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Networked Justice: Judges, the Diffusion of Ideas, and Legal Reform Movements in Mexico

Published online by Cambridge University Press:  24 October 2016

MATTHEW C. INGRAM
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Abstract

Existing research shows that the ideas of judges shape their behaviour. A natural next question to ask is, where do these ideas come from? Yet, there is little empirical evidence regarding the content and distribution of these ideas and even less evidence regarding the sources of these ideas, especially how ideas transfer or diffuse among judges. In this article, a survey of judges in the Mexican state of Michoacán generates original data on the attitudes and professional ties among these legal elites, and a mixed-methods design examines the diffusion of these attitudes along these ties, sequencing quantitative network analyses and interviews with judges to strengthen causal inferences. The core finding that the social structure of judges influences the attitudes judges hold contributes a valuable analytic complement to scholarship on comparative judicial behaviour, and clarifies our understanding of the role of judicial networks in strengthening democracy and the rule of law.

Spanish abstract

Estudios muestran que las ideas de los jueces moldean su comportamiento. Una segunda pregunta normal sería: ¿De dónde vienen esas ideas? Ahora bien, hay muy poca evidencia empírica en relación al contenido y distribución de tales ideas y todavía menos evidencia en relación a las fuentes de tales ideas, especialmente cómo las ideas se transfieren y difunden entre los jueces. En este artículo, una encuesta a jueces en el estado mexicano de Michoacán generaron datos sobre las actitudes y los lazos profesionales entre estas elites legales. Además, un diseño con varios métodos examina la difusión de estas actitudes a lo largo de tales lazos, enumerando análisis de redes cuantitativas y entrevistas con jueces para reforzar las inferencias causales. El hallazgo principal de que la estructura social de los jueces influye en las actitudes que ellos mismos mantienen contribuye a la academia con un complemento analítico valioso sobre el comportamiento judicial, y clarifica nuestro entendimiento sobre el papel de las redes judiciales en relación al fortalecimiento de la democracia y el estado de derecho.

Portuguese abstract

Pesquisas existentes mostram como as ideias dos juízes moldam seus comportamentos. A pergunta natural que se segue é: de onde vêm estas ideias? No entanto, há pouca evidência empírica com relação ao conteúdo e distribuição destas ideias e, ainda menos, em relação à origem destas ideias, especialmente, como estas são transferidas ou difundem-se entre juízes. Neste artigo, um levantamento entre juízes do estado mexicano de Michoacán produziu dados originais acerca das atitudes e laços profissionais entre estas elites legais. A partir de métodos mistos de pesquisa examina-se a difusão dessas atitudes ao longo destas relações, intercala-se análises quantitativas de rede e entrevistas com juízes para reforçar inferências causais. A observação central de que a estrutura social na qual os juízes se inserem influencia suas atitudes representa uma valiosa contribuição analítica complementar para estudos sobre comportamento judicial comparativo, além de clarificar nossa compreensão acerca do papel das redes judiciais para o fortalecimento da democracia e do Estado de direito.

Keywords

judgeslegal reformnetwork analysisMexicojuecesreforma legalanálisis de redesMéxicojuízesreforma legalanálise de redesMéxico
Type
Research Article
Information
Journal of Latin American Studies , Volume 48 , Issue 4 , November 2016 , pp. 739 - 768
Copyright
Copyright © Cambridge University Press 2016 

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References

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27 Patricia Woods, Judicial Power and National Politics: Courts and Gender in the Religious-Secular Conflict in Israel (Albany, NY: SUNY Press, 2008).

28 Ingram, Crafting Courts.

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30 Matthew C. Ingram, Octavio Rodríguez Ferreira and David A. Shirk, Justiciabarómetro (San Diego, CA: Trans-Border Institute, 2011). This was a survey of judges, prosecutors and defense attorneys and their attitudes towards legal reforms in nine Mexican states.

31 Doug McAdam, John D. McCarthy and Mayer N. Zald, ‘Introduction: Opportunities, Mobilizing Structures, and Framing Processes – Toward a Synthetic, Comparative Perspective on Social Movements’, in Doug McAdam, John D. McCarthy and Mayer N. Zald (eds.), Comparative Perspectives on Social Movements (New York: Cambridge University Press, 1996), p. 6.

