Do Citizens Know About Other States’ Policy Choices?

Abstract

While citizens’ knowledge of their own state’s policies has been investigated, little attention has been paid to citizens’ knowledge of policy differences across the United States. Citizen knowledge of out-of-state policy adoptions may drive diffusion, but a direct test of this knowledge has yet to be conducted. Using a national survey of US adults, I investigate the relationship between individual, state, and policy characteristics and out-of-state knowledge of four policies: recreational marijuana legalization, assault weapon bans, physician-assisted suicide, and in-state tuition for undocumented immigrants. I find that Americans know about out-of-state policies. However, this knowledge varies with an individual’s education attainment and ideological strength, a policy’s observability and complexity, and a state’s liberalism and population.

Keywords

political knowledge policy diffusion American state politics

“California bill would create Amber Alert system specifically for Black women, children” reports KGAN, a television station in Cedar Rapids, Iowa (Gonzales 2023). “Transgender adults in Florida ‘blindsided’ that new law also limits their access to health care” reports the Pittsburgh Post-Gazette (Beaty et al., 2023). “Hawaii allows more concealed carry after US Supreme Court ruling, but bans guns in most places” reports WBEN, a radio station in Buffalo, New York (WBEN 2023). Despite the lack of material interest Iowans should have in California policies, Pennsylvanians should have in Florida policies, or New Yorkers should have in Hawaii policies, these three local news agencies, like thousands of others across the United States (Hopkins, 2018; Kernell, 1986; Martin and McCrain, 2019), relay decisions made in faraway statehouses to their local audiences.

Does this reporting make Americans more knowledgeable about out-of-state policies? The expected answer varies across fields of study. Scholars of political knowledge would likely answer “no.” The average American’s level of political knowledge is notoriously low and most people are overconfident about the accuracy of their beliefs (Barker et al., 2022). Americans know even less about their state governments than the federal government (Burden and Ono, 2020; Lyons et al., 2013; S. Rogers, 2023). According to this research, we would not expect citizens to know much about state policies adopted elsewhere.

At the same time, a long tradition of research on policy diffusion expects citizens to know about some policy adoptions outside their home state. Policymakers are pressured to adopt policies by interest groups (Garrett and Jansa, 2015), donors (Reckhow, 2013), media coverage (Linos, 2013), and constituents. State policy tends to be responsive to public opinion (Caughey and Warshaw, 2022; Erikson et al., 1993) and the network of citizens’ perceptions of states’ similarity predicts policy diffusion (Bricker and LaCombe, 2021). F.S. Berry and Berry (1990, 400) argue that people “see a popular policy in place in nearby states and want it in their state as well.” This argument has been corroborated (Malet, 2022; Pacheco, 2012; Pacheco and Maltby, 2017) but only as analyses of opinion changes rather than as analyses of citizen knowledge.

Seeking to understand what Americans know about out-of-state policies, I asked a random sample of over 1,000 US adults in March 2023 to name up to three states that they believed currently had each of the following four policies: the legalization of recreational marijuana, a ban on assault weapons, the legalization of physician-assisted suicide, and in-state higher-education tuition for undocumented immigrants. These policies span the four possible combinations of two of E.M. Rogers and Shoemaker’s (1971) characteristics of innovations: observability and complexity. I find that out-of-state policy knowledge is not evenly distributed across the American public, varying substantially by the characteristics of the policy and of the states that have it. This implies that Americans are unevenly influenced by certain states’ adoptions of certain policies, limiting the effect that state policy adoptions have on public opinion and, consequently, limiting the public’s role in a policy’s diffusion.

Why Know About Out-of-State Policy?

Though perhaps normatively less important than knowledge of one’s own state’s policies, there is reason to know about policies in other states and reason to believe Americans know about some out-of-state policies. The United States Constitution guarantees freedom of movement across state lines, and Americans use that freedom to take advantage of policies their home state does not provide. There is evidence that Americans cross state lines to buy lottery tickets (Mikesell, 1987), marijuana (Wiseman and Walker, 2017), and cheap cigarettes (Hyland et al., 2005), to be reimbursed for recycling soda bottles (Niu, 2018), to enjoy (or avoid) smoking bans (Shipan and Volden, 2008), to gamble (Nichols, 1998), and to obtain abortions (Althaus and Henshaw, 1994; Rader et al., 2022). There is also evidence that policymakers adopt some of these policies for fear that their constituents will cross state lines and reduce the state tax base (W.D. Berry and Baybeck, 2005; Pacheco, 2012).

Scholars have argued that Americans may move to a new state for welfare benefits (Bailey, 2005; Cebula and Koch, 1989) or to live among the politically like-minded (Bishop and Cushing, 2008; Brown and Enos, 2021). Though there is little empirical evidence for large-scale migrations due to interstate variation in welfare policy (Allard and Danziger, 2000; Schram and Soss, 1998; Volden 2002) or partisanship (Abrams and Fiorina, 2012; Strickler, 2016), when people choose to move, knowledge of out-of-state political conditions can shape that choice. People in a position to move, such as college graduates (Ewers and Shockley, 2023) and physicians (Bonica et al., 2020), use their knowledge of out-of-state political conditions to pick their new home, and knowledge of policy consequences that generally lack partisan association, such as school quality, can also influence Americans’ choice for a new home (Mummolo and Nall, 2017).

