We live in the age of AI, it takes approximately 2-3 minutes to get a condensed report on why your paper is misleading or incorrect:

Control group mismatch: Treats non-profits as a clean counterfactual for for-profits, but the sectors have very different occupational mixes and shocks—so the triple-diff can pick up sectoral composition, not policy.

Short pre-trend: Only a tiny non-binding window to test parallel trends → weak pre-trend evidence.

Approvals ≠ demand: Uses approved H-1Bs (not applications), so results reflect rationed outcomes and USCIS processing quirks, not employer demand.

Strong wage-equalization assumption: Identification leans on wages equalizing across very different employers; if sector premia move (e.g., recession), estimates drift.

Relative, not absolute, effect: The estimate is “for-profit vs. non-profit”; if non-profits expand (cap-exempt), the “effect” can be reallocation, not a true for-profit decline.

Recession confound: Lottery years coincide with 2008–09; macro shocks can differentially hit new vs. established hires across sectors.

Noisy worker/firm measures: Experience is imputed (age minus stylized schooling ages); employer names are inconsistently harmonized → concentration trends may be artifacts; “large firm” cutoff is arbitrary.

Wage results are shaky: Based on offer wages (not realized), trimmed, and imprecise—tail stories are fragile.

Placebo underpowered/misaligned: Native “no effect” test is noisy and not analogous to “new vs. established” H-1Bs, so it’s weak evidence on substitutability.

Ignores geography: Cells are national; regional wage floors and local cycles could drive composition shifts.

Net: clever design but brittle; treat findings as suggestive reallocation under approvals data, not clean causal effects on demand, wages, or “top talent.”