metadata: authors: 0: author: Clemens,Michael A.
1: author: Tiongson,Erwin H. R.
count: World
docna: 0: docna: Split decisions : family finance when a
            policy discontinuity allocates overseas work
docty: Policy Research Working Paper
subtopic: Private Sector Economics,Health Care Services Industry,Labor Markets,Educational Sciences,Private Sector Development Law,Marketing
teratopic: Social Protections and Labor,Health, Nutrition and Population,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: Labor markets are increasingly global.
            Overseas work can enrich households but also split them
            geographically, with ambiguous net effects on decisions
            about work, investment, and education. These net effects,
            and their mechanisms, are poorly understood. This study
            investigates a policy discontinuity in the Philippines that
            resulted in quasi-random assignment of temporary,
            partial-household migration to high-wage jobs in Korea. This
            allows unusually reliable measurement of the reduced-form
            effect of these overseas jobs on migrant households. A
            purpose-built survey allows nonexperimental tests of
            different theoretical mechanisms for the reduced-form
            effect. The study also explores how reliably the
            reduced-form effect could be measured with standard
            observational estimators. It finds large effects on
            spending, borrowing, and human capital investment, but no
            effects on saving or entrepreneurship. Remittances appear to
            overwhelm household splitting as a causal mechanism.
repnme: repnme: Split decisions : family finance when a
            policy discontinuity allocates overseas work
display_title: Split decisions : family finance
            when a policy discontinuity allocates overseas work
listing_relative_url: /research/2012/12/17043015/split-decisions-family-finance-policy-discontinuity-allocates-overseas-work
topicv3: Private Sector Development m1326060 1921,Education m1327048 470,Jobs m1503931 2670,Labor Supply m1504055 2751,Health m1504661 3480
publishtoextweb_dt: 2012-12-01T00:00:00Z
historic_topic: Social Protections and Labor,Health, Nutrition and Population,Macroeconomics and Economic Growth,Finance and Financial Sector Development
no_of_pages: 52
author_keys: 0: author_key: 420948
1: author_key: 691152
model_extractions: 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 can enrich households but also split them geographically, with ambiguous net effects on decisions about work, investment, and education. These net effects, and their mechanisms, are poorly understood. This study investigates a policy discontinuity in the Philippines that resulted in quasi-random assignment of temporary, partial-household migration to high-wage jobs in Korea. This allows unusually reliable measurement of the reduced-form effect of these overseas jobs on migrant households. A purpose-built survey allows nonexperimental tests of different theoretical mechanisms for the reduced-form effect. The study also explores how reliably the reduced-form effect could be measured with standard observational estimators. It finds large effects on spending, borrowing, and human capital investment, but no effects on saving or entrepreneurship. Remittances appear to overwhelm household splitting as a causal mechanism.

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 finance when a policy discontinuity allocates overseas work

Michael A. Clemens<sup>_∗_</sup> Erwin R. Tiongson Center for Global World Bank, Development AIM, and IZA December 5, 2012

## _Sector board: POV (Poverty Reduction)_

> _∗_ **JEL Classification Numbers: J61, O15, R23.** We thank the Philippine Overseas Employment Administration (POEA) for their kind assistance, especially current and former POEA officials and staff Patricia Santo Tomas, Carmelita Dimzon, Hans Cacdac, Jennifer Manalili, and Helen Barayuga, as well as Vincent Olaivar of the National Statistical Office. We thank the John D. and Catherine T. MacArthur Foundation 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 participants 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 errors. 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.
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# 1. Introduction

Overseas workers are expected to send home over US$600 billion per year by 2014, double the amount just 8 years before (Ratha and Silwal 2012). This has raised interest in how international migration might finance investment in developing areas.

But migration affects more than just incomes. Another important effect is that overseas jobs, unlike many local jobs, often disrupt and split households for extended periods. 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 relative 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 identification. 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 households 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 survey allows nonexperimental tests of impact mechanisms.

We contribute to the literature in three principal ways. First, we offer well-identified 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 accompanies overseas work. Each of these bundled effects, separately, is the subject of an active literature. Second, we explore theoretically and empirically the relative importance 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 borrowing from family members outside the household. We find no significant effect on starting or investing in entrepreneurial activity or on labor suppy by other family members. Migration by the applicant causes children to be much more likely to attend private school and receive awards at school. Migration causes large changes in decisionmaking 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 find some evidence that migration affects home production technology, particularly in agriculture, by altering household composition. We find 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 constraints 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 discuss 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 observational estimators. We conclude with a nonexperimental exploration of impact mechanisms, 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 decisions on savings and investment. Recent influential studies trace the effects of increases in capital on investments in home production and new business.<sup>1</sup> Others stress the effects of credit constraints on investments in human capital,<sup>2</sup> and on consumption decisions.<sup>3</sup> A common theme is that access to capital substantially alters all of these decisions in many settings.

Second, and separately, economists have studied the effect of household composition and decision-making structure on finance 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 control over income shifts from one member to another.<sup>4</sup> A different strand traces the effects of parental absence—through one parent’s labor supply, departure, or death—on investments in children’s human capital.<sup>5</sup> These effects, too, are typically substantial.

Migration bundles these two effects.<sup>6</sup> The combined effect has been the subject of a

> 1These 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).

> 2See 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).

3The 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); Dupas and Robinson (2012); Kaboski and Townsend (2012).

> 4Research 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).

> 5Work 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).

> 6Migration 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 finance 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 investment 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,<sup>7</sup> a new literature seeks the effects of migration itself, comprising both channels above.<sup>8</sup> In this paper we contribute to these literatures by offering unusually well-identified 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 identification that are difficult 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 identified estimates, and have found large differences (McKenzie 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 decisionmaking (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 finance and investment decisions. First, new income can alleviate constraints on borrowing and consumption smoothing. Second, changes in the location 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.

> 7Rapoport 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).

> 8Studies 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 L ́opez-Feldman (2010); Amuedo-Dorantes et al. (2010); Mergo (2012).

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 fixed Hicksian composite good _c ≡ c_ 1 + _c_ 2 and from leisure ( _l_ 1 _, l_ 2) over lifetime _T_ . Member 1 can work in the home country at wage _w_ . Member 2 can migrate,<sup>9</sup> to spend a fraction 0 ⩽ _m_ ⩽ 1 of work time earning the overseas wage _wo > w_ , thus earning overall wage There is pure disutility of having a family member overseas, captured by 0 ⩽ _φ_ ⩽ 1, analogous in modeling terms to Moffitt’s (1983) “welfare stigma”. For given _m_ it solves _With borrowing constraints._ Suppose, for the moment, that the household cannot borrow or lend. Capital evolves subject to 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; Baland 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; Connell et al. 1976).<sup>10</sup> 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.

> 10We 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 _ul_ 1 = _μw_ ; ̇ _ul_ 2 = _μw_<sup>_∗_</sup> ; _uc_ = _μ_ ; and _μ_ = _μ_ ( _ρ − θf_<sup>_′_</sup> ( _k_ )), where the subscript denotes the partial derivative. The first two conditions imply 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 _l_<sup>1</sup> come from differentiating the fourth Pontryagin condition and substituting for _μ_ : Migration affects investment. Letting labor income net of consumption Ψ _≡ w_ (1 _−l_ 1)+ _w_<sup>_∗_</sup> (1 _− l_ 2) _− c_ , then (3) gives The first term on the right side captures increased investment as the borrowing constrained 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_<sup>_∗_</sup> . Each is a different aspect of a single channel of the effect of migration on investment: via higher wages. Barring large declines 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,<sup>11</sup> the effect of

> with respect to _t_ . We assume _u_ and _f_ are concave, continuous, and twice differentiable.

>  ̄ 11The equation of motion (3) becomes _k_  ̇ = _θf_ ( ̄ _k_ ) _− r_ ( ̄ _k − k_ ) + _w_ (1 _− l_ 1) + _w∗_ (1 _− l_ 2) _− c_ , where efficient and frictionless ( _k_ (5)=now _f_<sup>_′−_1</sup> follow( _r/θ_ ) is the unconstrained optimal investment. _c_  ̇ = _− u_<sup>_u′′c_(</sup> _r_<sup>_r −_</sup> _≈ ρ_<sup>_ρ_</sup> ) and the home wage is constant (<sup>)and</sup><sup>_l_ ̇1=</sup><sup>_−_</sup> _uul_<sup>_<u>′′</u>_</sup> 1 � _r − ρ −_ Optimal consumption and non-migrant leisure _ww_  ̇ �. In _w_ contrast ̇ = 0), then non-migrant labor supplyto (5), if the capital market is does not change over time (because _l_<sup> ̇1</sup> = 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_<sup>_∗_</sup> . 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 where the first term on the right is the return to saving the new income in the bank. Home production is fixed at _f_ ( _k_<sup> ̄</sup> )—where _k_<sup> ̄</sup> 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 borrowingunconstrained households whenever home production is less than optimal, i.e. _k < k_<sup> ̄</sup> .

