WPS6287
Policy Research Working Paper 6287
Impact Evaluation Series No. 78
Split Decisions
Family Finance When a Policy Discontinuity Allocates
Overseas Work
Michael A. Clemens
Erwin R. Tiongson
The World Bank
Europe and Central Asia Region
Poverty Reduction and Economic Management Unit
December 2012
Policy Research Working Paper 6287
Abstract
Labor markets are increasingly global. Overseas work migrant households. A purpose-built survey allows
can enrich households but also split them geographically, nonexperimental tests of different theoretical mechanisms
with ambiguous net effects on decisions about work, for the reduced-form effect. The study also explores how
investment, and education. These net effects, and reliably the reduced-form effect could be measured with
their mechanisms, are poorly understood. This study standard observational estimators. It finds large effects
investigates a policy discontinuity in the Philippines on spending, borrowing, and human capital investment,
that resulted in quasi-random assignment of temporary, but no effects on saving or entrepreneurship. Remittances
partial-household migration to high-wage jobs in appear to overwhelm household splitting as a causal
Korea. This allows unusually reliable measurement mechanism.
of the reduced-form effect of these overseas jobs on
This paper is a product of the Poverty Reduction and Economic Management, Europe and Central Asia Region. It is part
of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy
discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
The author may be contacted at etiongson@worldbank.org.
The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations
and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part
of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings,
interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the
International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of
the World Bank or the governments they represent.
Produced by the Research Support Team
Split Decisions: Family �?nance when a
policy discontinuity allocates overseas work
Michael A. Clemens∗ Erwin R. Tiongson
Center for Global World Bank,
Development AIM, and IZA
December 5, 2012
Sector board: POV (Poverty Reduction)
∗
JEL Classi�?cation Numbers: J61, O15, R23. We thank the Philippine Overseas Employment Admin-
istration (POEA) for their kind assistance, especially current and former POEA of�?cials and staff Patricia
Santo Tomas, Carmelita Dimzon, Hans Cacdac, Jennifer Manalili, and Helen Barayuga, as well as Vin-
cent Olaivar of the National Statistical Of�?ce. We thank the John D. and Catherine T. MacArthur Founda-
tion for generous support, and Paolo Abarcar and Tejaswi Velayudhan for excellent research assistance.
We received helpful suggestions from Jenny Aker, Christopher Blattman, William Easterly, Dean Karlan,
Jonathan Morduch, Sendhil Mullainathan, Kevin Thom, Shing-Yi Wang, Patricia Cort´ es and seminar par-
ticipants at NYU, Univ. of Michigan, Harvard Kennedy School, the Univ. of Minnesota, the Asian Dev. Bank,
the Asian Institute of Management, Ateneo de Manila, and Tufts Univ., but we are responsible for any er-
rors. The views expressed here are those of the authors only; they do not necessarily represent the views
of the POEA, the Center for Global Development, the World Bank, or any of their boards and funders.
SPLIT DECISIONS
1. Introduction
Overseas workers are expected to send home over US$600 billion per year by 2014, dou-
ble the amount just 8 years before (Ratha and Silwal 2012). This has raised interest in
how international migration might �?nance investment in developing areas.
But migration affects more than just incomes. Another important effect is that over-
seas jobs, unlike many local jobs, often disrupt and split households for extended pe-
riods. Substantial literatures have separately studied the effects of new income and
changes in household composition. These two effects are bundled in labor migration,
but research has only begun to accurately measure the net effects or gauge the rela-
tive importance of each mechanism. Existing efforts face major empirical challenges
in accounting for migrant self-selection.
We present estimates of the effects of temporary overseas work in a setting that allows
unusually reliable causal identi�?cation. We study a sample of the households of 25,320
workers in the Philippines who applied to high-wage temporary jobs in Korea between
2005 and 2007. Each applicant was required to pass a basic test in the Korean language,
and all those who failed were unable to take the job. In 2010 we surveyed the house-
holds of those who barely passed for comparison to the households of those who barely
failed. This regression discontinuity design allows uncommonly reliable attribution of
differences in those households to the effects of overseas work. Our purpose-built sur-
vey allows nonexperimental tests of impact mechanisms.
We contribute to the literature in three principal ways. First, we offer well-identi�?ed
estimates of the reduced-form effect of large increases in income for one member of
a low-income household bundled with the change in household composition that ac-
companies overseas work. Each of these bundled effects, separately, is the subject of an
active literature. Second, we explore theoretically and empirically the relative impor-
tance of these mechanisms for the effect of overseas work. Third, we offer evidence on
how the subjects of this natural experiment differ from representative Filipinos, in both
observed and unobserved traits. We explore unobserved differences by comparing our
1
CLEMENS & TIONGSON
quasi-experimental estimates with those we would have obtained with more common
observational estimates.
The results show that migration by one household member causes large increases in
remaining household members’ spending on health and education, quality of life, and
durables, but no increases in savings. Migration causes substantial reductions in bor-
rowing from family members outside the household. We �?nd no signi�?cant effect on
starting or investing in entrepreneurial activity or on labor suppy by other family mem-
bers. Migration by the applicant causes children to be much more likely to attend pri-
vate school and receive awards at school. Migration causes large changes in decision-
making power between household members, but nonexperimental evidence suggests
that most of the migration effect arises through the remittance mechanism rather than
through changing household decision-making. We do �?nd some evidence that migra-
tion affects home production technology, particularly in agriculture, by altering house-
hold composition. We �?nd that widely-used nonexperimental estimators of migration
effects can, in the same data, spuriously attribute causation to self-selection.
We begin by discussing the separate literatures on microeconomic effects of credit con-
straints and on household composition and decision-making, as well as the literature
on the bundle of these effects that arises from migration. The next section derives a
simple model of the migration effect and its mechanisms. The following sections dis-
cuss the natural experiment that created the policy discontinuity, and describe our new
survey data. We then present evidence on reduced-form impacts of migration, and
compare our quasi-experimental results to those obtained with more standard obser-
vational estimators. We conclude with a nonexperimental exploration of impact mech-
anisms, and a discussion of external validity.
2. Financial decisions in families split by work
Labor migration by one household member bundles different effects on the household.
Of these, two of the most important have each been the subject of a separate literature:
2
SPLIT DECISIONS
There is often an important change in household income that could alleviate credit
constraints, and there is a change to the migrant’s participation in household decisions
and in home production.
First, economists have long investigated how credit constraints shape household de-
cisions on savings and investment. Recent influential studies trace the effects of in-
creases in capital on investments in home production and new business.1 Others stress
the effects of credit constraints on investments in human capital,2 and on consumption
decisions.3 A common theme is that access to capital substantially alters all of these de-
cisions in many settings.
Second, and separately, economists have studied the effect of household composi-
tion and decision-making structure on �?nance and investment choices (Becker 1991;
Bergstrom 1997). An important strand of literature studies the effect of shifts in
decision-making power on household investments (broadly considered), especially
testing whether household savings and investment decisions change when direct con-
trol over income shifts from one member to another.4 A different strand traces the ef-
fects of parental absence—through one parent’s labor supply, departure, or death—on
investments in children’s human capital.5 These effects, too, are typically substantial.
Migration bundles these two effects.6 The combined effect has been the subject of a
1
These studies include Hubbard (1998); Hurst and Lusardi (2004); Udry and Anagol (2006); De Mel et
al. (2008); McKenzie and Woodruff (2008); Banerjee et al. (2010); Gertler et al. (2012); Wang (2012).
2
See for example Kane (1994); Blau (1999); Keane and Wolpin (2001); Carneiro and Heckman (2002);
Cameron and Taber (2004); Akee et al. (2010); Lochner and Monge-Naranjo (2011); Dahl and Lochner
(2012); Macours et al. (2012); Caucutt and Lochner (2012).
3
The effects of credits constraints on consumption choices is investigated by Hanushek (1986); Altonji
and Siow (1987); Paxson (1992); Morduch (1995); Case and Deaton (2001); Karlan and Zinman (2010); Du-
pas and Robinson (2012); Kaboski and Townsend (2012).
4
Research on the effects of intrahousehold shifts in power over income includes Duflo (2000); Qian
(2008); Agnew et al. (2008); Ashraf (2009); Ashraf et al. (2010).
5
Work on the effects of parental absence and household composition includes Blau and Grossberg
(1992); Angrist and Johnson (2000); Lang and Zagorsky (2001); Corak (2001); Gertler et al. (2004); Page and
Stevens (2004); Ruhm (2004); Gennetian (2005); Lyle (2006); Booth and Tamura (2009).
6
Migration certainly can affect households by other channels as well. In particular, economists have
studied the effect of social networks and information flows on household decisions about �?nance and
investment (e.g Jensen 2010; Alatas et al. 2012). This literature suggests that information on investment
returns often passes through social networks and can substantially influence household savings and in-
vestment decisions. We abstract from these effects for this study because the overseas jobs are short-term,
involve little social integration with people at the destination, and involve low-skill work unlikely to bring
3
CLEMENS & TIONGSON
recent and growing literature on the micro effects of migration, surveyed by Hanson
(2009) and Antman (2012). Though much of the early work on migration stresses the
effects of remittances alone,7 a new literature seeks the effects of migration itself, com-
prising both channels above.8
In this paper we contribute to these literatures by offering unusually well-identi�?ed
estimates of the reduced-form net effects of migration, and by theoretically-grounded
tests of the relative importance of different impact mechanisms. Much of the previous
research on reduced-form effects faces important challenges of causal identi�?cation
that are dif�?cult to address with the observational methods most commonly used. In
rare cases, researchers have been able to compare observational estimates of migration
effects to more reliably identi�?ed estimates, and have found large differences (McKen-
zie et al. 2010; Gibson et al. 2011). Moreover, existing research has only begun to sort out
the different mechanisms of migration impact. The latest work suggests that, beyond
remittances, also important are the ways that migration shapes household decision-
making (Ashraf et al. 2011) and perceived returns to investment (Kandel and Kao 2001;
McKenzie and Rapoport 2011).
