Migration, Remittances and Poverty:
Evidence from the Community-Based
Monitoring System (CBMS) Data in Selected
Communities in the Philippines
ALELLIE B. SOBREVIÑAS
GERMÁN CALFAT
Arnoldshain Seminar XI: “Migration, Development and
Demographic Change- Problems, Consequences, Solutions”
University of Antwerp
28 June 2013
OUTLINE
1.
2.
3.
4.
Introduction
Data and Methods
Empirical Results
Concluding Remarks
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
Trend in stock of Filipinos Overseas, 2005-2011
Source: Commission on Filipinos Overseas
• The stock of Filipinos overseas is about 10.5 million in 2011
- more than 10% of the country’s total population
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
Global Mapping of Filipinos Overseas
Source: Commission on Filipinos Overseas
• Majority of the Filipino migrants go to more developed
countries
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
Trend in cash remittances in the Philippines, 2007-2012
Amount (million US dollars)
25,000
12.0
10.0
20,000
8.0
15,000
6.0
10,000
4.0
5,000
2.0
-
0.0
2007
2008
2009
2010
2011
2012
Year
Total Cash Remittances (million US dollars)
Real Growth in Cash Remittances (%)
Source: Bangko Sentral ng Pilipinas
• The volume of cash remittances continued to increase since
2005 and reached US$21.4 billion in 2012 (about 6.5% of GNI)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
• Continuous increase in the number of
deployed OFWs in recent years
- Increased by 69.6% in 2012 when compared to
2006 figures (POEA, 2013)
• No significant reduction in poverty rates
- Poverty incidence (1st semester, 2012) = 27.9%
(0.9 percentage point lower compared to 2006
estimates) (NSCB, 2013)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
• Remittances may not necessarily flow to
the poor
• Remittance-recipient households have
more education (Adams, 2004)
• Better-off households are more capable of
producing migrants (Stahl, 1982)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
• Economy’s growth may be restricted =>
“brain drain”
• Social tensions may arise => if income
inequality increases between migrant and
non-migrant households.
• There may be costs to family members left
behind, especially the children
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
Existing research on poverty and migration/remittances
• limited
• no consensus in the literature with regards to the impact of
migration/remittances on poverty
Adams and Page
Acosta, et al
(2005)
(2007)
significant reduction in increase in poverty
the level, depth and (11 Latin American
severity of poverty
countries)
(71 developing
countries)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
Murata (2006)
increase in
livelihoods; but
contributed the
most to the rich
(Philippines)
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
Conceptual and Empirical Challenges
• endogeneity
• reverse causality
• selection bias
Possible solutions
•
•
•
•
randomized experiment (e.g., lottery system)
panel data
instrumental variables
Heckman selection model
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
1. INTRODUCTION
Counterfactual income approach
• first used by Adams (1989) – the regression of incomes of
non-migrant HHs was estimated and then, the resulting
parameters were used to estimate the counterfactual
income of migrant HHs
• also used and refined by other researchers
1. Rodriguez(1998) – assumed that the differences between
households with and without migrants are observable and
can be reduced in a constant term
2. Barham and Boucher (1998) – added a stochastic term
component to predicted incomes; migration choice and
labor-force participation
3. Acosta, et al. (2007) – used bootstrap prediction
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
2. DATA AND METHODS
Community-based Monitoring System (CBMS)
• an organized process of data collection,
processing and validating information at the local
level, and integration of data in the local
development process
• one of the tools developed in the early 1990s to
provide policymakers with a good information
base for tracking the impacts of economic
reforms and policy shocks on the vulnerable
groups in the society
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
2. DATA AND METHODS
Community-based Monitoring System (CBMS)
• promotes evidence-based policymaking and
program implementation while empowering
communities to participate in the process.
• entails the development of instruments and conduct
of training to build the capacities of local
stakeholders in implementing the system.
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
Key Features of CBMS
It is a census of all households in the community
and not a sample survey
It is rooted in local government and promotes
community participation.
It uses local personnel and community
volunteers as monitors
It establishes databases at each geopolitical
level. It uses freeware customized for CBMS-data
encoding, processing and poverty mapping.