I argue three sets of characteristics influence out-of-state policy knowledge: individual, policy, and state. Americans have different levels of political interest, and those characteristics can shape the extent to which a person acquires knowledge about out-of-state policies. Policies are not equally easy to see or understand, so policy characteristics can also influence policy knowledge. Finally, not all Americans are familiar with the same sets of states. Visiting states or hearing media coverage of state policy actions can change out-of-state knowledge, but the states a person is likely to visit or see in the media vary by the state’s characteristics.

Explaining Out-of-State Policy Knowledge: Individual Characteristics

As with all political knowledge, knowledge of out-of-state policies is likely not distributed uniformly across the American electorate. Delli Carpini and Keeter (1996, 154) argue that there is a “knowledge class” of Americans who are older, wealthier, and more educated than the less knowledgeable. Older people know more simply because they have had more life lived. People with more formal education should also have more knowledge than those lacking formal education, and the wealthy are more likely to have access to the means of acquiring information about out-of-state policies if so desired. Additionally, white people are more likely than non-white people to be in the “knowledge class” because of the historic and systemic marginalization of non-white people in American politics. It is less reasonable to acquire information about a system one has little means to control, so those outside the knowledge class are less likely to acquire that knowledge.

There is one “knowledge class” group I expect to have particularly strong knowledge of out-of-state policies: the educated. This expectation is, in part, normative: people who have spent more time in school ought to know more things. While support for education simply equipping the educated with general political knowledge is mixed (Galston, 2001; Joslyn and Haider-Markel, 2014; Lupia, 2016), studies of state political knowledge consistently find that knowledge of state politics increases with formal education (Burden and Ono, 2020; Fortunato and Stevenson, 2021; Lyons et al., 2013). This is due to two factors: the educated are more likely to build the skills to acquire state political knowledge (Brady et al., 1995) and the educated are more likely to engage in “political hobbyism,” enjoying politics with sport-like obsession because it piques may of the same interests as the occupation for which they achieved their high-ranking degrees (Hersh, 2020).

Knowledge of out-of-state policy adoptions increases with individuals’ formal education.

“Political hobbyism” is not solely the domain of the highly educated: Americans from all walks of life may find themselves glued to the television screen to keep up with policy wins and losses around the country. Most of these Americans identify as strong ideologues because ideology and political knowledge are mutually reinforcing. Though finding abysmal levels of political knowledge in the body politic, Converse (1964) and Stimson (1975) find that ideological consistency is correlated with political knowledge. The proportion of the American population that is politically knowledgeable and professes strong ideological attachment has grown since Converse and Stimson (Wattenberg, 2019), and it is strength of ideological label, not party identification, that best predicts attitude stability (Turner-Zwinkels and Brandt 2023).

Strong ideologues have more out-of-state policy knowledge than weak ideologues and moderates.

Explaining Out-of-State Policy Knowledge: Policy Characteristics

Policies vary substantially in their ease of understanding and in the likelihood that a person will encounter their effects or information about them. Of the five characteristics of a policy innovation defined by Rogers and Shoemaker, complexity and observability should have the most immediate effect on knowledge about the policy. Rogers and Shoemaker define these characteristics as follows:

Complexity: “The degree to which an innovation is perceived as relatively difficult to understand and use” (E.M. Rogers and Shoemaker 1971, 154).

Observability: “The degree to which the results of an innovation are visible to others” (E.M. Rogers and Shoemaker 1971, 155).

Complex policies are difficult to remember. Instead of relying on knowledge of the specific policy, citizens often use shortcuts to form opinions about complex policies, with opinions shaped either by knowledge of well-known groups’ positions on the policy (Lupia, 1994) or by forming opinions about issue areas instead of the policy itself (Alvarez and Brehm, 2002). Independent of whether a policy is complex is whether that policy can be easily observed, that is, whether its implementation and direct consequences can be seen in day-to-day life. Observable policies are more likely to be encountered in day-to-day life than policies without direct visible consequences. Knowledge of a policy’s existence is greater for observable policies, like smoking bans (Pacheco, 2012) and Amber alerts (Makse and Volden, 2011), than for difficult-to-observe policies, like Affordable Care Act grants (Pacheco and Maltby, 2017) and concealed carry of firearms (Makse and Volden, 2011).

Knowledge of observable policies out-of-state is higher than knowledge of non-observable policies out-of-state.

Knowledge of non-complex policies out-of-state is higher than knowledge of complex policies out-of-state.

Explaining Out-of-State Policy Knowledge: State Characteristics

Policy diffusion scholars have long argued that a state should be more likely to adopt a policy if that policy is present in a nearby state (F.S. Berry and Berry 1990; Walker 1969). This could be due to economic competition between states easily accessible to each other’s residents (W.D. Berry and Baybeck, 2005; Boehmke and Witmer, 2004; Shipan and Volden, 2008), the ease with which policymakers can imitate or learn from nearby states (Boushey, 2010; Nicholson-Crotty and Carley, 2016; Shipan and Volden, 2008), or because citizens approve of a policy in a nearby or culturally similar state and lobby for it in their home state (Bricker and LaCombe, 2021; Pacheco, 2012; Pacheco and Maltby, 2017). Policy feedback scholars have made a similar argument that geographically proximate policies are more likely to affect an individual’s opinion and behavior than a distant policy (Soss and Schram, 2007; Teodoro et al., 2022). Though analyzing a single state (namely, Wisconsin), Cramer (2016) argues that rural people have an inflated sense of how much public policy favors urban areas, in part because many rural people are physically distant from urban areas. In the study of political knowledge, Keeter and Zukin (1983, 163) find that New Jerseyans living closer to Pennsylvania, which had an earlier presidential primary than New Jersey in 1980, had more knowledge of presidential candidates than those living further from Pennsylvania. Across fields, there is a consistent argument that it is easier to know about nearby politics than faraway politics because the probability of being exposed to the politics of a place decreases with distance from that place.