The unitary household model makes two key predictions: When one household member 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 alternative optimization program allows each household member an additively separable egoistic utility term. For given _m_ the household solves 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 _ul_ 1 = _μw_ ; (1 _− φm_ ) _ul_ 2 = _μw_<sup>_∗_</sup> ; _uc_ 1 = _μ_ ; (1 _− φm_ ) _uc_ 2 = _μ_ ; ̇ and _μ_ = _μ_ ( _ρ − φ_ ( _m_ ) _f_<sup>_′_</sup> ( _k_ )). The first two of these give 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: The first 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 consumption by altering the wage _w_<sup>_∗_</sup> , as in (6), but in the fourth term there is a new and independent effect: Migration decreases the influence of the migrant in day-to-day household decisions, changing the balance of decision-making power between migrant and nonmigrants.

_Without borrowing constraints, f_<sup>_′_</sup> = _r_ in (10). As in (7), migration raises investment by more in borrowing-constrained households than in borrowing-unconstrained households (provided _k < k_<sup> ̄</sup> ). For the collective household, too, it can be shown in equations 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 channels 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 remaining spouse has different consumption preferences). Migration can separately alter 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 households in the passing and failing groups years later allows uncommonly reliable estimation 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 identification 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 Republic of Korea allowing participation of Filipino workers in Korea’s Employment Permit 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 Em9
CLEMENS & TIONGSON ployment Administration (POEA) of the national government. EPS job contracts are initially for three years, and are renewable up to five 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 vocational 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 enterprises (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.<sup>12</sup> 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 examination 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 five EPS recruitment rounds in the Philippines, each of which administered one KLT.<sup>13</sup> Table 1 shows the number of people who sat for each round of the KLT, and the numbers whose scores fell within five points of the 120-point cutoff. The large number of test-takers provides substantial density

> 12This 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 Korean Language Foundation, at five test centers across the Philippines (Manila, Pampanga, Baguio, Cebu, and Davao). The KLT has since been superseded by the Test of Proficiency in Korean (TOPIK), administered by the Human Resources Development Service of Korea.

> 13An 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 occurred 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 began.

# 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 nonparametric 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 proposed by Imbens and Kalyanaraman (2012).<sup>14</sup> For each outcome _μ_ we first 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:<sup>15</sup> We then estimate the treatment-on-treated (TOT) effect as fuzzy nonparametric RDD. This is the effect of migration by a household member: This again is the local average treatment effect on “compliers”, whose households com-

> 14We 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.”

> 15For 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 _≡_ lim _s→_ 0+ _Ei_ [ _μ_<sup>_i_</sup> ]; _μs↑_ 0 _≡_ lim _s→_ 0 _− Ei_ [ _μ_<sup>_i_</sup> ]; _τs↓_ 0 _≡_ lim _s→_ 0+ _Ei_ [ _τ_<sup>_i_</sup> ]; _τs↑_ 0 _≡_ lim _s→_ 0 _− Ei_ [ _τ_<sup>_i_</sup> ].

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 sufficient) 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).<sup>16</sup> In the sampling universe there is a very large jump in deployment probability at the cutoff score, but little evidence of a significant discontinuity in the baseline characteristics 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 significant 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 statistically significant jump in test-score density at the passing threshold. Figure 2 shows the

> 16Clean identification with RDD requires other assumptions that we cannot test but that appear plausible 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 consider it unrealistic for families’ financial decisions years later to depend substantially on such an effect.

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### 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 significant, 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 _first_ 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.<sup>17</sup> Third, the test was administered 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 applicants 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.<sup>18</sup>

## 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 firm in stages. First, we gave only the addresses of households whose applicant was within one point of the cutoff score. Only after the firm 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

> 17Because the test-score we use is only the first-attempt score, a small number of those with failing scores are deployed to Korea. These are people who failed the first attempt but passed on subsequent attempts. This is of concern to external validity, but not internal validity.

> 18Cull and Scott (2010) show that survey responses on the financial life of Ghanaian households provided by knowledgeable respondents are as good as full household enumeration and better than responses from randomly selected respondents.

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## the survey were exhausted (which occurred at 5 points from the cutoff).<sup>19</sup>

The survey was “blind” in two senses. First, no one at the survey firm knew which households were those whose member passed and which were those whose member failed. The firm 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 enumerators 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.<sup>20</sup> In the end, enumerators attempted to locate the permanent addresses 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 applicants’ 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.

> 19An 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 firm might have visited households that were easier to reach but further from the discontinuity before attempting to contact all households near the discontinuity. 20Following Duflo et al. (2007) we estimated that 900 households would give us a 92% chance of detecting a 10 percentage-point difference in household-level school enrollment fraction, a 94% chance of detecting 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% significance level).

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## 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 significantly 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 survey sample in Table 3. The first rows show that there is a very large discontinuity in household-level migration exposure at the test-score discontinuity. A graphical representation 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 significant difference in any of the baseline traits at the threshold 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 first the effects of test-passing and migration on households, then the effects on individual adults, and finally the effects on individual children. The first column of each table 15
CLEMENS & TIONGSON shows the average outcome for test-failers approaching the cutoff score. “Treatment” is defined as a household in which any member _ever_ worked in Korea. Defining 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 otherwise stated, household members are considered members even when abroad. There are no statistically significant 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 reject 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 significant effect on three separate 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 borrow16
# SPLIT DECISIONS

ing by the household members who remain in the Philippines. Migration by the jobapplicant causes monthly expenditures to rise substantially. The effect on overall pesovalue expenditures is on the order of 60% and significantly different from zero at the 3% level; the effect in _ln_ pesos is about 30% but not statistically precise. We cannot 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.<sup>21</sup> 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 savings ( _p_ = 0 _._ 11).<sup>22</sup> The remaining rows of the table show that migration by the job applicant causes households to borrow less from family members in the 6 months preceding the survey. There is no statistically significant 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 bedrooms 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 migrated. 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 borrowing.

We believe this disparity is caused by underreporting of specific types of income due to difficulties in eliciting information from survey respondents. For example, if a migrant

> 21These 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 significant at the 12% level or below for all three categories.

> 22“Food” = food, beverages, and tobacco. “Health & educ.” = school, medicine, and medical care. “Quality 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.

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## 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 significant 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 applicants’ 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.

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## 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 five areas. They could answer themselves, another identified member, or shared decision-making by themselves and another identified 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 significant in the case of “major purchases” and “weekend 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 statistically significant for “childcare”, “major purchases”, and “weekend activities”. A graphical 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 enrollment. 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 percentage 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 facilities, 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 Appendix section A1.

# 6.4. Selection bias and nonexperimental reduced-form results

Could the preceding effects have been well-identified 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.<sup>23</sup> 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 first column simply reproduces the ITT result from RDD analysis in an earlier table. The second column shows the coefficient on the treatment variable in an OLS regression including numerous controls. 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

> 23This 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 specific destinations nor on past migration experience.

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## SPLIT DECISIONS

households that self-select into this form of migration and those that do not.<sup>24</sup> 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 final column uses PSM with Mahalanobis-distance matching.

The observational estimators can lead to estimates of the “effects” of migration that are spurious. The first 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 households. The next row shows that the observational estimators exaggerate the magnitude 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 estimates spuriously find 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 unobservable.

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 synthetic 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-

> 24These 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 investment, migrants’ absence can alter their relative power over financial decisions, and migrants’ 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 empirically to the case of nonparametric RDD by Fr ̈olich (2007). These tests are not wellidentified 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 signify treatment via single mechanisms.

