{"args":{},"checks":[{"abs_tol":null,"bind":"any","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"answer_backed","min_lre":null,"note":"every number traced to a tool call or the question","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":null,"value":null,"weight":1.0,"where":null},{"abs_tol":null,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":true,"key":null,"kind":"answer_figure_close","min_lre":null,"note":"CRB estimate_within_solution_ci: the answer figure inside the gold 95% interval effect \u00b1 1.96\u00b7|SE| of source crb_papers.q006.gold (closed, evaluate_result.py; no --auto-rescale)","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":null,"value":[-0.11697425907320382,0.2542890886508108],"weight":1.0,"where":null},{"abs_tol":9.47100376846976e-08,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"answer_figure_close","min_lre":null,"note":"replication cell: the gold effect to rel 1e-6 (abs floor 1e-6\u00b7SE); never required","open_interval":false,"rel_tol":1e-06,"required":false,"source_id":"crb_papers.q006.gold","table":null,"tool":null,"value":0.06865741478880348,"weight":1.0,"where":null},{"abs_tol":9.47100376846976e-08,"bind":"answer_call","column":null,"failure_class":null,"field":"se","interval":false,"key":null,"kind":"answer_figure_close","min_lre":null,"note":"replication cell: the gold SE to rel 1e-6; never required","open_interval":false,"rel_tol":1e-06,"required":false,"source_id":"crb_papers.q006.gold","table":null,"tool":null,"value":0.0947100376846976,"weight":1.0,"where":null}],"datasets":{"ds_0003":{"columns":null,"description":"| Variable Name | Description |\n| --- | --- |\n| c6_ibge | Six-digit IBGE municipality identifier used in Brazilian statistical and administrative systems to uniquely identify each locality. |\n| codigouf | Numeric code of the state (Unidade da Federa\u00e7\u00e3o) in which the municipality is located. |\n| year | Calendar year of observation (primarily 2004\u20132015). |\n| term | Start year of the mayoral electoral term associated with the record when available (four-year cycles in Brazil). |\n| cad_update | Indicator equal to 1 in and after years when the municipality reports having completed a comprehensive cadastre (land-parcel register) update; 0 otherwise. |\n| planta_update | Indicator equal to 1 in years when the municipality reports revising the generic land valuation schedule (Planta Gen\u00e9rica de Valores) used to assess property values for taxation; 0 otherwise. |\n| iptu_law_update | Indicator equal to 1 in years when the municipality reports reforming or updating its local property tax (IPTU) law or regulations; 0 otherwise. |\n| logiptu | Natural log of annual IPTU (urban property tax) revenue collected by the municipality, in Brazilian reais (R$). |\n| logissqn | Natural log of annual ISSQN (services tax) revenue collected by the municipality, in Brazilian reais (R$). |\n| Llogtransfers | One-year lag of the natural log of total intergovernmental transfers received by the municipality (federal and state transfers), in Brazilian reais (R$). |\n| pre_2004_cad_update_yr | Year of the last comprehensive municipal cadastre update reported prior to 2004. |\n| t | Integer index for years relative to the last pre-2004 cadastre update (e.g., 0 in the update year, positive thereafter, negative before). |\n| switchcadaster | Binary flag tied to cadastre timing that marks the reference update year used to construct relative-year indicators. |\n| F3switchcadaster | Indicator equal to 1 three years before the referenced cadastre update; 0 otherwise. |\n| F2switchcadaster | Indicator equal to 1 two years before the referenced cadastre update; 0 otherwise. |\n| Lswitchcadaster | Indicator equal to 1 one year after the referenced cadastre update; 0 otherwise. |\n| L2switchcadaster | Indicator equal to 1 two years after the referenced cadastre update; 0 otherwise. |\n| L3cad_update | Indicator equal to 1 three years after the referenced cadastre update; 0 otherwise. |\n| F4cad_update | Indicator equal to 1 four years before the referenced cadastre update; 0 otherwise. |\n| anyswitch_period | Municipality-level flag equal to 1 for places that experience a change in cadastre-update status within the analysis window; 0 for those that remain in the same status throughout. |\n| loggdppc | Natural log of municipal GDP per