# Measuring Aggregate Policy Priorities of the U.S. House of Representatives

January 08, 2021

By Philip D. Waggoner, University of Chicago

Abstract: Tracing individual policy priorities through bill sponsorship is a relatively common occurrence in policy research. Yet, do individual priorities translate to institutional priorities? I address this by offering a statistical approach to measuring and mapping the policy priorities of the U.S. House of Representatives over the post-war period. Assuming individually sponsored bills are reflections of individual legislators’ priorities, and thus aggregate sponsored bills reflect aggregate chamber priorities, more frequently sponsored issues should be of higher priority than less frequently sponsored issues. Leveraging metric multidimensional scaling (MDS), I generate MDS solutions for nearly 70 years of bill sponsorship activity to measure and map aggregate chamber priorities. As a validity check, comparing the patterns of policy prioritization uncovered from the MDS solutions with the occurrence of major political events reveals pronounced congruence, suggesting the solutions are capturing real chamber priorities that shift over time. The goal at present is to offer a short paper presenting a new method for measuring aggregate policy priorities that can be used in future studies of policy making and institutional behavior.

Code and Replication data: https://github.com/pdwaggoner/MDS-and-Bills

Looking to sponsored bills to understand individual legislators’ individual policy priorities is a commonly pursued strategy in political research (e.g., Jones 1997; Schiller 1995; Sulkin 2009). Individual legislators with unique preferences and constituencies have a relatively unabated opportunity to sponsor any number of bills on any number of topics in the American Congress. Bill sponsorship has also been considered to reflect position-taking (Rocca and Gordon 2010). Further, there is recent evidence of legislators being indirectly influenced by constituents to take up specific issues in their sponsored bills, pointing to an electoral connection in this form or policy prioritization (Woon 2009).

As sponsored bills have been shown to reflect individual policy priorities, it follows that the sum of individual priorities should reflect aggregate chamber priorities. Frequently sponsored issues should be of greater priority than less frequently sponsored issues. By considering the differences in aggregate sponsorship frequencies of major issues as geometric distances, the degrees of dissimilarities in sponsorship behavior can be explored, allowing for the mapping of institutional policy priorities relative to all other issues. As the distance between issues grows, the differences in the prioritization of the issue also grows.

Given the value of bill sponsorship in understanding both individual and institutional priorities, I offer a new approach to measure and explore dynamic, aggregate institutional policy priorities of the U.S. House of Representatives. Leveraging metric multidimensional scaling (MDS), I generate solutions for nearly 70 years of bill sponsorship, from the 80th Congress (January 1947 to January 1949) to the first session of the 114th Congress (January 2015 to January 2016). Such an approach sheds light on the prioritization of individual issues by the chamber, in relation to all other issues simultaneously proposed in the given Congress. While my approach remains in the American context, this MDS procedure can be applied to any legislative context in which individual legislators are tasked with creating policy through sponsoring bills on single topics, allowing for aggregation of individual priorities.

This research reflects an effort to establish a contextually-rich and methodologically parsimonious, yet rigorous approach to mapping aggregate bill sponsorship and policy prioritization, such that a clearer understanding will aid in future research on legislative behavior. Specifically, this analysis offers an important methodological advance for studying the U.S. Congress for two main reasons, which are interrelated: multidimensional scaling is preferable to summing across individual issues, in that relative priorities more accurately revealed. Multidimensional scaling in this context allows issues to be placed in relation to each other in space and across time, rather than summing frequencies of sponsored bills and thus assuming their independence from each other. In brief, the summing approach treats issues as isolated, disallowing any contextual comparison to all other issue-specific sponsorship activity in the chamber, while the MDS approach iteratively pairs all issues to allow for a more realistic comparison of relative institutional policy priorities.

As Congress is a context of hundreds of competitors with thousands of policies competing for limited agenda space, assuming these issues and players are acting independently is unrealistic. Thus, placing all issues in space and time together, allows their relative prioritization to be mapped onto a common space, ultimately revealing broader patterns of chamber priorities of the U.S. House as an institution, beyond individual legislators. In sum, relational, dynamic differences are helpful in understanding policy priorities. Upon specification of the MDS solutions, as a face validity check, I compare the patterns of aggregate priorities over time with major political events. The MDS solutions mirror reality well, with shifts in priorities coinciding with key events, such as the bills sponsored on the issue of defense increasing in the time of war, or the bills on the environment rising in priority in the 1970s at the beginning of the environmental movement.

