Policy Diffusion and Polarization across U.S. States

DellaVigna and Kim study the innovation and diffusion of policies across U.S. states using a dataset of over 700 state laws spanning seven decades. The central question is what predicts whether a state adopts a policy — and how those predictors have changed over time. The paper draws on two primary data sources: the State Policy Innovation and Diffusion (SPID) Database (Boehmke et al., 2020), covering 676 policies, and a hand-collected sample of 57 policies from 91 NBER working papers (April 2012–September 2021) that feature state-level policy variation. The combined dataset covers 733 policies adopted from the 1950s onward across the contiguous 48 states.

On policy innovation, the paper finds that state capacity plays only a small role: larger and richer states are only slightly more likely to introduce new policies, innovation originates from both Republican and Democratic states, and the patterns are largely idiosyncratic with respect to observable state characteristics. California is the most frequent innovator, but large states like Florida and Texas rank in the middle.

For policy diffusion, the paper employs both a static Geary’s C clustering statistic (measuring whether the first 10 adopting states cluster geographically or politically relative to a random-diffusion benchmark) and a dynamic logit hazard model estimated separately by decade. The hazard model identifies three similarity channels — geographic, demographic, and political — and allows their coefficients to vary over time.

The central finding is a structural break in diffusion patterns around 2000. From the 1950s to the 1990s, geographic proximity is the dominant predictor of policy adoption: the coefficient on geographic similarity is 0.34 in the 1970s and remains roughly constant at 0.33 in the most recent decade. Demographic similarity is consistently positive and stable (approximately 0.20 in the 1980s, 0.22 in the 2010s). Political similarity — measured by closeness in Republican vote-share from the most recent presidential election — is a modest predictor before 2000, with coefficients between 0.14 (1970s) and 0.17 (1990s). Since 2000, the political similarity coefficient triples: 0.46 in the 2000s and 0.52 in the 2010s, making it by far the strongest predictor. The overall pseudo R-squared rises from 0.13 in the 1970s to 0.19 in the 2010s.

These patterns are more pronounced for policies studied by economists: in the NBER subsample, the political similarity coefficient reaches 0.66 (s.e.=0.09) in the most recent two decades, versus 0.42 (s.e.=0.04) in the SPID sample.

The paper tests whether the increased role of political similarity reflects correlated voter preferences, learning, or competition versus party discipline. Against correlated-preferences explanations: adding cross-state migration flows as a similarity measure reduces geographic predictive power but leaves the political similarity coefficient entirely unchanged; and typical policy-outcome variables (poverty rate, opioid mortality, income) have not become more correlated among politically similar states over time. In favor of party discipline: similarity in unified state government has zero predictive power through the 1990s but a coefficient of 0.42 (s.e.=0.06) in the 2000s–2010s. An event study of switches to unified party control confirms this causally for 1991–2020: switching to unified government raises the probability of passing ideologically aligned laws by approximately 2 percentage points in the four years following the switch, with no pre-trends and no effect on neutral-leaning laws; the same event study for 1950–1990 yields no detectable effect.

COVID policies (77 state laws since October 2019) show strong political similarity in adoption; historical vaccination mandate policies (28 laws since 1975) show no political similarity effect. The paper concludes that rising party polarization at the state level — detectable from the 2000s onward, lagging the Congressional trend by roughly four to five decades — is the primary driver of the shift in diffusion patterns. The authors additionally classify each of the 57 NBER-sample policies by type of diffusion as an input for difference-in-differences research design assessment.

Q: What data do the authors use and what is its scope? A: The main source is the SPID Database (Boehmke et al., 2020), covering 676 policies over seven decades. The authors supplement this with 57 policies hand-collected from 91 NBER working papers (2012–2021) that use state-level policy variation. The combined sample covers 733 policies adopted from the 1950s onward in the contiguous 48 states, with the SPID sample averaging 23 adopting states per policy and the NBER sample averaging 29.

Q: Do states with more resources or larger populations systematically innovate more policies? A: The evidence for a state-capacity hypothesis is weak. There is only suggestive evidence that higher per-capita income predicts being in the top-20% of innovators, and no clear difference in population between the top and bottom innovators. Innovations arise from both Republican and Democratic states. One consistent correlate is urban population share, but overall innovation is largely idiosyncratic with respect to observable characteristics.

Q: What was the dominant predictor of policy diffusion before 2000? A: Geographic proximity was the dominant predictor. The coefficient on geographic similarity in the hazard model is 0.34 in the 1970s and remains stable at approximately 0.33 in the 2010s. Demographic similarity contributes consistently at approximately 0.20. Political similarity before 2000 is modest, ranging from 0.14 in the 1970s to 0.17 in the 1990s — roughly one-third to one-half the magnitude of the geographic coefficient.

Q: How dramatically does political similarity change after 2000, and is this finding robust? A: The political similarity coefficient triples, rising from 0.17 in the 1990s to 0.46 in the 2000s and 0.52 in the 2010s, making it the largest single predictor in recent decades. This pattern is robust across linear probability models, alternative measures of political similarity, alternative thresholds for “closest” states (closest fifth, fourth, third, or half all yield comparable coefficients), and alternative ways of computing adoption counts.

Q: Is the shift toward political diffusion stronger for policies economists study? A: Yes. In the NBER subsample, the political similarity coefficient reaches 0.66 (s.e.=0.09) in the 2000s–2010s, compared to 0.42 (s.e.=0.04) in the SPID sample. Geographic similarity also has somewhat higher coefficients in the NBER sample throughout the period. This implies that the policies most studied for difference-in-differences evaluation are also those most subject to politically-driven diffusion.

