O15 | Macro Paper Warehouse

Abundance from Abroad: Migrant Income and Long-Run Economic Development

Mon, 01 Jan 0001 00:00:00 +0000

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).

In depth

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.

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.

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.

Civil War–Induced Displacement and Human Capital

Mon, 01 Jan 0001 00:00:00 +0000

This paper examines the impact of conflict-driven forced displacement on human capital accumulation using the Mozambican civil war (1977–1992) as the empirical setting. During this war, over four million civilians — roughly a third of the population — fled to rural areas, cities, neighboring countries, or UN-managed refugee camps. The study advances on prior work in three dimensions: it uses the full post-war population census (12 million individuals) rather than a small survey; it studies multiple displacement trajectories in a single framework; and it separately identifies place-based exposure effects from a general uprootedness effect.

The primary data source is the 1997 Mozambican census, which records each individual’s place of birth, residence in 1992 (the war’s end), and residence in 1997. Key outcomes are educational attainment and sectoral employment (agricultural versus services). The authors supplement the census with digitized colonial road and school maps, georeferenced conflict events, and landmine contamination data.

The main identification strategy compares approximately 135,000 siblings (from 45,000 families) separated during the war, using the sibling who stayed behind as a within-family counterfactual. This design controls for household-level characteristics including religious and ethnic background, aspirations, and exposure to violence.

The key findings are as follows. First, rural-born IDPs displaced to cities have a 7.3 percentage point higher likelihood of attending primary school and 0.53 more years of schooling compared to their siblings who stayed behind — roughly one-third of the non-displaced mean. Rural-born IDPs displaced to other rural areas also show gains, with a 3 percentage point higher likelihood of attending school and 0.24 additional years, supporting the uprootedness hypothesis even for displacements that did not reach urban centers. Urban-born IDPs forcibly relocated to the countryside — primarily through FRELIMO’s villagization scheme — experienced 9 percentage point lower primary school attendance and approximately 0.5 fewer years of schooling relative to siblings who remained in cities.

External displacement (to camps in Malawi or Zimbabwe) generated no significant schooling gains relative to staying siblings, despite UN-built schools in camps, likely because scarce employment opportunities reduced perceived returns to education.

Second, the paper jointly estimates place-based and uprootedness effects in a single within-family framework. Place effects are statistically significant: displacement to a district one standard deviation more developed than one’s birthplace raises schooling likelihood by approximately 3 percentage points (OLS) to 5 percentage points (2SLS reduced form). Crucially, a residual uprootedness effect of approximately 2–4 percentage points persists even after controlling fully for destination-origin differences in development and conflict intensity. This uprootedness effect is quantitatively comparable to being displaced to a district one standard deviation more developed than one’s birthplace.

Third, a primary survey of 208 Nampula residents conducted in early 2020 — three decades after the war — confirms lasting educational gains. IDPs displaced to Nampula have a 10 percentage point higher likelihood of completing primary school relative to their siblings who stayed in the countryside, and their educational attainment converged to levels of urban-born, never-displaced residents despite large urban-rural education gaps. However, IDPs report significantly lower social capital, civic participation, and community trust than urban-born respondents, and score significantly worse on mental health indicators, including depression, loneliness, and pessimism. These psychosocial costs persist three decades after the war’s end.

The findings apply to a low-income, post-colonial African setting characterized by widespread illiteracy (over 60%) and subsistence agriculture (over 85% of employment) at the war’s close. The results are robust to alternative age restrictions, extended family comparisons, dropping the oldest sibling, same-sex sibling pairs, and narrowing the age gap between sibling pairs to as few as two years.

Q: What is the core identification strategy and why is it preferred over cross-sectional estimates? A: The authors compare siblings within the same household who experienced different displacement trajectories during the war. Because siblings share household-level characteristics — parental preferences for education, ethnic and religious background, wealth, and local conflict exposure — the within-family design controls for confounders that would bias cross-sectional estimates. The within-family estimates are systematically smaller than cross-sectional ones (e.g., 7.3 pps vs. 24–30 pps for rural-to-urban displacement in primary school attendance), confirming that sorting was present even in the unpredictable civil war setting.

Q: What do the results show for rural-born IDPs displaced to urban centers? A: Within the sibling-pair framework, rural-born IDPs displaced to cities and towns have a 7.3 percentage point higher likelihood of attending primary school and 0.53 more years of schooling compared to their siblings who stayed in rural birthplaces, against a non-displaced sibling mean of approximately 20% primary school access and one year of formal schooling. These IDPs also show a 4 percentage point higher likelihood of non-agricultural employment five years after the war’s end.

Q: What do the results show for rural-born IDPs displaced to other rural areas? A: Even displacement to a different rural district — not a city — generates modest but statistically significant gains: a 3 percentage point higher likelihood of attending school and 0.24 additional years of schooling relative to siblings staying in their birthplace rural district. The authors interpret this as evidence for the uprootedness hypothesis, since rural Mozambique at the time was among the most impoverished and insecure environments in the world, meaning destination quality alone cannot explain the gain.

Q: What do the results show for externally displaced refugees? A: Refugees displaced to camps and settlements in Malawi, Zimbabwe, Tanzania, Zambia, and Swaziland show schooling levels statistically similar to their siblings who remained in their rural birthplaces, despite UN-built primary schools in camps. The authors attribute the absence of gains to low perceived returns to education stemming from scarce employment opportunities at displacement destinations. Externally displaced individuals do show a 5 percentage point lower likelihood of agricultural employment relative to staying siblings.

Q: What are the consequences of urban-to-rural forced displacement? A: Urban-born individuals forcibly relocated to the countryside — primarily through FRELIMO’s villagization and food production programs — have approximately 9 percentage point lower likelihood of attending primary school and 0.5 fewer years of schooling compared to siblings who remained in urban areas. These results indicate that FRELIMO’s coercive relocation policies imposed material human capital costs on the displaced.

Q: How are place-based and uprootedness effects separated empirically? A: The authors construct principal component indices for destination-origin differences in regional development (aggregating population density, Portuguese-speaking share, offspring mortality, road density, colonial market density, and school density) and conflict intensity (conflict events per capita and landmine contamination per capita). They then include these continuous exposure measures alongside a binary displacement indicator in within-family regressions. The coefficient on the binary displacement indicator — conditional on destination-origin development and conflict differences — isolates the uprootedness effect for individuals displaced to districts with identical characteristics to their birthplace.

Q: What are the magnitudes of the place-based and uprootedness effects? A: Under OLS, displacement to a district one standard deviation more developed than one’s birthplace raises schooling likelihood by approximately 3 percentage points. The residual uprootedness effect — displacement per se, controlling for destination quality — raises schooling likelihood by approximately 2 percentage points. Under 2SLS (instrumenting destination-origin development differences with the development of districts within 100 km of birthplace), the place-based effect rises to approximately 5 percentage points in the reduced form, and the uprootedness effect remains significant at approximately 4 percentage points. Both the uprootedness and place-based effects are of comparable magnitude.

