{"call_id":"call_7e4e0361","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q120/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.767650Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"ds_0085\",\n \"rows\": 2703,\n \"description\": \"| Variable Name                 | Description                                                                                         |\\n|-------------------------------|-----------------------------------------------------------------------------------------------------|\\n| cand_id                       | Unique identifier for the candidate                                                                 |\\n| okrug_id                      | Identifier for the single-member electoral district within a region                                 |\\n| okrug_win                     | Indicator for whether the candidate won the single-member district seat (1=yes, 0=no)              |\\n| region                        | Name of the Russian region (federal subject)                                                        |\\n| margin                        | Candidate’s vote margin relative to the nearest rival (in share points)                             |\\n| margin_i                      | Interaction/auxiliary term based on vote margin used in modeling                                    |\\n| margin_2                      | Squared term of vote margin                                                                         |\\n| margin_i2                     | Squared interaction/auxiliary term based on vote margin                                            |\\n| margin_3                      | Cubic term of vote margin                                                                           |\\n| margin_i3                     | Cubic interaction/auxiliary term based on vote margin                                              |\\n| actualendyear                 | Final year of the legislative convocation relevant to the candidacy                                 |\\n| actualstartyear               | First year of the legislative convocation relevant to the candidacy                                 |\\n| reg_id                        | Numeric region code                                                                                 |\\n| sector                        | Two‑digit industry code (All-Russian Classification/OKVED) for the firm                             |\\n| age_start_l                   | Natural log of the candidate’s age at the time of the election                                      |\\n| male                          | Indicator for candidate gender (1=male, 0=female)                                                   |\\n| incumbent                     | Indicator for whether the candidate served in the previous regional convocation                     |\\n| ur                            | Indicator for United Russia (UR) party membership                                                   |\\n| party_win                     | -                                                                                                   |\\n| foreign                       | Indicator for whether the firm has any foreign ownership                                            |\\n| soe                           | Indicator for whether the firm has any state ownership                                              |\\n| system_firm                   | Indicator for “systemically important” enterprise designation                                       |\\n| agriculture                   | Indicator for firm in agriculture/food-related sectors                                              |\\n| construction                  | Indicator for firm in the construction sector                                                       |\\n| resources                     | Indicator for firm in natural resource industries (e.g., mining, oil, gas)                          |\\n| immobile                      | Indicator for firm with immobile/specific assets (manufacturing, mining, energy, construction, ag.) |\\n| pr_and_smd                    | Indicator for candidates appearing on both party list and single-member district ballots            |\\n| totalassets.s                 | Total assets in the pre‑election year (rubles)                                                      |\\n| totalassets.s.l               | Natural log of total assets in the pre‑election year                                                |\\n| fullturnover.s                | Total revenue/turnover in the pre‑election year (rubles)                                            |\\n| fullturnover.s.l              | Natural log of total revenue in the pre‑election year                                               |\\n| fullturnover.average.l        | Average of logged revenue over the convocation window                                               |\\n| pmarg.s                       | Profit margin in the pre‑election year (net profit / revenue)                                       |\\n| leverage.s                    | Leverage in the pre‑election year (total liabilities / total assets)                                |\\n| leverage.s.ihs                | Inverse hyperbolic sine transform of leverage (pre‑election year)                                   |\\n| taxrate.s                     | Effective profit tax rate in the pre‑election year (profit tax paid / profit before tax)            |\\n| receivedcontracts_regmuni.s   | Regional/municipal procurement contracts prior to the election (count/value indicator)              |\\n| totalassets.e                 | Total assets in the final year of the relevant convocation (rubles)                                  |\\n| totalassets.e.l               | Natural log of total assets in the final year                                                        |\\n| fullturnover.e                | Total revenue/turnover in the final year (rubles)                                                   |\\n| fullturnover.e.l              | Natural log of total revenue in the final year                                                       |\\n| pmarg.e                       | Profit margin in the final year                                                                      |\\n| leverage.e                    | Leverage in the final year (total liabilities / total assets)                                       |\\n| leverage.e.ihs                | Inverse hyperbolic sine transform of leverage (final year)                                          |\\n| taxrate.e                     | Effective profit tax rate in the final year                                                          |\\n| fullturnover.e.l.d            | Change in logged revenue between pre‑election year and final year                                   |\\n| pmarg.d                       | Change in profit margin between pre‑election year and final year                                    |\\n| pmarg.average                 | Average profit margin over the convocation window                                                    |\\n| receivedcontracts_regmuni     | Regional/municipal procurement contracts over the convocation window (count/value indicator)        |\\n| reg_democracy_c               | Carnegie regional democracy index (composite score)                                                  |\\n| percurseats                   | Share of seats held by the ruling party in the regional legislature                                  |\\n| log_grp                       | Log of regional gross product per capita                                                             |\\n| oilgas                        | Indicator for presence of oil/gas in the region                                                      |\\n| ur_residuals                  | Residualized measure related to ruling party strength (centered)                                     |\\n| dem_residuals                 | Residualized measure related to regional democracy score (centered)                                  |\\n| reg_grp_pc_log_r              | Logged gross regional product per capita (regional measure, rescaled)                                |\\n| numcandspersector             | Number of candidates from the same sector in the convocation/region                                  |\\n| percbus                       | Share of legislators with business backgrounds in the convocation                                    |\\n| civil_soc_c                   | Carnegie component score: civil society                                                              |\\n| media_c                       | Carnegie component score: media                                                                      |\\n| openness_c                    | Carnegie component score: openness                                                                   |\\n| pluralism_c                   | Carnegie component score: political pluralism                                                         |\\n| elecs_c                       | Carnegie component score: elections                                                                  |\\n| pol_struc_c                   | Carnegie component score: political structure/institutions                                           |\\n| director_full                 | Indicator that the candidate held a director/deputy director role at the firm                        |\\n| full_firmname                 | Full legal name of the firm                                                                           |\\n| inn                           | Firm’s Russian taxpayer identification number (INN)                                                   |\\n\\n\\nData Description:\\nThis dataset links regional legislative candidates in Russia (2004–2011) to the private firms they led at the time of their campaigns, combining election records, firm financial statements, procurement records, and regional institutional indicators. Candidate information (districts, party affiliation, incumbency, basic demographics) is drawn from the Russian Central Election Commission and matched to firms via company registry data on directors, deputy directors, and board members.\\n\\nFirm-level financials (assets, revenue, profits, leverage, and tax rates) originate from the SPARK database aggregating Rosstat filings; public procurement information comes from the federal registry of regional and municipal contracts; sector codes follow the All-Russian industry classification; and regional context measures include the Carnegie democracy index, ruling-party seat shares, resource endowments, and gross regional product. Variables are provided for pre‑election and end‑of‑convocation periods, along with simple differences and averages, allowing analysts to explore patterns in firm performance and public contracting across regions, sectors, and political settings.