Abundance from Abroad: Migrant Income and Long-Run Economic Development
What this paper finds — and why it matters
Layer 1 — Overview
Research Question
This paper asks how persistent increases in international migrant income prospects affect long-run economic development in migrant-origin areas. The central question is whether Philippine provinces with persistent access to higher-income migration opportunities develop faster than provinces with less attractive migration opportunities, and through which channels.
Natural Experiment and Identification Strategy
The authors exploit the 1997 Asian Financial Crisis as a large-scale natural experiment. The crisis triggered sharp, heterogeneous, and persistent exchange rate changes across Philippine migrants’ destination countries — ranging from a 4% depreciation against the Philippine peso (Korea) to a 57% appreciation (Libya), with Japan and Saudi Arabia in between (appreciations of 32% and 52%, respectively). Because Philippine provinces differed in the pre-crisis distribution of migrant income across destinations (measured using unusual POEA/OWWA administrative contract data covering all overseas worker contracts, including migrant incomes, origins, and destinations), these exchange rate shocks generated exogenous, province-level variation in a shift-share instrument: the predicted change in province migrant income per capita due to the 1997 shocks. Identification follows the “exogenous shares” framework of Goldsmith-Pinkham et al. (2020). Pre-trend tests across up to 12 years of pre-shock panel data find no evidence of differential trends across provinces. The five destinations with the highest Rotemberg weights — Saudi Arabia, Japan, United States, Taiwan, and Hong Kong — collectively account for 75% of the identifying variation. The exchange rate shocks and the exposure weights both exhibit strong persistence over two decades post-1997.
Data
- Philippine government administrative data (POEA/OWWA) on all overseas worker contracts, 1992–2015, matched at 95% rate, providing province-of-origin and destination-specific migrant income.
- Philippine Family Income and Expenditure Survey (FIES), up to twelve triennial rounds from 1985–2018 (74 provinces, ~40,000 households per round), for domestic income and expenditure.
- Six rounds of the Philippine Census of Population (1990–2015) for education, migration rates, and sectoral employment shares.
- Province-level consumer price index data (1994–2017) and firm-level export survey data for robustness checks.
- Unit of analysis: 74 Philippine provinces (consistent 1990 borders).
Main Findings with Quantitative Magnitudes
Six-fold magnification of migrant income: Each unit of initial short-run shock (1997–1998) to migrant income per capita is magnified more than six-fold by 2009–2015. A one-standard-deviation shock (0.093) raises long-run migrant income per capita by 14.7% of the baseline mean (PhP 601 per capita, 0.2 standard deviations).
Domestic income gains predominate: A one-standard-deviation shock raises domestic income per capita (excluding migrant income and remittances) by 6.4% of the baseline mean (PhP 1,676, 0.18 standard deviations). Remarkably, 73.6% of the long-run global income increase comes from domestic income and only 26.4% from migrant income.
Global income and expenditure: A one-standard-deviation shock raises global income per capita by PhP 2,277 (0.2 standard deviations, or 7.5% of the baseline mean) in 2009–2015. Expenditure per capita rises by PhP 1,159 (0.13 standard deviations). Effects emerge gradually over two decades.
Education: A one-standard-deviation shock increases the college-educated share of the population by 0.46–0.51 percentage points (0.11–0.12 standard deviations) and secondary completion by 0.63 percentage points. There is no significant effect on primary completion.
Migration rates and skill composition: A one-standard-deviation shock increases the migration rate by 0.19 percentage points (0.22 standard deviations), raises the share of skilled migrants by 1.84 percentage points (0.19 standard deviations), and increases average migrant annual salary by PhP 23,703 (0.16 standard deviations). New migration concentrates in higher-education-quartile occupations.
Structural change: The shock reduces primary sector employment shares by 1.2 percentage points per standard deviation (0.06 standard deviations), with over 70% of that shift absorbed by non-tradable goods and services sectors. Domestic income gains are driven almost entirely by non-agricultural income, and roughly 55% of the increase in entrepreneurial income is from service sectors.
Education’s contribution to income: Model-based calculations assign 19.6% of the global income gain, 17.8% of the migrant income gain, and 20.2% of the domestic income gain to educational investments. Exchange rate persistence plus altered migration flows explain an additional 64.6% of the migrant income increase, so together these mechanisms account for 82.3% of the six-fold magnification. A demand multiplier (assuming 64% of migrant income returns to origin economies and a multiplier of 2.9, consistent with estimates from the literature) accounts for approximately 83.3% of the non-education-related portion of the domestic income increase.
