RETURNS TO MIGRATION OF FILIPINOS

Michael Reyes Cabalfin∗

May 22, 2013

A Research Paper

Supervisory Panel

Prof. Hal Hill

Dr. Robert Sparrow

Dr. Juergen Meinecke

Abstract

Theory suggests that migration is primarily motivated by income gain. Consistent with this, incomedifferentials between top destination countries of Filipino migrants and the Philippines are high and in-creasing, and the shares of Filipino migrants rise with the income level of destination countries. However,estimates of gains from migration may be biased due to migration selection in observed and unobservedcharacteristics. While a minority of Filipinos have higher education, most Filipino migrants to the OECDhave higher education. This suggests higher returns to education in the OECD and overestimation ofstandard estimates of gains from migration. The greater inequality in the Philippines relative to countrieslike the United States, Australia, Canada and Japan may be driving the less productive to migrate intomore egalitarian economies. On the other hand, the rising inequality in the United States and Australiais allowing Filipino migrants to earn above average and protecting their incomes. Conversely, decreasinginequalities in Canada and Japan threaten to tax migrant incomes attracting the less productive.

Microeconometric analysis of survey data using propensity score matching reveals that migrantsare only slightly negatively selected from the local labor force, in terms of observed characteristics.Accounting for time-varying unobserved heterogeneity using three-stage least squares regression alsosuggests that migration selection based on unobserved characteristics is not much of a problem. Estimatesof return on migration by country of destination confirms significant and variably substantial gains frommigration to Canada, Italy and the United States. However, these estimates indicate no significantmigration selection, except for Hong-Kong where migrants negatively select.

∗PhD Candidate in Economics, Arndt-Corden Department of Economics, Crawford School of Public Policy, The AustralianNational University

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Contents

1 Introduction 21.1 Impacts of Migration - World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Impacts of Migration - Philippines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Objective of Paper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Literature Review 32.1 Theory of Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Migration Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.3 Migration Impact Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3 Macroeconomic Context 73.1 Top Origin and Destination Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.2 Income and Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.3 Inequality and Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.4 Migration by Educational Attainment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4 Model and Methodology 84.1 Ordinary Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84.2 Propensity Score Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.3 Double-Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.4 Instrumental Variable Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

5 Data 95.1 Labor Force Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105.2 Survey of Overseas Filipinos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

6 Results 116.1 Ordinary Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116.2 Propensity Score Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116.3 Instrumental Variable Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126.4 By Country of Destination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

7 Summary and Conclusion 12

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1 Introduction

1.1 Impacts of Migration - World

Much has been written about the benefits and costs of migration. UNDP [2009] surveys the literature onthe impacts of migration to origin and host countries. In origin countries, at the household level, migrationprovides remittances that help improve nutrition, health and general household welfare. Remittances serveas an alternative income source and mitigate against income shocks. Remittances also encourage householdentrepreneurship, investments, and savings for further migration. Moreover, remittances raise educationspending, enhancing the formation of human capital. Even when used for consumption, remittances boostseconomic activity. There are also migration costs to the household, such as negative emotional effects andadverse effects on child and elderly care.

At the national level, emigration of skilled workers is feared to decrease the quality of services. On thepositive side, mobility is said to reduce income disparities across countries. On the other hand, remittancescan be a ‘resource curse’, resulting in currency appreciation and reducing competitiveness. Conversely,remittances provide more macroeconomic stability than do foreign aid and foreign investment. Migrationalso provides benefits in terms of ideas and good practices from abroad such as egalitarian gender relationsand a ‘culture of migration’. However, migration also has negative cultural effects such as the gang culturefrom the United States. Migration also affects income distribution depending on which segment of theeconomy moves more. Remittances from international migration tend to benefit the better off while thosefrom internal migration benefit the poor more. There are also collective remittances that finance communityinfrastructure and services. Migration has social and political effects as well such as political participationand the development of political institutions, but it can also spur civil wars.

In destination countries such as the OECD, immigration has been found to raise employment, stimulatebusiness, encourage investment, and enhance innovation. While there are concerns that immigrants drivewages down, the effect is found to be small and depends on whether the skills of immigrants complementor compete with the skills of locals. The effect on employment also depends on market segmentation;displacements are unlikely if migrants take on low-skilled jobs that allow locals to move up the value chain.The negative impact on employment is small in light of the institutional discrimination against migrantswhich compel them to settle for informal employment.

Migration can also foster rapid urbanization putting a stress on services and driving migrants into informalsettlement subject to environmental and social risks. Migrants can also be viewed as a fiscal burden, “tak(ing)more than they give”, as is the case in some European countries. In the U.S., the newer generations ofmigrants are a source of fiscal surplus rather than costs. Other concerns about migrants relate to securityand crime, and cultural diversity. In the U.S., proportionately less immigrants are in prison, while in Europe,there are twice more immigrants in prison than locals. Nations established by migrants are more acceptingof migrants, and some cultural characteristics such as food are easier to adopt than others such as religion.Attitude toward migrants is determined by education, age, employment, and migration background.

1.2 Impacts of Migration - Philippines

Orbeta [2008] reviews the literature on the economic impact of migration and remittances in the Philippineson households. He finds remittances raising household consumption, as well as the shares of housing, durablegoods, education, health, and recreation. While remittances increase education spending and promotes schoolenrolment over youth employment, they also increase inequality in this spending. Remittances also reducepoverty incidence. Labor force participation rates among households with overseas workers are lower thanamong households without, as household incomes and demand for leisure increase.

At the national level, the largest benefit from migration appears to be remittances, reaching over 17billion dollars in 2011, amounting to 7.6 percent of GDP. The national Socio-Economic Report 2010-2012identifies growth in remittances as complementary to the overall economic growth, contributing to a strongerdomestic currency, stabilizing private consumption, and keeping the current account in surplus [NEDA, 2013].Overseas Filipinos also provide funding support through government and NGOs for ”relief and rehabilitation,

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education and scholarships, health equipment/facilities and medical missions, water and sanitation facilities,and livelihood assistance” [CFO, 2010]. They also facilitate transfer of technology through return-visits toand sharing in the Philippines such as through the Department of Science and Technology’s Balik(Return)-Scientist Program.

Baggio [2009] analyzes the costs of Filipino international migration. The political costs of migrationincludes the excessive reliance on remittances that has prevented government from developing sustainabledevelopment policies. The deployment of huge numbers of emigrants has also placed Filipino migrants atthe mercy of destination countries, facing discrimination and human rights violations. The economic costsof migration include increasing inequality as remittances accrue to households with higher incomes andeducation. Overseas workers come from richer regions so remittances go to these regions widening regionalinequalities. The social costs of migration includes separation in the family which threatens relationshipsand the unity of the family. The stability of the family appears to be threatened more with the migration ofmothers, as certain roles such as care-giving are under stress. Children face school, emotional, psychologicaland health problems, especially in cases where the mothers migrate, although these are mitigated in extendedfamilies. A long history of migration has created a ‘culture of migration’ with 60 percent of children ofoverseas workers considering working abroad, and taking up education oriented toward the internationallabor market. The exodus of professionals such as doctors and nurses is also showing adverse effects on thehealth care system.

1.3 Objective of Paper

While much is known about the benefits of migration in the Philippines at the household and national levels,not much is known about the private returns that are motivating migration in the first place. This paperaims to determine the returns to migration of Filipinos and to the education of Filipino migrants. Thisextends earlier research on returns to education to local workers.