3. Mechanisms of impact
We posit three principal channels by which labor migration by one household member
can affect household �?nance and investment decisions. First, new income can alleviate
constraints on borrowing and consumption smoothing. Second, changes in the loca-
tion of family members can affect their power to influence decisions. Third, changes in
the composition of the household back home can affect home production technology.
Each emerges from a simple dynamic optimization model of household savings and in-
migrants important changes in transferable skills, cultural attitudes, or technical knowledge.
7
Rapoport and Docquier (2006) and Yang (2011) survey this literature, which includes Cox Edwards and
Ureta (2003); Yang (2008); Alcaraz et al. (2012). A focus of this work has been the effect of remittances and
similar transfers on labor force participation by recipients (Rodriguez and Tiongson 2001; G¨ orlich et al.
2007; Ardington et al. 2009; Edmonds and Schady 2012).
8
Studies of the effects of migration on source households, including but not principally focusing on
remittances, include Hanson and Woodruff (2003); Cort´ es (2010); Macours and Vakis (2010); Taylor and
opez-Feldman (2010); Amuedo-Dorantes et al. (2010); Mergo (2012).
L´
4
SPLIT DECISIONS
vestment. We consider unitary and collective households, with and without borrowing
constraints.
3.1. Unitary household model
We begin modeling the migrant’s household as unitary (Blundell and MaCurdy 1999)
with an available investment that requires no labor input (Bardhan and Udry 1999, pp.
7–18). The two household members (1, 2) get utility from consuming a �?xed Hicksian
composite good c ≡ c1 + c2 and from leisure ( 1 , 2 ) over lifetime T . Member 1 can work
in the home country at wage w. Member 2 can migrate,9 to spend a fraction 0 m 1
of work time earning the overseas wage wo > w, thus earning overall wage
w∗ = mwo + (1 − m)w. (1)
There is pure disutility of having a family member overseas, captured by 0 φ 1,
analogous in modeling terms to Mof�?tt’s (1983) “welfare stigma�?. For given m it solves
T
max e−�?t u(c, 1
, 2
) − φm dt. (2)
c, 1 , 2 0
With borrowing constraints. Suppose, for the moment, that the household cannot bor-
row or lend. Capital evolves subject to
˙ = θf (k ) + w(1 −
k 1
) + w∗ (1 − 2
) − c. (3)
where θ reflects the productivity of some home production process—a family business,
a farm, or (more abstractly) the production of high-quality children (Becker 1991; Ba-
land and Robinson 2000; Caucutt and Lochner 2012) or even investment in migration
by other family members as a form of human capital (Schultz 1961; Sjaastad 1962; Con-
nell et al. 1976).10 We solve (2) and (3) as an autonomous program of optimal control,
9
Here m is an exogenous parameter, not a choice variable. The reason is that the sampling universe
of our survey comprises exclusively households that took serious steps to acquire overseas work; for all of
these households, the perceived optimal m is 1. From the household’s point of view, either m = 1 as they
desire or it is exogenously set to zero by forces beyond their control.
10
We have the standard boundary conditions kt 0, the shadow value of capital µ(T ) > 0, and
µ(T )k(T ) = 0. The subscript t is suppressed for clarity, and a superscript dot indicates the derivative
5
CLEMENS & TIONGSON
for which the Pontryagin conditions on the current-value Hamiltonian are u 1 = µw;
u 2 = µw∗ ; uc = µ; and µ
˙ = µ (�? − θf (k )), where the subscript denotes the partial
derivative. The �?rst two conditions imply
1 2
m >0 �?⇒ m 0. (4)
That is, non-migrants supply less labor (consume more leisure) due to migration by one
household member, provided that migration does not cause the migrant to consume
much more leisure. The equations of motion for c and 1 come from differentiating the
fourth Pontryagin condition and substituting for µ:
uc ˙1 = − u 1 ˙
w
˙=−
c θf (k ) − �? ; θf (k ) − �? − . (5)
u u w
Migration affects investment. Letting labor income net of consumption Ψ ≡ w(1 − 1 )+
w∗ (1 − 2) − c, then (3) gives
˙ m = θf km + (wo − w) Ψw∗ ,
k (6)
The �?rst term on the right side captures increased investment as the borrowing con-
strained household uses migration earnings to raise home production. The second
term captures the effect of migration earnings on wage income net of consumption,
which acts exclusively through raising wage w∗ . Each is a different aspect of a single
channel of the effect of migration on investment: via higher wages. Barring large de-
clines in labor supply caused by the new earnings, home production rises and all new
income is (necessarily) reinvested in it.
Without borrowing constraints. If we allow the household to borrow,11 the effect of
with respect to t. We assume u and f are concave, continuous, and twice differentiable.
11
The equation of motion (3) becomes k ¯ ) − r (k
˙ = θf (k ¯ − k) + w(1 − 1 ) + w∗ (1 − 2 ) − c, where
¯ = f −1 (r/θ) is the unconstrained optimal investment. Optimal consumption and non-migrant leisure
k
u
(5) now follow c ˙ = −u uc
(r − �?) and ˙1 = − u 1 r − �? − w w
˙
. In contrast to (5), if the capital market is
ef�?cient and frictionless (r ≈ �?) and the home wage is constant (w ˙ = 0), then non-migrant labor supply
does not change over time (because ˙1 = 0). The entire timepath of non-migrant labor supply can fall
due to foreseen migration, but non-migrant labor supply is unchanged at the moment that migration
raises w∗ . Intuitively, non-migrants in this circumstance choose consumption of leisure according to the
household’s permanent income. This implies that shorter spells of migration, with a foreseeable end, will
have a smaller effect on non-migrant labor supply.
6
SPLIT DECISIONS
migration on investment is
˙ m = rkm + (wo − w) Ψw∗ ,
k (7)
where the �?rst term on the right is the return to saving the new income in the bank.
¯)—where k
Home production is �?xed at f (k ¯ is unconstrained optimal investment—and
is unaffected by migration. Comparing (6) and (7) shows that the effect of migration
on investment is greater for borrowing-constrained households than for borrowing-
¯.
unconstrained households whenever home production is less than optimal, i.e. k < k
The unitary household model makes two key predictions: When one household mem-
ber migrates for work, 1) non-migrants reduce labor supply, to a degree that increases
with borrowing constraints and capital market imperfections; and 2) the household
raises investment, to a degree that increases as the household faces greater borrowing
contraints, solely because earnings rise.
3.2. Collective household model
The unitary household model has been criticized both theoretically (Chiappori 1988,
1992) and empirically (Alderman et al. 1995; Fortin and Lacroix 1997). A unitary model
seems especially inappropriate for households split between two countries. An alter-
native optimization program allows each household member an additively separable
egoistic utility term. For given m the household solves
T
max e−�?t u(c1 , 1
) + (1 − φm)u(c2 , 2
) dt. (8)
c1 ,c2 , 1 , 2
0
The term −φm captures not just the disutility of partial-household migration, but also
the corresponding change in the “balance of power�? between household members (as
in Basu 2006; Udry 1996; Bobonis 2009) when one member is long absent.
With borrowing constraints. The equation of motion for capital (3) is unchanged, but
now θ ≡ θ(m). That is, migration can change home productivity: it can raise home
productivity by bringing in new ideas or inspiration, or lower home productivity by
7
CLEMENS & TIONGSON
taking away individuals that determine home production. The Pontryagin conditions
for this collective household are u 1 = µw; (1 − φm)u 2 = µw∗ ; uc1 = µ; (1 − φm)uc2 = µ;
˙ = µ (�? − φ(m)f (k )). The �?rst two of these give
and µ
1 1 2
m > 0, mφ >0 �?⇒ m 0. (9)
Thus non-migrants still respond to migration by consuming more leisure, but now to a
degree that gets smaller when migration causes a smaller shift in the balance of power
(φ is smaller).
In the collective household, migration affects investment through the earnings channel
in (7), plus two additional channels:
˙ m = f km + f θm + (wo − w) Ψw∗ + Ψm .
k (10)
The �?rst term of (10) is identical to (6), but in the second term an additional effect
arises: the migrant’s absence can directly change the home production technology by
altering household θ. In the third term migration affects labor income net of consump-
tion by altering the wage w∗ , as in (6), but in the fourth term there is a new and indepen-
dent effect: Migration decreases the influence of the migrant in day-to-day household
decisions, changing the balance of decision-making power between migrant and non-
migrants.
Without borrowing constraints, f = r in (10). As in (7), migration raises investment by
more in borrowing-constrained households than in borrowing-unconstrained house-
¯). For the collective household, too, it can be shown in equations
holds (provided k < k
of motion analogous to (5) that the labor supply effect (9) only arises when there are
borrowing constraints or capital market imperfections.
The collective household enriches the models’ predictions:
First, migration by one member can still reduce labor supply by other members, but
this effect varies. We saw in the unitary household that the labor supply effect will be
8
SPLIT DECISIONS
smaller when borrowing constraints are smaller and capital markets more frictionless.
In the collective household, the labor supply effect also depends on the degree to which
migration causes household decision-making power to shift.
Second, migration can affect household saving and investment through three chan-
nels in the collective household, and the net effect need not be positive. In the unitary
household, only the migrant’s earnings affect saving and investment. In the collective
household, migration can separately affect investment in two other ways. Migration
can alter the balance of power to make saving and investment decisions (e.g. the re-
maining spouse has different consumption preferences). Migration can separately al-
ter the technology of home production (e.g. the remaining spouse is better or worse at
some home production activity such as farming).
