Lawmaking and Law Unmaking:A Survival Model of Federal Law in the United States∗

Matthew Hindman†and Lada Adamic‡

ABSTRACT

While there has long been substantial research into the traits that promote bill passagein the U.S. Congress, political scientists have learned much less about the factors thatkeep federal laws on the books. This paper addresses that problem using a large new dataset containing the revision history of every section, part, and subpart of the U.S. Codefrom 1973 to 1998. While most laws are of little importance and remain on the booksindefinitely, our data show that sections of federal law created by significant legislation (asjudged by previous scholarship) are much more vulnerable to amendment and repeal. Weuse a proportional hazard model to jointly test competing theories of legislative behavior.One unexpected finding is that sections of the U.S. Code that are heavily referenced byother sections of federal law are less likely to be altered.

∗Paper prepared for presentation for the Annual Meeting of the Midwest Political Science Association, April5, 2009. The authors would like to acknowledge the work of Matthew Simmons and Anthony Hayes in collectingand analyzing these datasets.

†Matthew Hindman is Assistant Professor of Political Science at Arizona State University. Mail: PO Box873902, Tempe, AZ 85287-3902, tel: 480-965-4686, fax: 480-965-3929, hindman@asu.edu.

‡Lada Adamic is Assistant Professor at the School of Information and Center for the Study of ComplexSystems. ladamic@umich.edu.

I. Introduction

This article aims to address a curious asymmetry in political science knowledge. Once a bill

is introduced in congress—and even before—scholars know a great deal about whether it is

likely to become law. This subject has been the focus of scholarly attention since before the

founding of political science as a discipline, and despite persistent areas of controversy, con-

temporary scholarship has identified a list of consistent factors that influence lawmaking, such

the role of congressional parties, the committee system, members’ seniority and individual

policy preferences, and the influence of interest groups and bureaucratic actors. In recent

decades much work in this domain has also employed spatial models of congressional vot-

ing, often in combination with vote scaling procedures. It is well established that a spatial

model with a single policy dimension can capture most variance in voting behavior in the U.S.

Congress (Poole and Rosenthal 1997, McCarty, Poole, and Rosenthal 2006).

But if scholars can say a great deal about which bills are likely to pass, they know far less

about which laws to are likely to stay passed. There have been few comprehensive studies

about what happens to laws once they are on the books, and the factors that make laws more

or less likely to be amended, revised, or repealed. While studies of agenda setting and policy

stability have examined the issue, scholarship in this vein has been focused on wide-ranging

but scattershot collections of case studies across differing policy domains (Baumgartner and

Jones 1993, Baumgartner and Jones 2002)). While spatial models typically estimate a “sta-

tus quo point” (Romer and Rosenthal 1978, Krehbiel 1999), this has been a theoretical and

computational convenience unconnected to studies of actually existing laws.

Why has there been so little comprehensive work done on the persistence of federal laws?

The answers seem to be at once theoretical and practical. Even many scholars of congress

assume that the federal law, in practice, is mostly additive, and that only a small portion of laws

are ever amended or repealed. This is largely true; however, this fact comes with an important

qualification. Some laws are more important than others, as longstanding scholarship about

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what counts as “significant” legislation attests to (Mayhew 2005, Clinton and Lapinsky 2007).

As this paper will show, sections of the U.S. Code created by significant laws—as judged by

previous scholarship—are far more vulnerable to amendment.

Another barrier is practical. The corpus of federal law is immense, with 50 Titles, more

than 2,200 chapters, and roughly 50,0000 parts or subparts. Studying what happens to laws

once they have been passed requires us to associate to every title, chapter, subchapter, section,

subsection, part, and subpart of that code with the specific congressional bill that created it,

and with every subsequent bill that might amend it.

These difficulties notwithstanding, understanding what happens to laws after they have

been passed is a scholarly puzzle of the highest order. In this paper, we propose to take a

first step toward the comprehensive study of what happens to laws once they are enacted. We

start by adopting a methodology that would not have been feasible a decade ago, digitizing the

entirety of the federal law, and placing the complete text of U.S. Code in a database alongside

traditional political science variables.

