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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 Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"ds_0027\",\n \"rows\": 6887,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| year | Calendar year of observation for the district-level fiscal and political records (2001–2012). |\\n| kode_neil | Harmonized district identifier that tracks administrative units across boundary changes and splits to maintain longitudinal comparability. |\\n| personnel_sh | Share of total realized district expenditures allocated to personnel (wages, salaries, allowances); expressed as a fraction between 0 and 1. |\\n| capital_sh | Share of total realized district expenditures allocated to capital investment (e.g., infrastructure, equipment); expressed as a fraction between 0 and 1. |\\n| ltotalexp_all_pc | Natural log of total realized district government expenditures per capita (rupiah per resident). |\\n| lcapital_exp_pc | Natural log of realized district capital expenditures per capita (rupiah per resident). |\\n| incumbency | Indicator for whether the sitting district head is a candidate in that year (1=yes, 0=no); relevant in years when district head elections are held. |\\n| services_provision | Standardized index summarizing local service delivery (sanitation access, clean water access, school enrollment, skilled birth attendance, and road quality); higher values indicate better provision. |\\n| lgdppc_l | Natural log of district GDP per capita from official statistics, lagged one year. |\\n| lpop_l | Natural log of the district’s population, lagged one year. |\\n| rev_total_pc2_l | Total district revenue per capita from all sources (including general transfers and revenue sharing), lagged one year; measured in rupiah per resident. |\\n| rev_natural_pc_l | Shared natural resource revenues (intergovernmental revenue sharing tied to extractive resources) per capita, lagged one year; measured in rupiah per resident. |\\n| poverty_pc_l | Fraction of the district population below the official poverty line, lagged one year (0–1 scale). |\\n| gini_l | Gini index of consumption inequality from household survey data, lagged one year (0–100 scale; higher = more unequal). |\\n| elected_leader_l | Indicator that the district head position is filled by direct popular election (1) versus selection by the local legislature (0), lagged one year; direct elections were rolled out beginning in 2005. |\\n| golkar_share_all | Vote share of the Golkar party in the district’s legislative elections (compiled from 1999 and 2004 results); expressed as a fraction between 0 and 1. |\\n| pdip_share_all | Vote share of the Indonesian Democratic Party of Struggle (PDI–P) in the district’s legislative elections (compiled from 1999 and 2004 results); expressed as a fraction between 0 and 1. |\\n| enp_all | Effective number of parties in the district legislature (Laakso–Taagepera index); higher values indicate more fragmented party systems. |\\n| election_year | Indicator that a district head election is held in the current calendar year (1=yes, 0=no); reflects the local executive election calendar. |\\n| election_year_l | Indicator for the year immediately preceding a district head election (lead-up year; 1=yes, 0=no). |\\n| election_year_lead1 | Indicator for the year immediately following a district head election (post-election year; 1=yes, 0=no). |\\n\\nDataset Context:\\nThis panel assembles district-by-year information for Indonesia’s post-decentralization era, covering 2001–2012. Fiscal figures are realized (executed) budgets from the Ministry of Finance and use the national economic classification that separates outlays into capital investment, goods and services, and personnel. Revenue items distinguish general-purpose transfers and shared natural resource revenues alongside locally generated revenue. Where noted, variables are expressed per capita using contemporaneous district population counts, and several key covariates are provided in natural logs to reduce skew.\\n\\nPolitical characteristics capture the local partisan environment and institutional arrangements. District-level party vote shares are compiled from the 1999 and 2004 legislative elections to describe the baseline partisan landscape. The effective number of parties quantifies legislative fragmentation. Indonesia shifted from indirect selection of district heads by local legislatures (1999–2004) to direct popular elections beginning in 2005. The initial schedule of local executive contests was phased according to pre-existing appointment terms, producing staggered election years across districts. Indicators mark the election year itself as well as the years immediately before and after.\\n\\nSocioeconomic and service delivery measures come from official statistics agencies and large-scale household surveys (including Susenas). The services index combines access and quality proxies—sanitation, clean water, school enrollment, skilled birth attendance, and road conditions—standardized so that higher values indicate stronger provision. Inequality (Gini) and poverty rates are derived from household consumption data. Variables with the “_l” suffix are lagged one calendar year to reflect prior conditions relative to the current budget year.\\n\\nThe administrative map of Indonesia evolved substantially during this period due to district proliferation. The dataset harmonizes identifiers to follow units through splits, yielding an unbalanced panel by design. Users should note that national statistical agencies implemented notable survey and weighting changes around 2011; while the panel extends through 2012, comparability for certain survey-based indicators across this redesign may warrant caution. District governments largely finance spending through intergovernmental transfers and shared revenues, as local borrowing was highly restricted during these years. Overall, the dataset provides a consolidated view of local public finance, political structure, and socioeconomic context during Indonesia’s early decentralization and direct local election era.