### Congressional Context

The U.S. House of Representatives is charged with numerous responsibilities, from funding international conflicts in which the U.S. is engaged to establishing domestic programs. Much of this work gets accomplished beginning with bill sponsorship. While some of the work of Congress is to set up new programs and offices, other tasks are more routine aimed at reauthorizing various programs and positively responding to required jurisdictional realms (e.g., funding and operating national parks). Given the finite resources and time available to Congress, most notably time (Fenno 1978), it follows that Congress as a whole must make daily prioritization decisions; do we prioritize this program over that program? Given the primary responsibility of keeping the government operational, coupled with the opportunity cost structure of prioritizing some issues over others, the aggregate sponsorship of bills on individual issues should reveal aggregate institutional priorities.

While most sponsored bills do not pass out of the chamber due to majority party gatekeeping, bill sponsorship remains a consequential form of legislative behavior capturing the priorities of groups and individuals in the chamber (e.g., Barnello and Bratton 2007; Schiller 1995). Additionally, bill sponsorship avoids selection biases associated with only analyzing subgroups of legislators and/or bills, such as studies on policy output. Evaluating only those bills that are passed out of the chamber and then using them to draw generalizable inferences about the institution broadly (which is comprised of far more legislators who were unsuccessful at passing bills) for example, is vulnerable to bias. And finally, several studies tap policy priorities of legislators through event count models, where higher sponsorship rates suggest greater priority (Waggoner 2018; Woon 2009)

Given the substantive and analytical benefits associated with analyzing bill sponsorship, the priorities of the institution should be seen in the frequencies with which issues are the subjects of individual bills. High frequency should reflect high priority. For example, 100 sponsored bills in a single session on environmental policy versus 11 sponsored bills on housing policy in that same session would suggest that in the aggregate, the environment was a relatively higher legislative priority for Congress than housing in that session. Following this simple logic, the aggregate frequencies of sponsored bills should reveal information about the relative priorities of issues in a given Congress, in relation to all other issues in that same Congress. Distances from one issue to the next based on the aggregate frequencies should capture dynamic policy prioritization for the entire chamber.

Taken together, the focus of Congress should be seen in the aggregate of sponsored bills in a single Congress, which is made up of two “sessions,” each a year long. These priorities should include a mixture of required federal functional responsibilities (e.g., national parks, program reauthorizations) and also issues of particular salience in the given Congressional session (e.g., agriculture bills during a drought). The issues of higher salience should vary by Congress, with some issues be of higher priority in one Congress, but not in another as a function of various conditioning contexts (e.g., the national healthcare debate on the Affordable Care Act occurring mostly in 2010 influencing an increase in healthcare related legislation). In short, I call this dimension, federal focal prioritization. The priority scale captures targeted prioritization, or focus, at a given point, which is comprised of high salience issues as well as continued functional responsibilities of the federal government.

### A Multidimensional Scaling Approach to Mapping Institutional Priorities

MDS has a rich history with volumes of work from initial exploration of proximities (Torgerson 1952), to more technical iterations (e.g., jackknife (De Leeuw and Meulman 1986) or, e.g., Procrustes problem solutions (Borg and Groenen 2005)). [2] As such, MDS is a highly flexible and useful strategy to explore and recover the underlying structure in similarities and dissimilarities data. These types of data reflect geometric distances between objects of interest, either physically (e.g., distances between cities) or non-physically (e.g., differences between survey responses). Dissimilarities means that as the values grow larger, there is less similarity between the objects, whereas similarities data is the opposite. Thus, MDS estimates distances between pairs of observations to recover an inter-point configuration that mirrors the configuration in the data (Armstrong et al. 2014). The generated point configuration is measured by the stress function, $$\sigma$$,

\begin{align} \sigma = \frac{\sum \sum (f(x_{ij}) - d_{ij})^2}{\sum \sum d_{ij}^2} \end{align}

where, $$f(x_{ij})$$ is a function of aggregate sponsorships, and $$d_{ij}$$ is the calculated distance between the sponsorship frequencies of pairings of individual issues, summed over issue, $$i$$ and issue, $$j$$, where $$i$$ and $$j$$ define indices for $$i$$ and $$j \in \{1...N\}$$. The goal of MDS is to minimize the stress function to generate a highly accurate representation of the true distances between the points, which is the difference in sponsorship totals.

