ALELLIE B. SOBREVIÑAS GERM ÁN CALFAT

Migration, Remittances and Poverty: Evidence from the Community-Based Monitoring System (CBMS) Data in Selected Communities in the Philippines

ALELLIE B. SOBREVIÑASGERMÁN CALFAT

Arnoldshain Seminar XI: “Migration, Development and Demographic Change- Problems, Consequences, Solutions”

University of Antwerp 28 June 2013

OUTLINE

1. Introduction2. Data and Methods3. Empirical Results4. Concluding Remarks

University of Antwerp, June 25-28, 2013

Arnoldshain Seminar XI: Migration, Development and Demographic Change

1. INTRODUCTION

• The stock of Filipinos overseas is about 10.5 million in 2011

- more than 10% of the country’s total population

Trend in stock of Filipinos Overseas, 2005-2011

Source: Commission on Filipinos Overseas



1. INTRODUCTION

• Majority of the Filipino migrants go to more developed

countries

Global Mapping of Filipinos Overseas

Source: Commission on Filipinos Overseas



2007 2008 2009 2010 2011 2012 -

5,000

10,000

15,000

20,000

25,000

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Trend in cash remittances in the Philippines, 2007-2012

Total Cash Remittances (million US dollars) Real Growth in Cash Remittances (%)

Year

Amou

nt (m

illio

n US

dol

lars

)1. INTRODUCTION

• The volume of cash remittances continued to increase since 2005 and reached US$21.4 billion in 2012 (about 6.5% of GNI)

Source: Bangko Sentral ng Pilipinas



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)



• 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)



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



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



1. INTRODUCTION

Adams and Page (2005)

Acosta, et al (2007) Murata (2006)

significant reduction in the level, depth and severity of poverty

(71 developing countries)

increase in poverty(11 Latin American

countries)

increase in livelihoods; but contributed the most to the rich

(Philippines)

Conceptual and Empirical Challenges • endogeneity• reverse causality• selection bias

Possible solutions• randomized experiment (e.g., lottery system)• panel data• instrumental variables • Heckman selection model



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 researchers1. 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



1. INTRODUCTION

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

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.

2. DATA AND METHODS



Key Features of CBMS It is a census of all households in the community and not a sample surveyIt is rooted in local government and promotes community participation. It uses local personnel and community volunteers as monitorsIt 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.

Survival

Security

Enabling

• Health• Food & Nutrition• H20 & Sanitation

• Shelter• Peace & Order

• Income• Employment• Education

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 water5. HHs w/o access sanitary toilet

6. HHs who are informal settlers 7. HHs living in makeshift housing8. HHs victimized by crimes

9. HHs w/income below poverty threshold10. HHs w/income below food threshold11. HHs which experienced food shortage12. Unemployment13. Elementary school participation14. High school participation

CBMS Indicators Dimensions of Poverty Core Indicators

CBMS Core Indicators

The CBMS ProcessStep 1

Advocacy / Organization

Step 2Data Collection

and Field Editing

(Training Module 1)

Step 4Processing and

Mapping(Training Module 3)

Step 5Data validation

and Community Consultation

Step 7Plan Formulation(Training Module 4)

Step 8Dissemination/Implementation

andMonitoring

Step 3Data Encoding

and Map Digitizing

(Training Module 2)

Step 6Knowledge (Database)

Management

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

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)

STATUS OF CBMS IMPLEMENTATION

CBMS CountriesAfrica1. Benin2. Burkina Faso3. Ghana4. Kenya5. Nigeria 6. Senegal7. South Africa8. Tanzania9. ZambiaAsia1. Bangladesh2. Cambodia3. Indonesia4. Lao PDR5. Pakistan6. Philippines7. VietnamLatin America1. Peru2. Argentina

Argentina

South Africa

Benin

Pakistan

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)

2. DATA AND METHODS



Selected Sites

2. DATA AND METHODS

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%



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%

• 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)

