{"findings":[{"id":"stress-mig","title":"The 'AI exodus' is mostly the partisan and telework geography of migration","claim":"In 2020-22, one SD more AI exposure meant -0.86 points of households a year within the same state (t = -13.6). With density, education, income, housing costs, January temperature, the 2016 vote and telework held equal it is -0.11 (t = -2.2): 88% of the gradient goes. Year by year the controlled AI coefficient stays small and flat while the unadjusted one grows. The strongest predictor in the full model is the county's 2016 Republican vote share (+0.74, t = 15.4); the coefficient on the 2016 vote grew from +0.33 (2013-16) to +0.74 (2020-22).","theme":"Stress tests","level":"county","datasets":["IRS SOI migration","AIOE (AIGE)","County context","Dingel & Neiman telework"],"strength":"robust","stats":{"coef_base":-0.856,"t_base":-13.588,"coef_full":-0.10646,"t_full":-2.2467,"attenuation":0.87563,"rep16_2013_16":0.32842,"rep16_2020_22":0.73517,"telework_2020_22":-0.29831,"n":9245},"question":"Once AI exposure is read as a proxy for dense, Democratic, telework-intensive counties, the open question is the political one: do movers carry their workplace politics into destination firms?","next_data":"Movers' occupations and party (ACS migration microdata; L2 voter-file moves); county-pair flows across state borders","caveats":["County context is a 2019 cross-section (ACS 2019, CBP); telework comes from the county's industry mix, not its occupations.","The 2016 vote is fixed before the flows it predicts, but it stands for everything that differs between red and blue counties."],"views":["stress-mig-models","stress-mig-years"],"rank":1},{"id":"stress-occ","title":"AI exposure's Democratic lean is composition; automation risk's Republican lean is not","claim":"Generative-AI exposure (DAIOE) leans Democratic on its own (-2.0 points per SD, t = -3.3, 825 occupations), but the lean disappears once gender and race are held equal (-0.1, t = -0.1) and stays near zero with occupation-group effects (+0.1, t = 0.1). Computerisation risk (Frey & Osborne) goes the other way: +0.9 (t = 1.2) alone, +2.4 (t = 4.0) with education, pay, telework, gender, race and occupation group held equal. Measures whose sign flips and becomes clear (|t| of 2 or more) with every control: AIOE (Felten, Raj & Seamans).","theme":"Stress tests","level":"occupation","datasets":["VRscores","DAIOE (SOC 2018 panel)","AIOE inputs (CPS demographics)","Dingel & Neiman telework"],"strength":"robust","stats":{"genai_raw":-1.973,"genai_raw_t":-3.2672,"genai_demog":-0.068192,"genai_full":0.13246,"genai_full_t":0.10421,"comp_raw":0.87954,"comp_full":2.3671,"comp_full_t":3.9982,"n_full":293},"question":"If exposure's partisan face is who holds the jobs (educated women, teleworkers), does political conflict over AI inside firms run along occupational and gender lines rather than party lines?","next_data":"Worker-level panels with party, occupation and AI use (Cooperative Election Study; Gallup Workforce Panel); occupation x metro partisanship to hold geography equal","caveats":["Gender and race shares exist for about 300 occupations, so the last two models use a smaller sample.","Occupation partisanship is national; occupations concentrated in Republican regions look Republican for that reason alone."],"views":["stress-occ-models"],"rank":2},{"id":"stress-structure","title":"Most of an industry's partisanship, and half of its political balance, is the occupations it employs","claim":"Across 178 industries, the Republican share predicted from national occupation shares and each industry's staffing pattern explains 72% of the variance in the actual share (r = 0.85, weighted by workers) and 54% of the variance in political balance (how evenly split the workforce is; r = 0.74). Workforce 'ideology' and 'balance' measured at the organisation level therefore mix the occupational structure of the work with any organisational sorting.","theme":"Stress tests","level":"industry","datasets":["VRscores","OES staffing"],"strength":"robust","stats":{"n":178,"r2_share":0.71896,"r2_balance":0.54214},"question":"Does the part of workforce partisanship and balance that occupations do not explain (the elective part) predict innovation, where the