Susan Pozo Prepared for Conference on Regional Trade Agreements Migration and Remittances with Special Focus on CAFTA and Latin America Sam Houston State University April 12 2008 Much more attention paid to the migratory process in the past 5 years ID: 557113
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Slide1
Accounting for Migration and Remittance Effects
Susan
Pozo
Prepared for Conference on Regional Trade Agreements, Migration and Remittances with Special Focus on CAFTA and Latin America
Sam Houston State University
April 12, 2008Slide2
Much more attention paid to the migratory process in the past 5 yearsSlide3
1. Is this a research fad?
Source: Econ Lit database, 2008Slide4
Growth in the number of persons affected by
the migratory process?
Source: U.S. Bureau of the Census, 2008Slide5
Source: Data from UN (2008)Slide6
Source: Data from UN (2008)Slide7
Remittances to Mexico
(quarterly frequency, in millions of US dollars)
Source: Data from
Banco
Central de Mexico, 2008Slide8
Source: World Development Indicators, 2008
Remittances to Mexico
(yearly frequency, Percent of GDP)Slide9
Remittances to Italy as a percent of Italian GDP
(1880-1910)
Source: Computed by the author
with data from
Cinel
(1991)
and from
Flandreau
&
Zumer
(2004)Slide10
1990
2006
Source: US Census Bureau, http://factfinder.census.gov
Increased dispersion of the
foreign born?Slide11
Computed by the author from Census BureauSlide12
Computed by the author from Census BureauSlide13
Computed by the author from Census BureauSlide14
Increased spread of the
foreign-born in 2006 relative to 1990
1990
2006Slide15
3. Increased dispersion of the foreign-born?
Source: Computed by author from 1990, 2000 Decennial Censuses and 2006 American
Community Survey, US Census.Slide16
Economic Development Effects of the Migratory Process onSlide17
Tend to focus on only one facet of the migratory process…
Poverty
-- remittances
Labor force participation
– remittances
Education
—remittances
Business Investment
—(return) migration
Health
– emigration
Happiness -
migrationSlide18Slide19
Economic Development Effects of the Migratory Process onSlide20
Migrant HH and Remittance Receipt
Source:
Amuedo-Dorantes
, Georges and
Pozo
, (2007)Slide21
Source: Computed by author from LAMP and MMP databasesSlide22
Computed by the author from :
Discrimination and Economic Outcomes Survey
Database, IADB, 2006
Too large
Too smallSlide23
We miss out on the story when we focus on one or the other alone
In the modeling of education a typical strategy might be to estimate:
Education =
βRemit
+
δ
X +
Є
Several problems:
i
)
endogeneity
due to reverse causality
ii)
endogeneity
due to omitted variable biasSlide24
Type of Household
All
Model Specification
Probit
Variables
M.E.
Remittance Receipt
.0067
HH
Currently
Employed
-0.0199
Assets
0.0494***
%
dependent age
0.3121
Ed
17+
-0.2857*
Ed
female adult
0.0979
%
kids school age
-0.3581**
Own Child
0.1090*
Boy
-0.0210
Child’s Age
0.0075
Firstborn Child
-0.0326**
Urban
-0.1263
No. of Observations
327
Wald Chi2-test
23.71
Prob>Chi2
0.0222
Log pseudolikelihood
-104.4399
Source:
Amuedo-Dorantes
, Georges and
Pozo
, (2007)Slide25
Typical solution
Instrument for remittances:
Using migration or variables linked to long-standing migratory patterns, such as the mapping of railroads. Essentially migration networks.Slide26
Problems with this Approach…
1.
An instrument can’t be something that should be in the equation in the first place, i.e. migration and variables
proxying
for long-standing migratory patterns are likely to impact educational attainment via:
A disruptive effect, in the case of family migration
A network effect, in the case of both family and broadly defined migration networks
Slide27Slide28
Migration capital/networks
Expected value of additional education varies with the probability of future migration
EV
H
= (p
H
) R
H
,H
+ ( 1 - p
H
) R
H
,USSlide29
Type of Household
All
Model Specification
Probit
Coefficient
Migration
networks/capital
0.4827**
Household Head Currently Employed
0.0037
Current Household Assets
0.2743***
Percent of Non-working Age Household Members
1.8011
Mean Potential Education
of
17 Years
+
-1.7777**
Potential
Ed
Attainment
of
Spouse or Head
0.3882
Percent of School-age Children in the
HH
-2.1341***
Own Child
0.4865
Boy
-0.1973*
Child’s Age
0.0196
Firstborn Child
-0.1239Slide30
Problems with this Approach…
1. An instrument can’t be something that should be in the equation in the first place, i.e. migration and variables
proxying
for long-standing migratory patterns are likely to impact educational attainment via:
A disruptive effect, in the case of family migration
A network effect, in the case of both family and broadly defined migration networks
2. We notice significant differences in selectivity with respect to different types of HHs. HHs without migrants receiving remittances are very different from HHs with migrants receiving remittances. Slide31
Conclusions
1. Redesign of surveys to take into account the diversity in the incidence of migration and remittances.
2. Redesign of econometric methodology to recognize differential “migration,” “remittance” and “migration capital” effects.Slide32
Type of Household
All
Non-migrant
Model Specification
Probit
IV-Probit
Variables
M.E.
M.E.
Remittance Receipt
.0067
0.6791***
HH
Currently
Employed
-0.0199
-0.2073*
Assets
0.0494***
0.0213
%
dependent age
0.3121
0.0223
Ed
17+
-0.2857*
0.0182
Ed
female adult
0.0979
-0.2607
%
kids school age
-0.3581**
-0.2329
Own Child
0.1090*
0.1594**
Boy
-0.0210
0.0214
Child’s Age
0.0075
-0.0067
Firstborn Child
-0.0326**
0.0402
Urban
-0.1263
0.0216
No. of Observations
327
258
Wald Chi2-test
23.71
1181.35
Prob
>Chi2
0.0222
0.0000
Log
pseudolikelihood
-104.4399
-243.2202
IV
Exogeneity
Test
a
n.a.
0 < = 5.99
Wald Test of
Exogeneity
n.a.
Chi2(1)=19.85
Prob
>Chi2=0.0000
Source:
Amuedo-Dorantes
, Georges and
Pozo
, (2007)Slide33
Sources:
Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, Trends in Total Migrant Stock: The 2005 Revision http://esa.un.org/migration, Saturday, April 05, 2008; 8:31:39 AM.
Marc Flandreau and
Frédréric
Zumer
,
The Making of Global Finance, 1880-1913
, OECD 2004. (Italian GDP data)
Cinel
, Dino, “The national integration of Italian return migration, 1870-1929.
Cambridge, Cambridge University Press, 1991.