How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level

06/13/2018 ∙ by Vlad Eidelman, et al. ∙ FiscalNote, Inc. 0

Modeling U.S. Congressional legislation and roll-call votes has received significant attention in previous literature. However, while legislators across 50 state governments and D.C. propose over 100,000 bills each year, and on average enact over 30 less attention due in part to the difficulty in obtaining the necessary data. Since each state legislature is guided by their own procedures, politics and issues, however, it is difficult to qualitatively asses the factors that affect the likelihood of a legislative initiative succeeding. Herein, we present several methods for modeling the likelihood of a bill receiving floor action across all 50 states and D.C. We utilize the lexical content of over 1 million bills, along with contextual legislature and legislator derived features to build our predictive models, allowing a comparison of the factors that are important to the lawmaking process. Furthermore, we show that these signals hold complementary predictive power, together achieving an average improvement in accuracy of 18



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1 Introduction

This work is licensed under a Creative Commons Attribution 4.0 International License. License details:

Federal institutions in the U.S., like Congress and the Supreme Court, play a significant role in lawmaking, and in many observable ways define our legal system. Thus, as data and computational resources have become more readily available, political scientists have increasingly been adopting quantitative methods focused on understanding these entities and the role they play in our society [Katz et al.2017, Poole and Rosenthal2007, Slapin and Proksch2008, Lauderdale and Clark2014].

Although many issues are legislated and regulated primarily at the federal level, state governments have significant power over certain areas. An increasing number of important issues are being decided at the state or local levels, especially in emerging industries and technologies, such as the gig economy and autonomous vehicles [Hedge1998]. Moreover, there are 535 members of Congress who introduce over 10,000 pieces of legislation a session,111A session is the period of time a legislative body is actively enacting legislation, usually one to two years. of which less than 5% is enacted. Similar dynamics exist at the state level, except on a much broader scale. There are over 7,000 state legislators, in aggregate introducing over 100,000 pieces of legislation, with over 30% being enacted. In order to be enacted, every bill must pass through one or more legislative committees and be considered on the chamber floor, a process we refer to as receiving floor action. This process is one of the most pivotal steps during lawmaking [Rosenthal1974, Hamm1980, Francis1989, Rakoff and Sarner1975], as on average, only 41% of bills receive floor action, with most legislation languishing in the committees.

Legislative policymaking decisions are extremely complex, and are influenced by a myriad of factors, ranging from the content of the legislation, to legislators’ personal characteristics, such as profession, religion, and party and ideological affiliations, to their constituents’ demographics, to governor agendas, to interest group activities, and to world events [Canfield-Davis et al.2010, Hicks and Smith2009, Talbert and Potoski2002].

Despite this complexity, in this paper we present an approach to better understand state lawmaking dynamics and the legislative process by focusing on the task of predicting the likelihood that legislation will reach the floor in each state. As there are many dimensions underlying the content of the legislation, such as the policy area and ideology of the sponsor [Linder et al.2018], that may affect the likelihood of floor action, in addition to text we examine several established contextual legislature and legislator derived features. To the best of our knowledge, this is the first work quantitatively modeling the floor action process across all 50 states and using the text of legislation alongside traditional contextual information.

2 Related Work

Much of the work analyzing the federal legislature is aimed at understanding legislator preferences through the use of voting patterns. One of the most popular techniques in political science is the application of spatial, or ideal point, models built from voting records [Poole and Rosenthal1985, Poole and Rosenthal2007], that is often used to represent unidimensional or multidimensional ideological stances [Clinton et al.2004]. However, there is also an increasing literature examining broader legislative dynamics, such as measuring legislative effectiveness [Harbridge2016], evaluating the impact of legislation on stock prices using legislators’ constituents [Cohen et al.2012], creating cosponsorship networks [Fowler2006], and examining the role of lobbying [Bertrand et al.2018, Matthew et al.2013],

In recent years a variety of primary and secondary textual legal data, such as legislation, floor debates, and committee transcripts, has become increasingly available, enabling the NLP community to create richer multidimensional ideal point estimation 

[Gerrish and Blei2011, Nguyen et al.2015, Kornilova et al.2018], and examine ideology detection from political speech [Iyyer et al.2014], voting prediction from debates [Thomas et al.2006], committee referral [Yano et al.2012], and enactment [Nay2016].

While there is also an increasing amount of state legislative research, states have received significantly less attention [Hamm et al.2014]. One major reason for this is that quantitative methods require data, and the availability of data for Congress far exceeds that of states. In fact, Yano:2012 noted “When we consider a larger goal of understanding legislative behavior across many legislative bodies (e.g., states in the U.S., other nations, or international bodies), the challenge of creating and maintaining such reliable, clean, and complete databases seems insurmountable.” Thus, while there has been scholarship quantifying the role of committees, it has been limited in scope, to a few sessions or states, or reliant on survey data [Francis1989, Rakoff and Sarner1975, Rosenthal1974, Hamm1980]. More recently, as different kinds of state data has become more accessible, it has enabled studying the affect of interest groups on legislative activity [Gray and Lowery1995], the application of spatial models [Shor et al.2010, Shor and McCarty2011], and comparisons of textual similarity [Linder et al.2018].

