• Just a quick reminder that I am teaching DATASCI 185 - Introduction to AI Applications next semester 🤖
• The course is for everyone, no prior programming or AI experience is required
• If you (or anyone you know) is interested, please check it out and consider enrolling!
• A short course description:

This course offers a non-technical introduction to artificial intelligence and its effects on institutions, work and everyday life. The course is practical: students will learn how modern systems are designed and how to ask the right questions about data, reliability and harms. The course combines short demonstrations (no programming experience required), case studies, and project work. It is suitable for undergraduate students from any faculty who want a grounded understanding of what AI can and cannot do.

• For more information, please visit https://atlas.emory.edu/ or email me at danilo.freire@emory.edu to request the syllabus or discuss further!

• Do social networks improve labour market outcomes for Mexican migrants in the U.S.?
• Motivation:
  • Non-market institutions (like networks) are really important to the economy, especially where markets are imperfect (e.g., information gaps in labour markets).
  • Migrant communities often rely heavily on social ties for finding jobs, housing, etc. (Rees 1966, Montgomery 1991)
  • Particularly relevant for undocumented migrants who face barriers to formal channels
• Does a larger network (from the same origin community) lead to better employment prospects and higher-paying jobs?

• Why networks might matter:
  • Information Asymmetry: Employers don’t know worker quality; workers don’t know about all job openings. Networks provide referrals
  • Screening/Referrals: Incumbent workers might refer others from their network, potentially signalling quality (Montgomery 1991)
  • Support: Networks can provide financial help, housing, advice, reducing job search costs
• Who contributes? Established migrants (longer duration, more connections, more employed)
• Who benefits? New arrivals or those who would otherwise struggle to find employment/good jobs
• Networks could lead to higher employment rates and channel members into better (e.g., non-agricultural) jobs

• Conducted jointly by researchers in Mexico and the U.S. since 1982
• Surveys households in multiple Mexican communities with high migration rates
• Collects retrospective life histories: migration patterns, work history (location, job type, employment status) year-by-year
• An important strength is defining the social unit based on the origin community in Mexico – a well-established social unit (paisanaje, origin-community). This is arguably better than using arbitrary administrative boundaries
• Munshi uses data from 24 communities across 7 Mexican states, focusing on the 15 years prior to the survey year for each community

• Network Definition: For an individual \(i\) from community \(c\) in year \(t\), the network size (\(X_{t-1}\)) is measured as the proportion of sampled individuals from community \(c\) who were located in the U.S. in the previous year (\(t-1\))
• A lagged measure is used (\(t-1\) network size to predict \(t\) outcomes) to reduce immediate simultaneity concerns
• Network size changes over time within a community due to new migration and return migration. Munshi primarily uses this within-community time variation
• The analysis later refines this by distinguishing between new migrants (in the U.S. < 3 years) and established migrants (in the U.S. ≥ 3 years), based on theoretical predictions

• Migrants are not a random sample of the origin community
• Individuals who migrate might differ in unobserved ways (e.g., ability, motivation) that also affect their labour market success
• \(Y_{it} = \beta X_{t-1} + \alpha_i + \delta_t + \epsilon_{it}\)
  • \(Y_{it}\): Labour outcome (e.g., employment)
  • \(X_{t-1}\): Network size
  • \(\alpha_i\): Individual unobserved heterogeneity (ability)
  • \(\delta_t\): Year fixed effects (common shocks)
• If higher ability (\(\alpha_i\)) individuals are more likely to migrate and get better jobs, simply regressing \(Y_{it}\) on \(X_{t-1}\) will yield biased estimates of \(\beta\). The network effect might be confounded with it

• The author uses panel data (repeated observations for the same individual over time) and include individual fixed effects (\(\alpha_i\))
• Fixed effects control for any time-invariant unobserved characteristics of the individual (like innate ability, stable motivation)
• The effect of the network (\(\beta\)) is now identified from changes in network size over time for the same individual, comparing their outcomes when their network was larger versus smaller
• Important assumption: Unobserved individual factors (\(\alpha_i\)) are constant over the sample period (perhaps reasonable for low-skill jobs)
• Fixed effects do not solve simultaneity bias!

• Network size (\(X_{t-1}\)) itself might also be correlated with unobserved factors affecting current outcomes (\(Y_{it}\))
• For instance, positive economic shocks in the U.S. (\(\epsilon_{it}\)) could simultaneously:
  • Improve employment outcomes (\(Y_{it}\)) directly
  • Attract more migrants, increasing network size (\(X_{t-1}\) or \(X_t\))
  • Reduce return migration among established migrants
• This simultaneity would lead OLS (even with fixed effects) to potentially overestimate the positive effect of networks
• Alternatively, good U.S. conditions might allow migrants to reach savings targets faster and return home, reducing network size, biasing estimates downwards