structural part does not?","next_data":"Employer x occupation headcounts (Revelio positions behind VRscores) matched to firms; DIPI leadership ideology","caveats":["National occupation partisanship is applied to every industry, so the prediction ignores where industries are located."],"views":["stress-structure"],"rank":3},{"id":"mig-ai-exodus","title":"Households are leaving AI-exposed counties, but the gradient is mostly density, politics and telework","claim":"Across 3,130 counties, the correlation between AI exposure (AIGE) and net domestic migration went from -0.19 (2012-13) to -0.51 (2020-21), weighted by households. The most exposed fifth of counties lost 0.16% of households a year at the start and 0.80% at the worst. Comparing counties within the same state, one SD more exposure means -0.16 (t = -3.9) points a year in 2013-16, -0.56 (t = -11.1) in 2017-19 and -0.86 (t = -13.6) in 2020-22. Biggest losers 2020-22 among large counties: San Francisco County, CA (-3.9%); Suffolk County, MA (-3.7%); New York County, NY (-3.7%); Bronx County, NY (-3.4%); Kings County, NY (-2.7%). Exposed counties still gaining: Collin County, TX (+2.4%); Chester County, PA (+0.6%); Polk County, IA (+0.3%).","theme":"Migration x AI","level":"county","datasets":["IRS SOI migration","AIOE (AIGE)"],"strength":"fragile","stats":{"r_first":-0.1882,"r_peak":-0.51044,"fe_2013-16":-0.16417,"fe_2017-19":-0.56323,"fe_2020-22":-0.86409},"question":"Is this remote work and housing costs pulling knowledge workers out of dense cores, and does it move AI-exposed work, and its politics, into less exposed and more Republican places?","next_data":"County votes (MIT) for the partisan side; ACS remote-work shares and housing costs; movers' occupations (ACS migration microdata)","caveats":["AIGE describes the county's 2019 jobs, not the movers themselves.","IRS counts tax returns (households); year = the second year of each filing pair.","Stress test (stress-mig): with county controls the 2020-22 gradient falls from -0.86 to -0.11 points per SD; AIGE mostly stands for dense, Democratic, telework-intensive counties."],"views":["mig-quintiles","mig-aige-models","mig-aige-corr","county-migration-map","county-migration-explorer"],"rank":4},{"id":"occ-waves","title":"AI exposure measures disagree about who is exposed: benchmark and language-model measures lean Democratic, patent and automation measures Republican","claim":"Across 825 occupations (weighted by workers), the correlation with the Republican share of workers runs from -0.18 (DAIOE, generative AI) to +0.23 (AI patents (Webb)). Weighting occupations equally, the benchmark and GPT measures sit near -0.32, patent and automation measures near +0.25. Holding education and wage equal, measures with a clear Republican lean (|t| of 2 or more): Computerisation risk (Frey & Osborne); clear Democratic lean: DAIOE, generative AI.","theme":"Political ideology x AI","level":"occupation","datasets":["VRscores","DAIOE (SOC 2018 panel)","AIOE"],"strength":"fragile","stats":{"n":825,"r_min":-0.18345,"r_max":0.23358,"exp_cumul_genai":-0.183,"exp_cumul":-0.161,"open24_human_E1":-0.129,"frs21_aioe":-0.037,"open24_gpt_automation":-0.014,"open24_human_E1_E2":-0.01,"webb19_robot_score":0.063,"fo17_p_computerisation":0.099,"webb19_software_score":0.145,"webb19_ai_score":0.234},"question":"Which measure predicts what actually happens to workers (employment, wages, hours) since 2022, and do exposed occupations shift politically, as robot exposure did after 2016?","next_data":"VRscores occupation panel by year; OEWS employment and wages 2019-2025; CPS occupation panels","caveats":["VRscores occupations are linked to SOC 2018 by title, then code; 12% of workers are not linked.","Webb's and Frey-Osborne's scores come from older SOC versions mapped in the DAIOE panel.","Stress test (stress-occ): the Democratic lean of generative-AI and language-model measures disappears once gender and race are held equal; the Republican lean of computerisation risk survives every control."],"views":["occ-waves","occ-waves-models"],"rank":5},{"id":"mig-partisan-state","title":"Interstate movers go to more Republican states, and the gap widened after 2019","claim":"Weighted by households, the average interstate move