For my purposes to measure the aggregate policy priorities of the U.S. House, I generate MDS solutions for each Congress from the 80th (1947-1949) to the first session of the most recent full Congress, the 114th (2016), as well as the full dataset of all pooled Congresses. To do so, I use the majorization algorithm to minimize the stress function, $$\sigma$$, of a given configuration of points, $$X$$ (Armstrong et al. 2014; De Leeuw 1988). This is a process of approximating “unwieldy and complex functions using a series of simpler, auxiliary functions” (Armstrong et al 2014, 114). Majorization is an iterative data reduction algorithm used to recover the distances between pairs of objects in Euclidean space to approximate the underlying relational proximities as closely as possible. The idea is that pairs of issues are measured based on differences in sponsorship totals, and then a real, or “observed” point configuration is generated. Then, the majorization algorithm generates many synthetic point configurations based on the observed configurations, and then minimizes the sum of squared errors between the observed and synthetic point configurations. The algorithm stops when the global minimum is found, suggesting the synthetic configuration approximates the observed configuration as closely as possible. While this is the same goal as numerical optimization, majorization is preferable, because it is guaranteed to stop iterations once the minimum is found. [3]

Using the Adler and Wilkerson (2012) Congressional Bills Project data containing all sponsored bills from 1947-2016 coded for major and minor issue areas, I operationalize the dissimilarities in sponsorship frequencies as differences in priorities in each Congress. [4] As expected, priorities may shift from Congress to Congress. For visual interpretation of the full solution cross-section in Figure 1, outliers and groupings of objects are important. Outliers reveal significant differences between issues, while groupings of points reveal similarities among respective issues, and thus similarity in priority. For example, the government operations issue is often an outlier with greater positive distance from other issues, suggesting sponsoring bills related to keeping the government operating is a high priority.

### Recovering the Structure of Aggregate Policy Priorities

In this section, I visually present the structure of legislative policy priorities as a function of bill sponsorship activity. The MDS solution is well fitting as shown in the Shepard Diagram in Figure A1 in the Appendix, which plots the configuration distances as a function of the dissimilarities. Further, the values of the stress function for all MDS solutions were sufficiently low (e.g., 5.21e-16 for the full solution), per the goal of majorization and MDS of keeping the stress function below 1.0 (Borgatti 1997).

To evaluate the structure of the priority space underlying bill sponsorship, I first present a visualization of the full solution for all pooled Congresses in Figure 1. This step is beneficial as a first step in presenting a macro-level view of prioritization spanning 70 years. Then, I follow the pooled MDS plot in Figure 1 with several plots in Figures 2-5 showing the dynamic prioritization of issues over time, relative to the most prioritized issue, government operations.

Figure 1 acts as a cross-section of policy priorities, valuable only insofar as we can see where issues “hang” in space relative to all other issues over the full study period (1947-2016). The Y axis places issues on the federal focal prioritization dimension. Those near the top of the plot are of higher priority, while those issues lower on the plot are of lower priority. The X axis reflects the pre-assigned (arbitrary) ordering of the individual issues based on their index values in the coding scheme in the Baumgartner and Jones (1993) Policy Agendas Project (e.g., economics = issue 1, civil rights = issue 2, health = issue 3, and so on). [5] While the cross-section in Figure 1 serves as a useful starting place to begin to understand the relative policy priorities of the House, it is only minimally useful because shifts in prioritization of specific issues should vary across individual Congresses. Therefore, building on the visual pattern in Figure 1, the relative positions of individual issues are traced across Congresses in the subsequent plots in Figures 2-5.

