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Advances in Political Economy pp 249–262Cite as

Measuring the Latent Quality of Precedent: Scoring Vertices in a Network

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Abstract

In this chapter, we consider the problem of estimating the latent influence of vertices of a network in which some edges are unobserved for known reasons. We present and employ a quantitative scoring method that incorporates differences in “potential influence” between vertices. As an example, we apply the method to rank Supreme Court majority opinions in terms of their “citability,” measured as the likelihood the opinion will be cited in future opinions. Our method incorporates the fact that future opinions cannot be cited in a present-day opinion. In addition, the method is consistent with the fact that a judicial opinion can cite multiple previous opinions.

This research was supported by NIH Grant # 1RC4LM010958-01.

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Notes

  1. 1.

    The networks literature in political science is large and growing. Recent comprehensive reviews include Lazer (2011) and Ward et al. (2011). In addition, Fowler et al. (2011) summarize and discuss methodological issues with inference of causality in networks.

  2. 2.

    The word “quality” is simply a placeholder, though one that is roughly descriptive (at least in common parlance) of the characteristic that our method is estimating. While one might be precise and use a term such as “citability,” we note the traditional issues of scope and space constraints and, setting this larger issue to the side, default to the use of a real word to refer to the latent construct our method is attempting to detect and estimate.

  3. 3.

    In general network settings, we interpret a connection from v to w as implying that w “influences” or “is greater than” v. What is key for our purposes is that the notion of influence be conceptually tied to the notion of quality, as we have discussed earlier.

  4. 4.

    For reasons of space, we refer the interested reader to Schnakenberg and Penn (2012) for more details on the method.

  5. 5.

    Seminal offerings include Merryman (1954) and Landes and Posner (1976), while more recent, book-length analyses include Hansford and Spriggs II (2006) and Gerhardt (2008). Other relevant citations are provided where appropriate in our discussion.

  6. 6.

    In addition, there are many interesting theoretical and empirical questions regarding how one should conceive of the relationship between opinions and opinions (e.g., Bommarito et al. (2009)) that the data we employ here do not allow us to explore more fully.

  7. 7.

    Practically speaking, there are a number of ways that scholars have developed and employed to consider this aspect of how Justices cite earlier opinions. For recent examples, see Clark and Lauderdale (2010), Spriggs II et al. (2011).

  8. 8.

    We are not aware of any recent work that has differentiated citations by the number of times the citation occurs in the citing opinion.

  9. 9.

    Note that, for simplicity, we approximate this “later than” relation in the sense that we presume (unrealistically) that, in any year, the Court cannot cite one opinion that is decided in that year in another opinion that is decided in that same year. Given the number of years that we consider, this approximation affects a very small proportion of the number of potential citations we consider.

  10. 10.

    Note that this is true despite the presumption that an opinion might have been feasible only in a subset of observed and subsequent majority opinions.

  11. 11.

    This time period includes all cases in the Fowler and Jeon data for which Spaeth’s rich descriptive data (Spaeth 2012) are also available.

  12. 12.

    This time period includes all cases in the Fowler and Jeon data.

  13. 13.

    Note that the estimated scores for the top 100 opinions sum to 100, so these two opinions account for over 1/8th of the sum of the estimated scores. In other words, any opinion that cites exactly one of these 100 cases is predicted to cite either Chevron or Gregg almost 13 % of the time.

  14. 14.

    See, for example, Black and Spriggs II (2010).

  15. 15.

    Recall that the scores are identified only up to multiplication by a positive scalar, implying that they inherently relative scores.

  16. 16.

    In that case, the majority opinion affirmed an individual’s right to sue recipients of federal financial support for gender discrimination under Title IX, which calls for gender equity in higher education.

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Authors and Affiliations

  1. Center in Political Economy, Washington University in Saint Louis, 1 Brookings Drive, Saint Louis, MO, 63130, USA

    John W. Patty, Elizabeth Maggie Penn & Keith E. Schnakenberg

Authors
  1. John W. Patty
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  2. Elizabeth Maggie Penn
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  3. Keith E. Schnakenberg
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Correspondence to John W. Patty .

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Editors and Affiliations

  1. Center in Political Economy, Washington University in St.Louis, Brookings Drive 1, Saint Louis, 63130, Missouri, USA

    Norman Schofield

  2. , Faculty of Economics, University of Vigo, Lagoas Marcosende, Vigo, 36310, Spain

    Gonzalo Caballero

  3. Centro de Estudios Avanzados, en Ciencias Sociales, Juan March Institute, Calle Castelló 77, Madrid, 28006, Spain

    Daniel Kselman

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Patty, J.W., Penn, E.M., Schnakenberg, K.E. (2013). Measuring the Latent Quality of Precedent: Scoring Vertices in a Network. In: Schofield, N., Caballero, G., Kselman, D. (eds) Advances in Political Economy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35239-3_12

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