The contribution of this work is to continue building a broader understanding of state legislative dynamics by evaluating how predictable state lawmaking is, and what factors influence that process. We create a novel task, predicting the likelihood of legislation receiving floor action, and utilize a corpus of over 1 million bills to build computational models of all 50 states and D.C. We present several baseline models utilizing various features, and show that combining the legislative and legislator contextual information with the text content of bills consistently provides the best predictions. Our analysis considers various factors and their respective importance in the predictive models across the states, showing that although there are some consistent patterns, there are many variations and differences in what affects the likelihood in each state.

3 Data

There is state-to-state variation in the legislative procedure of how a bill becomes law, but the path is largely similar. Legislation is introduced by one or more members of the legislature in their respective chamber,222All legislatures are bicameral, with either a House or Assembly as the lower chamber, and the Senate as the upper chamber, except D.C. and Nebraska, which are unicameral. and assigned to one or more standing subject committees.333Depending on the state, other groups can introduce legislation, including legislative committees, legislative delegations, the governor, or non-elected individuals. For the purpose of this work we focus on legislator sponsored legislation. Committees are made up of a subset of members of their respective chamber, and are chaired by the majority party. Once in committee, legislation is subject to debate and amendment only by the committee members, with the successful outcome being a favorable referral, or a recommendation, to be considered by the full chamber on the floor.

The primary data we use to model floor action was scraped directly from each state legislatures’ website. For each state, we downloaded legislation, committee, and legislator pages for all sessions that were publicly accessible. Legislation pages were automatically parsed to determine legislative contextual metadata, which includes bill text versions, sponsors, committee assignments, and the timeline of actions. Legislator pages were parsed to obtain sponsor contextual metadata, which includes party affiliation, committee assignments, and committee roles.

As states demarcate legislative status in the timeline of actions differently, we automatically map and normalize all textual descriptions of legislative actions to a finite set of statuses.444The normalized statuses include introduced, assigned to committee, reported from committee, and passed. These statuses are used to determine whether a piece of legislation survived committee and received a floor action, or consideration on the floor. All bills having a status of passed in their introductory chamber, or having had a recorded floor vote are treated as positive examples, while any status prior to passed is considered failed, including legislation that was reported out of committee but not considered on the floor.

Finally, since each state follows their own conventions with regard to classifying the type of legislation, we normalize all legislation across states to two types: resolutions and bills.

555Resolutions are pieces of legislation of type appointment, resolution, joint resolution, concurrent resolution, joint memorial, memorial, proclamation, nomination. Bills are those of type bill, amendment, urgency, appropriation, tax levy, or constitutional amendment.

Figure 0(a) shows the total number of bills introduced and receiving floor action for each state. In total, our dataset consists of 1.3 million pieces of state legislation, broken into 1 million bills, with 360k receiving floor action, at an average rate across states of 41%, and 275k resolutions, with 210k receiving floor action. On average, we have 10 legislative sessions of data per state.666Full data statistics are given in Table 7 in Appendix A. As bills represent substantive legislation, with a much lower floor action rate, while resolutions are much more likely to receive floor action, for the rest of this paper we focus on bills only, and refer to bills and legislation interchangeably. We include 15 sessions of U.S federal legislation in our data for comparative purposes, with 23k of 172k bills receiving floor action.

Figure 0(b) presents the percent of bills receiving floor action. It is interesting to note the difference in difficulty for legislation to receive floor action in different states. For example, in New Jersey and Massachusetts, fewer than 15% of bills reach the floor, whereas 75% do in Colorado and Arkansas.777Our average across states, chambers, and sessions is in line with previous single state and session findings; in examining five states rosenthal found between 34% and 73% of legislation did not survive committee.

(a) Number of bills introduced and receiving floor action for each state.
(b) Percent of bills reaching floor per state.
Figure 1: Dataset characteristics.

4 Methods

4.1 Models

In order to not only be able to predict, but also examine the importance of features to our prediction, we chose three relatively interpretable models for our modeling framework. Formally, let our training data consist of pairs where, each is a bill and a binary indicator of whether received floor action. Let

be a feature vector representation of

, and the parameter vector indicating the weight of each feature learned by the model.

The first two models are linear classifiers, where the prediction of floor action, , is given by . The first is a regularized conditional log-linear model :


where is the partition function given by . The model optimizes according to


The second model is NBSVM [Wang and Manning2012]

, an interpolation between multinomial Naive Bayes and a support vector machine, which optimizes

according to:



is the log-count ratio of features occurring in positive and negative examples. The third model is non-linear, in the form of a tree-based gradient boosted machine 

[Friedman2000], which optimizes according to:


where is the number of trees,

is the loss function, typically binomial deviance, and

is given by where is a tree.

We use the scikit-learn [Pedregosa et al.2011] implementation for the log-linear and gradient boosted models, and implemented NBSVM based on the interpolated version in conf/acl/WangM12.

As hyperparameters, such as learning rate and regularization, have a significant impact on model performance, we use Bayesian hyperparameter optimization 

[Bergstra et al.2011] to select the optimal hyperparameters for each model on a held-out development set. We used the tree-structured Parzen Estimator (TPE) algorithm implemented in hyperopt for our sequential model-based optimization [Bergstra et al.2013]. After individually optimizing hyperparameters and training each of the three base models, we use their outputs to train a meta-ensemble model, a regularized conditional log-linear model, forming a linear combination over their predictions [Breiman1996].

