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{"call_id":"call_5ff14c6e","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q067/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"2925aeab2262","inputs":{"dataset":"ref_iv","index":2,"op":"dummies","args":{"column":"villages","prefix":"villages","drop_first":true}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":417,"rows_after":417,"frame_sha256_before":"6c5588b9ded5d938da44c282885e388e4661a4d04400127ea34a6338207b78ba","frame_sha256_after":"b404b85da47cd17ecc0f48608fa67a115542e841ac1b45a7c956032413471471"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:09:29.823943Z","duration_ms":0.3339999821037054,"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_4ce96c83","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q067/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":"2026-10-15T17:09:29.837334Z","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_0037\",\n \"rows\": 420,\n \"description\": \"| Variable Name | Description |\\n|---------------|-------------|\\n| cve_entIFE | Numeric code for the federal entity (state) as recorded by Mexico's electoral authority (IFE). |\\n| cve_mpioIFE | Numeric municipality code (IFE) corresponding to the locality's municipality. |\\n| seccion | Electoral \\\"sección\\\" (precinct) identifier used by IFE. |\\n| pobtot1994 | Total population in the locality/precinct from the 1994 census (count). |\\n| avgpoverty | Average poverty index for the locality (census-derived index, higher values indicate greater deprivation). |\\n| pobelegiblep | Proportion of the precinct population living in localities deemed eligible for the program (share between 0 and 1). |\\n| treatment | Precinct assignment indicator: 1 when the precinct contains an experimental village assigned to earlier enrollment; 0 for the delayed-assignment comparison group. This is an assignment label, not an indicator that every household or village in the precinct enrolled early. |\\n| numerotreated | Count of evaluation localities within the precinct that were flagged for earlier rollout phases (integer). |\\n| numerocontrol | Count of evaluation localities within the precinct that were flagged for later rollout phases (integer). |\\n| one_random | Legacy sample-selection flag, equal to 1 throughout this extract; it does not give the total number of experimental villages in a precinct. See numerotreated and numerocontrol for the separate counts. |\\n| two_random | Legacy sample-selection flag retained from the source data. It should not be interpreted as a direct count of experimental villages; see numerotreated and numerocontrol. |\\n| early_progresa_p | Number of households enrolled during the first four program-expansion phases divided by the total number of program-enrolled households by the eleventh phase. It measures early enrollment among program recipients, not among all precinct households. |\\n| villages | Total number of localities (villages) aggregated into the electoral precinct (count). |\\n| villages2 | Secondary village-count / grouping field included in the original data (alternate village categorization; sometimes missing). |\\n| pri1994 | Raw count of votes for the PRI in the 1994 election (integer). |\\n| votos_totales1994 | Total number of votes cast in the precinct in the 1994 election (integer). |\\n| pan1994 | Raw count of votes for the PAN in the 1994 election (integer). |\\n| prd1994 | Raw count of votes for the PRD in the 1994 election (integer). |\\n| t2000 | Total votes cast in the precinct in 2000 divided by its voting-age population. Values can exceed 1, for example where special polling places admit voters from outside the precinct. |\\n| t1994 | Total votes cast in the precinct in 1994 divided by its voting-age population; some recorded values exceed 1. |\\n| pri2000s | PRI vote count converted to a share of the voting-age population in 2000 (vote-share metric). |\\n| pri1994s | PRI vote count converted to a share of the voting-age population in 1994 (vote-share metric). |\\n| pan2000s | PAN vote count converted to a share of the voting-age population in 2000 (vote-share metric). |\\n| pan1994s | PAN vote count converted to a share of the voting-age population in 1994 (vote-share metric). |\\n| prd2000s | PRD vote count converted to a share of the voting-age population in 2000 (vote-share metric). |\\n| prd1994s | PRD vote count converted to a share of the voting-age population in 1994 (vote-share metric). |\\n| rep_total | Total number of party observers/representatives recorded at the