We analyze this data using a Cox proportional hazard model, measuring how long the

component sections of a federal law remain in force before being altered or amended. Some

of the largest and most consistent predictors of a statute’s durability concern the structural

characteristics of the legal code itself—particularly the references that a part or subpart makes

to, and receives from, other parts of federal law. Indeed, the network structure of Federal

law predicts as much variance as other variables that have long inspired more attention from

scholars.

II. The United States Code

Scholarship on congressional policymaking for many years has sought to quantify the level

of stability in federal policy. Arguably the most important measure of policy stability lies

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in federal law itself. If we want to understand federal law, we need to start with a detailed

examination of the United States Code.

According to the United States Government Printing Office, the United States Code is “the

codification by subject matter of the general and permanent laws of the United States.”1 En-

rolled bills passed by congress that are signed by the president, or passed over the president’s

veto, are presented to the archivist of the United States and published in United States Statutes

at Large, an annual compendium of all new laws. However, because the Statutes at Large are

not an easy source for legal research, the text of the statutes are sorted, classified and codified

by House of Representative’s Office of Law Revision Counsel, which also determines which

sections of existing law have been altered by these new acts of congress, or have expired on

their own.

The print version of the US code is published every six years by the Government Printing

Office, with annual supplemental volumes produced between these hexannual revisions. The

U.S. Code is organized logically and by subject area. For this reason, the text of new statutes

often do not appear in a single place in the Code. Focusing just on the important laws, we find

that some edit many more sections than others. While Pub. Law 99-514 revises the federal

income tax system by modifying 1463 parts (or subparts) of the code, and Pub. Law 94-455

reforms the tax code by modifying 1211 sections, many other laws accomplish their goals by

modifying only a handful of sections. However, most important laws touched more than a

single title of code. For example, Pub. Law 94-455, the “Tax Reform Act of 1976” modified

not only 1190 parts of Title 26 concerning the internal revenue service, but also modified 21

other parts in 7 other titles, including 28 (Judiciary), 29 (Labor) and 42 (Public Welfare).

1http://www.gpoaccess.gov/uscode/

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A. Data Gathering and Data Description

Our data sets were collected from publicly available sources as detailed in Table A. Data on

rollcall votes, congress membership, and correspondence between bill numbers and public

laws were already readily available in tabular format for download. The links within the

federal code, and the correspondence between public laws and the sections of code they edited

were parsed from document text. This process, as is true of all data extraction, was prone to

some error. However, given that our analysis is done in aggregate, we believe a small error

rate may be tolerated.

To obtain references between sections of the code, we parsed the full text of the code,

which can be downloaded as a set of plain text files, one for each title. Within each title file,

for each section, we found the “-SECREF- section referred to in other sections” portion and

used regular expression matching to match to sections referring to that particular section. For

example, when the text listed section numbers followed by the text “of this title” we mapped

the sections to the same title. Otherwise, we used the explicitly specified title the citing section

belonged to. This gave us a single snapshot of the citation structure, current as of 2004, but no

information about when particular sections first cited one another. Reconstructing the session-

by-session process by which section acquire new links will be possible in the future, however,

and this is a high priority for subsequent research.

The mapping from public laws to sections of the code they edited was extracted from

Table III of the US Code published in 2005. The document contained all changes starting

with the first congress, but listed public law numbers from the 85th congress onward. The

document was first converted from PDF to text format, and then parsed. Some changes were

designated as Rep. (repealed) or Elim. (Eliminated) while others were assumed to be additions

or modifications.