\",\n \"columns\": {\n  \"year\": \"float64\",\n  \"kode_neil\": \"int64\",\n  \"personnel_sh\": \"float64\",\n  \"capital_sh\": \"float64\",\n  \"ltotalexp_all_pc\": \"float64\",\n  \"lcapital_exp_pc\": \"float64\",\n  \"incumbency\": \"float64\",\n  \"services_provision\": \"float64\",\n  \"lgdppc_l\": \"float64\",\n  \"lpop_l\": \"float64\",\n  \"rev_total_pc2_l\": \"float64\",\n  \"rev_natural_pc_l\": \"float64\",\n  \"poverty_pc_l\": \"float64\",\n  \"gini_l\": \"float64\",\n  \"elected_leader_l\": \"float64\",\n  \"golkar_share_all\": \"float64\",\n  \"pdip_share_all\": \"float64\",\n  \"enp_all\": \"float64\",\n  \"election_year\": \"float64\",\n  \"election_year_l\": \"float64\",\n  \"election_year_lead1\": \"float64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 632,\n \"description\": \"District rows of 2009 and 2010: election_year 0 then 1 (treated) or 0 and 0\",\n \"columns\": {\n  \"lcapital_exp_pc\": \"float64\",\n  \"election_year\": \"float64\",\n  \"kode_neil\": \"int64\",\n  \"year\": \"float64\",\n  \"t0\": \"float64\",\n  \"t1\": \"float64\",\n  \"TreatGroup\": \"int64\",\n  \"Post\": \"int64\",\n  \"DiD\": \"int64\"\n }\n}\n{\n \"id\": \"ref_post\",\n \"rows\": 490,\n \"description\": \"Each district's election_year in 2010 (t1)\",\n \"columns\": {\n  \"kode_neil\": \"int64\",\n  \"t1\": \"float64\"\n }\n}\n{\n \"id\": \"ref_pre\",\n \"rows\": 356,\n \"description\": \"Each district's election_year in 2009 (t0)\",\n \"columns\": {\n  \"kode_neil\": \"int64\",\n  \"t0\": \"float64\"\n }\n}\n\n# Question\nHow did logged district capital expenditures per capita change in 2010 in districts holding a district head election that year versus districts that did not hold an election that year (restricting to districts without an election the year prior)?","response":"sm.OLS(y, add_constant([TreatGroup, Post, DiD])).fit(cov_type='cluster', groups=kode_neil) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"lcapital_exp_pc\", \"x\": [\"DiD\"], \"controls\": [\"TreatGroup\", \"Post\"], \"cluster\": \"kode_neil\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_37d45961","text":"sm.OLS(y, add_constant([TreatGroup, Post, DiD])).fit(cov_type='cluster', groups=kode_neil) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"lcapital_exp_pc","x":["DiD"],"controls":["TreatGroup","Post"],"cluster":"kode_neil"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"5189079beb55c953435f739ec314e91d949fb67397379fd58af8e07fbbad0ef1","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["c87fc70e2f8efe5540c45f2973a46ea09955c6810973bc0955762923d6243e3a"],"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":0.7642079144716263}}
{"call_id":"call_60c89d3a","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q051/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ref_panel","y":"lcapital_exp_pc","x":["DiD"],"controls":["TreatGroup","Post"],"fe":[],"cluster":"kode_neil","se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"04328710a1b35076dd1dc90e3b963633951a455112fb686c3a186a9da3602f4a","dataset_digests":{"ref_panel":"0a07692d62394106dfc25570fd5084a6fe2f87641078e23ec94d298f0b3388f1"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_60c89d3a","status":"ok","reason":"","figures":[{"key":"coef.DiD","value":-0.17599592472724745,"unit":"","label":"Coefficient on DiD in the regression of lcapital_exp_pc","se":0.06591232006923621,"ci_low":-0.3056799654917912,"ci_high":-0.04631188396270372,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de DiD na regressão por mínimos quadrados ordinários de lcapital_exp_pc.","formula":"","terms":[{"symbol":"n","value":632.0,"unit":"observações","note":""},{"symbol":"k","value":4.0,"unit":"parâmetros","note":""}],"result":-0.17599592472724745,"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":0.004344433936909439,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.06591232006923621,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 315 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.17599592472724745,"unit":"","note":""},{"symbol":"c","value":1.9675235316905832,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.06591232006923621,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":632,"flag":false,"note":"observations used"},{"name":"k","value":4,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.08736010450065912,"flag":false,"note":""},{"name":"df_resid","value":628,"flag":false,"note":""},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'kode_neil'"},{"name":"n_clusters","value":316,"flag":false,"note":"316 clusters of 'kode_neil'"}],"tables":{"coefficients":[{"term":"Intercept","coef":13.56176968242811,"se":0.08145235499795468,"t":166.4994177610783,"p":4.177683545219556e-309,"ci_low":13.40151025725802,"ci_high":13.722029107598201},{"term":"DiD","coef":-0.17599592472724745,"se":0.06591232006923621,"t":-2.6701521740150587,"p":0.00797507973248248,"ci_low":-0.3056799654917912,"ci_high":-0.04631188396270372},{"term":"TreatGroup","coef":-0.06226901763041496,"se":0.11970913191744383,"t":-0.5201693190236995,"p":0.6033109452956122,"ci_low":-0.29779955163623795,"ci_high":0.17326151637540804},{"term":"Post","coef":-0.558663617009702,"se":0.04504649369606437,"t":-12.401933450782906,"p":4.941858757829338e-29,"ci_low":-0.6472936533768602,"ci_high":-0.47003358064254386}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across clusters of 'kode_neil' and may be correlated within them"],"notes":""},"outputs":null,"output_digest":"7ede81b1373f98bc4c2e752f8fcf023930b365a28c8843df4b225db16c91cefa","params_versions":[],"sources":[],"started_at":"2026-10-15T17:09:29.070085Z","duration_ms":3.533707931637764,"error":null,"decided_by":"model","rationale":"sm.OLS(y, add_constant([TreatGroup, Post, DiD])).fit(cov_type='cluster', groups=kode_neil) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_37d45961","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"4e115d6a1eda135e6ba3e5fdd8ed0e61e54fe79b4ed35f2070c5de4d34827695"}}
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