{"call_id":"call_28cfbf8a","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q082/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"2925aeab2262","inputs":{"dataset":"ref_iv","index":0,"op":"select","args":{"columns":["fourpack_1_01","anger","treated","democrat"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":3416,"rows_after":3416,"frame_sha256_before":"797cbf0bfcbbfd6a34aae7cc1830cdf962c38cc317dbe9c6cfa3bb37f670acb3","frame_sha256_after":"60d004fb2385b0c8903d4b73569616adf232349deae86b6e12b7e89466b638ed"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:09:31.033527Z","duration_ms":0.460208160802722,"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_a181110e","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q082/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"2925aeab2262","inputs":{"dataset":"ref_iv","index":1,"op":"dropna","args":{"columns":["fourpack_1_01","anger","treated","democrat"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":3416,"rows_after":3410,"frame_sha256_before":"60d004fb2385b0c8903d4b73569616adf232349deae86b6e12b7e89466b638ed","frame_sha256_after":"9eb981d171957de3cf90ecab15ace89b0d6df00d45b53f10c68035b0ec2ff23f"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:09:31.081044Z","duration_ms":0.5357090849429369,"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_5c209365","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q082/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","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":"2026-10-15T17:09:31.166715Z","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\": \"ds_0052\",\n \"rows\": 3416,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| Record | Row index from the CSV export / internal record number |\\n| StartDate | Timestamp when the respondent began the survey (survey-platform metadata) |\\n| EndDate | Timestamp when the respondent finished the survey (survey-platform metadata) |\\n| Status | Survey metadata/status field (platform-provided) |\\n| IPAddress | Respondent IP address (as recorded by the survey platform) |\\n| Progress | Progress percent (completion progress reported by survey software) |\\n| Duration..in.seconds. | Time to complete the survey in seconds (survey-platform metadata) |\\n| Finished | Logical indicator whether the respondent completed the survey |\\n| RecordedDate | Timestamp when the response was recorded by the system (survey-platform metadata) |\\n| ResponseId | Unique respondent identifier assigned by the survey platform |\\n| RecipientLastName | Recipient last name field (often empty/anonymized) |\\n| RecipientFirstName | Recipient first name field (often empty/anonymized) |\\n| RecipientEmail | Recipient email field (often empty/anonymized) |\\n| ExternalReference | External reference field (study-specific metadata) |\\n| LocationLatitude | Latitude of respondent location (as recorded by platform) |\\n| LocationLongitude | Longitude of respondent location (as recorded by platform) |\\n| DistributionChannel | Distribution channel (e.g., \\\"anonymous\\\", panel source) |\\n| UserLanguage | Language of the respondent interface (e.g., EN) |\\n| irb_consent | Indicator of whether respondent provided IRB consent |\\n| birth_yr | Respondent birth year |\\n| gender | Self-reported gender (original survey response) |\\n| race_eth | Self-reported race/ethnicity (original survey response) |\\n| educ | Educational attainment (original survey response category) |\\n| pid7 | 7-point party identification measure (original item; typically coded 1–7) |\\n| ideo7 | 7-point ideological self-placement (original item; typically coded 1–7) |\\n| sm1 | Self-monitoring item 1 (original survey item) |\\n| sm2 | Self-monitoring item 2 (original survey item) |\\n| sm3 | Self-monitoring item 3 (original survey item) |\\n| gop_characteristics_1 | Respondent rating for GOP characteristic item #1 (original survey item) |\\n| gop_characteristics_2 | Respondent rating for GOP characteristic item #2 (original survey item) |\\n| gop_characteristics_3 | Respondent rating for GOP characteristic item #3 (original survey item) |\\n| gop_characteristics_4 | Respondent rating for GOP characteristic item #4 (original survey item) |\\n| gop_characteristics_5 | Respondent rating for GOP characteristic item #5 (original survey item) |\\n| gop_characteristics_6 | Respondent rating for GOP characteristic item #6 (original survey item) |\\n| gop_characteristics_7 | Respondent rating for GOP characteristic item #7 (original survey item) |\\n| dem_characteristics_1 | Respondent rating for Democratic characteristic item #1 (original survey item) |\\n| dem_characteristics_2 | Respondent rating for Democratic characteristic item #2 (original survey item) |\\n| dem_characteristics_3 | Respondent rating for Democratic characteristic item #3 (original survey item) |\\n| dem_characteristics_4 | Respondent rating for Democratic characteristic item #4 (original survey