Gerrymandering – The Salamander that Could… and Did

Written By: Liz Anusauskas

In 1812 Elbridge Gerry, the Democratic- Republican Governor of Massachusetts, did something practically unheard of. He let Democratic-Republicans in Massachusetts use their political power to redraw district lines to ensure a victory for the Democratic-Republican Party in the state senate election. After Gerry signed a bill to make this kind of redistricting legal, a cartoon was put into a local newspaper that made the Boston district he had drawn look like a salamander. The name “gerrymander” comes from a combination of Elbridge Gerry’s name and the famous salamander from the cartoon.[1] Ever since then, politicians have been altering district lines to fit their needs.


Cartoon from 1812 depicting a salamander in the shape of the congressional districts surrounding Boston. This cartoon highlighted the absurdity of gerrymandering early on. [2]

As required by federal law and the Voting Rights Act of 1965, the current system for drawing district lines takes population distribution, compactness of a district, and minority votes into account.[3] Every ten years the decennial census is administered to all citizens and the information gathered from that survey is used to both redistrict congressional lines and to reapportion the number of congressional representatives in each state. This census data contains important information that can tell those doing the redistricting how many citizens to put into each congressional district as well as the diversity of citizens that should be included in each district. A poor count by the census can lead to unfairly drawn districts and poorly apportioned states. In addition, census information is influential in determining federal funding for projects like schools, housing vouchers, and Medicare – and with 600 billion dollars of federal funding on the line, a dramatic miscount will lead to a poor dispersion of these funds.[4] However, a miscount can also give incumbent politicians an opportunity to map their districts in ways that guarantee future electoral victories, making the data from the census critical to this process.

In the 2018-midterm election, voters approved ballot measures in four states to prevent and limit gerrymandering. Colorado, Michigan, Missouri, Ohio and Utah all passed ballot questions to limit partisan redistricting. In Colorado, Michigan, and Utah voters approved the creation of an independent commission to draw congressional and legislative districts. In Missouri the people voted to appoint a state demographer and to use a statistical model for the redistricting process. Ohio passed a ballot measure that would ensure both parties are included in the process of approving new congressional districts. The push for these ballot questions showed the deliberate efforts of voters to curb gerrymandering before the 2020 Census, which will affect the results of the 2022-midterm election.[5]

Gerrymandering commonly occurs in two distinct ways, both of which heavily contribute to wasted votes. Wasted votes are defined as all the votes a party wins after they have won the majority. For example, if a party wins 80 percent of the votes, the 30 percent of votes won after winning over 50 percent are wasted. They are wasted because they are votes for a party that were not needed to win in a particular congressional district, but could have been influential in changing the results of another congressional district that hosted a closer race. The gerrymandering techniques that create wasted votes are known as “packing” and “cracking.” Packing occurs when politicians load up voters of the opposing party into one district leaving the rest of the districts empty of the opposing party’s voters. This creates easy, noncompetitive wins in the majority of the congressional districts. The party that has packed this district can then make other districts less competitive because the opposing party’s votes are “wasted” in the packed district. To crack districts, a party creates an artificially competitive district by dividing voters of the opposing parties into many districts so that they never hold the majority, but the districts seem a little more competitive. An easy way to tell if a congressional district has been gerrymandered is to look at whether or not it was a competitive district and to compare which party gathered the highest number of votes in the state with the number of seats in Congress they won.[6]

The graphic below showing results from Wisconsin elections from 2008-2012 highlights the fact that a party might gain the majority of votes, but still win less overall seats in the state assembly. Packing and cracking is further explained and visualized in the graphic under the Wisconsin chart.


These charts show how Wisconsin Democrats began with control of the state assembly prior to the 2010 census, but lost control after the subsequent redistricting that resulted from efforts by Republicans in 2010. From 2007 until 2010, Democrats controlled both the Governor’s seat and the Senate and in 2009 gained the majority in the House as well. In the 2010 election, Republicans won the Governor’s seat and the majority of seats in both the House and Senate giving them the power to redistrict congressional lines in their favor according to data from the results of the 2010 census. Ever since that year, Wisconsin Republicans have controlled all three majorities even in years when they do not garner the majority of votes. The only change to these results was during the midterm elections last fall when Democrats helped their governor get elected. [7] In the 2012 election, after the districts had been rearranged, the Democrats lost control of the state assembly even though they continued to get more votes in the state overall. In 2012, Democrats won 53 percent of the votes statewide, but Republicans won over 50 percent of the available seats in the state’s assembly. In 2016, Republicans garnered 53 percent of the votes statewide, but managed to win about 65 percent of the assembly seats. In a perfect world the percentage of votes statewide a party receives would be equivalent to the percentage of seats a party wins in the state legislature.
These two graphics show what packing and cracking look like using blue and orange houses to represent two different parties. Notice how the cracked districts look more competitive with more of the orange house spread out, but in the packed districts the only two districts that the orange party wins are the ones made up of only orange party voters.