4. A policy discontinuity in the Philippines
We identify these effects in a single setting with an unusual natural experiment. A large
group of Filipino applicants to high-wage jobs overseas—in Korea—were required to
pass a Korean language test. Large numbers of applicants either barely passed or barely
failed the exam, and those who failed could not migrate. Comparing applicant house-
holds in the passing and failing groups years later allows uncommonly reliable estima-
tion of the pure effect of migration on these households. This setting is well-suited for
the regression discontinuity design (RDD), which can approximate the causal identi�?-
cation offered by randomized experiments in real settings (Cook and Wong 2008).
4.1. A language test for high-wage jobs in Korea
In 2004, the government of the Philippines signed a bilateral agreement with the Re-
public of Korea allowing participation of Filipino workers in Korea’s Employment Per-
mit System (EPS). EPS issues temporary visas to work in Korea, visas today accessible
to workers from 16 developing countries across Asia. In the Philippines, EPS jobs are
advertised and recruitment takes place exclusively through the Philippine Oveseas Em-
9
CLEMENS & TIONGSON
ployment Administration (POEA) of the national government. EPS job contracts are
initially for three years, and are renewable up to �?ve years, but workers may not settle
in Korea.
EPS jobs are only accessible to people 18–39 years old, with either a high-school or vo-
cational degree and two years of work experience, or a tertiary degree and one year
of work experince. In Korea, most of the workers perform low-skill labor in small en-
terprises (fewer than 300 employees), almost all of which are manufacturing plants.
During 2008–2011 the average wage was about PHP35,000–38,000 per month (about
US$820–800). Employers pay for workers’ lodging and for some meals (usually daytime
meals only, but occasionally dinner as well). The typical one-time, all-inclusive cost
of starting an EPS-Korea job is approximately PHP25,500–32,500 (US$550–700), that is,
less than one month’s earnings.12
Starting with the second wave of workers, in 2005, all Filipino applicants to EPS jobs
were required to pass a Korean Language Test (KLT). This 90-minute, 200-question ex-
amination tests basic listening and reading in Korean. The test is administered at three
locations in the Philippines and graded in Korea. The maximum score is 200 points,
and a score of 120 points or greater is required to secure a work permit.
For the purposes of this study, there were �?ve EPS recruitment rounds in the Philip-
pines, each of which administered one KLT.13 Table 1 shows the number of people who
sat for each round of the KLT, and the numbers whose scores fell within �?ve points
of the 120-point cutoff. The large number of test-takers provides substantial density
12
This includes a one-time cost of PHP19,000–25,000 (US$410–540) for application fees and travel; this
comprises a PHP729 training fee, PHP1,500 medical examination fee, US$50 POEA processing fee, US$25
Overseas Workers Welfare Administration membership fee, PHP900 Philhealth/Medicare fee, PHP100
Home Development Mutual Fund membership (known as “Pag-Ibig �?), PHP2,500 visa fee, and PHP10,000
for one-way airfare to Korea (PHP16,000 for chartered flight). Beyond these POEA application fees are the
cost of the Korean language exam: This comprises a one-time KLT test fee of US$30. Many applicants also
take a preparatory course in Korean language, offered by numerous private teachers, which costs around
PHP5,000–7,000. The application fees, travel cost, test fee, and Korean language course costs sum to about
PHP25,500–32,500. In the years relevant to this study, the KLT was administered by the International Ko-
rean Language Foundation, at �?ve test centers across the Philippines (Manila, Pampanga, Baguio, Cebu,
and Davao). The KLT has since been superseded by the Test of Pro�?ciency in Korean (TOPIK), adminis-
tered by the Human Resources Development Service of Korea.
13
An additional EPS recruitment round occurred in 2004, before the KLT became a requirement, and
further EPS recruitment continued starting in mid-2010, after we conducted our survey.
10
SPLIT DECISIONS
near the cutoff, suggesting a regression discontinuity design to evaluate the effects of
migration on EPS job-applicants’ households. Migration by test-passers typically oc-
curred within a few months of the KLT, and our household survey occurred in early
2010. Households are therefore surveyed about 3–5 years after potential migration be-
gan.
4.2. The regression discontinuity design
We estimate the effects of migration with the regression discontinuity design or RDD.
Because RDD results can be sensitive to functional form assumptions, we use fully non-
parametric sharp and fuzzy RDD (Hahn et al. 2001; Porter 2003; Nichols 2007; Imbens
and Lemieux 2008). Because RDD results are also notoriously sensitive to bandwidth
selection, we tie our hands with the asymptotically optimal bandwidth recently pro-
posed by Imbens and Kalyanaraman (2012).14
For each outcome µ we �?rst estimate the intent-to-treat (ITT) effect (where treatment
τ is migration) with sharp nonparametric RDD. This is the effect of barely passing the
language test on “compliers�? (Angrist et al. 1996)—those whose migration decisions
were changed by the test result—whose families were willing to complete the survey,
and who had scores near the passing threshold s = 0:15
ITT = µs↓0 − µs↑0 . (11)
We then estimate the treatment-on-treated (TOT) effect as fuzzy nonparametric RDD.
This is the effect of migration by a household member:
µs↓0 − µs↑0
TOT = . (12)
τs↓0 − τs↑0
This again is the local average treatment effect on “compliers�?, whose households com-
14
We use the triangular kernel shown optimal by Fan and Gijbels (1996) and Cheng et al. (1997) for its
boundary properties; Lee and Lemieux (2010) argue that the choice of kernel “typically has little impact in
practice.�?
15
For individual i the outcome is µi , treatment status is τ i ∈ {0, 1} where treatment is migration, and
s ≡ [raw score] − 120. Then µs↓0 ≡ lims→0+ Ei [µi ]; µs↑0 ≡ lims→0− Ei [µi ]; τs↓0 ≡ lims→0+ Ei [τ i ]; τs↑0 ≡
lims→0− Ei [τ i ].
11
CLEMENS & TIONGSON
pleted the survey, and whose scores were near the passing threshold.
4.3. Checking the discontinuity: Sampling universe
Three testable conditions are necessary (not suf�?cient) for the test-passing threshold
to be useful in identifying the effect of migration. First, at the threshold, there must
be a large discontinuity in the treatment variable: deployment of the worker to Korea.
Second, there must be no discontinuity in baseline traits of the job applicants. Such a
discontinuity would suggest self-selection across the threshold. Third, there must be
no bunching of test-score density above or below the threshold. This would suggest
that test-takers are able to manipulate their scores—either through legitimate means
(putting extra effort when they know they’re about to barely fail) or illegitimate (such as
paying bribes for extra points).16
In the sampling universe there is a very large jump in deployment probability at the
cutoff score, but little evidence of a signi�?cant discontinuity in the baseline character-
istics of the job applicants. Table 2 shows that there is a jump of 68 percentage points
in the probability of deployment at the cutoff score.
The rest of the table tests for discontinuities in all known baseline characteristics of the
job applicants: age, sex, education, work experience, employment, civil status, and test
batch. None exhibit statistically signi�?cant jumps at the cutoff score. Figure 1 shows
some of these results in graphical form, displaying both an unsmoothed average at each
test score (gray circle) and a local linear regression. The upper-left panel shows the
jump in deployment probability. The upper-right and lower-left panels show the lack
of discontinuity in baseline education and employment of the applicant.
Were test-takers able to self-select across the discontinuity? First, there is no statisti-
cally signi�?cant jump in test-score density at the passing threshold. Figure 2 shows the
16
Clean identi�?cation with RDD requires other assumptions that we cannot test but that appear plau-
sible in this case. It requires there to be no “bitterness�? effect of barely failing the exam, so that some
outcome could be attributable to having come very close to passing without passing. In this case we con-
sider it unrealistic for families’ �?nancial decisions years later to depend substantially on such an effect.
12
SPLIT DECISIONS
McCrary (2008) nonparametric test for manipulation of the test score variable. While
there is an increase in the density at the passing threshold, it is small in magnitude, not
statistically signi�?cant, and well within the observed variance in score density at nearby
levels. This is reassuring but does not per se rule out self-selection. Second, in all of the
analysis to follow, the test score we use is exclusively the test score from each worker’s
�?rst attempt to pass the KLT. A small number of failers re-took the test in later rounds,
and if we were to use scores from subsequent attempts, this would raise the possibility
of workers self-selecting across the passing-score cutoff.17 Third, the test was adminis-
tered and scored by a Korean institution and we are not aware of any substantial reports
of corruption or other irregularities in scoring or record-keeping.
5. New survey data
We conducted a new, purpose-built survey of the households of EPS-Korea job appli-
cants who has scored near the passing threshold. Survey teams visited the households
in February and March of 2010. Any knowledgeable respondent present at the time of
the survey team’s visit was allowed to complete the survey.18
5.1. Sampling strategy
In order to ensure that the sample was as representative as possible of the sampling
universe, we provided target addresses to the survey �?rm in stages. First, we gave only
the addresses of households whose applicant was within one point of the cutoff score.
Only after the �?rm had attempted to contact all of those households did we provide a
second set of target households whose applicant was within two points of the cutoff.
We proceeded in this fashion, one point at a time, until our resources for conducting
17
Because the test-score we use is only the �?rst-attempt score, a small number of those with failing
scores are deployed to Korea. These are people who failed the �?rst attempt but passed on subsequent
attempts. This is of concern to external validity, but not internal validity.
18
Cull and Scott (2010) show that survey responses on the �?nancial life of Ghanaian households pro-
vided by knowledgeable respondents are as good as full household enumeration and better than responses
from randomly selected respondents.