As the lawmaking process in each state, and even within each chamber, is different, we divide the problem space by state and chamber, building separate models for each subset. Specifically, we consider each of these as separate problems: upper chamber bills and lower chamber bills. Thus, we have 2 predictions per state, and each prediction is comprised of 4 model outputs, three from the base models, and one from the meta-ensemble, resulting in 400 models.888There are only upper chamber bills in D.C. and Nebraska, resulting in 49 states x 2 prediction types + 2 states x 1 prediction type) x 4 models = 400.

4.2 Features

As there are many dimensions underlying bills that may affect the likelihood of floor action, we compute and utilize several established contextual legislature and legislator derived features. Previous literature has proposed various factors that may affect legislation, including the content of bills,999In most previous literature the content is determined via a manual analysis of each bill to establish the scope of impact, the complexity, or the incremental nature. number of and identity of sponsors, extra-legislative forms of support, timing of introduction, leadership’s position, seniority, identity of chairperson of the committee, identity of one’s own party, and membership of the dominant faction [Hamm1980, Rakoff and Sarner1975, Harbridge2016, Yano et al.2012].

In order to quantitatively evaluate these factors and establish a strong baseline from which to measure the affect of text, we include the contextual features shown in Table 1. These indicator features derived from the sponsors, committees, and bills are meant to capture many of the major factors that are proposed in the literature.101010Each count based feature, such as number of sponsors, also spawns a number of discretized features, including ranks, percentiles, and deviations from the mean thereof. We automatically compute companion bills using a cosine-based lexical similarity.

Feature Type Description
Sponsor primary and cosponsor(s) identity, primary and cosponsors(s) party affiliation, number of primary and sponsors, number of Republicans, number of Democrats, sponsors bicameral, sponsors bipartisan, sponsor in majority/minority, majority party Republican or Democrat
Committee identity of assigned committee(s), number of committee assignments, number of sponsors members of the committee, sponsor same party as committee chairman, sponsor role on the committee, referral rate of committee(s)
Bill chamber, bill type, session, introductory date, companion bill(s) existence, companion(s) current status.
Table 1: Contextual feature types and descriptions.

To strengthen the representation of legislators in our model beyond the basic features described above, we compute several measures of legislator effectiveness. The effectiveness score is calculated from the sponsoring and cosponsoring activity of each legislator, and meant to represent where they stand in relation to other legislators in successfully passing legislation.111111This is not a holistic representation of being an effective legislator, as someone may consider themselves effective by not passing anything, or preventing others from doing so. Members may also be highly influential and their support is needed behind the scenes but their names do not appear on the legislation. We can only account for recorded activity. Despite the limitations, we argue this is a fair, if incomplete, assessment of how well the legislator advances their agenda.

Similar to leg_eff_volden, the score we compute for each legislator is a combination of several partial scores, computed for each important stage of the legislative process. Each legislator gets a score for how many bills they sponsored, getting those bills out of committee, getting them to the floor, passing their own chamber, passing the legislature, and getting enacted. The score for each stage is further broken down by how many of those pieces of legislation were substantive, i.e. bills, attempting a meaningful legal change, versus non-substantive, i.e. resolutions. This results in 12 factors for each individual. To compute a score for each legislator’s relative performance at each stage to the other members in the chamber, we create a weighted combination of that legislator’s bills and resolutions, where bills get more weight, and compute the ratio based on the weighted contribution of the other members in the chamber. All the stage scores are then combined into a second weighted combination, where each successive stage in the process gets more weight, to get the final score. Finally, the scores are normalized to 0-10. In addition to using the effectiveness scores directly as features, we further compute and discretize several statistics derived from them, including ranks, percentiles, and deviations from the mean thereof.

To further enrich the bill representation beyond contextual information, we utilize the textual content of the bills. The legislation in our collection is comprised of long documents, with an average of 11 thousand words, often containing significant amounts of procedural language and pieces of extant statutes. As this can create additional challenges in identifying the salient points, for this work we chose to focus on a condensed amount of text, specifically the state provided title and description, that average 17 and 18 words, respectively.121212Although this is a coarse approximation of the bill content, we believe it should capture the substantive aspects of the bill. Full details of the length of documents in each state are given in Table 7 in Appendix A.

Both are preprocessed by lowercasing and stemming. We compute the tf-idf weighting for n-grams of size (1,3) on the training data for each prediction task, and select the top 10k n-grams from the title and description separately.

While we would like to study the predictability of reaching the floor upon first introduction, bills often change after introduction and are updated with additional information. Thus, we limit our features to those available at the time of first introduction.

5 Results

In order to clarify the impact that each set of features described in Section 4.2 has on predictive performance, we create five different subsets of features described in Table 2, and train models on each one of them separately.

Condition Feature Set
combined sponsor, committee, bill, text
no_txt sponsor, committee, bill
no_txt_spon committee, bill
just_txt text
just_spon sponsor
Table 2: The five feature settings with contextual and lexical features.

The first condition, combined contains all the contextual and text content features. The second condition, no_txt, removes text content, allowing us to study the importance of all contextual features, and by comparing combined to no_txt we can evaluate if text has any complementary information to contextual features. The third condition, no_txt_spon further removes sponsor features, essentially allowing us to study the importance of committee information. By comparing no_txt to no_txt_spon we can evaluate what sponsors contribute. The fourth and fifth conditions use only sponsor and only text features, respectively, to study the importance of each individually.

All models for a given condition are built from the same training data and feature space. We measure and report several performance metrics of our models using 10-fold cross validation. The baseline model represents guessing the majority class; for some states this means all fail, for others it is all receive floor action, based on the state specific rate.