Table 9 conducts these mechanism tests. The first three columns simply reproduce selected 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 five indicator variables for whether the applicants is a principal or joint decision-maker in the five 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 first 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 significance 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 significant effect on whether the household receives income from farming, but after controlling for remittances, migration has a significant negative effect on farming activity. The effect is intuitive: If the household member who would do most of the farming is abroad, farming can only continue if remittances pay for someone else to do it. Likewise, the applicant’s migration has no effect on days worked by non-applicants, but after controlling for remittances, the effect is significant 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 evidence that migration substantially alters investment in home production by altering the technology of home production. It is not definitive evidence, in part because the change in coefficients is not statistically precise. The opposite pattern is seen in the effects of migration on non-agricultural business: the magnitude of the negative coefficient on migration greatly diminishes when remittances are controlled for (though the change in coefficients is also not statistically precise). This pattern could arise because remittances decrease the propensity for remaining family members to start nonagricultural businesses. But this pattern is not compatible with important effects of migrant absence _per se_ on non-agricultural entrepreneurial activity. This form of migration 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.

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## 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.<sup>25</sup> We leave out test-passers so as to remove the effects of EPS-Korea migration.

Households in the survey sample are much more likely to already have a member abroad than typical households in the Philippines. Sample households have somewhat more income (about 35% more) than typical households, a difference entirely accounted 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 migration 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 pattern is that households in the survey sample emphasize investments in human capital (education, migration) over physical capital (entrepreneurship, savings).

# 9. Conclusion

We find 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) finds that remittances to the Philippines encourage some types of entrepreneurial activity, conditional on the household already

> 25We use a household-matched nationally representative sample from the 2006 Family Income and Expenditure Survey (FIES) and Labor Force Survey (LFS). 2006 is the most recent matched FIES-LFS microdata publicly-available from the National Statistical Office at the time of writing. We inflate all peso figures from 2006 to 2010 using the Consumer Price Index.

24
# SPLIT DECISIONS

having a migrant. This is compatible with our finding that migration has no significant 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-finance and alleviating any borrowing constraints. In collective households, there are two additional channels: migration can alter the balance of power in household decisionmaking and can alter the technology of home production. We find that the most important of these are far and away the financial 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 collective 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 find 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 explanations for this result: the predicted effect of migration on others’ labor force participation 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 findings are compatible with the households surveyed being creditconstrained human capital investors: Migration causes greater private schooling for children, more awards at school, and greater household expenditure on health and education. The findings are not broadly compatible with these households being credit-constrained physical capital investors: Migration has no significant 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 theoretical 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, positive 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 migration 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.

# References

- **Agnew, J.R., L.R. Anderson, J.R. Gerlach, and L.R. Szykman** , “Who chooses annuities? an experimental investigation of the role of gender, framing, and defaults,” _American Economic Review Papers & Proceedings_ , 2008, _98_ (2), 418–422.

- **Akee, R.K.Q., W.E. Copeland, G. Keller, Angold A., and Costello E.J.** , “Parents Incomes and Childrens Outcomes: A Quasi-Experiment Using Transfer Payments from Casino Profits,” _American Economic Journal: Applied Economics_ , 2010, _2_ (1), 86–115.

- **Alatas, V., A. Banerjee, A.G. Chandrasekhar, R. Hanna, and B.A. Olken** , “Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia,” NBER Working Paper 18351, National Bureau of Economic Research, Cambridge, MA 2012.

- **Alcaraz, C., D. Chiquiar, and A. Salcedo** , “Remittances, schooling, and child labor in Mexico,” _Journal of Development Economics_ , 2012, _97_ (1), 156–165.

- **Alderman, H., P.A. Chiappori, L. Haddad, J. Hoddinott, and R. Kanbur** , “Unitary versus collective models of the household: is it time to shift the burden of proof?,” _World Bank Research Observer_ , 1995, _10_ (1), 1–19.

- **Altonji, J.G. and A. Siow** , “Testing the response of consumption to income changes with (noisy) panel data,” _Quarterly Journal of Economics_ , 1987, _102_ (2), 293–328.

- **Amuedo-Dorantes, C., A. Georges, and S. Pozo** , “Migration, Remittances, and Childrens Schooling in Haiti,” _ANNALS of the American Academy of Political and Social Science_ , 2010, _630_ (1), 224–244.

26
SPLIT DECISIONS

- **Angrist, J.D. and J.H. Johnson** , “Effects of Work-Related Absences on Families: Evidence from the Gulf War,” _Industrial and Labor Relations Review_ , 2000, _54_ (1), 41–58.

- **, G.W. Imbens, and D.B. Rubin** , “Identification of causal effects using instrumental variables,” _Journal of the American Statistical Association_ , 1996, _91_ (434), 444–455.

- **Antman, F.** , “The impact of migration on family left behind,” in A. Constant and Zimmermann K.F., eds., _International Handbook on the Economics of Migration_ , forthcoming 2012.

- **Ardington, C., A. Case, and V. Hosegood** , “Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa,” _American Economic Journal: Applied Economics_ , 2009, _1_ (1), 22–48.

- **Ashraf, N.** , “Spousal Control and Intra-Household Decision Making: An Experimental Study in the Philippines,” _American Economic Review_ , 2009, _99_ (4), 1245–1277.

- **, D. Aycinena, A. Mart ́ınez, and D. Yang** , “Remittances and the Problem of Control: A Field Experiment Among Migrants from El Salvador,” Working Paper, Dept. of Economics, University of Michigan, Ann Arbor, MI 2011.

- **, D. Karlan, and W. Yin** , “Female empowerment: Impact of a commitment savings product in the Philippines,” _World Development_ , 2010, _38_ (3), 333–344.

- **Baland, J.M. and J.A. Robinson** , “Is child labor inefficient?,” _Journal of Political Economy_ , 2000, _108_ (4), 663–679.

- **Banerjee, A., E. Duflo, R. Glennester, and C. Kinnan** , “The Miracle of Microfinance? Evidence from a Randomized Evaluation,” BREAD Working Paper 278, Bureau for Research and Economic Analysis of Development 2010.

- **Bardhan, P. and C. Udry** , _Development Microeconomics_ , New York: Oxford University Press, 1999.

- **Baron, R.M. and D.A. Kenny** , “The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.,” _Journal of Personality and Social Psychology_ , 1986, _51_ (6), 1173.

- **Basu, K.** , “Gender and Say: A Model of Household Behaviour with Endogenously Determined Balance of Power,” _Economic Journal_ , 2006, _116_ (511), 558–580.

- **Batista, C., A. Lacuesta, and P.C. Vicente** , “Testing the brain gain hypothesis: Micro evidence from Cape Verde,” _Journal of Development Economics_ , 2012, _97_ (1), 32–45.

- **Becker, G.S.** , _A Treatise on the Family_ , Cambridge, Mass.: Harvard University Press, 1991.

- **Bergstrom, T.C.** , “A Survey of Theories of the Family,” _Handbook of Population and Family Economics_ , 1997, _1_ , 21–79.

- **Blau, D.M.** , “The effect of income on child development,” _Review of Economics and Statistics_ , 1999, _81_ (2), 261–276.

- **Blau, F. and A.J. Grossberg** , “Maternal Labor Supply and Children’s Cognitive Development,” _Review of Economics and Statistics_ , 1992, _74_ (3), 474–81.

- **Blundell, R. and T. MaCurdy** , “Labor supply: A review of alternative approaches,” _Handbook of Labor Economics_ , 1999, _3_ , 1559–1695.

- **Bobonis, G.J.** , “Is the allocation of resources within the household efficient? New evidence from a randomized experiment,” _Journal of Political Economy_ , 2009, _117_ (3), 453–503.

27
CLEMENS & TIONGSON

- **Booth, A.L. and Y. Tamura** , “Impact of paternal temporary absence on children left behind,” IZA Discussion Paper 4381, Institute for the Study of Labor, Bonn 2009.

- **Cameron, S.V. and C. Taber** , “Estimation of educational borrowing constraints using returns to schooling,” _Journal of Political Economy_ , 2004, _112_ (1), 132–182.

- **Carneiro, P. and J.J. Heckman** , “The Evidence on Credit Constraints in Post-Secondary Schooling,” _Economic Journal_ , 2002, _112_ (482), 705–734.

- **Case, A. and A. Deaton** , “Large cash transfers to the elderly in South Africa,” _Economic Journal_ , 2001, _108_ (450), 1330–1361.

