{"call_id":"call_92f27c16","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.mmv_2011.tab20_1_c2.agent/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"gov_transfers","index":0,"op":"derive","args":{"name":"Income_Below","op":"negate","args":{"column":"Income_Centered"}}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":1948,"rows_after":1948,"frame_sha256_before":"2600b9da8866ad58c495eb606b025cf7957d0969e750a6a86969bcd03e8faa76","frame_sha256_after":"ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:56.572156Z","duration_ms":0.35629095509648323,"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_cd5ade5c","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.mmv_2011.tab20_1_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":"2026-10-15T16:57:56.582846Z","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\": \"gov_transfers\",\n \"rows\": 1948,\n \"description\": \"Households near the income cutoff of Uruguay's PANES cash-transfer programme, the extract of the survey of Manacorda, Miguel and Vigorito (2011), 'Government Transfers and Political Support', distributed with the causaldata R package and used in chapter 20 of Huntington-Klein's 'The Effect': one row per household (1,948 rows), only households whose income measure is within 0.02 of the cutoff. Households below the cutoff were eligible for the transfers. Columns: rownames, the row number; Income_Centered, the household's income measure minus the eligibility cutoff (negative values are below the cutoff, hence eligible); Income_Below, minus Income_Centered, added by the task author: how far below the cutoff the household's income measure is (positive values are eligible); Education, average years of education of household members aged 16 and older (missing for some households); Age, average age of household members; Participation, 1 if the household participated in the transfer programme; Support, the household's support for the government (0, 0.5 or 1; higher means more support).\",\n \"columns\": {\n  \"rownames\": \"int64\",\n  \"Income_Centered\": \"float64\",\n  \"Education\": \"float64\",\n  \"Age\": \"float64\",\n  \"Participation\": \"int64\",\n  \"Support\": \"float64\",\n  \"Income_Below\": \"float64\"\n }\n}\n\n# Question\nManacorda, Miguel and Vigorito (2011), on Uruguay's PANES cash transfers, as replicated in Huntington-Klein's 'The Effect', chapter 20, Table 20.1, column 'Linear with Kernel Weight': households qualified for Uruguay's PANES cash transfers when their income measure fell below the eligibility cutoff, so treatment is on the low-income side of the cutoff ('you need low income to get paid'). Estimate, as a sharp regression discontinuity at the cutoff, the effect of receiving the payments on the household's backing for the government: fit a straight line in income on each side of the cutoff, weighting each household by one minus 100 times the distance of its income measure from the cutoff (weights that fall linearly from one at the cutoff to zero at a distance of 0.01, households beyond it weighing nothing). Report the effect of being on the eligible side of the cutoff, with a positive sign meaning more backing among the eligible households.","response":"Table 20.1, column 'Linear with Kernel Weight': WLS of support on participation with a line in centred income interacted with participation, weights 1 - 100|income| (a triangular kernel of bandwidth 0.01). The treated side is below the cutoff, so the running variable is the declared negation Income_Below.\ninfer__rdd({\"dataset\": \"gov_transfers\", \"y\": \"Support\", \"running\": \"Income_Below\", \"cutoff\": 0, \"bandwidth\": 0.01, \"kernel\": \"triangular\", \"polynomial\": 1})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_cd5ade5c","text":"Table 20.1, column 'Linear with Kernel Weight': WLS of support on participation with a line in centred income interacted with participation, weights 1 - 100|income| (a triangular kernel of bandwidth 0.01). 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{"call_id":"call_444b13b9","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.mmv_2011.tab20_1_c2.agent/e1a1","step":"analysis","kind":"tool","name":"infer.rdd","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"gov_transfers","y":"Support","running":"Income_Below","cutoff":0.0,"bandwidth":0.01,"kernel":"triangular","polynomial":1,"cluster":null,"subset":null,"level":0.95},"input_digest":"8b8ef036e90fe5e9f6e2330a6969dd7fb22a18198adac4d013de5f3e62cb5b3f","dataset_digests":{"gov_transfers":"ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113"},"result":{"tool":"infer.rdd","version":"0.1.0","call_id":"call_444b13b9","status":"ok","reason":"","figures":[{"key":"effect","value":0.03348175396085768,"unit":"","label":"Jump in Support at the cutoff of Income_Below","se":0.04419589592144589,"ci_low":-0.05314061030965694,"ci_high":0.12010411823137229,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (informada); o efeito é a diferença entre os dois ajustes no limiar.","formula":"","terms":[{"symbol":"h","value":0.01,"unit":"","note":"janela (informada)"},{"symbol":"n_esq","value":400.0,"unit":"observações abaixo do limiar","note":""},{"symbol":"n_dir","value":537.0,"unit":"observações acima do limiar","note":""}],"result":0.03348175396085768,"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":0.0019532772162992773,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.04419589592144589,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição normal vezes o erro-padrão.","formula":"IC = τ ± c · ep","terms":[{"symbol":"τ","value":0.03348175396085768,"unit":"","note":""},{"symbol":"c","value":1.959963984540054,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.04419589592144589,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"bandwidth","value":0.01,"flag":false,"note":"half-width of the window around the cutoff (informada)"},{"name":"bandwidth_method","value":"user","flag":false,"note":""},{"name":"n_left","value":400,"flag":false,"note":"observations with positive weight below the cutoff"},{"name":"n_right","value":537,"flag":false,"note":"observations with positive weight at or above the cutoff"},{"name":"kernel","value":"triangular","flag":false,"note":""},{"name":"polynomial","value":1,"flag":false,"note":""},{"name":"bandwidth_sensitive","value":false,"flag":false,"note":"the estimates at half and double the bandwidth fall inside the main interval"},{"name":"n_obs","value":1948,"flag":false,"note":"rows used to choose the bandwidth"},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"}],"tables":{"sensitivity":[{"multiplier":0.5,"bandwidth":0.005,"estimate":-0.027155292837106566,"se":0.07202392168810465,"ci_low":-0.16831958537112499,"ci_high":0.11400899969691185,"n_left":186,"n_right":276},{"multiplier":1.0,"bandwidth":0.01,"estimate":0.03348175396085768,"se":0.04419589592144589,"ci_low":-0.05314061030965694,"ci_high":0.12010411823137229,"n_left":400,"n_right":537},{"multiplier":2.0,"bandwidth":0.02,"estimate":0.09585269557379221,"se":0.03141937170055098,"ci_low":0.0342718586238353,"ci_high":0.15743353252374911,"n_left":821,"n_right":1127}]},"datasets":{},"warnings":[],"assumptions":["units cannot precisely control the running variable around the cutoff","every other determinant of the outcome is continuous at the cutoff"],"notes":""},"outputs":null,"output_digest":"c54e3387e8a458b6a8dcc0573128d1b7deb331569a170261c139b9a182ee104b","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:56.585480Z","duration_ms":9.865541942417622,"error":null,"decided_by":"model","rationale":"Table 20.1, column 'Linear with Kernel Weight': WLS of support on participation with a line in centred income interacted with participation, weights 1 - 100|income| (a triangular kernel of bandwidth 0.01). The treated side is below the cutoff, so the running variable is the declared negation Income_Below.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_cd5ade5c","llm_name":"infer__rdd","vendor_id":"script_1","result_for_model_ref":"6b1f40a0d72458169a361bf9b8d325968ab285cfe7abbb1acbd9fc97bfb13600"}}
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