precinct/polling station (count). |\\n| rep_PAN | Number of PAN party observers present at the precinct (count). |\\n| rep_PRD | Number of PRD party observers present at the precinct (count). |\\n| rep_PRI | Number of PRI party observers present at the precinct (count). |\\n| X_Ivillages_2 | Dummy = 1 if the precinct has 2 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_3 | Dummy = 1 if the precinct has 3 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_4 | Dummy = 1 if the precinct has 4 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_5 | Dummy = 1 if the precinct has 5 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_6 | Dummy = 1 if the precinct has 6 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_7 | Dummy = 1 if the precinct has 7 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_8 | Dummy = 1 if the precinct has 8 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_9 | Dummy = 1 if the precinct has 9 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_10 | Dummy = 1 if the precinct has 10 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_11 | Dummy = 1 if the precinct has 11 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_12 | Dummy = 1 if the precinct has 12 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_13 | Dummy = 1 if the precinct has 13 villages (indicator capturing number-of-villages category). |\\n| X_Ivillages_14 | Dummy = 1 if the precinct has 14 villages (indicator capturing number-of-villages category). |\\n\\nData Description: This precinct-level dataset combines official electoral returns and observer counts from Mexico's Federal Electoral Institute (IFE), population and poverty indicators from census/CONAPO sources, and Progresa program-enrollment records around the late 1990s and the 2000 presidential election. The released file contains 420 precinct observations.\\n\\nThe program evaluation initially assigned 506 eligible rural villages in seven Mexican states to different enrollment dates: 320 villages to earlier enrollment beginning in September 1998 and 186 to delayed enrollment beginning in January 2000. Assignment was randomized at the village level. Each village had a 60% probability of assignment to the earlier group. Eligible households in early-assigned villages were offered enrollment; the delayed group began receiving benefits later.\\n\\nElectoral outcomes are recorded for precincts rather than individual villages. Experimental villages were geographically linked to precincts and then to election returns, demographic data, and program-enrollment records. A precinct can contain several villages, including villages outside the experiment. Consequently, assignment and actual coverage are different measures: some households in early-assigned precincts enrolled later, and some households in delayed-assigned precincts enrolled early through the broader rollout. Some released precincts contain both early- and delayed-assigned experimental villages.\\n\\ntreatment records the precinct's assignment grouping, while early_progresa_p records actual early coverage among program recipients. t2000 is total votes cast in 2000 divided by the precinct's voting-age population. Geographic identifiers, population and poverty measures, village counts, historical and recent votes, turnout, party-observer counts, and village-count indicators describe the precincts and their composition.\\n\",\n \"columns\": {\n  \"cve_entIFE\": \"float64\",\n  \"cve_mpioIFE\": \"float64\",\n  \"seccion\": \"float64\",\n  \"pobtot1994\": \"float64\",\n  \"avgpoverty\": \"float64\",\n  \"pobelegiblep\": \"float64\",\n  \"treatment\": \"float64\",\n  \"numerotreated\": \"float64\",\n  \"numerocontrol\": \"float64\",\n  \"one_random\": \"float64\",\n  \"two_random\": \"float64\",\n  \"early_progresa_p\": \"float64\",\n  \"villages\": \"float64\",\n  \"villages2\": \"float64\",\n  \"pri1994\": \"float64\",\n  \"votos_totales1994\": \"float64\",\n  \"pan1994\": \"float64\",\n  \"prd1994\": \"float64\",\n  \"t2000\": \"float64\",\n  \"t1994\": \"float64\",\n  \"pri2000s\": \"float64\",\n  \"pri1994s\": \"float64\",\n  \"pan2000s\": \"float64\",\n  \"pan1994s\": \"float64\",\n  \"prd2000s\": \"float64\",\n  \"prd1994s\": \"float64\",\n  \"rep_total\": \"float64\",\n  \"rep_PAN\": \"float64\",\n  \"rep_PRD\": \"float64\",\n  \"rep_PRI\": \"float64\",\n  \"X_Ivillages_2\": \"float64\",\n  \"X_Ivillages_3\": \"float64\",\n  \"X_Ivillages_4\": \"float64\",\n  \"X_Ivillages_5\": \"float64\",\n  \"X_Ivillages_6\": \"float64\",\n  \"X_Ivillages_7\": \"float64\",\n  \"X_Ivillages_8\": \"float64\",\n  \"X_Ivillages_9\": \"float64\",\n  \"X_Ivillages_10\": \"float64\",\n  \"X_Ivillages_11\": \"float64\",\n  \"X_Ivillages_12\": \"float64\",\n  \"X_Ivillages_13\": \"float64\",\n  \"X_Ivillages_14\": \"float64\"\n }\n}\n{\n \"id\": \"ref_iv\",\n \"rows\": 417,\n \"description\": \"Complete rows of the script's columns, with village-count dummies\",\n \"columns\": {\n  \"t2000\": \"float64\",\n  \"early_progresa_p\": \"float64\",\n  \"treatment\": \"float64\",\n  \"avgpoverty\": \"float64\",\n  \"pobtot1994\": \"float64\",\n  \"votos_totales1994\": \"float64\",\n  \"pri1994\": \"float64\",\n  \"pan1994\": \"float64\",\n  \"prd1994\": \"float64\",\n  \"villages\": \"float64\",\n  \"villages_2.0\": \"int64\",\n  \"villages_3.0\": \"int64\",\n  \"villages_4.0\": \"int64\",\n  \"villages_5.0\": \"int64\",\n  \"villages_6.0\": \"int64\",\n  \"villages_7.0\": \"int64\",\n  \"villages_8.0\": \"int64\",\n  \"villages_9.0\": \"int64\",\n  \"villages_10.0\": \"int64\",\n  \"villages_11.0\": \"int64\",\n  \"villages_12.0\": \"int64\",\n  \"villages_13.0\": \"int64\",\n  \"villages_14.0\": \"int64\"\n }\n}\n\n# Question\nWhat is the causal effect of receiving earlier Progresa coverage on precinct-level turnout in the 2000 presidential election for the subpopulation of precincts whose early-coverage status was shifted by the program's enrollment timing?","response":"IV2SLS.from_formula('t2000 ~ 1 + controls + C(villages) + [early_progresa_p ~ treatment]').fit(cov_type='robust', debiased=True)\ninfer__iv_2sls({\"dataset\": \"ref_iv\", \"y\": \"t2000\", \"endog\": [\"early_progresa_p\"], \"instruments\": [\"treatment\"], \"exog\": [\"avgpoverty\", \"pobtot1994\", \"votos_totales1994\", \"pri1994\", \"pan1994\", \"prd1994\", \"villages_2.0\", \"villages_3.0\", \"villages_4.0\", \"villages_5.0\", \"villages_6.0\", \"villages_7.0\", \"villages_8.0\", \"villages_9.0\", \"villages_10.0\", \"villages_11.0\", \"villages_12.0\", \"villages_13.0\", \"villages_14.0\"], \"se\": \"HC1\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_4ce96c83","text":"IV2SLS.from_formula('t2000 ~ 1 + controls + C(villages) + [early_progresa_p ~ treatment]').fit(cov_type='robust', debiased=True)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"ref_iv","y":"t2000","endog":["early_progresa_p"],"instruments":["treatment"],"exog":["avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages_2.0","villages_3.0","villages_4.0","villages_5.0","villages_6.0","villages_7.0","villages_8.0","villages_9.0","villages_10.0","villages_11.0","villages_12.0","villages_13.0","villages_14.0"],"se":"HC1"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"cebc1b204c1b183a6f672045eb1d7f18b6467ae15bd2584d322a29f5b7b51b4e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["7ffdc9b4c1ae7de14b5074c37652a35c5a1472deffd419cb27501dd80fc5d633"],"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":0.7619170937687159}}
{"call_id":"call_abf20ac3","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q067/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ref_iv","y":"t2000","endog":["early_progresa_p"],"instruments":["treatment"],"exog":["avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages_2.0","villages_3.0","villages_4.0","villages_5.0","villages_6.0","villages_7.0","villages_8.0","villages_9.0","villages_10.0","villages_11.0","villages_12.0","villages_13.0","villages_14.0"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"70d341d33a39a5184f8814f9b4ff8fab0feae8200b4b0057ae1e129f88b93d22","dataset_digests":{"ref_iv":"b404b85da47cd17ecc0f48608fa67a115542e841ac1b45a7c956032413471471"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_abf20ac3","status":"ok","reason":"","figures":[{"key":"coef.early_progresa_p","value":0.15630190259608176,"unit":"","label":"2SLS effect of early_progresa_p on t2000","se":0.08920981043804706,"ci_low":-0.019082141131879726,"ci_high":0.33168594632404325,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de treatment na forma reduzida (t2000 sobre treatment e os controles) e no primeiro estágio (early_progresa_p sobre treatment e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":0.05315735471566051,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.34009409887370834,"unit":"","note":"primeiro estágio"}],"result":0.15630190259608176,"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":0.00795839027839229,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.08920981043804706,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da 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