{"attempt":1,"datasets":{"ds_0065":{"description_shown":"| Variable Name | Description |\n| --- | --- |\n| (index) | Row identifier from the source file (not part of the analytical variables). |\n| city | City name (normalized; typically lowercase). |\n| STATEAB | Two-letter state abbreviation. |\n| YearData_next | Year of the city\u2019s next mayoral election linked to this record (if a next election is matched/available). |\n| fips | Census place FIPS code identifying the municipality. |\n| Name | Official Census-style place name (e.g., \u201cABILENE CITY\u201d). |\n| JURIS | Jurisdiction display name as used by the city (e.g., \u201cCity of Abilene\u201d). |\n| YearData | Year of the current mayoral election for this record. |\n| elec_index | Within-city sequence number for the current election. |\n| elec_index_next | Within-city sequence number for the next election (if linked/available). |\n| elecdate | Calendar date of the current election. |\n| month | Month in which the current election occurred (1\u201312). |\n| population_est | Estimated city population at the time of the current election (persons). |\n| cand_name | Candidate\u2019s name as recorded in the election returns/source. |\n| cand_party | Candidate\u2019s party label when present (e.g., D, R); blank for nonpartisan ballots or when party is not reported. |\n| voteshare | Candidate\u2019s **two-candidate vote share** in the current election: the candidate\u2019s votes divided by the **sum of votes for the top two finishers** in that election (proportion between 0 and 1). For the two top candidates, their `voteshare` values sum to 1. |\n| next_VS | Candidate\u2019s two-candidate vote share in the next linked mayoral election (constructed analogously to `voteshare` using the top two finishers in that next election); typically missing if the candidate does not appear in the next election. |\n| run_next | Indicator for whether the candidate appears on the ballot in the next linked mayoral election (1/0). |\n| turnout | Total votes cast in the mayoral race in the current election (votes). |\n| fog | City\u2019s form of government (e.g., mayor\u2013council vs. council\u2013manager), as coded in the source institutional data. |\n| partisan | Indicator for whether the mayoral ballot lists party labels (1/0). |\n| initiative | Indicator for whether the city allows citizen initiatives (1/0). |\n| referendum | Indicator for whether the city allows referendums (1/0). |\n| concurrent_next | Indicator for the next linked election being held concurrently with state/national general elections (typically November of an even-numbered year; 1/0). |\n| concurrent_pres_next | Indicator for the next linked election coinciding with a presidential general election year (1/0). |\n| concurrent_mid_next | Indicator for the next linked election coinciding with a midterm general election year (1/0). |\n| run_and_win_next | Indicator for whether the candidate both runs in and wins the next linked election (1/0). |\n| gov_id | Internal numeric identifier for the municipal government entity. |\n| fullstate | Full state name. |\n| dailypaper | Local daily newspaper availability measure for the city at election time (as recorded/constructed in the source media data; may be a count or indicator depending on coding). |\n| dailypaper_titles | Names/titles of daily newspapers associated with the city (string field; may contain multiple titles). |\n| voteshare_adj | Two-candidate vote share centered at 0.5: `voteshare - 0.5`. |\n| fog2 | Alternate coding/version of the city\u2019s form of government. |\n| dailypaper_delta | Change in the `dailypaper` measure from the prior election to the current election (where prior election data exist). |\n| dailypaper_delta2 | Alternate coding/version of `dailypaper_delta`. |\n| dailypaper_delta_lead | Change in the `dailypaper` measure between the current election and the next linked election. |\n| dailypaper_delta_lead2 | Alternate coding/version of `dailypaper_delta_lead`. |\n| concurrent | Indicator for the current election being concurrent with state/national general elections (typically November of an even-numbered year; 1/0). |\n| concurrent_delta | Change in concurrency status from the prior election to the current election (where prior election data exist). |\n| concurrent_delta_lead | Change in concurrency status from the current election to the next linked election. |\n| concurrent_delta_lead2 | Alternate coding/version of `concurrent_delta_lead`. |\n| concurrent_delta2 | Alternate coding/version of `concurrent_delta`. |\n\nDataset Context:\nThere is no stored `incumbency` column in this extract. For nonmissing, untied top-two vote shares, a current-election victory is indicated by `voteshare > 0.5`, equivalently `voteshare_adj > 0`, because `voteshare_adj = voteshare - 0.5`. Values below the respective thresholds indicate a loss. Missing vote shares and exact ties (`voteshare = 0.5` or `voteshare_adj = 0`) do not independently establish a winner. These two stored columns encode the same assignment information on different scales.