2 Literature Review

2.1 Theory of Migration

Migration can be seen as an investment in human capital [Sjaastad, 1962]. As such, return to investment inmigration can be determined by the income gain from moving relative to the monetary and opportunity costsof migration. Migration is essentially a response to differences in earnings and a movement towards higherpay. Accordingly, the emigration rate is positively related to average income in the destination country,negatively related to average income in the home country, and negatively related to migration costs [Borjas,1987].1

UNDP [2009] puts the number of international migrants at 214 million (3.1 percent of world population).60 percent of international migration occurs between developing countries or between developed countries,whereas only 37 percent of migrants move from developing to developed countries. Almost half of interna-tional migrants move within their region and 40 percent move to a neighboring country. Nevertheless, mostinternational migrants move to a country with a higher development (HDI) than theirs.

Among migrants to OECD countries, those coming from least developed countries earn about 14 timesmore, those from moderately developed countries earn about 4.4 times more, highly developed countriesabout 2 times more, and very highly developed countries earn about 10 percent more - than the averageincome in their country [Ortega, 2009].

While migration is motivated by income gain, it is constrained by migration costs. Emigration rate isnegatively related to emigration costs. The income gain net of migration costs, both monetary and psychic,

1Given the distribution of earnings in the home country, lnw0 = µ0 + ε0, and the destination country, lnw1 = µ1 + ε1,a person decides to migrate if the index function I = ln(w1/(w0 + C)) ≈ (µ1 − µ0 − π) + (ε1 − ε0) > 0, where π = C/w0.The emigration rate is P = Pr[v − (µ1 − µ0 − π)] where v = ε1 − ε0, z = −(µ1 − µ0 − π)/σv , and Φ is the standard normaldistribution.

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together with institutional restrictions, determines the extent of migration [Borjas, 1987]. These constraintslimit mobility to a few and generate selection bias. It is no wonder that while the poorest countries standto gain the most from migration, emigration rates are lowest among them, constrained by poverty [UNDP,2009].

2.2 Migration Selection

The income gains from migration may be overstated given selectivity. Returns from migration are higherfor people with higher ability and motivation as these raise earnings relatively more than migration costsChiswick [1978].2 Migrants are also likely to have higher ability and motivation than natives of destinationcountries. The quality of migrants depends on the correlation between income disparities in the home anddestination countries on one hand and the ratio between the disparities on the other [Borjas, 1987].3 Positiveselection, where the most able persons migrate and earn more than the average person in the destinationcountry, occurs to the extent that income disparities between home and destination countries are highlycorrelated and income inequality is higher in the destination country. This suggests that the more able aretaxed more in their home country and less in the destination country so they choose to move. Otherwise, ifincome inequality is higher in the home country, negative selection occurs, where the less able migrate butearn lower than the average person in the destination country. This means that the less able benefit fromredistribution in the destination country. If the correlation in income disparities is lower than their ratio,the less able migrate but earn higher than average in the destination country, a phenomenon which Borjas[1987] calls Refugee Sorting.

Selection can also occur in observed characteristics, such as education. If return to education is higherin the host country, migrants tend to have more schooling [Borjas, 1999]. Otherwise, if return to educationis higher in the home country, migrants tend to have less schooling.4

Chiswick [1978] found that US immigrants have 3 percent higher earnings than native-born Americans,holding schooling, work experience, residence, and labor effort constant. While returns to schooling forimmigrants are lower than those of the native born, these rise when considering the number of years sincemigration. Borjas [1987] found that the quality of immigrants who arrived in the US in 1979 measured byentry wage is lower for those from higher inequality countries. Entry wage is 26 percent higher for thosewith English proficiency, is lower for the older, and rises 1.2 percent per 10 percent growth in origin country.Immigrants’ assimilation into the economy, measured by their earnings growth over ten years, is found to behigher for those with English proficiency, rise with age at immigration, and be higher for those from richercountries. The change in cohort quality measured by the difference in wages between the 1955 and 1979immigrant cohorts, is 13 percent higher for those from countries that shifted from political competition torepression, increased for those from Western Europe, decreased for those from less developed countries, andis higher for countries with quotas. Finally, emigration rate decreases with distance and origin country GDPper capita, and is lower for countries with higher inequality.

Chiquiar and Hanson [2005] found that returns to education among Mexican immigrants in the U.S. arelower than those in Mexico. Using wage densities and immigrant population shares by decile from the 1990and 2000 Mexican and U.S. Census of Population and Housing, Chiquiar and Hanson [2005] found that

2The rate of return to migration for person i is ri = (Wd,i −Wo,i)/(Co + Cd) where Wd,i and Wo,i are the earnings in thedestination and origin countries, respectively, Co = pWo,i is the opportunity cost of migration and is a fraction of domesticearnings, and Cd is the direct cost of migration. Return to migration for a more able person j, rj = (Wd,j−Wo,j)/(pWo,j+Cd),is greater than that for a less able person i, ri = (Wd,i −Wo,i)/[pWo,i + (Cd/1 + l)], if greater ability does not raise migrationcosts more than earnings.

3Given the conditional means E(lnw0|I > 0) = µ0 + σ0σ1σv

(ρ− σ0σ1

)λ and E(lnw1|I > 0) = µ1 + σ0σ1σv

(σ1σ0− ρ)λ, where ρ is

the correlation between ε0 and ε1, λ =φ(z)

1−Φ(z); E(lnw0|I > 0) > µ0 if ρ > σ0

σ1, and E(lnw1|I > 0) > µ1 if σ1

σ0> ρ. Otherwise,

E(lnw0|I > 0) < µ0 if ρ < σ0σ1

, and E(lnw1|I > 0) < µ1 if σ1σ0

< ρ.4Given the earnings functions, lnw0 = µ0 + δ0s + ε0 and lnw1 = µ1 + δ1s + ε1, for the home and destination countries,

respectively; the education distribution in the home country, s = µs + εs; the migration rate, P (z∗) = Pr[τ > −[(µ1 − µ0) −(δ1 − δ0)µs − π]] = 1−Φ(z∗), where τ = (ε1 − ε0) + (δ1 − δ0)εs, and z∗ = −[(µ1 − µ0 + (δ1 − δ0 − π]/στ . The mean schooling

of migrants, E(s|µs, I > 0) = µs +σ2sστ

(δ1 − δ0)λ is > µs if δ1 > δ0 and < µs if δ1 < δ0.

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Mexican male migrants come from the middle to upper-middle segment of the wage distribution, indicatingintermediate selection among Mexican males. Mexican female migrants, on the other hand, are drawn fromupper-middle segment of the wage distribution, indicating moderate positive selection. These, despite higherinequality in Mexico relative to the US. Moraga [2010] criticize these results on three grounds. Firstly, theseresults, estimated from the U.S. census, pertain more to the selection of the migrant stock. Secondly, themethodology does not account for unobserved characteristics. Thirdly, U.S. data underestimate immigrantsfrom Mexico.

Conversely, Moraga [2010] presents new evidence supporting the negative selection of Mexican migrantsto the U.S. He uses the nationally-representative Quarterly National Labor Survey (ENET) for 2000-2004,capturing the flow rather than the stock of migrants. Based on density and cumulative distribution func-tions, he finds that migrants have from 11 to 38 percent lower wages than non-migrants. Using frequencydistributions, he finds that male migrants have 3 years less schooling than non-migrants, but female migrantshave 3 years more schooling than non-migrants. Comparing the wage density distributions of non-migrantsand migrants, on one hand, and non-migrants and a counterfactual, on the other, he finds that 62 per-cent of the difference in wages is due to observable characteristics while the remaining 38 percent is due tounobservables.