Threats to Identification Ruled Out
Import and export shift-share controls (constructed analogously using bilateral trade data and province-level industry employment shares) are uncorrelated with the migrant income shock and leave coefficient estimates unchanged. Province-level manufactured exports, agricultural income, the CPI, and national-level FDI inflows show no statistically significant response to the shock. Internal migration rates are unaffected. Geographic spillover controls and tourism controls do not alter results. Placebo regressions in the pre-period yield small, statistically insignificant coefficients.
Scope Conditions
The paper studies formal, government-regulated temporary labor migration from the Philippines, where migrants sign contracts through POEA-licensed agencies and typically expect to return after one or more contracts. The findings apply specifically to settings where persistent (not transitory) migrant income shocks occur. Approximately 60% of contract migrants are female. The study period spans 1985–2018, with main long-run outcome analyses comparing 1994 (pre-shock) with 2009–2015 (post-shock).
Layer 2 — Q&A
Q1: What makes the 1997 Asian Financial Crisis useful as a natural experiment for this paper’s purposes?
A1: The crisis was largely unanticipated by policymakers, international organizations, and financial markets, making it implausible that pre-1997 migration destination choices reflected anticipation of the shocks. Exchange rate changes were heterogeneous across destinations (ranging from a 4% depreciation to a 57% appreciation), and crucially, these changes proved highly persistent over two decades — regression coefficients of long-run exchange rate changes on the initial 1997–1998 shock are close to and statistically indistinguishable from 1 in nearly all post-shock periods. Combined with the province-specific variation in migrant destination exposure, this generates persistent, exogenous, and heterogeneous shocks to migrant income prospects across provinces.
Q2: What is the shift-share variable, and how does it combine “shifts” and “shares”?
A2: The shift-share variable Shiftshareo equals the sum over destinations d of (ωdo0 × ΔRd), where ωdo0 is province o’s pre-shock migrant income per capita from destination d (the “exposure weight” or “share”), and ΔRd is the fractional change in destination d’s exchange rate from before to after the crisis (the “shift”). It captures the predicted change in province-level migrant income per capita due to the 1997 exchange rate shocks, and is derived directly from a theoretical model of migration. Identification relies on the “exogenous shares” approach of Goldsmith-Pinkham et al. (2020): the pre-1997 exposure weights are treated as as-good-as-randomly assigned conditional on controls, because they reflect historical migration networks formed well before the crisis.
Q3: Why is the six-fold magnification of the initial migrant income shock so striking, and what does the structural model say about its sources?
A3: The coefficient on migrant income per capita (6.463 in Panel D of Table 1) implies that for each unit of initial short-run migrant income shock, migrant income per capita is more than six units higher in 2009–2015 — a far larger response than a one-for-one pass-through would predict. The structural model, which augments a Fréchet-based gravity model of migration with endogenous education investments, accounts for 82.3% of this magnification. Education investments explain 17.8% of the migrant income increase; persistent favorable exchange rates and resulting shifts in migration flows across destinations explain an additional 64.6%. The Fréchet elasticity of migration flows with respect to destination wages is estimated at θ = 3.42 via PPML, implying that even partial reorientation of migrants toward now-higher-wage destinations substantially raises aggregate migrant income.
Q4: What evidence supports the parallel trends assumption in the pre-shock period?
A4: The authors present event study diagrams (Figure 2) showing no differential positive pre-trends in either expenditure per capita or domestic income per capita prior to 1997 — for domestic income, there is a statistically insignificant negative trend from 1985–1991 and no trend in 1991–1994. Placebo regressions estimated on the pre-period only (1985, 1988, 1991 as “pre,” 1994 and 1997 as “post”) yield small, statistically insignificant coefficients on both domestic income and expenditure. Balance tests focusing on the five high-Rotemberg-weight destination shares (Saudi Arabia, Japan, US, Taiwan, Hong Kong) — which collectively account for 75% of the identifying variation — also show no significant pre-trends in key outcomes across provinces with varying levels of exposure.
Q5: How do the authors rule out trade flows as an alternative mechanism for the estimated income effects?
