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January 2018

Redistricting and the Census

One of the critical applications of the Decennial Census is its use in the redrawing of Congressional districts, known commonly as redistricting. Redistricting determines how many representatives a state gets in the House of Representatives and the Electoral College, depending on whether the state gained or lost population since the last census.  State and local governments also utilize the census to redistrict representation. One of the current hot-topic issues around redistricting is gerrymandering, or the drawing of district boundaries so as to benefit one political party over another.

History of Redistricting

“Representatives and direct Taxes shall be apportioned among the several States which may be included within this Union, according to their respective Numbers, which shall be determined by adding to the whole Number of free Persons, including those bound to Service for a Term of Years, and excluding Indians not taxed, three fifths of all other Persons. The actual Enumeration shall be made within three Years after the first Meeting of the Congress of the United States, and within every subsequent Term of ten Years, in such Manner as they shall by Law direct.”  US Constitution, Article I Section 2 Clause 3

The Constitution of the United States was the first such document in the world to require a census to be used for the purpose of legislative representation. Worried about ensuring that citizens felt that their voices were heard, George Washington proposed that the initial number of people per representative be set at 30,000. The Constitutional Convention then apportioned the 1st Congress based on population estimates, with a total of 65 representatives from 13 states. After the first Decennial Census in 1790, the number of representatives jumped to 105, and steadily increased until 1912, with the admission of New Mexico and Arizona into the Union bumping the number of representatives up to the current 435. In 1921, Congress failed to reapportion the House based on political concerns that the Republican majority would be diminished by the most recent census results, and since then the number of representatives per state remained the same. Michigan has historically had a relatively large number of representatives, thanks to the population center of Detroit, and reached a height of 19 representatives in the 1960s and 1970s, before falling back to 14 representatives as of the most recent census.

Congressional Seat Assignment Today

Today, after the census is conducted and state populations tallied, the representative seats are distributed through the Huntington-Hill Method, given below:

In the equation, An is the priority number for every seat a state could get after their constitutionally-required one seat, P is the population of the state, and n is the number of seats that the state currently holds (begins at 1 for every state). Before every seat is distributed, the priority number for each state is calculated, and the seat is given to the state with the highest priority number. After the 2010 Census, the 51st House of Representatives seat was given to California, the 52nd to Texas, the 53rd to California again, and so forth, until the 435th seat is given out.

After the 2010 apportionment, North Carolina missed out on its 14th seat by just 15,754 residents, highlighting the importance of an accurate census count in legislative representation. As of the 2010 Census, each representative in the House represents an average of 710,767 people, up from 646,952 after the 2000 Census. The smallest congressional district by population in the country remains Wyoming’s at-large district, at 568,300 people, while the largest is Montana’s at-large district, with 994,416 people. Accurate population counts from the census play a critical role in the apportionment of representatives; without the census, we wouldn’t know how many seats to give each state!

Gerrymandering

The first use of the word “gerrymander” occurred in the Boston Gazette in 1812. At the time, Governor Elbridge Gerry of Massachusetts had just signed a bill that redistricted the state’s senate election districts, and the paper was calling into question the unusual shape of a district outside of Boston. The resemblance to a salamander was noted, and the term was coined. In June 2017, the Supreme Court agreed to hear a case regarding the constitutionality of the state house districts in Wisconsin drawn after the 2010 Census.  Gill v. Whitford is the first case in over a decade that addresses the question of gerrymandering of house districts and whether or not too much of a partisan advantage in elections should be illegal. To fully understand the question at hand, we’ll first provide an overview of how state and federal House districts are constructed, and then explain the basis of the quantitative model of gerrymandering used in this case, considered to be the most effective quantitative representation of gerrymandering to date.

Gerrymandering is defined as the drawing of district borders so as to disproportionately benefit the interests of one party at the expense of another. This can be accomplished in two main ways, known as ‘cracking’ or ‘packing’. Cracking is the spreading of voters of one party across multiple districts to deny them a large enough voting block to win any of the districts, while packing is concentrating many voters of one party in a single district in order to limit that party’s influence in other districts. These techniques are utilized by the drawers of district maps, typically the political party in charge of the state legislature to benefit their party.

It has been historically difficult to challenge these maps. In the past, tests were based on criteria such as how neat the district was or if it was too large. The Voting Rights Act of 1965 added an avenue to challenge district maps based on racial grounds, but it has proven difficult to apply this piece of legislation to voter discrimination based on party. Most claims today rest on the Equal Protection Clause of the 14th Amendment, under the theory that plaintiffs are not equally allowed to have a say in elections because some votes are worth more than others.

Most states allow the state legislature to draw up congressional and legislative districts after the decennial census; in fact, 37 states allow the state legislature to have the primary responsibility in creating state district maps, while 42 have primary control over congressional maps. This method of redistricting is inherently political in nature; whichever party controls the state legislature in the year of the census has an immense say in the divisions of districts (for example, after the 2010 Census, legislatures were told of their allowed seats in January of 2011, with the responsibility to submit a new congressional map before the 2012 election cycle).

As an example of just how political this process has become in some states, take Texas in 2003. The Republicans gained a majority the state legislature in 2002 for the first time in 130 years, and hoped to take the majority of Texas’ congressional seats in 2004. In order to do this, they drew up a new congressional district map in 2003.  The fight over the redistricting resulted in every Democrat Texas House member leaving the state house to prevent a quorum at different times and 11 Texas State Senators fleeing to Albuquerque, New Mexico to prevent a quorum. Due to a legislative rule, the new districts eventually passed, with one of them being stricken as unconstitutional on racial grounds by the Supreme Court. Outrage over the partisan nature of redistricting has led six states to creating independent commissions, consisting of neither lawmakers nor public officials, to oversee redistricting.  Some feel that this is an improvement on the legislative model of redistricting. More states may adopt this model in the future.