32 Several projects measure judicial ideology as a categorical variable capturing party affiliation and/or ideology as the party of the appointing executive, e.g., Sunstein et al., ‘Ideological Voting’, pp. 301–54. Segal-Cover scores code newspaper editorials for each Supreme Court nominee, yielding an index (–1 to 1) for ideology; see Segal, Jeffrey A. and Cover, Albert D., ‘Ideological Values and the Votes of U. S. Supreme Court Justices’, American Political Science Review, 83: 2 (1989), pp. 557–65CrossRefGoogle Scholar. Martin-Quinn scores use techniques from legislative roll-call analysis to estimate judicial ideal points based on the votes of judges. See Martin, Andrew D. and Quinn, Kevin M., ‘Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U. S. Supreme Court, 1953–1999’, Political Analysis, 10: 2 (2002), pp. 134–53CrossRefGoogle Scholar; ‘Assessing Preference Change on the U. S. Supreme Court’, Journal of Law, Economics, and Organisation, 23: 2 (2007), pp. 365–85CrossRefGoogle Scholar; for application in Brazil, see Desposato, Scott, Ingram, Matthew C. and Lannes, Osmar P. Jr., ‘Power, Composition, and Decision Making: The Behavioral Consequences of Institutional Reform on Brazil's Supremo Tribunal Federal’, Journal of Law, Economics & Organisation, 31: 3 (2015), pp. 534–67CrossRefGoogle Scholar.

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34 Woods, Judicial Power, p. 23.

35 For instance, a ‘justice network’ (red de justicia) might be a group of interested individuals or an informal association of groups, not a formal, structural representation of a network.

36 For example, Langer, ‘Revolution in Latin American Criminal Procedure’; Rodríguez-Garavito, ‘Toward a Sociology’, pp. 617–76.

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41 Myra Marx Ferree and Frederick D. Miller, ‘Winning Hearts and Minds: Some Psychological Contributions to the Resource Mobilisation Perspective on Social Movements’, Unpublished paper, 1977, p. 34, cited in McAdam, McCarthy and Zald, ‘Introduction: Opportunities, Mobilizing Structures, and Framing Processes’, p. 9.

42 Thomas W. Valente, Network Models of the Diffusion of Innovations (Cresskill, NJ: Hampton Press, 1995), p. 31.

43 For example, Dato Param Cumaraswamy, Independence of the Judiciary, Administration of Justice, Impunity: Report on the Mission to Mexico (2002). Report of the Special Rapporteur on the Independence of Judges and Lawyers, Economic and Social Council of the United Nations. Submitted in accordance with Commission on Human Rights resolution 2001/39.

44 Héctor Fix-Fierro, ‘Judicial Reform in Mexico: What Next?’, in Erik G. Jensen and Thomas C. Heller (eds.), Beyond Common Knowledge: Empirical Approaches to the Rule of Law (Stanford, CA: Stanford University Press, 2003), pp. 240–89; Finkel, Judicial Reform as Political Insurance; Ingram, Crafting Courts.

45 Langer, ‘Revolution in Latin American Criminal Procedure’, pp. 617–76.

46 Ingram, Crafting Courts; ‘Mandates, Geography, and Networks: Diffusion of Criminal Procedure Reform in Mexico’, Latin American Politics & Society, 58: 1 (2016), pp. 121–45CrossRefGoogle Scholar.

47 Hilbink, ‘Politicising Law to Liberalise Politics’, pp. 403–37.

48 Ingram, Crafting Courts.

49 Ingram, Crafting Courts.

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53 Fowler et al., ‘Causality in Political Networks’, ibid.

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55 Hollstein, ‘Mixed-Methods Social Networks Research: An Introduction’, pp. 19–20. For still other suggestions, albeit with a more pessimistic outlook about the prospect for disentangling the direction of causation, see Shalizi, Cosma Rohila and Thomas, Andrew C., ‘Homophily and Contagion Are Generically Confounded in Observational Social Network Studies’, Sociological Methods and Research, 40: 2 (2011), pp. 211–39CrossRefGoogle ScholarPubMed.

56 Web appendix available from author: mattingram.net.

57 J. Brewer and A. Hunter, Multimethod Research: A Synthesis of Styles (Thousand Oaks, CA: Sage Publications, 1989) pp. 16–17; Abbas Tashakkori and Charles Teddlie, Mixed Methodology: Combining Qualitative and Quantitative Approaches (Thousand Oaks, CA: Sage Publications, 1998), pp. 40–2.

58 Jason Seawright, Multi-Method Social Science: Combining Qualitative and Quantitative Tools (Cambridge: Cambridge University Press, forthcoming); see also Lieberman, ‘Nested Analysis’, pp. 435–52.