Individuals have more knowledge of policies in nearby states than of the same policies in faraway states.

Personal experience is not the only way to learn about a policy. Those who consume news media have more political knowledge than those who do not (Delli Carpini and Keeter 1996; Lyons et al. 2013), and even those who consume “soft” media learn knowledge of public affairs from those outlets (Bawn, 2002). If the media covered all states equally then there would be little else to say, but media coverage of states and their politicians is unequal because larger states get more media coverage (Dunaway, 2008). This is seen, for example, in policy diffusion, with increasing population being a strong predictor of whether a state is a persistent source for policies to diffuse (Desmarais et al., 2015). Graber (1980, 65) argues that the media’s over-coverage of larger states was originally due to those states having more media outlets, but, over time, their politics have become “‘local’ events in what Marshall McLuhan (1962) has called the ‘global village’ created by television.” Transposing this onto Soss and Schram’s theory of proximity’s effect on whether a person can be affected by a policy’s presence, it can be said that larger states are “proximate” to all Americans, even if not geographically proximate because those states are more familiar than geographically distant small states.

Knowledge of a more populous state’s policies is higher than knowledge of the same policies in a less populous state.

Another bias in media coverage must be tackled to understand out-of-state policy knowledge: a media bias toward coverage of the partisan extremes. Whether this bias exists at all is a matter of some debate. In the last half of the twentieth century, the dominant view was that the media set the policy agenda and had a bias towards more ideologically moderate viewpoints (Gans, 1979; Herman and Chomsky, 1988; McCombs and Shaw, 1972). However, news competition has increased as the dominant medium of communication moved from the technologically limited spectrum of broadcast radio and television to the near-limitless options provided by cable and satellite television and the internet around the turn of the millennium. This new competition has reversed media coverage trends because salacious extremism sells better than even-handed moderation (Bennett and Iyengar, 2008; Chaffee and Metzger, 2001). More ideologically extreme advocacy groups (McCluskey and Kim, 2012) and members of Congress (Padgett et al., 2019; Wagner and Gruszczynski, 2018) get more media coverage than their moderate counterparts, and the expansion of ideologically extreme news outlets has polarized voters and policymakers alike toward the ideological extremes (Arceneaux et al., 2016; Hedding et al., 2019; Martin and Yurukoglu, 2017). Because Americans are exposed to more extreme viewpoints than more moderate viewpoints, Americans should have more knowledge of the (possibly unusual or extreme) policy decisions of more ideologically extreme states than of more moderate states.

Knowledge of an ideologically extreme state’s policies is higher than knowledge of the same policies in a more ideologically moderate state.

Research Design and Policy Selection

To measure out-of-state policy knowledge, I placed four questions on an online, probability-based survey with a nationally representative sample of 1,123 adult Americans conducted from March 24 to April 6, 2023. This poll was conducted by a trusted third-party vendor. Respondents were emailed at random from the vendor’s panel of survey-takers with quotas for gender, age, and census region.¹ Survey weights are not used in this analysis. These four questions asked respondents, presented with a list of the 50 states, to select up to three states with each of the four policy areas: legal recreational marijuana, an assault weapon ban, legal physician-assisted suicide, and in-state tuition for undocumented immigrants. An example of these questions, as they appeared for respondents, is available in Figure A1 in the appendix.² These policies are intended to represent four combinations of Rogers and Schumaker’s observability and complexity, with an observable/non-complex policy (marijuana), an observable/complex policy (weapon ban), a non-observable/non-complex policy (suicide), and a non-observable/complex policy (undocumented tuition). Because of the structure of the data, I conducted three series of analyses: a series of logistic regressions modeling whether respondents answered “don’t know,” a series of choice logistic regressions modeling the states respondents chose to answer the questions, and a series of logistic regressions modeling whether respondents answers were correct.

These policies were chosen because all are considered “liberal” in the United States. However, there are differences in these policies besides their Rogers-Schumaker characteristics. Twenty-two states had in-state tuition for undocumented immigrants at the time of the survey, twenty-one had recreational marijuana, ten had physician-assisted suicide, and nine had an assault weapon ban. Undocumented tuition and recreational marijuana are on opposite ends of the observability-complexity axes, meaning statistical similarities between them are likely related to number of adoptions, while differences between them are likely related to policy characteristics. Additionally, Google Trends data from the survey period,³ which can be used as a measure of salience (Bromley-Trujillo and Poe, 2020), indicate the observable policies were generally more salient than the non-observable policies, but issue salience varies by state. To account for this, I control for each issue’s salience in the respondent’s home state.

Answering “Don’t Know”

The first measure of whether respondents have out-of-state policy knowledge is whether respondents choose to answer the question at all. Unlike knowledge of federal policies or of the policies of a person’s home state, there is little normative pressure to learn information about out-of-state policies. A person cannot vote for representatives or referenda in a state in which that person does not live. People may not believe it to be a personal weakness not to know another state’s governor or positions on policy issues, as a person might be considered for not knowing their own governor or the laws of their own state. Consequently, we should expect questions about out-of-state policy knowledge to have a high “don’t know” rate, as there is less pressure for the uninformed to pretend to be informed. Table 1 displays the distributions of the number of answers given by respondents to each of the four policy knowledge questions. The distribution of responses mostly aligns with hypotheses 3 and 4. The observable, non-complex policy (marijuana) has the most responses, followed by the observable but complex policy (assault weapon ban). In-state tuition for undocumented immigrants had the same level of non-response as physician-assisted suicide, but differences between these policies are investigated further with multivariate regression analyses.