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Table I: Data sourcesDataset time span URL/descriptionPublic Laws 1948-2005 http://policyagendas.org/codebooks/publiclawsdb.html

Public Law Number, Bill or Senate Number, CommitteesImportant Laws 1948-2005 http://policyagendas.org/txt/MostImptLaws48-98TabDelim.txt

Identifies significant laws during the time periodTable III 1789 - 2005 http://uscode.house.gov/pdf/2005/2005uscTableIII.pdf(US Code) Maps public laws to code edits 1957-2005

US Code2004 version http://uscode.house.gov/download/ascii.shtmlFull text used to extract links

Congressional 1946-2000 http://policyagendas.org/excel/House-Roll-Calls-Web.xlsRoll Call Votes http://policyagendas.org/excel/Senate-Roll-Calls-Web.xls

Y and N votes, DW-Nominate scores, sponsorICPSR rollcall 1789-1996 http://www.icpsr.umich.edu/cgi-bin/bob/newark?study=4&path=ICPSRvote records Individual votes and party affiliations of congressmen

B. The Network Structure of the Federal Code

Despite the recent proliferation of social network models in political science and related disci-

plines, little previous scholarship has looked at the network structure of U.S. law. Exceptions

include modeling networks of supreme court citation cases (Leicht, Clarkson, Shedden, and

Newman 2007, Fowler and Jeon 2008) and network analyses of bill-cosponsorship (Fowler

2006) and congressional committee interlocks (Porter, Mucha, Newman, and Warmbrand

2005). Yet The federal code itself is indeed a network, and references between different parts

of the law are an integral part of how federal law functions in practice. Moreover, unlike many

other politically relevant social networks, links between sections of the US code should be

fully observed, making analysis easier.

Figure 1 shows the citation patterns at the coarsest granularity: between the titles of the

code. The network is formed by adding a citation link if any part of one title links to any part

of another. An automated graph layout algorithm (Adar 2006, Frick, Ludwig, and Mehldau

1995) is used to place titles that cite one another close together while pushing unconnected

pairs apart. Two titles are highly central, both in terms of their placement near the middle

of the network and in terms of the many sections citing them. Title 5 concerns government

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organization and employees and Title 42 concerns public health and welfare. In addition,

related titles, such as “10. Armed Forces” and “38. Veterans’ Benefits” are brought close

together.

Figure 1: The largest connected component of the network of titles. The size of a node repre-sents the number of sections and parts. Yellow links represent a smaller number of citations,green ones a higher number. Edges with fewer than a given number of citations are omitted.Titles omitted (with no edges above threshold) include 1,4,9,13,17,27,48,36,47.

At the chapter level, there are 2,252 chapters, with 12,726 links between them. Figure 4

shows this dense network. As one might expect, chapters belonging to the same title are linked,

but there are many links across titles, making this network dense and difficult to interpret vi-

sually. But we can examine local linking patterns in the network using motif analysis. Motif

analysis (Milo, Shen-Orr, Itzkovitz, Kashtan, Chklovskii, and Alon 2002, Milo, Itzkovitz,

Kashtan, Levitt, Shen-Orr, Ayzenshtat, Sheffer, and Alon 2004) compares the frequencies of

all possible connection patterns between three nodes and compares them against expected fre-

quencies for randomized versions of the network. In a randomized version of this network,

all chapters have the same number of other chapters citing them, and the same number of

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chapters they cite, but the edge endpoints are chosen at random. A z-score is constructed by

comparing the observed frequencies against the mean and standard deviation of the random-

ized networks. A positive z-score indicates that a motif occurs more often than expected, a

negative one indicates the opposite.

Figure 2: The largest connected component of the network of chapters. Two chapters arelinked if one references the other. Each chapter is colored by the title it belongs to.

Figure 3 shows the motif frequencies for the network of chapters. The network exhibits a

high degree of clustering, which is reflected in high z-scores for all closed triads. For example,

if chapters A and B cite one another, and chapter B and C cite one another, it is also likely that

A cites C or C cites A or both. On the other hand, open triads tend to occur less often than

expected, with one exception: A and B both citing C but not citing one another is a common

motif. The opposite is less likely, C citing both A and B, but A and B not citing one another.

Overall the patterns closely resemble those of other document networks, such as the Web,

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motiffrequency 1.81% 0.11% 0.35% 0.53% 0.50% 0.47%

z-score 22.5 6.1 22.0 45.4 22.0 37.8

motiffrequency 0.13% 19.87% 10.6% 46.0% 1.79%

zscore 66.4 -5.8 -26.1 45.2 -59.3

Figure 3: Percentage of connected triads, by type.

and those of social networks. The common characteristic across these domains is that related

nodes tend to cluster together.

