.017
(0.011)
Permanent, Imm
0.021
(0.014)
Current, Imm

0.028
(0.039)
0.060
(0.060)
0.062
(0.055)
0.112
(0.060)
0.129
(0.045)**
0.054
(0.048)

NILF
Holds Assets in
HC

(5) (1) +
Country –
GDP and ur

0.000
(0.001)
-0.054
(0.022)*
0.017
(0.025)
0.021
(0.032)
-0.000
(0.007)
0.000
(0.002)

0.000
(0.001)
-0.037
(0.015)*
-0.036
(0.018)
0.000
(0.014)
0.007
(0.004)
0.000
(0.000)

0.018
(0.028)
0.059
(0.043)
0.063
(0.038)
0.116
(0.049)*
0.164
(0.039)**
0.093
(0.043)*

0.037
(0.016)*
0.089
(0.027)**
0.105
(0.026)**
0.112
(0.024)**
0.115
(0.024)**
0.059
(0.026)*

0.020
(0.007)**
0.017
(0.004)**

Family Income
Unemployed***

(4) Family
Income

0.014
(0.002)**
-0.058
(0.024)*
-0.100
(0.021)**
0.107

-0.069
(0.034)*

0.048
(0.039)

0.136

0.123

0.095

-0.042
(0.015)**
-0.091
(0.015)**
0.093

(0.040)**

(0.047)**

(0.051)*

(0.042)*

(0.021)**

40
Economic Resources of Family in the HC
Income Relative to Countrymen
Far Below
0.056
0.095
0.100
0.064
0.012
Average
(0.014)**
(0.022)**
(0.028)**
(0.020)**
(0.018)
Below Average
0.060
0.075
0.090
0.080
0.033
(0.019)**
(0.024)**
(0.025)**
(0.023)**
(0.015)*
Above Average
-0.019
-0.017
-0.007
-0.025
-0.028
(0.022)
(0.026)
(0.030)
(0.029)
(0.014)*
Far Above
-0.028
-0.041
-0.023
-0.020
-0.009
Average
(0.027)
(0.040)
(0.029)
(0.031)
(0.027)
Rural
0.021
0.029
0.004
0.018
-0.001
(0.013)
(0.014)*
(0.020)
(0.015)
(0.008)
Log GDP (OER) -0.050
-0.058
-0.059
-0.059
(0.015)**
(0.015)**
(0.016)**
(0.016)**
Unemployment
0.004
0.004
0.002
0.004
Rt.
(0.003)
(0.002)
(0.003)
(0.003)
Sample
21%
18%
21%
20%
17%
Frequency
Pseudo R
.07
.08
.08
.08
.08
Squared
Observations**
3529
2322
2394
2903
5916
Marginal Effects Evaluated at the Mean of Regressors
Robust standard errors in parentheses (clustered by country of nationality)
* significant at 5%; ** significant at 1%
Columns 2 and 3 exclude immigrants who are not in the labor force, and column 4 excludes immigrants for
whom the HH is not in the labor force
Unemployed refers to immigrant in all columns but last, in which it refers to the head of household
Not reported: effects on dummy for missing observations in “return” variable (questionnaire uses a random
skip on that question), effects on country indicators
Estimation sample consists of immigrants with a close relative in the home country