It generates a core set of indicators that are
being measured to determine the welfare status
of the population. These indicators capture the
multidimensional aspects of poverty.
CBMS Core Indicators
CBMS Indicators
Dimensions of Poverty
Core Indicators
Survival
•Health
•Food & Nutrition
•H20 & Sanitation
1. Child deaths (0-5 yrs. old)
2. Women deaths due to pregnancy
-related causes
3. Malnourished children (0-5 yrs. old)
4. HHs w/o access to safe water
5. HHs w/o access sanitary toilet
Security
•Shelter
•Peace & Order
6. HHs who are informal settlers
7. HHs living in makeshift housing
8. HHs victimized by crimes
•Income
•Employment
•Education
9. HHs w/income below poverty threshold
10. HHs w/income below food threshold
11. HHs which experienced food shortage
12. Unemployment
13. Elementary school participation
14. High school participation
Enabling
The CBMS Process
Step 1
Advocacy /
Organization
Step 2
Data Collection
and
Field Editing
(Training Module 1)
Step 8
Plan Formulation
(Training Module 4)
Data Encoding
and
Map Digitizing
(Training Module 2)
Step 4
Dissemination/
Implementation
and
Monitoring
Step 7
Step 3
Processing and
Mapping
(Training Module 3)
Step 6
Knowledge
(Database)
Management
Step 5
Data validation
and
Community
Consultation
STATUS OF CBMS
IMPLEMENTATION
Coverage of CBMS implementation in the
PHILIPPINES
as of April 8, 2013
21,424 barangays
in 791 municipalities and 63 cities
in 68 provinces (32 of which are provincewide)
With Technical Assistance from:
DILG-BLGD and CBMS Team with support from WB-ASEM
DILG-BLGD and CBMS Team with support from UNFPA
DILG-BLGD, DILG Regional offices and CBMS Team
Eastern Visayas CBMS TWG and CBMS Team
Bicol CBMS TWG and CBMS Team
Bicol CBMS TWG and CBMS Team with support from Spanish Government
MIMAROPA CBMS TWG and CBMS Team
NAPC and CBMS Team with support from UNDP
Dawn Foundation and CBMS Team
Social Watch Philippines and CBMS Team
SRTC, SUCs and CBMS Team
Kagabay and CBMS Team
SRTC, NEDA IV-A and CBMS Team
PRRM, SWP and CBMS Team
CBMS Team
CBMS Countries
Africa
1. Benin
2. Burkina Faso
3. Ghana
4. Kenya
5. Nigeria
6. Senegal
7. South Africa
8. Tanzania
9. Zambia
Asia
1. Bangladesh
2. Cambodia
3. Indonesia
4. Lao PDR
5. Pakistan
6. Philippines
7. Vietnam
Latin America
1. Peru
2. Argentina
Pakistan
Benin
Argentina
South Africa
2. DATA AND METHODS
Overseas Filipino Workers(OFWs)
• overseas contract workers who are “presently and temporarily
out of the country to fulfill an overseas work for a specific length
of time or who are presently at home on vacation but still has an
existing contract to work abroad”
• other Filipino workers abroad with valid working visa or work
permits
• those who had no working visa or work permits (tourist, visitor,
student, medical, and other types of non-immigrant visas) but
were presently employed and working full time in other
countries
Remittances
• money sent by migrant workers to their origin households
• in-cash and in-kind (past 12 months)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
2. DATA AND METHODS
Selected Sites
Limay, Bataan (Central
Luzon Region)
•
•
•
•
•
•
•
12 barangays
10,216 HHs
13.1% migrant HHs
Average HH size : 4.2
Dependency ratio : 0.7
Unemployment rate : 8.2%
Poverty rate: 38.5%
Arnoldshain Seminar XI: Migration, Development and Demographic Change
Pasay City (National
Capital Region)
•
•
•
•
•
•
•
201 barangays
70,430 HHs
7.3% migrant HHs
Average HH size – 3.8
Dependency ratio – 0.6
Unemployment rate- 3.8%
Poverty rate: 11.3%
University of Antwerp, June 25-28, 2013
2. DATA AND METHODS
• migrant HHs vs. non-migrant HHs
• profile of OFWs
• remittance patterns
• impact of migration and remittances on
poverty
-counterfactual income approach (using different
methods of estimation)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
2. DATA AND METHODS
Scenarios
Counterfactual 1
Remittances as exogenous transfer
Counterfactual 2
Imputing the income of migrant HHs in the counterfactual nomigration using the reduced form for the determinants of
income among HHs without migration
log Yi  1  X i  H i  i
where Yi is the no-migration household income, X i is the
vector of HH characteristics, H i is the set of characteristics
of the HH head,  i is the unobserved heterogeneity
Counterfactual 3
Using the Heckman estimation framework to address
selection bias
M i*  1  1 X i   1 H i  Z i  i
(1)
where M i* is the selection rule for having no migrant,
Z i is the exclusion restriction
log Yi   2   2 X i   2 H i  i   i
(2)
where i is the selection inverse Mill’s ratio
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
2. DATA AND METHODS
• indicators are estimated using the observed income
for non-migrant households and the counterfactual
income for migrant households
• poverty rates and poverty gaps are estimated based
on the official poverty threshold
• poverty impact: counterfactual scenarios vs.