ends in a state whose last presidential vote was +0.8 points more Republican than the origin in 2013, +2.5 at the peak (2021) and +2.2 in 2022; weighted by the income moved, +1.4 to +4.2. Moves from 2017 on hold the map at the 2016 election, so the widening comes from where people moved, not from how states voted.","theme":"Migration x AI","level":"state","datasets":["IRS SOI migration","CSPP"],"strength":"robust","stats":{"shift_first":0.80516,"shift_max":2.5277,"shift_agi_max":4.2375},"question":"Is partisan re-sorting by migration large enough to move state electorates, and is it driven by policy, housing or jobs?","next_data":"IRS state-to-state files (all interstate moves); county votes for the within-state picture; movers' ages","caveats":["CSPP's presidential share is the state's result in the last election before each year (through the 2016 election).","Flows are county-to-county pairs summed to states (about half of interstate moves)."],"views":["mig-partisan-state","mig-network"],"rank":6},{"id":"occ-ai-education","title":"AI exposure looks Democratic across occupations, but the link runs through education","claim":"Across 698 occupations, more AI-exposed ones have more Democratic workforces (r = -0.30); weighted by workers the link nearly vanishes (r = -0.03, 95% CI -0.15 to +0.08). With wage and education held equal, exposure's coefficient is -0.2 points per SD (t = -0.3); adding gender, race and occupation group gives +3.3 (t = +2.8).","theme":"Political ideology x AI","level":"occupation","datasets":["VRscores","AIOE"],"strength":"robust","stats":{"n":698,"r_unweighted":-0.29528,"r_weighted":-0.031408,"ci_weighted":[-0.14698,0.083232],"aioe_with_wage_edu_pp":-0.21939,"aioe_full_pp":3.306,"n_full":292},"question":"Will generative AI's labor-market effects land on educated, Democratic-leaning workers, and does exposure change political attitudes or sorting?","next_data":"Dynamic AIOE (exposure after 2022), VRscores occupation panel by year, worker panels (CPS, CES) with party","caveats":["Demographic controls exist for fewer occupations, so the full model has a smaller sample.","VRscores partisanship is party identity from voter files, not ideology.","Stress test (stress-occ): gender and race account for the rest of the lean."],"views":["occ-explorer","occ-models","occ-corr"],"rank":7},{"id":"state-environment","title":"Households left states with liberal economic policy and AI-exposed jobs; at the state level the two are hard to separate","claim":"Across states, net interstate migration (IRS, 2012-13 to 2021-22, weighted by households) correlates most negatively with Economic policy liberalism (Caughey and Warshaw) (-0.75); Income per capita ($) (-0.64); Public economic liberalism (Caughey and Warshaw) (-0.63) and most positively with Right-to-work law (1 = yes) (+0.62); Republican share of state legislators (%) (+0.61). Entered together, economic policy liberalism gives -0.42 (t = -5.5) points a year per SD and AIGE -0.05 (t = -0.5): the two move together across states. Within states, where policy is constant, county exposure still predicts out-migration (see the county finding).","theme":"Policy x AI","level":"state","datasets":["CSPP","IRS SOI migration","AIOE (AIGE)"],"strength":"suggestive","stats":{"env_policyeconlib_est":-0.75,"env_incomepcap":-0.637,"env_masseconlib_est":-0.631,"env_policysociallib_est":-0.631,"env_inst6014_nom":-0.601,"env_x_top_corporateincometaxrate":-0.565,"env_ranney4_control":-0.562,"aige":-0.544,"env_hincomemed":-0.511,"env_perc_college":-0.433,"env_masssociallib_est":-0.432,"env_unemployment":-0.174,"env_propgoppres":0.584,"env_propgopleg":0.611,"env_grtw":0.619},"question":"Which part is policy (taxes, housing regulation, labour law) and which is the kind of work a state does? Border-county comparisons across state lines can separate them.","next_data":"County-pair flows across state borders; state housing regulation (Wharton index); state AI legislation (NCSL)","caveats":["CSPP political variables mostly end in 2014-2017; the profile averages 2012-2016.","51 states: correlations, not causal estimates."],"views":["state-env-corr","state-env-explorer","state-env-models"],"rank":8},{"id":"mig-income","title":"AI-exposed counties lose richer