- **Caucutt, E.M. and L. Lochner** , “Early and Late Human Capital Investments, Borrowing Constraints, and the Family,” NBER Working Paper 18493, National Bureau of Economic Research, Cambridge, MA 2012.

- **Chen, S. and M. Ravallion** , “Absolute poverty measures for the developing world, 1981–2004,” _Proceedings of the National Academy of Sciences_ , 2007, _104_ (43), 16757–16762.

- **Cheng, M.Y., J. Fan, and J.S. Marron** , “On automatic boundary corrections,” _Annals of Statistics_ , 1997, _25_ (4), 1691–1708.

- **Chiappori, P.A.** , “Rational Household Labor Supply,” _Econometrica_ , 1988, _56_ (1), 63–90.

- , “Collective labor supply and welfare,” _Journal of Political Economy_ , 1992, _100_ (3), 437–467.

- **Connell, J., B. Dasgupta, R. Laishley, and M. Lipton** , _Migration from Rural Areas: The Evidence from Village Studies_ , London: Oxford University Press, 1976.

- **Cook, T.D. and V.C. Wong** , “Empirical tests of the validity of the regression discontinuity design,” _Annales d’Economie et de Statistique_<sup>_ ́_</sup> , 2008, _91–92_ , 127–150.

- **Corak, M.** , “Death and Divorce: The Long-Term Consequences of Parental Loss on Adolescents,” _Journal of Labor Economics_ , 2001, _19_ (3), 682–715.

- **Cort ́es, P.** , “The Feminization of International Migration and its effects on the Children Left behind: Evidence from the Philippines,” Working Paper, Boston University School of Management, Boston, MA 2010.

- **Cull, R. and K. Scott** , “Measuring Household Usage of Financial Services: Does it Matter How or Whom You Ask?,” _World Bank Economic Review_ , 2010, _24_ (2), 199–233.

- **Dahl, G.B. and L. Lochner** , “The impact of family income on child achievement: Evidence from the earned income tax credit,” _American Economic Review_ , 2012, _102_ (5), 1927–1956.

- **Duflo, E.** , “Child health and household resources in South Africa: Evidence from the Old Age Pension program,” _American Economic Review Papers & Proceedings_ , 2000, _90_ (2), 393–398.

- **, R. Glennerster, and M. Kremer** , “Using randomization in development economics research: A toolkit,” in T.P. Schultz and J. Strauss, eds., _T.P. Schultz and J. Strauss, eds._ , Amsterdam: North-Holland, 2007, pp. 3895–3962.

- **Dupas, P. and J. Robinson** , “Why Don’t the Poor Save More? Evidence from Health Savings Experiments,” _American Economic Review_ , 2012, _forthcoming._

- **Edmonds, E.V. and N. Schady** , “Poverty alleviation and child labor,” _American Economic Journal: Policy_ , 2012, _forthcoming._

- **Edwards, A. Cox and M. Ureta** , “International migration, remittances, and schooling: evidence from El Salvador,” _Journal of development economics_ , 2003, _72_ (2), 429–461.

28
SPLIT DECISIONS

- **Fan, J. and I. Gijbels** , _Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66_ , Vol. 66, London: Chapman & Hall/CRC, 1996.

- **Fortin, B. and G. Lacroix** , “A Test of the Unitary and Collective Models of Household Labour Supply,” _Economic Journal_ , 1997, _107_ (443), 933–955.

- **Fr ̈olich, M.** , “Regression Discontinuity Design with Covariates,” IZA Discussion Paper 3024, Bonn, Germany 2007.

- **Gennetian, L.A.** , “One or two parents? Half or step siblings? The effect of family structure on young children’s achievement,” _Journal of Population Economics_ , 2005, _18_ (3), 415–436.

- **Gertler, P., D.I. Levine, and M. Ames** , “Schooling and parental death,” _Review of Economics and Statistics_ , 2004, _86_ (1), 211–225.

- **Gertler, P.J., S.W. Martinez, and M. Rubio-Codina** , “Investing cash transfers to raise long-term living standards,” _American Economic Journal: Applied Economics_ , 2012, _4_ (1), 164–192.

- **Gibson, J., D. McKenzie, and S. Stillman** , “The impacts of international migration on remaining household members: omnibus results from a migration lottery program,” _Review of Economics and Statistics_ , 2011, _93_ (4), 1297–1318.

- **G ̈orlich, D., T. Omar Mahmoud, and C. Trebesch** , “Explaining Labour Market Inactivity in Migrant-Sending Families: Housework, Hammock, or Higher Education?,” Working Paper 1391, Kiel, Germany 2007.

- **Hahn, J., P. Todd, and W. Van der Klaauw** , “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design,” _Econometrica_ , 2001, _69_ (1), 201–09.

- **Hanson, G.** , “The Economic Consequences of the International Migration of Labor,” _Annual Review of Economics_ , 2009, _1_ (1), 179–208.

- **Hanson, G.H. and C. Woodruff** , “Emigration and educational attainment in Mexico,” IR/PS Working Paper, University of California San Diego 2003.

- **Hanushek, E.A.** , “Non-labor-supply responses to the income maintenance experiments,” in A.H. Munnell, ed., _Lessons from the Income Maintenance Experiments: Proceedings of a Conference Held at Melvin Village, New Hampshire_ , 1986, pp. 106–21.

- **Hubbard, R.G.** , “Capital-Market Imperfections and Investment,” _Journal of Economic Literature_ , 1998, _36_ (1), 193–225.

- **Hurst, E. and A. Lusardi** , “Liquidity constraints, household wealth, and entrepreneurship,” _Journal of Political Economy_ , 2004, _112_ (2), 319–347.

- **Imai, K., L. Keele, D. Tingley, and T. Yamamoto** , “Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies,” _American Political Science Review_ , 2011, _105_ (4), 765–789.

- **Imbens, G. and K. Kalyanaraman** , “Optimal Bandwidth Choice for the Regression Discontinuity Estimator,” _Review of Economic Studies_ , 2012, _79_ (3), 933–959.

- **Imbens, G.W. and T. Lemieux** , “Regression discontinuity designs: A guide to practice,” _Journal of Econometrics_ , 2008, _142_ (2), 615–635.

- **Jensen, R.** , “The (perceived) returns to education and the demand for schooling,” _Quarterly Journal of Economics_ , 2010, _125_ (2), 515–548.

- **and N. Miller** , “Keepin’ ‘em down on the farm: Migration and strategic investments in chil-

- drens’ schooling,” Working Paper, University of California Los Angeles, Los Angeles, CA 2012.

29
CLEMENS & TIONGSON

- **Kaboski, J.P. and R.M. Townsend** , “The Impact of Credit on Village Economies,” _American Economic Journal: Applied Economics_ , 2012, _4_ (2), 98–133.

- **Kandel, W. and G. Kao** , “The Impact of Temporary Labor Migration on Mexican Children’s Educational Aspirations and Performance,” _International Migration Review_ , 2001, _35_ (4), 1205– 1231.

- **Kane, T.J.** , “College Entry by Blacks since 1970: The Role of College Costs, Family Background, and the Returns to Education,” _Journal of Political Economy_ , 1994, _102_ (5), 878–911.

- **Karlan, D. and J. Zinman** , “Expanding credit access: Using randomized supply decisions to estimate the impacts,” _Review of Financial Studies_ , 2010, _23_ (1), 433–464.

- **Keane, M.P. and K.I. Wolpin** , “The effect of parental transfers and borrowing constraints on educational attainment,” _International Economic Review_ , 2001, _42_ (4), 1051–1103.

- **LaLonde, R.J.** , “Evaluating the Econometric Evaluations of Training Programs with Experimental Data,” _American Economic Review_ , 1986, _76_ (4), 604–620.

- **Lang, K. and J.L. Zagorsky** , “Does Growing up with a Parent Absent Really Hurt?,” _Journal of Human Resources_ , 2001, _36_ (2), 253–273.

- **Lee, D.S. and T. Lemieux** , “Regression Discontinuity Designs in Economics,” _Journal of Economic Literature_ , 2010, _48_ (2), 281–355.

- **Lochner, L.J. and A. Monge-Naranjo** , “The Nature of Credit Constraints and Human Capital,” _American Economic Review_ , 2011, _101_ (6), 2487–2529.

- **Lyle, D.S.** , “Using military deployments and job assignments to estimate the effect of parental absences and household relocations on childrens academic achievement,” _Journal of Labor Economics_ , 2006, _24_ (2), 319–350.