We preform our quantitative analysis at the level of the individual parts, of which there

are close to 50,000 with nearly 70,000 links between them. As is typical for many complex

networks growing and evolving over time, the federal code displays a highly skewed degree

distribution. 57% of the nodes have no outward citations, and 61% receive no citations. On

the other hand some parts receive upwards of a hundred citations. For example, Part I of Title

26, Chapter 1, Section 501, on tax exemptions, is referenced by 273 other parts, while Part III

of Title 5, Chapter 57, Section 5703, on per-diem rates for traveling government employees, is

referenced 216 times. The link popularity of these mundane aspects of government operation

show that the network is a practical codification of the way the country is to be run. Figure 3

shows the skew in degree (number of citations) for each piece of code.

III. Hypotheses

In putting our newly gathered data to the test, we begin by examining some basic hypotheses

about the factors that should be related to the persistence of federal statutes.

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100 101 102 10310−1

100

101

102

103

104

105

# of links

cum

ulat

ive

num

ber o

f par

ts

inlinksx−1.7 fitoutlinksx−2.5 fit

Figure 4: The largest connected component of the network of chapters. Two chapters arelinked if one references the other. Each chapter is colored by the title it belongs to.

Vote Margin: All else being equal, we expect that laws that pass by larger margins will be

altered less often. Whereas the coalition behind a closely fought bill might be disrupted by

a small number of defections or a small shift in the makeup of a chamber of Congress, on

balance larger coalitions should be more durable.

In our analysis here, we look at the average margin of the final House and the Senate votes

on the bill. Bill Margin is the average across the two chambers of the votes for a statute,

minus votes against, divided by the number of members voting. This produces possible values

between 0 and 1.

Ideological Position: We also expect that the ideological position of a bill will be related to

its survival time before amendment. First, over the long term, we expect that sections created

by bills that are more ideologically extreme will be shorter lived than bills that are closer to

the center of the ideological spectrum. Second, given the period that we are studying, we

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expect that more conservative pieces of legislation will survive longer. It is easier to obstruct

a bill than to pass one, and during most of the period studied in our survival analysis (the 93rd

through 105th congresses) the House was under solid Democratic control, while control of the

Senate alternated.

Of course, measuring the ideological position of a piece of legislation entails some diffi-

culties. Following previous scholarship, we rely on roll call vote scaling techniques to measure

the ideological position of bills, specifically DW-NOMINATE scores (Lewis and Poole 2004).

We take the DW-NOMINATE scores of the final votes in each chamber, average them to-

gether, and then use the absolute value as a measure of the bill’s ideological extremity. In the

tables below, this variable is represented as abs(Ideal). We also use the raw average of the

DW-NOMINATE scores (listed as Ideal) to measure any ideological drift.

Previous Revisions: We hypothesize that the more a section or part of the code has been

amended previously, the more likely subsequent amendments are. This appears in the analysis

below as # of Prev. Edits. Partly this variable is included as a control; given the sparseness of

our models, it is likely that there are unmeasured covariates that influence a section’s vulner-

ability to amendment. This variable also serves as an indirect measure of how dispersed edits

are throughout the code

Reference Structure: We expect that sections of the code that make and receive more refer-

ences to other sections of the law should last longer in the aggregate. There are several reasons

to expect that this is the case. The number of references that a section of blog receives is a

practical indication of its importance in the day-to-day operation of the federal government.

All else being equal, highly referenced and referential sections should be more difficult to alter

without creating unintended consequences; this should be especially true for laws drafted by

legislative counsel, rather than members of Congress themselves.

By the same token, references between sections of the law serve as a concrete indicator

of congruence with the rest of the legal code. In this vein, it is worth recalling Dworkin’s

metaphor of a superhuman judge named Hercules, who is capable of discerning consistent

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legal principles behind countless separate statutes, and seeing the law as a whole as a “seam-

less Web” (Dworkin 1986). If the goal is to validate as much of the law as possible, the most

connected parts of federal law would seem easiest to defend. The more peripheral a statute is

in the network of federal laws, the more likely we expect it is to be amended.