41

Table 5: Multivariate Analysis of Remittance Level: OLS Estimates
Dependent Variable: Log of positive remittances
(1) GDP—
(2) add time (3) add
Official
since entry
countries,
Exchange
take away
Rate
GDP, ur
Demographics
Age
0.006
0.006
0.003
(0.006)
(0.005)
(0.005)
Married, Female -0.081
-0.057
-0.007
(0.102)
(0.122)
(0.147)
Single, Female
0.044
0.145
0.170
(0.150)
(0.153)
(0.193)
Single, Male
-0.074
-0.014
0.030
(0.209)
(0.223)
(0.245)
# Children in
0.035
0.052
0.077
HH
(0.036)
(0.036)
(0.044)
# Siblings
0.010
-0.000
0.016
(0.013)
(0.014)
(0.013)
Migration
Intent to Return 0.255
0.189
0.187
(0.159)
(0.154)
(0.137)
Time Since First
Entry to US
1-2 yrs.
0.284
0.290
(0.233)
(0.236)
3-5 yrs.
0.406
0.533
(0.263)
(0.292)
6-10 yrs.
0.881
0.858
(0.146)**
(0.169)**
11-15 yrs.
0.593
0.638
(0.197)**
(0.254)*
16+ yrs.
0.512
0.576
(0.156)**
(0.185)**
Economic Resources of the Immigrant
Permanent
0.375
0.355
0.253
Income HH
(0.095)**
(0.089)**
(0.096)*
NILF
-0.368
-0.245
-0.358
(0.263)
(0.238)
(0.281)
Unemployed
-0.383
-0.161
-0.175
(0.166)*
(0.227)
(0.224)
Holds Assets in 0.078
0.241
0.241
HC
(0.122)
(0.109)*
(0.109)*
Economic Resources of Family in the Home Country
Income Relative to Countrymen

(4) GDP—
PPP

(5) add
time since
entry

(6) add
countries,
take away
GDP, ur

0.007
(0.006)
-0.056
(0.096)
0.136
(0.156)
0.016
(0.192)
0.044

0.005
(0.005)
-0.026
(0.115)
0.235
(0.164)
0.088
(0.204)
0.053

-0.001
(0.006)
-0.009
(0.122)
0.119
(0.193)
0.032
(0.250)

(0.036)
0.007
(0.013)

(0.034)
-0.002
(0.012)

0.064
(0.040)
0.015
(0.011)

0.297
(0.155)

0.224
(0.152)

0.216
(0.109)

0.232
(0.204)
0.389
(0.229)
0.882
(0.144)**
0.586
(0.191)**
0.483
(0.156)**

0.379
(0.167)*
0.736
(0.227)**
0.947
(0.154)**
0.992
(0.257)**
0.851
(0.168)

0.372

0.356

0.284

(0.089)**
-0.404
(0.244)
-0.377
(0.161)*
0.083

(0.080)**
-0.284
(0.231)
-0.141
(0.228)
0.248

(0.072)**
-0.363
(0.220)
-0.112
(0.155)
0.293

(0.112)

(0.105)*

(0.099)***

42
Far Below Avg
Below Avg
Above Avg
Far Above Avg
Rural
Log GDP per
capita (OER)
Unemployment
Rt
Log GDP per
capita (ppp)

0.414
(0.196)*
0.137
(0.143)
0.616
(0.122)**
0.845
(0.185)**
-0.105
(0.131)
0.075

0.410
(0.203)
0.139
(0.155)
0.530
(0.138)**
0.645
(0.171)**
-0.110
(0.133)
0.024

0.378
(0.177)*
0.136
(0.161)
0.473
(0.129)**
0.580
(0.170)**
-0.105
(0.158)
0.169

(0.094)
0.010

(0.091)
0.016

(0.075)*
0.051

(0.018)

(0.016)

(0.007)**

0.368
(0.176)*
0.129
(0.137)
0.595
(0.130)**
0.746
(0.169)**
-0.096
(0.129)

0.362
(0.182)
0.126
(0.142)
0.509
(0.136)**
0.541
(0.167)**
-0.110
(0.131)

0.167

0.132

0.361
(0.133)*
0.134
(0.148)
0.375
(0.136)*
0.449
(0.132)**
-0.196
(0.137)

(0.097)
(0.090)
2.274
2.408
1.891
1.476
1.578
3.276
(1.273)
(1.206)
(1.327)
(1.115)
(1.078)
(0.670)**
Observations
568
556
556
609
597
826
R-squared
0.11
0.14
0.19
0.11
0.15
0.18
Robust standard errors in parentheses
* significant at 5%; ** significant at 1%
Not reported: coefficient estimate for indicator for missing “return” responses—random skip question,
coefficient estimates on country indicators
Estimation sample consists of remitters with a close relative in the home country
Constant

43

Table 6a: Marginal Effects and Std Error on Country Indicators in Probit Analysis
Country

Canada

China

Colombia

Cuba

Dominican
Republic

El
Salvador

0.192

0.132

Ethiopia Guatemala

-0.056

0.001

-0.040

-0.008

(0.021)

