{"call_id":"call_5b4d5856","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.cheng_hoekstra_2013.tab5_pa_c2.agent/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":"2026-10-15T16:57:55.570163Z","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\": \"castle\",\n \"rows\": 550,\n \"description\": \"The state-year panel of Cheng and Hoekstra (2013), 'Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine', as distributed with Cunningham's Causal Inference: The Mixtape: one row per U.S. state and year, 2000-2010 (550 rows: 50 states, 11 years). Columns used most: state, the state's name; sid, a numeric state id; year; cdl, the castle doctrine law variable as the paper codes it: the fraction of the year in which the state's castle doctrine (stand-your-ground) law was in effect, 0 before adoption and 1 in full years after it; post, 1 in every year from the adoption year on (any part of the year); effyear and treatment_date, the year the law took effect (missing for states that never adopted one); homicide, the homicide rate per 100,000 residents (FBI Uniform Crime Reports), and l_homicide its natural log; murder and l_murder, the murder rate and its log; population; popwt, the state's average population over 2000-2010 (constant within a state), the weight the paper's weighted regressions use; northeast, midwest, south, west, census region indicators; r20001 to r20104, region-by-year indicators named r20<two-digit year><region 1-4> (for example r20063 is region 3 in 2006); trend_1 to trend_51, state-specific linear trends; lead1 to lead9 and lag0 to lag5, event-time indicators relative to adoption; other crime rates (robbery, assault, burglary, larceny, motor) and their logs (l_robbery and so on); and the time-varying controls of the paper (police, unemployrt, income, poverty, prisoner, lagprisoner, exp_subsidy, exp_pubwelfare, and the shares of black and white men aged 15-24 and 25-44).\",\n \"columns\": {\n  \"state\": \"str\",\n  \"year\": \"int16\",\n  \"sid\": \"int8\",\n  \"cdl\": \"float32\",\n  \"pre2_cdl\": \"float32\",\n  \"caselaw\": \"float32\",\n  \"anywhere\": \"int8\",\n  \"assumption\": \"int8\",\n  \"civil\": \"int8\",\n  \"homicide_c\": \"int16\",\n  \"robbery_gun_r\": \"float32\",\n  \"jhcitizen_c\": \"int8\",\n  \"jhpolice_c\": \"int16\",\n  \"homicide\": \"float32\",\n  \"robbery\": \"float32\",\n  \"assault\": \"float32\",\n  \"burglary\": \"float32\",\n  \"larceny\": \"float32\",\n  \"motor\": \"float32\",\n  \"murder\": \"float32\",\n  \"hc_felonywsus\": \"float32\",\n  \"jhcitizen\": \"float32\",\n  \"jhpolice\": \"float32\",\n  \"population\": \"int32\",\n  \"police\": \"float64\",\n  \"unemployrt\": \"float32\",\n  \"income\": \"int32\",\n  \"blackm_15_24\": \"float32\",\n  \"whitem_15_24\": \"float32\",\n  \"blackm_25_44\": \"float32\",\n  \"whitem_25_44\": \"float32\",\n  \"prisoner\": \"float32\",\n  \"lagprisoner\": \"float32\",\n  \"poverty\": \"float64\",\n  \"exp_subsidy\": \"float32\",\n  \"exp_pubwelfare\": \"float32\",\n  \"northeast\": \"int8\",\n  \"midwest\": \"int8\",\n  \"south\": \"int8\",\n  \"west\": \"int8\",\n  \"effyear\": \"float64\",\n  \"r20001\": \"float32\",\n  \"r20002\": \"float32\",\n  \"r20003\": \"float32\",\n  \"r20004\": \"float32\",\n  \"r20011\": \"float32\",\n  \"r20012\": \"float32\",\n  \"r20013\": \"float32\",\n  \"r20014\": \"float32\",\n  \"r20021\": \"float32\",\n  \"r20022\": \"float32\",\n  \"r20023\": \"float32\",\n  \"r20024\": \"float32\",\n  \"r20031\": \"float32\",\n  \"r20032\": \"float32\",\n  \"r20033\": \"float32\",\n  \"r20034\": \"float32\",\n  \"r20041\": \"float32\",\n  \"r20042\": \"float32\",\n  \"r20043\": \"float32\",\n  \"r20044\": \"float32\",\n  \"r20051\": \"float32\",\n  \"r20052\": \"float32\",\n  \"r20053\": \"float32\",\n  \"r20054\": \"float32\",\n  \"r20061\": \"float32\",\n  \"r20062\": \"float32\",\n  \"r20063\": \"float32\",\n  \"r20064\": \"float32\",\n  \"r20071\": \"float32\",\n  \"r20072\": \"float32\",\n  \"r20073\": \"float32\",\n  \"r20074\": \"float32\",\n  \"r20081\": \"float32\",\n  \"r20082\": \"float32\",\n  \"r20083\": \"float32\",\n  \"r20084\": \"float32\",\n  \"r20091\": \"float32\",\n  \"r20092\": \"float32\",\n  \"r20093\": \"float32\",\n  \"r20094\": \"float32\",\n  \"r20101\": \"float32\",\n  \"r20102\": \"float32\",\n  \"r20103\": \"float32\",\n  \"r20104\": \"float32\",\n  \"trend_1\": \"float32\",\n  \"trend_2\": \"float32\",\n  \"trend_3\": \"float32\",\n  \"trend_4\": \"float32\",\n  \"trend_5\": \"float32\",\n  \"trend_6\": \"float32\",\n  \"trend_7\": \"float32\",\n  \"trend_8\": \"float32\",\n  \"trend_9\": \"float32\",\n  \"trend_10\": \"float32\",\n  \"trend_11\": \"float32\",\n  \"trend_12\": \"float32\",\n  \"trend_13\": \"float32\",\n  \"trend_14\": \"float32\",\n  \"trend_15\": \"float32\",\n  \"trend_16\": \"float32\",\n  \"trend_17\": \"float32\",\n  \"trend_18\": \"float32\",\n  \"trend_19\": \"float32\",\n  \"trend_20\": \"float32\",\n  \"trend_21\": \"float32\",\n  \"trend_22\": \"float32\",\n  \"trend_23\": \"float32\",\n  \"trend_24\": \"float32\",\n  \"trend_25\": \"float32\",\n  \"trend_26\": \"float32\",\n  \"trend_27\": \"float32\",\n  \"trend_28\": \"float32\",\n  \"trend_29\": \"float32\",\n  \"trend_30\": \"float32\",\n  \"trend_31\": \"float32\",\n  \"trend_32\": \"float32\",\n  \"trend_33\": \"float32\",\n  \"trend_34\": \"float32\",\n  \"trend_35\": \"float32\",\n  \"trend_36\": \"float32\",\n  \"trend_37\": \"float32\",\n  \"trend_38\": \"float32\",\n  \"trend_39\": \"float32\",\n  \"trend_40\": \"float32\",\n  \"trend_41\": \"float32\",\n  \"trend_42\": \"float32\",\n  \"trend_43\": \"float32\",\n  \"trend_44\": \"float32\",\n  \"trend_45\": \"float32\",\n  \"trend_46\": \"float32\",\n  \"trend_47\": \"float32\",\n  \"trend_48\": \"float32\",\n  \"trend_49\": \"float32\",\n  \"trend_50\": \"float32\",\n  \"trend_51\": \"float32\",\n  \"l_murder\": \"float32\",\n  \"l_homicide\": \"float32\",\n  \"l_homicide_c\": \"float32\",\n  \"l_robbery\": \"float32\",\n  \"l_assault\": \"float32\",\n  \"l_burglary\": \"float32\",\n  \"l_larceny\": \"float32\",\n  \"l_motor\": \"float32\",\n  \"l_robbery_gun\": \"float32\",\n  \"l_jhcitizen\": \"float32\",\n  \"l_jhpolice\": \"float32\",\n  \"l_hc_felonywsus\": \"float32\",\n  \"l_robbery_gun_r\": \"float32\",\n  \"l_pop\": \"float32\",\n  \"l_police\": \"float32\",\n  \"l_income\": \"float32\",\n  \"l_prisoner\": \"float32\",\n  \"l_lagprisoner\": \"float32\",\n  \"l_exp_subsidy\": \"float32\",\n  \"l_exp_pubwelfare\": \"float32\",\n  \"treatment_date\": \"float32\",\n  \"post\": \"float32\",\n  \"time_til\": \"float32\",\n  \"lead1\": \"float32\",\n  \"lead2\": \"float32\",\n  \"lead3\": \"float32\",\n  \"lead4\": \"float32\",\n  \"lead5\": \"float32\",\n  \"lead6\": \"float32\",\n  \"lead7\": \"float32\",\n  \"lead8\": \"float32\",\n  \"lead9\": \"float32\",\n  \"lag0\": \"float32\",\n  \"lag1\": \"float32\",\n  \"lag2\": \"float32\",\n  \"lag3\": \"float32\",\n  \"lag4\": \"float32\",\n  \"lag5\": \"float32\",\n  \"_Iyear_2001\": \"int8\",\n  \"_Iyear_2002\": \"int8\",\n  \"_Iyear_2003\": \"int8\",\n  \"_Iyear_2004\": \"int8\",\n  \"_Iyear_2005\": \"int8\",\n  \"_Iyear_2006\": \"int8\",\n  \"_Iyear_2007\": \"int8\",\n  \"_Iyear_2008\": \"int8\",\n  \"_Iyear_2009\": \"int8\",\n  \"_Iyear_2010\": \"int8\",\n  \"popwt\": \"float32\"\n }\n}\n\n# Question\nCheng and Hoekstra (NBER Working Paper 18134, 2012; published in the Journal of Human Resources, 2013), 'Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine', Table 