observed scenario
• using entire sample and sub-sample of migrant HHs
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS: DESCRIPTIVES
Households with and without OFW
All Sites
No. of households
Proportion of HHs (%)
Household Composition
HH size
Mean HH members 15 years
old and above
Mean HH members less than
15 years old
Mean HH members 15 years
old & above who are
employed
School participation (%)
Members 6-21 years old
Members 6-16 years old
6-12 years old
13-16 years old
17-21 years old
With OFW Without OFW All HHs
6,481
74,165
80,646
8.0
92.0
100.0
3.8
2.6
3.8
2.7
3.8
2.7
1.2
1.2
1.2
0.9
1.3
1.3
81.4
95.4
97.5
66.8
42.8
73.6
92.8
96.4
62.2
26.7
74.2
93
96.5
62.6
28.1
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-11)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
• Less
employed
members
among HHs
with OFW
• Higher school
participation
among
school-aged
children in
HHs with
OFW
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS: DESCRIPTIVES
• Heads of HHs
with OFW are
older, mostly
female,
married and
generally
bettereducated
• Lower
proportion of
poor among
HHs with OFW
Households with and without OFW
All Sites
With OFW Without All HHs
OFW
Household Head
Mean age (years)
45.7
Male (%)
47.9
Married (%)
74.0
Employed (%)
40.6
Education level (%)
No grade
0.3
Elementary
9.2
Secondary/post-secondary
47.6
College/postgraduate
43.0
Welfare Level (Actual)
Mean annual per capita income (in P) 102,392
Proportion of poor HHs (%)
4.5
Proportion of poor population (%)
5.7
41.9
79.3
58.3
78.9
42.2
76.8
59.6
75.8
0.2
13.4
52.0
34.4
0.2
13.1
51.6
35.1
64,577
15.6
20.7
67,616
14.7
19.5
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-11)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS : DESCRIPTIVES
Characteristics of OFWs
All Sites
Sex
Female
Male
Relation to the HH head
Wife/Spouse
Son/Daughter
Son in law/Daughter in law
Grandson/Granddaughter
Father/Mother
Others
Unspecified
Proportion of male spouses
Pasay
Limay
City
34.3
65.7
38.5
61.5
17.4
82.6
49.5
29.1
46.7
30.7
60.9
22.8
4.5
0.3
4.0
12.3
0.2
40.1
4.8
0.3
4.4
13.1
36.1
3.2
0.3
2.6
9.1
1.1
56.1
• most of the
OFWs are
male
• most of the
OFWs are
spouses
(particularly,
male spouses)
of the current
HH head
Source of basic data: CBMS Census: Pasay City (2008) and Limay (2010-11)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS : DESCRIPTIVES
Type of occupation of OFWs
Sector
Trades and Related Workers
Service Workers and Shop and Market Sales
Workers
Laborers and Unskilled Workers
Plant and Machine Operators and Assemblers
Physical, Mathematical and Engineering Science
Professionals
Clerks
Technician and Associate Professionals
Officials of Government and Special-Interest
Organizations, Corporate Executives, Managers,
Managing Proprietors and Supervisors
Special Occupations
Farmers, Forestry Workers and Fishermen
Unspecified
All Areas
18.7
Pasay City
12.2
Limay
44.4
16.9
19.8
5
15.2
14.9
16.2
14.5
16
8.2
11.4
12.9
5.4
8.2
7.7
9.5
8.1
2.8
6.2
5.1
4.4
7.9
0.3
0.1
2.2
0.2
0.1
1.8
0.5
0
3.6
Source: CBMS Census: Pasay City (2008) and Limay (2010-11)
• A significant proportion of OFWs are skilled workers