(0.026) (0.044)** (0.031)** (0.031) (0.032)** (0.023)* (0.017)**

Nigeria

Peru

Philippines

Poland

Russia

Ukraine

United
Kingdom

Vietnam

-0.008

0.150

0.016

0.125

-0.022

-0.020

0.026

-0.137

-0.030

(0.023)

(0.024)

(0.030)

(0.009)**

(0.026)

(0.026)

-0.120

(0.018)** (0.038)** (0.028) (0.036)**

0.145

India

-0.065
0.065
Marg.
Effect
(0.017)** (0.031)*
Std
Error
Korea
Country Jamaica

Marg.
Effect
Std.
Error

0.050

Haiti

Table 6b: Marginal Effects and Std Error on Country Indicators in OLS Analysis
Country

Canada

China

Colombia

Cuba

Dominican
Republic

El
Salvador

Ethiopia Guatemala

Haiti

India

-0.058

-0.135

0.256

0.335

(0.104) (0.052)* (0.117)

(0.167)

(0.108)**

1.319
0.594
Marg.
Effect
(0.134)** (0.092)**
Std
Error
Korea
Country Jamaica

-0.048

-0.145

0.213

(0.135)

(0.139)

(0.118)

Nigeria

Peru

Philippines

Poland

Russia

Ukraine

United
Kingdom

Vietnam

-1.964

-0.204

-0.068

-0.130

0.349

0.684

0.090

0.106

-1.026

-0.350

(0.209)**

(0.139)

(0.075)

(0.131) (0.106)** (0.112)** (0.160)

Marg.
Effect
Std.
Error

0.058

(0.093)

(0.282)** (0.161)*

Effects on continent indicators (reported for those observations for which country of nationality is not
given) not reported in Tables 6a and 6b
Mexico is the reference category
Estimation sample consists of immigrants (6a) or remitters (6b) with a close relative in the home country

44

Table 7: Remittance-Income Elasticities
OLS Specification:
(1)
(2)
(3)
(4)
(5)
Income Type:
0.28
Permanent Income (HH)
0.38
0.36
0.37
0.36
(0.095)** (0.089)** (0.089)** (0.080)** (0.072)**
Permanent Income (Imm)
0.38
(0.082)**
Current Income (Imm)
0.12
(0.035)**
Family Income
0.07
(0.04)
Permanent income estimates and current income are calculated from wages and salary. Family income
includes wage and salary income as well as asset income.
Estimation sample consists of remitters with a close relative in the home country

45

Table 8: Determinants of the Differences in Remittance Frequency by Country of
Nationality, GLS Regression Results
Dependent Variable: Coefficients from Linear Probability Estimates
(1)
(2)
Log GDP per capita, 2003 PPP
-0.050
-0.001
(0.019)**
(0.023)
GDP growth 1970-2003
-0.019
-0.023
(0.032)
(0.027)
Openness (Sachs&Warner), 1965-90
-0.196
(0.065)***
Infant Mortality
Log(Distance)
Constant

0.446
(0.158)**
0.23

0.086
(0.177)
0.48

(3)
0.039
(0.030)
-0.013
(0.031)
-0.236
(0.064)***
0.004
(0.002)*
-0.004
(0.020)
-0.322
(0.347)
0.55

Adjusted R-squared
Standard errors in parentheses
* significant at 10% ** significant at 5%; *** significant at 1%
Not reported: coefficients on indicators for whether or not growth is missing or distance is missing

Table 9: Determinants of the Differences in Remittance Amount by Country of
Nationality, GLS Regression Results
Dependent Variable: Country Coefficients from OLS Estimates
(1)
GDP per capita, 2003 PPP
-0.124
(0.160)
GDP growth 1970-2003
0.350
(0.269)
Openness (Sachs&Warner), 1965-90

(2)
0.155
(0.213)
0.328
(0.252)
-1.131
(0.611)*

Infant Mortality
Log(Distance)
Constant

0.426
(1.309)
21
-.06

-1.648
(1.664)
21
0.08

(3)
0.393
(0.279)
0.681
(0.287)**
-1.440
(0.595)**
0.038
(0.023)
-0.302
(0.187)
-2.592
(3.229)
21
0.20