item) |\\n| dem_characteristics_5 | Respondent rating for Democratic characteristic item #5 (original survey item) |\\n| dem_characteristics_6 | Respondent rating for Democratic characteristic item #6 (original survey item) |\\n| dem_characteristics_7 | Respondent rating for Democratic characteristic item #7 (original survey item) |\\n| emp_characteristics_1 | - |\\n| emp_characteristics_2 | - |\\n| emp_characteristics_3 | - |\\n| emp_characteristics_4 | - |\\n| emp_characteristics_5 | - |\\n| emp_characteristics_6 | - |\\n| trump_approval | Respondent's approval rating for then-President (survey item) |\\n| house | Respondent item related to U.S. House (survey item label unspecified) |\\n| house_DO | - |\\n| senate | Respondent item related to U.S. Senate (survey item label unspecified) |\\n| senate_DO | - |\\n| whitehouse | Respondent item related to White House (survey item label unspecified) |\\n| whitehouse_DO | - |\\n| trust_govt | Respondent trust in government (survey item) |\\n| treatment_gop | Flag/indicator for a GOP-related prompt or condition (study-specific) |\\n| breakfast_gop | Flag indicating a \\\"breakfast\\\" prompt variation for GOP scenario (study-specific) |\\n| treatment_dems | Flag/indicator for a Democratic-related prompt or condition (study-specific) |\\n| breakfast_dems | Flag indicating a \\\"breakfast\\\" prompt variation for Democratic scenario (study-specific) |\\n| fourpack_1 | Out-partisan neighbor interaction battery item #1 (original response; later recoded/rescaled in *_01 version) |\\n| fourpack_2 | Out-partisan neighbor interaction battery item #2 (original response; later recoded/rescaled in *_01 version) |\\n| fourpack_3 | Out-partisan neighbor interaction battery item #3 (original response; later recoded/rescaled in *_01 version) |\\n| fourpack_4 | Out-partisan neighbor interaction battery item #4 (original response; later recoded/rescaled in *_01 version) |\\n| talking | Survey response about willingness to keep talking to an out-party supporter (original response; later recoded/rescaled in *_01 version) |\\n| meal | Survey response about willingness to share a meal with an out-party supporter (original response; later recoded/rescaled in *_01 version) |\\n| club | Survey response about willingness to join a club/social gathering likely attended by out-party supporters (original response; later recoded/rescaled in *_01 version) |\\n| date | Survey response about willingness to go on a first date with an out-party supporter (original response; later recoded/rescaled in *_01 version) |\\n| family_date | Survey response about approval if a close family member dated an out-party supporter (original response; later recoded/rescaled in *_01 version) |\\n| friendship | Survey response about continuing/ending friendship with an out-party supporter (original response; later recoded/rescaled in *_01 version) |\\n| family.x | Family-related survey response (duplicate/variant naming from export/merge; see also family_01 for rescaled version) |\\n| divorce_gop | Response about a scenario involving divorce from (or related to) an out-party supporter (original response; later recoded/rescaled in *_01 version) |\\n| friends_comfort | Comfort with close friends who support the opposing party (original response; later recoded/rescaled in *_01 version) |\\n| neighbors_comfort | Comfort with neighbors who support the opposing party (original response; later recoded/rescaled in *_01 version) |\\n| marry_upset | Upset if a close family member married an opposing-party supporter (original response; later recoded/rescaled in *_01 version) |\\n| news_share | Likelihood of sharing a news article with friends/family (original response; later recoded/rescaled in *_01 version) |\\n| rid | Another respondent ID field (platform/round identifier) |\\n| age | Respondent age (numeric) |\\n| gender.1 | Secondary gender variable (often duplicate/recode of gender) |\\n| hhi | Household income (self-reported / coded) |\\n| ethnicity | Ethnicity coding (recoded) |\\n| hispanic | Hispanic ethnicity indicator |\\n| education | Education recode (e.g., degree categories or BA+ indicator depending on coding) |\\n| political_party | Reported political party affiliation (text/category) |\\n| region | Geographic region of respondent |\\n| zip | ZIP/postal code (may be partial/anonymized) |\\n| other_party | Text field for “other party” specification |\\n| own_party | Text field describing respondent's own party label |\\n| other_party_plural | Flag/indicator for multiple party responses (study metadata) |\\n| betsy.squestionsforindependents_DO | - |\\n| experimentforrepublicans_DO | - |\\n| experimentfordemocrats_DO | - |\\n| pid | Party identification (alternate coding) |\\n| democrat | Binary indicator for Democratic identification (recoded from party ID) |\\n| female.x | Binary indicator for female (recoded) |\\n| nonwhite | Binary indicator for nonwhite respondents (recoded) |\\n| ba_plus | Indicator for BA