{"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.q049.gold (closed, evaluate_result.py; no --auto-rescale)","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":null,"value":[-0.01304470401956573,0.023135479202175756],"weight":1.0,"where":null},{"abs_tol":9.229638576974868e-09,"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.q049.gold","table":null,"tool":null,"value":0.005045387591305012,"weight":1.0,"where":null},{"abs_tol":9.229638576974868e-09,"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.q049.gold","table":null,"tool":null,"value":0.009229638576974869,"weight":1.0,"where":null}],"datasets":{"ds_0026":{"columns":null,"description":"| Variable Name | Description |\n| --- | --- |\n| fips | City identifier formed by concatenating the two-letter state abbreviation and the five-digit Census place code. |\n| year | Calendar year of the observation. |\n| cog.id | Census of Governments (CoG) unit code identifying the municipal government. |\n| abbr | Two-letter U.S. Postal Service state abbreviation. |\n| place.fips | Five-digit Census place FIPS code for the municipality. |\n| inflation | Price index factor supplied to convert nominal dollar amounts in this file to constant-dollar terms; values are multiplicative. |\n| state | State name. |\n| state.code | Numeric state identifier used in source files. |\n| state.fips | Two-digit FIPS code for the state. |\n| city | City name as recorded in the source administrative files. |\n| place.code | Census place code (numeric), without the state prefix. |\n| fips.city | City/place name in mixed case for readability (not an identifier). |\n| county | County or county-equivalent containing the city (as recorded by CoG/ACS). |\n| pop.orig | Municipal population from the original source (typically CoG/ACS), prior to any imputation. |\n| imputed.pop | Population value after applying the dataset\u2019s imputation procedure when pop.orig is missing. |\n| log.pop | Natural logarithm of the population measure used for analysis (typically imputed.pop where available). |\n| lobby.lag1 | Indicator equal to 1 if the city reported any state-level lobbying activity in the previous year, 0 otherwise. |\n| lobby | Indicator equal to 1 if the city reported any state-level lobbying activity in the current year, 0 otherwise. |\n| lobby.lead1 | Indicator equal to 1 if the city reported any state-level lobbying activity in the following year, 0 otherwise. |\n| ever.lobby | Indicator equal to 1 if the city reports any state-level lobbying in any year of the panel, 0 otherwise. |\n| lobby.amount | Reported municipal lobbying expenditures from state disclosure files (nominal dollars); available only where states report amounts. |\n| log.lobby.amount | Natural logarithm of lobbying.amount (where available). |\n| total.rev.orig | Total municipal revenue (nominal/original dollars) from the Census of Governments. |\n| total.rev.own.sources.orig | Own-source revenue (nominal/original dollars) from the Census of Governments. |\n| total.taxes.orig | Total tax revenue (nominal/original dollars) from the Census of Governments. |\n| property.tax.orig | Property tax revenue (nominal/original dollars). |\n| total.ig.rev.orig | Total intergovernmental revenue received (nominal/original dollars). |\n| total.fed.rev.orig | Federal intergovernmental revenue (nominal/original dollars). |\n| total.state.rev.orig | State intergovernmental revenue (nominal/original dollars). |\n| tot.chgs.and.misc.rev.orig | Charges for services plus miscellaneous revenue (nominal/original dollars). |\n| interest.rev.orig | Interest earnings revenue (nominal/original dollars). |\n| fines.and.forfeits.orig | Revenue from fines and forfeitures (nominal/original dollars). |\n| total.expenditure.orig | Total municipal expenditures (nominal/original dollars). |\n| total.current.operating.orig | Current operating expenditures (nominal/original dollars). |\n| total.capital.outlays.orig | Capital outlay expenditures (nominal/original dollars). |\n| total.construction.orig | Construction expenditures (nominal/original dollars). |\n| total.salaries.wages.orig | Salaries and wages (nominal/original dollars). |\n| general.expenditure.orig | General expenditures (nominal/original dollars) in CoG categories. |\n| housing.com.total.exp.orig | Housing and community development expenditures (nominal/original dollars). |\n| libraries.total.exp.orig | Library service expenditures (nominal/original dollars). |\n| parks.total.exp.orig | Parks and recreation expenditures (nominal/original dollars). |\n| police.prot.total.exp.orig | Police protection expenditures (nominal/original dollars). |\n| public.welf.total.exp.orig | Public welfare expenditures (nominal/original dollars). |\n| total.debt.outstanding.orig | Debt outstanding at fiscal year end (nominal/original dollars). |\n| mrp.estimate | City-level ideological position estimated via multilevel regression and poststratification (MRP); higher precision for larger cities. |\n| mrp.sd | Standard error (uncertainty) of the city-level MRP ideology