13
CLEMENS & TIONGSON
the survey were exhausted (which occurred at 5 points from the cutoff).19
The survey was “blind�? in two senses. First, no one at the survey �?rm knew which
households were those whose member passed and which were those whose member
failed. The �?rm was only given the name, permanent address, and phone number of
the applicant. Second, the survey enumerators and respondents were told only that
the study was a “follow-up survey on families of POEA job applicants�?. Neither enu-
merators nor respondents were told that a goal of the study was to identify the effect of
migration by a household member on the household.
Rough power calculations before the survey suggested a target sample size of roughly
900 households.20 In the end, enumerators attempted to locate the permanent ad-
dresses of 2,053 EPS applicants, of which they successfully located and visited 1,532. Of
these visits, 899 (59%) resulted in a completed survey. The rest were nonresponders—
either because the residents declined to complete the survey (9%), no one was home or
the residents were not the applicant’s family (15%), or neighbors indicated that the ap-
plicants’ family had once lived there but had moved away (17%) or died (0.1%). All 899
completed surveys represent the households of applicants who scored within 5 points
of the cutoff.
5.2. Checking the discontinuity: Survey sample
Because only 59% of located addresses produced a completed survey, we must check
for bias due to nonresponse. For example, if an important use of remittances were to
purchase a new residence and move away, passing the test itself may affect the response
rate. This could produce differences across the passing threshold not attributable to
passing the test or migration.
19
An alternative method might have led to a less representative sample. For instance, if we had provided
all of the target addresses at once, the survey �?rm might have visited households that were easier to reach
but further from the discontinuity before attempting to contact all households near the discontinuity.
20
Following Duflo et al. (2007) we estimated that 900 households would give us a 92% chance of detect-
ing a 10 percentage-point difference in household-level school enrollment fraction, a 94% chance of de-
tecting a 10 percentage-point difference in the fraction of household engaged in entrepreneurial activity,
and a 93% chance of detecting a change of 0.2 in household-level ln remittances (all at the 5% signi�?cance
level).
14
SPLIT DECISIONS
The lower-right panel of Figure 1 checks for a discontinuity in this response among
households in the sampling universe near the passing threshold. Barely passing the
test did not cause an applicant’s household to be statistically signi�?cantly more or less
likely to complete the survey.
It is nevertheless possible that passing the test altered the composition of households
completing the survey. For this reason we repeat the analysis of Table 2 on the sur-
vey sample in Table 3. The �?rst rows show that there is a very large discontinuity in
household-level migration exposure at the test-score discontinuity. A graphical repre-
sentation is shown in the upper-left panel of Figure 3. The rest of Table 3 tests whether
there is a discontinuity at the threshold score in applicants’ baseline traits, among
households in the survey sample.
There is no statistically signi�?cant difference in any of the baseline traits at the thresh-
old in the survey sample. A representative row of the table is shown graphically in the
upper-right panel of Figure 3: There is no discontinuity in the responding households’
applicants’ baseline education levels. The rows of Table 3 on geographic location are
shown graphically in the maps of Figure 4 (nationwide) and Figure 5 (zoomed in to the
National Capital Region). These maps show that the locations of the 899 households in
the survey sample are similar on both sides of the discontinuity.
Collectively, this evidence suggests that having a household member barely pass the
test is a strong source of exogenous variation in household exposure to having that
member work in Korea.
6. Quasi-experimental evidence on reduced-form impacts
We now report sharp (ITT) and fuzzy (TOT) nonparametric RDD regressions using the
job applicant’s Korean Language Test score as the running variable. We consider �?rst
the effects of test-passing and migration on households, then the effects on individual
adults, and �?nally the effects on individual children. The �?rst column of each table
15
CLEMENS & TIONGSON
shows the average outcome for test-failers approaching the cutoff score. “Treatment�? is
de�?ned as a household in which any member ever worked in Korea. De�?ning treatment
in this way, rather than as current presence of a household member in Korea, prevents
self-selected return migration from being a source of endogenous treatment.
6.1. Effects on households
Table 4a shows household-level effects on family composition and income. Unless oth-
erwise stated, household members are considered members even when abroad. There
are no statistically signi�?cant effects on the number of total household members, or
on the number who are working age, less than working age, or more than working age.
When household members in Korea are not included in household size, we cannot re-
ject the hypothesis that the TOT effect of migration is –1. Migration by the job applicant
causes a 21 percentage point rise in the fraction of working-age household members
(excluding those in Korea) who are female.
In the middle rows of the table, there is no statistically signi�?cant effect on three sep-
arate measures of the total income of the household (excluding members in Korea): a
dummy for nonzero income, the value of income (in pesos per month), and the natural
log of income. A graphical representation of the ln income regression is in the lower-left
panel of Figure 3. Migration by the job applicant causes a substantial rise in remittance
income that is mostly or fully offset by causing a decline in non-remittance income.
Remitters appear to send roughly the same amount that they would be contributing to
household income if they were working in the Philippines.
At the bottom of the table, we see no evidence of effects on entrepreneurial activity—
whether measured as a dummy for having any income from entrepreneurial activities,
or the natural log of that income—in the agricultural or non-agricultural sectors. There
is suggestive evidence that migration causes a decline in the fraction of households
engaged in farming, but this is statistically imprecise (p = 0.14).
Table 4b shows household-level effects on spending, saving, investing, and borrow-
16
SPLIT DECISIONS
ing by the household members who remain in the Philippines. Migration by the job-
applicant causes monthly expenditures to rise substantially. The effect on overall peso-
value expenditures is on the order of 60% and signi�?cantly different from zero at the
3% level; the effect in ln pesos is about 30% but not statistically precise. We can-
not reject the hypothesis that none of this increase comes from increased spending
on food. Rather, the increase principally comprises a 30–60% rise in “quality of life�?
spending, a 68–150% rise in “health and education�? spending, and a 91–146% rise
in “durable goods�? spending.21 Migration does not affect average monthly savings
when households with zero savings are included, but among households who have
nonzero monthly savings, there is statistically imprecise evidence of a 20% rise in sav-
ings (p = 0.11).22
The remaining rows of the table show that migration by the job applicant causes house-
holds to borrow less from family members in the 6 months preceding the survey. There
is no statistically signi�?cant effect on borrowing from sources outside the family. There
is no effect on whether or not the family owns its residence, nor on the number of bed-
rooms in the residence.
We note important differences between the effects of migration on reported income
and reported expenditure. Migration causes remittances at a level that roughly replace
the cash income that migrants would have brought to the household if they had not mi-
grated. This includes, if the survey question is correctly answered, in-kind remittances
such as purchased gifts. But migration causes increases in expenditure well beyond
these reported increases in income, without appearing to cause increases in borrow-
ing.
We believe this disparity is caused by underreporting of speci�?c types of income due to
dif�?culties in eliciting information from survey respondents. For example, if a migrant
21
These ranges represent the linear peso and ln peso results, respectively. The linear peso results are
statistically precise at the 3% level or below for “quality of life�? and “education & health�?, and the ln peso
results are signi�?cant at the 12% level or below for all three categories.
22
“Food�? = food, beverages, and tobacco. “Health & educ.�? = school, medicine, and medical care. “Qual-
ity of life�? = fuel, transportation, household & personal care, clothing, recreation, family occasions, gifts.
“Durables�? = durable goods, taxes, home improvement. “Savings�? includes deposits in banks, paying off
loans, extending loans.
17
CLEMENS & TIONGSON
used overseas earnings to purchase a motorcycle for the household on a home visit, the
survey respondent might not think of this as an in-kind remittance—it was not brought
from Korea—but is likely to report it when asked about major purchases the household
made recently. For many related reasons the literature broadly considers the economic
well-being of the poor to be more accurately reflected by expenditure and consumption
measures rather than income measures, in both developed and developing countries
(e.g. Chen and Ravallion 2007; Meyer and Sullivan 2008).
6.2. Effects on individual adults
Table 5 presents impacts of migration on individual adults: applicants, applicants’
spouses only, and all non-applicants (including spouses).
Migration does not cause a signi�?cant change in the fraction of applicants who are
employed at the time of the survey. Treatment (the applicant ever migrated to Korea)
causes an 84 percentage-point increase in the probability that the applicant is in Korea
at the time of the survey (a small portion had migrated and then returned). Having
ever migrated to Korea causes a 59 percentage point increase in the probability that the
applicant is anywhere outside the Philiippines at the time of the survey.
Migration by the applicant has no discernible effects on labor force participation by the
applicant’s spouse—whether measured as a dummy for working, the number of days
worked, a dummy for any wage earnings, or the natural log of wage earnings.
The bottom portion of the table considers all non-applicant adults, including appli-
cants’ spouses. There is likewise no effect of the applicant’s migration on their labor
force participation, by any measure. There is suggestive evidence that migration by the
applicant causes a 4 percentage point increase in the probability that a non-applicant
adult in the same household is in Korea, but this is not statistically precise (p = 0.12).
Migration by the applicant does not cause any changes in adults’ years of education,
visits to health facilities, or visits to private health facilities in particular.
18
SPLIT DECISIONS
Table 6 shows the impacts on household decision-making by applicants. Household
survey respondents were asked who bears the principal responsibility for household
decisions in �?ve areas. They could answer themselves, another identi�?ed member, or
shared decision-making by themselves and another identi�?ed member. The outcome
variable in this table is an indicator for whether or not the job applicant is a principal
or shared decision-maker in each area.
Migration by the applicant causes important changes in how household decisions are
made. It causes decision-making by the applicant to decline in all areas, though these
declines are only statistically signi�?cant in the case of “major purchases�? and “week-
end activities�?. The lower half of the table considers only applicants who were married
at baseline. For them, the magnitude of the declines is much larger, and they are statis-
tically signi�?cant for “childcare�?, “major purchases�?, and “weekend activities�?. A graph-
ical representation of the “childcare�? row is shown in the lower-right panel of Figure 3.
