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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 Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"ds_0006\",\n \"rows\": 7055,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| year | Fiscal year label for the observation. Sweden shifted to a calendar-aligned fiscal year in 1997; earlier years follow the pre-reform fiscal calendar but are recorded with a single year identifier. |\\n| agency_id | Stable numeric identifier for each central government agency, used to track agencies over time. |\\n| head_id | Identifier for the individual serving as agency head (Director General) in that year; constant for a person across years and agencies where applicable. |\\n| agency_name | Official agency name as reported in government publications (often in Swedish). |\\n| ministry_name | Name of the supervising ministry responsible for the agency in that year. |\\n| ministry_code | Numeric code corresponding to the supervising ministry in that year. |\\n| inflation | Annual price index factor used to express monetary amounts in constant prices; applied to convert nominal SEK to inflation-adjusted SEK for comparability over time. |\\n| incumbent | Indicator for leadership continuity: 1 if the listed agency head is a holdover continuing in office that year; 0 if newly installed. |\\n| conflict | Indicator of ideological misalignment: 1 if the cabinet that appointed the agency head belongs to a different ideological bloc than the parliamentary majority in that year; 0 if aligned. |\\n| academic | Indicator that the agency head holds a PhD or comparable academic-sector credential that year (1) versus not (0). |\\n| political | Indicator that the agency head has prior experience in political office/positions (1) versus not (0). |\\n| private | Indicator that the agency head has work experience from the private sector (1) versus not (0). |\\n| public | Indicator that the agency head has work experience from the public sector (1) versus not (0). |\\n| council | Indicator that the agency had an advisory council in place that year (1) versus not (0). |\\n| board | Indicator that the agency had a management board in place that year (1) versus not (0). |\\n| creator | Indicator that the agency was originally established by a political bloc opposed to the current parliamentary majority (1) versus not (0). |\\n| budget | Agency’s annual operating appropriation in current (nominal) SEK as recorded in the national budget; excludes programmatic cash transfers. |\\n| i_budget | Agency’s operating appropriation expressed in constant (inflation-adjusted) SEK using the provided price index for intertemporal comparability. |\\n| lni_budget | Natural logarithm of i_budget. |\\n| bgrowth | Year-over-year percentage change in the agency’s inflation-adjusted operating appropriation; may be missing for the first observed year of an agency or after structural breaks. |\\n| bs_agency_id | Technical helper field mirroring the agency identifier; retained from the original processing pipeline. |\\n| odd | Unlabeled technical field carried over from source files; not documented as substantively meaningful in this dataset version. |\\n| lni_budget_control | Natural logarithm of the agency’s inflation-adjusted operating appropriation in the previous fiscal year (lagged real budget). |\\n\\nDataset Context:\\nThis dataset compiles annual records for Sweden’s central executive agencies from 1971 to 2014, combining administrative registers and official publications. Agencies and their life spans were identified from the Swedish Code of Statutes, while annual operating appropriations and organizational details were taken from national budget bills, annexes, and government yearbooks such as the Swedish State Calendar. The resulting panel traces each agency’s supervising ministry, leadership, organizational arrangements (e.g., advisory councils and management boards), and budget in both nominal and constant SEK.\\n\\nBudgets refer to operating appropriations rather than programmatic transfer spending, allowing like-for-like comparisons of agency running costs across time. Monetary amounts are deflated using an annual price index supplied with the data. A major public finance reform in the mid-1990s realigned Sweden’s fiscal year to the calendar year starting in 1997; the dataset reflects the official year labels before and after this change.\\n\\nLeadership information centers on the Director General for each agency, including indicators of sectoral background (academic, political, private, public) and whether the leader is a continuing incumbent or newly installed. Swedish cabinet ministers appoint agency heads on fixed-term contracts, which—together with legislative control over appropriations—creates periods where leaders appointed under one political constellation continue to serve when the parliamentary majority shifts. The data also record whether agencies have advisory councils or management boards in a given year, institutional features that are regularly reported in official yearbooks.\\n\\nNot all agencies appear in every year: the panel includes agencies created, reorganized, or terminated over time. The coverage nonetheless spans hundreds of organizations and thousands of agency-year observations, providing a comprehensive view of the Swedish central administration’s structure and operating appropriations across more than four decades.