2.3 Migration Impact Evaluation

Determining the returns to migration can be considered as an evaluation problem. We adapt the theory ofimpact evaluation [Khandker et al., 2010] with migration as the treatment. It involves comparing the actualearnings of migrants with their counterfactual earnings had they not migrated. The difference is called theaverage treatment effect :

ATE = E(Yi(1)|Mi = 1)− E(Yi(0)|Mi = 1).

The problem is that the counterfactual is usually unobserved. Given this missing data problem, the secondbest option is to compare the earnings of migrants with those of non-migrants, endeavouring to choose anon-migrant comparison group that is much like the migrant group. The average effect of migration wouldthen be:5

D = E(Yi(1)|Mi = 1)− E(Yi(0)|Mi = 0).

The problem is that migration is not random; migrants and non-migrants may not be similar in the absenceof migration. Rather, migrants self-select based on observed as well as unobserved characteristics (e.g.ability).6 The difference in earnings between migrants and non-migrants may not due to migration alone.Adding and subtracting E(Yi(0)|Mi = 1) to/from D and rearranging yields:

D = [E(Yi(1)|Mi = 1)− E(Yi(0)|Mi = 0)] + [E(Yi(0)|Mi = 1)− E(Yi(0)|Mi = 1)].

D = ATE + [E(Yi(0)|Mi = 1)− E(Yi(0)|Mi = 0)].

D = ATE +Bias.

If migrants tend to earn more than non-migrants prior to migration, the effect of migration would be biasedupward.

If migrants were randomly selected, there would be no selection bias. However, if a randomized experimentis not feasible, non-experimental methods can be used to evaluate the impact of migration. One option isto use propensity score matching to create a comparison group based on a model of the probabilityof migration, using observed characteristics. Migrants are then matched to non-migrants based on the

5The evaluation problem can then be written as:

Yi = αXi + βMi + εi

where M is a dummy equal to 1 for migrants and 0 for non-migrants/domestic workers; β is the income effect of migration; Xis a vector of other observed characteristics; and ε is the error term. If migration were random, the migration dummy wouldbe uncorrelated with the error term, and the OLS estimate of the effect of migration would be unbiased.

6In the latter case, the migration dummy is correlated with the error term, making the estimates biased.

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likelihood of migration or propensity score. Matching is valid only if earnings are independent of migrationgiven observed characteristics [Rosenbaum and Rubin, 1983] (it implies that unobserved characteristics donot affect migration), and there is sufficient overlap in the propensity scores between migrants and non-migrants. The treatment of treated for the PSM is the mean difference in earnings between the migrantsand the comparison group:

TOTPSM = EP (X)|M=1[E[Y T |M = 1, P (X)]− E[Y C |M = 0, P (X)]].

Using the US Census and household survey data across various countries, Clemens, Montenegro, andPritchett [2009] estimate the ratio, R0, between the wage of workers in the United States and those ofobservably identical (in terms of country of birth, country of education, education level, age, sex, andurbanity) workers in 42 other countries, akin to the treatment of treated in proportion to the earnings ofthe non-migrant comparison group. They find a median real wage ratio of between 4 and 14 dependingon the exchange rate used and the proportion of income spent in the home country assumed. Observablecharacteristics account for a third of wage differences across countries.

As migration may be affected by unobserved characteristics such as ability, propensity score matchingestimates may still be biased. In this case, the double-difference method may be used as it assumes thepresence of unobserved heterogeneity affecting migration. However, these unobserved characteristics aretime-invariant and can be differenced out. The double-difference technique usually uses panel data. Thetechnique involves estimating the changes (differences) in earnings for both migrants and non-migrants overtime. The average income effect of migration is the difference between these differences:

DD = E(YM1 − YM0 |M1 = 1)− E(Y C1 − Y C0 |M1 = 0).

where YMt and Y Ct are the earnings of migrants and non-migrants, respectively, at time t.Clemens, Montenegro, and Pritchett [2009] also estimate the place premium, Re, the ratio of the wages

of observably and unobservably identical workers in the United States and 9 other countries, akin to thedouble-difference in proportion to the change in non-migrant income. They then derive the ratio, R0

Re, akin

to the bias as a proportion of the average treatment effect, to determine selection. They estimate the placepremia and R0

Re, respectively, at 3.5 and 1.08 for the Philippines, 2.49 and 1.03 for Mexico, and 2.3 and 1.2 for

South Africa, using panel data; and 2.35 and 1.07 for Mexico, 3.08 and 0.96 for Guatemala, 3.28 and 1.07 forNicaragua, 1.68 and 1.23 for Costa Rica, 1.87 and 1.06 for Dominican Republic, 7.84 and 1.32 for Haiti, and2.61 and 1.45 for Peru, using wage histories, suggesting modest positive selection for most. Using residualwage kernel densities, they find that the median migrant comes from the 58th percentile of non-migrants inthe Philippines, 56th percentile in Mexico, and 60th percentile in South Africa. They conclude that placepremia higher than 1.5 are due to policy barriers to labor mobility.

If unobserved characteristics are time-varying, the double difference method estimates may also be biased.To allow for time-varying unobserved heterogeneity, instrumental variable regression can be used. Itinvolves finding an instrument that is highly correlated with migration but not correlated with unobservedcharacteristics affecting earnings.

McKenzie, Gibson, and Stillman [2006] study the migration of Tongans to New Zealand, generating theonly random experimental estimate of the returns to migration together with non-experimental estimates.With higher inequality in New Zealand than in Tonga, they predict positive selection of Tongans. Theyanalyze New Zealand’s Pacific Access Category, which accepts a yearly quota of Tongans into Zealand froma lottery of applicants. They find that lottery winners earn 88 percent more income than non-winners withsimilar characteristics and that migrants earn 263 percent more than non-migrants. Using non-experimentalmethods, they find that the instrumental variable estimate using a bad instrument is biased upward by 82percent, while OLS overestimates by 31 percent. Propensity score matching overstates by 19-33 percent,while single-difference method is biased by 25 percent. Double-difference overestimates by 20 percent, whileinstrumental variable estimate with a good instrument has the lowest bias at 9 percent. The results confirmpositive selection in unobserved characteristics.

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3 Macroeconomic Context

3.1 Top Origin and Destination Countries

The Philippines is among the top ten source countries of migrants world-wide. Figure 1 shows the number ofmigrants from the top ten source countries in 2010 from the World Bank’s Bilateral Migration Matrix. Theten countries account for over a third (33.6 percent) of migrants worldwide, among them the Philippines,source of 4.3 million (2 percent) migrants worldwide. Filipino migrants comprise 4.6 percent of the country’spopulation. These figures suggest the significance of the Philippines in terms of migration.

The Commission of Filipinos Overseas puts the stock of overseas Filipinos much higher at 10.5 millionin 2011, growing at 2.6 percent annually from 7.4 million in 2000. 46.6 percent of overseas Filipinos arepermanent migrants, 43.2 percent are temporary migrants, and 10.3 percent are irregular migrants. 10destination countries account for 87.5 percent of Filipino migrants (Figure 2). The United States alone ishost to a third (33.4 percent) of Filipino migrants, followed by Saudi Arabia (15.1 percent), Canada (8.2percent), and UAE (6.6 percent). Malaysia hosts 5.5 percent of Filipino migrants, Australia 3.7 percent, andQatar 3.3 percent. Japan is host to 2.2 percent, United Kingdom 2.1 percent, Italy, Singapore and Kuwait1.8 percent each, and finally Hong Kong 1.7 percent.