59 Dominguez and Hollstein, Mixed-Methods Social Networks Analysis.

60 Hollstein, ‘Mixed-Methods Social Networks Research: An Introduction’, pp. 16–17.

61 Hollstein, ‘Mixed-Methods Social Networks Research: An Introduction’, p. 19.

62 Hollstein, ‘Qualitative Network Analysis’, pp. 404–16; ‘Mixed-Methods Social Networks Research: An Introduction’, pp. 18–21; see also Padgett in Fowler et al., ‘Causality in Political Networks’, 466–70.

63 The polling firm Data Opinión Pública y Mercados (DataOPM, Mexico City) conducted the telephonic interviews in June–July 2011. At DataOPM, Pablo Parás and Carlos López managed the survey administration, and both have conducted previous surveys in the justice sector in Mexico. I am grateful to them and to their staff for valuable feedback on early drafts of the questionnaire and for communications during the survey administration that enhanced its feasibility and interpretation.

64 High density could potentially also impede the entrance of new ideas; Valente, Network Models, p. 40, citing James A. Danowski, ‘Interpersonal Network Structure and Media Use: A Focus on Radiality and Non-Mass Media Use’, in Gary Gumpert and Robert Cathcart (eds.), Intermedia, 3rd ed., (New York: Oxford University Press, 1986), pp. 168–75.

65 Fowler et al., ‘Causality in Political Networks’, pp. 437–80.

66 Ingram et al., Justiciabarómetro.

67 One hundred and thirteen is more than the 110 judges listed in the official directory of the judiciary, but this directory does not account for recent personnel changes: indeed, the consultant who administered the survey noted that the interviewer was turned away from several courts because a judge had either been reassigned or no judge had yet been assigned to the court; in other cases, a new judge not yet on the official roster/directory was already there and completed the questionnaire.

68 Though these networks are technically incomplete, they are very nearly complete, and existing research includes examples of socio-centric analysis on networks ranging in completeness from 65.3 per cent to 77.7 per cent. See Ramiro Berardo, ‘Networking Networkers: An Initial Exploration of the Patterns of Collaboration among the Members of a New Community in Political Science’, PS: Political Science (2011), pp. 69.

69 Visualisation generated using Cytoscape 2.8.2; Smoot, Michael, Ono, Keiichiro, Ruscheinski, Johannes, Wang, Peng-Liang and Ideker, Trey, ‘Cytoscape 2.8: New Features for Data Integration and Network Visualisation’, Bioinformatics, 27: 3 (2011), pp. 431–2CrossRefGoogle Scholar. Colour images available from author at http://mattingram.net/.

70 The analysis includes four measures of centrality: degree (number of ties), betweenness, closeness, and eigenvector centrality. Betweenness centrality captures the extent to which a node is on the shortest path between two other nodes. Nodes with high values on this measure are often thought of as being good conduits, bridges, brokers, or gatekeepers between other nodes. Because more information should flow through these nodes than through others with lower values, these nodes are exposed to more information and should therefore adopt new ideas and attitudes faster or sooner than others. Closeness centrality captures the ease with which a node can reach all other nodes in the network. Lastly, eigenvector centrality counts ties to other nodes, but does so in a manner that gives more weight to connections to nodes that are themselves well-connected, capturing centrality in a way that takes the centrality of other nodes into consideration. See Stanley Wasserman and Katherine Faust, Social Network Analysis: Methods and Applications (Cambridge: Cambridge University Press, 1994), chap. 5.

71 Steve P. Borgatti, Martin G. Everett and Lin C. Freeman, Ucinet for Windows: Software for Social Network Analysis (Harvard, MA: Analytic Technologies, 2002).

72 Valente, Network Models, pp. 43–5.

73 Ordered logistic regressions must meet the parallel regression assumption, also called the probabilistic odds assumption. That is, ordered probit (and logit) assume that the effect of the explanatory variables (X) across all levels of the response variable (Y) is the same, i.e., the size of the coefficients does not change for different values of Y. A likelihood ratio test implemented at the bottom of each column shows whether the analysis meets that assumption. Test was implemented using omodel in Stata v.11. See also Dow, Malcolm M., ‘Network Autocorrelation Regression with Binary and Ordinal Dependent Variables: Galton's Problem’, Cross-Cultural Research, 42: 4 (2008), p. 407 CrossRefGoogle Scholar; Stata Data Analysis Examples: Ordered Logistic Regression, UCLA: Academic Technology Services, Statistical Consulting Group, available at http://www.ats.ucla.edu/stat/stata/output/stata_ologit_output.htm.

74 Age, salary and position (magistrado dummy) are theoretically capturing similar dynamics, and magistrado and salary are empirically correlated (0.66), so they are not included in the same model. Still, including (1) age and magistrado or (2) age and salary in the same model did not alter core results.

