April as the Cruellest Month? Data on the Law Clerkship Tournament

 

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Judge Wald’s classic article describing the market for judicial clerks reminds us how April was once the cruellest month.  Given the Federal Law Clerk Hiring plan has shifted the relevant window of discomfort, we thought it reasonable to ring in the spring season with some of our data on the law clerk tournament.  Using underlying information Derek Stafford and I collected for our article Hustle and Flow: A Social Network Analysis of the American Federal Judiciary, here is Federal Court Clerkship data for the period of the “Natural” Rehnquist Court.  The current offering is aimed at the US News Top 15 Law Schools.  Although this data terminates in the 2004- 2005 clerkship year, we still believe it offers useful empirical insight into the status of the law clerk tournament. 

Computer Programming and the Law — OR — How I Learned to Learn Live with Python and Leverage Developments in Information Science

 

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One of our very first posts highlighted a recent article in Science Magazine describing the possibilities of and perils associated with a computational revolution in the social sciences.  A very timely article by Paul Ohm (UC-Boulder Law School) entitled Computer Programming and the Law: A New Research Agenda represents the legal studies analog the science magazine article.  From information retrieval to analysis to visualization, we believe this article outlines the Computational Legal Studies playbook in a very accessable manner.

Prior to founding this blog, we had little doubt that developments in informatics and the science associated with Web 2.0 would benefit the production of a wide class of theoretical and empirical legal scholarship. In order to lower the costs to collective action and generate a forum for interested scholars, we believed it would be useful to produce the Computational Legal Studies Blog. The early results have been very satisfying. For example, it has helped us link to the work of Paul Ohm.  

For those interested in learning more about not only the potential benefits of a computational revolution in legal science but also some of the relevant mechanics, we strongly suggest you consider giving his new article a read!  

Coming Next Week on CLS Blog

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A Netlogo 3D screenprint of one of the classic agent based models—the Shelling Segregation Model is above. We offer it as a holdover until CLS Blog Returns Sunday Night with more exciting content…..

NEXT WEEK:
(1) Discussion of a New Paper: Computer Programming and the Law
(2) Visualizing the 110th Congress — The House of Representatives
(3) For Law Students and Law Professors — Data on the Law Clerk Tournament
(4) And More …..

Data Mining the News — J. Kleinberg Work Discussed in MIT Tech Review

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This short but cool article from MIT Technology Review discusses recent work by Computer Scientist Jon Kleinberg and his Cornell colleagues. This very nice visualization is the byproduct of their efforts at data mining more than 1 million online news items per day in the weeks leading up to the 2008 presidential election.

With Bankruptcy on Our Minds: The Structure of Title 11 U.S.C.


CLICK ON IT AND YOU CAN ZOOM IN and READ EVERY LABEL!

MOTIVATION:

We have become interesting in visualizing the structure of the law including its components and subcomponents.  In reduced form, statutes, regulations and certain other units of the law can be characterized in graph theoretical terms.  While we do not make deep inroads on the content of this above graph, we do generate a tree traversable visualization for its structure.

Much of my training in law school (particularly in the so called “code-based” classes) was focused upon developing mental models for the structure and content of graphs such as the one displayed above. In my case, I believe the usage of such a visualization early in a code-based course would have been beneficial. Thus, we offer this traversable visualization to the world for not only its research value but also for pedagogical purposes.

INSTRUCTIONS:

Start in the MIDDLE at the “11 U.S.C.__ ” Label and traverse out.

BREAKDOWN OF THE VISUAL:
GREEN NODE LABELS =   for SECTIONS  {In the Example below, 11 U.S.C. § 101}
YELLOW ARCS — Chapter 7 of Title 11 = LIQUIDATION
BLUE ARCS — Chapter 11 of Title 11 = REORGANIZATION  (aka “Filing Chapter 11“)
RED ARCS and GREY ARCS — Balance of the Chapters under Title 11
Red Arcs are for lines which lead to terminal nodes
Grey Arcs are for lines which do not immediately lead to terminal nodes


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FINAL THOUGHTS:
Please feel free to PLAY AROUND and TEST IT OUT!
This is an early production version so please provide us with any feedback and/or suggestions.

Co-Sponsorship Networks– Senators of the 108th Congress

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In the days and weeks to come we will turn our attenion away from Congress in favor of other institutions and substantive questions. However, given our prior posts focusing upon the structure of the 110th Congress, we thought it proper to highlight some relevant realted scholarship. James Fowler from the UCSD Political Science Department and leader of the Networks in Political Science movement has published several papers exploring the strucutre of legislative co-sponsorship.  You can find a link to these papers here. My favorite of these papers is Community Structure in Congressional Cosponsorship Networks published in Physica A by Yan Zhang, A. J. Friend, Amanda L. Traud, Mason A. Porter, James H. Fowler & Peter J. Mucha. The above figure, drawn from the paper, is a dendrogram for the legislative cosponsorship network of the Senate of the 108th Congress.  

Senators of the 110th Congress Take 2-Contributions by Industry/Sector

To view the full image, please click here.