\n\nThe dataset contains candidate-level records for U.S. mayoral elections compiled across cities from 1950 to 2014. Each row represents a candidate in a specific mayoral election and links, where possible, to that candidate\u2019s subsequent election appearance in the next mayoral election for the same city. The compiled coverage is broad (on the order of ~9,000+ elections across ~1,000+ cities and just under ~10,000 unique candidates, depending on matching/availability across sources). Variables include candidate names and (when available) party labels, vote-share measures, whether the candidate runs again, whether they run and win again, basic election details (year, date, month), city identifiers (place FIPS, jurisdiction names), estimated city population at the time, total votes cast, and several municipal institutional attributes.\n\nThe data were assembled by merging multiple large-scale collections of municipal election returns and institutional records used in prior research, supplemented with additional collection and verification from city, county, and state sources. The file includes indicators that classify whether a mayoral election was held concurrently with state/national general elections (operationalized using the typical November even-year election calendar), as well as flags for the availability of initiatives and referendums and the city\u2019s form of government. Additional fields summarize local media availability via daily newspapers. Some vote-share fields are standardized using a two-candidate (top-two) denominator so that elections with more than two candidates can be compared on a consistent scale. This structure enables analysts to study patterns in municipal contests, election timing, and candidate trajectories across a broad span of cities and years.\n","file_sha256":"a478d0e1d57c403a86d031f42ac4660328a55bea50f128360820d3c514d25939","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"Among mayoral candidates in elections decided by a razor-thin margin around 50% of the vote, what is the average effect of barely winning (gaining incumbency) on the probability of running in the next election?","question_sha256":"70a9fcca3e83a1cebbca926be499f9ef37217a244095f350da2bc9872439d4ae","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":0,"skills":[],"task_id":"crb_papers.q097","task_sha256":"24774f6b6558949aca9250a8b950b92957701f52aeab69a89e96ac5b5967efeb","timestamp":"2026-10-15T17:10:30.238171Z"}
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{"arguments":{"cluster":null,"controls":[],"dataset":"ds_0065","fe":[],"intercept":true,"level":0.95,"se":"HC1","subset":null,"weights":null,"x":["voteshare"],"y":"run_next"},"attempt":1,"cached":false,"call_id":"call_56f4876f","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"ds_0065":"60b5cb8255dce8557dbfc11b6b5bc524cac88cde74a31a7a023fcd6877f6d7fb"},"decided_by":"model","duration_ms":7.310167187824845,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"ce36c33a122cccd4ed7b53810f824dcbe19152c76c0ad51c69efb8e383736a86","llm_name":"infer__ols","output_digest":"662b9e46cbaf7ce446369493c2e8435371d90707938c47e3768833fd2e3dbde4","rationale":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","replay_class":"replayed","result":{"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"call_id":"call_56f4876f","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":8875},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":2},{"flag":false,"name":"r2","note":"","value":0.1953582362815529},{"flag":false,"name":"df_resid","note":"","value":8873},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null}],"figures":[{"ci_high":1.17554518286585,"ci_level":0.95,"ci_low":1.0831551202936525,"derivation":{"note_pt":"","steps":[{"formula":"","result":1.1293501515797513,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":8875.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":2.0}],"text_pt":"Coeficiente de voteshare na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de run_next.","unit":""},{"formula":"ep = \u221aV_jj","result":0.02356611152410411,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.0005553616123665126}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":1.1293501515797513},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9602313788105894},{"note":"","symbol":"ep","unit":"","value":0.02356611152410411}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 8873 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.voteshare","label":"Coefficient on voteshare in the regression of run_next","se":0.02356611152410411,"unit":"","value":1.1293501515797513}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":-0.1720685750665147,"ci_low":-0.21721062449115355,"coef":-0.19463959977883413,"p":4.0673134815198117e-63,"se":0.011514469646953029,"t":-16.903913575415075,"term":"Intercept"},{"ci_high":1.17554518286585,"ci_low":1.0831551202936525,"coef":1.1293501515797513,"p":0.0,"se":0.02356611152410411,"t":47.92263460285413,"term":"voteshare"}]},"tool":"infer.ols","version":"0.1.0","warnings":["7395 rows were dropped because one of 'run_next', 'voteshare' was missing"]},"result_for_model_ref":"a6c89493db27105524780bf5e3b63b96adaeabfec6e310f5d9f32019abe4ed4a","result_sha256":"25853f165f14de4a68c5b3d3630b269a585e7811399054fea46ea339f9cb7363","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":2,"synthetic":false,"task_id":"crb_papers.q097","timestamp":"2026-10-15T17:10:30.241559Z","tool_id":"infer.ols","turn_id":"call_21920a27"}
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