The Redistricting Controversy in Pennsylvania and the Importance of the Public Voice: Part II
The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.
By Sean L. Ziller
October 7, 2019
Congressional district map in preparation for the 2018 state election cycle.
As highlighted in Part I, the number of court cases impacting redistricting efforts across the country will have both immediate and lasting consequences on how government operates at all levels. This notion is put into stark relief when considering the upcoming census.
That is to say, judicial decisions made in these cases will likely have a colossal effect on the representation being afforded to each citizen as well as how subsequent redistricting is approached.
However, by utilizing as much of a non-partisan lens as possible we can begin to embark on the still important task of understanding how to properly evaluate the complex elements of redistricting as well as its outcomes. Additionally, thinking apolitically and recognizing the value of, for example, emerging technology relevant to this sector may begin to disentangle the partisanship that is, to many, seemingly a part of existing redistricting initiatives. By applying an altogether ,“Neutral,” approach to circumstances in real-time, such as with the partisan efforts occurring in Pennsylvania, casual observers and public administrators alike can begin to possible substitute what may be their own subjectivity in favor of more analytical analyses.
In discussing redistricting, being able to evaluate a contentious process like partisan gerrymandering pragmatically does not initially seem possible, let alone easy. However, Samuel S.H. Wang, in his 2016 article in the Stanford Law Review entitled, “Three Tests for Practical Evaluation of Partisan Gerrymandering,” attempts to do just that. Employing statistical techniques, Wang offers the following three tests to, in this case, “Reliably assess asymmetry in state-level districting schemes:”
- An unrepresentative distortion in the number of seats won based on expectations from nationwide district characteristics.
- A discrepancy in winning vote margins between the two parties.
- The construction of reliable wins for the part in charge of redistricting, as measured by either the difference between mean and median vote share, or an unusually even distribution of votes across district.
What implications can be drawn from the use of these tests, and what do these mean for evaluating progressively complex redistricting schemes three years onward? Wang notes immediately in the abstract that the first test requires computer simulations in order to measure representation. However, the second and third methods rely on statistical principles that can be accomplished more readily. The majority of his analysis discusses quantitively evaluating the, “Intents and effects,” of partisan gerrymandering, applying his testing to circumstances found in legislative districts such as Arizona and Maryland, while remaining within the, “Partisan asymmetry,” scope laid out previously in gerrymandering decisions handed down by the U.S. Supreme Court.
While the deeper quantitative implications of Wang’s work may not be relevant for us here, the largely academic approach and the discussion it stimulates are critical in being able to hold wider conversations about proper and fair redistricting. As expressed by Wang, similar statistical methodologies could aid in the judicial review of these types of cases. This would, presumably, standardize decisionmaking in redistricting controversies, creating greater impartiality in redrawing districts and their potential later review. For Wang, “Current approaches to proving gerrymanders focus on intent, are diverse in approach, and sometimes do not agree with one another”. While not foolproof, advocating for approaches to redistricting—and, importantly, for approaches of judicial review toward redistricting—where a more quantitative framework is employed could help to extradite partisanship from the larger practice. This is particularly relevant when examining the differing circumstances of cases from state to state. For public administrators involved in these processes, similar approaches, or methodologies geared toward neutrality, may ultimately be a manner through which fairer outcomes result for constituents.
Embracing relevant technology may also assist in reducing unjust gerrymandering practices. Teresa Mathew in, “Can Data Defeat Gerrymandering?” expands on a prediction made early on in the article by the Brennan Center’s Michael Li: “…Things that look like space age technology now will look run-of-the-mill when things are redrawn in 2021.” Mapping software and produced data has long been used by analysts to help skew districts during redrawing processes, leading to a greater potential for unfair and improper gerrymandering. However, there have been recent efforts to help control this practice and, instead, directly aid courts in, “[Navigating] the judgement process.” While there remain obstacles to an era of greater accessibility to open-source data, the potential capabilities to an actor, or actors, offering options of impartial mapping, in good faith, has implications for a healthier redistricting future.
The purpose of emphasizing statistical approaches to redistricting, as well as noting new areas of technology, is not to advocate for one option over another. Instead, it’s important for those in the public administration sphere to consider avenues through which overtly political conditions could be mitigated. For those in our field, collective alarm may arise each time redistricting or claims of partisan gerrymandering become a topic of focus throughout our respective states and localities. Why? Because redistricting can have a large impact on our own budgets, processes and operational longevity. Through a more balanced process, impacts on our profession can be, at the very least, viewed as originating from a place of fairness.
Author: Mr. Sean L. Ziller is a policy analyst/consultant with Conduent State and Local Solutions, Inc. in Philadelphia. He possesses a Bachelor of Arts in Political Science – King’s College (PA) and a Master of Public Administration – Penn State University. All opinions are his own and do not necessarily reflect those of his employer. He can be reached at [email protected].