Q: What does the Medicaid case study illustrate about political polarization? A: ACA Medicaid expansion spread almost exclusively along partisan lines, with Republican vote-share accurately predicting the year of adoption. Crucially, the states that delayed or declined adoption — higher Republican vote-share states — had a higher share of population that would benefit from the expansion and therefore face a worse policy-need match. By contrast, the original 1966 Medicaid rollout showed no relationship between state political leaning and timing of adoption, and neither did the 1960s–1970s food stamp program expansion.

Q: How do the authors distinguish party discipline from correlated voter preferences as the mechanism? A: Two tests point away from correlated preferences: (1) cross-state migration flows, when added as a similarity measure, absorb geographic predictive power but leave the political similarity coefficient entirely unaffected; (2) typical policy-outcome variables (opioid mortality, poverty rate, income, etc.) have not become more correlated among politically similar states over time, contradicting the hypothesis that local needs or environments have become politically correlated.

Q: What is the direct evidence for party discipline as the operative mechanism? A: The authors construct a measure of similarity based on unified party control (governor and both chambers of the same party). This variable has zero predictive power through the 1990s (point estimate near zero). In the 2000–2020s, the coefficient for unified-government similarity is 0.42 (s.e.=0.06), making it the strongest single predictor of adoption in those decades. States with divided governments show no predictive power of adoption by other divided-government states, further isolating the role of party control.

Q: What does the event-study of switches to unified party control show? A: Switches to unified party control in 1991–2020 produce a statistically significant increase of approximately 2 percentage points in the probability of adopting ideologically aligned laws within four years of the switch, relative to the year before. The effect emerges in year t+1 and is persistent, with no pre-trends, and the effect on neutral-leaning laws is zero, ruling out a simple reduced-gridlock story. The same event study for 1950–1990 detects no effect.

Q: How do COVID state policies compare to historical vaccination policies in terms of political diffusion? A: COVID policies (77 state laws, October 2019–August 2021) show significant political similarity in adoption, consistent with the recent-decade patterns. Vaccination mandate laws (28 policies since 1975) show no political similarity effect whatsoever, with demographic and modest geographic similarity being the relevant predictors. This contrast underscores that political polarization in policy adoption is a recent phenomenon that has spread even to policy areas without prior partisan patterning.

Q: How does partisan polarization at the state level compare temporally to polarization in Congress? A: Congressional polarization (measured by DW-NOMINATE) has been rising since the 1950s. State-level policy polarization, as documented here, does not emerge until the 2000s — a lag of roughly four to five decades. The paper notes it has risen rapidly and has already reached policy domains (such as COVID mandates) that showed no political patterning historically.

Q: Does the diffusion pattern vary across policy types? A: Yes. For economic policies, geography and demographics decline in importance over time with a smaller increase in political predictors. For non-economic (social) policies, geographic importance remains stable while political polarization is especially strong. Political polarization is strongest in Republican-leaning and Democratic-leaning states, and weaker among battleground states, consistent with a party-driven model where ideologically extreme states adopt from each other.

Q: How do the authors classify individual NBER-sample policies by diffusion type? A: Using Geary’s C statistics computed separately for geographic and political clustering for each of the 57 NBER policies, the authors identify three approximate clusters: (1) primarily politically-clustered (e.g., Medicaid expansion); (2) jointly geographically and politically clustered (e.g., ban on asking about past salary history); and (3) largely idiosyncratic, neither geographically nor politically clustered (e.g., anti-bullying laws). This classification has direct implications for assessing identification threats in difference-in-differences designs.

Q: What does the overall predictability of policy adoption look like over time? A: The pseudo R-squared from the logit hazard model rises from 0.13 in the 1970s to 0.19 in the 2010s. The increase in political similarity is large enough not only to surpass geographic similarity as a predictor but to make the overall process of state policy adoption more predictable over time.

Policy diffusion: The process by which a policy adopted in one state subsequently spreads to other states; measured here along geographic, demographic, and political dimensions using a logit hazard model estimated by decade.

Geary’s C statistic: A ratio of weighted to unweighted average pairwise squared differences in adoption status, adapted from spatial statistics (Geary, 1954). Values below 1 indicate clustering; values above 1 indicate anti-clustering. The paper reports 1−C so higher values mean more clustering among similar states.

Policy innovation: First-year adoption of a law in any state; a state is an “innovator” if it adopts in the first year the policy appears anywhere. The paper distinguishes innovation (origination) from diffusion (spread).

Logit hazard model: A discrete-time logit model estimated at the state-year-policy level for all states that have not yet adopted a given policy, with policy-decade fixed effects as a baseline hazard and three time-varying similarity measures (geographic, demographic, political) as key predictors.

Political similarity: Closeness of two states’ Republican vote-shares from the most recent presidential election; the closest third of states in this dimension are used to construct the diffusion measure. Shown to be independent of — and to have grown far more predictive than — geographic similarity since 2000.

Unified party control: A state government in which the governor and both state legislative chambers belong to the same party. The paper shows this is the variable most predictive of politically-driven policy diffusion in the 2000s–2020s, with a coefficient of 0.42 where it was effectively zero before 2000.

Party discipline / party polarization: The paper’s preferred explanation for post-2000 patterns: state politicians increasingly vote and adopt policies along party lines beyond what voter preferences alone would predict, with the effect detectable since the 2000s at the state level, lagging the Congressional polarization trend by roughly four decades.