- **Macours, K. and R. Vakis** , “Seasonal migration and early childhood development,” _World development_ , 2010, _38_ (6), 857–869.

- **, N. Schady, and R. Vakis** , “Cash Transfers, Behavioral Changes, and Cognitive Development in Early Childhood: Evidence from a Randomized Experiment,” _American Economic Journal: Applied Economics_ , 2012, _4_ (2), 247–273.

- **McCrary, J.** , “Manipulation of the running variable in the regression discontinuity design: A density test,” _Journal of Econometrics_ , 2008, _142_ (2), 698–714.

- **McKenzie, D. and C. Woodruff** , “Experimental evidence on returns to capital and access to finance in Mexico,” _World Bank Economic Review_ , 2008, _22_ (3), 457–482.

- **and H. Rapoport** , “Can migration reduce educational attainment? Evidence from Mexico,”

- _Journal of Population Economics_ , 2011, _24_ (4), 1331–1358.

- **, S. Stillman, and J. Gibson** , “How Important is Selection? Experimental VS. NonExperimental Measures of the Income Gains from Migration,” _Journal of the European Economic Association_ , 2010, _8_ (4), 913–945.

- **Mel, S. De, D. McKenzie, and C. Woodruff** , “Returns to capital in microenterprises: evidence from a field experiment,” _Quarterly Journal of Economics_ , 2008, _123_ (4), 1329–1372.

- **Mergo, T.** , “The Effects of International Migration on Source Households: Evidence from DV Lottery Migration,” Job Market Paper, Dept. of Economics, University of California Berkeley, Berkeley, CA 2012.

- **Meyer, B.D. and J.X. Sullivan** , “Changes in the Consumption, Income, and Well-Being of Single

30
SPLIT DECISIONS Mother Headed Families,” _American Economic Review_ , 2008, _98_ (5), 2221–41.

**Moffitt, R.** , “An economic model of welfare stigma,” _American Economic Review_ , 1983, _73_ (5), 1023–1035.

- **Morduch, J.** , “Income smoothing and consumption smoothing,” _Journal of Economic Perspectives_ , 1995, _9_ (3), 103–114.

- **Nichols, A.** , “Causal inference with observational data,” _Stata Journal_ , 2007, _7_ (4), 507–541.

- **Page, M.E. and A.H. Stevens** , “The economic consequences of absent parents,” _Journal of Human Resources_ , 2004, _39_ (1), 80–107.

- **Paxson, C.H.** , “Using Weather Variability to Estimate the Response of Savings to Transitory Income in Thailand,” _American Economic Review_ , 1992, _82_ (1), 15–33.

- **Porter, J.** , “Estimation in the regression discontinuity model,” Technical Report, Unpublished Manuscript, Department of Economics, University of Wisconsin at Madison 2003.

- **Qian, N.** , “Missing women and the price of tea in China: The effect of sex-specific earnings on sex imbalance,” _Quarterly Journal of Economics_ , 2008, _123_ (3), 1251–1285.

- **Rapoport, H. and F. Docquier** , “The economics of migrants’ remittances,” _Handbook on the Economics of Giving, Reciprocity and Altruism_ , 2006, _2_ , 1135–1198.

- **Ratha, D. and A. Silwal** , “Remittance Flows in 2011: An Update,” Migration and Development Brief 18, World Bank Migration and Remittances Unit, Washington, DC 2012.

- **Reinhold, S. and K. Thom** , “Migration Experience and Earnings in the Mexican Labor Market,” Working Paper, Dept. of Economics, New York University, New York, NY 2011.

- **Rodriguez, E.R. and E.R. Tiongson** , “Temporary migration overseas and household labor supply: evidence from urban Philippines,” _International Migration Review_ , 2001, _35_ (3), 709–725.

- **Ruhm, C.J.** , “Parental employment and child cognitive development,” _Journal of Human Resources_ , 2004, _39_ (1), 155–192.

- **Schultz, T.W.** , “Investment in human capital,” _American Economic Review_ , 1961, _51_ (1), 1–17.

- **Sjaastad, L.A.** , “The costs and returns of human migration,” _Journal of Political Economy_ , 1962, _70_ (5), 80–93.

- **Smith, J.A. and P.E. Todd** , “Does matching overcome LaLonde’s critique of nonexperimental estimators?,” _Journal of Econometrics_ , 2005, _125_ (1), 305–353.

- **Taylor, J.E. and A. L ́opez-Feldman** , “Does migration make rural households more productive? Evidence from Mexico,” _Journal of Development Studies_ , 2010, _46_ (1), 68–90.

- **Udry, C.** , “Gender, Agricultural Production, and the Theory of the Household,” _Journal of Political Economy_ , 1996, _104_ (5), 1010–1046.

- **and S. Anagol** , “The Return to Capital in Ghana,” _American Economic Review Papers & Pro-_

- _ceedings_ , 2006, _96_ (2), 388–393.

- **Wang, S.Y.** , “Credit Constraints, Job Mobility, and Entrepreneurship: Evidence from a Property Reform in China,” _Review of Economics and Statistics_ , 2012, _94_ (2), 532–551.

- **Yang, D.** , “International Migration, Remittances and Household Investment: Evidence from Philippine Migrants Exchange Rate Shocks,” _Economic Journal_ , 2008, _118_ (528), 591–630.

- , “Migrant remittances,” _Journal of Economic Perspectives_ , 2011, _25_ (3), 129–151.

31
CLEMENS & TIONGSON 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**

|Outcome|_μs↑_0|_μs↓_0_−μs↑_0|s.e.|_p_-val.|band-<br>width|
|---|---|---|---|---|---|
|_Migration behavior afte_|_r applicati_|_on_||||
|Applicant deployed?|0_._0175|0_._678|0_._0284|_<_0_._001|2_._113|
|_Traits of applicant at the_|_time of ap_|_plication_||||
|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. _N_ fail( _s<_ 0) = 12 _,_ 577, _N_ pass( _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
CLEMENS & TIONGSON <!-- Start of picture text --> 100<br>3<br>50 2<br>1<br>0 0<br>-1<br>-50 Points above cutoff -2 Points above cutoff<br>-3<br>-100 -4<br>N = 23448, bandwith = 2.149 Good addresses only. N = 1532, bandwith = 2.360<br>100 100<br> Discontinuities in sampling universe 50 50<br>Figure 1:<br>0 0<br>-50 Points above cutoff -50 Points above cutoff<br>-100 -100<br>N = 23448, bandwith = 2.113 N = 23448, bandwith = 2.211<br>1.5 1.0 0.5 0.0 -0.5 1.0 0.5 0.0<br>Applicant college grad. Completed survey<br>1.0 0.5 0.0 1.0 0.5 0.0 -0.5<br>Applicant deployed Applicant employed<br><!-- End of picture text --> 34
SPLIT DECISIONS Figure 2: **McCrary (2008) nonparametric test for score manipulation** <!-- Start of picture text -->
-40 -20 0 20 40<br>Points above cutoff<br>0.020<br>0.015<br>Density<br>0.010<br>0.005<br><!-- End of picture text -->

35
CLEMENS & TIONGSON Table 3: **Checking discontinuity for 899 households in survey sample**

|Outcome|_μs↑_0|_μs↓_0_−μs↑_0|s.e.|_p_-val.|band-<br>width|
|---|---|---|---|---|---|
|_Migration behavior after a_|_pplication_|||||
|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 t_<br>|_ime of appl_<br>|_ication_<br>||||
|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. _N_ fail( _s<_ 0) = 460, _N_ pass( _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
SPLIT DECISIONS <!-- Start of picture text --> 4 4<br>2 2<br>0 0<br>-2 -2<br>Points above cutoff Points above cutoff<br>-4 -4<br>-6 All HH, N = 899, bandwidth = 1.716 -6 Married applicants only, N = 389, bandwidth = 1.612<br>4 4<br> Discontinuities in survey sample 2 2<br>Figure 3:<br>0 0<br>-2 -2<br>Points above cutoff Points above cutoff<br>-4 -4<br>-6 All HH, N = 899, bandwidth = 1.307 -6 All HH with inc.>0, N = 881, bandwidth = 2.079<br>1.0 0.5 0.0 0.8 0.4 0.0<br>Applicant college grad. Decision maker (childcare)<br>1.0 0.5 0.0 10.0 9.6 9.2<br>Member ever in Korea ln(total income)<br><!-- End of picture text --> 37
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<!-- Start of picture text --> o o<br>Soo o<br>Oo mo<br>oo o<br>o AG Oo<br>iB Oo an<br>o oO oO of<br>a a o a Oo oo<br>q, Bo Oo oo a<br>a Qo o o<br>2 a i) Oo o Oo<br>Oo Oo oOo th oO B ma<br>oh foal Oo i<br>o s qo<br>Ce ie |<br>ep oO<br>jal if ‘<br>ia Qo i]<br>i<br>o f<br>“ahem<br><!-- End of picture text -->