Q: What instrument is used in the 2SLS specifications and what is its first-stage strength? A: The instrument exploits the fact that Mozambique’s heavily mined and rudimentary transportation network constrained civilian movement — the median displaced sibling ended up roughly 97 kilometers from birthplace. The authors instrument actual destination-origin development and conflict differences with the predicted differences based on the characteristics of districts within 100 km of the birthplace. The first-stage elasticity between actual and proximity-predicted differences in development is 0.86, and for conflict is 0.88, both precisely estimated.

Q: What do the long-run survey results from Nampula show about educational persistence? A: In a 2020 survey of 208 Nampula residents aged over 35, IDPs who fled to Nampula during the war have a 10 percentage point higher likelihood of completing primary school relative to their siblings who stayed in the countryside. Their educational attainment converges to the level of urban-born, never-displaced Nampula residents, despite large historical and contemporary urban-rural education gaps in northern Mozambique. The majority of IDPs (73%) report that extended relatives or friends advised them to attend school upon arriving in the city, and most believed education was necessary for urban employment.

Q: What are the long-run psychosocial costs documented in the Nampula survey? A: Even three decades after the war’s end, IDPs in Nampula report significantly lower social capital, civic participation, and community trust compared to urban-born never-displaced residents. IDPs also score significantly worse on mental health indicators including depression, loneliness, and pessimism. These findings suggest that forced displacement imposes persistent psychosocial costs that are not remediated by economic or educational convergence.

Q: What drives displacement in the data, and does selection threaten identification? A: Linear probability and multinomial logit models show that conflict intensity and geographic proximity (distance to the border for external displacement; distance to cities for urban displacement) are the primary correlates of displacement type, while differences in destination development are uncorrelated with displacement. Nevertheless, the overall explanatory power of these models is low, confirming many idiosyncratic and unpredictable features of the war. The within-family design addresses residual selection on household characteristics, and the 2SLS design addresses selection on destination-specific characteristics.

Q: How do educational gains translate into sectoral employment outcomes? A: Across specifications, gains in schooling move in tandem with a shift out of agriculture into services. Rural-to-urban IDPs have a 4 percentage point higher likelihood of non-agricultural employment five years after the war, while externally displaced show a 5 percentage point lower likelihood of agricultural employment. Urban-born IDPs displaced to the countryside are more likely to work in agriculture after the war. The authors interpret this co-movement as suggesting that conflict-driven human capital accumulation may contribute to structural transformation away from subsistence agriculture.

Q: How robust are the within-family estimates? A: The authors conduct six sensitivity checks: adding family fixed effects to cross-sectional regressions, restricting to individuals aged 12–18 in 1997 to address co-habitation concerns, extending comparisons to cousins and other relatives, dropping the oldest male sibling to minimize favoritism concerns, restricting to same-sex sibling pairs, and narrowing the age gap to two years. Across all permutations, the qualitative ordering is preserved: refugees show no significant schooling gains, rural-to-urban IDPs show gains of 5–6 percentage points in primary attendance and 0.35–0.5 extra years, rural-to-rural IDPs show small positive gains, and urban-to-rural IDPs show losses.

Uprootedness hypothesis: The idea, traced in the paper to Stigler and Becker (1977) and earlier scholars, that forced displacement incentivizes human capital investment precisely because education is a mobile asset that cannot be expropriated — distinct from place-based effects of destination quality.

Place-based (exposure) effects: The impact on human capital outcomes attributable to differences between the development level and conflict intensity of the displacement destination and the individual’s birthplace, measured as destination-origin differences in a principal component index of regional development.

Separated siblings design: An identification strategy that compares siblings from the same household who experienced different displacement trajectories during the war, holding constant all household-level characteristics including parental preferences, ethnicity, religion, wealth, and local conflict exposure.

Internal displacement (IDP): Conflict-driven movement within national borders to either rural areas or urban centers, constituting approximately 60% of global forced displacement and the majority of displacement in the Mozambican civil war context.

Source text origin: A categorization of the working paper text used for summarization — distinguishing full PDF or HTML text from abstract-only text. Abstract-only text is a hard block for summary generation in the pipeline.

Structural transformation: In this paper’s usage, the shift of workers out of subsistence agriculture into services associated with human capital accumulation triggered by conflict-driven displacement, treated as a potential mechanism of post-conflict recovery.

Psychosocial costs of displacement: Long-run deficits in social capital, civic engagement, community trust, and mental health (depression, loneliness, pessimism) reported by IDPs three decades after displacement, persisting despite convergence in educational attainment and employment.

How Do You Identify a Good Manager?

Mon, 01 Jan 0001 00:00:00 +0000

This paper develops a novel experimental method to identify the causal contribution of managers to team performance, and uses it to evaluate which characteristics predict managerial effectiveness and how manager selection mechanisms affect organizational outcomes.

The core identification challenge is that managers are not randomly assigned to teams in the field, and field managers are a highly non-random sample, making it difficult to infer which traits genuinely predict managerial performance. The authors address this by repeatedly randomly assigning managers to multiple teams in a controlled laboratory experiment, then estimating each manager’s average causal contribution to group output after conditioning on group members’ individual productive skills. The intuition is that a good manager is someone who consistently causes their team to produce more than the sum of their parts.

The experiment was conducted at the University of Essex lab with 555 participants (46% female, mean age 25, ethnically diverse) forming 728 groups of three across four rounds. Each group consisted of one manager and two workers who performed a Collaborative Production Task requiring coordination across three problem-solving modules (numerical, spatial, and analytical reasoning). The team score was the minimum module score — a weakest-link structure making coordination essential. Prior to group testing, all participants completed individual assessments of task-specific skill, fluid intelligence (CFIT), emotional perceptiveness (Reading the Mind in the Eyes Test, RMET), economic decision-making skill (the Assignment Game, which measures resource allocation under comparative advantage), Big 5 personality, and demographic characteristics. Manager selection was randomly varied at the session level: in 20 sessions, the participant with the strongest preference for leadership became manager (self-promotion); in 19 sessions, managers were assigned by lottery.

The main quantitative findings are as follows. First, there are large, stable, and statistically significant manager effects: a manager one standard deviation above average improves team performance by approximately 0.23 standard deviations (p = 0.04). This estimate is roughly 90% the size of the combined productive skill coefficient for the two workers (approximately 0.26 sd), indicating that a good manager is roughly twice as valuable as a good individual worker. Manager contributions predict out-of-sample group performance in a leave-one-out procedure (p < 0.01).

Second, among randomly assigned managers, only two predictors significantly explain managerial performance: fluid intelligence (CFIT) and economic decision-making skill (Assignment Game scores), both significant at below the 1% level. Gender, age, and ethnicity do not predict managerial performance.

