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Randomly assigned teacher effectiveness translated into actual teacher effectiveness at a rate of 0.293 (first stage) due to non-compliance. The IV coefficient of actual teacher effectiveness on student test achievement was 0.955 (p < 0.01, SE = 0.123), proving that a 1-unit predicted difference caused a statistically equivalent ~1-unit actual achievement gain.
In Year 2 in Mumbai, 33% of schools assigned to receive a balsakhi tutor did not receive one due to administrative difficulties; 2SLS instrumentation successfully eliminated non-compliance bias to yield unbiased direct treatment estimates.
LIML estimation yielded a pooled causal effectiveness coefficient of 0.955 (p < 0.01, SE = 0.123) while maintaining a minimum first-stage F-statistic of 38 across instrument specifications, eliminating weak-instrument bias concerns.
Because only 29.3% of the assigned teacher effectiveness translated into actual teacher effectiveness due to student and teacher movement, the LATE IV estimate scaled the reduced-form impact (~0.28) up to 0.955 (p < 0.01, SE = 0.123), proving that the true causal impact for compliant rosters was approximately 1:1 with predicted effectiveness.
The probabilistic job offer assignment strongly predicts short-term employment acquisition with a first-stage F-statistic of 76.36 [12, 13]. Assigning 50%, 75%, or 100% probabilities increases job acquisition by 40.2, 56.8, and 83.7 percentage points respectively (p < 0.01) [12, 13].
In Large-Country Virtual-Within networks, peer outcomes increase proposal submission rates ($\beta = 0.533, p < 0.01$). In Small-Country Virtual-Across networks, peer outcomes increase intensive business proposal quality ($\beta = 0.476, p < 0.01$).