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS : DESCRIPTIVES
Countries of Destination
Saudi Arabia
United States of America
Japan
Qatar
Canada
HongKong SAR of China
Singapore
United Arab Emirates
Australia
Italy
South Africa
Algeria
Kuwait
Taiwan
United Kingdom
Others countries
All Areas
Pasay City
Limay
38.5
11.6
5.8
4.9
4.0
4.0
3.8
2.7
1.8
1.7
1.0
0.4
1.5
0.3
0
18
38.3
13.9
6.9
3.5
4.8
4.8
3.8
0.4
2.0
2.1
0.3
1.5
1.8
16
39.4
2.0
1.4
10.7
1.0
0.9
3.8
11.9
1.1
0.2
3.5
2.2
1.6
1.5
0.4
18.5
• Saudi
Arabia is the
main
country of
destination
among
OFWs
Note: For countries of destination, figures in bold and italic are for countries that belong to the
top 10 destinations within each site.
Source of basic data: CBMS Census- Pasay City (2008) and Limay (2010-11)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS : DESCRIPTIVES
Remittances from OFW
Pasay City Limay All Sites
No. of households
Mean number of OFW per HH
HHs which received remittances from
OFW
Mean annual remittances (in pesos)
Mean share of remittance to total HH
income (%)
5,143
1.1
85.9
1,338
1.1
57.6
6,481
1.1
80.1
165,727 128,700 158,082
52.4
41.3
50.1
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-11)
• Not all households with OFW received remittances
• Migrant households relied heavily on remittance income
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS : DESCRIPTIVES
Average share of remittances to total income among migrant HHs
A. By site and urbanity
B. By income quintile
• Larger share of remittances to total income among migrant HHs in
Pasay City; in urban areas
• The richest HHs in Limay are more dependent on remittances;
Middle-income HHs in Pasay City on average relied more on
remittances as a source of income
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS: IMPACT ON POVERTY
Poverty measures for observed and counterfactual scenarios (All Households)
Observed
All Sites
Poverty rate
Poverty gap
Pasay City
Poverty rate
Poverty gap
Limay
Poverty rate
Poverty gap
Counterfactual 1:
Remittance as Exogenous Transfer
14.7
5.3
17.4
7.1
(-2.6)
(-1.8)
11.3
3.5
13.6
5.1
(-2.4)
(-1.6)
38.5
17.3
43.1 (-4.6)
20.3 (-3.0)
Note: Figures in parenthesis indicate the % change in poverty measures(observed – counterfactual)
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-2011)
• Poverty rate and poverty gap should have been 2.6 percentage
points higher and 1.8 percentage points higher in a no-migration
counterfactual scenario, respectively.
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS: IMPACT ON POVERTY
OLS and Heckman estimation results for income of non-migrant households
(Dependent Variable: Log of household income)
OLS
Coefficient
HH size
0.10812
HH size square
-0.00588
dependency ratio
-0.08966
male HH head
0.07975
age of HH head
0.00696
age of HH head square
-0.00002
married HH head
0.07642
no. of members with at least tertiary education
0.26149
location dummy (Pasay City=1)
0.45659
dummy for urbanity (urban=1)
0.09968
lambda (λ)
constant
10.52457
F-statistic
2956
Probability> F
0.000
R-squared
0.274
rho
Wald Chi2 (10)
Probability > Chi2
***
***
***
***
***
**
***
***
***
***
Std. Err.