Table 1.

Distribution of Number of Responses by Policy (N = 1,109).

	Policy Characteristics	No Response (%)	One Response (%)	Two Responses (%)	Three Responses (%)
Recreational marijuana	Observable, non-complex	15.0	21.6	15.1	48.3⁴
Assault weapon ban	Observable, complex	66.8	11.4	6.0	15.9
Physician-assisted suicide	Non-observable, non-complex	71.3	13.0	5.6	10.1
In-state immigrant tuition	Non-observable, complex	71.3	11.1	4.7	12.9

Data and Methods

I use a series of five logistic regression models to analyze what factors cause respondents to answer “don’t know.” Four of the regressions model the choice to answer “don’t know” for each policy, and the fifth model groups the four policies together. Standard errors for this model are clustered on respondents because each respondent appears four times in that model (once for each of the four policies).

The dependent variable for these models is 1 if a respondent refused to provide an answer to the question and 0 if a respondent chose at least one state as an answer to the question. (In this first set of models, it does not matter whether respondents provided a correct answer. Correctness is modeled in the third model series). Hypothesis 1 is tested with respondent self-reported education (an ordinal variable; the less than one percent of respondents who did not report their education level are dropped from the analysis).⁵ Other individual-level variables include age (a continuous variable from 18 to 90; all respondents reported their age), income (an ordinal variable with four percent of observations imputed due to non-response),⁶ and race and ethnicity (a series of binary variables for Black, Hispanic, and Asian people and for people reporting another race; white people are the reference category). A binary control variable is included for gender,⁷ and two binary control variables are included for whether the respondent identifies as a Democrat or a Republican, with independents as the reference category. Hypothesis 2 is tested with six binary variables, one for each point along a scale from strong liberal to strong conservative, with moderates as the reference category.⁸ To reduce multicollinearity, only two partisan control variables (Democrat and Republican) are in the main models. Models with four binary partisan controls (strong Democrat, weak Democrat, weak Republican, strong Republican) and two ideological controls (liberal and conservative) are in the appendix.

Hypotheses 3 and 4 are tested by comparing the four single-policy models against each other and with binary variables for each policy in the all-policy model (the reference category for this model is marijuana). Hypothesis 5 is tested with a count of the number of neighboring states that have the policy at the time of the survey (F.S. Berry and Berry 1990). A binary variable for whether the respondent’s own home state has the policy is also included. Information about the policy adoptions is from Boehmke et al. (2020),⁹ Britannica (2023a, 2023b), Giffords Law Center (2024), and National Conference of State Legislatures (2021). Because hypotheses 6 and 7 apply equally to all individuals, varying only as states adopt policies, they are not tested in these models. A state-level salience control (Google 2024) is included, except for physician-assisted suicide, whose salience did not vary across the states during the survey’s fielding. Models with standard errors clustered on respondent home state are presented in the appendix. The only substantial difference between the two pooled models is that the “strong liberal” coefficient is statistically less significant (p = .057 with errors clustered on states; p = .048 with errors clustered on respondents).

Findings

Tables 2 and 3 display the results of these analyses. As hypothesis 1 predicts, education is the only “knowledge class” element that consistently has the expected effect: a college graduate is five percentage points less likely than a technical school graduate to answer “don’t know” to an out-of-state policy knowledge question, all else equal (p = .002). By contrast, a person should gain more knowledge as they get older, yet older people seem more likely to answer “don’t know” overall (p = .01). Hypothesis 2 is also supported, as strong liberals (p = .048), conservatives identifying between strong and weak (p = .007), and strong conservatives (p < .001) are less likely to answer “don’t know” than moderates (Figure 1). However, as shown in Figure 2, the predicted probability that a strong liberal answers “don’t know” is not different, to a statistically significant degree, from the predicted probability that a moderate answers “don’t know.” Democrats are less likely to answer “don’t know” than moderates or Republicans (p = .03), and the appendix models indicate that effect is driven by strong Democrats. It may be the case that “political hobbyists” of the US left identify more with their party while those of the right identify more with their ideology.

Table 2.

Logistic Regression Analyses Predicting “Don’t Know” Answer.