households than they gain","claim":"In the most exposed fifth of counties, households moving out report more income than those moving in every year: -3.6 thousand dollars of AGI per return in 2013 and -7.5 in 2022. In the least exposed fifth the gap is +0.6 and +16.1. The correlation of exposure with the income gap is -0.36 in 2022.","theme":"Migration x AI","level":"county","datasets":["IRS SOI migration","AIOE (AIGE)"],"strength":"descriptive","stats":{"gap_exposed_first":-3.5861,"gap_exposed_last":-7.5204,"gap_least_first":0.56572,"gap_least_last":16.081},"question":"Do high earners in AI-exposed jobs carry their (remote) work with them, spreading AI-exposed employment to new places?","next_data":"IRS migration by income class (gross migration files); LinkedIn or Revelio worker moves","caveats":["AIGE stands for dense, Democratic, telework-intensive counties (see stress-mig); read as a description, not an AI effect."],"views":["mig-income-gap","county-migration-map"],"rank":9},{"id":"occ-education-vs-pay","title":"Education pulls occupations Democratic and pay pulls them Republican; the pay effect runs through who does the job","claim":"With both in the model, one SD more required education goes with -8.1 points Republican share (t = -9.3) and one SD higher pay with +6.1 (t = +7.6), 614 occupations. Adding gender and race shares and occupation group leaves education at -4.6 (t = -3.0) and pay at +0.6 (t = +0.5); the female share is the strongest single predictor (-7.0 points per SD, t = -7.0).","theme":"Political ideology x AI","level":"occupation","datasets":["VRscores","AIOE inputs (O*NET, OES wages, CPS demographics)"],"strength":"robust","stats":{"n_wage_edu":614,"n_full":292,"r2_full":0.67591},"question":"Is AI's partisan footprint just the education realignment, or does exposure add something within education levels?","next_data":"Worker-level data with education, occupation and party","caveats":[],"views":["occ-models"],"rank":10},{"id":"sorting","title":"Partisans are sorting into different employers while sorting across industries and occupations declines","claim":"Same-party over-exposure (colleagues share one's party more often than by chance), first to last year: employer: 8.43% -> 8.71%; industry: 2.74% -> 2.54%; metro: 7.36% -> 7.46%; occupation: 3.34% -> 3.08%. Rising: employer, metro; falling: industry, occupation. Employer sorting rose +0.28 points, metro sorting +0.10: most of the growth in workplace segregation happens between firms within the same kinds of jobs, not across occupations or industries.","theme":"Organizations","level":"employer","datasets":["VRscores"],"strength":"suggestive","stats":{"employer":{"first":1.0843,"last":1.0871},"industry":{"first":1.0274,"last":1.0254},"metro":{"first":1.0736,"last":1.0746},"occupation":{"first":1.0334,"last":1.0308}},"question":"Which organisational practices (hiring networks, location choice, mission statements, leadership politics) produce between-firm sorting?","next_data":"Firm-level leadership politics (DIPI), hiring sources, location histories","caveats":[],"views":["sorting"],"rank":11},{"id":"ind-ai-partisanship","title":"AI-exposed industries have more Democratic workforces, mostly because their workers are highly educated; language-model exposure keeps a modest link within sectors","claim":"Across 246 four-digit industries, AI exposure correlates -0.34 with the Republican share of the matched workforce (weighted; 95% CI -0.53 to -0.16). Language-model exposure is stronger (r = -0.43). Exposure tracks education closely, and with education and wages held equal the coefficient falls to -0.9 points per SD (t = -1.2); within sectors it is -1.7 (t = -1.9) and -2.5 for language-model exposure (t = -2.2).","theme":"Political ideology x AI","level":"industry","datasets":["VRscores","AIOE (AIIE)","OES staffing"],"strength":"fragile","stats":{"n":246,"r_weighted":-0.34398,"ci":[-0.52732,-0.16029],"coef_edu_wage_pp":-0.88146,"coef_sector_pp":-1.7332,"corr_aiie_education":0.85723},"question":"Is there an industry-level (firm culture, geography) component of AI exposure's partisan footprint beyond the education of the workforce?","next_data":"Employer-level VRscores linked to NAICS and location; BTOS adoption by