Although accuracy is informative with respect to how many correct binary decisions the model made, as noted in Bradley:1997:UAU:1746432.1746434 for imbalanced problems such as this, where one class dominates, the baseline accuracy can be very high. As a supplement, it is useful to measure a probabilistic loss, where there is a cost associated with how correct the decision was. Thus, we move beyond pure predictive performance and consider the actual probability distributions created by our models under different conditions. The log-linear and gradient boosted models are probabilistic, while NBSVM is not, thus we train a probability transformation on top of NBSVM using Platts Scaling to obtain probability estimates.

In additional to accuracy, we measure model performance on log-loss and AUROC (area under the receiver operating characteristic curve

[Bradley1997]. Log-loss, is defined as:


where is an binary indicator function equaling 1 if the model prediction was correct, and 0 otherwise. equals zero for a perfect classifier, and increases with worse probability estimates. Specifically, penalizes models more the more confident they are in an incorrect classification.

AUROC allows us to measure the relationship between a model’s true positive (TP), how many floor action bills were correctly predicted as floor action, and false positive rate (FP), how many failed bills were predicted as floor action. It is defined by:


By considering the TP and FP at different values, we can construct an ROC curve. The area under that curve, AUROC, can be interpreted as the probability that the model will rank a uniformly selected positive instance (floor action) higher than a uniformly selected negative instance (failure), or in other words, the average rank of a positive example. A random model will have a AUROC of 0.5, and a 45-degree diagonal curve, while a perfect model will have an AUROC of 1, and be vertical, then horizontal.

Feature Set Accuracy Log-Loss AUROC
Average Std Dev Average Std Dev Average Std Dev
baseline 0.68 0.1 0.6 0.09 0.5 0
just_txt 0.732 0.09 0.53 0.14 0.7 0.14
just_spon 0.759 0.102 0.48 0.16 0.74 0.15
no_txt_spon 0.81 0.113 0.39 0.18 0.8 0.18
no_txt 0.846 0.098 0.32 0.18 0.82 0.21
combined 0.859 0.093 0.31 0.17 0.85 0.21
Table 3:

Average and standard deviation across states on accuracy, log-loss, AUROC for bills on each feature set.

Table 3 shows the average accuracy, , and AUROC with standard deviations for each of the five conditions. The just_txt model achieves an accuracy of 73%, outperforming the baseline by 5%, and notably, shows that there is a predictive signal even within the limited amount of text available in the title and descriptions.

To examine where text content is most and least predictive on its own, we disentangle the average performance of the just_txt model in Figure 1(a), showing the per state and chamber pair change from baseline. The states that improve the most over baseline, with 15% improvement or more using only textual features are Oregon, Oklahoma, Tennessee, D.C., South Carolina, Louisiana (lower), Georgia (lower), and Alabama (lower). On the other hand, text is least predictive in Connecticut, Wyoming, Idaho, New Jersey, Utah (upper), New Hampshire (upper), North Dakota (upper), all underperforming the baseline.

The relatively small improvement over baseline of just_txt provides insight into the lawmaking process, raising the possibility that other contextual factors, outside the subject matter of the legislation, such as who the sponsors are and what committee the bill is assigned to, are often more important than the subject of the legislation.

The just_spon model achieves an average accuracy of 76%, slightly outperforming just_txt with an improvement over baseline of 8%. This further indicates that knowing sponsor related information, without reference to the subject of the legislation, is itself highly predictive. In fact, Figure 1(b) shows that except for New Hampshire (upper), almost all states achieve gains using sponsor only information, with Oklahoma, Texas, and Ohio achieving gains of 30% or more. The committee information in no_txt_spon, which includes the sponsor committee positions, is even more predictive than sponsor and text only, and the addition of sponsors in no_txt improves performance by 3.5%.

Including text in the combined model further improves performance by 1.3% over no_txt, and 18% over the majority class baseline, showing the complementary effects of contextual and lexical information, as this model consistently outperforms all others. Figure 1(c) shows the per state and chamber pair baseline and combined model performance. The AUROC performance follows a very similar trajectory.

On , the model performance follows a similar path, with all models showing improvement in probability estimates from the baseline. almost doubles from the combined model’s 0.31 to 0.6 on baseline. This reinforces that the combined model makes very confident correct predictions. Including text in the combined improves performance slightly over no_txt, while having just sponsors or just text decreases the to around 0.5.

(a) Change from baseline with text only features.
(b) Change from baseline with sponsor only features.
(c) Prediction accuracy on bills with combined model.
Figure 2: Performance improvements for text-only (1(a)) and sponsor-only models (1(b)), and combined performance (1(c))

6 Analysis

All contextual and lexical features considered above are available upon the introduction of a bill, or shortly thereafter,131313Some states do not indicate committee assignment immediately, for those we include the first assignment after introduction. thus the evaluation above indicates how well floor action can be predicted from the day of introduction. However, after the bill is introduced, subsequent legislative actions indicate further contextual information about the legislative process. As it is reasonable to assume these actions carry relevant predictive information, we further examine subsequent events in the legislative process in the combined+act feature set. We include a binary feature for the occurrence of amendment introduction and outcomes, votes, committee referral outcomes and readings up to the point of floor action. By comparing combined+act to combined we can examine how important different events in the legislative procedure are to predicting floor action.