75 There is no simple, straightforward method for interpreting substantive effect in ordered probabilistic regressions; Dow, ‘Network Autocorrelation Regression’. However, graphing the results offers one of the more intuitive ways of conveying substantive significance. Predicted probabilities were generated using margins and prgen commands in Stata v.11 and setting other variables at their means, and graphs by using the rarea option. See J. Scott Long and Jeremy Freese, Regression Models for Categorical Dependent Variables Using Stata (College Station, TX: Stata Press, 2006); Stata Annotated Output Ordered Logistic Regression. UCLA: Academic Technology Services, Statistical Consulting Group, available at http://www.ats.ucla.edu/stat/stata/output/stata_ologit_output.htm.

76 For example, Woods, Judicial Power. Though the role of ‘weak ties’ inherent in Woods's account is not directly tested here; the positive and significant effect of tie strength cuts against that argument.

77 There are 23 judicial districts in the state. Initially, 21 dummies captured the districts individually (two districts were unrepresented in the data). However, only one district had any significance (Zinapecuaro) relating to a single judge, and there were no meaningful departures from the results here. Judicial districts were then collapsed into three categories: Morelia, west of Morelia, and east of Morelia. Again, there were no meaningful differences compared with the results here.

78 This model was applied using lnam in the sna package in R; see Butts, Carter T., ‘Social Network Analysis with sna’, Journal of Statistical Software, 24: 6 (2008), pp. 1–51 CrossRefGoogle Scholar; R Core Team, ‘R: A Language and Environment for Statistical Computing’, R Foundation for Statistical Computing (2015), available at https://www.R-project.org.

79 Butts, ‘Social Network Analysis with sna’, pp. 1–51; Dow, Malcolm M., ‘Galton's Problem as Multiple Network Autocorrelation Effects: Cultural Trait Transmission and Ecological Constraint’, Cross-Cultural Research, 41: 4 (2007), pp. 336–63CrossRefGoogle Scholar.

80 Given the ordinal nature of the outcome of interest, rather than using the absolute value of residuals I rank observations within each category by the predicted probability of the outcome, in essence yielding typicality scores ranked by the ‘confidence’ in that typicality score. See Gerring, John and Seawright, Jason, ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’, Political Research Quarterly, 61: 2 (2008), pp. 294–308 Google Scholar.

81 Lieberman, ‘Nested Analysis’, pp. 435–52.

82 ‘Dolo’ is equivalent to deliberate criminal intent, intending to commit or allow an act to be committed knowing (or acknowledging the possibility) that said act is criminal, while ‘culpa’ is equivalent to criminal negligence, unintentionally committing a crime out of recklessness or carelessness. See Stephen Zamora, José Ramón Cossío, Leonel Pereznieto, José Roldán-Xopa and David López, Mexican Law (Oxford: Oxford University Press, 2004), pp. 352–3.

83 Katz et al., ‘Reproduction of Hierarchy?’, pp. 1–28.

84 Hilbink, Judges beyond Politics; Couso and Hilbink, ‘From Quietism’, pp. 99–127; Rodríguez-Garavito, ‘Toward a Sociology’, pp. 155–81; Ingram, Crafting Courts.

85 For example, Melanie Mitchell, Complexity: A Guided Tour (Oxford: Oxford University Press, 2011).

86 See Matthew C. Ingram and Diana Kapiszewski, ‘Introduction’, in Matthew C. Ingram and Diana Kapiszewski (eds.), Beyond High Courts: The Justice Complex in Contemporary Latin America (n.d.). Separately, the findings are also broadly suggestive of a relationship between structure and agency. If social structure has a powerful influence over ideas and behaviour, then agency may not always be a fully conscious, deliberative phenomenon, as work on ‘satisficing’ and mental shortcuts has suggested ( Simon, Herbert A., ‘Human Nature in Politics: The Dialogue of Psychology with Political Science’, American Political Science Review 79 (1985), pp. 293–304 CrossRefGoogle Scholar.). Nonconscious influences have deep implications for the nature of the relationship between structure and agency across multiple arenas, including rationalist, decision-theoretic approaches to behaviour. Beyond cognitive shortcuts, however, our individual decisions are not independent of other individuals; they are imbedded in a dependent web of relations. Thus, what appears to be an individualistic, conscious decision may in fact be the result of the non-conscious influence of social structure. Put simply if crudely, whom we know affects what we know, and perhaps without us even knowing it!

87 Hilbink, Judges beyond Politics; ‘Origins of Positive Judicial Independence’, pp. 587–621; Ingram, Crafting Courts.