industry","caveats":[],"views":["ind-explorer","ind-models"],"rank":12},{"id":"emp-bigtech","title":"AI and big-tech workforces barely joined the Democratic drift, and several moved Republican","claim":"The 34 large AI and big-tech employers (identified by name) changed -1.1 points on average, against -4.1 for all others. Moving Republican: Snowflake, Inc. (+11.9); Broadcom Inc. (+7.0); Uber Technologies, Inc. (+4.7); Amazon Web Services, Inc. (+4.4); Tesla, Inc. (+3.8); Palantir Technologies, Inc. (+3.3).","theme":"Political ideology x AI","level":"employer","datasets":["VRscores"],"strength":"suggestive","stats":{"n_bigtech":34,"change_bigtech_pp":-1.0788,"change_other_pp":-4.1267},"question":"Did AI and cloud firms' expansion (data centres, new hubs in Texas, Virginia, Utah) or the 2022-24 layoffs shift their workforces' politics?","next_data":"Employer x metro x occupation headcounts; data-centre locations; layoff notices (WARN)","caveats":[],"views":["emp-explorer","emp-groups"],"rank":13},{"id":"metro-regime","title":"Where party is inferred rather than registered, metro workforces drift Democratic much faster","claim":"Across 359 metros, 87% moved towards the Democrats between 2012 and 2024. Metros in states where party comes from primaries or L2's model drifted 1.8 points further than metros in registration states, holding the starting level and size equal (t = -5.6). The drift is a trait of the measurement regime, not only of places.","theme":"Measurement","level":"metro","datasets":["VRscores"],"strength":"robust","stats":{"n":359,"effect_pp":-1.7919,"t":-5.5949},"question":"How much of VRscores' Democratic drift is cohort replacement, and how much is the inference model aging with voters?","next_data":"VRscores by age cohort and regime; registration-state benchmarks","caveats":[],"views":["metro-explorer","metro-traj"],"rank":14},{"id":"ind-composition","title":"Occupational mix explains most of an industry's partisanship; the rest points to place and mission","claim":"Predicting each industry's Republican share from national occupation shares and its staffing pattern gives r = +0.85 with the actual share (178 industries with 70%+ of jobs linked). The remaining gap has an SD of 4.2 points (actual: 7.3). Most Democratic relative to their occupations: Footwear Manufacturing, Local Messengers and Local Delivery, Museums, Historical Sites, and Similar Institutions; most Republican: Merchant Wholesalers, Durable Goods, Other Wood Product Manufacturing, Rental and Leasing Services. The gap is unrelated to AI exposure once education is held equal (t = -0.1).","theme":"Organizations","level":"industry","datasets":["VRscores","OES staffing","AIOE"],"strength":"robust","stats":{"r_pred_actual":0.84792,"sd_gap_pp":4.2272,"sd_actual_pp":7.2885},"question":"How much of the residual is geography (where the industry sits) versus organisational culture or mission?","next_data":"Industry x metro employment (QCEW) with metro partisanship; employer-level data","caveats":[],"views":["ind-gap","ind-explorer"],"rank":15},{"id":"occ-two-ais","title":"Two kinds of AI exposure: language-AI jobs lean Democratic, perception-AI jobs Republican, until you control for education","claim":"Beyond overall exposure, occupations differ in which AI touches them: the second principal component contrasts image recognition and games with language modelling, translation and reading. It correlates +0.22 with Republican share (weighted), but with wage, education, gender, race and occupation group it is -0.6 points per SD (t = -0.7).","theme":"Political ideology x AI","level":"occupation","datasets":["AIOE inputs (O*NET abilities)","VRscores"],"strength":"fragile","stats":{"r_weighted":0.22158,"coef_controls_pp":-0.61059,"t":-0.74836},"question":"Does the generative-AI wave (language) shift the politics of exposure towards Democratic-leaning professions compared with automation and vision AI?","next_data":"Exposure measures by AI generation (Dynamic AIOE), task-level exposure","caveats":[],"views":["occ-pca","occ-explorer"],"rank":16},{"id":"metro-space","title":"Partisan drift clusters in space, along state lines","claim":"Metro drift is spatially autocorrelated (Moran's I = 0.24, permutation p = 0.001; level: I = 0.37). Clusters moving