In the analysis below, we use # of Inlinks to indicate the number of references that section

receives from other sections of the law; # of Outlinks measures the number of references that

a part or section makes to other areas of the code.

IV. Analysis

We limit our analysis here just to parts of the U.S. Code created or previously amended by

“significant” statutes. For our purposes here, we rely on the coding of Jones and Baumgartner

(2004), who make their determinations of significance based on the level of discussion that a

piece of legislation received in Congressional Quarterly.

We follow what happens to amendments or additions made from the 93rd congress through

the 105th congress. Each part of the code is assigned the legislative characteristics (spatial po-

sition, vote margin, sponsoring party, etc.) of the most recent bill to amend that part or section.

We also ignore revisions to sections of the code that appropriate and authorize federal spend-

ing. Future, more detailed analysis will compare appropriation levels across budget years,

and examine cuts and increases in funding. Here, however, these sections are omitted. In most

cases these amendments are simply reauthorizing the same level of funding that existed during

a previous year, and so an alteration in the code usually does not indicate a change in policy.

We test the hypotheses stated above using a Cox proportional hazard model covering the

93rd through the 105th Congress. A Cox model is chosen because we do not have strong prior

beliefs about what the baseline hazard should be for sections of the federal code. (If we did

have a strong belief, another model might be a more appropriate choice.) Figure 5 presents

11

0 5 10 15

0.0

0.2

0.4

0.6

0.8

1.0

Survival Time (in Sessions of Congress)

Per

cent

Par

ts/S

ubpa

rts

Sur

vivi

ng

Figure 5: This figure shows a step plot of the unamended survival time of parts and subpartsof the federal code. Dotted lines represent standard error bars.

12

Model: A B C D E F G HBill Margin -.91 -.54

(.53) (.47)abs(Ideal) 1.61 1.08 1.40 .71

(.43) (.47) (.44) (.56)Ideal 1.25 .63 .61

(.39) (.33) (.36)# of Prev. Edits .006 .122 .122

(.028) (.019) (.022)# of Inlinks -.048 -.055 -.054

(.005) (.006) (.006)# of Outlinks -.052 -.057 -.055

(.010) (.009) (.010)R2 .021 .065 .062 .077 .000 .067 .133 .147

Table II: This table presents the results of a Cox proportional hazards model. The units ofanalysis here are the parts and subparts of federal law created or amended by significant piecesof legislation. The dependent variable is the number of years before a section is amended oraltered. Regression coefficients are unstandardized. N=18405 for all regressions; howeverthe number of clusters (public laws) is far smaller, at 586. Robust, clustered standard errorsare reported in parentheses; as one would expect, this inflates the reported standard errorssubstantially, in some cases by a factor of ten.

a simple step plot of the fitted model. The figure shows that the most vulnerable years for

a section of the code are the first five sessions after its enactment. Slightly less than half of

sections created by important statutes survive this long. After making it to year five, the risk of

amendment declines, and the cumulative survival plateaus. The plot also shows risk increasing

again after 10 sessions. However, given that we have only 13 sessions in our time series, this

finding may result from the Republican takeover of Congress in the final few sessions.2

Table II presents the results of the Cox model more comprehensively. One surprising

finding here is that the margin by which a statute is passed seems only loosely related to the

persistence of a statute’s components. Model A presents a bivariate regression of the passage

margin on survival time. The sign is in the expected direction but the estimated coefficient is

2Because we only have 13 sessions of Congress in our time series, the right-hand tail of the graph, the apparentsteep drop at session 14 in the figure should be disregarded.

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slightly below conventional levels of statistical significance. Moreover, the R2 is small, at only

0.02. In the full model (Model H), the coefficient is even further from significance.

Somewhat stronger evidence emerges about the influence of a bill’s spatial position on its

durability. More ideologically extreme bills do seem to be shorter lived. The absolute value of

a bill’s average DW-NOMINATE score is strongly significant in a bivariate regression, and is

statistically significant in two of the three other models in which it is included. The coefficient

is smallest in the most inclusive model, where it suffers from some co-linearity with the bill

margin and with the raw DW-NOMINATE scores.