3.2 Income and Migration

The income differential between countries is the primary motivation for migration. Figure 3 shows the ratiosof the GDP per capita of top destination countries to that of the Philippines for 1960-2011. It shows thatin 1960, GDP per capita in the US was almost 20 times that of the Philippines, in the UK 14.5 timesmore, in Australia and Canada 13.6 times, and in Japan 11.2 times. Filipinos would have had sufficientmotivation to migrate to these countries. Income differentials have increased in the mid-1980s, primarilydue to the political crisis in the Philippines. Today, GDP per capita in the US is almost 27 times thatfor the Philippines, in the UK 20 times, in Australia and Canada 18 times, and in Japan 28 times. Theshare of Filipino migrants by destination is positively related to GDP per capita in the destination countries,consistent with migration theory (Figure 4). As GDP per capita in destination countries increase by 1percent, the share of Filipino migrants to that country increases by 2.7 percent, making Filipino migrantsvery responsive to destination country incomes. The share of migrants by status is shown in Figure 5. Mostof the Filipinos in the United States (89 percent), Canada (87 percent), Australia (85 percent), UnitedKingdom (73 percent), and Japan (70 percent) are permanent migrants. On the other hand, most of theFilipinos in Saudi Arabia (99 percent), UAE (97 percent), Kuwait and Qatar (96 percent each), and HongKong (90 percent) are temporary migrants. Most Filipinos in Malaysia (79 percent) are irregulars.

3.3 Inequality and Migration

The gini coefficients for the Philippines and the top 5 destination countries for which data are available forthe period 1957-2009 are given in Figure 6.7 The markers on the line graphs show actual data, with therest of the line interpolated for years without data. The actual and interpolated data are used to estimatethe ratios of the gini coefficients of the Philippines and each of the destination countries, and calculate thecorrelations in the ginis. The correlation coefficients and average ratio of ginis are given in Table 1. Figure 6shows greater inequality in the Philippines relative to the U.S., Australia, Canada and Japan over the period.Theory suggests that this higher inequality in the Philippines is driving the less productive to seek refuge inmore egalitarian economies to take advantage of better wealth distribution. This is reinforced by the negativecorrelations between inequality in the Philippines and those of the U.S. and Australia. Filipino migrantsare increasingly assimilating into the more productive segment of the U.S. and Australian labor force andthe rising inequality in these destination countries serve to protect their incomes. On the other hand, thepositive correlations between inequality in the Philippines and those of Canada and Japan suggest that

7Sources of data: Deininger [1996] for 1950s-1996, World Development Indicators for 1997-2009

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decreasing inequalities in these countries threathen to tax migrant incomes. Relative inequalities betweenthe Philippines and Malaysia are not that different nor highly correlated suggesting little if any selection.

3.4 Migration by Educational Attainment

The educational attainment of the Philippine labor force has increased over time. Figure 7 shows theeducational attainment of the labor force from 1950 to 2010. In 1950, almost 4 out of 5 in the labor forcehad at most incomplete primary schooling, with almost half without schooling at all. Only 1 in 5 had atleast completed primary school, 1 in 10 had secondary schooling, and 1 in 40 had tertiary education. In1990, 35 percent of the labor force have at least complete secondary education, with 13 percent havingcompleted college. By 2010, over half of the labor force have completed at least secondary school, and over1 in 5 completed tertiary education. Still, almost half have at most incomplete secondary schooling. Mostin the labor force have only primary to secondary educational attainment. A minority of the labor force hastertiary education.

Emigrants have more schooling than the local labor force. Table 2 shows the share of emigrants byeducational attainment. In 1990, over 6 in 10 emigrants have at least complete secondary education, and 3out of 10 are at least college graduates. By 2010, over 7 in 10 emigrants have at least complete secondaryeducation, with 35 percent having at least completed college.

Docquier and Marfouk [2005] show that the Philippines had the second highest emigration stock in 2000,next to the United Kingdom. It also has the fifth highest selection rate, the share of skilled emigrants tothe emigrant stock, at 67.1 percent compared to the of average 51.4 percent for the Southeast Asia and46.8 percent for Asia. Using the same methodology as Docquier and Marfouk [2005], Schiff and Sjoblom[n.d.] compile a panel dataset on international migration for 1975-2000. The shares of Filipino migrants to6 key OECD destination countries (U.S.A., U.K., Australia, Canada, France, and Germany) by educationlevel is given in Table 3. It shows the proportion of highly educated migrants rising from 51.5 percent in1975 to 72.2 percent in 2000. This is consistent with the earlier prediction based on inequality. In the U.S.,the proportion highly educated migrants rose from 51 percent in 1975 to 73 percent in 2000. In Canada,the proportion of Filipino migrants with high education rose from 71 percent in 1975 to over 81 percent in2000. In Australia, most (86 percent) Filipino migrants in 1975 had low education. However, since 1980,most migrants had high education although this dropped from 84 percent in 1980 to 66 percent in 2000.These figures show that Filipino migrants to the OECD, particularly in the U.S., Canada, and Australia arepositively selected in terms of education.

4 Model and Methodology

4.1 Ordinary Least Squares

The model is essentially an augmented human capital earnings function:

ln(Wi) = αi + βSi + γMi + δ(Si ∗Mi) +X ′iθ + εi (1)

where ln(Wi) is the natural logarithm of the wage of individual i, Si is years of schooling, and Mi isa dummy variable for migrants (non-migrants as reference). Xi is a vector of observable characteristicsincluding dummy variables for male (female as reference), married, 18 regions (the national capital regionas reference), and 9 occupations (Officials in government and special interest organizations, executives,managers, and supervisors as reference). ε is the error term. δ is the return to schooling for migrants, β isthe return to schooling for non-migrants, and γ is the return to migration without schooling.

If schooling and migration were random, these variables would be uncorrelated with the error termand their coefficients unbiased. However, if migrants and the more educated self-select based on observedand unobserved characteristics, migration and schooling will be correlated with the error term and theircoefficients would be biased.

8

4.2 Propensity Score Matching

If migrants self-select on observed characteristics, the bias can be corrected using propensity score matching.The first step is to estimate a probit/logit model of migration on observed characteristics (i.e. age/experience,schooling, sex, marital status, etc.).

Pr(Mi = 1|xi) = Φ(αi + βSi +X ′iθ + εi) (2)

The predicted values M̂ i of the probability model are derived, representing the estimated probability ofmigration or propensity score. We use the Stata estimation command pscore.ado developed by Becker andIchino [2002]. The second step is to identify the overlap in propensity scores between migrants and non-migrants. The third is to match migrants M to a non-migrant comparison group C based on the propensityscore. Finally, the average effect of migration is calculated as follows:

TOTPSM =1

NM[∑iεM

WMi −

∑jεC

ω(i, j)Y Cj ]. (3)

We use the nearest neighbor and stratification criteria to match migrants and non-migrants and computethe average migration effect.