6.3. Effects on individual children
Table 7 shows the effects on children. The table considers household members under
age 18 who are the children of the applicant or the applicant’s spouse.
Considering only those of schooling age ( 6), migration does not affect school enroll-
ment. This is to be expected given that over 98% of children in this age group are already
enrolled. Migration does cause a 41 percentage point increase in the probability that
a child is in private school (from a base of 28 percent). Migration also causes a 30 per-
centage point increase in the fraction of children who are receiving awards at school.
This could be due to better performance in school, or due to a different propensity for
private schools to give awards.
Considering children of all ages, migration by the applicant does not cause changes in
the probability that these children visited a health facility in the previous month. There
is suggestive evidence that it causes more of those visits to be at private health facili-
ties, but this is imprecisely measured (ITT p = 0.16, TOT p = 0.18). Migration by the
19
CLEMENS & TIONGSON
applicant does not affect the probability that children are working, the probability that
anyone in the household reads to the child in the evenings, or the years of education
that the respondent wishes the child to someday attain.
We explore heterogeneous reduced-form impacts by pre-treatment subgroups in Ap-
pendix section A1.
6.4. Selection bias and nonexperimental reduced-form results
Could the preceding effects have been well-identi�?ed without the quasi-experiment we
use? It is plausible that households with migrants differ in many ways from households
without migrants, ways that might not be the result of migration. If all such differences
were observable, quasi-experimental methods like RDD would have less value. Here
we test for differences in unobservable determinants of outcomes between the survey
sample and the national population.
We follow LaLonde (1986), Smith and Todd (2005), McKenzie et al. (2010), and others in
constructing analogous nonexperimental tests of migration treatment effects by using
the nationally representative data in Table 10 to construct a synthetic control group.
That is, we create a new dataset that retains only treated households from the survey
sample and stacks them with all households in the nationally representative sample.23
We then estimate treatement effects with ordinary least squares (OLS) and propensity
score matching (PSM), for comparison to the RDD estimates.
Table 8 shows this exercise for selected RDD regressions. The �?rst column simply repro-
duces the ITT result from RDD analysis in an earlier table. The second column shows
the coef�?cient on the treatment variable in an OLS regression including numerous con-
trols. If we could not carry out a quasi-experimental analysis, it might be tempting to
think that these controls capture much of the important economic difference between
23
This plausibly assumes that the fraction of Filipino households that have had a member in Korea is
very small. The FIES-LFS data contain an indicator of whether or not household members are currently
abroad, but do not contain information on speci�?c destinations nor on past migration experience.
20
SPLIT DECISIONS
households that self-select into this form of migration and those that do not.24 The
next three columns use PSM estimators with nearest-neighbor matching on 2, 5, and
10 neighbors. The matching variables are identical to the control variables in the OLS
column. The �?nal column uses PSM with Mahalanobis-distance matching.
The observational estimators can lead to estimates of the “effects�? of migration that are
spurious. The �?rst row shows that these observational estimators only capture about
half of the effect of migration on health and education spending. This is reasonable
since Table 10 shows that households that self-select into applying for these jobs have a
greater propensity to invest in adults’ and childen’s human capital than typical house-
holds. The next row shows that the observational estimators exaggerate the magni-
tude of the effect on running a business—again reasonable since households whose
members apply to jobs in Korea are much less likely to run a business, for reasons that
can include unobservable traits. The last two rows show that the observational esti-
mates spuriously �?nd positive effects of migration on children’s school enrollment and
adults’ education levels. Again this is likely because the households in the sample place
a greater emphasis on education than other households, for reasons that are in part un-
observable.
The success of these nonexperimental estimators obviously depends on the control
variables chosen, but whether any given set of controls is adequate is in most settings
untestable. Here we show that a plausible set of controls is inadequate to control away
large amounts of unobserved difference between the true control group and the syn-
thetic control group.
7. Nonexperimental evidence on mechanism of impact
By what mechanism do these effects arise? Above we offered theoretical support for
three candidates in a dynamically-optimizing, credit-constrained, collective house-
24
These controls are: household size, HoH (Head of Household) age, HoH years educ., plus dummies
for HoH female, HoH married, standalone house, family owns residence, strong wall materials, strong roof
materials, and three regions (one region omitted).
21
CLEMENS & TIONGSON
hold: Migrants’ earnings can alleviate credit constraints on consumption and invest-
ment, migrants’ absence can alter their relative power over �?nancial decisions, and mi-
grants’ absence can change the skill and technology of home production.
We offer suggestive, nonexperimental tests of these hypotheses following the logic of
Baron and Kenny (1986), extended theoretically by Imai et al. (2011) and extended em-
olich (2007). These tests are not well-
pirically to the case of nonparametric RDD by Fr¨
identi�?ed tests of the different causal mechanisms because, in the terminology of Imai
et al., they fail the sequential ignorability assumption: even if the migration treatment is
plausibly exogenous, the degree of exposure to different treatment mechanisms within
the treatment or control groups may not be exogenous. It is nevertheless informative
to explore correlations between the degree of treatment effect and observables that sig-
nify treatment via single mechanisms.
Table 9 conducts these mechanism tests. The �?rst three columns simply reproduce se-
lected TOT regressions from Tables 4a, 4b, 5, and 7. The center trio of columns include
as a covariate the household’s ln monthly remittance income. The rightmost trio of
columns include as covariates �?ve indicator variables for whether the applicants is a
principal or joint decision-maker in the �?ve areas of Table 6.
The patterns in Table 9 suggest that remittances are far and away the most important
mechanism of the migration effect. In the �?rst three rows, almost none of each effect
on spending is accounted for by controlling for changes in decision-making power, but
all of each effect is accounted for by controlling for remittances. In the following three
rows, the same is true for the effect on borrowing from family members: controlling
for changes in decision-making patterns does not substantially alter the result, while
controlling for remittances eliminates the statistical signi�?cance of the result.
Continuing down the table, controlling for changes in decision-making does little to
alter the treatment effect while controlling for remittances greatly alters the effect. In
some cases, remittances appear to be the reason for the migration treatment effect:
Migration causes more children to be in private school, but the result vanishes after
22
SPLIT DECISIONS
controlling for remittances. In other cases, remittances appear to be the reason why
migration does not affect the outcome: Migration has no statistically signi�?cant effect
on whether the household receives income from farming, but after controlling for re-
mittances, migration has a signi�?cant negative effect on farming activity. The effect is
intuitive: If the household member who would do most of the farming is abroad, farm-
ing can only continue if remittances pay for someone else to do it. Likewise, the appli-
cant’s migration has no effect on days worked by non-applicants, but after controlling
for remittances, the effect is signi�?cant and positive. This also is to be expected: If a
breadwinner is away but does not send remittances, other household members must
work more to supplement income.
The fact that migration affects agricultural activity—controlling for remittances—is ev-
idence that migration substantially alters investment in home production by altering
the technology of home production. It is not de�?nitive evidence, in part because the
change in coef�?cients is not statistically precise. The opposite pattern is seen in the
effects of migration on non-agricultural business: the magnitude of the negative co-
ef�?cient on migration greatly diminishes when remittances are controlled for (though
the change in coef�?cients is also not statistically precise). This pattern could arise be-
cause remittances decrease the propensity for remaining family members to start non-
agricultural businesses. But this pattern is not compatible with important effects of
migrant absence per se on non-agricultural entrepreneurial activity. This form of mi-
gration may alter home production by removing from the household the person who
would otherwise be engaged in farming, but does not appear to remove the person who
would otherwise be starting or operating a non-farm business.
8. External validity
The survey sample is far from representative of all Filipinos. The treatment effects we
measure are estimates of the effect on 1) households of people who applied for this
type of job in Korea, 2) who scored near the cutoff, 3) whose migration decisions were
altered by their test score, and 4) whose households yielded a completed survey.
23
CLEMENS & TIONGSON
Table 10 explores how the households of test-failers in the survey sample differ from
the same outcomes in a nationally-representative survey conducted by the Philippine
government.25 We leave out test-passers so as to remove the effects of EPS-Korea mi-
gration.
Households in the survey sample are much more likely to already have a member
abroad than typical households in the Philippines. Sample households have some-
what more income (about 35% more) than typical households, a difference entirely ac-
counted for by the fact that they have more remittance income. Sample households are
less likely to have monthly savings (and when they save, save less), are much less likely
to have businesses, and live in somewhat better-quality houses. They are more likely to
be in Luzon. Their heads of household are younger and have 3.5 years more education,
and their children are 12 percentage points more likely to be in school.
In short, relative to the country as a whole, the survey sample captures households that
have similar incomes in the absence of remittances, have more experience with migra-
tion and thus somewhat higher incomes due to remittances, are more likely to invest in
human capital and work for wages than to run a business, and save less. The broad pat-
tern is that households in the survey sample emphasize investments in human capital
(education, migration) over physical capital (entrepreneurship, savings).
9. Conclusion
We �?nd that migration from the Philippines to temporary jobs in Korea has important
effects on migrants’ households. These are theoretically and empirically different from
the effects of remittances, as remittances are just one portion of the bundled treatment
that is migration. For example, Yang (2008) �?nds that remittances to the Philippines
encourage some types of entrepreneurial activity, conditional on the household already
25
We use a household-matched nationally representative sample from the 2006 Family Income and Ex-
penditure Survey (FIES) and Labor Force Survey (LFS). 2006 is the most recent matched FIES-LFS micro-
data publicly-available from the National Statistical Of�?ce at the time of writing. We inflate all peso �?gures
from 2006 to 2010 using the Consumer Price Index.