Third, self-promoted managers perform substantially worse than lottery-assigned managers, by approximately 0.10 standard deviations — roughly equivalent to being assigned a manager with fluid intelligence one full standard deviation below average. The mechanism is overconfidence: people who strongly prefer management roles are significantly more overconfident (d = 0.41 sd, p < 0.01) and exhibit a strong negative correlation between self-reported social skills and actual emotional perceptiveness on the RMET (r = -0.37, p < 0.001). Among self-promoted managers, self-reported extraversion and political skill are negatively correlated with managerial performance (rho = -0.24 and -0.26, p < 0.05); no such negative relationship appears among lottery managers.

Fourth, selecting managers on economic decision-making skill rather than self-promotion improves average manager quality by 0.6 standard deviations — equivalent to replacing an average worker in every group with a worker at the 99th percentile of individual productivity.

The three mechanisms through which good managers improve performance are: (1) monitoring — good managers (1 sd above average) cut monitoring errors from 16% to 8%; (2) optimal task allocation according to comparative advantage — groups with optimally assigned workers score 0.52 sd higher (p < 0.01); (3) worker motivation in late-stage effort — teams led by a 1-sd-above-average manager solve 0.6 more problems in the final two minutes versus only 0.3 more in the first two minutes.

The experiment was conducted in a university lab in the UK, and the sample skews toward graduate students with limited work experience. Generalizability to field settings is supported by prior evidence that peer productivity spillover experiments yield similar magnitudes in lab versus field settings, and that the estimated manager effects are similar to Lazear et al. (2015) estimates from a large employer dataset.

Q: What is the core methodological innovation of this paper? A: The paper requires repeated random assignment of managers to multiple teams, combined with controls for individual productive skill measured prior to group work. This allows identification of each manager’s average causal contribution to group output, rather than confounding management quality with team composition or individual worker ability. The key estimand is the standard deviation of individual manager effects (sigma_alpha), interpreted as the impact of having a manager one standard deviation above average.

Q: How large is the estimated manager effect, and how does it compare to worker effects? A: A manager one standard deviation above average improves team performance by approximately 0.23 standard deviations (p = 0.04 by randomization inference). This is roughly 90% the size of the combined productive skill effect of both workers together (approximately 0.26 sd), implying a good manager is nearly twice as valuable as a good individual worker. Without conditioning on production skills, the manager effect rises to 0.29 sd.

Q: What characteristics predict managerial performance among randomly assigned managers? A: Only two measures predict managerial performance in the lottery arm: fluid intelligence (CFIT) and economic decision-making skill (scores on the Assignment Game), both significant at below the 1% level. These predictors are robust to controls for demographics, education, work experience, emotional perceptiveness, and personality traits. Gender, age, and ethnicity do not predict managerial performance.

Q: What is the “Assignment Game” and why is it a strong predictor? A: The Assignment Game (Caplin et al., 2024) places participants in a simulated managerial role where they must assign fictional workers to tasks. Performing well requires understanding comparative advantage intuitively, managing an attentionally demanding numerical environment, and avoiding biases such as anchoring. The paper argues its strong predictive power reflects that good managers excel at allocating workers according to comparative advantage — which the experiment directly identifies as a key mechanism.

Q: How do self-promoted managers perform relative to lottery-assigned managers? A: Self-promoted managers perform approximately 0.10 standard deviations below lottery managers, and this gap is robust across model specifications. The performance deficit is roughly equivalent to being assigned a manager whose fluid intelligence is one full standard deviation below average. This finding implies that common organizational practice of selecting managers partly via self-nomination actively reduces team productivity.

Q: Why do self-promoted managers underperform? A: The paper attributes underperformance primarily to overconfidence. People strongly preferring management roles are significantly more overconfident than those without strong preferences (d = 0.41 sd, p < 0.01). Self-promoted managers specifically overestimate their social skills: among them, self-reported people skills are strongly negatively correlated with actual emotional perceptiveness on the RMET (r = -0.37, p < 0.001), and self-reported extraversion and political skill are negatively correlated with managerial performance (rho = -0.24 and -0.26, p < 0.05). None of these negative relationships appear among lottery managers.

Q: Who wants to be a manager, and does it differ by gender? A: The three variables most strongly correlated with wanting to be in charge are extraversion, risk appetite, and being male. The relationship between high extraversion and preference for management is driven largely by men. Women are much less likely to nominate themselves for leadership roles despite being equally or more effective on average — a finding consistent with broader experimental evidence on gender and leadership self-selection.

Q: How large are the potential gains from skill-based manager selection? A: Compared to self-promotion, selecting managers based on economic decision-making skill yields managers who are 0.6 standard deviations better in terms of estimated manager effects. In terms of group performance, this is equivalent to replacing an average worker in every group with a worker at the 99th percentile of individual productivity. Selecting on both economic decision-making and fluid intelligence outperforms random assignment, selection on social skills, or selection on worker task performance (the Peter Principle).

Q: What are the three mechanisms through which good managers improve team performance? A: First, monitoring: good managers (1 sd above average) reduce monitoring errors — defined as having a worker on a module substantially above the minimum score at task end — from 16% to 8% (bivariate correlation with manager performance = -0.40, p < 0.001). Second, optimal task allocation: the probability of finding the optimal comparative-advantage-based assignment is positively associated with manager performance (rho = 0.19, p < 0.01), and groups with always-optimal starting assignments score 0.52 sd higher than those with never-optimal assignments (p < 0.01). Third, worker motivation: team performance in the final two-minute period is about 50% more influential for overall outcomes than the first two minutes (p = 0.038), and 1-sd-above-average managers generate 0.6 more problems solved in the final period versus 0.3 in the first, consistent with differential motivational effects emerging over time.

Q: What is the Peter Principle, and how does this paper relate to it? A: The Peter Principle refers to the practice of promoting employees based on their performance as line workers rather than their suitability for management — promoting individuals to their level of incompetence. Benson et al. (2019) document this selection pattern empirically. This paper shows that selecting managers on worker task skill is inferior to selecting on economic decision-making skill or fluid intelligence, confirming that task skill is not the right criterion for manager selection even if it predicts individual worker output.

Q: How does the paper validate that manager effects are real and not noise? A: The paper uses randomization inference with 5,000 simulated allocations to compute p-values, obtaining p = 0.04 for the main manager effect. Robustness checks include controlling for pre-existing social relationships, manager risk appetite, variance of individual scores, and granular skill measures — all yielding estimates near 0.22 sd. A leave-one-out out-of-sample prediction test confirms manager contributions significantly predict held-out group performance (p < 0.01), while the analogous worker out-of-sample estimate is less than half the magnitude and not statistically significant.