4.3 Double-Difference

As migration may be affected by unobserved characteristics such as ability, propensity score matching esti-mates may still be biased. To account for unobserved characteristics, we use a double-difference method, asit assumes the presence of unobserved heterogeneity affecting migration. Assuming that these unobservedcharacteristics, αi, are time-invariant, they can be differenced out. Using panel data, the model becomes:

ln(Wit)− ln(Wit−1) = (αi − αi) + β(Sit − Sit−1) + γ(Mit −Mit−1)

+ δ(Sit ∗Mit − Sit−1 ∗Mit−1) + (X ′it −X ′it−1)θ + (εit − εit−1)(4)

4.4 Instrumental Variable Regression

If unobserved characteristics are time-varying, the double difference method estimates may still be biased.To allow for time-varying unobserved heterogeneity, instrumental variable regression can be used. It involvesfinding an instrument that is highly correlated with migration, but not correlated with unobserved charac-teristics affecting earnings. Instrumental variable regression is done using two-stage least squares. The firststage is a probit regression of the Migration dummy on the instrument Z, the other covariates X and adisturbance u:

Pr(Mi = 1|xi) = Φ(αi + γZi + βSi +X ′iθ + ui) (5)

where the instrument Z is the number of members of the family / household. The idea is that a largerdependency ratio would motivate a worker to migrate to earn more for his/her family, but would not affecthis wage directly. The second stage involves substituting the predicted value of the migration model intothe wage model as follows:

ln(Wi) = αi + βSi + γM̂ i + δ(Si ∗ M̂ i) +X ′iθ + εi (6)

5 Data

The study aims to use the Labor Force Survey (LFS) for January 2004, 2008 and 2010, and the correspondingSurvey of Overseas Filipinos.

9

5.1 Labor Force Survey

The Labor Force Survey provides data on wages (for local workers), age, education, sex, marital status,occupation, industry and other demographic characteristics for household members. The LFS January 2004has 180,941 observations classified by labor force status, 44 percent of whom are employed, 3 percent areunemployed, and 53 percent are not in the labor force; another 2,380 are overseas workers. There are 126,645observations classified by labor force status in the January 2007 LFS, of whom 60 percent are employed,5 percent are unemployed, and 35 percent are not in the labor force; another 2,943 are overseas workers.There are 122,177 observations classified by labor force status in January 2010 LFS, of whom 60 percent areemployed, 4 percent are unemployed, and 36 percent are not in the labor force; another 3,386 are overseasworkers.

Earnings for local workers are derived from the Labor Force Survey and are measured in terms of hourlywage as annual or weakly wage would yield returns to education that include the effect of time spent atwork. Hourly wage is computed as basic pay per day divided by the normal number of hours worked perday.

5.2 Survey of Overseas Filipinos

The Survey of Overseas Filipinos provides data on remittances of overseas Filipinos, age, sex, education,marital status, occupation, industry, etc. Remittances will be used to proxy for total wages as are noindicators for wage rate and labor effort among overseas Filipinos, with adjustments to be made to accountfor spending not remitted. Total earnings is measured as the sum of remittances in cash and kind over thelast six months, and cash brought home for those who returned within during the period. While the datainclude remittances for the past six months, there is no indication as to how frequent the remittances are.We derive hourly wage by dividing total earnings by 25 weeks and further by 5 days, and finally by 8 hours.

The Survey of Overseas Filipinos (SOF) 2003 has 2,888 overseas workers. More recent years of the SOFdata are being obtained further.

Years of schooling is derived from the variable ‘highest grade completed’ where No Grade is assigned 0,Incomplete Elementary = 3, Elementary Graduate = 6, Incomplete High School = 8, High School Graduate= 10, College Undergraduate = 12, College Graduate =14, and Completed Post-Graduate = 16. Dummiesfor highest grade completed are also generated.

The educational system that applies to the data comprise 6 years of elementary education starting aroundage 7, 4 years of secondary education starting around age 13, and tertiary education starting around age 17.The Philippine Constitution promotes the right to education and provides a system of free public educationat the primary and secondary levels, and mandates primary education as compulsory. Starting in 2012, theDepartment of Education has implemented the K-12 program, a new system with Kindergarten, six years ofprimary education, 4 years of junior high school, and 2 years of senior high school.

Consistent with the literature, work experience is used rather than age as using age leads to omittedvariable bias that would underestimate return to schooling. Work experience is computed as age minusyears of schooling minus six (6), the normal age before the start of schooling. This assumes continuouswork experience after schooling and does not account for periods of unemployment and unpaid work. Sex isrecoded as a dummy variable where Male = 1 and Female = 0. A variable Occupation is created by lumpingvarious occupations into major occupations. The variable Region is recoded to make the National CapitalRegion the reference.

Results for this draft are based on the LFS January 2004 and SOF October 2003 alone while more recentSOF data are being obtained.

10

6 Results

6.1 Ordinary Least Squares

Table 4 shows the results using ordinary least squares. The basic mincerian with the migration dummyinteracted with schooling shows that on average, overseas workers earn 46 percent more than local workers.Including a male dummy raises return on migration to 63.3 percent. Including the dummy for marriedslightly decreases return on migration to 62.7 percent. Including regional dummies substantially decreasesreturn on migration to 55.4 percent, and including occupational dummies reduces it slightly more to 55.2percent.

The changes in return on migration with the inclusion of variables suggests estimation bias with theirommission. This can be seen in the interaction between the omitted variable’s relationship with earnings andthat with migration. Omitting males underestimates return on migration because while males earn more,they may be less likely to migrate. Omitting the dummy for married overestimates return on migration as themarried earn more wages and are more likely to migrate. Omitting regional dummies overestimates returnon migration as most regions have lower wages than the national capital and as individuals from most otherregions have less than or equal probability of migrating than the those from the national capital. Omittingoccupational dummies somewhat overestimates return on migration as most other occupations earn lowerthan the capital and the occupations that comprise majority of the sample tend to be negatively correlatedwith migration.

Return on schooling for local workers is 11 percent with the basic mincerian, and rises to 11.5 percentwith the inclusion of a male dummy. It decreases to 11.2 with the inclusion of the married dummy, andfurther to 10.5 percent with the inclusion of regional dummies. Return on schooling substantially decreasesto 5.4 percent with the inclusion of occupational dummies. Return on schooling for overseas workers is notsignificantly different from that of local workers in all specifications.

6.2 Propensity Score Matching

The first stage of propensity score matching is a probit or logistic regression of migration on other covariates.To appreciate migration probabilities, we rely on the odds ratio from a logistic regression (Table 5). Thetable shows that each year of schooling increases the likelihood of migration by 43 percent. Each year ofwork experience raises the likelihood of migration by 19 percent. Males have 15 percent less likelihoodof migration. Married individuals are 30 percent less likely to migrate. Those from the Ilocos, CagayanValley, Central Luzon, Western Visayas, Cordillera Administrative Region, and CALABARZON have greaterlikelihood to migrate than those from the National Capital Region. Those from Eastern Visayas, WesternMindanao, Southern Mindanao, Caraga, and MIMAROPA have less probability of migrating than those fromthe capital. Those from Bicol, Central Visayas, Northern Mindanao, Central Mindanao, and ARMM havethe same probability of migrating as those from the capital. The likelihood of migration does not appear tobe related to regional income. However, excepting outliers (in terms of standard errors, the Cordillera, NCRand ARMM), the likelihood of migration across regions is negatively related to regional inequality (standarderror), with 5 percent significance level. People are more likely to migrate from regions with low inequality.This seems to be consistent with positive selection in which the more able are taxed more so they choose tomigrate.