\",\n \"columns\": {\n  \"year\": \"int64\",\n  \"agency_id\": \"int64\",\n  \"head_id\": \"float64\",\n  \"agency_name\": \"str\",\n  \"ministry_name\": \"str\",\n  \"ministry_code\": \"float64\",\n  \"inflation\": \"float64\",\n  \"incumbent\": \"float64\",\n  \"conflict\": \"float64\",\n  \"academic\": \"float64\",\n  \"political\": \"float64\",\n  \"private\": \"float64\",\n  \"public\": \"float64\",\n  \"council\": \"float64\",\n  \"board\": \"float64\",\n  \"creator\": \"float64\",\n  \"budget\": \"float64\",\n  \"i_budget\": \"float64\",\n  \"lni_budget\": \"float64\",\n  \"bgrowth\": \"float64\",\n  \"bs_agency_id\": \"float64\",\n  \"odd\": \"float64\",\n  \"lni_budget_control\": \"float64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 210,\n \"description\": \"The script's long frame: one 1976 and one 1977 row per agency without conflict in 1976\",\n \"columns\": {\n  \"agency_id\": \"int64\",\n  \"post\": \"int64\",\n  \"conflict_post\": \"float64\",\n  \"conflict_pre\": \"float64\",\n  \"treat\": \"int64\",\n  \"bgrowth\": \"float64\",\n  \"treat_post\": \"int64\"\n }\n}\n{\n \"id\": \"ref_post\",\n \"rows\": 112,\n \"description\": \"Each agency's conflict and bgrowth in 1977\",\n \"columns\": {\n  \"agency_id\": \"int64\",\n  \"conflict_post\": \"float64\",\n  \"bgrowth1\": \"float64\"\n }\n}\n\n# Question\nBy how many percentage points did year-over-year agency budget growth in 1977 differ for agencies that became ideologically misaligned versus those that stayed aligned across 1976-1977?","response":"smf.ols('bgrowth ~ treat * post', data=long), cluster by agency_id\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"bgrowth\", \"x\": [\"treat_post\"], \"controls\": [\"treat\", \"post\"], \"cluster\": \"agency_id\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_2d140ea4","text":"smf.ols('bgrowth ~ treat * post', data=long), cluster by agency_id","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"bgrowth","x":["treat_post"],"controls":["treat","post"],"cluster":"agency_id"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"75f891373611ad7c36b1569f9cd0c028e15a531c9297d866f1b37f8de2e6761e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["9cc481b55ff730cf5d470ae1484ec96bdcc2705175d21485886c457664c2003b"],"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":0.7245000451803207}}
{"call_id":"call_4961a1ff","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q011/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ref_panel","y":"bgrowth","x":["treat_post"],"controls":["treat","post"],"fe":[],"cluster":"agency_id","se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"001934222429f5b3ce4776f918bbc9dabbc29893a0f737f39d753dda058b1fd2","dataset_digests":{"ref_panel":"4fce646ccda24a7d3c77cb21b16bb27adbc854525ef7b50a5fc54c582696e351"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_4961a1ff","status":"ok","reason":"","figures":[{"key":"coef.treat_post","value":4.255119515187237,"unit":"","label":"Coefficient on treat_post in the regression of bgrowth","se":6.392608438238797,"ci_low":-8.42166290995682,"ci_high":16.931901940331294,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de treat_post na regressão por mínimos quadrados ordinários de bgrowth.","formula":"","terms":[{"symbol":"n","value":210.0,"unit":"observações","note":""},{"symbol":"k","value":4.0,"unit":"parâmetros","note":""}],"result":4.255119515187237,"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":40.865442644641874,"unit":"","note":"diagonal da matriz de covariância"}],"result":6.392608438238797,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 104 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":4.255119515187237,"unit":"","note":""},{"symbol":"c","value":1.9830375264837259,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":6.392608438238797,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":210,"flag":false,"note":"observations used"},{"name":"k","value":4,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.059119550706493995,"flag":false,"note":""},{"name":"df_resid","value":206,"flag":false,"note":""},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'agency_id'"},{"name":"n_clusters","value":105,"flag":false,"note":"105 clusters of 'agency_id'"}],"tables":{"coefficients":[{"term":"Intercept","coef":1.0185036654584108,"se":5.594084493305162,"t":0.1820679803240953,"p":0.8558837852202841,"ci_low":-10.074775811086424,"ci_high":12.111783142003246},{"term":"treat_post","coef":4.255119515187237,"se":6.392608438238797,"t":0.665631182685037,"p":0.5071204985953194,"ci_low":-8.42166290995682,"ci_high":16.931901940331294},{"term":"treat","coef":0.5107257056836134,"se":5.890989580405798,"t":0.08669608029563554,"p":0.931079791895564,"ci_low":-11.171327700385703,"ci_high":12.19277911175293},{"term":"post","coef":4.443964720238,"se":5.8216377507040065,"t":0.7633530134540911,"p":0.4469810741050829,"ci_low":-7.100561405002354,"ci_high":15.988490845478355}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across clusters of 'agency_id' and may be correlated within them"],"notes":""},"outputs":null,"output_digest":"be32d23399f97e9a07b307c0e6a6080e55e5ff131c6bc3306e7e68ab0d1c6009","params_versions":[],"sources":[],"started_at":"2026-10-15T17:09:26.613195Z","duration_ms":3.1332920771092176,"error":null,"decided_by":"model","rationale":"smf.ols('bgrowth ~ treat * post', data=long), cluster by agency_id","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_2d140ea4","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"9874afe63ccc13954377d3f8ef821b375d387c37d631f21feccfe03833aaf209"}}
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