degree or higher (recoded) |\\n| ideology | Numeric ideology scale (recoded from ideo7) |\\n| selfmonitoring1 | Self-monitoring item 1 (recoded) |\\n| selfmonitoring2 | Self-monitoring item 2 (recoded) |\\n| selfmonitoring3 | Self-monitoring item 3 (recoded) |\\n| selfmonitoring | Composite self-monitoring score (derived from self-monitoring items) |\\n| treated | Binary indicator for randomized assignment to the emotion-induction writing prompt versus the neutral writing prompt (study assignment flag; see also control) |\\n| control | Binary indicator for assignment to the neutral writing prompt (complement/alternate assignment flag; see also treated) |\\n| fourpack_1_01 | Rescaled version of fourpack_1 on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| fourpack_2_01 | Rescaled version of fourpack_2 on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| fourpack_3_01 | Rescaled version of fourpack_3 on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| fourpack_4_01 | Rescaled version of fourpack_4 on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| talking_01 | Rescaled version of talking on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| meal_01 | Rescaled version of meal on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| club_01 | Rescaled version of club on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| date_01 | Rescaled version of date on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| family_date_01 | Rescaled version of family_date on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| friendship_01 | Rescaled version of friendship on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| family_01 | Rescaled version of the family item on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| divorce_gop_01 | Rescaled version of divorce_gop on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| friends_comfort_01 | Rescaled version of friends_comfort on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| neighbors_comfort_01 | Rescaled version of neighbors_comfort on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| marry_upset_01 | Rescaled version of marry_upset on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| news_share_01 | Rescaled version of news_share on a 0–1 scale (analysis-ready recode; derived from the original response scale) |\\n| Source..A. | - |\\n| Source..B. | - |\\n| Source..C. | - |\\n| Source..D. | - |\\n| Source..E. | - |\\n| Source..F. | - |\\n| Source..G. | - |\\n| Source..H. | - |\\n| Source..I. | - |\\n| Source..K. | - |\\n| Source..L. | - |\\n| Source..M. | - |\\n| Source..N. | - |\\n| Source..O. | - |\\n| Source..P. | - |\\n| Source..Q. | - |\\n| Source..R. | - |\\n| Source..S. | - |\\n| Source..T. | - |\\n| Source..U. | - |\\n| Source..V. | - |\\n| Source..W. | - |\\n| Source..X. | - |\\n| Source..Y. | - |\\n| Source..Z. | - |\\n| Source..AA. | - |\\n| Source..AB. | - |\\n| Source..AC. | - |\\n| Source..AD. | - |\\n| Source..AE. | - |\\n| Source..AF. | - |\\n| Source..AG. | - |\\n| Source..AH. | - |\\n| Source..AI. | - |\\n| Source..AJ. | - |\\n| Source..AK. | - |\\n| Source..AL. | - |\\n| Source..AM. | - |\\n| Source..AN. | - |\\n| Source..AO. | - |\\n| Source..AP. | - |\\n| Source..AQ. | - |\\n| Source..AR. | - |\\n| Source..AS. | - |\\n| Source..AT. | - |\\n| Source..AU. | - |\\n| Source..AV. | - |\\n| Source..AW. | - |\\n| Source..AX. | - |\\n| Source..AY. | - |\\n| Source..AZ. | - |\\n| Source..BA. | - |\\n| Source..BB. | - |\\n| Source..BC. | - |\\n| Source..BD. | - |\\n| Source..BE. | - |\\n| Source..BF. | - |\\n| Source..BG. | - |\\n| Source..BH. | - |\\n| Source..BI. | - |\\n| Source..BJ. | - |\\n| Source..BK. | - |\\n| Source..BL. | - |\\n| Source..BM. | - |\\n| Source..BN. | - |\\n| Source..BO. | - |\\n| Source..BP. | - |\\n| Source..BQ. | - |\\n| Source..BR. | - |\\n| Source..BS. | - |\\n| Source..BT. | - |\\n| Source..BU. | - |\\n| Source..BV. | - |\\n| Source..BW. | - |\\n| Source..BX. | - |\\n| Source..BY. | - |\\n| Source..BZ. | - |\\n| Source..CA. | - |\\n| Source..CB. | - |\\n| Source..CC. | - |\\n| Source..CD. | - |\\n| Source..CE. | - |\\n| Source..CF. | - |\\n| Source..CG. | - |\\n| Source..CH. | - |\\n| Source..CI. | - |\\n| WC | Word count of the free-text emotional-recall writing response (LIWC output; count of tokens/words) |\\n| Analytic | LIWC “Analytic thinking” summary score computed from the writing response (LIWC output; typically 0–100) |\\n| Clout | LIWC “Clout” summary score computed from the writing response (LIWC output; typically 0–100) |\\n| Authentic | LIWC “Authenticity” summary score computed from the writing response (LIWC output; typically 0–100) |\\n| Tone | LIWC “Tone” summary score computed from the writing response (LIWC output; typically 0–100) |\\n| WPS | Words per sentence in the writing response (LIWC output) |\\n| Sixltr | Percent of words with 6+ letters in the writing response (LIWC output; percent) |\\n| Dic | LIWC dictionary coverage (share/percent