Across occupations, officials in government and special interest organizations, executives, managers andsupervisors have the least probability of migrating. Workers in trades and related work have over 5 timesgreater likelihood, special cccupations almost 4.5 times, and plant and machine operators and assemblersalmost 3.5 times. Service workers and shop and market sales workers have over 3 times higher probability;laborers and unskilled workers, and clerks over twice more. Professionals, and technicians and associateprofessionals are about twice more likely to migrate. Farmers, forestry workers and fishermen are about 1.75times more likely to migrate. The likelihood of migration again does not appear to be related to averageoccupational income. However, the likelihood of migration is similarly negatively related to inequality acrossoccupations, significant at 10 percent level. People tend to migrate from occupations with lower inequality,

11

again suggesting positive selection.Running the pscore command, we find that the balancing property is satisfied with only two specifications,

the probit of overseas on schooling and the male dummy, and on schooling and the married dummy. Focusingon the former, the density of the pscore for migrants and non-migrants are shown in Figure 8. The regionof common support is [.0001897, .0772231], and the final number of blocks is 26. We estimate the averagetreatment effect using nearest-neighbor matching and stratification matching. The results are shown inTable 6. The nearest-neighbor matching result shows that overseas workers earn 57.6 percent more thanlocal workers. The stratification matching result is slightly lower at 55.7 percent. To make these comparableto the OLS estimate, we run OLS of overseas on schooling and the male dummy. This yields a return onmigration of 54.1 percent. The PSM figures are slightly higher than the OLS estimate meaning that OLSslightly underestimates return on migration based on observed characteristics. This suggests that migrantswould earn less than non-migrants in the absence of migration, consistent with the inferences based on themacroeconomic context.

6.3 Instrumental Variable Regression

To allow for time-varying unobserved heterogeneity, we use instrumental variable regression, using familysize as instrument. The idea is that supporting a large family motivates a person to move where incomesare generally higher but it does not affect the amount of income actually earned. The chosen instrumentproves valid as the reduced form equation shows that family size is partially correlated with migration butis uncorrelated with the residual. Two-stage least squares regression with all the covariates yields a returnon migration almost twice the average of local earnings. On the other hand, a three-stage least squaresregression with the same specification yields a return on migration of only 39.7 percent. A three-stage leastsquares with migration and schooling interacting yields a return on migration of 55.2 percent, the same asthe the OLS estimate and not much different from the propensity score matching estimates. This suggeststhat migration selection is not much of a problem. This is confirmed by a Hausman test which indicatesthat migration is not endogenous as the residuals of the structural equation and the reduced form equationare not correlated.

6.4 By Country of Destination

To determine return on migration and education by country of destination, we run OLS and IV regressionswith the interactions of migration, country of destination and schooling. OLS results show that return onmigration is highest for Canada at 2.4 times that of local workers, followed by Italy at 2.2 times. Return onmigration to Australia may be about 2 times the earnings of local workers but the sample is too small to besignificant. Return on migration to the United States is 91 percent. This is relatively smaller as migrants aremore permanent and may have brought their family with them, thereby sending less remittances to relatives.For other destinations, the estimates are insignificant. IV regression have essentially the same results butwith a significant return on migration to Hongkong at 71 percent.

OLS results show that return on schooling for local workers is 5.4 percent. Surprisingly, return onschooling in several destination countries, notably Canada, Hongkong, Italy, Singapore and the UnitedStates, are lower. Return on schooling in Australia, Japan, Malaysia, Saudi Arabia, Singapore, and Taiwanare not significantly different from that for local workers. Return on schooling in all other destinationcountries is 1.7 percent higher than for local workers. IV regression estimates are the same as the OLSestimates.

7 Summary and Conclusion

Migration theory suggests that differences in income are the primary motivation for migration. Consistentwith this, incomes in top destination countries of Filipino migrants are higher than that of the Philippines,and the gap increased over time further fueling migration. Moreover, the shares of Filipino migrants rise

12

with the income level of destination countries. Migration theory further suggests that selection of migrantsdepends on the relative inequalities between the origin and destination countries. This means that greaterinequality in the Philippines relative to the United States, Australia, Canada and Japan would be drivingthe less productive to migrate in more egalitarian economies. This is reinforced by the negative correlationsbetween inequality in the Philippines and those of the United States and Australia, drawing Filipino migrantsto labor markets where they earn above average and where the rising inequality serve to protect their incomes.On the other hand, decreasing inequalities in Canada and Japan threaten to tax migrant incomes attractingthe less productive.

The shares of Filipino migrants to key OECD countries including the United States, Canada and Australiawho were highly educated have risen from 1975 to 2000 suggesting positive selection in terms of education.This suggests higher returns to education in the OECD relative to the Philippines. This also implies thatsimply comparing migrant incomes from non-migrant incomes would over-estimate returns to migration.Furthermore, this is consistent with the prediction of positive selection into destination countries based onrelative inequalities, except for Canada.

Controlling for schooling, experience, sex, marital status, region, and occupation, return on migrationusing ordinary least squares regression is estimated at 55.2 percent. Comparing migrants and non-migrantswith the same education and sex using propensity score matching yields a return on migration of between55.7 and 57.6 percent. A comparative OLS regression controlling for education and sex is 54.1 percent.These figures suggest that migrants are only slightly negatively selected from the local labor force, in termsof observed characteristics, consistent with earlier predictions. Accounting for time-varying unobservedheterogeneity using three-stage least squares regression yields a return on migration of 55.2 percent, thesame as the OLS estimate, suggesting that migration selection based on unobserved characteristics is notmuch of a problem. Estimates of return on migration by country of destination confirms significant andvariably substantial gains from migration to Canada, Italy and the United States. However, these estimatesindicate no significant migration selection, except for Hong-Kong where migrants negatively select; the lessable are more likely to migrate.

References

Fabio Baggio. Enhancing the benefits and the reducing the costs of outward migration: Experiences andperspectives from the philippines. Working Paper 20, International Labour Organization, March 2009.

S.O. Becker and A. Ichino. Estimation of average treatment effects based on propensity scores. Stata Journal,2(4):358–377(20), 2002. URL http://www.stata-journal.com/article.html?article=st0026.

George J. Borjas. Self-selection and the earnings of immigrants. The American Economic Review, 77(4):pp.531–553, 1987. ISSN 00028282. URL http://www.jstor.org/stable/1814529.

George J. Borjas. Chapter 28 the economic analysis of immigration. volume 3, Part A of Handbookof Labor Economics, pages 1697 – 1760. Elsevier, 1999. doi: 10.1016/S1573-4463(99)03009-6. URLhttp://www.sciencedirect.com/science/article/pii/S1573446399030096.

CFO. Filipinos Overseas and their Contribution to National Development. Commission on Filipinos Overseas,2010.

Daniel Chiquiar and Gordon H. Hanson. International migration, self-selection, and the distribution ofwages: Evidence from mexico and the united states. Journal of Political Economy, 113(2):pp. 239–281,2005. ISSN 00223808. URL http://www.jstor.org/stable/10.1086/427464.

Barry R. Chiswick. The effect of americanization on the earnings of foreign-born men. Journal of PoliticalEconomy, 86(5):pp. 897–921, 1978. ISSN 00223808. URL http://www.jstor.org/stable/1828415.

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Michael A. Clemens, Claudio E. Montenegro, and Lant Pritchett. The place premium: Wage differences foridentical workers across the u.s. border. Technical Report RWP09-004, Harvard Kennedy School, John F.Kennedy School of Government, 2009.

Lyn Deininger, Klaus W.; Squire. A new data set measuring income inequality. The World Bank EconomicReview, 10(3):565–91, 1996.

Frederic Docquier and Abdelsam Marfouk. International Migration, Remittances and the Brain Drain. TheWorld Bank, 2005.

Shahidur R. Khandker, Gayatri B. Koolwal, and Hussain A. Samad. Handbook on Impact Evaluation. TheWorld Bank, 2010.