Observations
Adjusted R-squared
Standard errors in parentheses
* significant at 10% ** significant at 5%; *** significant at 1%
Not reported: coefficients on indicators for whether or not growth is missing or distance is missing

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Risk Overhang and Loan Portfolio Decisions
Robert DeYoung, Anne Gron and Andrew Winton

WP-05-04

Characterizations in a random record model with a non-identically distributed initial record
Gadi Barlevy and H. N. Nagaraja

WP-05-05

Price discovery in a market under stress: the U.S. Treasury market in fall 1998
Craig H. Furfine and Eli M. Remolona

WP-05-06

Politics and Efficiency of Separating Capital and Ordinary Government Budgets
Marco Bassetto with Thomas J. Sargent

WP-05-07

Rigid Prices: Evidence from U.S. Scanner Data
Jeffrey R. Campbell and Benjamin Eden

WP-05-08

Entrepreneurship, Frictions, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-05-09

Wealth inequality: data and models
Marco Cagetti and Mariacristina De Nardi

WP-05-10

What Determines Bilateral Trade Flows?
Marianne Baxter and Michael A. Kouparitsas

WP-05-11

Intergenerational Economic Mobility in the U.S., 1940 to 2000
Daniel Aaronson and Bhashkar Mazumder

WP-05-12

Differential Mortality, Uncertain Medical Expenses, and the Saving of Elderly Singles
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-05-13

Fixed Term Employment Contracts in an Equilibrium Search Model
Fernando Alvarez and Marcelo Veracierto

WP-05-14

1

Working Paper Series (continued)
Causality, Causality, Causality: The View of Education Inputs and Outputs from Economics
Lisa Barrow and Cecilia Elena Rouse

WP-05-15

Competition in Large Markets
Jeffrey R. Campbell

WP-05-16

Why Do Firms Go Public? Evidence from the Banking Industry
Richard J. Rosen, Scott B. Smart and Chad J. Zutter

WP-05-17

Clustering of Auto Supplier Plants in the U.S.: GMM Spatial Logit for Large Samples
Thomas Klier and Daniel P. McMillen

WP-05-18

Why are Immigrants’ Incarceration Rates So Low?
Evidence on Selective Immigration, Deterrence, and Deportation
Kristin F. Butcher and Anne Morrison Piehl

WP-05-19

Constructing the Chicago Fed Income Based Economic Index – Consumer Price Index:
Inflation Experiences by Demographic Group: 1983-2005
Leslie McGranahan and Anna Paulson

WP-05-20

Universal Access, Cost Recovery, and Payment Services
Sujit Chakravorti, Jeffery W. Gunther, and Robert R. Moore

WP-05-21

Supplier Switching and Outsourcing
Yukako Ono and Victor Stango

WP-05-22

Do Enclaves Matter in Immigrants’ Self-Employment Decision?
Maude Toussaint-Comeau

WP-05-23

The Changing Pattern of Wage Growth for Low Skilled Workers
Eric French, Bhashkar Mazumder and Christopher Taber

WP-05-24

U.S. Corporate and Bank Insolvency Regimes: An Economic Comparison and Evaluation
Robert R. Bliss and George G. Kaufman

WP-06-01

Redistribution, Taxes, and the Median Voter
Marco Bassetto and Jess Benhabib

WP-06-02

Identification of Search Models with Initial Condition Problems
Gadi Barlevy and H. N. Nagaraja

WP-06-03

Tax Riots
Marco Bassetto and Christopher Phelan

WP-06-04

The Tradeoff between Mortgage Prepayments and Tax-Deferred Retirement Savings
Gene Amromin, Jennifer Huang,and Clemens Sialm

WP-06-05

2

Working Paper Series (continued)
Why are safeguards needed in a trade agreement?
Meredith A. Crowley

WP-06-06

Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-06-07

A New Social Compact: How University Engagement Can Fuel Innovation
Laura Melle, Larry Isaak, and Richard Mattoon

WP-06-08

Mergers and Risk
Craig H. Furfine and Richard J. Rosen

WP-06-09

Two Flaws in Business Cycle Accounting
Lawrence J. Christiano and Joshua M. Davis