Q: What are the scope conditions on the experimental results? A: The experiment is conducted in a university lab in the UK with graduate students averaging 25 years of age and two years of work experience, limiting direct generalizability to experienced workers or senior management. The task lasts approximately 15 minutes, which may not capture longer-run managerial dynamics. Compensation equalized average earnings between managers and workers, which differs from most real-world settings. The authors note their effect-size estimates closely match Lazear et al. (2015) from a large employer, and that Herbst and Mas (2015) find lab peer-productivity experiments generalize to the field.

Manager Effect (sigma_alpha): The standard deviation of individual managers’ average causal contributions to group performance, estimated via repeated random assignment and conditioning on individual productive skill. Represents the impact of having a manager one standard deviation above average, estimated at approximately 0.23 standard deviations of group output.

Collaborative Production Task: A novel lab group task in which a manager and two workers solve problems across three modules (numerical, spatial, analytical reasoning), with team score defined as the minimum module score (weakest-link structure). Managers are responsible for worker assignment, monitoring, and motivation; workers face no financial performance incentives.

Economic Decision-Making Skill: Defined by Caplin et al. (2024) as the ability to make good resource allocation decisions, assessed via the Assignment Game in which participants must optimally assign workers to tasks under comparative advantage. The single strongest predictor of managerial performance in the lottery arm.

Monitoring Failure: Defined in the paper as having any group member working on a module at task end whose score is substantially greater (e.g., 10 points higher) than the minimum module score — meaning the worker’s effort is not contributing to the group score. Occurs in 16% of groups overall; managers one sd above average reduce this to 8%.

Self-Promotion (as selection mechanism): A treatment condition in which the participant with the strongest stated preference for being manager (on a 1-10 scale) is assigned the managerial role. Contrasted with lottery assignment; self-promoted managers perform approximately 0.10 sd worse than lottery managers.

Overconfidence (in managerial context): The gap between self-assessed skill (particularly social/interpersonal skill) and objectively measured skill (e.g., RMET score). Self-promoters are significantly more overconfident (d = 0.41 sd), and overconfidence is strongly negatively correlated with actual emotional perceptiveness (r = -0.33, p < 0.001).

Comparative Advantage Allocation: The practice of assigning each worker to the module in which they have the highest relative (not absolute) performance advantage. Captured via whether a manager selects the optimal one-to-one assignment given pre-measured individual module scores; groups with always-optimal allocation score 0.52 sd higher.

Intergenerational Impacts of Secondary Education: Experimental Evidence from Ghana

Mon, 01 Jan 0001 00:00:00 +0000

This paper provides experimental evidence on the intergenerational impacts of secondary education subsidies in a low-income context, leveraging a randomized controlled trial (RCT) conducted in rural Ghana with a 15-year longitudinal follow-up. The study exploits a 2008 scholarship lottery in which 682 students — drawn from 2,064 rural youth who had been admitted to public senior high school but had not enrolled due to financial constraints — were randomly selected to receive four-year secondary school scholarships covering full tuition and fees. Scholarship receipt increased senior high school completion by 27–28 percentage points for both men and women (from 39.8% to 67.2% for women; from 49.7% to 77.9% for men), and raised average years of education by 1.33 years.

The central research question is whether secondary education subsidies generate intergenerational benefits — specifically, whether children of scholarship recipients have better survival and cognitive development outcomes — and what mechanisms drive any such effects.

For female scholarship recipients, the scholarship significantly altered fertility timing and partnership. By 2013, female recipients were 6.9 percentage points less likely to have ever been pregnant (on a control-group base of 48.3%), with the decline driven almost entirely by a 7 percentage point (17%) reduction in unwanted pregnancies. Though total fertility eventually caught up by 2022, recipients were still less likely to be married or cohabiting as of 2019 and were significantly more likely to have a partner with tertiary education.

Children of female scholarship recipients experienced substantially lower mortality. Among control-group female respondents, 3.5% of children died before age one and 4.0% before age three. These rates fell to 1.7% (p=0.028) and 2.2% (p=0.065) respectively among children of female recipients — a roughly 45–51% reduction in under-one and under-three mortality.

Child cognitive development gains emerge only once children reach school age. Children of female recipients show no significant cognitive score differences at 18 months, 2.5 years, or 3.5 years, but score 0.238 standard deviations higher at age five (p=0.005) and 0.252 standard deviations higher at age seven (p=0.035). Effects span language, math and numeracy, spatial reasoning, and executive function, but not socio-cognitive development. These effect sizes fall between the 75th and 80th percentile of RCT-based educational intervention effect sizes in low- and middle-income countries.

The primary mechanism is not higher income or greater monetary investment in children. The study finds no significant treatment effect on household SES index (0.107 SDs, p=0.103), no impact on formal schooling inputs, and no difference in parental aspirations or knowledge of child stimulation’s importance. Instead, more-educated mothers seek more prenatal care, engage in more preventive health behaviors, and — critically — spend more time interacting with their children in stimulating ways. Day-long LENA (Language Environment Analysis) recordings at 18 months confirm 20% more adult-child conversational turns per minute (effect size 0.068, p=0.005) and 17% more child vocalizations per minute (effect size 0.32, p=0.014) for children of female recipients.

For male scholarship recipients, no analogous intergenerational benefits appear. Their partners are not more educated (in fact slightly less educated on tertiary rates), their children show no mortality improvement, and cognitive scores are if anything negative at age five (point estimate -0.22, p=0.069). The absence of effects is attributed to male scholarship recipients having caregivers — overwhelmingly mothers — with no more education than in the control group, and to children of male recipients being 8.7 percentage points less likely to live with their father.

A cost-benefit analysis finds internal rates of return (IRR) of 27%–76% for a female-only means-tested scholarship program and 20%–51% for a mixed-gender program. The cost per under-three death averted ($15,184 for female-only) places the scholarship program within the range of the 10th-percentile most cost-effective WHO-recommended child health interventions.

Scope conditions: the study estimates effects for students who qualified for senior high school but faced binding financial constraints in rural Ghana in 2008 — a population that is well-prepared academically but economically disadvantaged. Results may not generalize to students who would not have qualified for secondary school or to contexts where financial barriers are not binding.

Q: What was the experimental design and who was in the study sample? A: In 2008, 2,064 rural Ghanaian students who had been admitted to senior high school (SHS) but had not enrolled — typically due to inability to pay fees — were sampled. After a baseline survey, 682 were randomly selected (approximately one-third) by lottery to receive a four-year scholarship covering full tuition and fees for a day (non-boarding) student, stratified by district, school, gender, and exam-year cohort. The two-thirds comparison group received no scholarship. Students were on average 17 years old at baseline and just over 31 at the last follow-up in Spring 2023.