David McKenzie, John Gibson, and Steven Stillman. How important is selection? experimental vs. non-experimental measures of the income gains from migration. Technical report, Institute for the Study ofLabor, 2006.

Jess Fernndez-Huertas Moraga. New evidence on emigrant selection. Review of Economics and Statistics,93(1):72–96, July 2010. ISSN 0034-6535. doi: 10.1162/RESTa00050.URL

Figure 1: World Migrants by Source Country, 2010

11.9

11.411.0

8.3

6.5

5.4

4.7 4.7

4.3 4.3

0

2

4

6

8

10

12

14

Millions

Source of basic data: Commission on Filipinos Overseas

14

Figure 2: Share of Filipino Migrants by Destination Country, 2010

UNITED STATES

33.4%

SAUDI ARABIA

15.1%

CANADA

8.2%

UAE

6.6%

MALAYSIA

5.5%

AUSTRALIA

3.7%

QATAR

3.3%

JAPAN

2.2%

UNITED KINGDOM

2.1%

KUWAIT

1.8%

ITALY

1.8%

SINGAPORE

1.8%

HONGKONG

1.7%

OTHERS

12.7%

Table 1: Relative Inequalities and Correlations, Philippines vs. Destination CountriesDestination ρ σ0

σ1

σ1

σ0ρ− σ0

σ1

σ1

σ0-ρ

USA -0.78 1.32 0.77 -2.09 1.54Canada 0.41 1.52 0.66 -1.11 0.25Australia -0.54 1.30 0.78 -1.84 1.32Malaysia -0.04 0.99 1.02 -1.03 1.06Japan 0.33 1.40 0.72 -1.07 0.39

Table 2: Emigrants by Educational Attainment1990 1995 2000 2005 2010

No Education 2.1 0.8 0.7 0.2 0.1Elementary Level 14.4 14.3 13.2 13.4 13.9Elementary Graduate 5.6 5.0 3.9 3.5 2.8High School Level 14.1 14.5 13.5 12.0 11.9High School Graduate 11.8 14.0 13.4 11.7 10.5Vocational Level 1.9 2.1 1.8 1.5 1.8Vocational Graduate 3.2 4.1 4.8 4.3 5.7College Level 16.9 17.0 16.9 16.4 18.2College Graduate 28.2 25.8 28.5 32.5 31.5Post Graduate Level 1.1 1.4 2.3 2.1 1.5Post Graduate 0.7 1.1 1.1 2.5 2.0

15

Figure 3: Ratio of GDP per capita, Top Destination Countries to Philippines

0

5

10

15

20

25

30

35

40

19

60

19

62

19

64

19

66

19

68

19

70

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72

19

74

19

76

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78

19

80

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82

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84

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86

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88

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90

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92

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94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Japan

United States

Hong Kong

Qatar

Singapore

United Kingdom

Canada

Australia

Kuwait

United Arab Emirates

Saudi Arabia

Malaysia

Figure 4: Share of Filipino Migrants by GDP per capita in Destination Countries

y = 0.0272x - 0.2047

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

6 7 8 9 10 11

Sh

are

of

Fil

ipin

o M

igra

nts

, 2

01

0

Log of GDP per capita, 2000 (constant US$)

16

Figure 5: Migrants by Status, Top 10 Destination Countries

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Millions

IRREGULAR

TEMPORARY

PERMANENT

Figure 6: Gini Coefficients, Philippines and Top Desination Countries

0

10

20

30

40

50

60

1957

1958

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

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2004

2005

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2008

2009

Malaysia

Philippines

USA

Australia

Canada

Japan

17

Figure 7: Labor Force by Educational Attainment

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Complete Tertiary

Incomplete Tertiary

Complete Secondary

Incomplete Secondary

Complete Primary

Incomplete Primary

No schooling

Table 3: Share of Filipino immigrants to OECD6 by Educational AttainmentLow Medium High

1975 21.4 27.0 51.51980 19.4 19.9 60.71985 17.5 18.5 63.91990 13.2 18.3 68.21995 15.8 13.0 71.02000 9.4 18.3 72.2

18

Table 4: Ordinary Least SquaresVariable OLS1 OLS2 OLS3 OLS4 OLS5Overseas 0.461*** 0.633*** 0.627*** 0.554*** 0.552***Schooling 0.110*** 0.115*** 0.112*** 0.105*** 0.054***Overseas*Schooling 0.002 -0.012 -0.01 -0.004 0.001Experience 0.030*** 0.028*** 0.021*** 0.020*** 0.015***Experience Squared -0.000*** -0.000*** -0.000*** -0.000*** -0.000***Male 0.208*** 0.194*** 0.191*** 0.254***Married 0.124*** 0.131*** 0.111***REGION (National Capital Region as reference)Ilocos Region -0.284*** -0.247***Cagayan Valley -0.390*** -0.370***Central Luzon -0.137*** -0.137***Bicol Region -0.260*** -0.271***Western Visayas -0.441*** -0.423***Central Visayas -0.262*** -0.291***Eastern Visayas -0.277*** -0.314***Western Mindanao -0.333*** -0.369***Northern Mindanao -0.347*** -0.356***Southern Mindanao -0.339*** -0.335***Central Mindanao -0.365*** -0.354***Cordillera Administrative Region -0.120*** -0.157***Autonomous Region of Muslim Mindanao -0.189*** -0.310***Caraga -0.294*** -0.321***CaLaBaRZon -0.086*** -0.093***MiMaRoPa -0.275*** -0.277***OCCUPATION (Government Officials, Executives, Managers, and Supervisors as reference)Professionals 0.214***Technicians and Associate Professionals -0.210***Clerks -0.365***Service Workers and Shop and Market Sales Workers -0.662***Farmers, Forestry Workers and Fishermen -0.823***Trades and Related Workers -0.520***Plant and machine Operators and Assemblers -0.587***Laborers and Unskilled Workers -0.816***Special Occupation -0.492***constant 1.698*** 1.539*** 1.575*** 1.872*** 2.943***N 37949 37949 37949 37949 37949r2 0.33 0.346 0.351 0.382 0.483legend: * p<.05; ** p<.01; *** p<.001

19

Table 5: Logistic RegressionOVERSEAS Odds Ratio Std. Err. z P>zSchooling 1.470 0.013 44.54 0.000Experience 1.198 0.008 27.03 0.000Experience Squared 0.996 0.000 -23.9 0.000Male 1.110 0.049 2.38 0.017Married 0.624 0.030 -9.81 0.000REGION (National Capital Region as reference)Ilocos Region 1.591 0.131 5.63 0.000Cagayan Valley 1.941 0.171 7.53 0.000Central Luzon 1.123 0.091 1.43 0.152Bicol Region 0.688 0.081 -3.18 0.001Western Visayas 1.395 0.116 3.99 0.000Central Visayas 0.773 0.077 -2.6 0.009Eastern Visayas 0.537 0.076 -4.41 0.000Western Mindanao 0.611 0.087 -3.45 0.001Northern Mindanao 0.693 0.084 -3.02 0.003Southern Mindanao 0.707 0.087 -2.82 0.005Central Mindanao 0.888 0.102 -1.04 0.297Cordillera Administrative Region 1.371 0.140 3.09 0.002Autonomous Region of Muslim Mindanao 0.737 0.111 -2.02 0.043Caraga 0.409 0.066 -5.55 0.000CaLaBaRZon 1.419 0.101 4.9 0.000MiMaRoPa 0.600 0.089 -3.43 0.001OCCUPATION (Government Officials, Executives, Managers, and Supervisors as reference)Professionals 2.026 0.232 6.16 0.000Technicians and Associate Professionals 1.961 0.283 4.66 0.000Clerks 2.231 0.283 6.32 0.000Service Workers and Shop and Market Sales Workers 3.289 0.374 10.47 0.000Farmers, Forestry Workers and Fishermen 1.749 0.233 4.2 0.000Trades and Related Workers 5.193 0.582 14.7 0.000Plant and Machine Operators and Assemblers 3.486 0.419 10.38 0.000Laborers and Unskilled Workers 2.287 0.259 7.32 0.000Special Occupation 4.458 0.436 15.3 0.000constant 0.000 0.000 -62.63 0.000Number of obs 183713LR chi2(30) 6115.29Prob > chi2 0.0000Pseudo R2 0.2121legend: * p<.05; ** p<.01; *** p<.001