Feature Set Accuracy Log-Loss AUROC
Average SD Average SD Average SD
combined 0.859 0.093 0.31 0.17 0.85 0.21
combined+act 0.94 0.059 0.16 0.12 0.97 0.04
Table 4: Average accuracy, log-loss, AUROC for bills using legislative events post introduction.

Table 4 shows the results of the combined+act feature set. Accuracy improves to 0.94, while drops by half to 0.16, confirming that legislative events occurring up to the point point of floor action carry significant complementary information to other contextual factors and are highly indicative of floor action. While combined+act confirms the predictive power of procedural factors outside the legislative text, sponsor, and committee assignment, the combined model is arguably the most important result, as it indicates how well we can predict on features that are available upon introduction.

Beyond the predictions, we are interested in identifying the different features that contribute to legislative success across the states. As there are both a large number of models, and features in each model, in order to understand the relative predictive importance of contextual and legislature specific dynamics, we choose several previously proposed factors deemed to be important for floor action, and compare the rank and weight they received in each model.

We first examine the median rank and weight given to the following features in the just_spon condition across all states: bipartisan, sponsor in minority, sponsor in majority, and the number of sponsors. While many of these contextual features are highly ranked, there are many variations and differences across states. The top half of Table 5 shows the top ten states for which each feature was ranked among the top 20. For example, the bipartisan feature is ranked in the top 5 in Missouri, Virginia, Maine, and Mississippi, accounting for up to 6% of the explanatory power. As a comparison, in South Dakota, Hawaii, Minnesota, Wisconsin, and Pennsylvania, bipartisanship ranked lower than 200. Whether the sponsor is in the minority is important in the U.S. Congress, where it is ranked 6, along with Delaware, Tennessee, and West Virginia. Being in the majority accounts for 10% in Kentucky, and 7% in Wisconsin. This aligns with previous literature, as Wisconsin is known to have a strong party system [Hamm1980], and indeed we find sponsor in majority and in minority features to be ranked 1 and 9, respectively, while in Texas, which has a much weaker party system, those features are ranked among the lowest of all states.141414Full rankings and weights are presented in Table 8 in Appendix A.

Feature Median Top Top States Bottom States
Bipartisan 64 11 mo,va,me,ms,nc,sc,ak,de,wa,us sd,mn,wi,pa,ut,ne,id,fl,dc,ar
in Min 24 20 de,us,wv,tn,ia,wi,al,nd,md,mi ca, il,hi,tx,nj,ut,ne,fl,dc,ar
in Maj 23 22 wi,mn,tn,ky,nh,nc,co,il,al,oh ak,tx,ma,va,ne,nj,me,ut,fl,ar
Num Spon 28 20 co, ut,il,vt,in,ia,or,sd,oh,us pa, az,wa,wy,nm,nv,va,ms,mn,ar
Ranking Mbr 24 15 ne,vt,ar,ky,us,ga,ok,me,ny,or ks,mo,mt,oh,pa,ri,tn,ut,va,wy
No Cmte Mbr 17 23 ar,me,ne,il,sd,nc,nv,nm,de,ky hi,id,ia,ks,mt,oh,pa,tn,ut,wy
Members 6 33 de,ct,me,sd,nc,nv,ok,ny,ga,il hi,id,ia,ks,mt,oh,pa,tn,ut,wy
Table 5: Median ranking of across states for bipartisan, sponsor in minority, sponsor in majority, and number of sponsor features for sponsor only model, and having a ranking majority of the committee as a sponsor, not being a committee member as a sponsor, and being a member of the committee as a sponsor in the committee model. The top column indicates how many states have that feature ranked within the top 20 weighted features. The top states lists the ten states where each feature was ranked the highest and was one of the first 20 features. The bottom rows lists the ten states where each feature was ranked the worst.

Similar ranking is presented for committee features in the bottom half of Table 5 in the no_txt_spon condition. The committee features play a similarly predictive role, with the sponsors holding membership positions on the committee accounting for over 10% of explanatory power in Delaware, Connecticut, Maine, and South Dakota.

To examine the difference in probability assigned by the models under different conditions, we chose a representative example where neither contextual nor lexical features dominate, as shown in Figure 2, and show the boxplots for Pennsylvania’s lower chamber in Figure 3. Each subfigure shows the probability of floor action for legislation that received floor action (pass) and did not (failed). In all cases, the median of the probabilities on legislation that received floor action is higher than the median of the probabilities on legislation that failed. The combined models median and mean predictions on bills receiving action are above 90%, and it has the largest difference between the two cases. The no_txt model has a similar mean, but the probabilities become more distributed on both pass and fail. Removing sponsors significantly affects the distribution, and shifts the mean lower to 70%. just_spon and just_text both drop the mean to around 40%.

In addition, we show the calibration curves and distribution of predictions for the same settings in Figure 4 and ROC curves in Figure 5 in Appendix A. All models are well calibrated, closely following the diagonal line. The combined model is very confident in its predictions, resembling a bimodal distribution, placing most predictions close to either 0 or 1 probability. just_spon has the most distributed probability estimates, while just_txt moves the lower part of the distribution slightly forward. The combined model is quite accurate, with each subsequent model moving the ROC curve to the right, and thus allowing more false positives to reach the same true positive rate.151515For comparison to a state with a higher rate of floor action, we include analogous figures for California’s lower chamber in Appendix A.