The claim that sections of liberal bills should be more durable than sections of conservative

bills also finds some support in this data. The Ideal measure is highly significant by itself,

and in combination with the absolute value of the DW-NOMINATE score average. It nearly

reaches significance in the fullest model. While we believe the most likely explanation here is

the preponderance of Democratic control during the period that we study, other explanations

are possible. Future versions of this paper, which will include a more extended time series,

should allow a stronger test of both this finding and the potential mechanisms behind it.

On its own, the number of times a section has been previously edited has no discernible

relationship to future amendment activity. In combination with measures of link structure,

however, this variable is highly significant, with previously amended sections emerging as

consistently more likely to be altered.

The most precisely measured associations between a section and its risk of amendment,

however, concerns the internal reference structure of the U.S. Code. Both the number of

references a section makes, and the number of references received, are highly significant in

every specification we have tried. Though these findings add up to a consistent story, even

the most comprehensive model on display here has an R2 of only .14. This suggests that the

search for other explanatory variables should continue.

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V. Conclusion

The analysis in this paper relies on a new, large, complex data set. It is therefore not unusual

that analysis we offer should raise as many questions as it answers.

In part, the research presented here is intended to lay the groundwork for future research.

Qualitative accounts have emphasized that lawmaking behavior is influenced not just by inter-

congressional activity, but also by the actions of bureaucrats, interest groups, and the judiciary.

While not all of this interaction can be captured empirically, much of it can. The same tech-

niques we used to digitize the corpus of federal law can also be applied used to digitize the ad-

ministrative code that supplements congressional lawmaking, and the judicial decisions which

apply federal laws in court. The qualitative studies that have investigated policy stability have

a large number of moving parts, and are often not transferable across policy domains.

Still, the data we present in this paper does allow us to test a series of fundamental hy-

potheses about the persistence of federal law. On balance, these data suggest that the scholarly

models that are so successful in predicting roll call voting and bill passage are far somewhat

effective in explaining how long laws survive without amendment.

One finding here is that the margin by which a public law passes has only a modest

impact—at best—on how long that law remains on the books. It is curious that the strength

of the legislative coalition that passed a bill in the first place would such an inconsistent and

imprecise impact to a law’s durability.

The evidence is somewhat stronger for the claim that a law’s ideological content, as mea-

sured by its position in the Congressional “basic space,” has an impact on a law’s survival.

More ideologically extreme laws, and more liberal laws, do seem to survive somewhat longer.

In part, these finding may cause us to revisit the factors that map high dimensional policy

issues into a low-dimensional policy space in the first place. Existing scholarship has empha-

sized the bonding, bundling, and logrolling effects of political parties as an explanation for

the highly ordered voting behavior we observe in Congress (Poole and Rosenthal 1991, Cox

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and Mccubbins 1994, Jenkins 1999). Once passed, and spread throughout the U.S. Code, the

provisions of a bill are unbundled by default. Regardless, more work is needed to explain

these results.

The most consistent findings focus on the structural features of the US legal code. Larger

numbers of references to and from a section of code is associated strongly with lower vulner-

ability to amendment. The more tightly a section of the law is integrated with other parts of

the US Code, the more likely it is to escape alteration.

The mechanism behind the preservation of highly referenced and referential sections de-

serves greater inquiry. Given the newness of this area of investigation, and the nature of our

data, it is at plausible that the causal arrow could run either direction: not only might Congress

be less willing to alter heavily linked sections, sections that escape amendment for other rea-

sons might be more likely to acquire additional inlinks and outlinks.

What is clear, though, is that the structural characteristics of the federal code tell us as

much about whether a law survives as the legislative activity that created it does. As new

scholarship goes beyond just tracking bills through Congress, and starts tracking what happens

to bills after they are enacted, investigating the evolving structure of the federal law should be

a top priority.

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