{"call_id":"call_17a1961a","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q090/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":"2026-10-15T17:10:30.540090Z","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_0059\",\n \"rows\": 66804,\n \"description\": \"| Variable Name             | Description                                                                                              |\\n|---------------------------|----------------------------------------------------------------------------------------------------------|\\n| (index)                   | Row index from the source file.                                                                          |\\n| cntry                     | Country code (ESS two-letter code for country of interview).                                             |\\n| freehms                   | ESS item: attitude toward whether gay and lesbian people should be free to live as they wish (Likert).   |\\n| euftf                     | Feeling thermometer toward the European Union (0–10 scale).                                              |\\n| blgetmg                   | Self-identifies as belonging to a minority group in the country.                                         |\\n| facntr                    | Father’s country of birth (coded; 0 = country of interview, 1 = other).                                  |\\n| mocntr                    | Mother’s country of birth (coded; 0 = country of interview, 1 = other).                                  |\\n| ipeqopt                   | Personal values: important that people are treated equally and have equal opportunities (1–6).           |\\n| ipudrst                   | Personal values: important to understand different people (1–6).                                         |\\n| year                      | Survey year.                                                                                             |\\n| plus6                     | Indicator: completed at least 6 years of education (1 = yes).                                            |\\n| plus7                     | Indicator: completed at least 7 years of education (1 = yes).                                            |\\n| plus8                     | Indicator: completed at least 8 years of education (1 = yes).                                            |\\n| plus9                     | Indicator: completed at least 9 years of education (1 = yes).                                            |\\n| plus10                    | Indicator: completed at least 10 years of education (1 = yes).                                           |\\n| plus11                    | Indicator: completed at least 11 years of education (1 = yes).                                           |\\n| plus12                    | Indicator: completed at least 12 years of education (1 = yes).                                           |\\n| plus13                    | Indicator: completed at least 13 years of education (1 = yes).                                           |\\n| close_far_right           | Indicator: respondent reports feeling closest to a far-right party (any closeness level).                |\\n| running                   | Cohort index: birth year distance from a country-specific reference cohort (negative = earlier).         |\\n| reform                    | Country-specific label for the relevant compulsory schooling change (reference point for cohorts).       |\\n| eduyrs_actual             | Completed years of education, capped at 13 to reflect secondary schooling.                               |\\n| eduyrs                    | Standardized (z-score) years of education based on eduyrs_actual.                                        |\\n| any_tertiary              | Indicator: has any tertiary (post-secondary) education (1 = yes).                                        |\\n| round_1                   | Indicator: European Social Survey Round 1 (2002) sample (1 = yes).                                      |\\n| round_2                   | Indicator: European Social Survey Round 2 (1 = yes).                                                     |\\n| round_3                   | Indicator: European Social Survey Round 3 (1 = yes).                                                     |\\n| round_4                   | Indicator: European Social Survey Round 4 (1 = yes).                                                     |\\n| round_5                   | Indicator: European Social Survey Round 5 (1 = yes).                                                     |\\n| round_6                   | Indicator: European Social Survey Round 6 (1 = yes).                                                     |\\n| round_7                   | Indicator: European Social Survey Round 7 (1 = yes).                                                     |\\n| female                    | Gender indicator (1 = female, 0 = male).                                                                 |\\n| anti_immi1_or             | Binary: “none” on at least one of the three immigrant inflow items (OR aggregator).                      |\\n| anti_immi2_or             | Binary: “none” or “few” on at least one of the three immigrant inflow items (OR aggregator).             |\\n| anti_immi1_and            | Binary: “none” on all three immigrant inflow items (AND aggregator).                                     |\\n| anti_immi2_and            | Binary: “none” or “few” on all three immigrant inflow items (AND aggregator).                            |\\n| anti_immi1_ave            | Mean of the three “none” indicators across immigrant inflow items (0–1).                                 |\\n| anti_immi2_ave            | Mean of the three “none or few” indicators across immigrant inflow items (0–1).                          |\\n| imdfetn_dummy             | Binary: restrictive response (“none or few”) for immigrants of a different race/ethnic group.            |\\n| imdfetn_dummy_strong      | Binary: most restrictive response (“none”) for immigrants of a different race/ethnic group.              |\\n| imsmetn_dummy             | Binary: restrictive response (“none or few”) for immigrants of the same race/ethnic group.               |\\n| imsmetn_dummy_strong      | Binary: most restrictive response (“none”) for immigrants of the same race/ethnic group.                 |\\n| impcntr_dummy             | Binary: restrictive response (“none or few”) for immigrants from poorer countries outside Europe.        |\\n| impcntr_dummy_strong      | Binary: most restrictive response (“none”) for immigrants from poorer countries outside Europe.          |\\n| imbgeco_dummy             | Binary: rating 0–4 on “immigration is bad or good for the economy” (indicates negative view).            |\\n| imueclt_dummy             | Binary: rating 0–4 on “cultural life undermined or enriched” (indicates negative view).                  |\\n| imwbcnt_dummy             | Binary: rating 0–4 on “country made worse or better place to live” (indicates negative view).            |\\n| mother_secondary          | Indicator: mother completed secondary education (1 = yes).                                               |\\n| father_secondary          | Indicator: father completed secondary education (1 = yes).                                               |\\n| live_with_partner         | Indicator: currently living with a partner/spouse (1 = yes).                                             |\\n| never_married             | Indicator: never married (1 = yes).                                                                      |\\n| ever_divorced             | Indicator: ever divorced/separated (1 = yes).                                                            |\\n| child_at_home             | Indicator: has a child currently living in the household (1 = yes).                                      |\\n| ever_child_at_home        | Indicator: ever had a child living in the household (1 = yes).                                           |\\n| imdfetn                   | Standardized score from “allow immigrants of a different race/ethnic group” item (higher = more restrictive). |\\n| imsmetn                   | Standardized score from “allow immigrants of the same race/ethnic group” item (higher = more restrictive).    |\\n| impcntr                   | Standardized score from “allow immigrants from poorer countries outside Europe” item (higher = more restrictive). |\\n| imbgeco                   | Standardized score from “immigration is bad/good for the economy” item (higher = more negative).         |\\n| imueclt                   | Standardized score from “cultural life undermined/enriched by immigration” item (higher = more negative).|\\n| imwbcnt                   | Standardized score from “country made worse/better place to live by immigration” item (higher = more negative). |\\n| close_far_right_scale     | Closeness scale (1–4) to the far-right party identified as closest; missing if none named.               |\\n| scale                     | Composite anti-immigration scale (average of standardized items; across-country standardization).        |\\n| cont_scale                | Composite continuous anti-immigration scale built from the underlying continuous items.                  |\\n| factor                    | Latent factor score summarizing the six immigration attitude items.                                      |\\n| scale_within              | Composite anti-immigration scale standardized within country.                                            |\\n| cont_scale_within         | Within-country composite continuous scale from the immigration items.                                    |\\n| factor_within             | Within-country latent factor score summarizing the six immigration attitude items.                       |\\n\\nData Description: \\nThis dataset is constructed from the European Social Survey (ESS) rounds 1–7 (2002–2014) for Denmark, France, Great Britain (excluding Northern Ireland), the Netherlands, and Sweden. It merges original ESS responses with harmonized education measures, demographic controls, party closeness, personal values, and a detailed set of immigration-attitude recodes. The file also includes composite indices and standardized versions of key attitudes to facilitate cross-country and within-country comparisons.\\n\\nThe ESS collects nationally representative samples of residents aged 15+ using random probability designs and face-to-face interviews, typically around 2,000 respondents per country per round. For this study’s context, respondents’ years of education are capped at 13 to reflect secondary schooling, and multiple derived variables summarize immigration preferences: binary indicators for restrictive choices, averages across items, and composite scales combining economic, cultural, and overall livability assessments. Additional fields identify survey round, country, parental background, household structure, and a cohort index anchored to country-specific education policy reference points.