<!-- Start of picture text --> o<br>Oo & ia o<br>oda ae a<br>o oO<br>Oa “a<br>oO a BH<br>o Oo OOg daa og<br>a B sl Oo im] Oo Oo ina<br>o<br>gd a o Oom, 90oOo oOo 78<br>Oo o)6h|OU0 oO o o<br>A on a a Oo a o<br>mon Oo oOo i oO<br>ho = la 4<br>o i<br>a oo Oo o .<br>“noom<br><!-- End of picture text -->
CLEMENS & TIONGSON

|band-<br>width|2_._056<br>2_._505<br>1_._943<br>1_._754<br>2_._134<br>2_._543|1_._785<br>1_._836<br>1_._997<br>1_._858<br>1_._467<br>1_._897<br>2_._079<br>1_._877<br>1_._944<br>1_._819<br>2_._236<br>2_._184<br>2_._577<br>s.<br>s income.<br>, fshing).|
|---|---|---|
|effect<br>_p_-val.|0_._798<br>0_._708<br>0_._120<br>_<_0_._001<br>0_._520<br>0_._660|0_._851<br>_<_0_._001<br>0_._114<br>0_._520<br>_<_0_._001<br>0_._115<br>0_._844<br>0_._113<br>0_._0648<br>0_._139<br>0_._751<br>0_._204<br>0_._713<br>revious month<br>inc.” = busines<br>stock, forestry|
|nt-on-treated<br>s.e.|1_._013<br>0_._841<br>0_._437<br>0_._0608<br>0_._617<br>0_._332|0_._0327<br>0_._121<br>0_._0655<br>5711_._1<br>2764_._7<br>5183_._9<br>0_._459<br>0_._342<br>0_._298<br>0_._0878<br>16_._03<br>0_._180<br>1_._760<br>, average over 6 p<br>r in Korea. “Bus.<br>ture (farming, live|
|Treatme<br>_μs↓_0_−μs↑_0<br>_τs↓_0_−τs↑_0|0_._260<br>_−_0_._315<br>_−_0_._679<br>0_._209<br>0_._397<br>0_._146|0_._00616<br>0_._472<br>_−_0_._104<br>3675_._2<br>11853_._9<br>_−_8178_._7<br>0_._0901<br>0_._543<br>_−_0_._550<br>_−_0_._130<br>5_._083<br>_−_0_._229<br>0_._648<br>pesos per month<br>_ever_ had a membe<br>el. “Agr.” = agricul|
|fect<br>_p_-val.|0_._799<br>0_._709<br>0_._129<br>0_._00108<br>0_._523<br>0_._660|0_._851<br>_<_0_._001<br>0_._109<br>0_._520<br>_<_0_._001<br>0_._121<br>0_._845<br>0_._117<br>0_._0730<br>0_._136<br>0_._562<br>0_._193<br>0_._711<br>unts in Philippine<br>ent = household <br>). Triangular kern|
|t-to-treat ef<br>s.e.|0_._464<br>0_._387<br>0_._215<br>0_._0304<br>0_._284<br>0_._152|0_._0158<br>0_._0614<br>0_._0311<br>2739_._7<br>1371_._5<br>2533_._9<br>0_._203<br>0_._156<br>0_._145<br>0_._0418<br>0_._834<br>0_._0803<br>1_._038<br>5. Money amo<br>_ppines_. Treatm<br>araman(2012|
|Inten<br>_μs↓_0_−μs↑_0|0_._118<br>_−_0_._145<br>_−_0_._326<br>0_._0995<br>0_._182<br>0_._0671|0_._00296<br>0_._226<br>_−_0_._0498<br>1764_._3<br>5690_._4<br>_−_3926_._2<br>0_._0397<br>0_._244<br>_−_0_._260<br>_−_0_._0623<br>_−_0_._483<br>_−_0_._105<br>0_._384<br>means18⩽Age_<_6<br>members_in the Phili_<br>ofImbens and Kalyan|
|_μs↑_0|_members_<br>5_._194<br>3_._369<br>3_._276<br>0_._537<br>1_._456<br>0_._322|0_._983<br>0_._388<br>0_._957<br>21405_._8<br>4734_._6<br>16671_._2<br>9_._652<br>9_._141<br>9_._240<br>0_._155<br>7_._396<br>0_._162<br>6_._972<br>vel. Working age<br>g to household<br>by the method|
|Outcome|_Number of household_<br>Total<br>Working age<br>_excl. Korea_<br>_% female_<br>Age⩾65<br>Age_<_18|_Any income?_: Total<br>From remittances<br>Non-remittances<br>_Income_: Total<br>From remittances<br>Non-remittances<br>ln (_Income_): Total<br>From remittances<br>Non-remittances<br>Business? (agr.)<br>ln (_bus. inc._agr.)<br>Business? (non-agr.)<br>ln (_bus. inc._non-agr.)<br>All variables at household le<br>‘Income’ means income goin<br>Optimal bandwidth selected|

40
SPLIT DECISIONS

|band-<br>width<br>3_._091<br>2_._518<br>3_._551<br>1_._875<br>2_._204<br>3_._251<br>2_._724<br>1_._468<br>1_._523<br>1_._875<br>1_._960<br>3_._458<br>1_._684<br>1_._887<br>2_._030<br>1_._626<br>1_._746<br>2_._228<br>2_._318<br>f loans,|
|---|
|d effect<br>_p_-val.<br>0_._0317<br>0_._757<br>0_._0341<br>0_._00772<br>0_._375<br>0_._834<br>0_._301<br>0_._955<br>0_._0601<br>0_._115<br>0_._0818<br>0_._942<br>0_._106<br>0_._0718<br>0_._236<br>0_._0479<br>0_._263<br>0_._996<br>0_._996<br>stock.<br>nks, paying of<br>ular kernel.|
|nt-on-treate<br>s.e.<br>5379_._8<br>3435_._4<br>1878_._7<br>1005_._5<br>1470_._7<br>2773_._6<br>0_._271<br>0_._165<br>0_._162<br>0_._435<br>0_._525<br>0_._888<br>0_._123<br>0_._0938<br>0_._159<br>0_._0363<br>0_._0867<br><br>0_._195<br>0_._478<br>ngs is fow, not<br>s deposits in ba<br>casions, gifts.<br>n(2012), triang|
|Treatme<br>_μs↓_0_−μs↑_0<br>_τs↓_0_−τs↑_0<br>11554_._2<br>1061_._3<br>3981_._7<br>2678_._6<br>1303_._8<br>_−_581_._4<br>0_._280<br>0_._00930<br>0_._304<br>0_._684<br>0_._913<br>_−_0_._0648<br>0_._199<br>_−_0_._169<br>0_._188<br>_−_0_._0718<br>_−_0_._0970<br>0_._000919<br>_−_0_._00263<br>e over 6 mos. Savi<br>“Savings” include<br>creation, family oc<br>and Kalyanarama|
|ect<br>_p_-val.<br>0_._0245<br>0_._757<br>0_._0296<br>0_._00650<br>0_._383<br>0_._834<br>0_._301<br>0_._955<br>0_._0595<br>0_._110<br>0_._0958<br>0_._942<br>0_._102<br>0_._0656<br>0_._227<br>0_._0427<br>0_._264<br>0_._996<br>0_._996<br>/mo., averag<br>medical care.<br>, clothing, re<br>wingImbens|
|t-to-treat eff<br>s.e.<br>2417_._7<br>1577_._2<br>892_._5<br>472_._5<br>683_._6<br>1332_._7<br>0_._124<br>0_._0795<br>0_._0775<br>0_._195<br>0_._285<br>0_._500<br>0_._0585<br>0_._0440<br>0_._0710<br>0_._0170<br>0_._0417<br>0_._0897<br>0_._220<br> . Money in PHP<br>medicine, and<br>& personal care<br>h selection follo|
|Inten<br>Outcome<br>_μs↑_0<br>_μs↓_0_−μs↑_0<br>_Expenditures_: Total<br>18374_._8<br>5436_._0<br>Food<br>9451_._2<br>487_._1<br>Quality of life<br>6898_._2<br>1941_._2<br>Educ. & health<br>1054_._3<br>1285_._9<br>Durables<br>893_._4<br>596_._5<br>_Savings_<br>1422_._1<br>_−_278_._9<br>ln (_expenditures_): Total<br>9_._657<br>0_._129<br>Food<br>9_._001<br>0_._00446<br>Quality of life<br>8_._609<br>0_._146<br>Educ. & health<br>6_._349<br>0_._312<br>Durables<br>5_._989<br>0_._475<br>ln (_Savings_)<br>7_._399<br>_−_0_._0363<br>Any savings?<br>0_._276<br>0_._0956<br>_Borrowed for business reasons?_<br>from family<br>0_._181<br>_−_0_._0810<br>from other<br>0_._106<br>0_._0859<br>_Borrowed for non-business reasons?_<br>from family<br>0_._0345<br>_−_0_._0345<br>from other<br>0_._147<br>_−_0_._0466<br>Family owns residence?<br>0_._794<br>0_._000421<br>Number of bedrooms<br>2_._253<br>_−_0_._00121<br>All variables at household level. Working age means18⩽Age_<_65<br>“Food” = food, beverages, and tobacco. “Health & educ.” = school,<br>extending loans. “Quality of life” = fuel, transportation, household<br>“Durables” = durable goods, taxes, home improvement. Bandwidt<br>Treatment = household_ever_ had a member in Korea.|