0.00497
0.00046
0.00490
0.00769
0.00113
0.00001
0.00652
0.00250
0.01336
0.01919
***
0.02800
***significant at 1% level; ** significant at 5% level; * significant at 10% level
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-2011)
Heckman
Coefficient
Std. Err.
0.10567 ***
0.00575
-0.00640 ***
0.00045
-0.07474 ***
0.00621
0.02667 ***
0.01188
0.00763 ***
0.00123
-0.00004 ***
0.00001
0.10742 ***
0.00752
0.26343 ***
0.00254
0.48684 ***
0.01039
0.07564 ***
0.01485
0.26879 ***
0.03862
10.53994
0.03076
0.392
27081
0.000
3. EMPIRICAL RESULTS: IMPACT ON POVERTY
Poverty measures for observed and counterfactual scenarios (ALL HOUSEHOLDS)
Observed
All Sites (in %)
Poverty rate
Poverty gap
Pasay City
Poverty rate
Poverty gap
Limay
Poverty rate
Poverty gap
Counterfactual (No Migration)
Counterfactual 1:
Counterfactual 2: Counterfactual 3:
Remittance as
Using OLS
Heckman
Exogenous Transfer
regression
selection model
14.7
5.3
17.4 (-2.6)
7.1 (-1.8)
15.1 (-0.4)
5.3 (0.0)
15.4 (-0.7)
5.4 (-0.1)
11.3
3.5
13.6 (-2.4)
5.1 (-1.6)
11.2 (0.0)
3.5 (0.0)
11.3 (-0.1)
3.5 (0.0)
38.5
17.3
43.1 (-4.6)
20.3 (-3.0)
42.2 (-3.7)
17.6 (-0.3)
43.3 (-4.8)
18.0 (-0.7)
Note: Figures in parentheses indicate the % change in poverty measures(observed – counterfactual)
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-2011)
• Larger impact when remittances are treated simply as exogenous
transfer (Counterfactual 1) compared to other methods
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS: IMPACT ON POVERTY
Poverty incidence for observed and counterfactual scenarios (MIGRANT HOUSEHOLDS)
Poverty Measures
All Sites
Poverty rate (%)
% Change in poverty
Pasay City
Poverty rate (%)
% Change in poverty
Limay
Poverty rate (%)
% Change in poverty
Counterfactual (No-Migration)
Counterfactual 1: Counterfactual 2: Counterfactual 3:
Observed
Remittance as
Using OLS
Using Heckman
Exogenous Transfer
regression
selection model
4.5
37.4
-32.9
9.9
-5.4
12.9
-8.4
2.5
34.8
-32.3
2.0
-0.4
3.5
-1.1
12.3
47.5
-35.2
40.3
-27.9
49.0
-36.6
Note: The percent change in poverty rate is estimated by subtracting the counterfactual estimates from the observed estimates.
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-2011)
• The impact is larger than the impact obtained using the entire sample
(e.g., a reduction in the proportion of poor among migrant HHs by
8.4% vs. 0.7% in counterfactual 3)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS: IMPACT ON POVERTY
Poverty status among migrant households: No-migration and Observed
Scenario (% of households)
No-migration Scenario
Observed Scenario
ALL SITES
Nonpoor
Poor
Total
Nonpoor
97.3
2.7
100.0
Poor
84.3
15.7
100.0
Total
95.6
4.4
100.0
Note: No-migration scenario is based on the Counterfactual 3 results.
• Migrant households do not necessarily benefit from migration and
remittances
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS: IMPACT ON POVERTY
Poverty status among migrant households: No-migration and Observed Scenario (% of households)
Changes in poverty status
Poor-Poor
Poor-Nonpoor Nonpoor-Nonpoor Nonpoor-Poor
All Migrant Households
2.1
10.8
84.7
2.4
Location
Pasay City
0.6
2.9
94.6
1.9
Limay
8.0
41.0
46.6
4.4
Urbanity
Urban
1.4
7.2
89.2
2.2
Rural
9.1
46.6
39.6
4.7
Household Composition
HH size (including OFW
member)
6.7
6.4
4.7
5.4
Dependency ratio (including
OFW member)
1.3
1.4
0.7
0.9
Remittances
Mean annual per capita
3,210
32,983
58,008
4,365
remittances (in pesos)
Notes: It is assumed that the OFW member is 15 years old and above. No-migration scenario is based on the Counterfactual 3 results.