WP-06-10

Do Consumers Choose the Right Credit Contracts?
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles

WP-06-11

Chronicles of a Deflation Unforetold
François R. Velde

WP-06-12

Female Offenders Use of Social Welfare Programs Before and After Jail and Prison:
Does Prison Cause Welfare Dependency?
Kristin F. Butcher and Robert J. LaLonde
Eat or Be Eaten: A Theory of Mergers and Firm Size
Gary Gorton, Matthias Kahl, and Richard Rosen
Do Bonds Span Volatility Risk in the U.S. Treasury Market?
A Specification Test for Affine Term Structure Models
Torben G. Andersen and Luca Benzoni

WP-06-13

WP-06-14

WP-06-15

Transforming Payment Choices by Doubling Fees on the Illinois Tollway
Gene Amromin, Carrie Jankowski, and Richard D. Porter

WP-06-16

How Did the 2003 Dividend Tax Cut Affect Stock Prices?
Gene Amromin, Paul Harrison, and Steven Sharpe

WP-06-17

Will Writing and Bequest Motives: Early 20th Century Irish Evidence
Leslie McGranahan

WP-06-18

How Professional Forecasters View Shocks to GDP
Spencer D. Krane

WP-06-19

Evolving Agglomeration in the U.S. auto supplier industry
Thomas Klier and Daniel P. McMillen

WP-06-20

3

Working Paper Series (continued)
Mortality, Mass-Layoffs, and Career Outcomes: An Analysis using Administrative Data
Daniel Sullivan and Till von Wachter
The Agreement on Subsidies and Countervailing Measures:
Tying One’s Hand through the WTO.
Meredith A. Crowley

WP-06-21

WP-06-22

How Did Schooling Laws Improve Long-Term Health and Lower Mortality?
Bhashkar Mazumder

WP-06-23

Manufacturing Plants’ Use of Temporary Workers: An Analysis Using Census Micro Data
Yukako Ono and Daniel Sullivan

WP-06-24

What Can We Learn about Financial Access from U.S. Immigrants?
Una Okonkwo Osili and Anna Paulson

WP-06-25

Bank Imputed Interest Rates: Unbiased Estimates of Offered Rates?
Evren Ors and Tara Rice

WP-06-26

Welfare Implications of the Transition to High Household Debt
Jeffrey R. Campbell and Zvi Hercowitz

WP-06-27

Last-In First-Out Oligopoly Dynamics
Jaap H. Abbring and Jeffrey R. Campbell

WP-06-28

Oligopoly Dynamics with Barriers to Entry
Jaap H. Abbring and Jeffrey R. Campbell

WP-06-29

Risk Taking and the Quality of Informal Insurance: Gambling and Remittances in Thailand
Douglas L. Miller and Anna L. Paulson

WP-07-01

Fast Micro and Slow Macro: Can Aggregation Explain the Persistence of Inflation?
Filippo Altissimo, Benoît Mojon, and Paolo Zaffaroni

WP-07-02

Assessing a Decade of Interstate Bank Branching
Christian Johnson and Tara Rice

WP-07-03

Debit Card and Cash Usage: A Cross-Country Analysis
Gene Amromin and Sujit Chakravorti

WP-07-04

The Age of Reason: Financial Decisions Over the Lifecycle
Sumit Agarwal, John C. Driscoll, Xavier Gabaix, and David Laibson

WP-07-05

Information Acquisition in Financial Markets: a Correction
Gadi Barlevy and Pietro Veronesi

WP-07-06

Monetary Policy, Output Composition and the Great Moderation
Benoît Mojon

WP-07-07

4

Working Paper Series (continued)
Estate Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-07-08

Conflict of Interest and Certification in the U.S. IPO Market
Luca Benzoni and Carola Schenone

WP-07-09

The Reaction of Consumer Spending and Debt to Tax Rebates –
Evidence from Consumer Credit Data
Sumit Agarwal, Chunlin Liu, and Nicholas S. Souleles

WP-07-10

Portfolio Choice over the Life-Cycle when the Stock and Labor Markets are Cointegrated
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-07-11

