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The Dynamics of State Policy Liberalism, 1936-2014

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Citation Caughey, Devin, and Christopher Warshaw. “The Dynamics of State
Policy Liberalism, 1936-2014: THE DYNAMICS OF STATE POLICY
LIBERALISM.” American Journal of Political Science 60.4 (2016):
899–913.

As Published http://dx.doi.org/10.1111/ajps.12219

Publisher Wiley Blackwell

Version Author's final manuscript

Citable link http://hdl.handle.net/1721.1/105870

Terms of Use Creative Commons Attribution-Noncommercial-Share Alike

Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/

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http://hdl.handle.net/1721.1/105870
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The Dynamics of State Policy Liberalism, 1936–2014

Devin Caughey∗

Department of Political Science
MIT

Christopher Warshaw†

Department of Political Science
MIT

First draft: March 5, 2014

This draft: March 4, 2015

Abstract

Applying a dynamic latent-variable model to data on 148 policies collected over eight
decades (1936–2012), we produce the first yearly measure of the policy liberalism of U.S.
states. Our dynamic measure of state policy liberalism marks an important advance
over existing measures, almost all of which are purely cross-sectional and thus cannot be
used to study policy change. We find that, in the aggregate, the policy liberalism of U.S.
states steadily increased between the 1930s and 1970s and then largely plateaued. The
policy liberalism of most states has remained stable in relative terms, though several
states have shifted considerably over time. We also find surprisingly little evidence
of multidimensionality in state policy outputs. Our new estimates of state policy
liberalism have broad application to the study of political development, representation,
accountability, and other important issues in political science.

We appreciate the excellent research assistance of Melissa Meek, Kelly Alexander, Aneesh Anand, Ti↵any
Chung, Emma Frank, Jose↵ Kolman, Mathew Peterson, Steve Powell, Charlotte Swasey, Lauren Ullmann,
and Amy Wickett. We also appreciate the willingness of Frederick Boehmke and Carl Klarner to generously
share their data. We are grateful for research support from the Dean of the School of Humanities, Arts, and
Social Sciences at MIT. All mistakes, however, are our own.

∗Assistant Professor, Department of Political Science, Massachusetts Institute of Technology,
caughey@mit.edu

†Assistant Professor, Department of Political Science, Massachusetts Institute of Technology, cwar-
shaw@mit.edu



“Change,” Chandler et al. (1974, 108) noted four decades ago, “is both methodologically

and substantively critical for any theory of policy.” This is true of both of the determinants of

government policies, such as shifts in public mood or changes in the eligible electorate (e.g.,

Stimson, MacKuen, and Erikson 1995; Husted and Kenny 1997), and of policy feedback on

political and social outcomes (e.g., Wlezien 1995; Campbell 2012). Theories of all these

phenomena rely explicitly or implicitly on models of policy change. Moreover, many of

the most ambitious theories focus not on individual policies or policy domains, but on the

character of government policy as a whole. In short, most theories of policymaking are

both dynamic and holistic: they are concerned with changes in the general orientation of

government policy.

Unfortunately, the literature on U.S. state politics, perhaps the most vibrant field for

testing theories of policymaking, relies almost exclusively on policy indicators that are either

measured at a single point in time (e.g., Wright, Erikson, and McIver 1987) or else cover

only a partial subset of state policy outputs (e.g., Besley and Case 2003).1 Static measures

are poorly suited to studying causes of policy change over time (Lowery, Gray, and Hager

1989; Ringquist and Garand 1999; Jacoby and Schneider 2009). And while domain-specific

measures may provide useful summaries of some aspects of state policy, such as welfare

spending (Mo�tt 2002) or gay rights (Lax and Phillips 2009a), they are at best imperfect

proxies for what is often the outcome of interest, the overall orientation of state policy.

In this paper, we develop a holistic yearly summary of the ideological orientation of state

policies, which we refer to as state policy liberalism. This measure is based on a unique

dataset of 148 policies, which covers nearly eight decades (1936–2014) and includes policy

domains ranging from labor regulation and civil rights to gun control and gay rights.2 Based

on these data, we estimate policy liberalism in each year using a dynamic Bayesian latent-

1. To our knowledge, the only existing holistic yearly summary of state policies is Jacoby and Schneider’s
(2009) measure of particularistic versus collective state spending priorities between 1982 and 2005. As we
discuss below, our measures di↵er substantially in time coverage, conceptual interpretation, and the data
used to construct them.

2. Both the policy data and our policy liberalism estimates will be made available to the public upon
publication of this article.

1



variable model designed for a mix of continuous, ordinal, and dichotomous policy indicators.

This measurement model enables us to make use of many indicators of policy liberalism,

thus substantially reducing measurement error on the estimates of our construct of interest.

Despite the disparate policy domains covered by our dataset, allowing for additional

latent policy dimensions does little to improve the predictive accuracy of the model. This

suggests that contrary to previous claims (e.g., Sorens, Muedini, and Ruger 2008), a single

latent dimension su�ces to capture the systematic variation in state policies. Consistent

with this conclusion, our dynamic measure is highly correlated with existing cross-sectional

measures of state policy liberalism as well as with issue-specific ideological scales.

Substantively, we find that while U.S. states as a whole have drifted to the left (that

is, they have increasingly adopted liberal policies), most have remained ideologically sta-

ble in relative terms. Across our entire time series, the most conservative states are in the

South, whereas California, New York, Massachusetts, and New Jersey are always among the

most liberal. The relative policy liberalism of a few states, however, has changed substan-

tially. Several Midwestern and Mountain states have become considerably more conservative

relative to the rest of the nation, whereas most of the Northeast has become more liberal.

Our new dynamic estimates can be used to study a wide variety of possible questions,

many of which are not easily investigated using cross-sectional measures. Potential topics

of study include the short- and long-term determinants of policy outputs, such as economic

development, political institutions, mass policy preferences, and electoral outcomes. Policy

liberalism could also be used as an independent variable, as a means of examining policy

feedback or other consequences of policy change. These measures thus o↵er new research av-

enues onto political development, representation, accountability, and other important issues

in political science.

The remainder of the paper is organized as follows. We begin by defining the concept of

policy liberalism and situating it in the literature on U.S. state politics and policy. Next, we

describe our policy dataset, our measurement model, and our yearly estimates of state policy

2



liberalism. We then provide evidence for the validity of our measure. We show that it is

highly correlated with existing measures of policy liberalism and related concepts, and that a

one-dimensional scale adequately accounts for systematic policy variation across states. The

penultimate section discusses potential applications of our measure, illustrating its usefulness

with an analysis of the policy e↵ects of voter registration laws. The final section concludes.

Measuring State Policies

Studies of state policy generally employ one of two measurement strategies: they either con-

sider policy separately using policy-specific indicators, or they construct composite measures

intended to summarize the general orientation of state policies within or across domains

(Jacoby and Schneider 2014, 568). Among studies in the first camp, some have focused on

whether or not states have particular policies. Lax and Phillips (2009a), for example, exam-

ine the representational congruence between a series of dichotomous state gay-rights policies

and state opinion majorities. Other studies have employed continuous policy-specific indi-

cators, such as welfare expenditures (Husted and Kenny 1997), tax rates (Besley and Case

2003), or minimum wages (Leigh 2008), which potentially have greater sensitivity to dif-

ferences between states. Whether dichotomous or continuous, policy-specific measures are

appropriate when the research question is limited to a particular policy area. But they are

suboptimal as summary measures of the general orientation of state policies, though this is

how they are often used.3

For this reason, a number of scholars have sought to combine information from multiple

policies, using factor analysis or other dimension-reduction methods to summarize them in

terms of one or more dimensions of variation. Dimension reduction has several advantages

over policy-specific measures. First, from a statistical point of view, using multiple indica-

3. Lax and Phillips (2009a, 369) claim that “using. . . policy-specific estimates” allows them to “avoid
problems of inference that arise when policy and opinion lack a common metric.” On a policy-by-policy basis
this is probably true. But evaluating congruence on state policy in general, or even just in the domain of
gay rights, requires that the policy-specific estimates of congruence be weighted or otherwise mapped onto a
single dimension. Thus, dimension reduction must occur at some point, whether at the measurement stage
or later in the analysis.

3



tors for a latent trait usually reduces measurement error on the construct of interest, often

substantially (Ho↵erbert 1966; Ansolabehere, Rodden, and Snyder 2008). Secondly, many

concepts require multiple indicators to adequately represent the full content or empirical do-

main of the concept. For example, the concept of liberalism, in its contemporary American

meaning, encompasses policy domains ranging from social welfare to environmental protec-

tion to civil rights. A measure of liberalism based on only a subset of these domains would

thus fare poorly in terms of content validation (Adcock and Collier 2001, 538–40). A final

benefit is parsimony. If a single measure can predict variation in disparate domains, then we

have achieved an important desideratum of social science: “explaining as much as possible

with as little as possible” (King, Keohane, and Verba 1994, 29).

Di↵erent works have identified di↵erent traits or dimensions underlying state policies.

Walker (1969), for example, creates an “innovation score” that captures the speed with

which states adopt new programs. Sharkansky and Ho↵erbert (1969) identify two latent

factors that structure variation in state policies, as do Sorens, Muedini, and Ruger (2008).

Hopkins and Weber (1976) uncover a total of five. But primarily the state politics literature

has focused on a single left–right policy dimension (e.g., Ho↵erbert 1966; Klingman and

Lammers 1984; Wright, Erikson, and McIver 1987; Gray et al. 2004). As a number of studies

have confirmed, states with minimal restrictions on abortion tend to ban the death penalty,

regulate guns more tightly, o↵er generous welfare benefits, and have progressive tax systems,

and vice versa for states with more restrictive abortion laws. Following Wright, Erikson, and

McIver (1987), we label this dimension policy liberalism.

What is policy liberalism? We conceptualize liberalism not as a logically coherent ideol-

ogy, but as a set of ideas and issue positions that, in the context of American politics, “go

together” (Converse 1964). Relative to conservatism, liberalism involves greater government

regulation and welfare provision to promote equality and protect collective goods, and less

government e↵ort to uphold traditional morality and social order at the expense of personal

autonomy. Conversely, conservatism places greater emphasis on the values of economic free-

4



dom and cultural traditionalism (e.g., Ellis and Stimson 2012, 3–6). Although the definitions

of liberalism and conservatism have evolved over time, with civil rights and then social issues

becoming more salient relative to economics (Ladd 1976, 589–93), these ideological cleavages

have existed in identifiable form since at least the mid-20th century (Schickler 2013; Noel

2014).