Senator By Industry

 

This represents a deeper cut on campaign contributions to the Senators of 110th Congress. Again, we rely upon data from the Center for Responsive Politics.  The CRP aggregates contribution data up to the industry or economic sector. Thus, as before, we adopt their classification scheme and methodology herein.  While aggregating to the industry/sector level removes the degree of specificity we offered in our earlier post, it provides a cleaner representation for the graph.  For those interested in the other chamber, click here for the House of Representatives.

Click on the picture above and it will take you to our flash where you can zoom in and read the labels.

As you review the graph, please consider the following:

(1) Industries locate in the center of the graph because they provide significant funding to both Democrats and Republicans.

(2) Industries which generally only fund one political party are located toward the respective red/blue boundary.  For example, it is hardly surprising to observe the location of “Oil and Gas” relative to “Environmental” groups.

(3) It is important to note that we do not impose the partisan separation or the placement of party outliers apparent in the image. Rather, the algorithm places Red Senators in Blue Territory and Blue Senators in Red Territory because they receive significant sums from industries who typically fund the opposing party. For example, consider Senator Olympia J Snowe (R-ME) who is typically characterized as a moderate Republican.  Since she receives money from more industries that typically fund Democrats than Republicans, she is placed in Blue Territory by the algorithm.

(4) It is important not to over read the position of Senator Herb Kohl (D-WI).  Over the relevant time window, Senator Kohl received 94% of his resources through self-financing.

Google for Government? Broad Representations of Large N DataSets

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In our previous post, a post which has generated tremendous interest from a variety of sources, we demonstrated how applying the tools of network science can provide a broad representation for thousands of lines of information.  Throughout the 2008 Presidential Campaign then Senator Obama consistently discussed his Google for Government initiative.  

From the Obama for America Website:

Google for Government: Americans have the right to know how their tax dollars are spent, but that information has been hidden from public view for too long. That’s why Barack Obama and Senator Tom Coburn (R-OK) passed a law to create a Google-like search engine to allow regular people to approximately track federal grants, contracts, earmarks, and loans online. 

We agree with both President Obama and Senator Coburn that universal accessibility of such information is worthwhile goal.  However, we believe this is only a first step.  

In a deep sense, our prior post is designed to serve as a demonstration project.  We are just two graduate students working on a shoestring budget.  With the resources of the federal government, however, it would certainly be possible to create a series of simple interfaces designed to broadly represent of large amounts of information.  While these interfaces should rely upon the best available analytical methods, such methods could probably be built-in behind the scenes.   At a minimum, government agencies should follow the suggestion of David G. Robinson and his co-authors who argue the federal government “should require that federal websites themselves use the same open systems for accessing the underlying data as they make available to the public at large.”

Anyway, will be back on Monday providing more thoughts on our initial representation of the 110th Congress.  In addition, we hope to highlight other work in the growing field of Computational Legal Studies.  Have a good rest of the weekend!

Visualizing the Campaign Contributions to Senators in the 110th Congress — The TARP EDITION (Documentation for the Network)

 

Visualizing the Campaign Contributions to the Senators of the 110th Congress —
The TARP EDITION

By Michael Bommarito & Daniel Katz

University of Michigan
Center for the Study of Complex Systems
Department of Political Science


BASIC OVERVIEW:

110th Congress = January 3, 2007 – January 3, 2009

100 Members of the United States Senate

Click Here for the House of Representatives

BASIC RULE:

Squares (Institutions) Introduce Money into the System and Circles (Senators) Receive Money.

DATA OVERVIEW:

Using recently published data on campaign contributions collected by the Federal Election Commission and aggregated by the Center for Responsive Politics at http://www.opensecrets.org, our visualizations track large money donations to members of the 110th Congress over the 2003-2008 window.

Given that some senators resign or lose reelection, a subset of the senators of the 110th Congress have served less than the full 2003-2008 window. While this imposes some comparability issues, many of these new members faced challenging races and thus attracted significant sums of money.

It is important to note that most of these organizations did not directly donate. Rather, as noted by the Center for Responsive Politics “the money came from the organization’s PAC, its individual members or employees or owners, and those individuals’ immediate families. Organization totals include subsidiaries and affiliates. Of course, it is impossible to know either the economic interest that made each individual contribution possible or the motivation for each individual giver. However, the patterns of contributions provide critical information for voters, researchers and others.” The Center describes its methodology here http://www.opensecrets.org/politicians/method_pop.php.   We strike a tradeoff  between information overload and incomplete disclosure.  To provide for an optically tractable view of the top contributions, we impose the limiting requirement that to be included in our tally a given group’s contribution must fall within a given senator’s top contributor list.  For a first cut on the data, we believe this reaches an appropriate balance.  However, in subsequent work we plan to go much deeper and probe a much larger set of contribution information.   

CONTRIBUTORS & CONTRIBUTIONS:

1,050 of the Donors are captured in the Graph.

Total Recorded Donations Introduced into the System by these Entities Total to  $94, 138,917.

(1) SIZING of the SENATOR NODES — Each Circular node representing a U.S. Senator is sized according the amount of incoming donations. Thus, larger U.S. Senator nodes are the recipients of larger sums of money while the smaller nodes received smaller amounts of money.