capita, in Brazilian reais (R$), based on national accounts statistics. |\n| logdespobras | Natural log of annual municipal expenditures on public works (obras), in Brazilian reais (R$). |\n| id_c6_electoral_term | Concatenated identifier for municipality-by-electoral-term (c6_ibge + term start year), allowing records to be grouped by mayoral administration. |\n| yr_adm | Year index within the current mayoral administration (typically 1\u20134), counting elapsed years since the start of the term; resets at the beginning of a new administration. |\n\nDataset Context:\nThis municipality\u2013year panel brings together administrative and survey-based information to follow local public finance and land administration practices across Brazil during the mid-2000s to mid-2010s. Fiscal data on locally collected taxes and expenditures are drawn from Brazil\u2019s Treasury systems\u2014FINBRA for earlier years and SICONFI for later years\u2014harmonized to annual totals and recorded in Brazilian reais. These sources capture the main municipal own-revenue streams, notably the urban property tax (IPTU) and the services tax (ISSQN), as well as the flows of intergovernmental transfers from federal and state governments. Continuous variables appearing in logarithms are natural logs of annual amounts.\n\nInformation on land administration comes from IBGE\u2019s Pesquisa de Informa\u00e7\u00f5es B\u00e1sicas Municipais (MUNIC), an institutional survey fielded to all municipalities. MUNIC asks local governments about the status and timing of their real estate cadastre\u2014an official register of parcels, ownership, and fiscal valuation\u2014and related legal or administrative changes. The dataset records whether and when municipalities report: conducting a comprehensive cadastre update; revising the generic valuation schedule (Planta Gen\u00e9rica de Valores) used to appraise taxable values; and reforming local property tax rules. Because the cadastre underpins assessment and billing for the IPTU, these administrative markers help track when municipalities refresh the underlying property records and valuation parameters.\n\nSocioeconomic indicators such as GDP per capita are taken from IBGE national accounts and municipal statistics, allowing fiscal figures to be interpreted in light of local economic conditions. Additional budget aggregates, like spending on public works, come from the same Treasury systems as revenue data, providing a view of how local governments allocate resources. The panel further includes identifiers that align fiscal and administrative observations with mayoral terms. Brazil\u2019s municipalities elect mayors to four-year administrations; the dataset includes a composite municipality-by-term key and an index of years elapsed within each administration to facilitate analyses organized by political cycles.\n\nFinally, a small set of variables encodes the calendar position of observations relative to reported cadastre updates (e.g., indicators for specific years before or after an update). These timing markers are constructed from MUNIC responses on the last update year and allow users to describe patterns around the moments when registers are overhauled. Overall, the dataset enables longitudinal tracking of land administration practices, revenue collection, and budgetary activity across thousands of municipalities under a common institutional framework.","file_sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"redistributed by CausalReasoningBenchmark (MIT) under the original replication data's terms","member":null,"object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"agent","sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","transforms":[],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0003/data/data.csv"},"ref_f0":{"columns":null,"description":"First year each municipality has cad_update == 0","file_sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"redistributed by CausalReasoningBenchmark (MIT) under the original replication data's terms","member":null,"object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"reference","sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","transforms":[{"args":{"columns":["c6_ibge","year","cad_update","logiptu"]},"op":"select"},{"args":{"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"},"op":"derive"},{"args":{"columns":["logiptu"]},"op":"dropna"},{"args":{"query":"cad_update == 0"},"op":"filter"},{"args":{"aggs":[{"column":"year","fn":"min","name":"f0"}],"by":["c6_ibge"]},"op":"collapse"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0003/data/data.csv"},"ref_f1":{"columns":null,"description":"First year each municipality has cad_update == 1","file_sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"redistributed by CausalReasoningBenchmark (MIT) under the original replication data's terms","member":null,"object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"reference","sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","transforms":[{"args":{"columns":["c6_ibge","year","cad_update","logiptu"]},"op":"select"},{"args":{"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"},"op":"derive"},{"args":{"columns":["logiptu"]},"op":"dropna"},{"args":{"query":"cad_update == 1"},"op":"filter"},{"args":{"aggs":[{"column":"year","fn":"min","name":"f1"}],"by":["c6_ibge"]},"op":"collapse"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0003/data/data.csv"},"ref_panel":{"columns":null,"description":"Rows of 2012 and 2014: the 2013 adopters switching 0->1 and the not-yet/never adopters at 0 in both years","file_sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"redistributed by CausalReasoningBenchmark (MIT) under the original replication data's terms","member":null,"object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"reference","sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","transforms":[{"args":{"columns":["c6_ibge","year","cad_update","logiptu"]},"op":"select"},{"args":{"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"},"op":"derive"},{"args":{"columns":["logiptu"]},"op":"dropna"},{"args":{"query":"year in [2012, 2014]"},"op":"filter"},{"args":{"aggs":[{"column":"cad_update","fn":"max","name":"cad_update"},{"column":"logiptu","fn":"mean","name":"logiptu"}],"by":["c6_ibge","year"]},"op":"collapse"},{"args":{"how":"left","on":["c6_ibge"],"with":"ref_f1"},"op":"merge"},{"args":{"how":"left","on":["c6_ibge"],"with":"ref_f0"},"op":"merge"},{"args":{"how":"left","on":["c6_ibge"],"with":"ref_wpre"},"op":"merge"},{"args":{"how":"left","on":["c6_ibge"],"with":"ref_wpost"},"op":"merge"},{"args":{"query":"(f1 == 2013 and f0 < f1 and w2012 == 0 and w2014 == 1) or ((f1 != f1 or f0 != f0 or f0 > f1 or f1 > 2014) and w2012 == 0 and w2014 == 0)"},"op":"filter"},{"args":{"args":{"cmp":"eq","column":"w2014","value":1},"name":"G","op":"indicator"},"op":"derive"},{"args":{"args":{"cmp":"eq","column":"year","value":2014},"name":"Post","op":"indicator"},"op":"derive"},{"args":{"args":{"columns":["G","Post"]},"name":"DiD","op":"interaction"},"op":"derive"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0003/data/data.csv"},"ref_wpost":{"columns":null,"description":"Each municipality's max cad_update in 2014","file_sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"redistributed by CausalReasoningBenchmark (MIT) under the original replication data's terms","member":null,"object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"reference","sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","transforms":[{"args":{"columns":["c6_ibge","year","cad_update","logiptu"]},"op":"select"},{"args":{"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"},"op":"derive"},{"args":{"columns":["logiptu"]},"op":"dropna"},{"args":{"query":"year == 2014"},"op":"filter"},{"args":{"aggs":[{"column":"cad_update","fn":"max","name":"w2014"}],"by":["c6_ibge"]},"op":"collapse"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0003/data/data.csv"},"ref_wpre":{"columns":null,"description":"Each municipality's max cad_update in 2012","file_sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"redistributed by CausalReasoningBenchmark (MIT) under the original replication data's terms","member":null,"object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"reference","sha256":"782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e","transforms":[{"args":{"columns":["c6_ibge","year","cad_update","logiptu"]},"op":"select"},{"args":{"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"},"op":"derive"},{"args":{"columns":["logiptu"]},"op":"dropna"},{"args":{"query":"year == 