There are several things to note about this definition of policy liberalism. First, it is

comprehensive, in that it covers most if not all domains of salient policy conflict in American

domestic politics.4 This is not to say that policy liberalism explains all variation in state

policy, or that all policies are equally structured by this latent dimension. But it is a concept

that attempts to summarize, holistically, all the policy outputs of a state. Second, we define

policy liberalism solely in terms of state policies themselves. By contrast, some previous

measures (e.g., Sharkansky and Ho↵erbert 1969; Hopkins and Weber 1976) incorporate so-

cietal outcomes like infant mortality rates and high school graduation rates, muddying the

distinction between government policies and socio-economic conditions (Sorens, Muedini,

and Ruger 2008).

A final characteristic of our conceptualization of policy liberalism, which is particularly

crucial for our purposes, is that it is dynamic. Unlike, say, state political culture (Elazar

1966), which changes slowly if at all, policy liberalism can and does vary across time in

response to changes in public opinion, partisan control, and social conditions. Defining policy

liberalism as a time-varying concept is hardly controversial, but it does conflict with previous

operationalizations of this concept, all of which are cross-sectional. Cross-sectional measures

are problematic for two reasons. First, many are based on data from a long time span—over

a decade, in the case of Wright, Erikson, and McIver (1987)—averaging over possibly large

year-to-year changes in state policy (Jacoby and Schneider 2001). More importantly, cross-

sectional measures preclude the analysis of policy change, which not only is theoretically

limiting, but also inimical to strong causal inference since the temporal order of the variables

4. We do not include foreign policy in the domain of policy liberalism because states typically do not make
foreign policy.

5



cannot be established (Lowery, Gray, and Hager 1989; Ringquist and Garand 1999).

To our knowledge, the only existing time-varying measure that provides a holistic sum-

mary of state policy outputs is the measure of policy spending priorities developed by Jacoby

and Schneider (2009).5 This measure, available annually between 1982 and 2005, is estimated

with a spatial proximity model using data on the proportions of state budgets allocated to

each of nine broad policy domains (corrections, education, welfare, etc.). Jacoby and Schnei-

der interpret their measure as capturing the relative priority that states place on collective

goods versus particularized benefits, an important concept in the theoretical literature on

political economy (e.g., Persson and Tabellini 2006) as well as in empirical work on state

politics (e.g., Gamm and Kousser 2010).

Despite both being holistic yearly policy measures, policy liberalism and policy priorities

di↵er in important ways. As Jacoby and Schneider emphasize, policy liberalism and policy

priorities are conceptually distinct; indices of policy liberalism “simply do not measure the

same thing”as their policy priorities scale (2009, 19). For example, the policy priorities scale

is not intended to capture “how much states spend” but rather “how states divide up their

yearly pools of available resources” (Jacoby and Schneider 2009, 4). Consequently, variation

in the size of government, which lies at the heart of most liberal–conservative conflict (e.g.,

Meltzer and Richard 1981; Stimson 1991), is orthogonal to their measure. Another salient

di↵erence is that the policy priorities scale is based solely on state spending data. This

endows their measure with a direct and intuitive interpretation, but at the cost of excluding

taxes, mandates, prohibitions, and other non-spending policies that shape the lives of citizens

in equally important ways. Our policy liberalism measure resolves this trade-o↵ di↵erently,

emphasizing broad policy coverage at the possible expense of intuitive interpretation.

In summary, there is no existing time-varying measure of state policy liberalism, one of the

central concepts of state politics. Nearly all existing summaries of state policy orientations

are cross-sectional. Those that are dynamic either examine policy liberalism in a particular

5. For a cross-sectional implementation of this measure, see Jacoby and Schneider (2001).

6



policy area or, in the case of Jacoby and Schneider’s policy priorities scale, measure a di↵erent

concept entirely. Thus what is required is a measurement strategy that summarizes the global

ideological orientation of state policies using time-varying data that capture the full empirical

domain of policy liberalism.

Policy Data

As Jacoby and Schneider (2014) observe, composite measures of policy liberalism risk tau-

tology if they are derived from policy indicators selected for their ideological character.

Although the resulting scale may be a valid measure of policy liberalism, selection bias in

the component indicators undermines any claim that state policies vary along a single di-

mension. For this reason, we sought to make our dataset of state policies as comprehensive

as possible, so as to allow ideological structure to emerge from the data rather than imposing

it a priori. Given resource constraints and data limitations, we cannot claim to have con-

structed a random sample of the universe of state policies (if such a thing is even possible).

We are confident, however, that our dataset of 148 distinct policies is broadly representative

of the policy outputs of states across a wide range of domains. (For complete details on the

policies in our dataset, see the online appendix accompanying this article.)

To be included in our dataset, a policy had to meet the following criteria. First, it had

to be a policy output rather than a policy outcome (i.e., an aspect of the social environment

a↵ected by policy) or a government institution (i.e., one of the basic structures or rules of

the government). For example, we excluded state incarceration and infant-mortality rates,

which we considered outcomes. We also excluded indicators for whether states had particular

legislative rules or government agencies, which we classified as institutions.6 Second, the

policy had to be politically salient. To identify salient policies, we canvassed books and

articles on state politics, legal surveys of state policies, state party platforms, governors’

biographies, state-specific political histories, and government and interest-group websites.

6. The dataset used in this paper excludes electoral policies as well. We do this for the pragmatic reason
that scholars may want to use our measure to examine the e↵ect of such policies.

7



Third, the policies had to be comparable across all states. Many environmental, parks, and

farm policies, for example, are not comparable across states due to fundamental di↵erences

in state geography (e.g., coastal versus non-coastal). Some policies we normalized by an

appropriate baseline to make them more comparable.7 Finally, in keeping with our focus

on dynamics, data on a given policy had to be available in comparable form in at least five

di↵erent years.

The actual policy data themselves were obtained from many di↵erent sources, including

government documents, the Book of the States, interest-group publications, and various

secondary sources.8 Over four-fifths of the policies are ordinal (primarily dichotomous), but

the 26 continuous variables provide disproportionate information because they di↵erentiate

more finely between states.9 The policy domains covered by the dataset include

• abortion (e.g., parental notification requirements for minors)

• criminal justice (e.g., the death penalty)

• drugs and alcohol (e.g., marijuana decriminalization)

• education (e.g., per-pupil education spending; ban on corporal punishment)

• the environment (e.g., protections for endangered species)

• civil rights (e.g., fair employment laws; gay marriage)

• gun control (e.g., handgun registration)

• labor (e.g., right-to-work laws)

• social welfare (e.g., AFDC/TANF benefits)

• taxation (e.g., income tax rates)

and miscellaneous other regulations, such as fireworks bans and bicycle helmet laws.

To validate the comprehensiveness of our dataset, we can compare its coverage to other

datasets that were constructed for di↵erent purposes. For example, our policies cover 17

7. We adjusted all monetary expenditure and welfare benefit policies into 2012 dollars. We also adjusted
for cost-of-living di↵erences between states (Berry, Fording, and Hanson 2000).

8. In general, we tried to obtain primary sources for each policy indicator. When this proved impossible,
we obtained multiple secondary sources to corroborate the information about each policy in our database.

9. We standardized each continuous policy to ensure that the scales were comparable across policy areas.

8



of the 20 non-electoral policy areas contained in Sorens, Muedini, and Ruger’s (2008) state

policy database. Similarly, seven of the eight policy categories in the National Survey of State

Laws, a lengthy legal compendium of“the most-asked about and controversial”state statutes,

are represented in our dataset (Leiter 2008, xii).10 Our data also include 40 of the 56 policy

outputs in Walker’s (1969) policy innovation dataset and 21 of the 34 non-electoral policies

examined by Lax and Phillips (2011).11 The overlap between these last three datasets and

ours is particularly significant, because none of the three were constructed for the purpose of

studying the ideological structure of state policies. Even Sorens, Muedini, and Ruger (2008),

who do analyze policy in ideological terms, conceive of state policies as varying along two

dimensions. In sum, our dataset, while not a random sample of the universe of policies, is

broadly representative of available data on the salient policy activities of U.S. states.

Measurement Model

We use the policy dataset described above to construct yearly measures of state policy

liberalism. Like most previous work on the subject, we treat policy liberalism as a latent

variable whose values can be inferred from observed policy indicators. Our latent-variable

model (LVM), however, o↵ers several improvements over previous measurement strategies,

most of which have relied on factor analysis applied to cross-sectional data. First, we use

a Bayesian LVM, which unlike classical factor analysis provides straightforward means of

characterizing the uncertainty of the latent scores and also easily handles missing data by

imputing estimates on the fly (Jackman 2009, 237–8). Second, most of our policy indicators

are dichotomous variables, a poor fit for a factor-analytic model, which assumes that the

observed indicators are continuous. We therefore follow Quinn (2004) and specify a mixed

LVM that models continuous indicators with a factor-analytic model and ordinal (including

10. The categories are Business and Consumer, Criminal, Education, Employment, Family, General Civil,
Real Estate, and Tax. There are no real estate laws in our dataset because we could not locate comparable
time-varying data on these laws.
11. The remaining policies are missing either because time-varying data were not available or because the

policies are not su�ciently comparable across states.

9



dichotomous) variables with an item-response model. Third, our measurement model is

dynamic, both in that it allows policy liberalism to vary by year and in that it specifies a

dynamic linear model that links the measurement model between periods.

We parameterize policy liberalism as a latent trait ✓st that varies across states and years.

For each state s and year t, we observe a mix of J continuous and ordinal policies, denoted

yst = (y1st, . . . , yjst, . . . , yJst), whose distribution is governed by a corresponding vector of

latent variables y⇤st. We model y
⇤
st as a function of policy liberalism (✓st) and item-specific

parameters ↵t = (↵1t, . . . , ↵jt, . . . , ↵Jt) and � = (�1, . . . , �j, . . . , �J),

y⇤st ⇠ NJ(�✓st � ↵t,  ), (1)

where NJ indicates a J-dimensional multivariate normal distribution and  is a J ⇥ J

covariance matrix. In this application, we assume  to be diagonal, but this assumption

could be relaxed to allow for correlated measurement error across variables. Note that ↵jt,

which is analogous to the “di�culty” parameter in the language of item-response theory,

varies by year t, whereas the “discrimination” �j is assumed to be constant across time.