estimate. |\n| pres.dem.2008 | Democratic presidential vote share in 2008 aggregated to city boundaries. |\n| pres.dem.tercile | Tercile group of pres.dem.2008 (e.g., lowest/middle/highest third). |\n| pres.dem.median | Indicator denoting whether pres.dem.2008 is above (1) or below (0) the median across cities. |\n| mrp.tercile | Tercile group of the city\u2019s MRP ideology estimate. |\n| log.total.rev | Natural logarithm of total municipal revenue. |\n| log.own.source | Natural logarithm of own-source revenue. |\n| log.taxes | Natural logarithm of total tax revenue. |\n| log.state | Natural logarithm of state intergovernmental revenue. |\n| log.salaries | Natural logarithm of salaries and wages. |\n| log.exp | Natural logarithm of total expenditures. |\n| log.debt | Natural logarithm of total debt outstanding. |\n| state.by.year | Convenience label combining state and year (e.g., \u201cAK 2006\u201d). |\n| acs.id | ACS GEOID for the place (state FIPS + place FIPS). |\n| acs.pop | Population estimate from the American Community Survey (ACS). |\n| white | Count of residents identifying as White (ACS). |\n| black | Count of residents identifying as Black (ACS). |\n| pct.white | Percentage of the population identifying as White (ACS). |\n| pct.black | Percentage of the population identifying as Black (ACS). |\n| unemployed | Labor-market indicator from the ACS related to unemployment; definition follows the ACS measure used in construction. |\n| median.income | Median household income (ACS; nominal dollars). |\n| mean.income | Mean household income (ACS; nominal dollars). |\n| poverty | Share of individuals below the federal poverty threshold (ACS, percent). |\n| median.house.value | Median value of owner-occupied housing units (ACS; nominal dollars). |\n| bachelors.degree | Share of adults (typically age 25+) with a bachelor\u2019s degree or higher (ACS, percent). |\n| govparty | Party affiliation of the state governor in that year (e.g., D/R). |\n| sen.rep.prop | Proportion of state senate seats held by Republicans in that year. |\n| sen.dem.prop | Proportion of state senate seats held by Democrats in that year. |\n| house.dem.prop | Proportion of state lower-house seats held by Democrats in that year. |\n| house.rep.prop | Proportion of state lower-house seats held by Republicans in that year. |\n| log.income | Natural logarithm of median household income. |\n| log.income.sq | Square of log.income. |\n| pct.white.sq | Square of pct.white. |\n| log.med.house | Natural logarithm of median owner-occupied home value. |\n| mean.own.source | City-level average of own-source revenue across years (construction as in source files; constant within city). |\n| num.reps | Count of state lower-house legislators whose districts overlap the city. |\n| num.D | Number of Democratic state lower-house legislators representing the city. |\n| num.R | Number of Republican state lower-house legislators representing the city. |\n| min.np | Minimum Shor\u2013McCarty ideological score among the city\u2019s lower-house delegation. |\n| max.np | Maximum Shor\u2013McCarty ideological score among the city\u2019s lower-house delegation. |\n| sd.np | Standard deviation of Shor\u2013McCarty ideological scores in the city\u2019s lower-house delegation. |\n| mean.np.lower | Mean Shor\u2013McCarty ideological score of the city\u2019s lower-house delegation. |\n| np.quantile | Qualitative bin of the delegation\u2019s ideological position (e.g., Conservative/Liberal categories based on quantiles). |\n| prop.R | Share of the city\u2019s lower-house delegation that is Republican. |\n| prop.D | Share of the city\u2019s lower-house delegation that is Democratic. |\n| mismatch.lag1 | Indicator for partisan incongruence between the city and its state lower-house delegation in the prior year. |\n| mismatch | Indicator for partisan incongruence between the city and its state lower-house delegation in the current year. |\n| mismatch.lead1 | Indicator for partisan incongruence between the city and its state lower-house delegation in the next year. |\n| mismatch.lead2 | Indicator for partisan incongruence two years ahead. |\n| median.mismatch | Summary classification indicating whether the city is above/below the panel median in frequency of incongruence. |\n| scaled.year1 | Rescaled time index used for modeling convenience (e.g., centered or standardized year). |\n\nDataset Context:\nThis file is a city\u2013year panel covering U.S. municipalities, generally those with populations of at least 20,000, from 2006 through 2014. It integrates several administrative and survey-based sources to characterize local finances, demographics, political context, and intergovernmental activity.\n\nState lobbying information was compiled from all 50 states\u2019 disclosure systems. States require lobbyists and, in many cases, client organizations to report activity; however, reporting rules, thresholds, and data formats vary. Some states record only whether a client lobbied in a year, while others also report spending amounts. The dataset standardizes these records into annual city-level indicators of any lobbying and, where available, expenditures. Because disclosure regimes differ, the presence of expenditure data varies by state; the binary indicator of any reported activity is more consistently observed.