41
# CLEMENS & TIONGSON

|band-<br>width<br>2_._227<br>1_._406<br>1_._603<br>2_._147<br>3_._276<br>2_._138<br>3_._390<br>2_._103<br>3_._192<br>2_._490<br>1_._873<br>2_._041<br>2_._402<br>4_._436<br>2_._356<br>2_._904|
|---|
|ated effect<br>_p_-val.<br>0_._682<br>_<_0_._001<br>_<_0_._001<br>0_._983<br>0_._802<br>0_._955<br>0_._661<br>0_._528<br>0_._476<br>0_._838<br>0_._489<br>0_._117<br>0_._285<br>0_._993<br>0_._977<br>0_._969<br>evious months.<br>ngular kernel.|
|nt-on-tre<br>s.e.<br>0_._182<br>0_._0854<br>0_._120<br>0_._337<br>1_._627<br>0_._289<br>0_._615<br>0_._133<br>0_._426<br>0_._110<br>0_._235<br>0_._0263<br>0_._0677<br>0_._503<br>0_._0921<br>0_._285<br>ge over 6 pr<br>ect.<br>n(2012), tria|
|Treatme<br>_μs↓_0_−μs↑_0<br>_τs↓_0_−τs↑_0<br>_−_0_._0744<br>0_._838<br>0_._586<br>0_._00716<br>0_._409<br>0_._0163<br>_−_0_._270<br>0_._0840<br>0_._304<br>_−_0_._0225<br>_−_0_._163<br>0_._0412<br>0_._0723<br>0_._00413<br>_−_0_._00262<br>_−_0_._0110<br>er month, avera<br>er on each subj<br>d Kalyanarama|
|effect<br>_p_-val.<br>0_._683<br>_<_0_._001<br>_<_0_._001<br>0_._983<br>0_._804<br>0_._955<br>0_._656<br>0_._526<br>0_._477<br>0_._839<br>0_._493<br>0_._122<br>0_._284<br>0_._993<br>0_._977<br>0_._969<br>ilippine pesos p<br>t decision-mak<br>wingImbens an|
|t-to-treat<br>s.e.<br>0_._0830<br>0_._0541<br>0_._0612<br>0_._157<br>0_._814<br>0_._134<br>0_._505<br>0_._0703<br>0_._226<br>0_._0591<br>0_._133<br>0_._0141<br>0_._0361<br>0_._269<br>0_._0493<br>0_._173<br>ounts in Ph<br>mary or join<br>ction follo|
|Inten<br>_μs↓_0_−μs↑_0<br>_−_0_._0339<br>0_._403<br>0_._281<br>0_._00331<br>0_._202<br>0_._00755<br>_−_0_._225<br>0_._0446<br>0_._161<br>_−_0_._0120<br>_−_0_._0910<br>0_._0218<br>0_._0387<br>0_._00221<br>_−_0_._00140<br>_−_0_._00662<br>_<_65. Money am<br>cant was the pri<br>. Bandwidth sele|
|_μs↑_0<br>0_._833<br>0_._142<br>0_._336<br>)<br>0_._492<br>0_._471<br>0_._251<br>9_._262<br>= 2142)<br>0_._493<br>0_._262<br>0_._232<br>9_._171<br>0_._00730<br>0_._0695<br>11_._03<br>0_._141<br>0_._697<br>ans18⩽Age <br>ether the appli<br>dults_N_=2142<br>r in Korea.|
|Outcome<br>_Applicants only_(_N_ = 875)<br>Worked in past 6 months?<br>Now in Korea?<br>Now abroad?<br>_Applicant’s spouses only_(_N_ = 421<br>Worked in past 6 months?<br>Days worked, previous mo.<br>Any wage income?<br>_ln_wage income<br>_All non-applicant adults only_(_N_ <br>Worked in past 6 months?<br>Days worked, previous mo.<br>Any wage income?<br>_ln_wage income<br>Now in Korea?<br>Now abroad?<br>Years of education<br>Visited health facility past mo.<br>_if so, private facility?_<br>All variables at individual level. ‘Adult’ me<br>‘Decisions’ is an indicator variable for wh<br>For applicants_N_=875, for non-applicant a<br>Treatment = household_ever_ had a membe|

42
SPLIT DECISIONS

|band-<br>width|2_._087<br>2_._275<br>1_._967<br>2_._151<br>1_._955|1_._612<br>2_._033<br>1_._895<br>2_._010<br>1_._782<br>n(2012),|
|---|---|---|
|ted effect<br>_p_-val.|0_._486<br>0_._415<br>0_._0297<br>0_._502<br>0_._0130|0_._00278<br>0_._192<br>0_._00671<br>0_._286<br>0_._00223<br>Kalyanarama<br>ach subject.|
|t-on-trea<br>s.e.|0_._236<br>0_._239<br>0_._129<br>0_._245<br>0_._127|0_._212<br>0_._446<br>0_._214<br>0_._406<br>0_._208<br>Imbens and<br>-maker on e|
|Treatmen<br>_μs↓_0_−μs↑_0<br>_τs↓_0_−τs↑_0|_−_0_._165<br>_−_0_._195<br>_−_0_._281<br>_−_0_._164<br>_−_0_._315|_−_0_._635<br>_−_0_._582<br>_−_0_._579<br>_−_0_._432<br>_−_0_._635<br>lection follows <br>r joint decision|
|effect<br>_p_-val.|0_._484<br>0_._410<br>0_._0286<br>0_._501<br>0_._0119|_<_0_._001<br>0_._136<br>0_._00211<br>0_._264<br>_<_0_._001<br>. Bandwidth se<br>s the primary o|
|-to-treat<br>s.e.|0_._107<br>0_._108<br>0_._0618<br>0_._111<br>0_._0601|0_._0870<br>0_._165<br>0_._0910<br>0_._164<br>0_._0898<br>ber in Korea<br>pplicant wa|
|Intent<br>_μs↓_0_−μs↑_0|_−_0_._0748<br>_−_0_._0889<br>_−_0_._135<br>_−_0_._0747<br>_−_0_._151|_−_0_._307<br>_−_0_._246<br>_−_0_._280<br>_−_0_._183<br>_−_0_._307<br>d_ever_ had a mem<br>for whether the a|
|_μs↑_0|0_._367<br>0_._377<br>0_._451<br>0_._456<br>0_._416|9)<br>0_._510<br>0_._508<br>0_._608<br>0_._589<br>0_._588<br>= househol<br>tor variable|
|Outcome|_Applicants_(_N_ = 875)<br>Decisions:_childcare_<br>Decisions:_home repairs_<br>Decisions:_major purchases_<br>Decisions:_entrepreneurship_<br>Decisions:_weekend activities_|_Married applicants only_(_N_ = 38<br>Decisions:_childcare_<br>Decisions:_home repairs_<br>Decisions:_major purchases_<br>Decisions:_entrepreneurship_<br>Decisions:_weekend activities_<br>Observations are individuals. Treatment<br>triangular kernel. ‘Decisions’ is an indica|