	(1)	(2)	(3)	(4)	(5)
	Marijuana	Gun ban	Suicide	Tuition	Pooled
Age	−0.004	0.01***	0.01	0.01***	0.01***
Age	(0.01)	(0.00)	(0.00)	(0.00)	(0.00)
Income	0.10**	0.03	0.05	−0.02	0.03
Income	(0.05)	(0.03)	(0.03)	(0.03)	(0.03)
Education	−0.14*	−0.09	−0.15***	−0.10*	−0.12***
Education	(0.07)	(0.05)	(0.06)	(0.06)	(0.04)
Black	0.35	−0.26	−0.45**	−0.53**	−0.29
Black	(0.28)	(0.21)	(0.22)	(0.21)	(0.18)
Hispanic	0.82**	−0.28	0.09	−0.23	0.02
Hispanic	(0.34)	(0.28)	(0.30)	(0.29)	(0.24)
Asian	0.92**	−0.29	0.41	−0.60*	−0.005
Asian	(0.37)	(0.32)	(0.36)	(0.32)	(0.28)
Other race	0.28	−0.31	−0.82**	−0.06	−0.35
Other race	(0.48)	(0.37)	(0.37)	(0.41)	(0.29)
Woman	0.85***	0.59***	0.42***	0.49***	0.53***
Woman	(0.19)	(0.14)	(0.14)	(0.14)	(0.11)
Democrat	−0.28	−0.41**	−0.23	−0.25	−0.30**
Democrat	(0.23)	(0.17)	(0.18)	(0.18)	(0.14)
Republican	0.12	−0.15	−0.13	−0.05	−0.07
Republican	(0.26)	(0.20)	(0.21)	(0.21)	(0.16)
Strong liberal	−0.25	−0.29	−0.47**	−0.24	−0.33**
Strong liberal	(0.28)	(0.21)	(0.22)	(0.22)	(0.17)
Liberal	−0.43	−0.06	−0.25	0.22	−0.08
Liberal	(0.39)	(0.28)	(0.28)	(0.30)	(0.20)
Weak liberal	−0.39	−0.17	−0.04	−0.05	−0.14
Weak liberal	(0.47)	(0.34)	(0.35)	(0.35)	(0.22)
Weak conservative	−0.26	−0.36	−0.20	−0.37	−0.31
Weak conservative	(0.44)	(0.34)	(0.36)	(0.35)	(0.27)
Conservative	−0.90**	−0.47*	−0.16	−0.68***	−0.51***
Conservative	(0.38)	(0.25)	(0.27)	(0.25)	(0.19)
Strong conservative	−0.56**	−0.83***	−0.79***	−0.86***	−0.79***
Strong conservative	(0.29)	(0.22)	(0.22)	(0.22)	(0.18)
Neighboring states with policy	0.02	0.17**	−0.25***	−0.01	0.01
Neighboring states with policy	(0.07)	(0.07)	(0.09)	(0.06)	(0.04)
Own state has policy	−0.74***	−0.27	−0.64***	−0.04	−0.37***
Own state has policy	(0.20)	(0.18)	(0.16)	(0.14)	(0.10)
Policy salience in home state	0.18	0.02		3.63	0.06
Policy salience in home state	(0.21)	(0.39)		(2.48)	(0.18)
Assault weapon ban					2.54***
Assault weapon ban					(0.15)
In-state tuition for undocumented imm.					2.86***
In-state tuition for undocumented imm.					(0.16)
Physician-assisted suicide					2.75***
Physician-assisted suicide					(0.17)
Constant	−1.56***	0.46	1.67***	1.11***	−1.57***
Constant	(0.47)	(0.34)	(0.36)	(0.37)	(0.31)
Observations	1,109	1,109	1,109	1,109	4,436

Standard errors in parentheses. ***p < .01, **p < .05, *p < .1.

Table 3.

Average Marginal Effects of Significant Independent Variables in Model 5.

Variable	± 1 SD range¹⁰	$P (DV = 1) at \bar{x} - σ$	$P (DV = 1) at \bar{x} + σ$	Average Effect
Age	(32, 66)	.53	.59	.06
Education	(4, 6)	.58	.54	−.04
Woman¹¹	(0, 1)	.51	.61	.10
Democrat¹²	(0, 1)	.58	.53	−.05
Own state	(0, 1)	.59	.52	−.07

Figure 1.

Predicted probability of answering “don’t know” varying ideology, model 5.

Figure 2.

Predicted probability of answering “don’t know” varying policy, model 5.

Additionally, women give “don’t know” answers at higher rates than men. This is consistent with the literature on gender and public opinion, where scholars have long argued that the gender gap in guessing is because women are generally socialized to be less risk averse (Mondak and Anderson, 2004; Morehouse Mendez and Osborn, 2010). Though women may also have less out-of-state political knowledge than men for the same reason that non-white people might (systemic political discrimination), the gendered gap in the propensity to guess when uninformed is a distinct factor to account for in analyzing out-of-state policy knowledge.

Policy-wise, marijuana is unusual. This is expected, to an extent, as marijuana is the policy for which hypotheses 3 and 4 predict a person is most likely to have knowledge. Marijuana is both observable and non-complex. However, it should be the case that out-of-state policy knowledge of in-state tuition for undocumented immigrants, the non-observable and complex policy, is substantially more likely than all other policies to be answered “don’t know.” The tuition policy does have the highest predicted probability of being answered “don’t know,” but this is statistically insignificant from the predicted probability of a “don’t know” answer for the other two non-marijuana policies, as seen in Figure 2.

Though respondents are less likely to answer “don’t know” when their own state has the policy (p < .001), a neighboring state having a policy does not seem to consistently impact out-of-state policy knowledge. The neighboring-state coefficient is statistically significant (p = .01) and negative for physician-assisted suicide, but statistically significant (p = .01) and unexpectedly positive for assault weapon bans. It is unlikely that a person would be more likely to encounter a physician-assisted suicide in a neighboring state, as opposed to assault weapon bans or recreational marijuana. The relationship between neighboring states having physician-assisted suicide and knowledge of physician-assisted suicide may be coincidental.

Choosing States to Answer

Data and Methods

The second series of models measure whether the state-level hypothesized determinants of out-of-state policy knowledge affect respondents’ choices of answers (i.e., the states they name) for the policy knowledge question. In addition to buttressing the seven hypotheses, these analyses can also help determine whether respondents know other state’s policy conditions are whether they are relying on heuristics alone to determine their answers. People often use heuristics to try to shortcut their way to making decisions (Lupia, 1994; Steenbergen and Colombo, 2018), though overreliance on heuristics can cause a person to be misinformed (Cohen, 2003; Dancey and Sheagley, 2013; Plutzer et al., 1998). If respondents are using measurable heuristics to answer the policy knowledge questions, then whether a state has the policy should have no effect on whether respondents choose that state to answer the question, all else equal.