2. DATA AND METHODS



2. DATA AND METHODSScenarios

Counterfactual 1 Remittances as exogenous transfer

Counterfactual 2 Imputing the income of migrant HHs in the counterfactual no-migration using the reduced form for the determinants of income among HHs without migration

where is the no-migration household income, is the vector of HH characteristics, is the set of characteristics of the HH head, is the unobserved heterogeneity

Counterfactual 3 Using the Heckman estimation framework to address selection bias (1)

where is the selection rule for having no migrant, is the exclusion restriction

(2)

where is the selection inverse Mill’s ratio

iiii HXY 1log

iY iXiH

i

iiiii ZHXM 111*

iiiii HXY 222log

*iM

iZ

iUniversity 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



3. EMPIRICAL RESULTS: DESCRIPTIVES Households with and without OFW

All SitesWith OFW Without OFW All HHs

No. of households 6,481 74,165 80,646Proportion of HHs (%) 8.0 92.0 100.0Household CompositionHH size 3.8 3.8 3.8Mean HH members 15 years old and above

2.6 2.7 2.7

Mean HH members less than 15 years old

1.2 1.2 1.2

Mean HH members 15 years old & above who are employed

0.9 1.3 1.3

School participation (%)Members 6-21 years old 81.4 73.6 74.2Members 6-16 years old 95.4 92.8 93 6-12 years old 97.5 96.4 96.5 13-16 years old 66.8 62.2 62.6 17-21 years old 42.8 26.7 28.1

Source of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-11)

• Less employed members among HHs with OFW

• Higher school participation among school-aged children in HHs with OFW



3. EMPIRICAL RESULTS: DESCRIPTIVES • Heads of HHs

with OFW are older, mostly female, married and generally better- educated

• Lower proportion of poor among HHs with OFW

Households with and without OFW

All SitesWith OFW Without

OFWAll HHs

Household HeadMean age (years) 45.7 41.9 42.2Male (%) 47.9 79.3 76.8Married (%) 74.0 58.3 59.6Employed (%) 40.6 78.9 75.8Education level (%) No grade 0.3 0.2 0.2 Elementary 9.2 13.4 13.1 Secondary/post-secondary 47.6 52.0 51.6 College/postgraduate 43.0 34.4 35.1Welfare Level (Actual)Mean annual per capita income (in P) 102,392 64,577 67,616Proportion of poor HHs (%) 4.5 15.6 14.7Proportion of poor population (%) 5.7 20.7 19.5




3. EMPIRICAL RESULTS : DESCRIPTIVES • most of the

OFWs are male

• most of the OFWs are spouses (particularly, male spouses) of the current HH head

Characteristics of OFWs

All Sites Pasay City Limay

SexFemale 34.3 38.5 17.4Male 65.7 61.5 82.6

Relation to the HH headWife/Spouse 49.5 46.7 60.9Son/Daughter 29.1 30.7 22.8

Son in law/Daughter in law 4.5 4.8 3.2Grandson/Granddaughter 0.3 0.3 0.3Father/Mother 4.0 4.4 2.6Others 12.3 13.1 9.1Unspecified 0.2 - 1.1

Proportion of male spouses 40.1 36.1 56.1Source of basic data: CBMS Census: Pasay City (2008) and Limay (2010-11)



3. EMPIRICAL RESULTS : DESCRIPTIVES

• A significant proportion of OFWs are skilled workersUniversity of Antwerp, June 25-28, 2013


Type of occupation of OFWs Sector All Areas Pasay City Limay Trades and Related Workers 18.7 12.2 44.4

Service Workers and Shop and Market Sales Workers 16.9 19.8 5

Laborers and Unskilled Workers 15.2 14.9 16.2

Plant and Machine Operators and Assemblers 14.5 16 8.2

Physical, Mathematical and Engineering Science Professionals

11.4 12.9 5.4

Clerks 8.2 9.5 2.8 Technician and Associate Professionals 7.7 8.1 6.2

Officials of Government and Special-Interest Organizations, Corporate Executives, Managers, Managing Proprietors and Supervisors