43
CLEMENS & TIONGSON

|band-|width|2_._108|1_._599|1_._732|2_._156|2_._109<br>1_._396|1_._930<br>3_._163<br>Korea.|
|---|---|---|---|---|---|---|---|
|ted effect|_p_-val.|0_._832|0_._0328|0_._0370|0_._556|0_._182<br>0_._316|0_._283<br>0_._489<br>a member in|
|t-on-trea|s.e.|0_._145|0_._190|0_._145|0_._237|0_._512<br>0_._0217|0_._144<br>2_._602<br>ld_ever_ had|
|Treatmen|_μs↓_0_−μs↑_0<br>_τs↓_0_−τs↑_0|_−_0_._0309|0_._405|0_._302|_−_0_._139|0_._683<br>_−_0_._0217|_−_0_._154<br>_−_1_._799<br>ent = househo|
|fect|_p_-val.|0_._830|0_._0202|0_._0300|0_._555|0_._159<br>0_._317|0_._279<br>0_._483<br>117. Treatm|
|-to-treat ef|s.e.|0_._0402|0_._0754|0_._0676|0_._0840|0_._322<br>0_._0105|0_._0690<br>1_._007<br>age⩾12,_N_ =<br>gular kernel.|
|Intent|_μs↓_0_−μs↑_0|_−_0_._00862|0_._175|0_._147|_−_0_._0496|0_._453<br>_−_0_._0105|_−_0_._0747<br>_−_0_._707<br>ns age_<_18. With<br>aman(2012), trian|
||_μs↑_0|0_._984|0_._275|0_._340|0_._144|0_._525<br>0_._0105|0_._537<br>11_._51<br>‘Child’ mea<br>d Kalyanar|
||Outcome|In school? (if age⩾6)|_if so, private facility?_|_Any awards at school?_|<br>Visited health facility past mo.?|_if so, private facility?_<br>Working?|Does anyone read to child?<br>Desired years of education<br>_N_ = 729. All variables at individual level.<br>Bandwidth selection followingImbens an|

44
SPLIT DECISIONS

|Matching|Mahala-<br>nobis|0_._435|(0_._130)|_−_0_._313<br>(0_._0318)|0_._0202|(0_._0227)|0_._616<br>(0_._213)|
|---|---|---|---|---|---|---|---|
|eighbors|10|0_._298|(0_._0922)|_−_0_._317<br>(0_._0195)|0_._0360|(0_._0123)|0_._370<br>(0_._129)|
|g, nearest n|5|0_._355|(0_._0985)|_−_0_._323<br>(0_._0212)|0_._0348|(0_._0138)|0_._298<br>(0_._144)|
|Matchin|2|0_._387|(0_._112)|_−_0_._320<br>(0_._0250)|0_._0164|(0_._0175)|0_._278<br>(0_._175)|
||OLS|0_._406|(0_._0779)|_−_0_._297<br>(0_._0155)|0_._0372|(0_._01000)|0_._212<br>(0_._102)|
||RDD_∗_|0_._684|(0_._435)|_−_0_._229<br>(0_._180)|_−_0_._00862|(0_._0402)|0_._00221<br>(0_._269)|
||Outcome|_Households_<br>ln Expenditures: Educ. & med.||Business (non-agr.)?|_Children (6_⩽_age < 18)_<br>In school?||_Adults (age_⩾_18)_<br>Years of educ.|

45
CLEMENS & TIONGSON

|king<br>_p_-val.<br>0_._0762<br>0_._0908<br>0_._0824<br>0_._0538<br>0_._134<br>0_._222<br>0_._581<br>0_._477<br>0_._867<br>0_._506<br>0_._578<br>0_._0686<br>0_._323|
|---|
|ision-ma<br>s.e.<br>0_._172<br>0_._460<br>0_._543<br>0_._0384<br>0_._0952<br>0_._189<br>0_._135<br>0_._434<br>0_._113<br>0_._253<br>0_._295<br>0_._272<br>0_._185<br>rnel.|
|Dec<br>_μs↓_0_−μs↑_0<br>_τs↓_0_−τs↑_0<br>0_._306<br>0_._778<br>0_._943<br>_−_0_._0741<br>_−_0_._143<br>_−_0_._231<br>0_._0744<br>0_._309<br>_−_0_._0189<br>_−_0_._168<br>_−_0_._165<br>0_._495<br>_−_0_._183<br>, triangular ke<br>as ofTable 6.|
|nces<br>_p_-val.<br>0_._274<br>0_._989<br>0_._598<br>0_._163<br>0_._0654<br>0_._789<br>0_._314<br>_<_0_._001<br>0_._422<br>0_._122<br>0_._955<br>0_._259<br>0_._242<br>anaraman(2012)<br>aker in all fve are|
|Remitta<br>s.e.<br>0_._267<br>0_._650<br>1_._031<br>0_._0766<br>0_._175<br>0_._277<br>0_._209<br>0_._338<br>0_._155<br>0_._602<br>0_._649<br>2_._548<br>0_._425<br>ns and Kaly<br>ecision-m|
|_ln_ <br>_μs↓_0_−μs↑_0<br>_τs↓_0_−τs↑_0<br>0_._292<br>_−_0_._00899<br>0_._544<br>_−_0_._107<br>_−_0_._322<br>_−_0_._0741<br>0_._210<br>1_._145<br>0_._124<br>_−_0_._930<br>_−_0_._0364<br>2_._877<br>0_._497<br> ̈olich(2007)<br>n followsImbe<br>imary or joint d|
|_p_-val.<br>0_._0601<br>0_._115<br>0_._0818<br>0_._0479<br>0_._139<br>0_._205<br>0_._526<br>0_._504<br>0_._835<br>0_._489<br>0_._832<br>0_._0328<br>0_._283<br>followingFr<br>idth selectio<br>pplicant is pr|
|None<br>s.e.<br>0_._162<br>0_._435<br>0_._525<br>0_._0363<br>0_._0878<br>0_._180<br>0_._133<br>0_._442<br>0_._110<br>0_._235<br>_age <_18<br>0_._145<br>0_._190<br>0_._144<br>tes included<br>orea. Bandw<br>r whether a|
|_μs↓_0_−μs↑_0<br>_τs↓_0_−τs↑_0<br>0_._304<br>0_._684<br>0_._913<br>_−_0_._0718<br>_−_0_._130<br>_−_0_._228<br>0_._0842<br>0_._295<br>_−_0_._0229<br>_−_0_._163<br>_ouse)_,6⩽ <br>_−_0_._0309<br>0_._405<br>_−_0_._154<br>only. Covaria<br>member in K<br>ve dummies fo|
|Covariates:<br>Outcome<br>_Households_<br>_ln_Exp.: Quality of life<br>_ln_Exp.: Educ. & med.<br>_ln_Exp.: Durables<br>Borrowed (family)?<br>Business? (agr.)<br>Business? (non-agr.)<br>_Non-applicant adults_<br>Worked (6 mos.)?<br>Days worked (past mo.)<br>Any wage income?<br>_ln_wage income<br>_Children (of applicant or sp_<br>In school?<br>Private?<br>Anyone read to child?<br>Family borrowing is non-business<br>Treatment = household_ever_ had a<br>‘Decision-making’ variables are f|

46
SPLIT DECISIONS Table 10: **Compare barely-failing sampled households to whole country**

||Sample,_s <_0|Whole|country||
|---|---|---|---|---|
|Outcome|Mean (_μ_1)|Mean (_μ_2)|s.d.|_p_(_μ_1 =_μ_2)|
|_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? (fow)|0_._263|0_._496|[0_._498]|_<_0_._001|
|Savings (fow)|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: _N_ samp _,s<_ 0 = 460, _N_ ctry = 38 _,_ 453. Children: _N_ samp _,s<_ 0 = 433, _N_ ctry = 55 _,_ 642. Nationwide data weighted with frequency weights. Expenditures defined in Table 4b.

47