of words matched by the LIWC dictionary; LIWC output) |\\n| function. | Percent function words in the writing response (LIWC category output; percent) |\\n| pronoun | Percent pronouns in the writing response (LIWC category output; percent) |\\n| ppron | Percent personal pronouns in the writing response (LIWC category output; percent) |\\n| i | Percent first-person singular pronouns in the writing response (LIWC category output; percent) |\\n| we | Percent first-person plural pronouns in the writing response (LIWC category output; percent) |\\n| you | Percent second-person pronouns in the writing response (LIWC category output; percent) |\\n| shehe | Percent third-person singular pronouns (she/he) in the writing response (LIWC category output; percent) |\\n| they | Percent third-person plural pronouns in the writing response (LIWC category output; percent) |\\n| ipron | Percent impersonal pronouns in the writing response (LIWC category output; percent) |\\n| article | Percent articles (a, an, the) in the writing response (LIWC category output; percent) |\\n| prep | Percent prepositions in the writing response (LIWC category output; percent) |\\n| auxverb | Percent auxiliary verbs in the writing response (LIWC category output; percent) |\\n| adverb | Percent adverbs in the writing response (LIWC category output; percent) |\\n| conj | Percent conjunctions in the writing response (LIWC category output; percent) |\\n| negate | Percent negations in the writing response (LIWC category output; percent) |\\n| verb | Percent verbs in the writing response (LIWC category output; percent) |\\n| adj | Percent adjectives in the writing response (LIWC category output; percent) |\\n| compare | Percent comparative language in the writing response (LIWC category output; percent) |\\n| interrog | Percent interrogatives/question markers in the writing response (LIWC category output; percent) |\\n| number | Percent number words in the writing response (LIWC category output; percent) |\\n| quant | Percent quantifiers in the writing response (LIWC category output; percent) |\\n| affect | Percent affective-process words in the writing response (LIWC category output; percent) |\\n| posemo | Percent positive-emotion words in the writing response (LIWC category output; percent) |\\n| negemo | Percent negative-emotion words in the writing response (LIWC category output; percent) |\\n| anx | Percent anxiety words in the writing response (LIWC category output; percent) |\\n| anger | **Expressed anger in the emotional-recall writing task (LIWC “anger” category): percentage/share of words in the respondent’s open-ended recall paragraph that match the LIWC anger dictionary** (LIWC output; typically reported as a percent of total words, i.e., anger-word share × 100) |\\n| sad | Percent sadness words in the writing response (LIWC category output; percent) |\\n| social | Percent social-process words in the writing response (LIWC category output; percent) |\\n| family.y | Percent family-related words in the writing response (LIWC category output; percent) |\\n| friend | Percent friend-related words in the writing response (LIWC category output; percent) |\\n| female.y | Percent female-referenced words in the writing response (LIWC category output; percent) |\\n| male | Percent male-referenced words in the writing response (LIWC category output; percent) |\\n| cogproc | Percent cognitive-process words in the writing response (LIWC category output; percent) |\\n| insight | Percent insight words in the writing response (LIWC category output; percent) |\\n| cause | Percent causal words in the writing response (LIWC category output; percent) |\\n| discrep | Percent discrepancy words in the writing response (LIWC category output; percent) |\\n| tentat | Percent tentative words in the writing response (LIWC category output; percent) |\\n| certain | Percent certainty words in the writing response (LIWC category output; percent) |\\n| differ | Percent differentiation words in the writing response (LIWC category output; percent) |\\n| percept | Percent perceptual-process words in the writing response (LIWC category output; percent) |\\n| see | Percent “see” words in the writing response (LIWC subcategory output; percent) |\\n| hear | Percent “hear” words in the writing response (LIWC subcategory output; percent) |\\n| feel | Percent “feel” words in the writing response (LIWC subcategory output; percent) |\\n| bio | Percent biological-process words in the writing response (LIWC category output; percent) |\\n| body | Percent body-related words in the writing response (LIWC category output; percent) |\\n| health | Percent health-related words in the writing response (LIWC category output; percent) |\\n| sexual | Percent sexual words in the writing response (LIWC category output; percent) |\\n| ingest | Percent ingestion words in the writing response (LIWC category output; percent) |\\n| drives | Percent drives-related words in the writing