2012"},"op":"filter"},{"args":{"aggs":[{"column":"cad_update","fn":"max","name":"w2012"}],"by":["c6_ibge"]},"op":"collapse"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0003/data/data.csv"}},"difficulty":"M","id":"crb_papers.q006","level":"T2","meta":{"agent_reach":{"blocked_by":["reference_frame:ref_f0","reference_frame:ref_f1","reference_frame:ref_panel","reference_frame:ref_wpost","reference_frame:ref_wpre"],"dtypes":{"ds_0003":{"cad_update":"float64","logiptu":"float64"}},"named":{"outcome":["logiptu"],"treatment":["cad_update"]},"reachable":false,"rule":"magi_bench.reach/1"},"answer_format":{"figure_transform":"identity","round_to":null,"unit_scale":1.0},"cluster":"ds_0003","crb":{"data_path":"data/ds_0003/data/data.csv","dataset_id":"ds_0003","gold":{"ci95":[-0.11697425907320382,0.2542890886508108],"ci_rule":"effect \u00b1 1.96\u00b7|standard_error| (evaluate_result.ci_bounds)","effect":0.06865741478880348,"standard_error":0.0947100376846976},"identification":{"bad_controls":["logdespobras","anyswitch_period","switchcadaster","F3switchcadaster","F2switchcadaster","Lswitchcadaster","L2switchcadaster","L3cad_update","F4cad_update"],"causal_quantity":"ATT","group_variable":"c6_ibge","outcomes":["logiptu"],"reason_for_bad_controls":"Post-treatment or mechanically treatment-defined variables are bad controls because adjusting for descendants of D blocks part of the causal effect and can introduce collider bias. The guidance notes: \u201cThe adjustment set S is valid if the backdoor criterion is satisfied\u2026 No element of S is a descendant of D\u201d and that \u201cexplicitly adjusting for post-treatment variables is almost always a bad idea,\u201d including mediators that \u201clead to over-control bias\u201d (Applied Causal Inference, Ch. 11: pp. 300\u2013301, 309\u2013311). In this application: (i) logdespobras is a mediator/outcome of treatment\u2014\u201cwe show that cadastre updates \u2026 also lead to greater municipal spending on public works projects (see table B.4)\u201d (Christensen & Garfias 2021, p. 1402). Conditioning on logdespobras would block part of the causal pathway from cad_update to logiptu and can open collider paths (Ch. 11, Fig. 11.9a\u2013b, 11.11). (ii) anyswitch_period is mechanically defined by whether a municipality changes cadastre status within the window; conditioning on it amounts to conditioning on a function of treatment timing (a post-treatment selection variable), risking Heckman/collider selection (Ch. 11, Fig. 11.12). (iii) switchcadaster and its leads/lags (F3switchcadaster, F2switchcadaster, Lswitchcadaster, L2switchcadaster, L3cad_update, F4cad_update) are deterministic functions of treatment timing and include post-treatment indicators; including them as controls in a 2\u00d72 DiD would condition on descendants of D, violating the backdoor criterion and potentially absorbing the treatment effect itself.","strategy":"Difference-in-Differences","time_variable":"year","treatments":["cad_update"]},"identification_strategy":"Difference-in-Differences"},"headline_keys":{"infer.ols":["coef.DiD"]},"item_caveats":[{"kind":"implicit_window","note":"the question leaves implicit the comparison years the gold's script uses (pre 2012, post 2014)"}],"naive":{"dataset":"ds_0003","treatment":"cad_update","y":"logiptu"},"native_id":"6","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"cluster":"c6_ibge","controls":["G","Post"],"dataset":"ref_panel","x":["DiD"],"y":"logiptu"},"tool":"infer.ols","why":"sm.OLS(logiptu ~ const + G + Post + DiD), cluster by c6_ibge"}]},"reference_path":{"calls":[{"args":{"cluster":"c6_ibge","controls":["G","Post"],"dataset":"ref_panel","x":["DiD"],"y":"logiptu"},"tool":"infer.ols"}],"needs":[],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_f0","ref_f1","ref_panel","ref_wpost","ref_wpre"],"source_id":"crb_papers.q006.script"}},"question":"How much did logged municipal property tax (IPTU) revenue change after municipalities that updated the cadastre in 2013, relative to municipalities that had not yet updated by 2013?","requires":[],"runner":null,"sources":["crb_papers.q006.gold","crb_papers.q006.script"],"split":"public","suite":"crb_papers","tags":["econ-bench","external","crb","did","ref_benchmark_script"],"target":"agent","tool":null,"truth":"CausalReasoningBenchmark gold at the pinned revision: causal_queries.json effect and standard_error (the value the item's estimation.py prints), scored as evaluate_result.py scores estimate_within_solution_ci"}