The structure of these data (three opportunities to make a choice from among fifty alternatives) make McFadden’s (1974) choice logistic regression model ideal for analysis. Choice logistic regressions model the selection each “case” makes across several “alternatives,” with one alternative as the baseline against which all others are compared. The dependent variable, having chosen the state, is binary, and choice logistic regression can be thought of as a special case of logistic regression. The “cases” in these analyses are the respondent-opportunity¹³ and the “alternatives” are the states that a respondent can choose to answer the question. Standard errors are clustered on respondents.

I have no reason to believe education affects the state a person chooses, so I do not include it in these analyses. Hypotheses 3 and 4 can be tested indirectly, as I include an alternative-level binary variable for whether the alternative has the policy being analyzed. If hypotheses 3 and 4 are correct, the coefficient for this variable should be stronger for observable and non-complex policies than for non-observable or complex policies. Hypothesis 5 is tested with a binary variable for whether the alternative is a respondent’s neighboring state. Because Alaska and Hawaii have no neighbors, they and their respondents are dropped from the analysis. This is a statistical necessity because these variables do not vary across cases for these alternatives and vice versa. (Every respondent faces the same neighboring-state pressure to select Alaska and Hawaii, and every Alaska and Hawaii respondent faces the same neighboring-state pressure for all states). Hypothesis 6 is tested with the alternative’s population in millions, from the 2022 American Community Survey 1-year estimates (United States Census Bureau 2023). Because all four policies are generally considered liberal, hypothesis 7 is tested with a continuous variable for the alternative’s elite liberalism. This measure was calculated by the author using congressional NOMINATE scores (W.D. Berry et al., 1998, 2010; Lewis et al., 2024).¹⁴ Additionally, models in the appendix interact hypothesis 5 through 7’s variables with the binary for whether a state has the policy; statistical significance in a positive direction would bolster support for the hypotheses.

The variables used to measure hypotheses 6 and 7 can also be used to control for heuristic use. Ideology is an oft-used heuristic in politics (e.g., Bayram and Shields, 2021; Plutzer et al., 1998): a respondent with no knowledge of these policy adoptions would still be wise to guess that liberal states have these general liberal policies. Additionally, more populous states tend to adopt policies first (Boehmke and Skinner, 2012; Walker, 1969). Whether the American public knows this is unknown, but it would still be wise to guess more populated states have a policy.

Findings

These models (Table 4) provide support for hypotheses 3 and 4. In contrast to the “don’t know” analyses, where marijuana stands out, the tuition policy is exceptional here. Whether a state has the tuition policy does not change whether respondents will choose it as an answer to the question. Knowledge of marijuana is high, as is knowledge of other the two non-tuition policies. Respondents know some of the states that have these policies, rather than guessing that the most liberal or populous states have these policies.

Table 4.

Choice Logistic Regression Analyses of Choice of State for Knowledge Questions.

	(6)	(7)	(8)	(9)
	Marijuana	Weapon Ban	Suicide	Tuition
State has policy	1.72***	1.06***	1.28***	0.06
State has policy	(0.08)	(0.13)	(0.11)	(0.10)
Neighboring state	1.00***	0.55***	0.60***	0.71***
Neighboring state	(0.07)	(0.12)	(0.14)	(0.13)
Respondent’s home state	2.61***	2.13***	2.09***	2.01***
Respondent’s home state	(0.06)	(0.12)	(0.14)	(0.13)
Elite liberalism	0.02***	0.01***	0.01***	0.03***
Elite liberalism	(0.00)	(0.00)	(0.00)	(0.00)
Population in millions	0.05***	0.04***	0.02***	0.06***
Population in millions	(0.00)	(0.00)	(0.00)	(0.00)
Observations	104,736	37,728	28,992	31,488
Cases	2,182	786	604	656
Alternatives	48	48	48	48
Respondents	943	368	318	318

Robust standard errors in parentheses.

***p < .001.

Respondents are more likely to pick neighboring states, their own state, liberal states, and populous states than otherwise, all else equal. Respondents seem especially likely to pick their own state, regardless of whether it has the policy. Note that these coefficients are independent of the coefficient controlling for whether the state has the policy, meaning these states, of which respondents should have more knowledge, are being picked regardless of whether they actually have the policy.¹⁵ This means these states are more likely to appear in the third series of analyses. Should the effect of any of these state-level variables also be substantial in the third series of analyses, this model bolsters that: respondents were more likely to pick those states and more likely to be wrong about those states.

Are the Answers Correct?

Data and Methods

The third series of models is a set of five logistic regression analyses of whether respondents’ choices are correct. As with the first series of models, four of the third-series models measure each policy separately, and the fifth models all policies together. Because respondents appear in these analyses as many times as they gave an answer to the knowledge question, standard errors are clustered on respondents in all five models. A control variable is included in these analyses measuring the number of responses a respondent gave, since respondents are more likely to be wrong by random chance if they guess more states. Hypotheses 1 and 2 are tested with the variables from the first model series, and hypotheses 5 through 7 are tested with the same variables as in the second model series. Hypotheses 3 and 4 can be tested by comparing relative correctness across the policies. As in the second series of analyses, these analyses are conditional on the respondent having provided an answer, that is, “don’t know” is not measured. Models with strong/weak partisan variables and models with standard errors clustered on states are in the appendix. The only substantial difference between the pooled model clustered on respondents and the pooled model clustered on states is that the Black coefficient is statistically significant in the model clustered on states (p = .02 in the state model; p = .08 in the main model). Variance inflation factor tests are also in the appendix; the models do not appear to suffer from multicollinearity.