Current Litigation on Gerrymandering

In 2015, Nicholas Stephanopoulos and Eric McGhee published a paper titled “Partisan Gerrymandering and the Efficiency Gap”, which promoted a new quantitative model for computing one party’s advantage in an election, the efficiency gap. The efficiency gap is a comparison of the number of “wasted votes”, votes that were cast for a losing candidate or votes cast for the winner of a district greater than the smallest number of votes needed to win that district, to the number of seats won by a party. When completed, it shows the percentage of seats that a party won over what it should have won based on its vote share. It can be calculated using either of the following formulas:

Where EG is the efficiency gap, WD is total wasted Democrat votes, WR is total wasted Republican votes, and TV is total votes.

Where EG is the efficiency gap, SM is the seat margin, or (Congressional Seats Won/Total Congressional Seats)-0.5, and VM is the vote margin, or (Votes Won/Total Votes)-0.5.

Both equations yield the same results if two conditions are met: if the total number of votes in every district is the same and if there are no seats that are uncontested. In the real world this doesn’t usually happen, as districts never have an equal number of voters show up on Election Day and many seats, especially State House seats are uncontested (out of the 5,923 state legislative seats up for grabs in 2016, 2,477 only had one major party candidate running, 41.8%). The formula computes a decimal that can be translated into the number of additional seats won by multiplying it by the number of total congressional seats. Stephanopoulos and McGhee recommend a threshold of 0.08, above which plans should be considered to give an unfair advantage to one party.

In the complaint filed to the United States District Court for the Western District of Wisconsin, the plaintiffs of Gill v. Whitford noted that the calculated efficiency gap of 0.12 for the 2012 state election was the 28th worst score in modern American history, and that no other plan constructed after 2010 had seen an efficiency gap that high through 2014. Coincidentally, released documents by Dr. Ronald Gaddie, who helped judge the partisan effects of the new plan for the Republican State Assembly when it was being drafted, predicted that the plan would have an efficiency gap of 0.12. Partisan redistricting of the Wisconsin State Assembly districts led to demonstrable gerrymandering that disadvantaged Democrats. It should be noted that while there are also plans that dramatically favor Democrats, Stephanopoulos found that the average net efficiency gap has gone increasingly in the favor of Republicans since the 1990s, and is currently at the worst level since the 1965 Voting Rights Act.

Filling out the census has implications that we might not realize. Making sure the census count is accurate, especially in cities and other areas with difficult-to-count populations, can have dramatic effects on our representation at both the state and the federal level. In 2020, we have a chance to ensure that our voices are heard, and redistricting depends fully on accurate census results.

 

 

 

 

InnovateGov Intern Spotlight –Where are they now?

In Summer of 2016, Data Driven Detroit (D3) entered a partnership with Michigan State University’s InnovateGov internship program. Through this program, MSU students get the opportunity for an immersive experience working at an organization in the city of Detroit. During the internship, they live on Wayne State University’s campus in the heart of Midtown Detroit. Their time is split between working with the organization at which they’re placed and participating in learning activities carefully curated by InnovateGov’s expert program administrators.

Introducing: Jordon Newton

During the summer of 2016, we had the pleasure of welcoming two students into D3’s first InnovateGov cohort. We wanted to take a moment to spotlight the awesome work they did with us last summer, provide an update on what they’re up to these days, and explain how their time at D3 helped to elevate their path and future endeavors.

Following our profile on intern Boitshoko Molefhi, we have Jordon Newton. During his time at D3, Jordon was working toward his Masters in Public Policy.

Tell us a little bit about yourself.

I have a Bachelors in Economics from Gonzaga University and Masters in Public Policy from Michigan State. I chose that combination because I’m interested in statistical and data focused policy analysis, and wanted to have a little more depth in the tools necessary to understand budget and economic data that I might not have gotten from a pure Political Science track.

What’s your favorite type of data to work with?

I like working with all types of data, but in particular I like working with larger-scale economic and budget focused data.

What did you do when you interned at D3?

The primary project I worked on was helping to develop an indicator for urban displacement in the city of Detroit. I also worked on a number of other projects ranging from conducting a community survey in a Detroit neighborhood, to helping research and collect data for grant proposals and project backgrounds, to developing maps for a variety of projects.

What was your favorite project at D3?

I would have to say the community development indicator project. My work on that project I think best encompassed all the different tools I had to use while at D3 from conducting background research on potential data sources to collecting data, to requiring me to think outside the box on how to best use data to show what was happening in communities.

What was your favorite thing you did in Detroit during your time in the city?

My favorite thing I did in Detroit was going to a baseball game at Comerica. I’m a huge baseball fan so that alone made it fun, but being in the environment during and after the game in the downtown are helped kind of demonstrate to me the potential that the city has, even though there’s still a long way to go.

Where are you working now?

I’m working at the Citizen Research Council of Michigan (CRC).

Tell us a little bit about your role there.

I’m a research associate focused on state policy issues. I primarily work on legislative issues and the state budget, writing reports to help citizens and policymakers alike understand key issues the state is facing and occasionally present my research to a variety of organizations and the public. I have also conducted some research on ballot initiatives and have helped develop data visualizations for research that my coworkers have conducted.

How did you come across your current position?

It was advertised through my Master’s program.

How did your time at D3 equip/prepare you for this position?

Aside from developing skills working with mapping data, I think the most helpful aspect was working in an environment that was rigorous about finding the underlying data behind issues. This created good practices that help with the non-partisan, fact-based research I do with the CRC.