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NOTE ON SELF-FINANCING — Some candidates use personal funds to finance their campaigns. For example, Senator Herb Kohl (D-WI) spent $5,922,759 of which $5,575,000 (94%) came from his personal assets. In this respect, Senator Kohl has a significant “self-loop” but is sized very small because he accepts very little outside monies.


(2) COLORING of the SENATOR NODES — Each node representing a US Senator is colored according their Political Party. Using popular convention, we color members of the Republican Party as Red, members of the Democratic Party as Blue and Independents as Purple. For the 110th Congress, there are two Independents—Bernie Sanders (I- VT), Joe Lieberman (I- CT), respectfully.

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(3) COLORING of the INSTITUTIONAL NODES — Each square node represents institutions who are top contributors to at least one Senator in the 110th Congress. The full graph contains 1,050 institutions of two separate classes. Green institutions are either primary TARP recipients or now components of primary recipients of resources under the Troubled Asset Relief Program. For example, we color Wachovia as Green even though they are now owned by Wells Fargo, a TARP recipient.

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(4) SIZING of the INSTITUTIONAL NODES Each square node representing a TARP or Non-TARP institution is sized according their relative financial contribution to the over all system. Thus, larger institutions make larger contributions and smaller institutions make smaller contributions.

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(5) SIZING of the CONNECTIONS Each Connection (Arc) between an Institution and a Senator is sized according to the amount of money flowing through a connection. Darker connections represent larger flows of money while lighter connections represent smaller amounts of money.

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(6) COLORING of the CONNECTIONS — Each connection representing a campaign contribution from an institution to a US Senator is colored according to partisan affiliation of the receiving senator. Using popular convention, we color members of the Republican Party as Red, members of the Democratic Party as Blue and Independents as Purple. For the 110th Congress, there are two Independents—Bernie Sanders (I- VT), Joe Lieberman (I- CT), respectfully.

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(7) STRUCTURE OF THE GRAPH The Graph is Visualized Using the Kamada-Kawai Visualization Algorithm.  This is an automated spring embedded, force directed placement algorithm often used in the network science literature to visualize graphs of this size.

(8) ACKNOWLEDGEMENTS  We thank Rick Riolo, Jon Zelner, Carl SimonScott Page and the Center for Responsive Politics for their comments, contributions and/or data.  

 

Hustle & Flow: A Network Analysis of the American Federal Judiciary

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This paper written by CLS Blog Co-Founder Daniel Katz and Derek Stafford from the University of Michigan Department of Political Science representes an initial foray into Computational Legal Studies by the graduate students here at the University of Michigan Center for the Study of Complex Systems.  The full paper contains a number of interesting visualizations where we draw various federal judges together on the basis of their shared law clerks (1995-2004).  The screen print above is a zoom very center of the center of the network.  Yellow Nodes represent Supreme Court Justices, Green Nodes represent Circuit Court Justices, Blue Nodes represent Circuit Court Justices.  Here is a wide shot of the broader network visualized using the Kamada-Kawai visualization algorithm:    

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Here is the abstract:      Scholars have long asserted that social structure is an important feature of a variety of societal institutions. As part of a larger effort to develop a fully integrated model of judicial decision making, we argue that social structure-operationalized as the professional and social connections between judicial actors-partially directs outcomes in the hierarchical federal judiciary. Since different social structures impose dissimilar consequences upon outputs, the precursor to evaluating the doctrinal consequences that a given social structure imposes is a descriptive effort to characterize its properties. Given the difficulty associated with obtaining appropriate data for federal judges, it is necessary to rely upon a proxy measure to paint a picture of the social landscape. In the aggregate, we believe the flow of law clerks reflects a reasonable proxy for social and professional linkages between jurists. Having collected available information for all federal judicial law clerks employed by an Article III judge during the “natural” Rehnquist Court (1995-2004), we use these roughly 19,000 clerk events to craft a series of network based visualizations.   Using network analysis, our visualizations and subsequent analytics provide insight into the path of peer effects in the federal judiciary. For example, we find the distribution of “degrees” is highly skewed implying the social structure is dictated by a small number of socially prominent actors. Using a variety of centrality measures, we identify these socially prominent jurists. Next, we draw from the extant complexity literature and offer a possible generative process responsible for producing such inequality in social authority. While the complete adjudication of a generative process is beyond the scope of this article, our results contribute to a growing literature documenting the highly-skewed distribution of authority across the common law and its constitutive institutions. 

Print 'Hello World'

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In the days and weeks ahead, we hope to outline why we believe the application of a computational and complexity informed approach to legal studies will serve as a useful method to consider a wide class of substantive questions.  Standing at the intersection of a variety of fields including computer science, applied mathematics, physics, political science, social network analysis as well as others, we hope scholars will be able to leverage relevant techniques to help enrich positive legal theory.

As a entry point, we will highlight relevant developments to date in this new field–including our own work as well as the work of others.   So we offer this initial post to say ‘Hello World’ with a promise of more to come….