Q: How large was the scholarship’s effect on educational attainment? A: Scholarship receipt raised SHS completion from 39.8% to 67.2% among women (a 69% increase) and from 49.7% to 77.9% among men (a 57% increase). Overall, the scholarship led to an average of 1.33 more years of education. For women only, it also significantly raised tertiary education: by 2023, scholarship receipt increased tertiary completion by 10.8 percentage points for women, but had no significant tertiary effect for men.

Q: What were the effects on fertility and family formation for female scholarship recipients? A: By 2013, female recipients were 6.9 percentage points less likely to have ever been pregnant (base: 48.3% in control), driven almost entirely by a 7 percentage point (17%) reduction in unwanted pregnancies. By 2019, recipients were still 6 percentage points less likely to have started childbearing and had 0.152 fewer children on average (p=0.065). Total fertility eventually caught up by 2022. By 2016, female recipients were 12.1 percentage points (24% of control mean) less likely to have ever lived with a partner, and by 2019 were 6.2 percentage points less likely to be married or cohabiting. Conditional on having a partner, they were significantly more likely to have a partner who completed tertiary education (p=0.071).

Q: What were the effects on fertility and family formation for male scholarship recipients? A: Male recipients showed few changes in fertility or marriage behavior. They were 7.8 percentage points (30% of control mean) more likely to still be living with their parents as of 2019. Their partners were not more educated; in the cognitive games subsample, treatment actually reduced the share of partners with tertiary education by 3.6 percentage points from a control base of 4.3%.

Q: What were the child mortality results for children of female scholarship recipients? A: Among children of female control respondents, 3.5% died before age one and 4.0% before age three. These fell to 1.7% (p=0.028) and 2.2% (p=0.065), respectively, among children of female recipients — approximately a halving of under-one and under-three mortality. These point estimates are robust to varying the covariates (linear vs. fixed effects for birth year, dropping or adding controls). After multiple-hypothesis testing adjustment using the Romano-Wolf step-down procedure, the p-value for survived-to-one rises from 0.028 to 0.119.

Q: What were the child mortality results for children of male scholarship recipients? A: The estimated effects for children of male recipients were smaller and statistically insignificant: a 1.4 percentage point increase in survived-to-one (p=0.161) and 0.9 percentage points in survived-to-three (p=0.549). These estimates are not significantly different from those for female recipients. Results were sensitive to sample perturbations given the smaller sample: only 26 of 1,016 children of male respondents died before age one.

Q: What child cognitive development gains did children of female scholarship recipients show, and at what ages? A: No significant differences emerged at 18 months (-0.066 SDs, p=0.489), 2.5 years (-0.024 SDs, p=0.850), or 3.5 years (0.026 SDs, p=0.736). Significant gains appeared at age five (0.238 SDs, p=0.005) and age seven (0.252 SDs, p=0.035). Effects span language (0.15 SDs at five; 0.27 SDs at seven), math and numeracy (0.15 SDs; 0.26 SDs), spatial reasoning (0.20 SDs; 0.12 SDs), and executive function (0.25 SDs; 0.20 SDs), but not socio-cognitive development. These effect sizes fall between the 75th and 80th percentile of educational RCT effect sizes in low- and middle-income countries.

Q: What cognitive development effects did children of male scholarship recipients show? A: No significant positive effects emerged at any age. Point estimates were negative at all ages except 18 months, and marginally significantly negative at age five (-0.22 SDs, p=0.069). The difference in treatment effects between children of male and female recipients is statistically significant at age five (p=0.005).

Q: Why do cognitive gains appear only at age five and not earlier? A: The authors offer three interpretations: first, that the cognitive tests for younger children are noisier instruments (cross-sectional and longitudinal correlations within domains are much lower for 1.5-year tests than 5-year tests); second, that impacts on cognitive development may take time to materialize; third, that marginal survivors in the treatment group may start with a cognitive deficit (e.g., surviving a cerebral malaria episode), and maternal education effects require time to overcome this initial handicap. Gains concentrate on skills underlying literacy and numeracy, consistent with more educated mothers bridging home and school environments.

Q: What is the primary mechanism driving intergenerational effects? A: The primary mechanism is changes in parenting behaviors, not income. Female recipients do not invest more money in children (no significant difference in SES index or child investment index). Instead, they seek more prenatal care, engage in significantly more preventive health behaviors, and interact more with their children in cognitively stimulating ways. Day-long LENA recordings at 18 months show 20% more conversational turns per minute (effect size 0.068, p=0.005) and 17% more child vocalizations per minute (effect size 0.32, p=0.014). Caregiver reports confirm more playing, singing, and doing simple mathematics with children.

Q: Does the income effect of scholarship receipt explain the child outcomes? A: No. Duflo et al. (2024) find no significant earnings impacts until 2019 or later, meaning children tested at ages five and seven by 2023 largely grew up before their mothers’ earnings improved. The household SES index shows only a 0.107 SD gain (p=0.103), indistinguishable from the effect for children of male recipients. There is also no evidence of a quality-quantity trade-off: caregivers of scholarship recipients do not have fewer children to care for.

Q: Does the increase in maternal age at birth explain the child mortality reduction? A: It is not the primary driver. Maternal age at birth increases by only 0.349 years on average (p=0.142) for children of female recipients, and 0.64 years for first-born children (p=0.040). Point estimates on mortality for first-born children are somewhat smaller than for the full sample, suggesting maternal age is not the main channel. Moreover, maternal age at birth falls for children of male recipients yet their survival point estimates are positive, which further argues against maternal age as the primary mechanism.

Q: How does the education of the primary caregiver mediate the results? A: For 84% of children in the sample, the primary caregiver is the child’s mother. Children of female scholarship recipients have caregivers who are 25 percentage points more likely to have completed secondary school and 5 percentage points more likely to have completed tertiary education. Children of male scholarship recipients have caregivers with no more education than the control group, because the recipients’ partners — the typical caregivers — are not more educated. Treatment effects for female recipients are not altered when father’s education is added as a control, confirming maternal education as the main driver.

Q: What threat to validity arises from co-residence of the father? A: Children of male scholarship recipients are 8.7 percentage points less likely to live with their father (p=0.024), compared to no such effect for children of female recipients (92% of whom live with their scholarship-recipient mother). LENA recordings show negative treatment effects for children of male recipients — fewer adult words and conversational turns — consistent with father absence mechanically reducing auditory engagement and possibly leaving single mothers less time to verbally interact with each child.

Q: How are multiple-hypothesis testing concerns addressed? A: The pre-analysis plan pre-specified child survival and child cognitive development as primary outcomes. The authors apply the Romano-Wolf step-down procedure for multiple hypothesis testing adjustment. After adjustment, the p-value for survived-to-one for children of female recipients rises from 0.028 to 0.119; the cognitive development effects at age five and seven remain significant.