###### Figure 1: Cross-Section of the Pooled MDS Solution, 1947-2016

Figure 1 reveals that government operations, public lands, and defense bills stand alone and are high in federal focal prioritization. This is consistent with the underpinnings of this structure in that bills authorizing and reauthorizing the government’s work, operating the national parks and lands, and keeping the military funded and operational are of unique, consistently high priority over the course of nearly 70 years of bill sponsorship. Lower on the pooled federal priorities are immigration, civil rights, international affairs and science bills. [6] While the lower priorities of these issues could be a function of exogenous events, shifting functional responsibilities of the government, or another cause entirely, the following figures serve to add more clarity to the shifting nature of policy priorities overtime.

### Do Institutional Priorities Mirror Contextual Reality?

While the MDS solutions reveal relative priorities of the U.S. House, this pattern could be a function of anything, whether real priorities of the institution or otherwise. Thus, to gain some traction on that which the MDS solutions are precisely revealing, I turn now to offer a validity check. Specifically, in pursuit of face validity, I evaluate patterns of a few issues over time, and compare to major legislative action and key political events where appropriate. The goal is to assess whether the rises and falls of unique issues in relative priority mirrors contextual reality. Figures 2-5 show the evolution of policy priorities of several issues, relative to the highest priority issue, government operations. This provides a baseline against which to evaluate the evolution of policy prioritization. Figure 2 compares two issues of frequently high priority in recent Congresses: defense and healthcare.

###### Figure 2: Comparing Defense and Health Bills to Government Operations, 1947-2016

A striking trend in Figure 2 is that evolution of healthcare related legislation, moving from low priority to surpassing government operations in the most recent Congresses. This could be due to the shift in saliency of healthcare in the Obama years. [7] Specifically, clear spikes in healthcare prioritization occur in the 106th Congress (final years of the Clinton presidency) and 111th Congress (first two years of the Obama presidency). In addition to healthcare being a top priority for both of these presidents and their copartisans in Congress, these trends in prioritization are in line with the issue ownership literature, demonstrating healthcare as a Democratic-owned issue on average since the 1970s (Egan 2013, 67).

Regarding defense policy, the starting place as a high priority in Figure 2 is consistent with the global war environment at the time (post-WWII and pre-Korean war) both in which the United States was heavily engaged. Though the prioritization of defense decreases thereafter, spikes in prioritization occur periodically throughout the 20 years of the Vietnam War (1955-1975), and spike again in the 108th Congress (2003-2005), which was a high point in the War in Iraq and the War on Terror. Federal policy prioritization of defense bills seems to generally track with historical and exogenous influences and events.

Next, Figure 3 shows the evolution of prioritization of labor and welfare bills over time.

###### Figure 3: Comparing Labor and Welfare Bills to Government Operations, 1947-2016

As one may expect, these issues track together relatively closely overtime, with the exception of the deviation beginning around the 100th Congress, yet coming back together in the most recent full Congress (114th). This trend suggests that, while prioritization shifts, the institution broadly prioritizes these issues the same, sponsoring legislation to this effect.

Next, Figure 4 shows the prioritization of energy and environment bills over time.

###### Figure 4: Comparing Energy and Environment Bills to Government Operations, 1947-2016

While these similar issues mostly track together except for the range between the 100th and 108th Congresses, the most striking trend in line with historical action by the government is the stark jump in prioritization of the environment around the 91st and 92nd Congresses. During these years, the Nixon administration and Congress placed a premium on environmental issues, resulting in the passage of policies such as the Marine Mammal Protection Act, and the Coastal Zone Management Act among others, followed by the more notable Endangered Species Act and the Safe Drinking Water Act in the subsequent Congress.

Finally, Figure 5 is illuminating, not only from a policy perspective, but also to demonstrate that the measure is capturing relative prioritization.

###### Figure 5: Comparing Science/Technology Bills to Government Operations, 1947-2016

Notably, the issue of science/technology is one of the lowest priorities in the pooled solution shown above in Figure 1. In comparison to the highest priority of government operations, Figure 5 powerfully demonstrates the difference in prioritization of science related legislation in comparison to government operations. Notably, relative to government operations, science is a negative priority of the chamber (being below the 0.0 value on the Y axis).