Findings

Tables 5 and 6 show the results of these analyses. As respondents get older, their answers to the out-of-state policy knowledge questions appear to be more likely to be correct (p < .001 in the pooled model). However, older respondents are more likely to give “don’t know” responses, so the overall effect of this on policy knowledge is a wash. While the correctness of responses does not significantly (p = .26) change with education, the educated were less likely to answer “don’t know,” meaning the number of correct answers given increases with education. The same is true of ideologues, though Democrats, who are more likely to have not answered “don’t know” than moderates and Republicans, may be less likely to be correct than independents and Republicans (p = .09), and weak conservatives, who are as likely as moderates to have answered “don’t know,” may also be less likely to be correct than all other ideological groups (p = .09).

Table 5.

Logistic Regression Analyses of whether Respondents’ Answers are Correct.

	(10)	(11)	(12)	(13)	(14)
	Marijuana	Gun ban	Suicide	Tuition	Pooled
Age	0.004	0.04***	0.03***	−0.003	0.02***
Age	(0.01)	(0.02)	(0.01)	(0.01)	(0.00)
Income	0.07	−0.07	0.05	−0.03	0.03
Income	(0.05)	(0.10)	(0.05)	(0.06)	(0.03)
Education	0.09	−0.01	0.13	−0.08	0.05
Education	(0.08)	(0.17)	(0.10)	(0.12)	(0.05)
Black	−0.44	0.43	−0.51	−0.24	−0.31*
Black	(0.34)	(0.68)	(0.32)	(0.39)	(0.18)
Hispanic	−0.91***	0.84	−0.49	0.48	−0.23
Hispanic	(0.32)	(0.86)	(0.58)	(0.56)	(0.24)
Asian	0.89	0.40	0.99	1.48**	0.86**
Asian	(0.66)	(1.11)	(0.81)	(0.61)	(0.35)
Other race	−0.03	−0.21	−0.13	−0.30	−0.11
Other race	(0.63)	(1.01)	(0.38)	(0.47)	(0.28)
Woman	0.09	−0.52	0.13	0.33	0.12
Woman	(0.24)	(0.49)	(0.23)	(0.30)	(0.12)
Democrat	−0.25	−0.45	−0.36	.05	−0.26*
Democrat	(0.29)	(0.57)	(0.30)	(0.31)	(0.16)
Republican	−0.38	−0.43	−0.33	−0.10	−0.12
Republican	(0.31)	(0.62)	(0.34)	(0.40)	(0.17)
Strong liberal	−0.15	−0.03	0.55	0.07	0.15
Strong liberal	(0.35)	(0.62)	(0.38)	(0.41)	(0.19)
Liberal	−0.24	0.67	0.63	0.52	0.43
Liberal	(0.45)	(0.80)	(0.55)	(0.70)	(0.29)
Weak liberal	0.11	−0.06	−1.06**	−0.54	−0.28
Weak liberal	(0.47)	(0.91)	(0.53)	(0.49)	(0.24)
Weak conservative	−0.21	−0.46	−0.70	−0.98	−0.53*
Weak conservative	(0.52)	(0.96)	(0.60)	(0.60)	(0.31)
Conservative	0.11	−0.48	−0.31	−0.92**	−0.26
Conservative	(0.38)	(0.55)	(0.36)	(0.42)	(0.20)
Strong conservative	−0.02	0.01	−0.31	−0.15	−0.26
Strong conservative	(0.33)	(0.65)	(0.37)	(0.40)	(0.19)
Neighboring state	0.23	0.04	−0.08	−0.44	−0.002
Neighboring state	(0.34)	(0.58)	(0.29)	(0.38)	(0.17)
Own state	0.22	−0.40	−0.85***	−1.03***	−0.43***
Own state	(0.29)	(0.59)	(0.29)	(0.35)	(0.14)
State elite liberalism	0.13***	0.60***	0.07***	0.09***	0.11***
State elite liberalism	(0.01)	(0.11)	(0.01)	(0.01)	(0.00)
State population in mil.	−0.01	0.10***	0.02***	0.21***	0.06***
State population in mil.	(0.01)	(0.02)	(0.01)	(0.02)	(0.01)
Number of answers respondent gave	0.65***	0.08	−0.36**	0.19	0.08
Number of answers respondent gave	(0.17)	(0.30)	(0.14)	(0.18)	(0.08)
Policy salience in home state	0.16	−0.19		−3.53***	−0.10
Policy salience in home state	(0.28)	(0.94)		(1.25)	(0.21)
Assault weapon ban					−2.83***
Assault weapon ban					(0.19)
In-state tuition for undoc. immigrants					−1.21***
In-state tuition for undoc. immigrants					(0.24)
Physician-assisted suicide					−2.48***
Physician-assisted suicide					(0.22)
Constant	−7.01***	−41.06***	−5.19***	−5.60***	−5.34***
Constant	(0.83)	(7.45)	(0.94)	(0.98)	(0.46)
Observations	2,182	786	604	656	4,228

Robust standard errors in parentheses. ***p < .01, **p < .05, *p < .1.

Table 6.

Average Marginal Effects of Significant Independent Variables in Model 14.