We accommodate data of mixed type via the function linking latent and observed vari-

ables. If policy j is continuous, we assume y⇤jst is directly observed (i.e., yjst = y
⇤
jst), just

as in the conventional factor analysis model. If policy j is ordinal, we treat the observed

yjst as a coarsened realization of y
⇤
jst whose distribution across Kj > 1 ordered categories is

determined by a set of Kj + 1 thresholds ⌧j = (⌧j0, . . . , ⌧jk, . . . , ⌧j,Kj ). Following convention,

we define ⌧j0 ⌘ �1, ⌧j1 ⌘ 0, and ⌧jKj ⌘ 1, and we set the diagonal elements of  that cor-

respond to ordinal variables equal to 1. As in a ordered probit model, yjst falls into category

k if and only if ⌧j,k�1 < y
⇤
jst  ⌧jk. Thus for ordinal variable j, the conditional probability

that y⇤jst ⇠ N(�j✓st � ↵jt, 1) is observed as yjst = k is

Pr(⌧j,k�1 < y
⇤
jst  ⌧jk | �j✓st � ↵jt) = Pr(y

⇤
jst  ⌧jk | �j✓st � ↵jt) � Pr(y

⇤
jst  ⌧j,k�1 | �j✓st � ↵jt)

=�(⌧jk � [�j✓st � ↵jt]) � �(⌧j,k�1 � [�j✓st � ↵jt]), (2)

10



where � is the standard normal CDF (Fahrmeir and Raach 2007, 329). In the dichotomous

case, where there are Kj = 2 categories (“0” and “1”), the conditional probability that yjst

falls in the second category (i.e., “1”) is

Pr(⌧j1 < y
⇤
jst  ⌧j2 | �j✓st � ↵jt] = �(⌧j2 � [�j✓st � ↵jt]) � �(⌧j1 � [�j✓st � ↵jt])

= �(�j✓st � ↵jt), (3)

which is identical to the conventional probit item-response model (Quinn 2004, 341).

We allow the ↵jt to vary by year to account for the fact that many policies (e.g., seg-

regation laws) trend over time towards universal adoption or non-adoption. The simplest

way to deal with this problem is to estimate the di�culty parameters anew in each year. A

more general approach, however, which pools information about ↵jt over time, is to model

the evolution of the ↵jt with a dynamic linear model, or DLM (West and Harrison 1997;

Jackman 2009, 471–2). In this application we use a local-level DLM, which models ↵jt using

a “random walk” prior centered on ↵j,t�1:

↵jt ⇠ N(↵j,t�1, �2↵). (4)

If there is no new data for an item in period t, then the transition model in Equation 4 acts

as a predictive model, imputing a value for ↵jt (Jackman 2009, 474). The transition variance

�2↵ controls the degree of smoothing over time. Setting �
2
↵ = 1 is equivalent to estimating

↵jt separately each year, and �
2
↵ = 0 is the same as assuming no change over time. We take

the more agnostic approach of estimating �2↵ from the data, while also allowing it to di↵er

between continuous and ordinal variables.

The parameters in an LVM cannot be identified without restrictions on the parameter

space (e.g., Clinton, Jackman, and Rivers 2004). In the case of a one-dimensional model,

the direction, location, and scale of the latent dimension must be fixed a priori. We identify

the location and scale of the model by post-processing the latent measure of state policy

11



liberalism to be standard normal. For the prior on the innovation parameter �↵, we use a

half-Cauchy distribution with a mean of 0 and a scale of 2.5 (Gelman 2006). The di�culty

and discrimination parameters are drawn from normal distributions with a mean of 0 and

a standard deviation of 10. We fix the direction of the model by constraining the sign of

a small number of the item parameters (Bafumi et al. 2005).12 We further constrain the

polarity by assigning an informed prior to the policy measure for four states in year t = 0

(Martin and Quinn 2002).13 We estimated the model using the program Stan, as called

from R (Stan Development Team 2013; R Core Team 2013).14 Running the model for 1,000

iterations (the first 500 used for adaptation) in each of 4 parallel chains proved su�cient to

obtain satisfactory samples from the posterior distribution.

Estimates of State Policy Liberalism

Estimating our measurement model using the policy data described earlier produces a mea-

sure of the policy liberalism of each state in each year 1936–2014. When interpreting these

estimates, one should bear in mind that the model allows the di�culty parameters ↵t to

evolve over time. As a result, aggregate ideological shifts common to all states will be par-

tially assigned to the policy di�culties. Since states did adopt increasingly liberal policies

over this period, the model partially attributes this trend to the increasing di�culty of con-

servative policies (and increasing “easiness” of liberal ones). If we modify the model so as to

hold the item di�culties constant over time, the policies of all U.S. states are estimated to

12. Specifically, we constrain continuous measures of state spending to have a positive discrimination pa-
rameter, which implies that more liberal states spend more money. We also constrain the polarity of four
dichotomous items. The discrimination of ERA ratification and prevailing wage laws are constrained to be
positive, while the discrimination of right to work laws and bans on interracial marriage are constrained to
be negative.
13. Note that we started the model in 1935 (t = 0) and discarded the first year of estimates. As a result, the

informed priors on ✓ for four states in year t = 0 have little e↵ect on the estimates of state policy liberalism
that we report in our analysis. We assign a N(1, 0.22) prior on ✓s0 to New York and Massachusetts, and a
N(�1, 0.22) prior for Georgia and South Carolina. Other states are given di↵use priors for ✓st.
14. Stan is a C++ library that implements the No-U-Turn sampler (Ho↵man and Gelman, Forthcoming), a

variant of Hamiltonian Monte Carlo that estimates complicated hierarchical Bayesian models more e�ciently
than alternatives such as BUGS.

12



1940 1975 2010

Figure 1: The geographic distribution of government policy liberalism in 1940, 1975, and
2010. Darker shading indicates liberalism; lighter shading indicates conservatism. The esti-
mates have been centered and standardized in each year to accentuate the shading contrasts.

have become substantially more liberal, especially between the 1930s and 1970s.15 We use a

time-varying model instead because it helps avoid the interpretational di�culties of assum-

ing that policies have the same substantive meaning across long stretches of time. The price

of this flexibility is that states’ policy liberalism scores are comparable over time primarily

in a relative sense.

Figure 1 maps state policy liberalism in 1940, 1975, and 2010. As is clear from this figure,

the geographic distribution of policy liberalism has remained remarkably stable, despite huge

changes in the distribution of mass partisanship, congressional ideology, and other political

variables over past seven decades. Throughout the period, Southern states had the most

conservative policies. This holds not only on civil rights, but on taxes, welfare, and a host

of social issues. By contrast, the most liberal states have consistently been in the Northeast,

Pacific, and Great Lakes regions. New York, for example, has consistently had the most

liberal tax and welfare policies in the nation, and it was also among the first states to adopt

liberal policies on cultural issues such as abortion, gun control, and gay rights.

The overall picture of aggregate stability, however, masks considerable year-to-year fluc-

tuation in policy liberalism as well as major long-term trends in certain states. These details

can be discerned more easily in Figure 2, which plots the yearly time series of individual states

15. In these years, U.S. states expanded their welfare responsibilities and tax bases while loosening a variety
of social restrictions. This aggregate trend towards more liberal policies largely ceased after 1980.

13



AL

AR

AZ

CA
CO

CT

DE

FL

GA

IAID

IL

IN
KS

KY LA

MA

MD

ME

MI
MN

MO

MS

MT

NC

NDNE
NH

NJ

NM
NV

NY

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VA

VT

WAWI

WV

WY

AK

AL AR

AZ

CA

CO

CT

DE

FL

GA

HI

IA

ID

IL

IN

KS

KY

LA

MA

MD

ME

MI

MN

MO

MS

MT

NC
ND

NE

NH

NJ

NM

NV

NY

OH

OK

OR

PA

RI

SC

SD
TN

TX

UT

VA

VT

WA

WI

WV

WY

−2

−1

0

1

2

3

1936 1950 1975 2000 2014
Year

S
ta

te
 G

ov
er

m
en

t P
ol

ic
y 

Li
be

ra
lis

m

South Mountain West Midwest Northeast Pacific Coast

Figure 2: State government policy liberalism, 1936–2014. The thicker black line tracks the
mean in each year, and the colored lines indicate the means in five geographic regions.

14



between 1936 and 2014. Due to explicit policy revisions as well as to policy“drift”relative to

other states, policy liberalism can change substantially between years, though cross-sectional

di↵erences between states are generally much larger than within-state changes. The variance

across states has also increased over time, possibly due to growing geographic polarization.

Figure 2 also shows that not all states have been ideologically stable. The policies of

Northeastern states became steadily more liberal over this time period. Whereas states like

Delaware, Maryland, and Vermont were once more conservative than average, by 2014 all

three had joined most of the rest of the Northeast in the top quartile of liberalism. Their

early adoption of gay marriage and other rights for homosexuals, for example, contrasts with

their slowness in passing racial anti-discrimination laws in the 1950s and 1960s. The welfare

benefits and regulatory policies of these states exhibited a similar liberalizing trajectory.

Several Midwestern, Mountain, and Southern states have followed the opposite trajectory.

Idaho, for example, became much more conservative over this period. In the 1930s–1950s,

Idaho actually had some of the most generous welfare benefits in the nation, but by the

early 2000s they were among the least generous. Louisiana too has shifted substantially to

the right. In the 1930s, Louisiana’s welfare benefits were the most generous in the South

and roughly equivalent to those of several Northern states, but they gradually become less

generous over the next few decades. Louisiana also waited longer than any other Southern

state to pass a durable right-to-work law, but it finally did so in 1976.16

These states’ shifts in policy liberalism track the evolution of their presidential partisan-

ship. For instance, in the presidential election of 1936, the first year in our dataset, Maine,

Vermont, and New Hampshire were the three most Republican states in the nation, but by

2012 all three (especially Vermont) were more Democratic than average. The opposite is true

of the Mountain West, which transformed from Democratic-leaning to solidly Republican.

On the whole, the 2010 map in Figure 1 matches contemporaneous state partisanship much

16. Louisiana passed a right-to-work law in 1954 but repealed it in 1956, when the populist Long faction of
the Democratic Party recaptured control of state government (Canak and Miller 1990). The unusual power
of this faction, forged by Governor and Senator Huey Long in the late 1920s, may help explain Louisiana’s
anomalously (for the region) liberal state policies in that era (Key 1949, 156–82).

15



better than the earlier maps, primarily because the South’s shift to the Republicans finally

aligned its partisanship to match its consistently conservative state policies.

Measurement Validity

Having illustrated the face validity of the policy liberalism estimates, we now conduct a

more systematic validation of our measure. We begin with convergent validation (Adcock

and Collier 2001), documenting the very strong cross-sectional relationships between our

estimates’ and existing measures of policy liberalism. We then turn to construct validation,

demonstrating that our policy liberalism scale is also highly correlated with measures of

theoretically related concepts, such as presidential partisanship. Finally, we show that our

policy liberalism scale is strongly related to domain-specific policy measures, and that the

predictive fit of the model barely increases if a second dimension is added to the measure-

ment model. Overall, this evidence corroborates our claim that a one-dimensional model

adequately captures the systematic variation in state policies, and that this dimension is

properly interpreted as policy liberalism.