5.1 4.4 7.9

Special Occupations 0.3 0.2 0.5 Farmers, Forestry Workers and Fishermen 0.1 0.1 0 Unspecified 2.2 1.8 3.6Source: CBMS Census: Pasay City (2008) and Limay (2010-11)


• Saudi Arabia is the main country of destination among OFWs

Countries of Destination All Areas Pasay City LimaySaudi Arabia 38.5 38.3 39.4United States of America 11.6 13.9 2.0Japan 5.8 6.9 1.4Qatar 4.9 3.5 10.7Canada 4.0 4.8 1.0HongKong SAR of China 4.0 4.8 0.9Singapore 3.8 3.8 3.8United Arab Emirates 2.7 0.4 11.9Australia 1.8 2.0 1.1Italy 1.7 2.1 0.2South Africa 1.0 0.3 3.5Algeria 0.4 - 2.2Kuwait 1.5 1.5 1.6Taiwan 0.3 - 1.5United Kingdom 0 1.8 0.4

Others countries 18 16 18.5Note: 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)




• Not all households with OFW received remittances• Migrant households relied heavily on remittance income

Remittances from OFW Pasay City Limay All Sites

No. of households 5,143 1,338 6,481Mean number of OFW per HH 1.1 1.1 1.1HHs which received remittances from

OFW85.9 57.6 80.1

Mean annual remittances (in pesos) 165,727 128,700 158,082 Mean share of remittance to total HH

income (%)52.4 41.3 50.1




• 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

3. EMPIRICAL RESULTS : DESCRIPTIVES Average share of remittances to total income among migrant HHs

A. By site and urbanity B. By income quintile



3. EMPIRICAL RESULTS: IMPACT ON POVERTY Poverty measures for observed and counterfactual scenarios (All Households)

Observed Counterfactual 1:

Remittance as Exogenous TransferAll Sites

Poverty rate 14.7 17.4 (-2.6)Poverty gap 5.3 7.1 (-1.8)

Pasay CityPoverty rate 11.3 13.6 (-2.4)Poverty gap 3.5 5.1 (-1.6)

LimayPoverty rate 38.5 43.1 (-4.6)Poverty gap 17.3 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.



3. EMPIRICAL RESULTS: IMPACT ON POVERTY OLS and Heckman estimation results for income of non-migrant households

(Dependent Variable: Log of household income) OLS Heckman

Coefficient Std. Err. Coefficient Std. Err.HH size 0.10812 *** 0.00497 0.10567 *** 0.00575HH size square -0.00588 *** 0.00046 -0.00640 *** 0.00045dependency ratio -0.08966 *** 0.00490 -0.07474 *** 0.00621male HH head 0.07975 *** 0.00769 0.02667 *** 0.01188age of HH head 0.00696 *** 0.00113 0.00763 *** 0.00123age of HH head square -0.00002 ** 0.00001 -0.00004 *** 0.00001married HH head 0.07642 *** 0.00652 0.10742 *** 0.00752no. of members with at least tertiary education 0.26149 *** 0.00250 0.26343 *** 0.00254location dummy (Pasay City=1) 0.45659 *** 0.01336 0.48684 *** 0.01039dummy for urbanity (urban=1) 0.09968 *** 0.01919 0.07564 *** 0.01485lambda (λ) 0.26879 *** 0.03862constant 10.52457 *** 0.02800 10.53994 0.03076F-statistic 2956 Probability> F 0.000R-squared 0.274rho 0.392Wald Chi2 (10) 27081Probability > Chi2 0.000 ***significant at 1% level; ** significant at 5% level; * significant at 10% levelSource of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-2011)

3. EMPIRICAL RESULTS: IMPACT ON POVERTY Poverty measures for observed and counterfactual scenarios (ALL HOUSEHOLDS)

Observed

Counterfactual (No Migration)Counterfactual 1: Counterfactual 2: Counterfactual 3:

Remittance as Exogenous Transfer

Using OLS regression

Heckman selection model

All Sites (in %)Poverty rate 14.7 17.4 (-2.6) 15.1 (-0.4) 15.4 (-0.7)Poverty gap 5.3 7.1 (-1.8) 5.3 (0.0) 5.4 (-0.1)