A5: They construct separate import and export shift-share variables, analogous to the “China shock” of Autor et al. (2013), using baseline bilateral trade values (from COMTRADE, disaggregated to 36 ISIC industries), province-level employment shares in import and export industries (from the 1990 Census), and the same destination exchange rate shocks. These trade shift-share variables are uncorrelated with the migrant income shock after conditioning on baseline controls (Appendix Table A5). Including them as additional controls in Panel D of all main regression tables leaves the migrant income coefficient stable. Further, province-level manufactured exports per capita show no large or statistically significant response to the migrant income shock, agricultural income similarly shows no significant response, and consumer price indices are unresponsive — ruling out import price changes as a confound. FDI inflows at the national level also show no significant relationship with destination-country exchange rate shocks.
Q6: What is the composition of the domestic income gains — where do they come from?
A6: Both wage income and entrepreneurial/rental income rise significantly and in similar magnitude, while “other income” (pensions, interest, dividends) shows no robust increase (Table 4). Non-agricultural income drives virtually the entire domestic income gain; agricultural income per capita is statistically insignificant (Table 5, columns 1–2). Within entrepreneurial income, approximately 55% of the increase is from service sectors, with manufacturing and primary sector entrepreneurial income showing insignificant effects at the 10% level (Table 5, columns 3–5). These patterns are consistent with the structural change finding: the shock shifts labor from primary sectors toward non-tradable goods and services rather than toward tradable manufacturing.
Q7: What is the “global income” concept and what share does each component contribute?
A7: Global income per capita is defined as the sum of domestic income per capita (earned within the Philippine economy, excluding all international transfers) and migrant income per capita (the full income earned abroad by a province’s international migrants, calculated from contract data). Of the long-run global income increase, 73.6% comes from domestic income and 26.4% from migrant income. A one-standard-deviation shock raises global income by PhP 2,277 per capita in 2009–2015 (0.2 standard deviations, or 7.5% of the baseline mean).
Q8: How do education effects translate into more and higher-skilled migration?
A8: A one-standard-deviation migrant income shock increases college completion by 0.46 percentage points and secondary completion by 0.63 percentage points (with no significant effect on primary completion), consistent with the shock raising the return to higher education in the broader population. These better-educated workers then migrate at higher rates: the share of migrants who are skilled (college-educated) rises by 1.84 percentage points per standard deviation. Migration increases are concentrated in the two highest-education quartiles of occupations (engineers, medical professionals, teachers in the 4th quartile; caregivers, restaurant workers, performing artists in the 3rd quartile), with no significant effect in the two lowest quartiles. Average annual migrant salary rises by PhP 23,703 per standard deviation (0.16 standard deviations).
Q9: What mechanisms does the structural model invoke to explain the domestic income gains?
A9: The model treats domestic income changes as arising through at least two channels: (1) the education channel, which the model assigns 20.2% of the domestic income increase (using the estimated college completion response of 0.046 per unit shock, baseline skill-migration probabilities, and baseline skill premia for domestic income); and (2) a demand multiplier operating on the portion of migrant income remitted to origin provinces, combined with capital accumulation from sustained migrant income flows. Assuming 64% of migrant income returns to origin economies (estimated indirectly from KNOMAD/ILO and Survey on Overseas Filipinos data) and a multiplier of 2.9 (consistent with estimates from Kenya and India), this demand-plus-investment channel can explain approximately 83.3% of the remaining (non-education-related) domestic income increase of PhP 14.4 per unit shock. Under baseline assumptions (α = 0.64), the stylized dynamic model generates PhP 18.88 of domestic income by 2015 from a PhP 1 initial shock — close to the empirical estimate of PhP 18.02.
Q10: How do the authors assess SUTVA and internal migration?
A10: They test whether the migrant income shock affects net internal migration rates at the provincial level (Appendix Table A6) and find no large or statistically significant impact. There is a small negative effect on outmigration of young adults (aged 16–24) that the authors judge cannot account for the documented income impacts. The Philippines’ archipelago geography (over 7,000 islands) is noted as likely limiting inter-provincial economic spillovers; to the extent spillovers occur, they would be positive (demand spillovers from provinces experiencing income gains to neighboring provinces), making estimates conservative lower bounds. Direct tests controlling for the inverse-distance-weighted migrant income shock in neighboring provinces leave main estimates unchanged.
Q11: Are the exposure weights (migration shares) persistent, and does this support interpreting the shock as persistent?