Q: How does the study address potential sample selection bias in the child outcomes sample? A: The authors use entropy balancing (Hainmueller, 2012) to reweight observations so that baseline (2008) characteristics are balanced between treatment and control within the subsample of recipients who had children. Results are qualitatively unchanged for both female and male recipients. The authors also note that children of female recipients are younger on average (4.71 months, p=0.067), which is why the study collects data at fixed age windows (14-22 months, 2.5 years, 3.5 years, 5 years, 7 years) rather than in a single cross-sectional wave.

Q: What is the cost-effectiveness and cost-benefit result for secondary school scholarships? A: Social costs are estimated at $585 per recipient for a mixed-gender program and $505 for a female-only program (combining school fees, materials, and foregone wages). The cost per under-three death averted is $23,582 for mixed-gender and $15,184 for female-only — placing the female-only program within the range of the 10th-percentile most cost-effective WHO-recommended child health interventions. The IRR is 27%–76% for a female-only means-tested scholarship program and 20%–51% for a mixed-gender program. These are likely conservative, as they exclude welfare gains from avoiding unwanted pregnancies, greater female agency, and recipient health benefits.

Q: What is the scope of the experiment and to what population do findings generalize? A: The study estimates ITT effects for students in rural Ghana who qualified for SHS on exam performance but faced binding financial constraints in 2008 — a population that is academically prepared but economically disadvantaged. Results do not directly apply to students who would not have qualified, to contexts without binding financial barriers, or to settings where secondary school quality or the marriage market differs substantially. The study also cannot yet observe complete fertility, since scholarship-lottery participants were only 31 years old on average at last follow-up.

LENA (Language Environment Analysis): A day-long recording device worn by a child that uses speech recognition software to generate count-based metrics — adult word count, adult-child conversational turns, and child vocalizations per minute — providing an objective measure of the child’s auditory environment and caregiver engagement quality without reliance on self-report.

IRT Score (Item Response Theory Score): A latent-trait measure of child cognitive ability estimated from a one-parameter logistic model applied to binary correct/incorrect responses across cognitive game questions, assigned a difficulty level to each question and a latent ability to each child, then standardized. Used as the primary cognitive development outcome across age windows.

Incarceration Effect: The hypothesis that education delays fertility mechanically only while students are in school (analogous to incarceration preventing activity), with no persistent effect once they exit. The authors rule this out by showing that the fertility gap between female treatment and control groups persists well after the majority of scholarship recipients have graduated.

Quality-Quantity Trade-off (Becker 1991): The economic framework predicting that more educated parents, facing higher opportunity costs of children and lower costs of investing in child quality, will have fewer but better-invested-in children. The authors find delayed and reduced fertility but do not find that recipients have fewer children to care for in the cognitive assessment sample, suggesting the child quality gains operate primarily through parenting practices rather than resource concentration.

Intent-to-Treat (ITT) Effect: The treatment effect estimated by comparing all lottery winners to all losers regardless of whether winners actually enrolled, which captures the effect of the scholarship offer (including compliance costs). The cost-benefit analysis uses ITT estimates, so the cost of subsidizing inframarginal students who would have attended anyway is incorporated.

Entropy Balancing: A reweighting procedure (Hainmueller, 2012) that assigns weights to observations in the control group so that the weighted distribution of baseline covariates matches that of the treatment group, used to assess whether imbalances in the subsample of participants who had children drive the results. The authors apply this as a robustness check for both mortality and cognitive development outcomes.

Unwanted Pregnancy: A pregnancy reported by the respondent as unplanned at the time of conception, which the authors use to distinguish fertility reduction from a change in desired fertility versus a reduction in unintended out-of-wedlock pregnancies. The scholarship’s early fertility impact is almost entirely a reduction in unwanted pregnancies (7 percentage point decline, 17% reduction).

Rural Migrants and Urban Informality: Evidence From Brazil

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Research Question. Does rural-urban migration increase or decrease urban informality, and through what mechanisms — and does the answer depend on the time horizon?

Setting and Data. The paper studies internal migration in Brazil over 2000–2010. The empirical analysis combines: (i) two waves of the Decennial Population Census (2000 and 2010) covering working-age adults (ages 15–64) across 3,548 Minimum Comparable Areas (MCAs); (ii) the universe of formal firms and workers from the matched employer-employee administrative dataset RAIS (1997–2018); (iii) the ECINF informal firm survey (2003); and (iv) the annual National Household Survey (PNAD, 2001–2009) for year-on-year short-run analysis in 700 identifiable municipalities. Internal immigration to the average urban destination was large: 17.6 percent overall over the decade, 7 percent for state-to-state migration.

Empirical Design. The authors use a shift-share instrumental variable (IV) design. The shares are pre-existing migration networks (migrant flows by origin-destination pair, 1995–2000). The shifts are drought shocks constructed from the Standardized Precipitation-Evapotranspiration Index (SPEI) interacted with agricultural crop calendars and the value share of each crop in each origin municipality — accumulated over the 2000–2010 decade. A second independent instrument uses international commodity price shocks as push factors (following a China-analogous construction); the two instruments are nearly uncorrelated across origins (0.007) and only weakly correlated across destinations (-0.3), providing an independent validation.

Long-Run Findings (decadal changes, 2000–2010). A one-percentage-point increase in the immigration rate (equal to 18.5 percent of a standard deviation):

Increases the share of workers in formal wage employment by 0.27 percentage points (a 1.2 percent increase from the mean of 23 percent).
Decreases the share in informal wage employment by 0.29 percentage points (a 2.9 percent decrease from the mean of 10 percent).
Has no effect on overall wage employment, unemployment, or self-employment — the formalization effect is a reallocation from informal to formal jobs, not net job creation.
Reduces formal sector wages by 0.6 percent, with no effect on informal wages.
Increases the number of formal establishments by 1.6 percent and the number of formal jobs by 2 percent.
Raises gross firm entry by 2.8 percent and gross firm exit by 3 percent (higher churn), with effects stable or slightly increasing through 2017–18.

These firm-creation effects are not driven by migrants starting businesses: migrants are not more likely to be business owners in high-immigration municipalities.

Short-Run Findings. Using year-on-year specifications with the PNAD (2001–2009), the authors replicate the results in the prior literature: municipalities receiving more migrants experience a reduction in formal wage employment, with no change in informal employment or non-employment — so the share of informal jobs rises. These short-run informality-increasing effects coexist with the long-run formalization results, and are not a sample artifact (the long-run results are unchanged when restricted to the same 700 PNAD municipalities).

Mechanism — Downward Nominal Wage Rigidity (DNWR). DNWR in the formal sector is the key mechanism reconciling short- and long-run effects. In Brazil, nominal wage cuts were illegal, and the national minimum wage rose regularly during the 2000s. Two municipality-level DNWR proxies are used: (i) the Kaitz index (national minimum wage / municipality median wage in 2000); (ii) the share of workers with negative year-on-year nominal wage changes (from RAIS, 1997–2000). In municipalities with higher DNWR: the positive formalization effects of immigration are smaller or fully muted; non-employment increases; and formal wages decline less. These cross-sectional patterns echo the Harris-Todaro-Fields prediction, and are consistent with DNWR being more binding in the short run (when nominal rigidities bind) than in the long run (when inflation and worker turnover allow real wage adjustment).