• Checking Relevance: Is Mexican rainfall correlated with network size in the U.S.?
  • This is plausible: Droughts push people to migrate
  • Munshi provides empirical evidence in the first-stage regressions (Table V), showing low rainfall increases migration, particularly affecting the stock of established migrants later on
• Checking the Exclusion Restriction: Does Mexican rainfall affect U.S. employment only via network size?
  • This seems plausible too: Origin communities are far from U.S. destinations. Mexican weather shocks should be uncorrelated with U.S. labour market shocks. (Munshi reports a correlation coefficient of 0.01 between origin rainfall and U.S. destination rainfall)
  • Could low rainfall affect migrant characteristics (e.g., desperation, reservation wage) directly influencing U.S. employment? Munshi argues the observed timing (long lags) makes this less likely to be the primary channel for the employment effect

• This study finds that
  • Social networks significantly improve labour market outcomes for Mexican migrants in the U.S., increasing both employment probability and access to better (non-agricultural) jobs
  • Established migrants are the crucial part of the network driving these positive effects
• Methodologically, it demonstrates how to combine IV (using a plausible natural instrument - rainfall) with fixed effects to identify network effects while addressing selection and endogeneity concerns
• It highlights the important role non-market institutions play in modern economies, particularly for vulnerable populations, and shows that network size and composition matter

• Worm infections spread through contact with contaminated soil/water. Treating infected children reduces the overall pool of infection in the environment
• Externality: Treating child A can benefit untreated child B if they live/play/attend school nearby, because B is less likely to get infected or re-infected
• This creates issues for individual-level randomisation:
  • The comparison group (untreated individuals within a treated community/school) benefits from reduced transmission from the treated group
  • The estimated treatment effect (Treated Outcome - Control Outcome) is biased downwards because the control outcome is better than it would be without the programme
  • This represents a violation of the Stable Unit Treatment Value Assumption (SUTVA) (but a good one!)

• Overall Effect (ITT at School Level): Compare mean outcomes in treatment schools vs. comparison schools in a given year, controlling for baseline differences \(Y_{ijt} = \alpha + \beta_1 T_{1it} + \beta_2 T_{2it} + X_{ijt}\delta + u_i + \epsilon_{ijt}\) (Where T1, T2 indicate year 1 / year 2 treatment assignment)
• Cross-School Externalities: Exploit the exogenous variation in treatment density induced by school-level randomisation \(Y_{ijt} = \alpha + ... + \sum_d (\gamma_d \cdot N^T_{dit}) + \sum_d (\phi_d \cdot N_{dit}) + u_i + \epsilon_{ijt}\)
  • \(N^T_{dit}\): Number of treated pupils within distance d of school i.
  • \(N_{dit}\): Total number of pupils within distance d.
  • \(\gamma_d\) captures the externality effect from nearby treated pupils, after controlling for overall density \(\phi_d\).
• Within-School Externalities: Harder to identify experimentally due to lack of within-school randomisation. The paper uses non-experimental comparisons (e.g., untreated pupils in T schools vs C schools - Table VI), acknowledging potential selection issues

• Table V compares infection rates in Group 1 (treated ’98) vs Group 2 (control ’98) based on parasitological exams in early 1999
• Group 1 pupils had significantly lower rates of moderate-to-heavy infection (any worm) compared to Group 2 (27% vs 52%, a 25 percentage point difference). Significant reductions were seen for hookworm, roundworm, and schistosomiasis
• Table VII (Regression) confirms these health benefits and estimates externalities:
  • Within-School Externality (Col 2): Untreated pupils in Group 1 schools had ~12 pp lower infection rates than comparison pupils (\(\beta_1\) estimate).
  • Direct Effect + Within-School (Col 2): Treated pupils in Group 1 had ~26 pp lower infection rates (\(\beta_1 + b_2\) estimate).
  • Cross-School Externality (Col 1): Nearby treatment pupils had significant negative effects on infection rates (esp. within 3km). Each 1000 treated pupils within 3km reduced infection risk by ~26 pp (\(\gamma_{03}\) estimate).

• Deworming drugs are extremely inexpensive (< $0.50 per child per year via PCD estimates)
• Health Perspective: Cost per infection averted is estimated around $5 (including externalities), making it highly cost-effective by WHO standards, especially driven by schistosomiasis impact. Geohelminth treatment alone appears less cost-effective based only on health metrics
• Education Perspective: Cost per additional year of school participation gained is approx. $3.50 (including externalities). This is substantially cheaper than alternative participation-boosting interventions evaluated in the area (e.g., free uniforms ~$99)
• Human Capital View: Rough calculations suggest future wage benefits (>$30) greatly exceed program costs ($0.49), implying a potentially high rate of return on investment

• This study demonstrated that:
  • School-based deworming significantly reduced worm infections and increased school participation (~7.5 pp), though had no discernible effect on test scores.
  • There were large positive externalities (spillovers) affecting the health and participation of untreated children, both within the same school and in nearby schools. These externalities constitute a major portion of the total benefits.
  • Deworming is a highly cost-effective intervention, particularly for boosting school participation.
• Methodologically, it nicely illustrates the importance of accounting for externalities in program evaluation. It shows how school-level (or cluster) randomisation can facilitate the identification of overall effects and the estimation of spillovers
• A key policy insight is that ignoring externalities likely leads to under-investment in public health programmes like deworming. Full subsidies (or even payments, considering the large externalities) might be optimal