5, Panel A, column (2): in the state panel for 2000-2010, estimate by ordinary least squares, weighted by the state's average population over the period (the paper's population weight), the effect of castle doctrine laws on the log homicide rate, with state fixed effects, year fixed effects and region-by-year fixed effects (the four census regions) and no other controls. As in the paper, the law is measured as the fraction of each year in which it was in effect. Report the coefficient on the castle doctrine law.","response":"Table 5, Panel A, column (2): column (1) plus region-by-year fixed effects, entered as the 44 region-by-year indicators of the file (those collinear with the year effects are dropped), weighted by population, errors clustered by state.\ninfer__ols({\"dataset\": \"castle\", \"y\": \"l_homicide\", \"x\": [\"cdl\"], \"controls\": [\"r20001\", \"r20002\", \"r20003\", \"r20004\", \"r20011\", \"r20012\", \"r20013\", \"r20014\", \"r20021\", \"r20022\", \"r20023\", \"r20024\", \"r20031\", \"r20032\", \"r20033\", \"r20034\", \"r20041\", \"r20042\", \"r20043\", \"r20044\", \"r20051\", \"r20052\", \"r20053\", \"r20054\", \"r20061\", \"r20062\", \"r20063\", \"r20064\", \"r20071\", \"r20072\", \"r20073\", \"r20074\", \"r20081\", \"r20082\", \"r20083\", \"r20084\", \"r20091\", \"r20092\", \"r20093\", \"r20094\", \"r20101\", \"r20102\", \"r20103\", \"r20104\"], \"fe\": [\"sid\", \"year\"], \"weights\": \"popwt\", \"cluster\": \"sid\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_5b4d5856","text":"Table 5, Panel A, column (2): column (1) plus region-by-year fixed effects, entered as the 44 region-by-year indicators of the file (those collinear with the year effects are dropped), weighted by population, errors clustered by state.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"castle","y":"l_homicide","x":["cdl"],"controls":["r20001","r20002","r20003","r20004","r20011","r20012","r20013","r20014","r20021","r20022","r20023","r20024","r20031","r20032","r20033","r20034","r20041","r20042","r20043","r20044","r20051","r20052","r20053","r20054","r20061","r20062","r20063","r20064","r20071","r20072","r20073","r20074","r20081","r20082","r20083","r20084","r20091","r20092","r20093","r20094","r20101","r20102","r20103","r20104"],"fe":["sid","year"],"weights":"popwt","cluster":"sid"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"3e405409ff9b234c71b55f899412215931f375d5086600a3fde8b9f48588338f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3f95b21b3a0103ad03b2d8e39157984b1ed1d73ae7605c08b9539458fc1f18a4"],"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":0.840415945276618}}
{"call_id":"call_99bea445","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.cheng_hoekstra_2013.tab5_pa_c2.agent/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"castle","y":"l_homicide","x":["cdl"],"controls":["r20001","r20002","r20003","r20004","r20011","r20012","r20013","r20014","r20021","r20022","r20023","r20024","r20031","r20032","r20033","r20034","r20041","r20042","r20043","r20044","r20051","r20052","r20053","r20054","r20061","r20062","r20063","r20064","r20071","r20072","r20073","r20074","r20081","r20082","r20083","r20084","r20091","r20092","r20093","r20094","r20101","r20102","r20103","r20104"],"fe":["sid","year"],"cluster":"sid","se":"HC1","weights":"popwt","subset":null,"intercept":true,"level":0.95},"input_digest":"ee9d245e20ade2189fc3124ffe13f9693f45b3820828ecc2cfd5007d4934e050","dataset_digests":{"castle":"6a9af2b4a14b985d82e0f3aea93c535c24cfc2497c4d9f07242bac5f84ae11c1"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_99bea445","status":"ok","reason":"","figures":[{"key":"coef.cdl","value":0.09458774356441414,"unit":"","label":"Coefficient 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