\",\n \"columns\": {\n  \"cand_id\": \"int64\",\n  \"okrug_id\": \"int64\",\n  \"okrug_win\": \"int64\",\n  \"region\": \"str\",\n  \"margin\": \"float64\",\n  \"margin_i\": \"float64\",\n  \"margin_2\": \"float64\",\n  \"margin_i2\": \"float64\",\n  \"margin_3\": \"float64\",\n  \"margin_i3\": \"float64\",\n  \"actualendyear\": \"int64\",\n  \"actualstartyear\": \"int64\",\n  \"reg_id\": \"int64\",\n  \"sector\": \"int64\",\n  \"age_start_l\": \"float64\",\n  \"male\": \"int64\",\n  \"incumbent\": \"int64\",\n  \"ur\": \"int64\",\n  \"party_win\": \"int64\",\n  \"foreign\": \"int64\",\n  \"soe\": \"int64\",\n  \"system_firm\": \"int64\",\n  \"agriculture\": \"int64\",\n  \"construction\": \"int64\",\n  \"resources\": \"int64\",\n  \"immobile\": \"int64\",\n  \"pr_and_smd\": \"int64\",\n  \"totalassets.s\": \"int64\",\n  \"totalassets.s.l\": \"float64\",\n  \"fullturnover.s\": \"float64\",\n  \"fullturnover.s.l\": \"float64\",\n  \"fullturnover.average.l\": \"float64\",\n  \"pmarg.s\": \"float64\",\n  \"leverage.s\": \"float64\",\n  \"leverage.s.ihs\": \"float64\",\n  \"taxrate.s\": \"float64\",\n  \"receivedcontracts_regmuni.s\": \"float64\",\n  \"totalassets.e\": \"int64\",\n  \"totalassets.e.l\": \"float64\",\n  \"fullturnover.e\": \"float64\",\n  \"fullturnover.e.l\": \"float64\",\n  \"pmarg.e\": \"float64\",\n  \"leverage.e\": \"float64\",\n  \"leverage.e.ihs\": \"float64\",\n  \"taxrate.e\": \"float64\",\n  \"fullturnover.e.l.d\": \"float64\",\n  \"pmarg.d\": \"float64\",\n  \"pmarg.average\": \"float64\",\n  \"receivedcontracts_regmuni\": \"float64\",\n  \"reg_democracy_c\": \"float64\",\n  \"percurseats\": \"float64\",\n  \"log_grp\": \"float64\",\n  \"oilgas\": \"int64\",\n  \"ur_residuals\": \"float64\",\n  \"dem_residuals\": \"float64\",\n  \"reg_grp_pc_log_r\": \"float64\",\n  \"numcandspersector\": \"float64\",\n  \"percbus\": \"float64\",\n  \"civil_soc_c\": \"float64\",\n  \"media_c\": \"float64\",\n  \"openness_c\": \"float64\",\n  \"pluralism_c\": \"float64\",\n  \"elecs_c\": \"float64\",\n  \"pol_struc_c\": \"float64\",\n  \"director_full\": \"int64\",\n  \"full_firmname\": \"str\",\n  \"inn\": \"int64\"\n }\n}\n\n# Question\nAmong firms whose directors narrowly won or lost regional single-member district elections, what is the average effect of winning office on the firm's end-of-term profit margin at the win threshold?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0085\", \"y\": \"pmarg.e\", \"x\": [\"okrug_win\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_7e4e0361","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ds_0085","y":"pmarg.e","x":["okrug_win"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"f758113676ca2408d88889d20688c81b9e2541049b93241af645f538c57c90b8","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["8ed2d8208e32d18cbfa8e54dd5bffcdce9edb37790757ce31c420c1f7e5de838"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.7435830775648355}}
{"call_id":"call_5ca6af7c","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q120/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0085","y":"pmarg.e","x":["okrug_win"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"5e2cf85793cc9e070dd8e2ea762db4e499ea15e2935ad3ed27ce7fff5a71cb36","dataset_digests":{"ds_0085":"a031319452b722537cf763706f4c794f104c2abeda0a6a276fd432e65e241409"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_5ca6af7c","status":"ok","reason":"","figures":[{"key":"coef.okrug_win","value":-0.019062475495879,"unit":"","label":"Coefficient on okrug_win in the regression of pmarg.e","se":0.028445939115208495,"ci_low":-0.07484217673884866,"ci_high":0.03671722574709065,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de okrug_win na regressão por mínimos quadrados ordinários de pmarg.e.","formula":"","terms":[{"symbol":"n","value":2532.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.019062475495879,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.0008091714521461487,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.028445939115208495,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2530 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.019062475495879,"unit":"","note":""},{"symbol":"c","value":1.9609020822640826,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.028445939115208495,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":2532,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.00015382797594110098,"flag":false,"note":""},{"name":"df_resid","value":2530,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":-0.04511320515426494,"se":0.013008527029519492,"t":-3.4679718197065794,"p":0.0005331592212193388,"ci_low":-0.07062165289363831,"ci_high":-0.019604757414891566},{"term":"okrug_win","coef":-0.019062475495879,"se":0.028445939115208495,"t":-0.6701299408212307,"p":0.502836119156369,"ci_low":-0.07484217673884866,"ci_high":0.03671722574709065}]},"datasets":{},"warnings":["171 rows were dropped because one of 'pmarg.e', 'okrug_win' was missing"],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"d3ad46dec5a68865674b3119edc62062e540c95df9747175441ba196f68dec17","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.770287Z","duration_ms":2.25958414375782,"error":null,"decided_by":"model","rationale":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_7e4e0361","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"a08645e11c7c3cd3e8293c7ecc79e5d2bcdc6bf9d3e3271f2a54df84f966c7a4"}}
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