24
SPLIT DECISIONS
having a migrant. This is compatible with our �?nding that migration has no signi�?cant
effect on household entrepreneurial activity, since migration is a different treatment: It
both puts remittances into the household and takes potential entrepreneurs out of the
household.
The model predicts that in unitary households, migration affects investment behavior
solely by raising earnings—increasing self-�?nance and alleviating any borrowing con-
straints. In collective households, there are two additional channels: migration can al-
ter the balance of power in household decisionmaking and can alter the technology of
home production. We �?nd that the most important of these are far and away the �?nan-
cial effects, suggesting that the simplicity of the unitary household model is adequate
to explain the most important economic impacts of migration in this setting. While
migration causes large changes in how household decisions are made, these changes
explain almost none of the important impacts of migration on spending, borrowing,
or investment. There is suggestive but statistically imprecise evidence that the col-
lective household is relevant: migration does appear to alter the technology of home
production, perhaps by drawing breadwinners out of farming, though this evidence is
statistically imprecise.
We �?nd no evidence to support any effect of migration on labor force participation by
the spouses or other family members of migrants. The model provides potential expla-
nations for this result: the predicted effect of migration on others’ labor force partic-
ipation is smaller when borrowing constraints are smaller. Households in the sample
borrow extensively and the increase in income accompanying migration causes them
to borrow less, not more. They may therefore face small borrowing constraints, though
we do not have direct evidence of this.
The above �?ndings are compatible with the households surveyed being credit-
constrained human capital investors: Migration causes greater private schooling for
children, more awards at school, and greater household expenditure on health and
education. The �?ndings are not broadly compatible with these households being
credit-constrained physical capital investors: Migration has no signi�?cant effects on
25
CLEMENS & TIONGSON
entrepreneurial activity—except perhaps drawing some families’ breadwinners out of
farming. It does not raise savings, but causes borrowing to markedly decrease.
Our research design cannot answer several questions about the effects of migration. It
cannot measure how the effect depends purely on the gender of the migrant for theoret-
ical reasons (women who self-select to apply for an overseas job could be quite different
from men who do so) and empirical reasons (the applicant is female in only 179 [20%]
of our sampled households). Our design also cannot measure any external effects, pos-
itive or negative, on other households—households from which no member applied to
an EPS-Korea job. It cannot measure the effect of strategic decisions made prior to mi-
gration caused by foresight of the future option to migrate (Batista et al. 2012; Jensen
and Miller 2012). And it cannot reliably measure the effects of migration experience on
return migrants (Reinhold and Thom 2011), because return migrants are self-selected
from current migrants.
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31
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Table 1: The Korean Language Test
5 pts from
Batch Date Total # cutoff score
1 Sep 2005 411 56
2 Nov 2005 2,811 435
3 Jun 2006 6,110 1,045
4 Oct 2006 7,586 1,291
5 May 2007 8,402 589
Total 25,320 3,416
32
SPLIT DECISIONS
Table 2: Checking discontinuity for 23,448 households in sampling universe
band-
Outcome µs↑0 µs↓0 − µs↑0 s.e. p-val. width
Migration behavior after application
Applicant deployed? 0.0175 0.678 0.0284 < 0.001 2.113
Traits of applicant at the time of application
Age 30.21 −0.270 0.387 0.486 4.428
Female 0.210 0.000834 0.0548 0.988 2.073
College grad. 0.326 0.0397 0.0632 0.529 2.149
Months experience 70.56 −2.536 3.456 0.463 8.112
Employed 0.291 0.0103 0.0609 0.865 2.211
Married 0.447 −0.0491 0.0666 0.460 2.203
Test batch 1 0.0244 −0.0131 0.0102 0.199 1.610
Test batch 2 0.155 −0.0317 0.0483 0.511 2.151
Test batch 3 0.279 0.0647 0.0602 0.282 2.381
Test batch 4 0.368 0.00516 0.0647 0.936 2.413
Test batch 5 0.174 −0.0247 0.0504 0.625 2.165
Data for households in survey sample. Nfail(s<0) = 12, 577, Npass(s 0) = 10, 871.
µs↑0 is the mean of the local regression using data for test-failers only, evaluated at s = 0.
Optimal bandwidth selected by the method of Imbens and Kalyanaraman (2012).
Triangular kernel.
33
Figure 1: Discontinuities in sampling universe
0.5 1.0
Applicant deployed
Applicant college grad.
0.0
-0.5 0.0 0.5 1.0 1.5
-100 -50 0 50 100 -100 -50 0 50 100
Points above cutoff Points above cutoff
N = 23448, bandwith = 2.113 N = 23448, bandwith = 2.149
34
1.0
1.0
CLEMENS & TIONGSON
0.5
0.5
0.0
Completed survey
Applicant employed
0.0
-0.5
-100 -50 0 50 100 -4 -3 -2 -1 0 1 2 3
Points above cutoff Points above cutoff
N = 23448, bandwith = 2.211 Good addresses only. N = 1532, bandwith = 2.360
SPLIT DECISIONS
Figure 2: McCrary (2008) nonparametric test for score manipulation
0.020
0.015
Density
0.010
0.005
-40 -20 0 20 40
Points above cutoff
35
CLEMENS & TIONGSON
Table 3: Checking discontinuity for 899 households in survey sample
band-
Outcome µs↑0 µs↓0 − µs↑0 s.e. p-val. width
Migration behavior after application
Applicant deployed? 0.0172 0.683 0.0407 < 0.001 1.157
Anyone now in Korea? 0.155 0.402 0.0540 < 0.001 1.379
Anyone ever in Korea? 0.241 0.480 0.0551 < 0.001 1.307
Anyone now abroad? 0.422 0.256 0.0608 < 0.001 1.560
Anyone ever abroad? 0.672 0.199 0.0522 < 0.001 1.801
Traits of applicant at the time of application
Age 30.16 0.450 0.556 0.418 1.768
Female 0.216 0.00591 0.0521 0.910 1.941
College grad. 0.345 −0.0305 0.0593 0.607 1.716
Months experience 64.27 10.93 6.808 0.108 1.472
Employed 0.216 0.0416 0.0533 0.435 1.956
Married 0.460 0.0246 0.110 0.823 2.373
Region: NCR 0.198 −0.0340 0.0487 0.485 1.834
Region: Luzon 0.681 0.0118 0.0585 0.840 1.857
Region: Visayas 0.0862 0.0138 0.0365 0.706 1.844
Region: Mindanao 0.0345 0.00837 0.0242 0.729 1.825
Test batch 1 0.0259 −0.0187 0.0164 0.255 1.819
Test batch 2 0.144 −0.0755 0.0775 0.329 2.268
Test batch 3 0.276 0.0710 0.0991 0.474 2.245
Test batch 4 0.366 0.00947 0.107 0.929 2.413
Test batch 5 0.181 −0.00961 0.0481 0.842 1.911
Data for households in survey sample. Nfail(s<0) = 460, Npass(s 0) = 439.
µs↑0 is the mean of the local regression using data for test-failers only, evaluated at s = 0.
NCR = National Capital Region. “Luzon�? omits NCR. “Anyone�? means any household member.
Optimal bandwidth selected following Imbens and Kalyanaraman (2012). Triangular kernel.
36
Figure 3: Discontinuities in survey sample
1.0
1.0
0.5
0.5
Member ever in Korea
Applicant college grad.
0.0
0.0
-6 -4 -2 0 2 4 -6 -4 -2 0 2 4
Points above cutoff Points above cutoff
All HH, N = 899, bandwidth = 1.307 All HH, N = 899, bandwidth = 1.716
37
SPLIT DECISIONS
0.8
9.6 10.0
0.4
ln(total income)
9.2
0.0
Decision maker (childcare)
-6 -4 -2 0 2 4 -6 -4 -2 0 2 4
Points above cutoff Points above cutoff
All HH with inc.>0, N = 881, bandwidth = 2.079 Married applicants only, N = 389, bandwidth = 1.612
Figure 4: Locations of surveyed households: nationwide
38
CLEMENS & TIONGSON
(a) Barely failing (b) Barely passing
Figure 5: Locations of surveyed households: National Capital Region only
39
SPLIT DECISIONS
(a) Barely failing (b) Barely passing
Table 4a: Impacts on households: Composition and income
Intent-to-treat effect Treatment-on-treated effect band-
µs↓0 −µs↑0
Outcome µs↑0 µs↓0 − µs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val. width
Number of household members
Total 5.194 0.118 0.464 0.799 0.260 1.013 0.798 2.056
Working age 3.369 −0.145 0.387 0.709 −0.315 0.841 0.708 2.505
excl. Korea 3.276 −0.326 0.215 0.129 −0.679 0.437 0.120 1.943
% female 0.537 0.0995 0.0304 0.00108 0.209 0.0608 < 0.001 1.754
Age 65 1.456 0.182 0.284 0.523 0.397 0.617 0.520 2.134
Age < 18 0.322 0.0671 0.152 0.660 0.146 0.332 0.660 2.543
Any income? : Total 0.983 0.00296 0.0158 0.851 0.00616 0.0327 0.851 1.785
From remittances 0.388 0.226 0.0614 < 0.001 0.472 0.121 < 0.001 1.836
40
Non-remittances 0.957 −0.0498 0.0311 0.109 −0.104 0.0655 0.114 1.997
Income : Total 21405.8 1764.3 2739.7 0.520 3675.2 5711.1 0.520 1.858
From remittances 4734.6 5690.4 1371.5 < 0.001 11853.9 2764.7 < 0.001 1.467
CLEMENS & TIONGSON
Non-remittances 16671.2 −3926.2 2533.9 0.121 −8178.7 5183.9 0.115 1.897
ln (Income ): Total 9.652 0.0397 0.203 0.845 0.0901 0.459 0.844 2.079
From remittances 9.141 0.244 0.156 0.117 0.543 0.342 0.113 1.877
Non-remittances 9.240 −0.260 0.145 0.0730 −0.550 0.298 0.0648 1.944
Business? (agr.) 0.155 −0.0623 0.0418 0.136 −0.130 0.0878 0.139 1.819
ln (bus. inc. agr.) 7.396 −0.483 0.834 0.562 5.083 16.03 0.751 2.236
Business? (non-agr.) 0.162 −0.105 0.0803 0.193 −0.229 0.180 0.204 2.184
ln (bus. inc. non-agr.) 6.972 0.384 1.038 0.711 0.648 1.760 0.713 2.577
All variables at household level. Working age means 18 Age < 65. Money amounts in Philippine pesos per month, average over 6 previous months.