### Conclusion

It has been my goal to leverage a wealth of bill sponsorship data available for all legislators spanning nearly 70 years, regardless of majority party status or legislative ability, to offer fresh insight into institutional policy priorities of the U.S. House of Representatives. By considering the aggregate frequencies with which specific issues are introduced in the chamber and the distances between issues as signals of relative priorities, I have taken an initial step in recovering the structure underlying bill sponsorship. Specifically, I have offered a new methodological approach with a high degree of face validity, to map the policy prioritization of major issues in the House over the entire post-war period. The results displayed in Figures 1-5, serve as highlights to support my conception of aggregate institutional policy priorities. The patterns in sponsorship behavior mirror the external political climate, suggesting the relative priorities of the chamber track with major political and substantive events (e.g., the mid-late 1940s being an era of global war influencing a high degree of prioritization of defense related legislation; see, e.g., Figure A3 in the Appendix). [8]

While the structure recovered here is useful in strengthening a greater understanding of legislative institutional behavior and prioritization over time, there remains much work to be done. Future research would be useful to combine theoretical innovation with these solutions to expound upon the role bill sponsorship plays in institutional as well as individual level legislative behavior. For example, these MDS solutions could be used to answer substantive questions, such as, how do priorities shift when a new party takes control? Dynamic shifts in aggregate priorities would be a useful analytical focus in such a study. Further, future work could take up this same methodological exercise in the Senate, to compare the degrees to which the chambers are similar or different as they prioritize unique policies at unique points in time. And finally, as mentioned in the introduction, future work could apply a similar MDS approach to study aggregate policy prioritization in comparative, non-American legislative bodies to assess dynamic policy priorities.

### References

Adler, E. Scott, and John Wilkerson. 2012. Congressional Bills Project. NSF 00880066 and 00880061.

Armstrong, David A., Ryan Bakker, Royce Carroll, Christopher Hare, Keith T. Poole, and Howard Rosenthal. 2014. Analyzing spatial models of choice and judgment with R. Boca Raton: CRC Press.

Austen-Smith, David. 1990. “Information Transmission in Debate.” American Journal of Political Science, 34: 124-52.

Baumgartner, Frank R., and Bryan D. Jones. 1993. Agendas and Instability in American Politics. Chicago: University of Chicago Press.

Barnello, Michelle A., and Kathleen A. Bratton. 2007. “Bridging the Gender Gap in Bill Sponsorship.” Legislative Studies Quarterly, 32(3): 449-474.

Borg, Ingwer, and Patrick JF Groenen. 2005. Modern Multidimensional Scaling: Theory and Applications, 2nd edition. New York: Springer.

Borgatti, Stephen P. 1997. “Multidimensional Scaling.” Geneva97, http://www.analytictech.com/borgatti/mds.htm (accessed February 20, 2017).

De Leeuw, Jan. 1988. “Convergence of the Majorization Method for Multidimensional Scaling.” Journal of Classification, 5(2): 163-180.

De Leeuw, Jan, and Patrick Mair. 2009. “Multidimensional Scaling Using Majorization: SMACOF in R.” Journal of Statistical Software, 31(3): 1–30.

De Leeuw, Jan, and Jacqueline Meulman. 1986. “A Special Jackknife for Multidimensional Scaling.” Journal of Classification, 3(1): 97–112.

Egan, Patrick J. 2013. Partisan Priorities: How Issue Ownership Drives and Distorts American Politics. New York: Cambridge University Press.

Fenno, Richard F. 1978. Home Style: House Members in their Districts. New York: Harper Collins.

Jones, Mark P. 1997. "Legislator Gender and Legislator Policy Priorities in the Argentine Chamber of Deputies and the United States House of Representatives." Policy Studies Journal, 25(4): 613-629.

Mair, Patrick, Jan de Leeuw, and Patrick JF Groenen. n.d. “Multidimensional Scaling in R: SMACOF.”

Rocca, Michael S., and Stacy B. Gordon. 2010. “The Position-taking Value of Bill Sponsorship in Congress.” Political Research Quarterly, 63(2): 387-397.

Schiller, Wendy J. 1995. “Senators as Political Entrepreneurs: Using Bill Sponsorship to Shape Legislative Agendas.” American Journal of Political Science, 39(1): 186-203.

Sulkin, Tracy. 2009. “Campaign Appeals and Legislative Action.” Journal of Politics, 71 (3): 1093-1108.