Convergent Validation

If our estimates provide a valid measure of policy liberalism, they should be strongly related

to other (valid) measures of the same concept. Since ours is the first time-varying measure

of state policy liberalism, we must content ourselves with examining the cross-sectional

relationship between our measure and ones developed by other scholars at various points in

time. Figure 3 plots the cross-sectional relationships between our measure of policy liberalism

and six existing measures:

• “liberalness”/“welfare orientation” rank circa 1957 (Ho↵erbert 1966)17

• welfare-education liberalism in 1962 (Sharkansky and Ho↵erbert 1969)18

17. This index is based on mean per-recipient expenditures for 1952–61 for aid to the blind, old age
assistance, unemployment compensation, expenditure for elementary and secondary education, and aid to
dependent children. We compare Ho↵erbert’s (1966) scale with our measure of state policy liberalism in
1957 since this is the midpoint of the years he includes in his index.
18. This index is based on about twenty education and welfare policies. Note, however, that this index

16



AL AR

AZ

CA

CO

CT

DE

FLGA

IA

ID

IL

IN

KS

KY

LA

MA

MD

ME

MI
MN

MO

MS

MT

NC

ND

NE

NH

NJ

NM

NV

NY

OH
OK

OR

PA

RI

SC

SD

TN

TX

UT

VA

VT

WA

WI

WV

WYr=−0.76

1957

0

10

20

30

40

50

−2 −1 0 1 2
Policy Liberalism

Po
lic

y 
in

 ~
19

57
 (H

of
fe

rb
er

t, 
19

66
)

AL

AR
AZ

CA
CO

CT

DE

FL
GA

IA ID
IL

IN

KS

KY
LA

MA

MD
ME

MIMN

MO

MS

MT

NC

ND NE

NH

NJ

NM

NV

NY

OH

OK

OR
PA

RI

SC

SD

TN

TX
UTVA

VT
WA

WI

WV

WY

r=0.9
1973

−2

−1

0

1

2

−2 −1 0 1 2
Policy Liberalism

Po
lic

y 
in

 ~
19

73
 (K

lin
gm

an
 &

 L
am

m
er

s,
 1

98
4)

ALAR

AZ

CA

CO

CT

DE

FLGA

IA

ID

IL

INKS KY

LA

MA

MD
ME

MI

MN

MO

MS

MT

NCND

NE

NH NJ
NM

NV

NY

OH

OK

OR

PA

RI

SC

SD
TN

TX

UT
VA

VT

WA

WI

WV

WY

r=0.88

2000

−5.0

−2.5

0.0

2.5

5.0

7.5

−2 −1 0 1 2
Policy Liberalism

Po
lic

y 
in

 2
00

0 
(G

ra
y,

 e
t a

l, 
20

04
)

AL
AR

AZ

CA

CO CT

DE
FL

GA

IA

ID

IL

IN

KS

KY
LA

MA

MD
ME

MI

MN

MO

MS

MT

NC

ND
NE

NH NJ

NM

NV

NY

OH
OK OR

PA

RI

SC

SD

TN

TX

UT

VA
VT

WA

WI

WV

WY

r=0.85
1962

−2

−1

0

1

2

−2 −1 0 1
Policy Liberalism

Po
lic

y 
in

 1
96

2 
(S

ha
r. 

&
 H

of
f.,

 1
96

9)

AL AR

AZ

CA

CO

CTDE

FL

GA

IA

ID

IL

IN

KS

KY

LA

MA

MD

ME

MI
MN

MO

MS

MT

NC
ND

NH

NJ

NM

NY

OH

OK

OR
PA

RI

SC

SDTN
TX

UT
VA

VT
WA

WI

WV

WY

r=0.9

1980

−1

0

1

2

−1 0 1 2
Policy Liberalism

Po
lic

y 
in

 ~
19

80
 (E

W
M

, 1
99

3)

AK
AL AR

AZ

CA

CO

CT

DE

FL

GA

HI

IA

ID

IL

INKSKYLA

MA

MD

ME
MI

MN

MO
MS

MT

NC

ND

NE
NH

NJ

NM
NV

NY

OH

OK

ORPA

RI

SC

SD
TNTX

UT

VA

VTWA

WIWV

WY

r=0.84

2006

−5

0

5

10

−2 −1 0 1 2
Policy Liberalism

Po
lic

y 
in

 2
00

6 
(S

or
en

s,
 e

t a
l, 

20
08

)

Figure 3: Convergent validation: relationships between our policy liberalism estimates and
six existing measures. Fitted lines indicate loess curves.

• policy liberalism circa 1973 (Klingman and Lammers 1984)19

• policy liberalism circa 1980 (Wright, Erikson, and McIver 1987)20

• policy liberalism in 2000 (Gray et al. 2004)21

also includes several social outcomes, such as school graduation rates.
19. This index is based on data measured at a variety of points between 1961 and 1980 on state innovative-

ness, anti-discrimination policies, monthly payments for Aid to Families with Dependent Children (AFDC),
the number of years since ratification of the Equal Rights Amendment for Women, the number of consumer-
oriented provisions, and the percentage of federal allotment to the state for Title XX social services programs
actually spent by the state. We compare Klingman and Lammers’s (1984) scale with our measure of state
policy liberalism in 1973 since this is the midpoint of the years they include in their index.
20. This measure is based on state education spending, the scope of state Medicaid programs, consumer

protection laws, criminal justice provisions, whether states allowed legalized gambling, the number of years
since ratification of the Equal Rights Amendment for Women, and the progressivity of state tax systems.
We compare Wright, Erikson, and McIver’s (1987) scale with our measure of state policy liberalism in 1980
since this is roughly the midpoint of the years they include in their index.
21. This index is based on state firearms laws, state abortion laws, welfare stringency, state right-to-work

laws, and the progressively of state tax systems.

17



• policy liberalism in 2006 (Sorens, Muedini, and Ruger 2008)22

Each panel plots the relationship between our policy liberalism estimates (horizontal axis)

and one of the six existing measures listed above. A loess curve summarizes each relationship,

and the bivariate correlation is given on the left side of each panel.

Notwithstanding measurement error and di↵erences in data sources, our estimates are

highly predictive of other measures of policy liberalism. The weakest correlation, 0.76 for

Ho↵erbert (1966), is primarily the result of a few puzzling outliers (Washington, for example,

is the seventh-most conservative state on Ho↵erbert’s measure, whereas Wyoming is the

ninth-most liberal). In addition, all the relationships are highly linear. The only partial

exception is for Sorens, Muedini, and Ruger (2008), whose measure of policy liberalism

does not discriminate as much between Southern states as our measure, resulting in a flat

relationship at the conservative end of our scale.

In short, the very strong empirical relationships between our policy liberalism scale and

existing measures of the same concept provide compelling evidence for the validity of our

measure. It is worth noting that most of the existing scales were constructed explicitly with

the goal of di↵erentiating between liberal and conservative states. Thus their tight relation-

ship with our measure, which is based on a much more comprehensive policy dataset and

was estimated without regard to the ideological content of the policy indicators,23 suggests

in particular that we are on firm ground in calling our latent dimension “policy liberalism.”

Construct Validation

The purpose of construct (a.k.a. “nomological”) validation is to demonstrate that a mea-

sure conforms to well-established hypotheses relating the concept being measured to other

concepts (Adcock and Collier 2001, 542–3). One such hypothesis is that the liberalism of

a state’s policies is strongly related to the liberalism of its state legislature, though due to

22. This is the first principal component uncovered by Sorens, Muedini, and Ruger’s (2008) analysis of
over 100 state policies. They label this dimension “policy liberalism” and give the label “policy urbanism” to
the second principal component.
23. This is true except for the hard coding required to identify the latent scale.

18



factors such as legislative gridlock the relationship may not be perfect (e.g., Krehbiel 1998).

To measure legislative liberalism on a common scale, we rely on Shor and McCarty’s (2011)

estimates of the conservatism of members of state legislative lower houses. As Figure 4

demonstrates for presidential years between 1996 and 2008, states with more liberal policies

tend to have more liberal median legislators. Due possibly to the lingering Democratic ad-

vantage in Southern state legislatures, the relationship at the conservative end of the policy

spectrum is fairly flat, though by 2008 the relationship had become much more linear. The

correlation between legislative conservatism and policy liberalism has also strengthened over

time, from �0.51 in 1996 to �0.80 in 2008.

AK

AL

AZ CA

CO

CT

DE
FL

GA

HI

IA

ID

IL

IN

KS
KY

LA

MA

MD

ME

MI

MN

MO

MS

NC
NH

NJ

NM

NY

OH

OK

PA

RI

SC

SD

TN

TX

UT

VA
VT

WA

WI

WV

WY

r=−0.51

AKAK

ALALARAR

AZAZ

CACA

COCO

CTCT

DEDE

FLFL

GAGA

HI

IAIA

IDID

ILILININ

KSKS

KYKY
LALA

MAMA

MDMD

MEME

MIMI
MNMN

MOMO

MSMS

MTMT

NCNC

NDND

NHNH

NJNJ

NMNM

NVNV

NYNY

OHOH

OKOK ORORPAPA

RIRI

SCSC
SDSD

TNTN

TXTX

UTUT

VAVA

VTVT

WAWA

WIWI

WVWV

WYWY

r=−0.56

AK

ALAR

AZ

CA

CO

CT

DE

FL

GA

HI

IA

ID

IL

IN

KS
KY

LA

MA

MD

ME

MI MN

MO

MS
MT

NC

ND

NH

NJ

NM

NV

NY

OH

OK
ORPA

RI

SC

TN

TXUT

VA

VT

WA

WI

WV

WY

r=−0.69

AK

AL
AR

AZ

CA

CO

CT

DE

FL

GA

HI

IA

ID

IL
IN

KSKYLA

MD

ME
MI

MN

MO

MS

MT
NC

ND

NH

NJ
NY

OH

OR
PA

RI

SC SD

TN

TX

VA

WA

WI

WV

WY

r=−0.8

1996 2000

2004 2008

−1.0

−0.5

0.0

0.5

−1.0

−0.5

0.0

0.5

−1.5

−1.0

−0.5

0.0

0.5

−1.5

−1.0

−0.5

0.0

0.5

−2 −1 0 1 2 −2 −1 0 1 2

−2 −1 0 1 2 −2 −1 0 1 2

Policy Liberalism

M
ed

ia
n 

Le
gi

sl
at

or
 in

 S
ta

te
 H

ou
se

 (S
ho

r &
 M

cC
ar

ty
, 2

01
1)

Figure 4: The relationship between state policy liberalism and the conservatism of the median
member of the lower house of the state legislature (Shor and McCarty 2011), 1996–2008.