State Top Phrases Bottom Phrases
New Mexico (upper) day, campus, recognit, month, defin, alcohol, date, recipi, procur, cours, registr plate, revis, tax credit chang, enmu, residenti, lobbi, statewid, or, abort, safeti, date for, test for, primari care, analysi,
New Mexico (lower) day, studi, length, citi, of nm, fingerprint, geotherm, fund project, dog, definit, loan for, month, of game fish, peac, senior citizen, math scienc, transfer of, state fair, self, bachelor, develop tax credit, nmhu, wolf, equip tax,
Pennsylvania (upper) provid for alloc, creation of board, manufactur or, an appropri to, of applic and, medic examin, fiscal offic, for request for, corpor power, within the general, for the offic, an act amend, as the tax, known the, act provid, known the tax, wage, act prohibit, citizen, of pennsylvania further, tax, youth, requir the depart,
Pennsylvania (lower) or the, contract further, and for special, memori highwai, within the general, in game, first class township, whistleblow, emerg telephon, offens of sexual, for promulg, act amend titl, an act amend, act provid, known, act prohibit, amend the, pennsylvania, an act provid, code of, act establish, an act relat, the constitut,
New York (upper) fiscal year relat, memori highway, year relat to, implement the health, for retroact real, portion of state, the public protect, implement the public, inc to appli, budget author, program in relat, which are necessari languag assist, direct the superintend, the develop of, author shall, subsidi, automobil insur, such elect, limit profit, disabl act, polici base, polici to provid
New York (lower) care insur, applic for real, physic educ, fire district elect, establish credit, to file an, abolit or, hous program, the suspens of, are necessari to, the membership of, relat to hous appropri, fuel and, numer, school ground, vehicular, incom tax for, prohibit public, tag, senat and assembl, on school, on school, class feloni
Table 6: Top and bottom ranked phrases for New Mexico, Pennsylvania, and New York.

Finally, we examine language ranked most and least predictive on the just_txt condition for New Mexico, Pennsylvania, and New York in Table 6.161616Addition states are presented in Table 9 in Appendix A. Previous literature has proposed several theories on how content affects legislative passage, including that the more redistributive a policy is perceived, the higher in controversy, or the greater in scope, the lower the passage likelihood [Rakoff and Sarner1975, Hamm1980]. While each state has a unique set of issues that are likely to be taken to the floor, and conversely, to be left in committee, there is also evident overlap. In the top phrases, several states contain budgetary issues, expressed with fiscal and appropriation language, as most states have to pass budgetary measures. We also see commendation and procedural language, which is often less contentious. In the bottom phrases, several states have tax related language, and several education related topics.

While outside the scope of this work, in future work we hope to explore the differing language identified by the model to help identify important questions about the policymaking process in each state, and allow comparison within states of what successful legislation contains, and across states, of how different issues take shape. In addition, as we only included a limited amount of text, we would like to explore how to incorporate the full body text of legislation effectively.

(a) combined
(b) no_txt
(c) no_txt_spon
(d) just_spon
(e) just_txt
Figure 3:

Box plot distributions of predicted probabilities for legislation in Pennsylvania lower chamber. The box extends from the lower to upper quartile values of the predictions, with a line at the median, and triangle at the mean.

7 Conclusion

In this paper we explored the state legislative process by introducing the task of predicting floor action across all 50 states and D.C. We presented several baseline models and showed that combining contextual information about the legislators and legislatures with bill text consistently provides the best predictions, achieving an accuracy of 86% on which legislation will reach the floor upon first introduction. We further analyzed various factors and their respective importance in the predictive models across the states, gaining a broader understanding of state legislative dynamics. While the factors that influence legislative floor action success are diverse and understandably inconsistent among states, by examining them we can empirically help elucidate the similarities and differences of the policymaking processes.