‘Income’ means income going to household members in the Philippines. Treatment = household ever had a member in Korea. “Bus. inc.�? = business income.
Optimal bandwidth selected by the method of Imbens and Kalyanaraman (2012). Triangular kernel. “Agr.�? = agriculture (farming, livestock, forestry, �?shing).
Table 4b: Impacts on households: Spending, saving, investing, borrowing
Intent-to-treat effect Treatment-on-treated effect band-
µs↓0 −µs↑0
Outcome µs↑0 µs↓0 − µs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val. width
Expenditures : Total 18374.8 5436.0 2417.7 0.0245 11554.2 5379.8 0.0317 3.091
Food 9451.2 487.1 1577.2 0.757 1061.3 3435.4 0.757 2.518
Quality of life 6898.2 1941.2 892.5 0.0296 3981.7 1878.7 0.0341 3.551
Educ. & health 1054.3 1285.9 472.5 0.00650 2678.6 1005.5 0.00772 1.875
Durables 893.4 596.5 683.6 0.383 1303.8 1470.7 0.375 2.204
Savings 1422.1 −278.9 1332.7 0.834 −581.4 2773.6 0.834 3.251
ln (expenditures ): Total 9.657 0.129 0.124 0.301 0.280 0.271 0.301 2.724
Food 9.001 0.00446 0.0795 0.955 0.00930 0.165 0.955 1.468
Quality of life 8.609 0.146 0.0775 0.0595 0.304 0.162 0.0601 1.523
Educ. & health 6.349 0.312 0.195 0.110 0.684 0.435 0.115 1.875
41
Durables 5.989 0.475 0.285 0.0958 0.913 0.525 0.0818 1.960
ln (Savings ) 7.399 −0.0363 0.500 0.942 −0.0648 0.888 0.942 3.458
Any savings? 0.276 0.0956 0.0585 0.102 0.199 0.123 0.106 1.684
SPLIT DECISIONS
Borrowed for business reasons?
from family 0.181 −0.0810 0.0440 0.0656 −0.169 0.0938 0.0718 1.887
from other 0.106 0.0859 0.0710 0.227 0.188 0.159 0.236 2.030
Borrowed for non-business reasons?
from family 0.0345 −0.0345 0.0170 0.0427 −0.0718 0.0363 0.0479 1.626
from other 0.147 −0.0466 0.0417 0.264 −0.0970 0.0867 0.263 1.746
Family owns residence? 0.794 0.000421 0.0897 0.996 0.000919 0.195 0.996 2.228
Number of bedrooms 2.253 −0.00121 0.220 0.996 −0.00263 0.478 0.996 2.318
All variables at household level. Working age means 18 Age < 65. Money in PHP/mo., average over 6 mos. Savings is flow, not stock.
“Food�? = food, beverages, and tobacco. “Health & educ.�? = school, medicine, and medical care. “Savings�? includes deposits in banks, paying off loans,
extending loans. “Quality of life�? = fuel, transportation, household & personal care, clothing, recreation, family occasions, gifts.
“Durables�? = durable goods, taxes, home improvement. Bandwidth selection following Imbens and Kalyanaraman (2012), triangular kernel.
Treatment = household ever had a member in Korea.
Table 5: Impacts on individual adults
Intent-to-treat effect Treatment-on-treated effect band-
µs↓0 −µs↑0
Outcome µs↑0 µs↓0 − µs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val. width
Applicants only (N = 875)
Worked in past 6 months? 0.833 −0.0339 0.0830 0.683 −0.0744 0.182 0.682 2.227
Now in Korea? 0.142 0.403 0.0541 < 0.001 0.838 0.0854 < 0.001 1.406
Now abroad? 0.336 0.281 0.0612 < 0.001 0.586 0.120 < 0.001 1.603
Applicant’s spouses only (N = 421)
Worked in past 6 months? 0.492 0.00331 0.157 0.983 0.00716 0.337 0.983 2.147
Days worked, previous mo. 0.471 0.202 0.814 0.804 0.409 1.627 0.802 3.276
Any wage income? 0.251 0.00755 0.134 0.955 0.0163 0.289 0.955 2.138
ln wage income 9.262 −0.225 0.505 0.656 −0.270 0.615 0.661 3.390
42
All non-applicant adults only (N = 2142)
Worked in past 6 months? 0.493 0.0446 0.0703 0.526 0.0840 0.133 0.528 2.103
CLEMENS & TIONGSON
Days worked, previous mo. 0.262 0.161 0.226 0.477 0.304 0.426 0.476 3.192
Any wage income? 0.232 −0.0120 0.0591 0.839 −0.0225 0.110 0.838 2.490
ln wage income 9.171 −0.0910 0.133 0.493 −0.163 0.235 0.489 1.873
Now in Korea? 0.00730 0.0218 0.0141 0.122 0.0412 0.0263 0.117 2.041
Now abroad? 0.0695 0.0387 0.0361 0.284 0.0723 0.0677 0.285 2.402
Years of education 11.03 0.00221 0.269 0.993 0.00413 0.503 0.993 4.436
Visited health facility past mo. 0.141 −0.00140 0.0493 0.977 −0.00262 0.0921 0.977 2.356
if so, private facility? 0.697 −0.00662 0.173 0.969 −0.0110 0.285 0.969 2.904
All variables at individual level. ‘Adult’ means 18 Age < 65. Money amounts in Philippine pesos per month, average over 6 previous months.
‘Decisions’ is an indicator variable for whether the applicant was the primary or joint decision-maker on each subject.
For applicants N =875, for non-applicant adults N =2142. Bandwidth selection following Imbens and Kalyanaraman (2012), triangular kernel.
Treatment = household ever had a member in Korea.
Table 6: Impacts on decision-making by applicant
Intent-to-treat effect Treatment-on-treated effect band-
µs↓0 −µs↑0
Outcome µs↑0 µs↓0 − µs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val. width
Applicants (N = 875)
Decisions: childcare 0.367 −0.0748 0.107 0.484 −0.165 0.236 0.486 2.087
Decisions: home repairs 0.377 −0.0889 0.108 0.410 −0.195 0.239 0.415 2.275
Decisions: major purchases 0.451 −0.135 0.0618 0.0286 −0.281 0.129 0.0297 1.967
Decisions: entrepreneurship 0.456 −0.0747 0.111 0.501 −0.164 0.245 0.502 2.151
43
Decisions: weekend activities 0.416 −0.151 0.0601 0.0119 −0.315 0.127 0.0130 1.955
Married applicants only (N = 389)
SPLIT DECISIONS
Decisions: childcare 0.510 −0.307 0.0870 < 0.001 −0.635 0.212 0.00278 1.612
Decisions: home repairs 0.508 −0.246 0.165 0.136 −0.582 0.446 0.192 2.033
Decisions: major purchases 0.608 −0.280 0.0910 0.00211 −0.579 0.214 0.00671 1.895
Decisions: entrepreneurship 0.589 −0.183 0.164 0.264 −0.432 0.406 0.286 2.010
Decisions: weekend activities 0.588 −0.307 0.0898 < 0.001 −0.635 0.208 0.00223 1.782
Observations are individuals. Treatment = household ever had a member in Korea. Bandwidth selection follows Imbens and Kalyanaraman (2012),
triangular kernel. ‘Decisions’ is an indicator variable for whether the applicant was the primary or joint decision-maker on each subject.
Table 7: Impacts on individual children of applicant or applicant’s spouse
Intent-to-treat effect Treatment-on-treated effect band-
µs↓0 −µs↑0
Outcome µs↑0 µs↓0 − µs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val. width
In school? (if age 6) 0.984 −0.00862 0.0402 0.830 −0.0309 0.145 0.832 2.108
if so, private facility? 0.275 0.175 0.0754 0.0202 0.405 0.190 0.0328 1.599
44
Any awards at school? 0.340 0.147 0.0676 0.0300 0.302 0.145 0.0370 1.732
Visited health facility past mo.? 0.144 −0.0496 0.0840 0.555 −0.139 0.237 0.556 2.156
if so, private facility? 0.525 0.453 0.322 0.159 0.683 0.512 0.182 2.109
Working? 0.0105 −0.0105 0.0105 0.317 −0.0217 0.0217 0.316 1.396
CLEMENS & TIONGSON
Does anyone read to child? 0.537 −0.0747 0.0690 0.279 −0.154 0.144 0.283 1.930
Desired years of education 11.51 −0.707 1.007 0.483 −1.799 2.602 0.489 3.163
N = 729. All variables at individual level. ‘Child’ means age < 18. With age 12, N = 117. Treatment = household ever had a member in Korea.
Bandwidth selection following Imbens and Kalyanaraman (2012), triangular kernel.