response (LIWC category output; percent) |\\n| affiliation | Percent affiliation words in the writing response (LIWC category output; percent) |\\n| achieve | Percent achievement words in the writing response (LIWC category output; percent) |\\n| power | Percent power words in the writing response (LIWC category output; percent) |\\n| reward | Percent reward words in the writing response (LIWC category output; percent) |\\n| risk | Percent risk words in the writing response (LIWC category output; percent) |\\n| focuspast | Percent past-focus words in the writing response (LIWC category output; percent) |\\n| focuspresent | Percent present-focus words in the writing response (LIWC category output; percent) |\\n| focusfuture | Percent future-focus words in the writing response (LIWC category output; percent) |\\n| relativ | Percent relativity words in the writing response (LIWC category output; percent) |\\n| motion | Percent motion words in the writing response (LIWC category output; percent) |\\n| space | Percent space words in the writing response (LIWC category output; percent) |\\n| time | Percent time words in the writing response (LIWC category output; percent) |\\n| work | Percent work-related words in the writing response (LIWC category output; percent) |\\n| leisure | Percent leisure-related words in the writing response (LIWC category output; percent) |\\n| home | Percent home-related words in the writing response (LIWC category output; percent) |\\n| money | Percent money-related words in the writing response (LIWC category output; percent) |\\n| relig | Percent religion-related words in the writing response (LIWC category output; percent) |\\n| death | Percent death-related words in the writing response (LIWC category output; percent) |\\n| informal | Percent informal-language markers in the writing response (LIWC category output; percent) |\\n| swear | Percent swear words in the writing response (LIWC category output; percent) |\\n| netspeak | Percent netspeak/internet shorthand in the writing response (LIWC category output; percent) |\\n| assent | Percent assent words in the writing response (LIWC category output; percent) |\\n| nonflu | Percent nonfluencies in the writing response (LIWC category output; percent) |\\n| filler | Percent filler words in the writing response (LIWC category output; percent) |\\n| AllPunc | Total punctuation count in the writing response (LIWC output; count) |\\n| Period | Period/full-stop count in the writing response (LIWC output; count) |\\n| Comma | Comma count in the writing response (LIWC output; count) |\\n| Colon | Colon count in the writing response (LIWC output; count) |\\n| SemiC | Semicolon count in the writing response (LIWC output; count) |\\n| QMark | Question mark count in the writing response (LIWC output; count) |\\n| Exclam | Exclamation mark count in the writing response (LIWC output; count) |\\n| Dash | Dash count in the writing response (LIWC output; count) |\\n| Quote | Quote mark count in the writing response (LIWC output; count) |\\n| Apostro | Apostrophe count in the writing response (LIWC output; count) |\\n| Parenth | Parentheses count in the writing response (LIWC output; count) |\\n| OtherP | Other punctuation count in the writing response (LIWC output; count) |\\n\\nDataset Context:\\nThis dataset is an individual-level online survey experiment fielded via a commercial survey-panel platform in winter 2020. Respondents first answered pre-prompt background items (e.g., demographics, party identification and ideology, and a short self-monitoring battery). The study then presented respondents with one of two writing prompts: an emotion-focused “recall and describe” prompt (with the target emotion directed at the opposing political party) or a neutral recall prompt (e.g., describing what they ate for breakfast). Prompt wording varies with respondents’ stated partisanship so that the opposing party is referenced appropriately.\\n\\nAfter the writing task, respondents answered a battery of social-interaction and relationship questions involving supporters of the opposing party (neighbors, social gatherings, friendships, dating/marriage-related scenarios, and related comfort/approval items). Many of these items appear both as original survey responses (e.g., Likert-type categories) and as analysis-ready recodes. In particular, several variables are provided in both raw form and as 0–1 rescaled versions with “_01” suffixes.\\n\\nThe file also includes computational text-analysis outputs from the written recall paragraph, generated using Linguistic Inquiry and Word Count (LIWC). These include word count (WC), LIWC summary scores (e.g., Analytic/Clout/Authentic/Tone), and a large set of LIWC dictionary category measures that are typically expressed as the percentage of total words in the respondent’s paragraph that match each category (e.g., affective categories such as anger, anxiety, and positive/negative emotion; pronouns and other function-word categories; topical categories). Additionally, the dataset contains standard survey-platform metadata fields (timestamps, progress/duration, response IDs, location fields) and numerous study-specific flags and merge/export artifacts (e.g., “Source..