An analogous pattern of increasing association over time can be seen in an examination

of the relationship between policy liberalism and Democratic presidential vote share. It is

natural to hypothesize that both presidential vote and state policy liberalism are responsive

19



to the party and policy preferences of mass publics and thus should be correlated at the state

level. Since the anomalously Democratic partisanship of the “Solid South” would distort this

relationship, we focus on the non-South only. Even without Southerns states, however, pol-

icy liberalism and presidential vote are only weakly related in the early part of the period,

as Figure 5 shows. The correlation jumped to 0.58 in 1960 and continued to increase grad-

ually through 2012, when it reached nearly 0.9. This increasing association between policy

liberalism and presidential vote mirrors the growing alignment of party identification, pol-

icy preferences, and presidential vote at the mass level (Fiorina and Abrams 2008, 577–82).

The analysis of presidential vote thus provides further evidence for the validity of our pol-

icy liberalism scale. At same time, however, it suggests the limitations of presidential vote

share as a proxy for mass preferences before the 1960s, even in the non-South (contra, e.g.,

Canes-Wrone, Brady, and Cogan 2002).

Finally, we examine the relationship between our policy liberalism measure and its clos-

est analogue, Jacoby and Schneider’s (2009) policy priorities scale. As we emphasize above,

policy liberalism and policy priorities are di↵erent concepts. Moreover, the theoretical rela-

tionship between policy liberalism and preference for collective over particularistic spending

is not self-evident. Nevertheless, Jacoby and Schneider convincingly argue that in U.S. states

tend to target particularized policies at needy constituencies. Consistent with that expec-

tation, they find a moderately negative cross-sectional correlation between policy liberalism

and preference for collective goods.

Based on a similar analysis, we too find policy liberalism and policy priorities to be

negatively correlated, on the order of �0.5. As Figure 6 shows, their relationship atten-

uated somewhat between 1982 and 2005. Also, like Jacoby and Schneider (2009, 18–20),

we find that non-linearity in the measures’ relationship contributes to the weak correlation:

their association is much stronger among relatively liberal and particularistic states than on

the conservative/collective-good end of the spectrum. This seems to be driven in part by

Southern states, which always anchor the conservative end of our scale but seem to favor par-

20



AZ
CA

CO
CT

DE IA

ID

ILIN
KS MA

MD

ME

MI

MN
MO

MT
ND

NE

NH

NJ
NM

NV

NYOH

OR

PA RISD

UT

VT

WAWI

WY

r=0.12

AZ

CA

CO
CTDE

IA

ID
ILIN

KS

MA
MD

ME MI
MNMO

MT

NDNE

NH NJ
NM
NV

NYOH
OR PA

RI

SD

UT

VT

WA

WI
WY

r=0.12

AZ
CA

CO

CTDE

IA
ID IL
IN

KS

MAMD
ME

MI
MNMO

MT

ND
NE

NH NJ
NMNV NY

OH
ORPA

RI

SD

UT

VT

WA

WIWY

r=0.29

AZ
CA

CO
CTDE

IAID ILIN
KS

MA

MD

ME

MI

MNMO
MT

NDNE NH
NJ

NM
NV

NYOHOR PA

RI

SD

UT

VT

WA
WIWY

r=0.16

AZ CA
CO

CT
DE

IAID

IL
IN

KS

MAMD

ME

MIMN
MO

MT

ND
NE

NH
NJNM

NV
NYOH

OR

PA
RI

SD

UT

VT

WA

WIWY

r=0.44

AZ
CA

CO
CT

DE
IA ID ILIN

KS

MAMD

ME

MIMN
MO

MT
ND

NE NH NJ

NMNV
NYOH

ORPARISD

UT

VT

WA

WIWY

r=0.12

AK
AZ

CA
CO

CT
DE HI

IA
ID

IL
IN

KS

MA

MD

ME

MIMNMO MT
ND

NE

NH
NJNMNV

NY

OH OR
PA

RI

SD
UT

VT

WAWI
WY

r=0.58

AK

AZ

CA
CO

CT

DEIA

ID

IL
INKS

MD
ME MI

MNMO
MTND

NE

NH
NJ

NMNV

NY

OH OR
PA

SD UT

VT
WA WI

WY

r=0.58

AK

AZ

CA
CO

CT
DE

HI

IA

ID

IL
IN

KS

MA

MD
ME

MI
MN

MO
MT

ND

NE

NH
NJ

NMNV

NY
OHOR

PA

RI

SD
UT

VT

WA
WI

WY

r=0.61

AK
AZ

CA

CO
CTDE

HI
IA

ID

IL

IN
KS

MA

MDME
MI

MN

MOMTND

NE

NH
NJNMNV
NY

OH

OR
PA

RISD

UT

VT
WA

WI

WY

r=0.52

AK
AZ

CA
CO

CT
DE HIIA

ID

IL
INKS

MA
MD

ME
MI

MN
MO

MTND

NE
NH

NJNMNV
NYOH ORPA

RI

SD

UT

VT
WA

WI

WY

r=0.54
AKAZ

CA
CO

CT
DE

HI

IA

ID

IL
IN

KS

MAMDMEMI

MN

MO

MT

ND NE
NH

NJ
NM

NV

NY
OH ORPA

RI

SD

UT

VT
WA

WI

WY

r=0.71

AKAZ

CA

CO
CTDE

HIIA

ID

IL

IN
KS

MAMD

MEMI

MN

MO MT
ND

NE
NH

NJNM

NV

NY
OH

OR
PA RI

SD

UT

VT
WAWI

WY
r=0.72

AKAZ

CA
CO CT

DE

HIIA

ID

IL

IN
KS

MA
MD
MEMI

MN

MO MT
ND

NE
NH

NJ
NM

NV

NY

OH

OR
PA

RI

SD

UT

VT
WAWI

WY

r=0.67

AK
AZ

CA

CO CT
DE HIIA

ID

IL

INKS

MA
MD

MEMI
MNMO

MT

ND
NE

NH NJ
NM

NV

NY

OH
ORPA

RI

SD

UT

VT
WA
WI

WY

r=0.66

AK

AZ

CA

CO

CTDE

HI

IA

ID

IL

IN

KS

MA

MD
ME
MI

MN

MO
MT

ND

NE

NH
NJ

NM
NV

NY

OH ORPA

RI

SD

UT

VT

WAWI

WY

r=0.76

AKAK

AZAZ

CACA

COCO

CTCT
DEDE

HI

IAIA

IDID

ILIL

ININ
KSKS

MAMA

MDMD

MEMEMIMI MNMN
MOMO

MTMTNDND NENE

NHNH

NJNJ

NMNMNVNV

NYNY

OHOH OROR
PAPA

RIRI

SDSD

UTUT

VTVT
WAWA

WIWI

WYWY
r=0.81

AK

AZ

CA

CO

CTDE HI
IA

ID

IL

IN
KS

MA

MDME
MI MN

MO

MT
ND

NE

NH
NJ

NMNV

NY

OH
ORPA

RI

SD

UT

VT

WA
WI

WY

r=0.82

AK

AZ

CA

CO

CTDE

HI

IA

ID

IL

IN

KS

MAMD
MEMI

MN
MO MT

ND
NE

NH
NJNMNV

NY

OH

OR
PA

RI

SD

UT

VT

WAWI

WY

r=0.82

AK
AZ

CA

CO

CTDE

HI

IA

ID

IL

IN

KS

MAMD
ME

MI MN

MO MT
ND NE

NH

NJ
NMNV

NY

OH
OR

PA

RI

SD

UT

VT

WA
WI

WY
r=0.89

1936 1940 1944 1948

1952 1956 1960 1964

1968 1972 1976 1980

1984 1988 1992 1996

2000 2004 2008 2012

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

20

30

40

50

60

70

−1 0 1 2 −1 0 1 2 −1 0 1 2 −1 0 1 2
Policy Liberalism

D
em

oc
ra

tic
 P

re
si

de
nt

ia
l V

ot
e 

%
 (N

on
−S

ou
th

)

Figure 5: The relationship between state policy liberalism and Democratic presidential vote
share, 1936–2012 (non-South only).

21



ticularistic spending. The sources of this discrepancy between the two measures—perhaps

di↵erences in political culture, budgetary decentralization, or economic need—could be an

interesting topic for future research.

AK

AL
AR

AZ

CA

CO

CT

DE
FL

GA IA

ID

IL

IN

KS
KY

LA

MA

MD

ME

MI

MN
MO

MS
MT

NC
ND

NE

NH
NJ

NM
NV

NY

OH

OK OR

PA

RI

SC SD
TN

TX UT

VA
VT

WA

WI

WV

WY

r=−0.57

AK

AL
AR

AZ

CA

CO

CT

DE

FLGA IA

ID

IL

IN
KS

KY
LA

MA

MD
ME

MI

MN

MO
MS MT

NCND

NE
NH NJ

NM

NV

NY

OH

OK OR

PA RI

SC
SD

TN

TX
UTVA

VTWA

WI

WV

WY

r=−0.55

AK

AL
AR

AZ

CA

CO

CT

DE

FLGA IA

ID

IL

IN
KS

KYLA

MA

MD

ME

MI
MNMOMS

MT

NC
ND

NE

NH

NJ
NM

NV

NY

OH

OK
OR

PA RI
SC

SD

TN

TX

UT

VA

VTWA

WI
WV

WY

r=−0.44

AK

AL

AR
AZ

CA

CO

CT

DE

FLGA IA

ID

IL

IN

KS

KYLA

MA

MD

ME

MI
MN

MO
MS

MT

NC

ND

NE NH

NJ
NM

NV

NY

OH

OK OR

PA RI

SC

SD

TN

TX
UT

VA VT
WAWI

WV

WY

r=−0.33

1982 1990

1998 2005

−0.2

−0.1

0.0

0.1

0.2

−0.2

−0.1

0.0

0.1

0.2

−2 −1 0 1 2 −2 −1 0 1 2
Policy Liberalism

Po
lic

y 
P

rio
rit

ie
s 

(J
ac

ob
y 

&
 S

ch
ne

id
er

, 2
00

9)

Figure 6: The relationship between policy liberalism and policy priorities (Jacoby and Schnei-
der 2009) in selected years, 1982–2005.