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Appendix A Appendix

State Floor Action Introduced Rate Sessions Title Desc Body

6697 14327 0.467 16 21 - 11280
ak 781 2527 0.309 4 34 - 12054
az 3719 9308 0.4 24 4 - 13284
ar 3809 5076 0.75 8 17 - 7297
ca 18978 32143 0.59 17 5 186 16631
co 4808 6428 0.748 11 5 20 10671
ct 3044 16236 0.187 8 12 19 4748
de 3185 4858 0.656 6 19 89 9327
dc 8515 15593 0.546 9 11 - 7083
fl 6592 21298 0.31 15 6 29 16471
ga 7416 15379 0.482 13 8 43 5654
hi 5630 21615 0.26 5 9 28 6910
id 3259 4446 0.733 8 40 - 10458
il 14106 66926 0.211 10 3 46 7022
in 1958 5291 0.37 4 5 55 16918
ia 3434 21457 0.16 7 28 - 12740
ks 2123 6324 0.336 6 12 - 22294
ky 3149 8185 0.385 20 51 52 14218
la 18346 35277 0.52 32 20 - 7454
me 9268 17095 0.542 8 14 - 5583
md 9857 26125 0.377 14 10 59 8107
ma 6862 52467 0.131 7 11 26 14535
mi 14520 41730 0.348 11 27 - 10929
mn 4494 27240 0.165 10 11 - 10251
ms 6621 25450 0.26 21 12 77 14475
mo 2736 14143 0.193 8 18 - 16486
mt 5910 9905 0.597 8 7 - 13758
ne 1837 4829 0.38 6 12 - 11775
nv 2614 4163 0.628 7 14 12 21165
nh 3243 6793 0.477 6 12 - 3269
nj 6900 59861 0.115 8 16 - 12883
nm 4253 10909 0.39 8 5 - 11639
ny 23071 89072 0.259 4 16 23 8913
nc 6922 25152 0.275 10 4 - 7039
nd 5735 8089 0.709 9 42 3 5847
oh 4356 8605 0.506 9 8 - 33216
ok 16579 36827 0.45 10 13 - 10333
or 5240 14404 0.364 12 9 54 14500
pa 2887 16414 0.176 5 32 - 5466
ri 6596 16584 0.398 5 31 - 8838
sc 3269 11532 0.283 6 77 7 6255
sd 1647 2539 0.649 9 16 - 6437
tn 33936 77331 0.439 12 30 - 3256
tx 8371 25771 0.325 9 16 - 5145
ut 7816 11072 0.706 23 7 - 24623
vt 1035 4520 0.229 4 14 - 6046
va 14215 27813 0.511 24 9 37 8310
wa 8317 24578 0.338 6 10 - 14735
wv 4308 23917 0.18 12 12 10 11501
wi 4982 13761 0.362 14 40 2 12090
wy 2825 4223 0.669 11 4 38 7032
us 22973 172921 0.133 15 14 178 14043
Table 7: Data statistics for number of bills introduced and receiving floor action for each state. Word counts are given for title, description, and bill body.
State Bipartisan in Min in Maj Num Spon AP Eff BP Eff
Rank Weight Rank Weight Rank Weight Rank Weight Rank Weight Rank Weight
al 57 0.003 12 0.012 5 0.017 54 0.003 17 0.015 95 0.003
ak 9 0.029 22 0.009 57 0.002 62 0.006 3 0.059 3 0.046
az 133 0.003 46 0.004 28 0.008 69 0.006 89 0.003 11 0.012
ar - - - - - - - - 0 0.326 1 0.236
ca 43 0.006 108 0.003 29 0.007 13 0.01 70 0.003 3 0.027
co 49 0.006 19 0.014 3 0.025 0 0.137 94 0.002 135 0.0
ct 128 0.001 75 0.003 26 0.008 58 0.004 210 0.001 - -
de 13 0.013 3 0.023 36 0.013 22 0.009 79 0.003 115 0.002
dc - - - - 16 0.015 33 0.011 - - - -
fl - - - - - - 27 0.012 1 0.072 0 0.137
ga 80 0.003 23 0.01 9 0.015 32 0.006 303 0.0 291 0.0
hi 224 0.0 120 0.001 26 0.006 12 0.009 136 0.001 0 0.321
id - - 24 0.001 28 0.001 24 0.002 14 0.018 - -
il 159 0.001 109 0.015 3 0.037 2 0.015 179 0.0 24 0.006
in 43 0.005 101 0.002 50 0.009 2 0.044 42 0.007 48 0.004
ia 98 0.001 8 0.026 14 0.015 3 0.086 192 0.0 - -
ks 112 0.0 69 0.001 32 0.007 16 0.016 39 0.009 61 0.005
ky 35 0.004 38 0.006 2 0.103 67 0.004 - - - -
la 32 0.004 30 0.005 24 0.006 14 0.053 23 0.005 35 0.003
me 5 0.055 21 0.012 - - 21 0.013 40 0.001 - -
md 36 0.006 14 0.011 22 0.008 33 0.006 4 0.049 7 0.029
ma 151 0.004 69 0.003 66 0.003 16 0.009 35 0.009 3 0.045
mi 80 0.002 14 0.01 10 0.033 28 0.006 98 0.002 138 0.001
mn 231 0.0 60 0.003 1 0.046 283 0.0 156 0.003 140 0.003
ms 5 0.029 30 0.01 32 0.018 107 0.003 58 0.005 32 0.007
mo 3 0.049 19 0.008 17 0.019 49 0.003 90 0.001 118 0.001
mt 123 0.003 18 0.022 11 0.019 16 0.017 - - - -
ne - - - - 275 0.0 19 0.01 0 0.057 3 0.037
nv 20 0.01 33 0.006 35 0.006 101 0.002 2 0.072 6 0.044
nh 21 0.012 57 0.011 2 0.089 42 0.004 44 0.005 27 0.009
nj 66 0.004 352 0.0 363 0.0 14 0.01 397 0.0 340 0.0
nm 225 0.0 60 0.006 34 0.009 91 0.002 69 0.005 101 0.004
ny 167 0.002 20 0.012 20 0.014 36 0.004 47 0.007 49 0.006
nc 8 0.017 18 0.026 3 0.058 30 0.007 37 0.005 43 0.003
nd 121 0.002 13 0.016 14 0.02 12 0.014 - - - -
oh 207 0.001 18 0.01 5 0.033 10 0.026 29 0.019 21 0.019
ok 116 0.0 82 0.0 21 0.001 66 0.46 134 0.0 174 0.0
or 32 0.012 34 0.006 27 0.007 4 0.025 40 0.007 2 0.029
pa 333 0.001 18 0.014 20 0.013 68 0.004 16 0.01 36 0.01
ri 126 0.002 16 0.022 24 0.008 42 0.006 - - - -
sc 8 0.019 19 0.01 14 0.014 37 0.007 - - - -
sd 227 0.0 46 0.006 27 0.0 7 0.042 - - - -
tn 62 0.004 7 0.016 2 0.023 17 0.012 96 0.003 68 0.006
tx 147 0.003 212 0.001 61 0.003 22 0.014 112 0.001 108 0.002
ut - - - - - - 1 0.032 - - 19 0.003
vt 46 0.004 53 0.003 44 0.004 2 0.084 20 0.019 50 0.097
va 4 0.048 59 0.003 139 0.001 101 0.002 136 0.001 222 0.001
wa 16 0.014 20 0.011 24 0.011 73 0.003 51 0.004 111 0.002
wv 35 0.004 7 0.018 6 0.042 34 0.003 2 0.099 7 0.021
wi 232 0.002 9 0.024 1 0.072 47 0.007 5 0.022 1 0.103
wy 162 0.0 49 0.004 48 0.004 85 0.002 - - - -
us 20 0.016 6 0.029 6 0.025 10 0.01 16 0.02 16 0.013

