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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, 2008. - PowerPoint PPT Presentation
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Accounting for Migration and Remittance Effects
Susan PozoPrepared for Conference on Regional Trade Agreements, Migration
and Remittances with Special Focus on CAFTA and Latin AmericaSam Houston State University
April 12, 2008
Much more attention paid to the migratory process in the past 5 years
1998 1999 2000 2001 2002 2003 2004 2005 2006 20070
20
40
60
80
100
120
140
Econ Lit Hits
1. Is this a research fad?
Source: Econ Lit database, 2008
10.47.9
6.24.7
5.46.9
8.811.6
13.214.7
13.614.8
13.314.4
13.29.7
0 5 10 15
2000199019801970196019501940193019201910190018901880187018601850
United StatesPercent Foreign Born
2. Growth in the number of persons affected by the migratory process?
Sour
ce: U
.S. B
urea
u of
the
Cens
us, 2
008
050
100
150
200
Mig
rant
s
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
(in millions)World Migrants
Source: Data from UN (2008)
01
23
wor
ld m
igra
nts/
wor
ld p
opul
atio
n
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
(percent of world population)World Migrant Stock
Source: Data from UN (2008)
0
1000
2000
3000
4000
5000
6000
7000
1996 1998 2000 2002 2004 2006
REMITTANCES
Remittances to Mexico (quarterly frequency, in millions of US dollars)
Source: Data from Banco Central de Mexico, 2008
.000
.005
.010
.015
.020
.025
.030
1980 1985 1990 1995 2000 2005
WR_GDP
Source: World Development Indicators, 2008
Remittances to Mexico (yearly frequency, Percent of GDP)
1
2
3
4
5
6
7
80 85 90 95 00 05 10 15 20
PERCENT
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)
1990
2006
Source: US Census Bureau, http://factfinder.census.gov
3. Increased dispersion of the foreign born?
0.0
5.1
.15
.2.2
5.3
Den
sity
0 5 10 15 20 25 30p1990
1990Percent population foreign born
Computed by the author from Census Bureau
0.0
5.1
.15
.2.2
5.3
Den
sity
0 5 10 15 20 25 30p2000
2000Percent population foreign born
Computed by the author from Census Bureau
0.0
5.1
.15
.2.2
5.3
Den
sity
0 5 10 15 20 25 30p2006
2006Percent population foreign born
Computed by the author from Census Bureau
0.0
5.1
.15
.2.2
5.3
Den
sity
0 5 10 15 20 25 30p1990
1990Percent population foreign born
0.0
5.1
.15
.2.2
5.3
Den
sity
0 5 10 15 20 25 30p2006
2006Percent population foreign born
Increased spread of the foreign-born in 2006 relative to 1990
1990
2006
3. Increased dispersion of the foreign-born?
1990 2000 20060
5
10
15
20
25
30
Source: Computed by author from 1990, 2000 Decennial Censuses and 2006 American Community Survey, US Census.
Economic Development Effects of the Migratory Process on
Labor supply Health
Education Happiness
Poverty levels
Business Investments
Tend to focus on only one facet of the migratory process…
Poverty -- remittancesLabor force participation – remittancesEducation—remittancesBusiness Investment—(return) migrationHealth – emigrationHappiness - migration
Migratory Process
Remittances Migration
Economic Development Effects of the Migratory Process on
Labor supply Health
Education Happiness
Poverty levels
Business Investments
Migrant HH and Remittance Receipt
40%
4%
38%
17%
neithermigrant onlyremit onlyremit and migrant
Haiti
Source: Amuedo-Dorantes, Georges and Pozo, (2007)
Haiti
DR
Mexico
38
65
74
3.5
13
9
33
10
4
25
12
14
HH type in %R&M Remit migrant neither
Source: Computed by author from
LAMP and M
MP databases
18%
14%
4%64%
Cuenca, Ecuador
M&R Remit Migrant Neither
Computed by the author from : Discrimination and Economic Outcomes Survey Database, IADB, 2006
Too large
Too small
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 +δX +ЄSeveral problems: i) endogeneity due to reverse causalityii) endogeneity due to omitted variable bias
Type of Household All
Model Specification ProbitVariables M.E.Remittance Receipt .0067HH Currently Employed -0.0199Assets 0.0494***% dependent age 0.3121Ed 17+ -0.2857*Ed female adult 0.0979% kids school age -0.3581**Own Child 0.1090*Boy -0.0210Child’s Age 0.0075Firstborn Child -0.0326**Urban -0.1263No. of Observations 327Wald Chi2-test 23.71Prob>Chi2 0.0222Log pseudolikelihood -104.4399
Source: Amuedo-Dorantes, Georges and Pozo, (2007)
Typical solution
Instrument for remittances:Using migration or variables linked to long-standing migratory patterns, such as the mapping of railroads. Essentially migration networks.
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
Education
Migratory Process
Remittances
Migration
Migration K/networks
Everything else
Migration capital/networks
Expected value of additional education varies with the probability of future migration
EVH = (pH) RH,H + ( 1 - pH) RH,US
Type of Household All Model Specification Probit
CoefficientMigration networks/capital 0.4827**Household Head Currently Employed 0.0037Current Household Assets 0.2743***Percent of Non-working Age Household Members 1.8011Mean Potential Education of 17 Years + -1.7777**Potential Ed Attainment of Spouse or Head 0.3882Percent of School-age Children in the HH -2.1341***Own Child 0.4865Boy -0.1973*Child’s Age 0.0196Firstborn Child -0.1239
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.
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.
Type of Household All Non-migrant
Model Specification Probit IV-ProbitVariables 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 0.0223Ed 17+ -0.2857* 0.0182Ed female adult 0.0979 -0.2607% kids school age -0.3581** -0.2329Own Child 0.1090* 0.1594**Boy -0.0210 0.0214Child’s Age 0.0075 -0.0067Firstborn Child -0.0326** 0.0402Urban -0.1263 0.0216No. of Observations 327 258Wald Chi2-test 23.71 1181.35Prob>Chi2 0.0222 0.0000Log pseudolikelihood -104.4399 -243.2202IV Exogeneity Testa n.a. 0 < = 5.99Wald Test of Exogeneity n.a. Chi2(1)=19.85
Prob>Chi2=0.0000
Source: Amuedo-Dorantes, Georges and Pozo, (2007)
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.
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