Dimensionality

Our one-dimensional model of state policies implies that a single latent trait captures system-

atic policy variation across states. This is not to say that it captures all policy di↵erences,

but it does imply that once policies’ characteristics and states’ policy liberalism are ac-

counted for, any additional variation in state policies is essentially random. This assumption

would be violated if there were instead multiple dimensions of state policy, as some schol-

22



ars have claimed. Given that roll-call alignments in the U.S. Congress were substantially

two-dimensional for much of the 20th century (Poole and Rosenthal 2007), it is not un-

reasonable to suspect that state policies might be as well. As we demonstrate, however, a

one-dimensional model captures state policy variation surprisingly well, and there is little

value to increasing the complexity of the model by adding further dimensions.

AK

ALAR

AZ

CA

CO

CT

DE

FL

GA

HI

IA

ID

IL

INKSKYLA

MA

MDME

MI

MN

MOMS

MT

NC

ND NE

NH

NJNMNV NY

OHOK

OR

PA

RI

SC SD

TN

TXUTVA

VT

WA

WI

WV

WY

r=0.79

2011

5

10

−2 −1 0 1 2
Policy Liberalism

A
bo

rt
io

n 
Po

lic
y 

(N
A

R
A

L,
 2

01
2)

AK

ALAR

AZ

CA

CO

CT

DE

FLGA

HIIA

ID

IL

INKS

KY

LA

MA

MD

ME

MI

MN

MOMS

MT

NC

ND NE

NH

NJ

NM

NV

NY

OHOK

OR

PA

RI

SC

SDTN

TXUT VA

VT

WA

WI

WVWY

r=0.85

2008

0.00

0.25

0.50

0.75

1.00

−2 −1 0 1 2
Policy Liberalism

G
ay

 R
ig

ht
s 

(L
ax

 a
nd

 P
hi

lli
ps

, 2
00

9)

AK
AL

AR

AZ

CA

CO

CT

DE

FL

GA

HI

IA

ID

IL
IN

KS
KY

LA

MA
MD

ME

MI

MN

MOMS
MT

NC

ND

NE

NH

NJ

NM
NV

NY

OH

OK

OR

PA

RI

SC

SD
TN

TX

UT

VA

VTWA
WI

WV
WY

r=0.72

1991

10

20

30

−2 −1 0 1 2
Policy Liberalism

E
nv

iro
nm

en
ta

l I
nn

ov
at

io
n 

(G
re

en
 In

de
x,

 1
99

1−
19

92
)

AL

AR

AZ

CA

CO

CT

DEFL
GA

HI

IA

ID
IL

IN

KS

KY

LA

MA

MDME

MI
MN

MO

MS

MT

NC

ND

NENH

NJ

NM

NV

NY

OH
OK

OR

PA

RI

SC

SD

TNTX

UT

VA

VT

WAWI

WV
WY

r=0.76
1988

200

400

600

800

1000

−2 −1 0 1 2
Policy Liberalism

Av
er

ag
e 

M
on

th
ly

 A
FD

C
 B

en
ef

its
 P

er
 F

am
ily

 (2
01

0 
do

lla
rs

) 

Figure 7: Relationships between policy liberalism and four issue-specific scales (abortion
rights, environmental protection, gay rights, and welfare benefits).

One fact in support for unidimensionality is that the most discriminating policies in our

dataset—those most strongly related to the latent factor—span a wide range of issues, in-

cluding racial discrimination, women’s rights, gun control, labor law, energy policy, criminal

23



Table 1: Correlations between policy liberalism scales estimated using economic, social,
racial, and all policies. The unit of analysis is the state-year. The racial policy scale is
estimated for the 1950–70 period only.

All Economic Social

Economic 0.92

Social 0.84 0.69

Racial 0.86 0.68 0.55

rights, and welfare policy. Additional evidence is provided by the relationships between pol-

icy liberalism and four issue-specific scales: NARAL’s abortion rights scale (NARAL 2012),

the Green Index of Environmental Innovation in 1991–92 (Hall and Kerr 1991; Ringquist

and Garand 1999), a gay rights index derived from Lax and Phillips (2009b), and average

AFDC benefits per family in each state (Mo�tt 2002). As Figure 7 shows, policy liberalism

accurately predicts variation within each of these disparate policy areas.

We can explore this question at a higher level of generality by scaling state policies within

each of three broad issue domains: economic, social, and racial.24 Policy cleavages in the

mass public and in the U.S. Congress are often considered to di↵er across these domains,

especially earlier in the 1936–2014 period (e.g., Layman, Carsey, and Horowitz 2006; Poole

and Rosenthal 2007). As the first column of the correlation matrix in Table 1 shows, however,

each domain-specific scale is strongly related to the policy liberalism scale based on all

policies. The domain-specific scales are also highly correlated with each other, with the

correlation being weakest for racial and social policies (estimated for 1950–70 only). On the

whole, Table 1 provides strong evidence that variation in state policies is one-dimensional

and does not vary importantly across issue domains.

As a final piece of evidence, we show that allowing for multiple latent dimensions does not

24. Because cross-state variation in civil rights policies is concentrated in the 1950–70 period, we estimate
the racial policy dimension for these two decades only.

24



substantially improve our ability to predict policy di↵erences between states. As our measure

of model fit we use percentage correctly predicted (PCP), which for binary variables is the

percentage of cases for which the observed value corresponds to its model-based predicted

value (0 or 1). In order to include ordinal and continuous variables in this calculation, we

convert them into binary variables by dichotomizing them at a threshold randomly generated

for each variable. We estimate one and two-dimensional probit IRT models separately in each

year using the R function ideal (Jackman 2012), which automatically calculates PCP. We

then evaluate how much the second dimension improves PCP (adding dimensions cannot

decrease PCP).

Based on this method, we find little evidence that adding dimensions improves our ability

to account for the data. In the average year, a one-dimensional model correctly classifies

82% of all dichotomized policy observations. Adding a second dimension increases average

PCP by only 1.5 percentage points. This improvement in model fit is less than the increase

in fit that is used in the congressional literature as a barometer of whether roll-call voting

in Congress has a one-dimensional structure (Poole and Rosenthal 2007, 33–4). Further,

the minimal improvement in model fit gained from adding a second dimension is consistent

across time—even during the mid-century heyday of two-dimensional voting in Congress.

Taken as a whole, the evidence supports two conclusions. First, a single latent dimension

captures the vast majority of policy variation across states across disparate policy domains.

This is true even at times when national politics was multidimensional. Second, the approxi-

mately 20% of cross-sectional policy variation not captured by a one-dimensional model does

not seem to have a systematic structure to it, or at least not one that can be described by

additional dimensions.

Substantive Applications

Our dynamic measure of policy liberalism opens up multiple avenues of research not possible

with cross-sectional measures. Most obviously, as we have shown, it permits descriptive

25



analyses of the ideological evolution of state policies over long periods of time. But the

availability of a dynamic measure also facilitates causal analyses that incorporate policy

liberalism as an outcome, treatment, or control variable. In particular, because it is available

for each state-year, our measure can be used in time-series–cross-sectional (TSCS) research

designs, which leverage variation across both units and time. The fact that our estimates

are available for nearly 80 years is especially valuable because TSCS estimators can perform

poorly unless the number of time units is large (e.g., Nickell 1981).

For example, scholars could examine how the cross-sectional relationship between state

public opinion and policy liberalism has evolved over time (Burstein 2003); estimate the state-

level relationship between changes in opinion and changes in policy (cf. Stimson, MacKuen,

and Erikson 1995); or analyze how interest groups or electoral institutions moderate the

opinion–policy link (cf. Gray et al. 2004; Lax and Phillips 2011). Or scholars could evaluate

the policy e↵ects of electoral outcomes or the partisan composition of state government

(cf. Erikson, Wright, and McIver 1989; T. Kousser 2002; Besley and Case 2003; Leigh 2008).

An alternative approach would be to analyze policy liberalism as a cause rather than an

e↵ect. For example, one prominent view is that citizens respond“thermostatically”to changes

in policy by moving in the ideologically opposite direction (Wlezien 1995). A related perspec-

tive argues that voters compensate for partisan e↵ects on policy through partisan balancing

(e.g., Erikson 1988; Alesina, Londregan, and Rosenthal 1993). Other scholars, however,

highlight the positive feedback e↵ects of policy changes (e.g., Pierson 1993; Campbell 2012).

Our policy liberalism estimates open up ways of adjudicating among these theories using

state-level TSCS designs.

The Policy E↵ects of Voter Registration Reforms

To illustrate the kinds of analyses made possible by our estimates, we conduct a brief inves-

tigation into the policy e↵ects of reforms designed to make voter registration easier. While

debate over such reforms often focuses on e↵ects on turnout or partisan advantage, their ef-

26



fects on policy are arguably most important.25 One intuitive theoretical prediction, derived

from median-voter models of redistribution, is that lowering registration barriers makes the

electorate larger and poorer, which in turn increases political support for redistributive (i.e.,

liberal) policies (Meltzer and Richard 1981; Husted and Kenny 1997).

The policy consequences of registration regulations specifically have been examined by

Besley and Case (2003, 35–7), who using a fixed-e↵ect (FE) framework find liberalizing

e↵ects of lower registration barriers on five state taxation and spending policies in the period

1958–98. Besley and Case’s two-way FE specification improves substantially over cross-

sectional comparisons, which cannot control for unobserved di↵erences between states. An

important weakness of their specification, however, is that it assumes that states did not

trend in di↵erent directions over the period they examine.26 Figure 2 suggests, however,

that this assumption is false (see, e.g., the liberalizing trend among Northeastern states).

The likely consequence is that Besley and Case’s e↵ect estimates are much too large.

We replicate and extend Besley and Case’s analysis, examining the policy e↵ects of three

electoral policies—“motor voter”laws, election-day registration, and mail-in registration—on

state policy liberalism between 1950 and 2000.27 To guard against di↵erential time trends,

we use a more conservative specification that includes a lagged dependent variable (LDV)

as well as state and year FEs.28 One advantage of a long time series is the finite-sample

bias of LDV-FE models is of order 1/T and thus decreases rapidly as the number of time

units increases (Beck and Katz 2011, 342). Table 2 reports the estimated e↵ect estimates,

all of which are positive and, except for motor voter registration, distinguishable from 0.

In terms of substantive magnitude, these estimates imply that making voter registration

easier increases the probability of a state adopting a liberal law by about 1 percentage point.