Model. The paper develops and estimates a dynamic model of firm dynamics and informality, extending the canonical Hopenhayn framework with (i) two margins of informality — the extensive margin (whether a firm registers) and the intensive margin (whether a registered formal firm hires workers formally) — and (ii) heterogeneous long-run productivity parameters (nu) that generate firm-specific life-cycle growth profiles. Formal firms cannot revert to informality; informal firms can formalize by paying the cost differential between formal and informal entry costs.

Counterfactuals. A simulated once-and-for-all 10 percent labor supply shock (approximately the 80th percentile of observed immigration shocks) produces: a 4.1 percent decline in the share of informal workers (IV: 7.5 percent); a 16.1 percent increase in formal firms (IV: 21.1 percent); and a 3.4 percent wage decline (IV: 5 percent). Of the increase in formal firms, 40 percent is accounted for by formalization of previously informal firms, highlighting the stepping-stone role of informality that a static or dual-economy model would miss. Average firm productivity declines by 1.4 percent due to worsening firm composition (the share of formal firms in the lowest productivity quartile rises by more than 4 percentage points). A counterfactual that nearly eliminates the extensive margin of informality (via steep enforcement costs) raises total output by 8.6 percent vs. 7 percent in the baseline shock, and increases average firm productivity by 2.1 percent vs. a decline of 1.4 percent — at the cost of displacing the least productive informal firms.

Scope Conditions. Results pertain to internal (not international) migration; drought-induced migrants do not change the skill composition of the labor force at destination, justifying a homogeneous worker assumption. The formalization effects hold for migrants and non-migrants separately, and for high- and low-skilled workers separately. The model is calibrated to the average urban destination in Brazil, not a spatial general equilibrium.

In depth

Q1. What is the identification strategy, and what are the key threats to validity the authors address?

The authors use a shift-share IV where shifts are drought shocks at origin municipalities (constructed from SPEI x crop calendar x crop revenue share, accumulated over 2000–2010) and shares are pre-2000 migration networks. Threats addressed: (i) pre-trends — no evidence of differential pre-trends in firm outcomes between 1997–98 and 1999–2000; (ii) demand channel — controlling for local drought shocks and distance-weighted neighboring shocks leaves results unchanged; (iii) capital reallocation — adding a bank-network-based shift-share control (following prior literature) does not change results; (iv) agricultural processing linkages — results hold after excluding agricultural firms and food/beverage/tobacco manufacturers; (v) migration persistence — controlling for baseline log population and 1995–2000 migration rates leaves results unchanged. The commodity-price-shock instrument provides an independent validation, yielding similar results despite near-zero cross-origin correlation with drought shocks and only -0.3 correlation across destinations.

Q2. How do the authors reconcile the long-run formalization result with the short-run informality-increasing result, and what role does DNWR play?

DNWR is the key mechanism. Nominal wage cuts are illegal in Brazil’s formal sector, and the minimum wage rose through the 2000s, making DNWR binding especially in the short run. In the year-on-year specification (PNAD, 2001–2009), immigration reduces formal wage employment with no change in informal employment, raising the informal share — consistent with prior literature. Over the decade, inflation and worker turnover permit real formal wage adjustment, enabling formal sector expansion. Cross-sectional heterogeneity confirms this: in municipalities with above-median Kaitz index or below-median share of negative wage changes, the formalization effect of immigration is smaller or zero, and non-employment rises — precisely the Harris-Todaro-Fields prediction for rigid-wage environments.

Q3. What is the exact magnitude of the firm-level effects and how persistent are they?

A one-percentage-point increase in the immigration rate increases formal establishments by 1.6 percent, formal jobs by 2 percent, firm entry by 2.8 percent, and firm exit by 3 percent — all decadal effects (1999–2000 to 2011–12). Effects on firms, entry, exit, and jobs remain stable or slightly increasing through 2017–18 as estimated using RAIS panel data, with no evidence of pre-trends (effects near zero in 1997–98 to 1999–2000 period). The effect on firm-level average wages is negative (consistent with the worker-level wage effect) but not statistically significant.

Q4. Are migrants themselves the source of new formal firm creation?

No. The authors directly test and reject this channel. Migrants are not more likely to be business owners — either of small firms (fewer than 5 employees) or larger firms (6 or more employees) — in municipalities that receive more immigration. The increase in formal firm entry is driven by non-migrants responding to cheaper labor.

Q5. What are the two margins of informality in the model, and why does the intensive margin matter for the migration-formality nexus?

The extensive margin is whether a firm registers formally (firm-level binary). The intensive margin is whether a formally registered firm hires workers without formal labor contracts (worker-level, within formal firms). The intensive margin is crucial because it links formal firms to migrants: newly arrived migrants may take informal jobs within formal firms, allowing formal firm creation to respond to the immigration shock even before the labor market fully formalizes. In the transition dynamics after an immigration shock with DNWR, new formal firms tend to be small and lower-productivity, and hire a substantial fraction of their workforce informally — so labor informality hovers near its initial level for several years even as firm informality declines quickly.

Q6. What fraction of the increase in formal firms in the counterfactual comes from stepping-stone formalization versus new formal entry?

In the baseline 10 percent labor supply counterfactual, approximately 40 percent of the increase in the number of formal firms comes from formalization of previously informal firms across their life cycles. The remaining 60 percent comes from new formal firm creation. A static framework would miss the stepping-stone channel entirely and substantially underestimate total formalization.

Q7. How does the model’s calibration pin down the cost structure of informal vs. formal firms?

The model is calibrated using a two-step minimum distance procedure. First-step parameters include the persistence of formal firms’ productivity process (estimated from RAIS: rho_f = 0.92), and statutory tax rates (payroll tax tau_w = 0.375; revenue VAT tau_y = 0.293). Second-step parameters (12 total, including entry costs, exogenous death rates, productivity dispersion, and cost-function curvatures for both margins of informality) are estimated by minimizing the distance between simulated and observed moments from RAIS (2003 cross-section for static moments; 2000–2011 panel for growth moments) and ECINF (informal firms with up to 5 employees, 2003). Key calibrated values: formal entry costs are more than twice informal entry costs and correspond to over 30 times the 2003 monthly national minimum wage; the informal sector exogenous death rate (delta_i = 0.148) is more than twice the formal rate; productivity variance and persistence are similar across sectors.

Q8. What happens to firm productivity and output per worker in the long-run counterfactual?