*” columns, duplicated variable names with “.x/.y” suffixes).\\n\",\n \"columns\": {\n  \"StartDate\": \"str\",\n  \"EndDate\": \"str\",\n  \"Status\": \"str\",\n  \"IPAddress\": \"str\",\n  \"Progress\": \"int64\",\n  \"Duration..in.seconds.\": \"int64\",\n  \"Finished\": \"bool\",\n  \"RecordedDate\": \"str\",\n  \"ResponseId\": \"str\",\n  \"RecipientLastName\": \"float64\",\n  \"RecipientFirstName\": \"float64\",\n  \"RecipientEmail\": \"float64\",\n  \"ExternalReference\": \"float64\",\n  \"LocationLatitude\": \"float64\",\n  \"LocationLongitude\": \"float64\",\n  \"DistributionChannel\": \"str\",\n  \"UserLanguage\": \"str\",\n  \"irb_consent\": \"str\",\n  \"birth_yr\": \"float64\",\n  \"gender\": \"str\",\n  \"race_eth\": \"str\",\n  \"educ\": \"str\",\n  \"pid7\": \"str\",\n  \"ideo7\": \"str\",\n  \"sm1\": \"str\",\n  \"sm2\": \"str\",\n  \"sm3\": \"str\",\n  \"gop_characteristics_1\": \"float64\",\n  \"gop_characteristics_2\": \"float64\",\n  \"gop_characteristics_3\": \"float64\",\n  \"gop_characteristics_4\": \"float64\",\n  \"gop_characteristics_5\": \"float64\",\n  \"gop_characteristics_6\": \"float64\",\n  \"gop_characteristics_7\": \"float64\",\n  \"dem_characteristics_1\": \"float64\",\n  \"dem_characteristics_2\": \"float64\",\n  \"dem_characteristics_3\": \"float64\",\n  \"dem_characteristics_4\": \"float64\",\n  \"dem_characteristics_5\": \"float64\",\n  \"dem_characteristics_6\": \"float64\",\n  \"dem_characteristics_7\": \"float64\",\n  \"emp_characteristics_1\": \"float64\",\n  \"emp_characteristics_2\": \"float64\",\n  \"emp_characteristics_3\": \"float64\",\n  \"emp_characteristics_4\": \"float64\",\n  \"emp_characteristics_5\": \"float64\",\n  \"emp_characteristics_6\": \"float64\",\n  \"trump_approval\": \"float64\",\n  \"house\": \"float64\",\n  \"house_DO\": \"float64\",\n  \"senate\": \"float64\",\n  \"senate_DO\": \"float64\",\n  \"whitehouse\": \"float64\",\n  \"whitehouse_DO\": \"float64\",\n  \"trust_govt\": \"float64\",\n  \"treatment_gop\": \"str\",\n  \"breakfast_gop\": \"str\",\n  \"treatment_dems\": \"str\",\n  \"breakfast_dems\": \"str\",\n  \"fourpack_1\": \"float64\",\n  \"fourpack_2\": \"float64\",\n  \"fourpack_3\": \"float64\",\n  \"fourpack_4\": \"float64\",\n  \"talking\": \"float64\",\n  \"meal\": \"float64\",\n  \"club\": \"float64\",\n  \"date\": \"float64\",\n  \"family_date\": \"float64\",\n  \"friendship\": \"float64\",\n  \"family.x\": \"float64\",\n  \"divorce_gop\": \"float64\",\n  \"friends_comfort\": \"float64\",\n  \"neighbors_comfort\": \"float64\",\n  \"marry_upset\": \"float64\",\n  \"news_share\": \"float64\",\n  \"rid\": \"str\",\n  \"age\": \"float64\",\n  \"gender.1\": \"int64\",\n  \"hhi\": \"int64\",\n  \"ethnicity\": \"int64\",\n  \"hispanic\": \"int64\",\n  \"education\": \"int64\",\n  \"political_party\": \"int64\",\n  \"region\": \"int64\",\n  \"zip\": \"int64\",\n  \"other_party\": \"str\",\n  \"own_party\": \"str\",\n  \"other_party_plural\": \"str\",\n  \"betsy.squestionsforindependents_DO\": \"float64\",\n  \"experimentforrepublicans_DO\": \"str\",\n  \"experimentfordemocrats_DO\": \"str\",\n  \"pid\": \"float64\",\n  \"democrat\": \"float64\",\n  \"female.x\": \"float64\",\n  \"nonwhite\": \"float64\",\n  \"ba_plus\": \"float64\",\n  \"ideology\": \"float64\",\n  \"selfmonitoring1\": \"float64\",\n  \"selfmonitoring2\": \"float64\",\n  \"selfmonitoring3\": \"float64\",\n  \"selfmonitoring\": \"float64\",\n  \"treated\": \"float64\",\n  \"control\": \"float64\",\n  \"fourpack_1_01\": \"float64\",\n  \"fourpack_2_01\": \"float64\",\n  \"fourpack_3_01\": \"float64\",\n  \"fourpack_4_01\": \"float64\",\n  \"talking_01\": \"float64\",\n  \"meal_01\": \"float64\",\n  \"club_01\": \"float64\",\n  \"date_01\": \"float64\",\n  \"family_date_01\": \"float64\",\n  \"friendship_01\": \"float64\",\n  \"family_01\": \"float64\",\n  \"divorce_gop_01\": \"float64\",\n  \"friends_comfort_01\": \"float64\",\n  \"neighbors_comfort_01\": \"float64\",\n  \"marry_upset_01\": \"float64\",\n  \"news_share_01\": \"float64\",\n  \"Source..A.\": \"int64\",\n  \"Source..B.\": \"str\",\n  \"Source..C.\": \"str\",\n  \"Source..D.\": \"str\",\n  \"Source..E.\": \"str\",\n  \"Source..F.\": \"int64\",\n  \"Source..G.\": \"int64\",\n  \"Source..H.\": \"bool\",\n  \"Source..I.\": \"str\",\n  \"Source..K.\": \"float64\",\n  \"Source..L.\": \"float64\",\n  \"Source..M.\": \"float64\",\n  \"Source..N.\": \"float64\",\n  \"Source..O.\": \"float64\",\n  \"Source..P.\": \"float64\",\n  \"Source..Q.\": \"float64\",\n  \"Source..R.\": \"str\",\n  \"Source..S.\": \"str\",\n  \"Source..T.\": \"str\",\n  \"Source..U.\": \"float64\",\n  \"Source..V.\": \"str\",\n  \"Source..W.\": \"str\",\n  \"Source..X.