{"call_id":"call_7d8ea9f6","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q080/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","index":0,"op":"select","args":{"columns":["e_vote_buying","lm_pob_mesa","lpopulation","lpotencial","lz_pob_mesa_f","muni_code"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":4352,"rows_after":4352,"frame_sha256_before":"40a0ce2605caee3814388883402a6cbe3aca70170da6de9b5ee4bc485533b679","frame_sha256_after":"98d485bbb714d22b22ebc635f55a26f316c7d307ad083d1a904b3ba362f172f0"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:55.467913Z","duration_ms":0.8310410194098949,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_96dcfde8","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q080/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","index":1,"op":"dropna","args":{"columns":["e_vote_buying","lm_pob_mesa","lpopulation","lpotencial","lz_pob_mesa_f","muni_code"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":4352,"rows_after":4352,"frame_sha256_before":"98d485bbb714d22b22ebc635f55a26f316c7d307ad083d1a904b3ba362f172f0","frame_sha256_after":"98d485bbb714d22b22ebc635f55a26f316c7d307ad083d1a904b3ba362f172f0"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:55.504777Z","duration_ms":1.0729581117630005,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_28d06e8e","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q080/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","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":"2026-10-15T17:00:55.553681Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","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_0050\",\n \"rows\": 4352,\n \"description\": \"| Variable Name        | Description                                                                                         |\\n|----------------------|-----------------------------------------------------------------------------------------------------|\\n| muni_code            | Municipality numeric identifier (unique code for each municipality).                               |\\n| year                 | Calendar year / election year of the observation.                                                   |\\n| departamento         | Department (first-level administrative division) name or code.                                      |\\n| municipio            | Municipality name.                                                                                  |\\n| sum_vb               | Total count / aggregate of reported vote-buying incidents (municipality-year) (aggregate measure).  |\\n| e_vote_buying        | Citizens' reports of vote-buying incidents filed in regional offices of the Attorney General (count).|\\n| e_neg_t_buying       | Citizens' reports of turnout-suppression / negative-turnout incidents (municipality-year count).     |\\n| neg_t_buying_moe     | MOE monitors' reports of turnout suppression (municipality-year count).                             |\\n| pob_mesa             | Number of registered voters per polling station (polling-place size, raw).                           |\\n| lpob_mesa            | Natural log of pob_mesa (logged registered voters per polling station).                             |\\n| m_pob_mesa           | Mean registered voters per polling station in the municipality (municipality average).              |\\n| lm_pob_mesa          | Natural log of m_pob_mesa (logged mean polling-place size).                                         |\\n| z_pob_mesa_f | Average polling-station size calculated by dividing the potential electorate by the number of stations implied by the election-year voter-cap rule. This is a rule-based value, distinct from the observed average m_pob_mesa. |\\n| lz_pob_mesa_f        | Natural log of z_pob_mesa_f (logged rule-predicted size).                                          |\\n| lown_resources       | Log of municipality own resources (local revenue; logged local fiscal resources).                   |\\n| larmed_actor         | Lagged indicator for presence of nonstate armed actors in the municipality (e.g., guerrillas/paramilitaries). |\\n| margin_index2        | Index measuring electoral competitiveness / margin (competitiveness indicator).                     |\\n| nbi_i                | Unsatisfied Basic Needs (NBI) poverty index (possibly standardized / imputed).                      |\\n| nbi                  | Unsatisfied Basic Needs (NBI) raw measure (poverty indicator).                                      |\\n| lnbi_i               | Natural log of nbi_i (logged poverty index variant).                                                |\\n| llnbi_i              | Transformed / double-log version of the NBI index (log of log or alternative transform).            |\\n| lsize                | Logged version of a polling-station size measure (log of size).                                     |\\n| lpotencial           | Natural log of 'potencial' (logged measure of potential electorate or voting potential).            |\\n| potencial_cu         | Cubed transformation of 'potencial' (potencial^3).                                                  |\\n| potencial_sq         | Squared transformation of 'potencial' (potencial^2).                                                 |\\n| trend_f              | Time trend or election-wave variable (numeric trend / period indicator).                            |\\n| Population20         | Population aged 20 or older (used to construct per–polling‑station measures).                        |\\n| lroyalties2_d_pc     | Log of royalties (natural-resource transfers) per capita (logged royalties per person).              |\\n| local_e              | Indicator for local/regional election year (binary flag).                                           |\\n| ingresos_totales     | Total municipal revenues / total income (nominal value).                                            |\\n| own_resources_p      | Share (proportion) of municipal revenue coming from own sources (self-generated revenue).           |\\n| revenue_current_p    | Share (percent) of current revenue in total municipal revenue.                                       |\\n| tax_revenue_p        | Share (percent) of revenue coming from taxes.                                                        |\\n| population           | Total municipal population (raw count).                                                              |\\n| depto_code           | Department code (numeric identifier for department).                                                 |\\n| pot_mesa_mean        | Mean of 'potential per mesa' (average potential electorate per polling station).                     |\\n| potencial            | Measure of potential electorate / voting potential (raw value used in controls).                     |\\n| mesas                | Number of polling stations (\\\"mesas\\\") in the municipality.                                            |\\n| pot_mesa_med         | Median potential per mesa in the municipality.                                                       |\\n| lpopulation          | Natural log of population (logged municipal population).                                             |\\n| lpot_mesa_med        | Natural log of pot_mesa_med (logged median potential per mesa).                                      |\\n| lpot_mesa_mean       | Natural log of pot_mesa_mean (logged mean potential per mesa).                                       |\\n| lvoter_mesa          | Natural log of voter_mesa (logged number of actual voters per mesa / turnout per mesa).              |\\n| voter_mesa           | Number of actual voters per mesa (turnout per polling station).                                      |\\n| armed_actor          | Indicator for presence of nonstate armed actors (guerrillas/paramilitaries) in the municipality.     |\\n| size2                | Alternative polling-station size measure (variant / secondary definition of size).                   |\\n| lsize2               | Natural log of size2 (logged alternative size).                                                      |\\n| size                 | Polling-station size (alternative / additional size metric).                                         |\\n| le_parties_av        | Logged average of party-related measure (e.g., logged average number of parties or lists).           |\\n| closeness_CG         | Measure of electoral closeness (e.g., closeness of congressional/general contest).                   |\\n| l4margin_index2      | Four-period lag (or 4-election lag) of margin_index2 (lagged competitiveness measure).               |\\n| lcoca_area           | Log of area under coca cultivation (proxy for illicit-crop presence / local illicit activity).       |\\n| l4lsize              | Four-period lag of lsize (lagged log-size of polling stations).                                      |\\n| l4lpob_mesa          | Four-period lag of lpob_mesa (lagged logged registered voters per polling station).                  |\\n| pob_mesa_q           | Polling-station size quartile (categorical indicator for pob_mesa quartiles).                        |\\n| discont_f | Retained indicator for inclusion in a near-threshold subset of municipalities around the electorate levels at which the voter-cap rule adds a polling station; 1 denotes inclusion and 0 exclusion. |\\n| e_manipulating_results | Citizens' reports of manipulated election results / result tampering (municipality-year count).    |\\n| moving_votes_moe     | MOE monitors' reports of votes being moved or misallocated between stations (count).                 |\\n| vote_buying_moe      | MOE monitors' reports of vote-buying incidents (municipality-year count).                            |\\n| manipulating_results_moe | MOE monitors' reports of manipulated results (municipality-year count).                         |\\n| ltrend_f             | Logged / transformed version of trend_f (transformed time trend variable).                           |\\n| VB_pc                | Vote-buying reports per capita (vote-buying incidents scaled by population).                         |\\n| monitor_MOE          | Indicator for presence of MOE domestic election monitors in the municipality (binary).               |\\n| lpot_mesa            | Natural log of pot_mesa (logged potential per polling station).                                      |\\n| pot_mesa             | Potential electorate per mesa (potential votes / potential voters per polling station).               |\\n| sample               | Indicator for inclusion in the analytical sample (binary flag).                                      |\\n\\nData Description: This is a municipality‑year panel assembled for Colombian elections in the mid‑2000s to early‑2010s (covering multiple general and regional election cycles). The file merges administrative incident records (citizen reports filed with regional offices of the Attorney General), incident logs collected by the domestic observer Misión de Observación Electoral (MOE), official polling‑station result files from the National Registrar (used to derive polling‑station size and rule‑based size measures), municipal demographic and fiscal indicators from administrative sources, and survey‑based individual measures where available (LAPOP). Variables include identifiers (department and municipality), election‑year indicators, several alternative and transformed measures of polling‑station and electorate size, presence/lagged indicators for nonstate armed actors and monitors, municipal socioeconomic controls (population, revenue and poverty measures), and multiple municipality‑level aggregates of reported electoral irregularities.\\n\\nThe released extract contains 4,352 municipality-year observations from elections in 2006, 2007, 2010, and 2011. The National Registrar sets voter-cap rules, and registered voters within a precinct are allocated to stations alphabetically. In the supplied construction, the cap is 400 in 2006 and 2007, 500 in 2010, and 350 in 2011. For potential electorate V and cap C, z_pob_mesa_f is V divided by (floor((V−1)/C)+1). Crossing a multiple of the cap adds a predicted station and lowers the predicted average size. Actual station arrangements can differ because of geography and special voting locations. The near-threshold subset flag is retained as supplied; it is separate from the full-file sample flag.\\n\",\n \"columns\": {\n  \"muni_code\": \"float64\",\n  \"year\": \"float64\",\n  \"departamento\": \"float64\",\n  \"municipio\": \"float64\",\n  \"sum_vb\": \"float64\",\n  \"e_vote_buying\": \"float64\",\n  \"e_neg_t_buying\": \"float64\",\n  \"neg_t_buying_moe\": \"float64\",\n  \"pob_mesa\": \"float64\",\n  \"lpob_mesa\": \"float64\",\n  \"m_pob_mesa\": \"float64\",\n  \"lm_pob_mesa\": \"float64\",\n  \"z_pob_mesa_f\": \"float64\",\n  \"lz_pob_mesa_f\": \"float64\",\n  \"lown_resources\": \"float64\",\n  \"larmed_actor\": \"float64\",\n  \"margin_index2\": \"float64\",\n  \"nbi_i\": \"float64\",\n  \"nbi\": \"float64\",\n  \"lnbi_i\": \"float64\",\n  \"llnbi_i\": \"float64\",\n  \"lsize\": \"float64\",\n  \"lpotencial\": \"float64\",\n  \"potencial_cu\": \"float64\",\n  \"potencial_sq\": \"float64\",\n  \"trend_f\": \"float64\",\n  \"Population20\": \"float64\",\n  \"lroyalties2_d_pc\": \"float64\",\n  \"local_e\": \"float64\",\n  \"ingresos_totales\": \"float64\",\n  \"own_resources_p\": \"float64\",\n  \"revenue_current_p\": \"float64\",\n  \"tax_revenue_p\": \"float64\",\n  \"population\": \"float64\",\n  \"depto_code\": \"float64\",\n  \"pot_mesa_mean\": \"float64\",\n  \"potencial\": \"float64\",\n  \"mesas\": \"float64\",\n  \"pot_mesa_med\": \"float64\",\n  \"lpopulation\": \"float64\",\n  \"lpot_mesa_med\": \"float64\",\n  \"lpot_mesa_mean\": \"float64\",\n  \"lvoter_mesa\": \"float64\",\n  \"voter_mesa\": \"float64\",\n  \"armed_actor\": \"float64\",\n  \"size2\": \"float64\",\n  \"lsize2\": \"float64\",\n  \"size\": \"float64\",\n  \"le_parties_av\": \"float64\",\n  \"closeness_CG\": \"float64\",\n  \"l4margin_index2\": \"float64\",\n  \"lcoca_area\": \"float64\",\n  \"l4lsize\": \"float64\",\n  \"l4lpob_mesa\": \"float64\",\n  \"pob_mesa_q\": \"float64\",\n  \"discont_f\": \"float64\",\n  \"e_manipulating_results\": \"float64\",\n  \"moving_votes_moe\": \"float64\",\n  \"vote_buying_moe\": \"float64\",\n  \"manipulating_results_moe\": \"float64\",\n  \"ltrend_f\": \"float64\",\n  \"VB_pc\": \"float64\",\n  \"monitor_MOE\": \"float64\",\n  \"lpot_mesa\": \"float64\",\n  \"pot_mesa\": \"float64\",\n  \"sample\": \"float64\"\n }\n}\n{\n \"id\": \"ref_iv\",\n \"rows\": 4352,\n \"description\": \"Complete rows of the script's columns\",\n \"columns\": {\n  \"e_vote_buying\": \"float64\",\n  \"lm_pob_mesa\": \"float64\",\n  \"lpopulation\": \"float64\",\n  \"lpotencial\": \"float64\",\n  \"lz_pob_mesa_f\": \"float64\",\n  \"muni_code\": \"float64\"\n }\n}\n\n# Question\nWhat is the local causal effect of a higher average polling-station size on municipalities' counts of citizens' reports of vote buying? Report the local average effect for municipalities whose mean station size is shifted by exogenous institution-determined changes in the number of stations, controlling for municipal population and potential electorate and clustering standard errors by municipality.","response":"IV2SLS(y, [Intercept, lpopulation, lpotencial], lm_pob_mesa, lz_pob_mesa_f).fit(cov_type='clustered', clusters=muni_code)\ninfer__iv_2sls({\"dataset\": \"ref_iv\", \"y\": \"e_vote_buying\", \"endog\": [\"lm_pob_mesa\"], \"instruments\": [\"lz_pob_mesa_f\"], \"exog\": [\"lpopulation\", \"lpotencial\"], \"cluster\": \"muni_code\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_28d06e8e","text":"IV2SLS(y, [Intercept, lpopulation, lpotencial], lm_pob_mesa, lz_pob_mesa_f).fit(cov_type='clustered', clusters=muni_code)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"ref_iv","y":"e_vote_buying","endog":["lm_pob_mesa"],"instruments":["lz_pob_mesa_f"],"exog":["lpopulation","lpotencial"],"cluster":"muni_code"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"7670488aeeb06548e095163131bc9348ae10e61c4e7958a5ba17ec637c52fa72","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["41650c955eee9a4e73534851a729c235cfef06abc57860993389cf69f0175c67"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.7482499349862337}}
{"call_id":"call_72c7a9d4","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q080/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","y":"e_vote_buying","endog":["lm_pob_mesa"],"instruments":["lz_pob_mesa_f"],"exog":["lpopulation","lpotencial"],"cluster":"muni_code","se":"HC1","subset":null,"level":0.95},"input_digest":"cbbd4c9d811ad40c6b0a875c7b6f3b820af210d479ae155a599b45470959ace1","dataset_digests":{"ref_iv":"98d485bbb714d22b22ebc635f55a26f316c7d307ad083d1a904b3ba362f172f0"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_72c7a9d4","status":"ok","reason":"","figures":[{"key":"coef.lm_pob_mesa","value":-0.9835113358719844,"unit":"","label":"2SLS effect of lm_pob_mesa on e_vote_buying","se":0.14239177652238938,"ci_low":-1.26290234714472,"ci_high":-0.7041203245992487,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de lz_pob_mesa_f na forma reduzida (e_vote_buying sobre lz_pob_mesa_f e os controles) e no primeiro estágio (lm_pob_mesa sobre lz_pob_mesa_f e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":-0.7826113037496055,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.795731859110422,"unit":"","note":"primeiro estágio"}],"result":-0.9835113358719844,"unit":""},{"text_pt":"Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.020275418021202078,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.14239177652238938,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1097 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.9835113358719844,"unit":"","note":""},{"symbol":"c","value":1.9621288398547707,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.14239177652238938,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.lm_pob_mesa","value":8598.326402210985,"flag":false,"note":"Wald F of the excluded instruments in the first stage for lm_pob_mesa (cluster-robust standard errors (CR1) by 'muni_code')"},{"name":"partial_r2.lm_pob_mesa","value":0.41671930271655977,"flag":false,"note":"share of the variation the instruments explain beyond the controls"},{"name":"weak_instrument","value":false,"flag":false,"note":"every first-stage F is at least 10"},{"name":"n_obs","value":4352,"flag":false,"note":""},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'muni_code'"},{"name":"n_clusters","value":1098,"flag":false,"note":"1098 clusters of 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