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-2011)
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
3. EMPIRICAL RESULTS: IMPACT ON POVERTY
Distribution of migrant households by income quintile
(No-migration and Observed scenarios)
A. Pasay City
B. Limay
• Changes in the distribution of migrant households by income
quintile is evident in both sites
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
4. CONCLUSION AND RECOMMENDATIONS
• The magnitude of impact varies depending on the
method of estimating the counterfactual income.
- treating remittances as an exogenous transfer leads to
underestimation of income and overestimation of the
impact of migration
- Heckman estimation method is preferred
• The impact on poverty among migrant households is
larger than the impact obtained using the entire
sample.
- not all migrant households benefitted from migration
through improved welfare
- need for a much deeper analysis why migration and
remittances are effective in helping certain groups of
migrant households move out of poverty but not others
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
4. CONCLUSION AND RECOMMENDATIONS
• Expansion of this study
- looking at impact on other dimensions of poverty (e.g., education,
health)
- determining the link between destination countries and poverty
(“worse” destinations)
- employing other estimation methods (e.g., propensity score
matching, instrumental variables)
- using a good panel data
- exploring the possibility of surveying both areas of origin and areas
of destination depending on migrant concentration in destination
countries
- incorporating the insights from different fields (e.g., sociology)
- presenting the results and getting feedback from the communities
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
4. CONCLUSION AND RECOMMENDATIONS
• Module on “migration and remittances” as a rider to
CBMS
- collecting additional information on migration and
remittances issues
examples:
1. migration history (length of stay abroad)
2. retrospective questions about pre-migration
characteristics (e.g., income, work history)
3. specific migration locations within destination countries
4. information on family networks abroad
5. dynamics of how money is sent (e.g., how often the
migrant workers make transfers, how they make them and
who precisely the money is sent to)
6. spending patterns of remittance-recipient households
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
Thank You!
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013
Migration, Remittances and Poverty:
Evidence from the Community-Based
Monitoring System (CBMS) Data in Selected
Communities in the Philippines
ALELLIE B. SOBREVIÑAS
GERMÁN CALFAT
Arnoldshain Seminar XI: “Migration, Development and
Demographic Change- Problems, Consequences, Solutions”
University of Antwerp
28 June 2013
3. EMPIRICAL RESULTS: IMPACT ON POVERTY
First step (probit) results in estimating income of non-migrant households using
Heckman framework (Dependent Variable: No migration=1)
Coefficient
Std. Err.
HH size
-0.51558 ***
0.01414
HH size square
0.02900 ***
0.00119
dependency ratio
0.51336 ***
0.01570
male HH head
1.28624 ***
0.01850
age of HH head
0.06924 ***
0.00320
age of HH head square
-0.00074 ***
0.00003
married HH head
-0.79413 ***
0.02006
no. of members with at least tertiary education
0.06773 ***
0.00653
location dummy (Pasay City=1)
0.09127 **
0.04746
dummy for urbanity (urban=1)
0.17073 ***
0.04541
proportion of migrant HHs within the village
-0.00734 ***
0.00222
constant
0.62919
0.07638
Probability > Chi2
0.000
Pseudo R-squared
0.239
***significant at 1% level; ** significant at 5% level; * significant at 10% level
Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-2011)
3. EMPIRICAL RESULTS
Propensity Score Matching: Preliminary Results
ATT estimation with Nearest Neighbor Matching method
(random draw version); Analytical standard errors
n. treat.
6481
n. contr.
64773
ATT
0.743
Std. Err.
0.010
t
71.054
Note: the numbers of treated and controls refer to actual nearest neighbour
matches
• Having an OFW increases income of households by
74.3 percent
Arnoldshain Seminar XI: Migration, Development and Demographic Change
University of Antwerp, June 25-28, 2013