\": \"str\",\n  \"Source..Y.\": \"str\",\n  \"Source..Z.\": \"str\",\n  \"Source..AA.\": \"str\",\n  \"Source..AB.\": \"str\",\n  \"Source..AC.\": \"str\",\n  \"Source..AD.\": \"float64\",\n  \"Source..AE.\": \"float64\",\n  \"Source..AF.\": \"float64\",\n  \"Source..AG.\": \"float64\",\n  \"Source..AH.\": \"float64\",\n  \"Source..AI.\": \"float64\",\n  \"Source..AJ.\": \"float64\",\n  \"Source..AK.\": \"float64\",\n  \"Source..AL.\": \"float64\",\n  \"Source..AM.\": \"float64\",\n  \"Source..AN.\": \"float64\",\n  \"Source..AO.\": \"float64\",\n  \"Source..AP.\": \"float64\",\n  \"Source..AQ.\": \"float64\",\n  \"Source..AR.\": \"float64\",\n  \"Source..AS.\": \"float64\",\n  \"Source..AT.\": \"float64\",\n  \"Source..AU.\": \"float64\",\n  \"Source..AV.\": \"float64\",\n  \"Source..AW.\": \"float64\",\n  \"Source..AX.\": \"float64\",\n  \"Source..AY.\": \"float64\",\n  \"Source..AZ.\": \"float64\",\n  \"Source..BA.\": \"float64\",\n  \"Source..BB.\": \"float64\",\n  \"Source..BC.\": \"str\",\n  \"Source..BD.\": \"str\",\n  \"Source..BE.\": \"str\",\n  \"Source..BF.\": \"str\",\n  \"Source..BG.\": \"str\",\n  \"Source..BH.\": \"str\",\n  \"Source..BI.\": \"str\",\n  \"Source..BJ.\": \"str\",\n  \"Source..BK.\": \"str\",\n  \"Source..BL.\": \"str\",\n  \"Source..BM.\": \"str\",\n  \"Source..BN.\": \"str\",\n  \"Source..BO.\": \"str\",\n  \"Source..BP.\": \"str\",\n  \"Source..BQ.\": \"str\",\n  \"Source..BR.\": \"str\",\n  \"Source..BS.\": \"str\",\n  \"Source..BT.\": \"str\",\n  \"Source..BU.\": \"str\",\n  \"Source..BV.\": \"str\",\n  \"Source..BW.\": \"str\",\n  \"Source..BX.\": \"int64\",\n  \"Source..BY.\": \"int64\",\n  \"Source..BZ.\": \"int64\",\n  \"Source..CA.\": \"int64\",\n  \"Source..CB.\": \"int64\",\n  \"Source..CC.\": \"int64\",\n  \"Source..CD.\": \"int64\",\n  \"Source..CE.\": \"int64\",\n  \"Source..CF.\": \"int64\",\n  \"Source..CG.\": \"str\",\n  \"Source..CH.\": \"str\",\n  \"Source..CI.\": \"str\",\n  \"WC\": \"int64\",\n  \"Analytic\": \"float64\",\n  \"Clout\": \"float64\",\n  \"Authentic\": \"float64\",\n  \"Tone\": \"float64\",\n  \"WPS\": \"float64\",\n  \"Sixltr\": \"float64\",\n  \"Dic\": \"float64\",\n  \"function.\": \"float64\",\n  \"pronoun\": \"float64\",\n  \"ppron\": \"float64\",\n  \"i\": \"float64\",\n  \"we\": \"float64\",\n  \"you\": \"float64\",\n  \"shehe\": \"float64\",\n  \"they\": \"float64\",\n  \"ipron\": \"float64\",\n  \"article\": \"float64\",\n  \"prep\": \"float64\",\n  \"auxverb\": \"float64\",\n  \"adverb\": \"float64\",\n  \"conj\": \"float64\",\n  \"negate\": \"float64\",\n  \"verb\": \"float64\",\n  \"adj\": \"float64\",\n  \"compare\": \"float64\",\n  \"interrog\": \"float64\",\n  \"number\": \"float64\",\n  \"quant\": \"float64\",\n  \"affect\": \"float64\",\n  \"posemo\": \"float64\",\n  \"negemo\": \"float64\",\n  \"anx\": \"float64\",\n  \"anger\": \"float64\",\n  \"sad\": \"float64\",\n  \"social\": \"float64\",\n  \"family.y\": \"float64\",\n  \"friend\": \"float64\",\n  \"female.y\": \"float64\",\n  \"male\": \"float64\",\n  \"cogproc\": \"float64\",\n  \"insight\": \"float64\",\n  \"cause\": \"float64\",\n  \"discrep\": \"float64\",\n  \"tentat\": \"float64\",\n  \"certain\": \"float64\",\n  \"differ\": \"float64\",\n  \"percept\": \"float64\",\n  \"see\": \"float64\",\n  \"hear\": \"float64\",\n  \"feel\": \"float64\",\n  \"bio\": \"float64\",\n  \"body\": \"float64\",\n  \"health\": \"float64\",\n  \"sexual\": \"float64\",\n  \"ingest\": \"float64\",\n  \"drives\": \"float64\",\n  \"affiliation\": \"float64\",\n  \"achieve\": \"float64\",\n  \"power\": \"float64\",\n  \"reward\": \"float64\",\n  \"risk\": \"float64\",\n  \"focuspast\": \"float64\",\n  \"focuspresent\": \"float64\",\n  \"focusfuture\": \"float64\",\n  \"relativ\": \"float64\",\n  \"motion\": \"float64\",\n  \"space\": \"float64\",\n  \"time\": \"float64\",\n  \"work\": \"float64\",\n  \"leisure\": \"float64\",\n  \"home\": \"float64\",\n  \"money\": \"float64\",\n  \"relig\": \"float64\",\n  \"death\": \"float64\",\n  \"informal\": \"float64\",\n  \"swear\": \"float64\",\n  \"netspeak\": \"float64\",\n  \"assent\": \"float64\",\n  \"nonflu\": \"float64\",\n  \"filler\": \"float64\",\n  \"AllPunc\": \"float64\",\n  \"Period\": \"float64\",\n  \"Comma\": \"float64\",\n  \"Colon\": \"float64\",\n  \"SemiC\": \"float64\",\n  \"QMark\": \"float64\",\n  \"Exclam\": \"float64\",\n  \"Dash\": \"float64\",\n  \"Quote\": \"float64\",\n  \"Apostro\": \"float64\",\n  \"Parenth\": \"float64\",\n  \"OtherP\": \"float64\"\n }\n}\n{\n \"id\": \"ref_iv\",\n \"rows\": 3410,\n \"description\": \"Complete rows of fourpack_1_01, anger, treated and democrat\",\n \"columns\": {\n  \"fourpack_1_01\": \"float64\",\n  \"anger\": \"float64\",\n  \"treated\": \"float64\",\n  \"democrat\": \"float64\"\n }\n}\n\n# Question\nWhat is the causal effect of an increase in a respondent's expressed political anger (measured by the share of angry words they produced in the emotional-recall text) on