Literature

PDF storage. All file: <citekey>.pdf entries below resolve to bi-dropbox:poll-sponsor-bias/literature/<citekey>.pdf — the project's canonical Dropbox folder going forward. Citekeys, BibTeX keys in paper/references.bib, the [cite:<key>] tokens, and the PDF filenames all share the same identifier. As of 2026-06-16 the folder has 40 PDFs and covers all 12 papers currently cited in paper/paper.tex; the broader curated set in this document is ~63 entries and the gap is paywalled classics in queue for the next campus-network fetch round.

Brazilian polling accuracy and methodology

Poll regulation, disclosure regimes, and pre-registration

Effects of polls on voters and candidates

Selective publication of polls and survey selection

Brazilian electoral context, campaign finance, and parties

Forecast accuracy and audits in other settings

Methodological references (fixed effects, hierarchical Bayes, meta-analysis)

Notes on positioning

Has the paper been done? No. Six closest predecessors, none doing the same combination: (i) [cite:leeper2019sponsorship] Leeper & Thorson (2019) experimentally vary survey sponsor (university vs marketing firm) but in online opinion surveys, not pre-election polls, and not by candidate sponsor; (ii) [cite:lee2024korean] Lee, Zhang & Pak (2024) Bayesian-decompose Korean news-media selective reporting of polls — same spirit, but for the publication-selection problem the TSE pre-registration regime explicitly removes; (iii) [cite:gramacho2013margem] Gramacho (2013) and [cite:gramacho2015preelection] Gramacho (2015) audit Brazilian poll accuracy at the president/governor level but do not link polls to sponsors and do not exploit mandatory pre-registration; (iv) [cite:meireles2022pesquisas] Meireles & Russo (2022) is the closest existing audit on the same data (2k+ TSE-registered polls, 2012–2020, including municipal races), but analyzes only sample-design predictors of error and does not link polls to sponsors or use within-candidate FE — direct benchmark our sponsor-bias result must beat; (v) [cite:batistapereira2024pesquisas] Batista Pereira & Nunes (2024) explain the 2022 presidential polls-vs-results gap with late voter-side change (strategic voting + undecided alignment) on the same TSE-registered universe — competing alternative explanation we must distinguish sponsor bias from; (vi) [cite:cantu2016utility] Cantú, Hoyo & Morales (2016) build a Kalman-filter pipeline for firm-level pre-election poll bias in multiparty Mexican races — closest Latin American methodological predecessor, treats pollster as the unit (no sponsor split, no mandatory pre-registration). The Italian house-effects pipeline ([cite:destefano2022preelectoral], [cite:pauli2013hierarchical], [cite:selb2023bias]) gives us the estimator template but at the pollster level only. Nothing in the pool combines (a) mandatory pre-registration to kill publication selection with (b) a within-candidate FE design that splits sponsor from pollster house effects.

Industry-insider angle. Felipe Nunes — co-author of [cite:batistapereira2024pesquisas] and the main academic voice on Brazilian poll performance — is the co-founder of Quaest, one of the major Brazilian polling firms. He continues as a researcher (UFMG, now FGV-EESP) while running the firm. Implications: (1) the most academically engaged Brazilian pollster has not analyzed sponsor effects; (2) our paper needs an intro framing that does not claim malfeasance, only documents an average bias, and acknowledges that the industry has serious academic voices.

Cleanest positioning. "We use Brazil's mandatory pre-registration regime (TSE PesqEle) to estimate sponsor bias in pre-election polls free of publication-selection contamination, identifying the effect from a within-candidate fixed-effects design that compares the same mayoral candidate across polls with different sponsors." Two distinguishing claims: (1) the regime removes the selection on publication that limits Leeper-Thorson and Lee-Zhang-Pak; (2) the candidate×race fixed effect identifies the client-specific effect separately from the pollster's generic house effect — most prior work does only one or the other.

Citation-graph expansion targets (most central nodes the next pipeline step should follow forward and backward):

  1. Leeper & Thorson (2019), DOI 10.1017/xps.2019.25 — direct sponsor-bias predecessor.
  2. Lee, Zhang & Pak (2024), DOI 10.20879/kjjcs.2024.68.6.007 — selective-reporting predecessor.
  3. De Stefano, Pauli & Torelli (2022), DOI 10.1214/21-aoas1507 — house-effects estimator.
  4. Selb, Chen, Körtner & Bosch (2023), DOI 10.1093/poq/nfad046 — multiparty bias-variance decomposition (pulls in Shirani-Mehr et al. 2018, which is not in candidates).
  5. Gramacho (2013), DOI 10.1590/S0104-62762013000100004 — the Brazilian benchmark on poll accuracy.
  6. Schmitt-Beck, Faas & Mackenrodt (2008), DOI 10.1093/IJPOR/EDN034 — voter-side effects of media polls, motivation anchor.
  7. Castro Cornejo (2024), DOI 10.1080/17457289.2024.2409642 — Mexican partisan-bias-in-poll-perception, closest Latin American analog.
  8. Chen, Körtner, Selb & Wiederspohn (2023), DOI 10.33774/apsa-2023-t1vh8 — large-N hierarchical model on US Senate polls, methodological scale-up template.

Missing from this pool — flag for next pipeline step / hand-add. Shirani-Mehr, Rothschild, Goel & Gelman (2018) "Disentangling Bias and Variance in Election Polls" (JASA) is the parent paper of half the house-effects work in the pool and should be pulled in explicitly. It did not surface in the citation-graph expansion of Selb 2023 / Chen 2023 / De Stefano 2022 either — needs hand-add via direct DOI lookup. Also worth searching: Holland on poll release and political behavior in Latin America; David Samuels' / Bruno Speck's broader Brazilian campaign-finance work; the AAPOR ad-hoc committee reports on 2020 US polls; Soroka et al. on media-bias-in-polls.

Supply-side theory: persuasion, disclosure, career concerns

Theoretical anchors for the project's supply-side mechanisms (docs/theory.md §"Polls as Bayesian persuasion", §"Polls as verifiable disclosure", §"Polls as career-concerns games"). Added 2026-06-02 in a targeted refresh focused on filling theory.md placeholders. Canonical 1980s-1990s references were added by direct Crossref lookup; modern Bayesian-persuasion follow-ups came from pollster reputation accuracy, Bayesian persuasion information design, and citation-graph expansion on Kamenica-Gentzkow 2011.

These 12 entries close the theory-side gaps surfaced in docs/theory.md §"Polls as Bayesian persuasion (supply-side / Channel A)", §"Polls as verifiable disclosure", §"Polls as career-concerns games", and §"Reputation as premium-supported quality". The [ref needed] placeholders in theory.md can now be replaced with the citekeys above. No PDF download attempted yet — many are paywalled (JPE, REStud, GEB, AER, QJE) and require campus access.