\",\n \"columns\": {\n  \"cntry\": \"str\",\n  \"freehms\": \"float64\",\n  \"euftf\": \"float64\",\n  \"blgetmg\": \"float64\",\n  \"facntr\": \"float64\",\n  \"mocntr\": \"float64\",\n  \"ipeqopt\": \"float64\",\n  \"ipudrst\": \"float64\",\n  \"year\": \"int64\",\n  \"plus6\": \"int64\",\n  \"plus7\": \"int64\",\n  \"plus8\": \"int64\",\n  \"plus9\": \"int64\",\n  \"plus10\": \"int64\",\n  \"plus11\": \"int64\",\n  \"plus12\": \"int64\",\n  \"plus13\": \"int64\",\n  \"close_far_right\": \"float64\",\n  \"running\": \"int64\",\n  \"reform\": \"str\",\n  \"eduyrs_actual\": \"int64\",\n  \"eduyrs\": \"float64\",\n  \"any_tertiary\": \"float64\",\n  \"round_1\": \"int64\",\n  \"round_2\": \"int64\",\n  \"round_3\": \"int64\",\n  \"round_4\": \"int64\",\n  \"round_5\": \"int64\",\n  \"round_6\": \"int64\",\n  \"round_7\": \"int64\",\n  \"female\": \"int64\",\n  \"anti_immi1_or\": \"int64\",\n  \"anti_immi2_or\": \"int64\",\n  \"anti_immi1_and\": \"int64\",\n  \"anti_immi2_and\": \"int64\",\n  \"anti_immi1_ave\": \"float64\",\n  \"anti_immi2_ave\": \"float64\",\n  \"imdfetn_dummy\": \"int64\",\n  \"imdfetn_dummy_strong\": \"int64\",\n  \"imsmetn_dummy\": \"int64\",\n  \"imsmetn_dummy_strong\": \"int64\",\n  \"impcntr_dummy\": \"int64\",\n  \"impcntr_dummy_strong\": \"int64\",\n  \"imbgeco_dummy\": \"int64\",\n  \"imueclt_dummy\": \"int64\",\n  \"imwbcnt_dummy\": \"int64\",\n  \"mother_secondary\": \"float64\",\n  \"father_secondary\": \"float64\",\n  \"live_with_partner\": \"int64\",\n  \"never_married\": \"float64\",\n  \"ever_divorced\": \"float64\",\n  \"child_at_home\": \"int64\",\n  \"ever_child_at_home\": \"int64\",\n  \"imdfetn\": \"float64\",\n  \"imsmetn\": \"float64\",\n  \"impcntr\": \"float64\",\n  \"imbgeco\": \"float64\",\n  \"imueclt\": \"float64\",\n  \"imwbcnt\": \"float64\",\n  \"close_far_right_scale\": \"float64\",\n  \"scale\": \"float64\",\n  \"cont_scale\": \"float64\",\n  \"factor\": \"float64\",\n  \"scale_within\": \"float64\",\n  \"cont_scale_within\": \"float64\",\n  \"factor_within\": \"float64\"\n }\n}\n\n# Question\nAmong individuals whose schooling increased due to compulsory schooling laws and who were born near the reform thresholds, what is the effect of one additional year of completed secondary schooling on the probability of saying at least one immigrant group should be allowed 'none' to enter?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0059\", \"y\": \"anti_immi1_or\", \"x\": [\"eduyrs\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_17a1961a","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_0059","y":"anti_immi1_or","x":["eduyrs"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"28319ac683d8dce8e48cb5a2a33ad3f45b603e74e9396abf43f4ef5ccc16b577","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["9f14fab6d5c22acf2b87b5faa2eb02bb9eaf84c88fd8316cd162f65e05a57c9a"],"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":0.81829191185534}}
{"call_id":"call_bdcd10d6","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q090/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0059","y":"anti_immi1_or","x":["eduyrs"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"14290eb406fa6438f6f6c804a7d711f30d32c851daae89a766be9ce01346e06c","dataset_digests":{"ds_0059":"4f144871f4e518bfe4b6a2a0e86791677492429d810b5b620628d1447411f254"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_bdcd10d6","status":"ok","reason":"","figures":[{"key":"coef.eduyrs","value":-0.027617425177981686,"unit":"","label":"Coefficient on eduyrs in the regression of anti_immi1_or","se":0.0007231964854532023,"ci_low":-0.02903488992581183,"ci_high":-0.02619996043015154,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de eduyrs na regressão por mínimos quadrados ordinários de anti_immi1_or.","formula":"","terms":[{"symbol":"n","value":66804.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.027617425177981686,"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":5.230131565718639e-7,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.0007231964854532023,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 66802 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.027617425177981686,"unit":"","note":""},{"symbol":"c","value":1.9599994971516912,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.0007231964854532023,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":66804,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.026044489613302413,"flag":false,"note":""},{"name":"df_resid","value":66802,"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":0.17370217342949856,"se":0.0014465909985190459,"t":120.07690743778093,"p":0.0,"ci_low":0.17086685579981706,"ci_high":0.17653749105918007},{"term":"eduyrs","coef":-0.027617425177981686,"se":0.0007231964854532023,"t":-38.187996946189244,"p":0.0,"ci_low":-0.02903488992581183,"ci_high":-0.02619996043015154}]},"datasets":{},"warnings":[],"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":"666cdc3a32f9da48a6cb53e5f106a5650b29623790b23ec7dad9bd3cf8cd5541","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.543101Z","duration_ms":16.550667118281126,"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_17a1961a","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"c412675a8f7faeb5d826364d2768aae53725030381fd69e8d885366da1116326"}}
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