25. See, for example, Key’s (1949) and J. M. Kousser’s (1974) analyses of the policy e↵ects of su↵rage
restrictions in the post-Reconstruction South.
26. Besley and Case (2003) do include a few time-varying demographic controls, but these are unlikely to

account for di↵erential state trends.
27. We obtained data on the first two policies from Besley and Case (2003) and data on the third from

Springer (2014).
28. Following Besley and Case (2003), we define a unit-year as “treated” by a registration policy if that

policy was in e↵ect at the last election.

27



Consistent with our concern about state-specific trends, the estimates from a simple two-way

FE model (not shown) are all an order of magnitude larger than their LDV-FE counterparts.

Table 2: E↵ect of Electoral Reforms on State Policy Liberalism

Policy

Motor voter registration 0.012
(0.013)

Election day registration 0.035⇤⇤

(0.017)

Mail-in registration 0.021⇤⇤

(0.011)

Lagged Policy 0.925⇤⇤

(0.008)

Constant 0.007
(0.026)

FE for state X
FE for year X

Observations 2,581
R2 0.983
Adjusted R2 0.983

Note: ⇤p<0.1; ⇤⇤p<0.05

Though brief, this application highlights several advantages of our measure of policy lib-

eralism. First, its TSCS structure enables us to exploit within-state variation in institutions

such as registration regulation. Second, its long time series permits the use of estimators,

such as LDV-FE models, whose performance improves as T increases. Third, the precision

of our composite measure relative to any individual indicator of liberalism means allows us

to detect small but meaningful e↵ects, such as the ones reported in Table 2.

28



Conclusion

This paper has addressed a major gap in the state politics literature: the lack of a measure

of state policy liberalism that varies across time. Using a dataset covering 148 policies and a

latent-variable model designed for a mix of ordinal and continuous data, we have generated

estimates of the policy liberalism of every state in every year for the past three-quarters

of a century. As indicated by their high correlations with existing measures of state policy

liberalism as well as with domain-specific indices, our estimates exhibit strong evidence of

validity as a measure of policy liberalism.

Our yearly estimates of policy liberalism are illuminating for their own sake, revealing

historical patterns in the development of state policymaking that would be hard to discern

otherwise. But they also open up research designs that leverage temporal variation in state

policies to explore questions involving the causes and e↵ects of policy outcomes. These

topics include the policy e↵ects of public mood, electoral outcomes, interest groups, and

institutions, as well as the consequences of policy change on political attitudes and behavior.

The relevance of this paper extends well beyond the field of state politics. In addition to

facilitating the study of topics of general significance, our measurement model could be ap-

plied to policymaking by local governments (cf. Tausanovitch and Warshaw 2014) as well as

in cross-national studies. Even more generally, our dynamic approach to measurement helps

to illustrate the value of data-rich, time-varying measures of important political concepts

like policy liberalism.

29



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Springer.

Wlezien, Christopher. 1995. “The Public as Thermostat: Dynamics of Preferences for Spend-

ing.” American Journal of Political Science 39 (4): 981–1000.

Wright, Gerald C., Robert S. Erikson, and John P. McIver. 1987.“Public Opinion and Policy

Liberalism in the American States.” American Journal of Political Science 31 (4): 980–

1001.

36



Online Appendix: “The Dynamics of State Policy Liberalism,

1936–2012”

Table 1: Description of Policies

Policy Years Description Sources

Abortion Policies:

Access for Contraceptives 1974-2014 Can pharmacies dispense emergency contraception with-
out a prescription?

[78, 100, 106, 104]

Forced Counseling before Abortions 1992-2014 Does the state mandate counseling before an abortion? [78, 100, 136]
Forced Counseling before Abortions 1973-1991 Does the state mandate counseling before an abortion? [78, 100, 136]
Legal Abortion Pre-Roe 1967-1973 Did the state allow abortion before Roe v. Wade? [84, 48]
Parental Notification/Consent Required for
Abortion

1983-2014 Does the state require parental notification or consent
prior to a minor obtaining an abortion?

[78, 100, 99, 52]

Partial Birth Abortion Ban 1996-2000 Does the state ban late-term or partial birth abortions? [78, 100, 8, 51]
Medicaid for Abortion 1981-2014 Does the state’s Medicaid system pay for abortions? [78, 100, 53, 8, 96]
Criminal Justice Policies:

Age Span Provisions for Statutory Rape 1950-1998 Does a state adopt an age span provision into its statutory
rape law which e↵ectively decriminalizes sexual activity
between similar-aged teens?

[26]

Death Penalty 1936-2014 Has the state abolished the death penalty? [32]
Drug & Alcohol Policies:

Beer Keg Registration Requirement 1978-2013 Does the state require the registration upon purchase of a
beer keg?

[78, 120, 176]

Decriminalization of Marijuana Possession 1973-2014 Is marijuana possession a criminal act? [127, 86]
Medical Marijuana 1996-2014 Is it legal to use marijuana for medical purposes? [91, 109]
Minimum Legal Drinking Age 21 1936-1985 Does the state have a minimum legal drinking age of 21? [119]
Smoking Ban - Workplaces 1995-2014 Does the state ban smoking in all workplaces? [11, 25]
Smoking Ban - Restaurants 1995-2014 Does the state ban smoking in restaurants? [11, 25]
Zero Tolerance (<.02 BAC) for Underage
Drinking

1983-1995 Does the state have a Zero Tolerance law for blood alcohol
levels <0.02 for individuals under age 21?

[119]

Education Policies:

Allow Ten Commandments in Schools 1936-2013 Does the state allow the Ten Commandments to be posted
in educational institutions?

[35, 5]

Ban on Corporal Punishment in Schools 1970-2014 Does the state ban corporal punishment in schools? [78, 50]
Education Spending Per Pupil 1936-2009 What is the per capita spending on public education per

pupil based on daily average attendance?
[151]

Moment of Silence Required 1957-2014 Does the state have a mandatory moment of silence period
at the beginning of each school day?

[35, 108, 79]

Per Student Spending on Higher Education 1988-2013 What is the per student subsidy for higher education? [144]
Teacher Degree Required - High School 1936-1963 In what year does the state require high school teachers

to hold a degree?
[112]

Teacher Degree Required - Elementary 1936-1969 In what year does the state require elementary school
teachers to hold a degree?

[112]

School for Deaf 1936-1950 In what year did the state establish residential schools for
the deaf?

[163]

State Library System 1936-1955 In what year did the state establish a library system? [162]
Compulsory Education - Age 1936-1939 At what age are children allowed to leave school? [154]
Environmental Policies:

Air Pollution Control Acts (Pre-CAA) 1947-1967 Does the state have an air pollution control act (Pre-Clean
Air Act)?

[6, 102]

Bottle Bill 1970-2014 Does the state require a deposit on bottles paid by the
consumer and refunded when the consumer recycles?

[29]

CA Car Emissions Standard 2003-2012 Does the state adopt California’s car emissions standards
(which are more stringent than the federal level)?

[92]

Electronic Waste Recycling Program 2000-2014 Does the state have a recycling program for electronic
waste?

[37, 146, 36]

Endangered Species Act 1969-2014 Does the state have an endangered species act? [78, 146, 13]
Environmental Protection Act 1969-2014 Does the state have its own version of the federal National

Environmental Policy Act?
[78, 90, 178]

Greenhouse Gas Cap 2006-2014 Does the state have a binding cap on greenhouse gas emis-
sions in the utility sector?

[134, 19, 23]

1



Description of Policies – Continued from previous page

Policy Years Description Sources

Public Benefit Fund 1996-2014 Does the state have a public benefit fund for renewable
energy and energy e�ciency?

[24, 118, 31]

Solar Tax Credit 1975-2014 Does the state have a tax credit for residential solar in-
stallations?

[78, 121, 177]

Gambling Policies:

Casinos Allowed 1977-2012 Does the state allow casinos? [10]
Lottery Allowed 1964-2014 Does the state have a lottery? [132, 97]
Gay Rights Policies:

Ban on Discrimination - Public Accommoda-
tion

1989-2014 Does the state ban discrimination against gays in public
accommodations?

[58]

Civil Unions and Gay Marriage 2000-2012 Does the state allow civil unions or gay marriage (ordinal)? [78, 115, 110]
Employment Discrimination Protections 1982-2014 Does the state forbid employment discrimination on the

basis of sexual orientation and/or sexual identity?
[78, 115, 116]

Hate Crimes Ban 1999-2014 Are hate crimes explicity illegal in the state? [78, 58, 131]
Sodomy Ban 1962-2003 Does the state forbid sodomy? [43, 9]
Gun Control Policies:

Assault Weapon Ban 1989-2014 Are assault weapons banned in the state? [78, 68, 175]
Background Check - Dealer Purchase 1936-1993 Does the state require a background check on gun pur-

chases from dealers?
[78, 175, 69]

Background Check - Private Sales 1936-2014 Does the state require a background check on privately
sold guns?

[78, 175, 74]

Gun Dealer Licenses 1936-2014 Does the state have any license requirements for manufac-
turers or dealers?

[78, 175, 70]

Gun Purchases - Waiting Period 1936-2014 Does the state have a waiting period for gun purchases? [78, 175, 75]
Open Carry Law 1961-2014 Is there an open carry law for guns? [78, 175]
Saturday Night Special 1974-2013 Does the state ban “Saturday Night Special” handguns? [78, 175, 71]
“Stand Your Ground” Law 1993-2014 Does the state have a “stand your ground” law? [78, 175, 73]
Gun Registration 1936-2014 Does the state have a registration requirement for guns? [78, 175, 72]
Immigration Policies:

English as O�cial Language 1961-2014 Is English the state’s o�cial language? [38]
Instate Tuition for Immigrants 2001-2014 Does the state allow in-state tuition for illegal immigrants? [105]
Labor Rights Policies:

Age discrimination ban 1936-1999 Does the state ban age discrimination in hiring? [124, 123, 67]
Anti-Injunction Act 1936-1966 Does the state have an anti-injunction law? [170]
Collective Bargaining - State Employees 1966-1996 Does the state have collective bargaining rights for state

government employees?
[46, 27, 174]

Collective Bargaining - Teachers 1960-1996 Does the state have collective bargaining rights for local
teachers?

[34, 113, 114, 80,
46, 27, 174]

Disability Discrimination Ban 1965-1990 Does the state ban discrimination against disabled people? [62]
Merit System for State Employees 1936-1953 Does the state have a merit system for state employees? [148]
Minimum Wage above Federal Level 1968-2012 Is the state’s minimum wage above the federal level? [139, 140, 141, 166,

167, 168, 171]
Minimum Wage for Men 1944-1968 Does the state have a minimum wage for men? [139, 140, 141, 166,

167, 168, 171]
Minimum Wage for Women 1936-1980 Does the state have a minimum wage for women? [139, 140, 141, 166,

167, 168, 171]
Prevailing Wage Law 1936-2014 Does the state have prevailing wage laws? [157, 66]
Right to Work Law 1944-2014 Is the state a right-to-work state? [4, 28, 85]
State Pension System 1936-1960 When did the state establish its pension system? [149]
Temporary Disability Insurance 1945-2014 Does the state have a temporary disability insurance pro-

gram?
[158]

Unemployment Compensation 1937-2014 What is the maximum weekly amount of unemployment
benefits?