Torgerson, Warren S. 1952. “Multidimensional Scaling: I. Theory and Method.” Psychometrika, 17(4): 401-419.

Waggoner, Philip D. 2018. “Do Constituents Influence Issue-Specific Bill Sponsorship?” American Politics Research, doi: 1532673X18759644.

Woon, Jonathan. 2009. “Issue Attention and Legislative Proposals in the U.S. Senate.” Legislative Studies Quarterly, 34(1): 29-54.

### Appendix of Supplemental Materials

###### Figure A2: Full Solution Plot, without Immigration Bills

Individual Congress “Cross-Sectional” Case Studies: 80th and 111th. While Figure 1 in the main text is beneficial in providing an overview of issue priorities across all aggregated bill sponsorship frequencies for nearly 70 years, individual Congresses have unique priorities, as the theoretical context suggests, especially as world and other exogenous events condition the salience of issues. This seems likely to be the case in the cross-section of the 80th Congress, shown in Figure A3 below.

###### Figure A3: Federal Focal Prioritization in the 80th Congress

While Figure 1 in the main text is beneficial in providing an overview of issue priorities across all aggregated bill sponsorship frequencies for nearly 70 years, individual Congresses have unique priorities, as the theoretical context suggests, especially as world and other exogenous events condition the salience of issues. This seems likely to be the case in the cross-section of the 80th Congress, shown in Figure A3 below.

###### Figure A3: Federal Focal Prioritization in the 80th Congress

Here, defense bills are uniquely prioritized in the 80th Congress as they sit strikingly at the top of the plot, surpassing even government operations and public lands. This trend is likely influenced by the global war environment in which the United States was mired at the time, although notice how the functional need for the government to continue in its responsibilities do not diminish as defense bills are augmented in their prioritization (notably, see the continued high prioritization of government operations and public lands bills). This high priority of defense bills in the 80th Congress is demonstrated by greater distance (or dissimilarity) from most other issues comprising sponsored bills in that Congress. To further validate the defense-related tone of the 80th Congress, major pieces of passed legislation included the National Security Act of 1947, the Marshall Plan, the Civil Air Patrol Act, and the War Claims Act of 1948 among others.

Figure A4 shows the bill sponsorship structure in the 111th Congress, which included passage of several prominent healthcare bills. To this end, note the drastic elevation of health bills in federal focal prioritization compared to other issues (including routine government responsibilities) as well as compared to health bills in previous Congresses. The dissimilarities of health from most other issues in the 111th Congress underscores the unique prioritization of these issues during the Obama years and a supportive Democratically-controlled House. Among the prominent health bills to emerge from the chamber were the Affordable Care Act (“Obamacare”), the Children’s Health Insurance Program Reauthorization Act, and the Health Care and Education Reconciliation Act of 2010.

An additionally prominent issue as well, is the issue of commerce which surpasses even defense. This is in line with both theoretical expectations and the salience of the economy as well, in the wake of the “Great Recession” in 2008 just before the commencement of the 111th Congress. To illustrate the federal focal prioritization of these issues, some key commerce policies focusing on financial institutions and domestic commerce issues, included the American Recovery and Reinvestment Act of 2009, Lilly Ledbetter Fair Pay Act of 2009, and the Helping Families Save Their Homes Act of 2009 among many others.9

###### Figure A4: Federal Focal Prioritization in the 111th Congress

Comparing Sponsored Bills to Public Law Bills for Selected Issues. To compare prioritization of issues across different forms of behavior, such as bills that emerge from the chamber and are signed into law, versus bills that are only sponsored, consider the following brief analysis. The benefit of this is to take a step in determining whether sponsored bills reveal different information compared to passed bills. Specifically, I seek to follow the same process as in the main paper, but for bills signed into law. Such a procedure will allow for comparison of different forms of behavior to diagnose whether, and the degree to which different expressions of legislative output may be similar.

To do so, I first diagnose the dimensionality of the space, by assessing the Scree plot of the Eigen values based on a correlation matrix aggregate public law bills across specific issues. The plot in Figure A5 similarly shows that there is a single prominent dimension of prioritization underlying the bills that emerge from the chamber.