Pasay CityPoverty rate 11.3 13.6 (-2.4) 11.2 (0.0) 11.3 (-0.1)Poverty gap 3.5 5.1 (-1.6) 3.5 (0.0) 3.5 (0.0)

LimayPoverty rate 38.5 43.1 (-4.6) 42.2 (-3.7) 43.3 (-4.8)Poverty gap 17.3 20.3 (-3.0) 17.6 (-0.3) 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



3. EMPIRICAL RESULTS: IMPACT ON POVERTY

• 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)

Poverty incidence for observed and counterfactual scenarios (MIGRANT HOUSEHOLDS)

Poverty Measures Observed

Counterfactual (No-Migration) Counterfactual 1: Counterfactual 2: Counterfactual 3:

Remittance as Exogenous Transfer

Using OLS regression

Using Heckman selection model

All Sites Poverty rate (%) 4.5 37.4 9.9 12.9% Change in poverty -32.9 -5.4 -8.4

Pasay CityPoverty rate (%) 2.5 34.8 2.0 3.5% Change in poverty -32.3 -0.4 -1.1

LimayPoverty rate (%) 12.3 47.5 40.3 49.0% Change in poverty -35.2 -27.9 -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)




• Migrant households do not necessarily benefit from migration and remittances



Observed Scenario

No-migration 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.

Poverty status among migrant households: No-migration and Observed Scenario (% of households)




Poverty status among migrant households: No-migration and Observed Scenario (% of households) Changes in poverty status

Poor-Poor Poor-Nonpoor Nonpoor-Nonpoor Nonpoor-PoorAll Migrant Households 2.1 10.8 84.7 2.4Location Pasay City 0.6 2.9 94.6 1.9 Limay 8.0 41.0 46.6 4.4Urbanity Urban 1.4 7.2 89.2 2.2 Rural 9.1 46.6 39.6 4.7Household 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

remittances (in pesos) 3,210 32,983 58,008 4,365

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)

• Changes in the distribution of migrant households by income

quintile is evident in both sites

A. Pasay City B. Limay

3. EMPIRICAL RESULTS: IMPACT ON POVERTY Distribution of migrant households by income quintile

(No-migration and Observed scenarios)



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



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



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 abroad5. 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



Thank You!



Migration, Remittances and Poverty: Evidence from the Community-Based Monitoring System (CBMS) Data in Selected Communities in the Philippines

ALELLIE B. SOBREVIÑASGERMÁN CALFAT

Arnoldshain Seminar XI: “Migration, Development and Demographic Change- Problems, Consequences, Solutions”

University of Antwerp 28 June 2013

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 *** 0.01414HH size square 0.02900 *** 0.00119dependency ratio 0.51336 *** 0.01570male HH head 1.28624 *** 0.01850age of HH head 0.06924 *** 0.00320age of HH head square -0.00074 *** 0.00003married HH head -0.79413 *** 0.02006no. of members with at least tertiary education 0.06773 *** 0.00653location dummy (Pasay City=1) 0.09127 ** 0.04746dummy for urbanity (urban=1) 0.17073 *** 0.04541proportion of migrant HHs within the village -0.00734 *** 0.00222constant 0.62919 0.07638Probability > Chi2 0.000Pseudo R-squared 0.239 ***significant at 1% level; ** significant at 5% level; * significant at 10% levelSource of basic data: CBMS Census, Pasay City (2008-09) and Limay (2010-2011)


3. EMPIRICAL RESULTS

• Having an OFW increases income of households by 74.3 percent

ATT estimation with Nearest Neighbor Matching method (random draw version); Analytical standard errors

n. treat. n. contr. ATT Std. Err. t6481 64773 0.743 0.010 71.054

Note: the numbers of treated and controls refer to actual nearest neighbour matches

Propensity Score Matching: Preliminary Results



ALELLIE B. SOBREVIÑAS GERM ÁN CALFAT

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