[137, 165]

Workers’ Compensation 1936-1947 Has the state established workers’ compensation? [41, 169, 41]
Child Labor Work Certificates 1936-1939 Does the state require employment certificates for child

labor (14 and 15)?
[93, 173, 94, 95,
161, 81, 88, 89, 83]

Labor Relations Acts 1937-1966 Does the state have a Labor Relations Act? [103, 164, 172, 88,
89, 83]

Licensing Policies:

Chiropractor Licensing 1936-1951 When did the state require licensing for chiropractors? [150]
Dentist Licensing 1936-1951 When did the state require licensing for dentists? [150]
Architect Licening 1936-1951 When did the state require licensing for architects? [150]
Beautician Licensing 1936-1951 When did the state require licensing for beauticians? [150]
Pharmacist Licensing 1936-1951 When did the state require licensing for pharmacists? [150]
Engineer Licensing 1936-1951 When did the state require licensing for engineers? [150]

2



Description of Policies – Continued from previous page

Policy Years Description Sources

Nurse Licensing 1936-1951 When did the state require licensing for nurses? [150]
Accountant Licensing 1936-1951 When did the state require licensing for accountants? [150]
Real Estate Licensing 1936-1951 When did the state require licensing for real estate agents? [150]
Misc. Regulatatory Policies:

Anti-sedition Laws 1936-1955 Does the state have anti-sedition laws? [1]
Compulsory Sterilization 1945-1974 Does the state have a forced sterilization program (di-

rected toward the disabled, delinquent, etc.)?
[63]

Grandparents’ Visitation Rights 1964-1987 Does the state have a law guaranteeing grandparents’ vis-
itation rights?

[45, 57]

Hate Crimes Ban 1981-2014 Are hate crimes explicity illegal in the state? [60]
Urban Housing - Enabling Federal Aid 1936-1953 Does the state have a law enabling federal housing aid? [59]
Urban Housing - Direct State Aid 1939-1951 Does the state provide direct aid for urban housing? [30]
Living Wills 1976-1992 Does the state have a law permitting individuals control

over the use of heroic medical treatment in the event of a
terminal illness?

[55]

Pain and Su↵ering Limits in Lawsuits 1961-2012 Are there limits on damages for pain and su↵ering in law-
suits?

[78, 160]

Physician-assisted suicide 1998-2014 Does the state allow physician-assisted suicide? [78, 130, 126]
Planning Laws Required for Local Gov. 1961-2007 Does the state have a law authorizing or requiring growth-

management planning?
[128]

Protections Against Compelling Reporters to
Disclose Sources

1936-2013 Does the state have a Shield Law protecting journalists
from revealing their sources?

[78, 135, 129]

Rent Control Prohibition 1950-2014 Does the state prohibit the passage of rent control laws in
its cities or municipalities?

[78, 101, 61]

Religious Freedom Restoration Act 1993-2014 Did the state pass the Religious Freedom Restoration Act? [54]
State Debt Limitation 1936-1966 In what year did the state establish debt limitation? [56]
Municipal Home Rule 1936-1961 Did the state pass a law enabling voters to adopt a mu-

nicipal home rule charter?
[87]

Lemon Laws 1970-2014 Did the state pass a law protecting consumers who pur-
chase automobiles which fail after repeated repairs?

[78, 3, 76, 138]

Utility Regulation 1936-1960 In what year did the state regulate utilities? [145]
Cruelty to Animals 1936-2014 Has the state made aggravated animal cruelty a first- or

second-o↵ense felony?
[78, 14, 12, 42]

Racial Discrimination Policies:

School Segregation 1936-1953 Did the state require segregation in public schools? [78, 98, 133, 33, 16]
Ban on Interracial Marriage 1936-1967 Does the state have a law banning interracial marriages? [78, 98, 133, 33, 16]
Banning discrimination in public accommoda-
tions (pre-CRA)

1936-1963 Does the state pass a law (with adminstrative enforce-
ment) banning discrimination in public accomodations
(pre-Civil Rights Act)?

[78, 82, 22]

Banning discrimination in public accommoda-
tions (post-CRA)

1964-2010 Does the state pass a law (with adminstrative enforce-
ment) banning discrimination in public accomodations
(post-Civil Rights Act)?

[78, 82, 22]

Fair Employment Laws 1945-1964 Does the state have a fair employment law? [16, 156, 39]
Fair Employment Laws (post-1964) 1965-2014 Does the state have a fair employment law? (post-1964) [16, 156, 39]
Fair Housing - Private Housing 1959-1968 Does the state ban discrimination in private housing? [16, 156, 39]
Fair Housing - Public Housing 1937-1964 Does the state ban discrimination in public housing? [16, 156, 39]
Fair Housing - Urban Renewal Areas 1945-1964 Does the state have urban renewal areas? [59]
Cigarette Tax 1936-1946 Does the state have a cigarette tax? [151, 147]
Cigarette Tax Rate 1947-2014 What is the state’s tax on a pack of cigarettes? [151, 147]
Earned Income Tax Credit 1988-2014 Does the state have an earned income tax credit? [151, 147]
Income Tax 1936-2014 Does the state have an income tax? [151, 147]
Income Tax Rate - Wealthy 1977-2012 What is the state individual income tax rate for an indi-

vidual that makes more than 1.5 million real dollars?
[151, 147]

Sales Tax 1936-1945 Does the state have a sales tax? [151, 147]
Sales Tax Rate 1946-2014 What is the sales tax rate? [151, 147]
Tax Burden 1977-2010 What is the state’s tax burden (per capita taxes/per capita

income)?
[151, 147]

Corporate Income Tax 1936-1940 Is there a corporate income tax? [151, 147]
Top Corporate Tax Rate 1941-2014 What is the highest corporate tax rate? [151, 147]
Estate Tax 2009-2014 Is there a state estate tax? [151, 147]
Transportation Policies:

Controlled Access Highways 1937-1946 In what year did the state enact controlled-access high-
ways?

[77, 20]

Bicycle Helmets Required 1985-2014 Does the state require that people use helmets while on
bicycles?

[18]

3



Description of Policies – Continued from previous page

Policy Years Description Sources

Mandatory Seat Belts 1984-2014 Does the state require the usage of seat belts (either pri-
mary or secondary enforcement)?

[40]

Motorcycle Helmets Required 1967-2014 Does the state require the usage of helments by people on
motorcycles?

[47]

Mandatory Car Insurance 1945-2012 Does the state require drivers to obtain car insurance? [159]
Welfare Policies:

AFDC - Benefits for Average Family 1936-1992 What is the average level of benefits per family under the
Aid for Families with Dependent Children program?

[17, 142, 143, 155]

AFDC-UP Policy 1961-1990 What is the average level of benefits under the Aid for
Families with Dependent Children program?

[15]

Aid to Blind - Average Payment per Recipient 1936-1965 What is the average monthly payment per recipient for
the permanently blind or disabled?

[17, 142, 143, 155]

Aid to Blind - Avgerage Payment per Recipi-
ent (post-1965)

1966-1972 What is the average monthly payment per recipient for
the permanently blind or disabled? (post-1965)

[142, 143, 155]

Aid to Disabled - Average Payment per Recip-
ient

1951-1965 What is the average monthly payment per recipient for
the permanently blind or disabled?

[142, 143, 155]

Aid to Disabled - Average Payment per Recip-
ient (post-1965)

1966-1972 What is the average monthly payment per recipient for
the permanently blind or disabled? (post-1965)

[142, 143, 155]

CHIP - Eligibility Level for Children 1998-2012 What is the CHIP eligibility level for children? [117]
CHIP - Eligibility Level for Infants 1998-2012 What is the CHIP eligibility level for infants? [117]
CHIP - Eligibility Level for Pregnant Women 1998-2012 What is the CHIP eligibility level for pregnant women? [117]
General Assistance Payment Per Case 1937-1963 What is the average monthly payment per case for general

assistance (an early form of welfare)?
[17, 142, 143, 155]

General Assistance Payment Per Recipient 1964-1980 What is the average monthly payment per recipient for
general assistance (an early form of welfare)?

[142, 143, 155]

Old Age Assistance - Average Payment per Re-
cipient

1936-1965 What is the average monthly payment per recipient per
recipient for old age assistance?

[17, 142, 143, 155]

Old Age Assistance - Average Payment per Re-
cipient (post-1965)

1966-1972 What is the average monthly payment per recipient per
recipient for old age assistance? (post-1965)

[142, 143, 155]

Senior Prescription Drugs 1975-2001 Does the state provide pharmaceutical coverage or assis-
tance for seniors who do not qualify for Medicaid?

[111, 65]

State Adoption of Medicaid 1966-1983 Does the state have a Medicaid program? [49]
Medicaid - Eligibility Level for Pregnant
Women

1990-1997 What is the Medicaid eligibility level for pregnant women? [117]

TANF - Average Payment per Family 2006-2010 What is the average monthly level of benefits per family
under the Temporary Aid for Needy Families program?

[152]

TANF - Initial Eligibility Level 1996-2013 What is the initial eligibility level for benefits for a fam-
ily of three under the Temporary Aid for Needy Families
Program?

[152]

TANF - Max Payments 1990-2013 What is the maximum level of benefis under the Tempo-
rary Aid for Needy Families program for a family of three
with no income?

[152]

Women’s Rights Policies:

Equal Pay 1936-1972 Does the state have a law providing for equal pay for
women working in the same job?

[78, 107, 2, 21]

ERA Ratification 1972-2014 Has the state ratified the Equal Rights Amendment? [7, 122, 64]
State Equal Rights Law 1971-2014 Has the state passed a state-level equivalent to the Equal

Rights Amendment?
[78, 44, 153]

Gender Discrimination Laws 1961-1964 Does the state ban hiring discrimination on the basis of
gender?

[125]

Gender Discrimination Laws (post-1964) 1965-2014 Does the state ban hiring discrimination on the basis of
gender? (post-1964)

[125]

No Fault Divorce 1966-2014 Does the state have a no-fault divorce policy? [151]
Jury Service for Women 1936-1967 Can women serve on juries? [151]

4



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