\n\nFinancial variables come from the U.S. Census Bureau\u2019s Census of Governments and reflect municipal revenues, expenditures, and debt. These amounts are provided in nominal dollars as reported by governments for their fiscal years. An accompanying inflation factor enables conversion to constant dollars if desired. Demographic and socioeconomic measures\u2014population, racial composition, educational attainment, household income, poverty, and housing values\u2014are merged from the American Community Survey to align with city boundaries over time.\n\nPolitical environment measures include the party of the state governor and the partisan composition of each state\u2019s legislative chambers by year. The dataset also maps each city to the state lower-house districts that overlap it and summarizes the size and partisan/ideological makeup of the city\u2019s state legislative delegation using widely used legislator ideology scores. City-level political tendencies are summarized using a presidential vote-share measure from 2008 and a separate public-opinion-based ideology index estimated via multilevel regression and poststratification. Together, these components provide a consistent, longitudinal view of municipal characteristics, finances, and their interactions with state-level political institutions during a period marked by substantial variation in state policies and disclosure practices.","file_sha256":"74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55","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":"74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55","transforms":[],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0026/data/data.csv"},"ref_panel":{"columns":null,"description":"City rows of 2008 and 2009: mismatch 0 then 1 (treated) or 0 and 0","file_sha256":"74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55","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":{"low_memory":false},"records":null,"role":"reference","sha256":"74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55","transforms":[{"args":{"columns":["fips","year","lobby","mismatch","log.pop"]},"op":"select"},{"args":{"columns":["fips","year","lobby","mismatch","log.pop"]},"op":"dropna"},{"args":{"query":"year in [2008, 2009]"},"op":"filter"},{"args":{"how":"inner","on":["fips"],"with":"ref_pre"},"op":"merge"},{"args":{"how":"inner","on":["fips"],"with":"ref_post"},"op":"merge"},{"args":{"query":"m0 == 0 and (m1 == 1 or m1 == 0)"},"op":"filter"},{"args":{"args":{"cmp":"eq","column":"year","value":2009},"name":"post","op":"indicator"},"op":"derive"},{"args":{"args":{"cmp":"eq","column":"m1","value":1},"name":"treat_group","op":"indicator"},"op":"derive"},{"args":{"map":{"log.pop":"log_pop"}},"op":"rename"},{"args":{"args":{"columns":["treat_group","post"]},"name":"post_treat_group","op":"interaction"},"op":"derive"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0026/data/data.csv"},"ref_post":{"columns":null,"description":"Each city's mismatch in 2009 (m1)","file_sha256":"74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55","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":{"low_memory":false},"records":null,"role":"reference","sha256":"74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55","transforms":[{"args":{"columns":["fips","year","lobby","mismatch","log.pop"]},"op":"select"},{"args":{"columns":["fips","year","lobby","mismatch","log.pop"]},"op":"dropna"},{"args":{"query":"year == 2009"},"op":"filter"},{"args":{"columns":["fips","mismatch"]},"op":"select"},{"args":{"map":{"mismatch":"m1"}},"op":"rename"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0026/data/data.csv"},"ref_pre":{"columns":null,"description":"Each city's mismatch in 2008 (m0)","file_sha256":"74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55","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":{"low_memory":false},"records":null,"role":"reference","sha256":"74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55","transforms":[{"args":{"columns":["fips","year","lobby","mismatch","log.pop"]},"op":"select"},{"args":{"columns":["fips","year","lobby","mismatch","log.pop"]},"op":"dropna"},{"args":{"query":"year == 2008"},"op":"filter"},{"args":{"columns":["fips","mismatch"]},"op":"select"},{"args":{"map":{"mismatch":"m0"}},"op":"rename"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0026/data/data.csv"}},"difficulty":"M","id":"crb_papers.q049","level":"T2","meta":{"agent_reach":{"blocked_by":["reference_frame:ref_panel","reference_frame:ref_post","reference_frame:ref_pre"],"dtypes":{"ds_0026":{"lobby":"float64","mismatch":"float64"}},"named":{"outcome":["lobby"],"treatment":["mismatch"]},"reachable":false,"rule":"magi_bench.reach/1"},"answer_format":{"figure_transform":"identity","round_to":null,"unit_scale":1.0},"cluster":"ds_0026","crb":{"data_path":"data/ds_0026/data/data.csv","dataset_id":"ds_0026","gold":{"ci95":[-0.01304470401956573,0.023135479202175756],"ci_rule":"effect \u00b1 1.96\u00b7|standard_error| (evaluate_result.ci_bounds)","effect":0.005045387591305012,"standard_error":0.009229638576974869},"identification":{"bad_controls":["lobby.amount","log.lobby.amount","lobby.lead1","ever.lobby","mismatch.lead1","mismatch.lead2","median.mismatch","total.state.rev.orig","total.ig.rev.orig","total.fed.rev.orig","total.rev.orig","property.tax.orig","total.taxes.orig","tot.chgs.and.misc.rev.orig","interest.rev.orig","total.expenditure.orig","total.current.operating.orig","total.capital.outlays.orig","total.construction.orig","total.salaries.wages.orig","total.debt.outstanding.orig","log.total.rev","log.own.source","log.taxes","log.state","log.salaries","log.exp","log.debt"],"causal_quantity":"ATT","controls":["log.pop"],"group_variable":"fips","minimal_controlling_set":["log.pop"],"outcomes":["lobby"],"reason_for_bad_controls":"These variables are post-treatment or mechanically downstream of treatment/outcome for the DiD estimating equation of mismatch -> lobby. The paper explicitly shows that cities hire lobbyists to influence funding and fiscal outcomes: they aim to \u201cprotect local revenue sources,\u201d \u201cprotect and increase funding for specific programs and services,\u201d and oppose policies that reduce local authority (p. 680). It further states that cities target \u201clegislative earmarks, budget and appropriations bills, policies with a local impact, and executive grants\u201d (p. 680). Thus fiscal inflows (e.g., state intergovernmental revenue), revenues, expenditures, and debt are mediators or outcomes of lobbying/representation and controlling for them would induce over-control bias (Cinelli\u2013Forney\u2013Pearl \u2018bad controls\u2019 and Chernozhukov et al. Section 11.3: post-treatment/mediator controls create over-control bias). Variables such as lobby.amount/log.lobby.amount, lobby.lead1, ever.lobby are outcomes or future outcomes and are classic post-treatment controls (Chernozhukov et al., Sec. 11.3\u201311.4: controlling for outcomes or outcomes-of-outcomes introduces bias). Future treatment indicators mismatch.lead1/mismatch.lead2 or panel summaries like median.mismatch are also post-treatment relative to the current period and should not be conditioned on (anticipation/selection per Chernozhukov et al., Sec. 11.3\u201311.4).","reason_for_minimal_controlling_set":"The core DiD identification in the paper relies on city and time (or state-by-year) fixed effects, which absorb time-invariant city traits and common or state-specific shocks: \u201cLobby_it = \u03b21 Mismatch_it + X_it \u03b22 + \u03b3_i + \u03b7_t + \u03b5_it,\u201d with \u03b3_i (city fixed effects) and \u03b7_t (year or state-by-year fixed effects) (pp. 681\u2013682). \u201cThe city fixed effects control for time invariant local characteristics\u2026 The year fixed effects account for temporal shocks\u2026 The state-by-year fixed effects go a step further and control for state specific conditions\u2026 [and] proxy for changes to the state legislative agenda\u201d (p. 682). Population (log) is the only systematic time-varying city covariate included across specifications (Table 1 shows \u201cPopulation (log)\u201d as a regressor; the California robustness note states, \u201cThe models control for population,\u201d Table 3, p. 687). Given FE plus state-year FE, log.pop is a parsimonious adjustment for evolving city scale that may correlate with both lobbying propensity and exposure to mismatch, while avoiding post-treatment fiscal variables.","strategy":"Difference-in-Differences","time_variable":"year","treatments":["mismatch"]},"identification_strategy":"Difference-in-Differences"},"headline_keys":{"infer.ols":["coef.post_treat_group"]},"naive":{"dataset":"ds_0026","treatment":"mismatch","y":"lobby"},"native_id":"49","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"cluster":"fips","controls":["post","treat_group","log_pop"],"dataset":"ref_panel","x":["post_treat_group"],"y":"lobby"},"tool":"infer.ols","why":"smf.ols('lobby ~ post + treat_group + post:treat_group + log_pop').fit(cov_type='cluster', groups=fips) (CR1 errors with statsmodels' default G/(G-1)\u00b7(N-1)/(N-K) correction)"}]},"reference_path":{"calls":[{"args":{"cluster":"fips","controls":["post","treat_group","log_pop"],"dataset":"ref_panel","x":["post_treat_group"],"y":"lobby"},"tool":"infer.ols"}],"needs":[],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_panel","ref_post","ref_pre"],"source_id":"crb_papers.q049.script"}},"question":"Did cities that newly experienced partisan incongruence with their state lower-house delegation between 2008 and 2009 change their likelihood of reporting state-level lobbying relative to comparable cities that remained aligned?","requires":[],"runner":null,"sources":["crb_papers.q049.gold","crb_papers.q049.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"}