Average firm productivity declines by 1.4 percent despite lower informality. The composition of formal firms worsens: the share of firms in the lowest productivity quartile rises by more than 4 percentage points, while the share in the top quartile falls by about 3 percentage points. Total output and tax revenues increase (7 and 8.6 percent, respectively), but both decline in per capita terms. The authors note these are likely lower bounds because the model assumes no technological differences between formal and informal sectors and no differential capital access.

Q9. What does the enforcement counterfactual reveal about the dual role of informality?

When the extensive margin of informality is nearly shut down (by making the informal cost function very steep), a 10 percent labor supply shock produces: output increase of 8.6 percent (vs. 7 percent with informality present); average firm productivity increase of 2.1 percent (vs. decline of 1.4 percent); much higher tax revenues due to greater formality. However, this comes at the cost of a sizable reduction in total firm count as the least productive informal firms are displaced. This illustrates the dual role: in the short run, the informal sector acts as an employment buffer and stepping-stone, which is more important when formal wage rigidity is stronger; but in the long run, it dampens aggregate economic benefits from immigration by sheltering low-productivity firms.

Q10. Do the results hold for both migrants and non-migrants, and across skill levels?

Yes. Appendix results show similar employment and wage effects for migrants and non-migrants separately, though formal wage declines are more pronounced for non-migrants. Results are also similar for high- and low-skilled workers — which the authors attribute to the fact that drought-induced migration does not change the skill composition of the workforce at destination (confirmed empirically). Price-shock-induced migrants differ: they are more likely to be young and male, and do change workforce composition, providing a different set of compliers that strengthens external validity.

Q11. How does the paper relate to the “startup deficit” literature on demographic decline?

The paper’s findings are the mirror image of the US startup deficit literature, which argues that demographic slowdown reduced firm entry, labor reallocation, and employment growth. The magnitudes are comparable in scale: the US startup deficit corresponds to a 5-percentage-point decline in firm entry between 1980 and 2012, while the rural-urban migration shocks studied here produce first-order effects on firm entry of similar or larger magnitude (2.8 percent per percentage point of immigration rate), suggesting labor supply growth is a primary driver of formal firm dynamics in both directions.

Key Concepts

Downward Nominal Wage Rigidity (DNWR). In the paper’s usage, the binding constraint that formal sector wages cannot be cut in nominal terms — in Brazil, both legal prohibition of nominal wage cuts and a rising national minimum wage. DNWR is the paper’s central mechanism explaining why immigration increases informality in the short run (wages cannot adjust) but reduces it over the decade (inflation and turnover permit real adjustment). Measured empirically via the municipality-level Kaitz index (national minimum wage / local median wage) and via the share of workers with negative year-on-year nominal wage changes in RAIS.

Extensive Margin of Informality. Whether a firm is registered with the government (formal) or not (informal). In the model, informal firms can avoid taxes but face a size-increasing cost of informality and the option to formalize by paying the difference in entry costs. This margin captures the firm’s legal registration status.

Intensive Margin of Informality. Whether a formally registered firm hires individual workers with or without formal labor contracts (signed work booklet, carteira de trabalho). Formal firms face increasing costs for informal hiring but exploit this margin for lower-cost labor, especially when small or young. This margin is critical because it links formal firms to migration-induced informal labor supply and allows formal firms to absorb migrants before full wage adjustment occurs.

Stepping-Stone Role of Informality. The paper’s term for the dynamic channel through which the informal sector facilitates transitions to formality for both firms and workers. Informal firms accumulate productivity experience and formalize when productivity crosses the formalization threshold; informal workers within formal firms transition to formal contracts as firms grow. In the counterfactuals, 40 percent of the increase in formal firms following a labor supply shock is attributable to this channel. The stepping-stone role is most valuable during the short-run period of formal wage rigidity.

Shift-Share Instrumental Variable. The identification design combining pre-existing migration network shares (fraction of prior migrants to destination d from each origin o, computed 1995–2000) with exogenous push shocks at origin (drought shocks or commodity price shocks). The instrument predicts which destination municipalities receive more migrants based purely on exogenous origin-level shocks, purging the endogeneity from migrants self-selecting into prosperous cities.

Minimum Comparable Area (MCA). The paper’s geographic unit of analysis: a harmonized aggregation of Brazilian municipalities whose administrative borders changed during the study period, yielding 3,548 stable units covering all urban destinations studied. The authors call these “municipalities” for convenience.

Harris-Todaro-Fields Framework. The theoretical benchmark against which the paper’s results are compared — the view (from Harris and Todaro 1970 and Fields) that rural-urban migration increases urban unemployment or informality because DNWR prevents the formal sector from absorbing migrants, who instead queue for formal jobs or enter the informal sector. The paper shows this prediction holds in the short run and in high-DNWR municipalities, but not in the long run where real wage adjustment occurs.

O15 | Macro Paper Warehouse

Abundance from Abroad: Migrant Income and Long-Run Economic Development

Layer 1 — Overview

In depth

Q1. What makes the 1997 Asian Financial Crisis useful as a natural experiment for this paper’s purposes?

Q2. What is the shift-share variable, and how does it combine “shifts” and “shares”?

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?

Q4. What evidence supports the parallel trends assumption in the pre-shock period?

Q5. How do the authors rule out trade flows as an alternative mechanism for the estimated income effects?

Q6. What is the composition of the domestic income gains — where do they come from?

Q7. What is the “global income” concept and what share does each component contribute?

Q8. How do education effects translate into more and higher-skilled migration?

Q9. What mechanisms does the structural model invoke to explain the domestic income gains?

Q10. How do the authors assess SUTVA and internal migration?

Q11. Are the exposure weights (migration shares) persistent, and does this support interpreting the shock as persistent?

Q12. What are the policy implications and how do the authors connect findings to migration policy?

Key Concepts

Civil War–Induced Displacement and Human Capital

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Intergenerational Impacts of Secondary Education: Experimental Evidence from Ghana

Rural Migrants and Urban Informality: Evidence From Brazil

Overview

In depth

Q1. What is the identification strategy, and what are the key threats to validity the authors address?

Q2. How do the authors reconcile the long-run formalization result with the short-run informality-increasing result, and what role does DNWR play?

Q3. What is the exact magnitude of the firm-level effects and how persistent are they?

Q4. Are migrants themselves the source of new formal firm creation?

Q5. What are the two margins of informality in the model, and why does the intensive margin matter for the migration-formality nexus?

Q6. What fraction of the increase in formal firms in the counterfactual comes from stepping-stone formalization versus new formal entry?

Q7. How does the model’s calibration pin down the cost structure of informal vs. formal firms?

Q8. What happens to firm productivity and output per worker in the long-run counterfactual?

Q9. What does the enforcement counterfactual reveal about the dual role of informality?

Q10. Do the results hold for both migrants and non-migrants, and across skill levels?

Q11. How does the paper relate to the “startup deficit” literature on demographic decline?

Key Concepts