least towards the Democrats (or towards the Republicans): Rome GA MSA (+7.0); Dalton GA MSA (+5.2); Gainesville GA MSA (+4.8); Cleveland TN MSA (+2.4); Brunswick GA MSA (+5.8). Clusters moving most: Columbia MO MSA (-13.2); Springfield MO MSA (-10.1); Jefferson City MO MSA (-7.2); Grand Rapids-Wyoming MI MSA (-8.0); Lansing-East Lansing MI MSA (-6.8). Clusters line up with states' party-data regimes.","theme":"Geography","level":"metro","datasets":["VRscores"],"strength":"robust","stats":{"moran_drift":0.23554,"p":0.001,"moran_level":0.36943},"question":"Are the Georgia and Missouri clusters real political change or artefacts of primary-based and modelled party?","next_data":"Party registration records for the same voters; primary crossover rates","caveats":[],"views":["metro-map","metro-explorer"],"rank":17},{"id":"state-aige","title":"AI-exposed states have more Democratic workforces, and VRscores covers them more densely","claim":"State AI exposure (AIGE) correlates -0.63 with the Republican share of the matched metro workforce in 2024 (51 states, weighted by jobs). The same states' Republican vote share correlates -0.72 with AIGE. Coverage (matched workers per job) correlates +0.60 with AIGE: the data lean towards AI-exposed labour markets.","theme":"Political ideology x AI","level":"state","datasets":["VRscores","AIOE (AIGE)","QCEW"],"strength":"suggestive","stats":{"n":51,"r_aige_rep":-0.62541,"r_aige_coverage":0.60164},"question":"Is the partisan gap in AI exposure a feature of places or of the people the data happen to cover?","next_data":"County-level partisanship (votes) with AIGE; coverage weights for VRscores","caveats":[],"views":["state-map"],"rank":18},{"id":"occ-daioe","title":"Dynamic exposure mostly rises for everyone; where it rose most, workforces are more Democratic","claim":"DAIOE grows about 51-fold between 2012 and 2024 for every occupation alike (levels in 2010 and 2024 correlate 0.98), so the change is mostly a shared trend. Relative standing moved a little: occupations that gained standing since 2012 have more Democratic workforces (r = -0.26, 95% CI -0.44 to -0.09, weighted by workers), led by Telemarketers, Technical Writers, Historians, Door-to-Door Sales Workers, Sales and Related Workers. The correlation of standing with the Republican share drifted from -0.12 (2010) to -0.17 (2024).","theme":"Political ideology x AI","level":"occupation","datasets":["VRscores","DAIOE (SOC 2010)"],"strength":"descriptive","stats":{"r_rise":-0.26054,"ci":[-0.43727,-0.086611],"levels_corr":0.97673,"r_first":-0.11668,"r_last":-0.17473},"question":"Does a rise in an occupation's exposure precede changes in who works in it (entry by younger, more Democratic cohorts)?","next_data":"VRscores occupation panel by year and age; OEWS by year","caveats":["Partisanship is measured once (2024), so this is a cross-section of changes, not a panel."],"views":["daioe-trend","daioe-explorer"],"rank":19},{"id":"mig-exposure-gap","title":"Movers trade down in AI exposure, and more so since 2020","claim":"Weighted by households, the average county-to-county move ends in a county 0.020 SD of AIGE less exposed than its origin in 2013 and 0.073 SD in 2022 (largest: 0.096 in 2021); weighted by income moved it is 0.204 at its largest.","theme":"Migration x AI","level":"county","datasets":["IRS SOI migration","AIOE (AIGE)"],"strength":"descriptive","stats":{"gap_first":-0.019662,"gap_last":-0.07342,"gap_min":-0.096124},"question":"Does the down-trading in exposure change the local AI adoption and politics of destination counties?","next_data":"BTOS AI use by state and MSA; county votes","caveats":["County pairs with fewer than 20 returns are suppressed by the IRS, so small flows are missing.","AIGE stands for dense, Democratic, telework-intensive counties (see stress-mig); read as a description, not an AI effect."],"views":["mig-exposure-gap"],"rank":20},{"id":"state-regime","title":"States where party is inferred rather than registered drift Democratic faster","claim":"Holding the starting level equal, states whose party is inferred (primary participation or L2's model) drifted 1.9 points further towards the Democrats between 2012 and 2024 (t = -3.9, 51 