Nonparametric Analysis of Intergenerational Income Mobility
with Application to the United States
Debopam Bhattacharya and Bhashkar Mazumder

WP-07-12

How the Credit Channel Works: Differentiating the Bank Lending Channel
and the Balance Sheet Channel
Lamont K. Black and Richard J. Rosen

WP-07-13

Labor Market Transitions and Self-Employment
Ellen R. Rissman

WP-07-14

First-Time Home Buyers and Residential Investment Volatility
Jonas D.M. Fisher and Martin Gervais

WP-07-15

Establishments Dynamics and Matching Frictions in Classical Competitive Equilibrium
Marcelo Veracierto

WP-07-16

Technology’s Edge: The Educational Benefits of Computer-Aided Instruction
Lisa Barrow, Lisa Markman, and Cecilia Elena Rouse

WP-07-17

The Widow’s Offering: Inheritance, Family Structure, and the Charitable Gifts of Women
Leslie McGranahan

WP-07-18

Demand Volatility and the Lag between the Growth of Temporary
and Permanent Employment
Sainan Jin, Yukako Ono, and Qinghua Zhang

WP-07-19

A Conversation with 590 Nascent Entrepreneurs
Jeffrey R. Campbell and Mariacristina De Nardi

WP-07-20

Cyclical Dumping and US Antidumping Protection: 1980-2001
Meredith A. Crowley

WP-07-21

The Effects of Maternal Fasting During Ramadan on Birth and Adult Outcomes
Douglas Almond and Bhashkar Mazumder

WP-07-22

5

Working Paper Series (continued)
The Consumption Response to Minimum Wage Increases
Daniel Aaronson, Sumit Agarwal, and Eric French

WP-07-23

The Impact of Mexican Immigrants on U.S. Wage Structure
Maude Toussaint-Comeau

WP-07-24

A Leverage-based Model of Speculative Bubbles
Gadi Barlevy

WP-08-01

Displacement, Asymmetric Information and Heterogeneous Human Capital
Luojia Hu and Christopher Taber

WP-08-02

BankCaR (Bank Capital-at-Risk): A credit risk model for US commercial bank charge-offs
Jon Frye and Eduard Pelz

WP-08-03

Bank Lending, Financing Constraints and SME Investment
Santiago Carbó-Valverde, Francisco Rodríguez-Fernández, and Gregory F. Udell

WP-08-04

Global Inflation
Matteo Ciccarelli and Benoît Mojon

WP-08-05

Scale and the Origins of Structural Change
Francisco J. Buera and Joseph P. Kaboski

WP-08-06

Inventories, Lumpy Trade, and Large Devaluations
George Alessandria, Joseph P. Kaboski, and Virgiliu Midrigan

WP-08-07

School Vouchers and Student Achievement: Recent Evidence, Remaining Questions
Cecilia Elena Rouse and Lisa Barrow

WP-08-08

Does It Pay to Read Your Junk Mail? Evidence of the Effect of Advertising on
Home Equity Credit Choices
Sumit Agarwal and Brent W. Ambrose

WP-08-09

The Choice between Arm’s-Length and Relationship Debt: Evidence from eLoans
Sumit Agarwal and Robert Hauswald

WP-08-10

Consumer Choice and Merchant Acceptance of Payment Media
Wilko Bolt and Sujit Chakravorti

WP-08-11

Investment Shocks and Business Cycles
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

WP-08-12

New Vehicle Characteristics and the Cost of the
Corporate Average Fuel Economy Standard
Thomas Klier and Joshua Linn

WP-08-13

6

Working Paper Series (continued)
Realized Volatility
Torben G. Andersen and Luca Benzoni

WP-08-14

Revenue Bubbles and Structural Deficits: What’s a state to do?
Richard Mattoon and Leslie McGranahan

WP-08-15

The role of lenders in the home price boom
Richard J. Rosen

WP-08-16

Bank Crises and Investor Confidence
Una Okonkwo Osili and Anna Paulson

WP-08-17

Life Expectancy and Old Age Savings
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-08-18

Remittance Behavior among New U.S. Immigrants
Katherine Meckel

WP-08-19

7