###### Figure A5: SCREE Plot of Public Law Bills, 1947-2016

Next, I generate MDS solutions for each Congress, as well as the pooled data for all Congresses. Similar to Figure 1 in the main paper, Figure A6 below shows that the positioning of these issues in the prioritization space is very similar to those from bill sponsorship. Specifically, of highest priority are Government Operations, Public Lands, and Defense bills.

###### Figure A6: Full Solution for Public Law Bills, 1947-2016

Then, finally, to compare at a finer grained level, I show the following two plots in Figures A7 and A8, which compare sponsored and public law bills on the same issue, relative to sponsored government operations bills for comparison purposes, similar to the main paper. The first issue is civil rights (Figure A7), and the second is healthcare (Figure A8).

###### Figure A7: Comparing Evolution of Prioritization of Civil Rights Bills, 1947-2016

Figure A7 shows that both sponsored and public law civil rights bills track relatively well together, and are of notably lower priority compared to government operations bills. This comparison indicates that the priorities of individual bill sponsors mostly matches the priorities of the chamber as a whole.

The next plot in figure A8 on healthcare, though, shows a different story. This plot seems to show that healthcare sponsored bills are different than those that are signed into law. Specifically, bill sponsors seem to view this issue as a higher priority than the majority party, which is in charge of determining that which emerges from the chamber.

###### Figure A8: Comparing Evolution of Prioritization of Healthcare Bills, 1947-2016

While sponsored healthcare bills tracked relatively well public law healthcare bills from about the 80th Congress to the 90th Congress, the 90th Congress marks significant divergence between sponsored bills and those that became law, resulting in bill sponsors prioritizing healthcare legislation to a greater degree than government operations in the most recent Congress. Given the higher prioritization of this issue by bill sponsors relative to the chamber, the majority party could be suppressing the prioritization of this issue relative to the demand from bill sponsors. Or, this could be a functional reality, where the majority party must keep government operations as a higher priority, while bill sponsors are beholden to no such constraint, resulting in sponsorship of healthcare legislation vastly outpacing bills on that topic that emerge from the chamber. Still, to precisely say that which is causing or influencing this pattern is well beyond the scope of this analysis, though these patterns merit more scrutiny and future research.

### Notes

1. I thank Justin Esarey, Justin Kirkland, Carlisle Rainey, and participants of the 2017 Texas Methods Meeting for helpful comments on earlier iterations of this project. All mistakes remain my own.

2. See De Leeuw and Mair (2009) and Mair, De Leeuw, and Groenen (n.d.) for an overview of more technical iterations.

3. See Armstrong et al. (2014, 109-118) for an extended discussion on tradeoffs and estimation for each of these approaches.

4. In using these data, “the views expressed are those of the author and not the National Science Foundation.”

5. Given that numeric values and specific coordinates of points in Euclidean space are meaningless on their own, I omit them from the pooled plot in Figure 1 to allow for a simplified rendering of the dissimilarities across bill sponsorship activity.

6. The low prioritization of immigration bills could be due to the change in coding from the Policy Agendas Project, resulting in several congresses with missing immigration bills. Inclusion of immigration in the full solution is not worrisome as Figure A3 demonstrates, though. Results and positions of all issues remains consistent with and without immigration bills.

7. Select individual Congress cross-sections of federal focal priorities are shown in the Appendix in Figures A3 and A4, and are combined with an overview of major pieces of passed legislation corresponding with high priority issues in the respective Congress. These additional plots and discussion serve to strengthen the expectations and findings that federal focal prioritization shifts as issues rise and fall in saliency.

8. While the pooled solutions for both sponsored and public law bills were nearly identical suggesting a high degree of similarity between these forms, comparing MDS solutions for sponsored bills and bills that became public law on the same topics for the same years, shown in the Appendix, revealed that certain issues track well together, while others diverge. Whether sponsorship is capturing different behavior than bills that emerge from the chamber is beyond the scope of this analysis, but would benefit from future research.

9. The coding of the commerce policy issue area following the Comparative Agendas Project coding scheme (Baumgartner and Jones 1993), includes banking system regulation, insurance and commodities regulation, consumer finance and protection, small business administration issues, and copyright and patents, among several other key domestic commerce issues.