states).","theme":"Measurement","level":"state","datasets":["VRscores"],"strength":"suggestive","stats":{"t":-3.9437,"n":51},"question":"Validate inferred party against registration in states that switched regime, or against survey self-reports.","next_data":"L2 modelled-party accuracy by age; CES validated vote/party","caveats":[],"views":["state-map"],"rank":21},{"id":"ind-drift","title":"Nearly every industry drifted Democratic, and AI exposure does not predict by how much","claim":"98% of 246 industries moved towards the Democrats between the first and last year (weighted mean -3.6 points). Exposure does not predict the size of the move (t = -0.6); industries that started more Republican moved more (t = -2.0), consistent with party being fixed at 2024 while younger cohorts enter.","theme":"Measurement","level":"industry","datasets":["VRscores","AIOE"],"strength":"robust","stats":{"n":246,"share_moving_dem":0.97561},"question":"Separate cohort replacement from real change: does drift vanish once workforce age is held constant?","next_data":"VRscores by worker age cohort; a second voter-file vintage","caveats":[],"views":["ind-explorer"],"rank":22},{"id":"emp-drift","title":"Large employers drift Democratic eight times as often as Republican; the most Republican and the largest drift most","claim":"Of 6,000 large employers present every year, 2,127 moved 5+ points towards the Democrats and 269 towards the Republicans (ratio 7.9 to 1). Drift is larger for more Republican employers (t = -13.4) and for larger ones (t = -11.1).","theme":"Organizations","level":"employer","datasets":["VRscores"],"strength":"robust","stats":{"n":6000,"drift_dem":2127,"drift_rep":269},"question":"Is drift hiring (new cohorts), attrition, or relocation? Which organisational events precede it?","next_data":"Employer x year hires and exits by age; M&A events (SDC); locations","caveats":[],"views":["emp-explorer"],"rank":23},{"id":"policy-twfe","title":"Which state policy variables move with AI outcomes within states over time","claim":"1 of 23 policy-outcome pairs pass a 10% false-discovery threshold. Strongest: Median household income ($) -> net interstate migration (t = -4.2).","theme":"Policy x AI","level":"state","datasets":["CSPP","PatentsView/BTOS/IRS"],"strength":"suggestive","stats":{"pairs":23,"passing_fdr10":1},"question":"Use policy adoptions as events (staggered difference-in-differences) for the variables that pass.","next_data":"Policy adoption dates (e.g. NCSL AI legislation, R&D tax credits)","caveats":[],"views":["policy-table"],"rank":24},{"id":"mig-network","title":"The US migration network in the latest year","claim":"2021-22: the largest net gainers of households were FL, TX, NC, SC, the largest net losers CA, NY, IL, MA. Biggest corridors: CA -> TX (44,415 returns); NY -> NJ (44,325 returns); NY -> FL (43,518 returns); CA -> AZ (30,784 returns); CA -> NV (28,869 returns). The network splits into 5 communities: AK, AR, AZ, CA, CO, HI, ID, LA, MT, NM, NV, OK, OR, TX, UT, WA, WY | IA, IL, IN, KS, KY, MI, MN, MO, ND, NE, OH, SD, WI | CT, DE, FL, GA, MA, ME, NH, NJ, NY, PA, RI, VT | DC, MD, NC, SC, VA, WV | AL, MS, TN.","theme":"Migration x AI","level":"state","datasets":["IRS SOI migration"],"strength":"descriptive","stats":{},"question":"Do migration communities follow political regions, and do AI hubs sit at their centre or their edge?","next_data":"IRS state-to-state files (complete flows); earlier years for trends","caveats":["Corridors come from county-to-county pairs, which show about half of interstate moves; net totals use all moves."],"views":["mig-network"],"rank":25},{"id":"landscape","title":"State archetypes across politics, AI and innovation","claim":"22 state measures (Workforce Republican share, Republican vote share, AI exposure, Net migration, Gross migration, Movers' income gap, Republican share of the last presidential vote (%), Republican share of state legislators (%)...) reduce to components explaining 50% and 12% of the variation. 3 archetypes: type 1: AL, AK, AZ, AR, FL, GA, ID, IN, IA, KS, KY, LA, MI, MS, MO, MT, NE, NV, NM, NC, ND, OH, OK, SC, SD, TN, TX, UT, WV, WI, WY; type 2: CA, CO, CT, DE, HI, IL, ME, MD, MA, MN, NH, NJ, NY, OR, PA, RI, VT, VA, WA; type 3: DC. 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