A11: Yes. Regressions of dyadic migrant income per capita in post-shock years (2009, 2012, 2015) on dyadic migrant income per capita in 1995 yield coefficients ranging from 0.4 to 0.6, each statistically significantly different from zero (and from 1, indicating partial but substantial persistence). The exchange rate shocks ΔRd are even more persistent: regression coefficients on the initial 1997–1998 shock are close to 1 and statistically indistinguishable from 1 in nearly all post-shock periods (with the only exceptions in 2009–2012 during the Great Recession). Both components of the shift-share variable thus show persistence over two decades, supporting interpretation of the long-run effects as responses to a persistent (not transitory) income shock.
Q12: What are the policy implications and how do the authors connect findings to migration policy?
A12: The findings suggest migration policy should be an important part of the development policy toolkit. The results are directly relevant to origin-country policies facilitating formal, contract-based labor migration (e.g., regulation of recruitment agencies, educational investments to raise worker skills and competitiveness for overseas employment) and destination-country policies governing legal immigration opportunities. The authors also note implications for overseas development assistance: development agencies could consider supplementing traditional foreign aid with programs that facilitate international labor migration. The paper’s context — formal, government-regulated migration through POEA and OWWA — is described as highly policy-relevant, with 94% of developing countries with populations exceeding 1 million having a dedicated government migration agency and 78% having policies promoting migrant remittances.
Key Concepts
Shift-share variable (Shiftshareo): The paper’s primary independent variable, equal to the sum over all overseas destinations d of (ωdo0 × ΔRd) — the province’s pre-shock migrant income per capita from each destination (the exposure weight or “share”) multiplied by that destination’s exchange rate shock (the “shift”). It is the predicted change in province migrant income per capita due to the 1997 Asian Financial Crisis exchange rate shocks, and is derived directly from the theoretical model of migration (Equation A9). Identification treats the exposure weights as exogenous following the “exogenous shares” approach of Goldsmith-Pinkham et al. (2020).
Exposure weights (ωdo0): Province o’s pre-shock aggregate migrant income per capita earned in destination d, calculated from administrative POEA/OWWA contract data for 1995. These serve as the “shares” in the shift-share and capture the extent to which a province’s residents are exposed to a given destination’s exchange rate shock. They reflect historically-formed migration networks rather than anticipation of future shocks.
Global income per capita: The sum of domestic income per capita and migrant income per capita. Domestic income is household income earned within the Philippine economy (wages, entrepreneurial, and other sources), explicitly excluding all income from international sources including remittances. Migrant income is the full income earned abroad by all international migrants from the province, calculated from contract data (not remittances sent home). Global income thus captures the full resource gain available to a province from the combination of domestic production and international migration.
Magnification (of migrant income shock): The empirical finding that the long-run coefficient on migrant income per capita (6.463 in Panel D, Table 1) far exceeds 1 — meaning each unit of initial short-run shock becomes more than six units of migrant income per capita in 2009–2015. The paper decomposes this magnification into contributions from persistent exchange rates, educational investments raising skill levels and migration, and shifts in migration flows toward now-higher-wage destinations.
Brain gain: The paper’s term for the process by which improved migrant income prospects raise educational investments among the broader population (not just among migrants), leading to higher skill levels among non-migrants as well. The paper distinguishes this from “brain drain” (where migration of skilled workers reduces origin-area human capital) and provides evidence of a “virtuous cycle”: education raises migration rates and migrant skill levels, which in turn raises migrant and domestic incomes, potentially funding further education.
Rotemberg weights: Province-destination-level weights (following Goldsmith-Pinkham et al. 2020) characterizing which destination-specific exchange rate shocks drive the estimates most. Saudi Arabia (0.20), Japan (0.19), United States (0.18), Taiwan (0.10), and Hong Kong (0.08) together account for 75% of the total Rotemberg weight. These weights guide which destination-specific exposure shares receive the most scrutiny in pre-trend and balance tests.
Fréchet elasticity (θ): The elasticity of migration flows from an origin province to a destination with respect to destination wages (in Philippine pesos), estimated at 3.42 via PPML using the exchange rate shocks. This parameter governs how much migration flows — and thereby migrant income — respond to the persistent exchange rate changes, and is central to the model’s decomposition of the six-fold magnification of migrant income effects.
Domestic income multiplier: The ratio of long-run domestic income increase to the portion of the migrant income shock that returns to origin provinces. Assuming 64% of migrant income returns to origin economies (estimated from multiple administrative data sources), the implicit demand multiplier in the paper’s context ranges from about 2.9 to 3.4, consistent with multipliers found in related literature on cash transfers and credit supply shocks in low-income settings.