Variable	±1 SD Range	$P (DV = 1) at \bar{x} - σ$	$P (DV = 1) at \bar{x} + σ$	Average Effect
Age	(32, 66)	.72	.77	.05
Asian	(0, 1)	.74	.80	.06
Own state	(0, 1)	.75	.72	−.03
State elite lib.	(41.59, 75.23)	.54	.94	.40
State population	(1.15, 28.67)	.69	.83	.14

These results bode well for hypotheses 6 and 7. Respondents are substantially more likely to be correct about a state having a policy as that state’s population and liberalism increase, even though those states were more likely to be chosen regardless of whether they had the policy. By contrast, hypothesis 4 has little support. Despite respondents being more likely to answer that their neighbors had a policy, whether a state neighbors a respondent has no significant effect (p = .99) on whether a respondent’s answer to the policy knowledge question is correct.

As in the first series of models, marijuana stands out. Respondents are not only more confident about their knowledge of other states’ recreational marijuana policies, but they are also more likely to be correct about them. Respondents’ answers are also more likely to be correct about in-state tuition for undocumented immigrants than assault weapon bans and physician-assisted suicide, as shown in Figure 3. But, given that whether a state has in-state tuition for undocumented immigrants has no significant effect on whether a respondent picks it as an answer, it is likely the case that respondents correctly assume that liberal states are more likely to have the policy than not without actually knowing about the policy. Additionally, respondents could just be right by chance. Twenty-two states had in-state tuition for undocumented immigrants at the time of the survey. Only ten states had physician-assisted suicide, only nine had an assault weapon ban, and twenty-one had recreational marijuana.

Figure 3.

Predicted probability of policy knowledge answer being correct, model 14.

Discussion

Do Americans know about policies outside their own state? The answer requires more nuance than “yes” or “no.” In this narrow subset of political knowledge, the main individual characteristic influencing policy knowledge is education. Age, income, and race have a little noticeable effect. As expected, strong ideologues have more out-of-state policy knowledge, but contrary to expectation, strong conservatives are more likely than strong liberals to have out-of-state policy knowledge. In an era of negative polarization, it could be that conservative media report on liberal policies more than liberal media do.

Additionally, Americans may know more about what is going on in larger states and more liberal states, but it is difficult to disentangle where there is actually more knowledge of these states’ policies or whether respondents happen to be more likely to be correct about these state’s policies by chance. This corroborates research indicating a policy’s first adopters affect its speed of diffusion (Boehmke et al. 2017) and implies policy proponents may want to target larger and more ideologically extreme states over smaller or more moderate states (Table 7).

Table 7.

Summary of Analyses.

Hypothesis	Model Set 1	Model Set 2	Model Set 3	Overall
1 (Education)	Supported			Supported
2 (Ideologues)	Supported			Supported
3 (Observability)	Supported	Supported	Supported	Supported
4 (Complexity)	Supported	Supported	Supported	Supported
5 (Nearby states)
6 (Populous states)	Supported		Supported	Supported
7 (Less moderate states)	Supported		Supported	Supported

Hypothesis 5 is the only hypothesis for which these analyses could not provide support. Americans seem not to know what is going on in nearby states, even when the policy is easy to observe. Barring the discovery of long-lost out-of-state knowledge surveys, we will probably never know whether the lack of knowledge of one’s neighbors is a product of the internet age or has always been the case. The lack of support for the nearby-state hypothesis speaks to a growing body of research indicating partisanship and ideology have replaced geography as the dominant driver of state-to-state diffusion (Grumbach, 2022; Mallinson, 2021). This shift has often been attributed to policymakers, but the ultimate cause may be the American people as a whole.

Furthermore, different policies are known to different extents. Though an individual can pursue more education and a policy can be popularized through its adoption by a populous or ideologically extreme state, policy characteristics do not change. These findings lend credence to there being a “submerged state” (Mettler, 2011) where citizen preferences have little impact on policy diffusion. Perhaps knowledge of these policies could increase with increased access to quality state political media, but the inequity in knowledge of observable and non-observable and non-complex and complex policies will likely always remain.

As always, more work needs to be done. Though there is evidence that policies shape public opinion (Pacheco, 2012), variation in policies’ effect on opinion due to individual, state, and policy characteristics remains mostly unexplored. Finally, the policies in this analysis are not representative of the length and breadth of American state policies. To reduce the possibility that differences across findings were due to variations in the policies’ perceived ideological placement, only liberal policies were chosen; knowledge of conservative and ideologically ambiguous policies still needs to be analyzed. In any case, there is evidence citizens know about some out-of-state policies, partially preserving their role in the see-policy/want-policy/get-policy chain of citizen-led policy diffusion.

Supplemental Material

Supplemental Material - Do Citizens Know About Other States’ Policy Choices?

Supplemental Material for Do Citizens Know About Other States’ Policy Choices? by Samuel F. Harper in Political Research Quarterly

Supplemental Material

Supplemental Material - Do Citizens Know About Other States’ Policy Choices?

Supplemental Material for Do Citizens Know About Other States’ Policy Choices? by Samuel F. Harper in Political Research Quarterly

Footnotes

Acknowledgments

I thank the staff at the University of Iowa Center for Social Science Innovation for managing the survey and assisting with the data. I also thank Frederick Boehmke, Julianna Pacheco, Caroline Tolbert, Samantha Zuhlke, Eric Plutzer, and discussants and audiences at the 2024 Midwest Political Science Association Annual Meeting and State Politics and Policy Conference and at the Universities of Iowa and Tampa, Mississippi State University, and the United States Air Force Academy for their helpful comments.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Samuel F. Harper

Supplemental Material

Supplemental material for this article is available online. Supplemental materials, including the online appendix and replication data, are available at .

Notes

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