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The Redistricting Controversy in Pennsylvania and the Importance of the Public Voice: Part II
The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.
By Sean L. Ziller
October 7, 2019
Congressional district map in preparation for the 2018 state election cycle.
As highlighted in Part I, the number of court cases impacting redistricting efforts across the country will have both immediate and lasting consequences on how government operates at all levels. This notion is put into stark relief when considering the upcoming census.
That is to say, judicial decisions made in these cases will likely have a colossal effect on the representation being afforded to each citizen as well as how subsequent redistricting is approached.
However, by utilizing as much of a non-partisan lens as possible we can begin to embark on the still important task of understanding how to properly evaluate the complex elements of redistricting as well as its outcomes. Additionally, thinking apolitically and recognizing the value of, for example, emerging technology relevant to this sector may begin to disentangle the partisanship that is, to many, seemingly a part of existing redistricting initiatives. By applying an altogether ,“Neutral,” approach to circumstances in real-time, such as with the partisan efforts occurring in Pennsylvania, casual observers and public administrators alike can begin to possible substitute what may be their own subjectivity in favor of more analytical analyses.
In discussing redistricting, being able to evaluate a contentious process like partisan gerrymandering pragmatically does not initially seem possible, let alone easy. However, Samuel S.H. Wang, in his 2016 article in the Stanford Law Review entitled, “Three Tests for Practical Evaluation of Partisan Gerrymandering,” attempts to do just that. Employing statistical techniques, Wang offers the following three tests to, in this case, “Reliably assess asymmetry in state-level districting schemes:”
What implications can be drawn from the use of these tests, and what do these mean for evaluating progressively complex redistricting schemes three years onward? Wang notes immediately in the abstract that the first test requires computer simulations in order to measure representation. However, the second and third methods rely on statistical principles that can be accomplished more readily. The majority of his analysis discusses quantitively evaluating the, “Intents and effects,” of partisan gerrymandering, applying his testing to circumstances found in legislative districts such as Arizona and Maryland, while remaining within the, “Partisan asymmetry,” scope laid out previously in gerrymandering decisions handed down by the U.S. Supreme Court.
While the deeper quantitative implications of Wang’s work may not be relevant for us here, the largely academic approach and the discussion it stimulates are critical in being able to hold wider conversations about proper and fair redistricting. As expressed by Wang, similar statistical methodologies could aid in the judicial review of these types of cases. This would, presumably, standardize decisionmaking in redistricting controversies, creating greater impartiality in redrawing districts and their potential later review. For Wang, “Current approaches to proving gerrymanders focus on intent, are diverse in approach, and sometimes do not agree with one another”. While not foolproof, advocating for approaches to redistricting—and, importantly, for approaches of judicial review toward redistricting—where a more quantitative framework is employed could help to extradite partisanship from the larger practice. This is particularly relevant when examining the differing circumstances of cases from state to state. For public administrators involved in these processes, similar approaches, or methodologies geared toward neutrality, may ultimately be a manner through which fairer outcomes result for constituents.
Embracing relevant technology may also assist in reducing unjust gerrymandering practices. Teresa Mathew in, “Can Data Defeat Gerrymandering?” expands on a prediction made early on in the article by the Brennan Center’s Michael Li: “…Things that look like space age technology now will look run-of-the-mill when things are redrawn in 2021.” Mapping software and produced data has long been used by analysts to help skew districts during redrawing processes, leading to a greater potential for unfair and improper gerrymandering. However, there have been recent efforts to help control this practice and, instead, directly aid courts in, “[Navigating] the judgement process.” While there remain obstacles to an era of greater accessibility to open-source data, the potential capabilities to an actor, or actors, offering options of impartial mapping, in good faith, has implications for a healthier redistricting future.
The purpose of emphasizing statistical approaches to redistricting, as well as noting new areas of technology, is not to advocate for one option over another. Instead, it’s important for those in the public administration sphere to consider avenues through which overtly political conditions could be mitigated. For those in our field, collective alarm may arise each time redistricting or claims of partisan gerrymandering become a topic of focus throughout our respective states and localities. Why? Because redistricting can have a large impact on our own budgets, processes and operational longevity. Through a more balanced process, impacts on our profession can be, at the very least, viewed as originating from a place of fairness.
Author: Mr. Sean L. Ziller is a policy analyst/consultant with Conduent State and Local Solutions, Inc. in Philadelphia. He possesses a Bachelor of Arts in Political Science – King’s College (PA) and a Master of Public Administration – Penn State University. All opinions are his own and do not necessarily reflect those of his employer. He can be reached at [email protected].
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