their willingness to engage in an out-party social interaction (the 0-1 social-polarization index fourpack_1_01), focusing on respondents whose expressed anger responded to the study's emotion induction?","response":"IV2SLS.from_formula('fourpack_1_01 ~ 1 + democrat + [anger ~ treated]').fit(cov_type='robust')\ninfer__iv_2sls({\"dataset\": \"ref_iv\", \"y\": \"fourpack_1_01\", \"endog\": [\"anger\"], \"instruments\": [\"treated\"], \"exog\": [\"democrat\"], \"se\": \"HC1\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_5c209365","text":"IV2SLS.from_formula('fourpack_1_01 ~ 1 + democrat + [anger ~ treated]').fit(cov_type='robust')","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"ref_iv","y":"fourpack_1_01","endog":["anger"],"instruments":["treated"],"exog":["democrat"],"se":"HC1"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"d578cba746c23858c5bd908b0fa381d90f145ea03054a06ef3adf1184018ea91","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["23014cd0de5418264b2cec43347c898f2f7e677b4b2c445f24b109389011b411"],"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":0.7040409836918116}}
{"call_id":"call_a5e7f955","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q082/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ref_iv","y":"fourpack_1_01","endog":["anger"],"instruments":["treated"],"exog":["democrat"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"5013b1d7e1bca1f858f8bf1bda01146f1e3a268c8e012b478f2bd6c23f5b6217","dataset_digests":{"ref_iv":"9eb981d171957de3cf90ecab15ace89b0d6df00d45b53f10c68035b0ec2ff23f"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_a5e7f955","status":"ok","reason":"","figures":[{"key":"coef.anger","value":0.010848449162245937,"unit":"","label":"2SLS effect of anger on fourpack_1_01","se":0.0038574338400275453,"ci_low":0.003285330919539163,"ci_high":0.01841156740495271,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de treated na forma reduzida (fourpack_1_01 sobre treated e os controles) e no primeiro estágio (anger sobre treated e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":0.03101012188208508,"unit":"","note":"forma reduzida"},{"symbol":"π","value":2.858484325114782,"unit":"","note":"primeiro estágio"}],"result":0.010848449162245937,"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":0.000014879795830189654,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.0038574338400275453,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 3407 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.010848449162245937,"unit":"","note":""},{"symbol":"c","value":1.960660521050639,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.0038574338400275453,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.anger","value":773.5894300442613,"flag":false,"note":"Wald F of the excluded instruments in the first stage for anger (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.anger","value":0.19052930712420246,"flag":false,"note":"share of the variation the instruments explain beyond the controls"},{"name":"weak_instrument","value":false,"flag":false,"note":"every first-stage F is at least 10"},{"name":"n_obs","value":3410,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.3135950405022685,"se":0.009827553605714101,"t":31.909776642676647,"p":9.952012780872178e-196,"ci_low":0.294326544129036,"ci_high":0.33286353687550096},{"term":"democrat","coef":0.11613008052463146,"se":0.011031301253926401,"t":10.52732382621651,"p":1.5870173965052651e-25,"ci_low":0.09450144366024156,"ci_high":0.13775871738902137},{"term":"anger","coef":0.010848449162245937,"se":0.0038574338400275453,"t":2.8123487303073147,"p":0.004946268698528295,"ci_low":0.003285330919539163,"ci_high":0.01841156740495271}],"first_stage":[{"endog":"anger","instrument":"treated","coef":2.858484325114782,"se":0.10277335910173759,"t":27.81347569154674,"p":1.3162660029490694e-153,"ci_low":2.6569806573082446,"ci_high":3.0599879929213194}],"reduced_form":[{"instrument":"treated","coef":0.03101012188208508,"se":0.010974101389733395,"t":2.8257550008692274,"p":0.0047444034563073785,"ci_low":0.009493634533227862,"ci_high":0.052526609230942295}]},"datasets":{},"warnings":[],"assumptions":["the instruments affect the outcome only through the endogenous regressors (exclusion restriction)","the instruments are as good as randomly assigned given the controls","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"bd8867f88d783e2d8404d4e5aa45b155fb94414338041ca4f0bfd17db0f2ba8b","params_versions":[],"sources":[],"started_at":"2026-10-15T17:09:31.169297Z","duration_ms":4.565707873553038,"error":null,"decided_by":"model","rationale":"IV2SLS.from_formula('fourpack_1_01 ~ 1 + democrat + [anger ~ treated]').fit(cov_type='robust')","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_5c209365","llm_name":"infer__iv_2sls","vendor_id":"script_1","result_for_model_ref":"2afeb75cbbf5bde9cca379db580ffad7487eb47ac37b39f1b432aab49644a207"}}
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