Table 8: Compare policy discontinuity estimates with observational estimates
Matching, nearest neighbors Matching
Mahala-
Outcome RDD∗ OLS 2 5 10
nobis
Households
ln Expenditures: Educ. & med. 0.684 0.406 0.387 0.355 0.298 0.435
(0.435) (0.0779) (0.112) (0.0985) (0.0922) (0.130)
Business (non-agr.)? −0.229 −0.297 −0.320 −0.323 −0.317 −0.313
(0.180) (0.0155) (0.0250) (0.0212) (0.0195) (0.0318)
45
Children (6 age < 18)
In school? −0.00862 0.0372 0.0164 0.0348 0.0360 0.0202
SPLIT DECISIONS
(0.0402) (0.01000) (0.0175) (0.0138) (0.0123) (0.0227)
Adults (age 18)
Years of educ. 0.00221 0.212 0.278 0.298 0.370 0.616
(0.269) (0.102) (0.175) (0.144) (0.129) (0.213)
∗
Regression Discontinuity Design estimates from Tables 4a, 4b, 5, and 7. Standard errors in parentheses.
Treatment = household ever had a member in Korea. Control variables in OLS and matching variables in PSM:
Household size, HoH (Head of Household) age, HoH years educ., plus dummies for HoH female, HoH married, standalone
house, family owns residence, strong wall materials, strong roof materials, four regions.
Table 9: Nonexperimental evidence on effect mechanisms
Covariates: None ln Remittances Decision-making
µs↓0 −µs↑0 µs↓0 −µs↑0 µs↓0 −µs↑0
Outcome τs↓0 −τs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val.
Households
ln Exp.: Quality of life 0.304 0.162 0.0601 0.292 0.267 0.274 0.306 0.172 0.0762
ln Exp.: Educ. & med. 0.684 0.435 0.115 −0.00899 0.650 0.989 0.778 0.460 0.0908
ln Exp.: Durables 0.913 0.525 0.0818 0.544 1.031 0.598 0.943 0.543 0.0824
Borrowed (family)? −0.0718 0.0363 0.0479 −0.107 0.0766 0.163 −0.0741 0.0384 0.0538
Business? (agr.) −0.130 0.0878 0.139 −0.322 0.175 0.0654 −0.143 0.0952 0.134
Business? (non-agr.) −0.228 0.180 0.205 −0.0741 0.277 0.789 −0.231 0.189 0.222
46
Non-applicant adults
Worked (6 mos.)? 0.0842 0.133 0.526 0.210 0.209 0.314 0.0744 0.135 0.581
Days worked (past mo.) 0.295 0.442 0.504 1.145 0.338 < 0.001 0.309 0.434 0.477
CLEMENS & TIONGSON
Any wage income? −0.0229 0.110 0.835 0.124 0.155 0.422 −0.0189 0.113 0.867
ln wage income −0.163 0.235 0.489 −0.930 0.602 0.122 −0.168 0.253 0.506
Children (of applicant or spouse), 6 age < 18
In school? −0.0309 0.145 0.832 −0.0364 0.649 0.955 −0.165 0.295 0.578
Private? 0.405 0.190 0.0328 2.877 2.548 0.259 0.495 0.272 0.0686
Anyone read to child? −0.154 0.144 0.283 0.497 0.425 0.242 −0.183 0.185 0.323
olich (2007)
Family borrowing is non-business only. Covariates included following Fr¨
Treatment = household ever had a member in Korea. Bandwidth selection follows Imbens and Kalyanaraman (2012), triangular kernel.
‘Decision-making’ variables are �?ve dummies for whether applicant is primary or joint decision-maker in all �?ve areas of Table 6.
SPLIT DECISIONS
Table 10: Compare barely-failing sampled households to whole country
Sample, s < 0 Whole country
Outcome Mean (µ1 ) Mean (µ2 ) s.d. p(µ1 = µ2 )
Households
No. members 5.128 4.952 [2.238] 0.0663
Member overseas? 0.380 0.0695 [0.253] < 0.001
Total income 23015.9 17143.8 [20601.0] 0.00425
Remittance income 5059.9 1945.9 [8073.1] < 0.001
Expenditures: total 17403.9 14639.0 [14789.6] < 0.001
Food 8980.4 7083.4 [4524.4] < 0.001
Quality of life 6603.3 3112.3 [4601.0] < 0.001
Educ. & med. 1095.2 1057.6 [3002.0] 0.738
Durables 725.0 717.7 [3184.6] 0.945
Any savings? (flow) 0.263 0.496 [0.498] < 0.001
Savings (flow) 1088.1 1811.0 [8208.3] 0.00140
Business (agr.)? 0.0870 0.397 0.487 < 0.001
ln(bus. income, agr.) 7.305 7.580 1.199 0.177
Business (non-agr.)? 0.113 0.395 0.487 < 0.001
ln(bus. income, non-agr.) 6.972 7.926 1.376 < 0.001
Own residence? 0.796 0.705 0.454 < 0.001
Strong wall material 0.822 0.598 0.488 < 0.001
Region: NCR 0.265 0.131 [0.335] < 0.001
Region: Luzon (not NCR) 0.654 0.220 [0.412] < 0.001
Region: Visayas 0.0543 0.419 [0.491] < 0.001
Region: Mindanao 0.0261 0.231 [0.420] < 0.001
Head of household
Age 40.84 47.34 [13.91] < 0.001
Female? 0.184 0.174 [0.377] 0.560
Years education 11.42 7.864 [3.774] < 0.001
Married? 0.779 0.814 [0.387] 0.0699
Children (6 age < 18)
In school? 0.968 0.842 [0.363] < 0.001
Sample households restricted to those whose applicant barely failed exam. “Agr.�? = agriculture.
Money in 2010 PHP/mo. Nationally representative data from 2006, inflated with CPI.
Households: Nsamp,s<0 = 460, Nctry = 38, 453. Children: Nsamp,s<0 = 433, Nctry = 55, 642.
Nationwide data weighted with frequency weights. Expenditures de�?ned in Table 4b.
47
SPLIT DECISIONS
Appendix
A1. Heterogeneous reduced-form impacts by pre-treatment
subgroups
Table A1 explores the heterogeneity of selected reduced-form impacts by pre-treatment
subgroups. The �?rst three columns repeat results from the full sample, for reference
only (896 households). The second trio of columns are restricted to the subsample in
which the applicant was married at the time of application (398 households). The third
trio of columns are restricted to the subsample in which the applicant was unemployed
at the time of application (649 households).
We do not �?nd strong patterns of heterogeneity in the results by these two sub-
groups. Standard errors are much larger in the subgroups; the samples are substantially
smaller. The positive effect on education/health expenditures and the negative effect
on whether the family engages in farming may both be larger among already-married
applicants, but these changes are not statistically precise. The effect of the applicant’s
migration on OFW status of other adults in the household is signi�?cant at the 12% level
in households whether the applicant is already married.
Among households where the applicant was initially unemployed, the positive effect on
durable goods expenditures greatly decreases and becomes statistically insigni�?cant.
The effect on private schooling of children of the applicant or applicant’s spouse may
be larger in households where the applicant was initially unemployed, but this change
is not statistically precise.
A-1
Appendix Table A1: Heterogeneous reduced-form effects by pre-treatment subgroups
Sample: Full Applicant already married Applicant not already employed
µs↓0 −µs↑0 µs↓0 −µs↑0 µs↓0 −µs↑0
Outcome τs↓0 −τs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val. τs↓0 −τs↑0 s.e. p-val.
Households
ln Exp.: Quality of life 0.304 0.162 0.0601 0.287 0.214 0.180 0.429 0.195 0.0281
ln Exp.: Educ. & med. 0.684 0.435 0.115 1.260 1.237 0.308 0.660 0.512 0.197
ln Exp.: Durables 0.913 0.525 0.0818 1.102 1.344 0.412 0.143 1.029 0.890
Borrowed (family)? −0.0718 0.0363 0.0479 −0.0615 0.132 0.643 −0.0471 0.0340 0.166
Business? (agr.) −0.130 0.0878 0.139 −0.210 0.115 0.0675 −0.150 0.109 0.168
Business? (non-agr.) −0.228 0.180 0.205 −0.155 0.269 0.564 −0.205 0.207 0.323
Non-applicant adults
A-2
Worked (6 mos.)? 0.0842 0.133 0.526 −0.0668 0.141 0.635 −0.115 0.0950 0.224
Days worked (past mo.) 0.295 0.442 0.504 −0.801 1.312 0.542 0.294 0.766 0.701
Any wage income? −0.0229 0.110 0.835 −0.0131 0.210 0.950 −0.0987 0.0792 0.213
CLEMENS & TIONGSON
ln wage income −0.163 0.235 0.489 0.102 0.498 0.838 −0.241 0.288 0.402
Currently in Korea? 0.0412 0.0263 0.118 0.0381 0.0367 0.300 0.0352 0.0242 0.146
Currently OFW? 0.0723 0.0676 0.285 0.115 0.0742 0.120 0.0714 0.0490 0.145
Children (of applicant or spouse), 6 age < 18
In school? −0.0309 0.145 0.832 −0.0442 0.135 0.744 0.0190 0.149 0.898
Private? 0.405 0.190 0.0328 0.388 0.175 0.0265 0.667 0.251 0.00798
Anyone read to child? −0.154 0.144 0.283 −0.268 0.370 0.468 −0.121 0.170 0.479
Family borrowing is non-business only. Sample sizes (HHs): Full 896; applicant married 398; applicant unemployed 649.
Treatment = household ever had a member in Korea. Bandwidth selection follows Imbens and Kalyanaraman (2012), triangular kernel.
‘Already married’ and ‘Not already employed’ refer to the time at which the applicant applied to the overseas job (pre-treatment).