Table 6: Propensity Score MatchingMatching criterion n. treat. n. contr. ATT Std. Err. tNearest-neighbor 2886 35712 0.576 0.019 29.623Stratification 2886 183139 0.557 0.019 29.907

20

Figure 8: Histogram and Normal Density Plots of pscore for Migrants (1) and Non-Migrants(0)

050

100

150

0 .05 .1 .15 0 .05 .1 .15

0 1

Densitynormal pscore

Den

sity

Estimated propensity score

Graphs by =1 if REC_TYPE==3

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Table 7: Instrumental Variable Regression2SLS 3SLS-1 3SLS-2

coef se coef se coef seOverseas 1.975*** 0.103 0.397** 0.158 0.552*** 0.064Schooling 0.049*** 0.001 0.057*** 0.003 0.054*** 0.001Overseas*Schooling 0.001 0.005Experience 0.012*** 0.001 0.016*** 0.001 0.015*** 0.001Experience Squared -0.000*** 0.000 -0.000*** 0.000 -0.000*** 0.000Male 0.238*** 0.006 0.252*** 0.007 0.254*** 0.006Married 0.127*** 0.007 0.108*** 0.008 0.111*** 0.007REGION (National Capital Region as reference)Ilocos Region -0.256*** 0.015 -0.240*** 0.016 -0.247*** 0.014Cagayan Valley -0.397*** 0.015 -0.365*** 0.015 -0.370*** 0.014Central Luzon -0.134*** 0.012 -0.135*** 0.012 -0.137*** 0.012Bicol Region -0.263*** 0.015 -0.272*** 0.015 -0.271*** 0.015Western Visayas -0.438*** 0.013 -0.420*** 0.013 -0.423*** 0.013Central Visayas -0.282*** 0.014 -0.289*** 0.013 -0.291*** 0.013Eastern Visayas -0.306*** 0.016 -0.314*** 0.016 -0.314*** 0.016Western Mindanao -0.361*** 0.019 -0.371*** 0.018 -0.369*** 0.018Northern Mindanao -0.352*** 0.015 -0.356*** 0.015 -0.356*** 0.015Southern Mindanao -0.330*** 0.015 -0.336*** 0.015 -0.335*** 0.014Central Mindanao -0.364*** 0.015 -0.356*** 0.015 -0.354*** 0.015Cordillera Administrative Region -0.171*** 0.018 -0.153*** 0.018 -0.157*** 0.018Autonomous Region of Muslim Mindanao -0.293*** 0.029 -0.306*** 0.029 -0.310*** 0.028Caraga -0.309*** 0.017 -0.324*** 0.017 -0.321*** 0.017CaLaBaRZon -0.106*** 0.012 -0.089*** 0.012 -0.093*** 0.011MiMaRoPa -0.274*** 0.017 -0.280*** 0.017 -0.277*** 0.017OCCUPATION (Government Officials, Executives, Managers, and Supervisors as reference)Professionals 0.142*** 0.018 0.210*** 0.018 0.214*** 0.018Technicians and Associate Professionals -0.249*** 0.021 -0.211*** 0.020 -0.210*** 0.020Clerks -0.417*** 0.018 -0.367*** 0.018 -0.365*** 0.018Service Workers and Shop and Market Sal -0.701*** 0.017 -0.658*** 0.017 -0.662*** 0.017Farmers, Forestry Workers and Fishermen -0.802*** 0.023 -0.805*** 0.029 -0.823*** 0.022Trades and Related Workers -0.562*** 0.017 -0.512*** 0.019 -0.520*** 0.017Plant and machine Operators and Assembl -0.611*** 0.018 -0.580*** 0.019 -0.587*** 0.017Laborers and Unskilled Workers -0.833*** 0.016 -0.809*** 0.017 -0.816*** 0.016Special Occupation -0.397*** 0.019 -0.437*** 0.057 -0.492*** 0.019constant 3.027*** 0.026 2.913*** 0.037 2.943*** 0.024r2 0.464N 37,949 37,949 37,949legend: * p<.05; ** p<.01; *** p<.001

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Table 8: By DestinationOLS IV

coef se coef seOverseas 0.380*** 0.121 0.380*** 0.121Australia 2.061 1.506 2.061 1.505Canada 2.395*** 0.717 2.395*** 0.716Hongkong (dropped) 0.707*** 0.230Italy 2.178*** 0.695 2.178*** 0.695Japan 0.314 0.269 0.314 0.269Malaysia (dropped) -0.473 0.365Saudi Arabia 0.056 0.166 0.056 0.166Singapore 0.471 0.302 0.471 0.302Taiwan 0.035 0.351 0.035 0.351United States 0.906*** 0.238 0.906*** 0.238SCHOOLING 0.054*** 0.001 0.054*** 0.001Overseas*Australia (dropped) (dropped)Overseas*Canada (dropped) (dropped)Overseas*Hongkong 0.707*** 0.230Overseas*Italy (dropped) (dropped)Overseas*Japan (dropped) (dropped)Overseas*Malaysia -0.473 0.365Overseas*SaudiArabia (dropped) (dropped)Overseas*Singapore (dropped) (dropped)Overseas*Taiwan (dropped) (dropped)Overseas*UnitedStates (dropped) (dropped)Overseas*Schooling 0.017* 0.010 0.017* 0.010Australia*Schooling -0.141 0.115 -0.141 0.115Canada*Schooling -0.197*** 0.055 -0.197*** 0.055Hongkong*Schooling -0.071*** 0.020 -0.071*** 0.020Italy*Schooling -0.163*** 0.057 -0.163*** 0.057Japan*Schooling 0.004 0.022 0.004 0.022Malaysia*Schooling 0.019 0.033 0.019 0.033SaudiArabia*Schooling -0.021 0.014 -0.021 0.014Singapore*Schooling -0.052** 0.026 -0.052** 0.026Taiwan*Schooling 0.000 0.029 0.000 0.029UnitedStates*Schooling -0.067*** 0.019 -0.067*** 0.019Overseas*Australia*Schooling (dropped) (dropped)Overseas*Canada*Schooling (dropped) (dropped)Overseas*Hongkong*Schooling (dropped) (dropped)Overseas*Italy*Schooling (dropped) (dropped)Overseas*Japan*Schooling (dropped) (dropped)Overseas*Malaysia*Schooling (dropped) (dropped)Overseas*SaudiArabia*Schooling (dropped) (dropped)Overseas*Singapore*Schooling (dropped) (dropped)Overseas*Taiwan*Schooling (dropped) (dropped)Overseas*UnitedStates*Schooling (dropped) (dropped)Constant 2.943*** 0.023 2.943*** 0.023r2 0.486N 37,949 37,949legend: * p<.05; ** p<.01; *** p<.001

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