Table 8: Feature ranks and weight for bipartisan, sponsor in minority, sponsor in majority, number of sponsors, average primary sponsor effectiveness and best primary sponsor effectiveness features in the gradient boosted model with just sponsor features across all states. Dash indicates feature was not ranked within the top 400.
(a) combined
(b) no_txt
(c) no_txt_spon
(d) just_spon
(e) just_txt
Figure 4: Calibration plots for Pennsylvania lower chamber.
(a) combined
(b) no_txt
(c) no_txt_spon
(d) just_spon
(e) just_txt
Figure 5: ROC curves and AUC for Pennsylvania lower chamber. Green pointers indicate probability thresholds on the Recall-Precision curve, and the title includes accuracy at the best performing threshold.
(a) combined
(b) no_txt
(c) no_txt_spon
(d) just_spon
(e) just_txt
Figure 6: Distributions of predicted probabilities for legislation in California lower chamber that received floor action (pass) and did not (fail) in a box plot. The box extends from the lower to upper quartile values of the predictions, with a line at the median, and triangle at the mean.
(a) combined
(b) no_txt
(c) no_txt_spon
(d) just_spon
(e) just_txt
Figure 7: Calibration plots for California lower chamber.
(a) combined
(b) no_txt
(c) no_txt_spon
(d) just_spon
(e) just_txt
Figure 8: ROC curves and AUC for California lower chamber. Green pointers indicate probability thresholds on the Recall-Precision curve, and the title includes accuracy at the best performing threshold.
State Top Phrases Bottom Phrases
New Jersey (upper) for farmland preserv, preserv trust, green acr fund, acquisit and, mmvv million from, vehicl from, budget for, fund for state, in feder fund, unemploy, for state acquisit, infrastructur trust retir benefit for, of educ for, school board member, clarifi law, contract and, tax reimburs program, appropri mmvv for, to develop and, tax rate, credit under corpor, certain vehicl
New Jersey (lower) environment infrastructur, to dissemin, farm to, dmva to, concern certain, and dhs, unsolicit, atm, contract law, link to, manufactur rebat, limit liabil polit, import, all school, for water, facil to be, respons for, grant program for, relat crime, state administ, from tax, chair, to all
Maryland (upper) financ the construct, festiv licens, issu the licens, grante provid and, to effect, advisori commiss, an evalu of, that provis of, financ statement, board licens, to borrow, defer not to, phase, be use as, use as, facil locat in, to own, law petit, or expenditur, trust establish, expend match, and expend match
Maryland (lower) charl counti alcohol, improv or, to financ the, termin provis relat, counti sale, sanction, alter, counti special tax, montgomeri counti alcohol, report requir repeal, length, licens mc grant to the, creation state debt, educ fund, state debt baltimor, establish the amount, elimin, disclos to, propos amend, incom tax rate, purpos relat, crimin gang, deced die after
California (upper) ab, revolv fund, household, intent that, these provis until, restitut, counsel, employe, onli if ab, properti, if ab, would incorpor addit legisl, cost of, veterinari, enact legisl, to the, law, regul econom, governor, incom tax deduct, hour, motor vehicl recreat, decis
California (lower) add articl, to amend repeal, bill would incorpor, to add and, budget act of, urgenc statut, make nonsubstant, and make, as bill provid, relat the budget, and of the, ab would make nonsubstant, enact legisl, make technic nonsubstant, would make technic, unspecifi, code to add, baccalaur degre, salari, fraud prevent, flexibl, of the state, would
Florida (upper) ogsr, abrog provis relat, grant trust fund, govern act, person inform, to supplement, employ contribut to, legisl audit committe, jac, maintain by the, insur regul, financi inform senat relat to, senat relat, to, ssb, elder, school, municip that, and legislatur by, that law enforc, provid minimum, admiss to, local law enforc
Florida (lower) etc, certain propos, re creat, repeal under, to qualifi, boundari, program revis requir, environment permit, counti hospit district, alcohol beverag licens, except under, ranch hous relat, day, renew energi, provid for alloc, make recommend, for employ of, of damag, from particip, week, catastroph, dhsmv to develop, employ from
Delaware (upper) uniform, would increas the, amend chapter volum, person convict, relat the delawar, dealer, child support, bureau, violenc, associ, charter chang, for fiscal year rent, state languag, the content, act regul, certain licens, give local, assembl from, delawar code establish, for citizen, reimburs, propos constitut amend, salari
Delaware (lower) of the th, tax refund, thi act also, amend of the, this section of, the titl, the act to, of member of, electron transmiss, for in the, and the date, parent guardian predatori, hour per, relat state employe, unfair practic, communic, open meet, equal the, to the construct, the construct, medicaid, state agenc, relat to prevail
Table 9: Top and bottom ranked phrases for New Jersey, Maryland, California, and Florida.