The idea of wasted votes contributes heavily to one solution brought to the Supreme Court from Wisconsin: a call for gerrymandering to be identified according to the efficiency gap. In recent history, the Supreme Court has seen an abundance of cases citing significant gerrymandering and in each case the largest difficulty has been in finding proof that gerrymandering is the singular reason a party lost. The efficiency gap has been utilized in these instances to explore the idea that state legislatures, who agreed upon redistricted maps, understood that these maps would leave a lot of wasted votes for a specific party in the majority of congressional districts. To explain how this is identified I will use an example of Party A and Party B. Let us say that Party A always wins the majority number of votes by citizens across the state, but is the minority party within the state legislature (meaning it holds less seats in the assembly). Since Party A always wins more votes than Party B statewide, Party A argues that Party B gerrymandered the districts to ensure Party B wins more seats in the state legislature. To prove that Party B gerrymandered congressional districts, Party A shows that the efficiency gap is so wide that it benefits Party B. To begin, Party A looks at one of the congressional districts they won and subtracts the total number of votes cast for their party by the number of votes they need to win a simple majority in that district. This solution is the number of wasted votes in that district. This subtraction is done the same way in all the other congressional districts they won. In the districts they lost, Party B’s votes are subtracted instead. Party A the adds up the total number of wasted votes from each district, adds together totals for all the districts Party A and B won, and then subtracts Party B’s wasted votes from Party A’s wasted votes. This number represents the efficiency gap between the two parties that benefited Party B. The further the number is from zero the more wasted votes that party had. Since the number of votes that contributed to Party A’s wasted votes will be a negative number, the lower the efficiency gap is the more it is clear that Party B gerrymandered.  This example, highlighting how effective wasted votes can be, is important in explaining how gerrymandering can be a completely biased process.[8]

The Supreme Court has postponed three of the four cases about gerrymandering that started last year, to be addressed later this year, meaning a final verdict on these problems will not be heard for a while. In Wisconsin, the Supreme Court decided there was not enough evidence to prove redistricting from the efficiency gap and their three-part test. The Supreme Court ruled to hear the case after the North Carolina and Maryland cases have been heard. [9] In the case heard for League of Women Voters of Pennsylvania v Commonwealth of Pennsylvania, Pennsylvania had success in changing congressional lines. The Pennsylvania Supreme Court ruled that the General Assembly of Pennsylvania had to redraw the congressional districts in time for the 2018 midterm elections. The governor and legislature did not agree on the redrawn map so, the Pennsylvania Supreme court released the congressional map that was used during the midterm election.[10] North Carolina will see its case heard in the Supreme Court in late March after a panel granted the motion to stay the opinion to redraw the lines until further review. [11] All of these cases prove how difficult it is for courts to make decisions on gerrymandering and how each case of gerrymandering is uniquely different.

Another way to prove that gerrymandering contains a partisan bias was developed by two mathematicians. In 2014, Johnathan Mattingly and Christy Vaugh created a method to randomly simulate the drawing of congressional districts. Using the outcome of North Carolina’s 2012 election, in which Democratic House candidates received a majority of the vote but only won 4 out of 13 districts, they ran a bootstrap estimate of 100 simulations to determine how many Democratic candidates would win, on average, if districts were randomly drawn. They found that, for all simulations, the Democrats always won between six and nine seats – randomly drawn districts never produced a map where only four candidates triumphed. This proved that the probability of only four Democrats being elected was incredibly low, meaning North Carolina’s 2012 districts were unfairly gerrymandered, and did not reflect the “will of the people.”[12]

Independent commissions have become a common way for states to realign their gerrymandered congressional districts. Right now there are 13 states that use independent commissions as the means for drawing district lines. Instead of partisan commissions that could have a discernible bias, these independent commissions are usually made up of an equal number of Democrats, Republicans, and sometimes even a few independents. Whether these people are legislators, nonpolitical officials, or members of the public varies by state, but in every case an effort is made to decrease the effects of partisan bias. There are three types of commissions that are used by states that do not grant the full power of redistricting to the legislature: backup commissions, advisory commissions, and commissions who are tasked with drawing a plan for congressional districts. The number of people in these commissions and who nominates or appoints the people in the commission varies by state.[13]

In addition, Iowa uses its own method that is completely different than all of the other states. The Iowa commission is called the Temporary Redistricting Advisory Commission, and state law requires that the legislature vote on plans created by a group of nonpartisan legislative staff members. The commissioners are required to create electoral maps without looking at political or election data so they can focus on population size and fitting the correct number of state house and state senate seats into each district.[14]

This map shows the types of commissions that each state utilizes in their redistricting efforts. The states in grey do not have an independent commission. It is not updated to show the changes to commission type that resulted from the 2018 ballot questions.

One proposed solution to unfair gerrymandering is known as the “shortest splitline algorithm,” or splitline districting. Splitline districting uses mathematical equations to divide districts by straight lines according to population density. Using Census data to calculate population and state shape, splitline districting can be completed with a computer program created by Ivan Ryan or by using a mathematical equation to determine where to draw the lines.[15] The biggest problem with splitline districting is that it ignores all political and geographical boundaries meaning, it might divide a house or a yard making it hard to tell which district people are in. The equations used to divide districts is shown below.

To the right is what Massachusetts looks like with districts divided by the splitline districting method. Below is the most up to date version of what congressional district lines in Massachusetts look like.
Photo on the right is from: https://rangevoting.org/SplitLR.html

North Carolina (shown below) has an even worse track record of gerrymandering. The original map depicts how district lines were drawn in 2014. The splitline districting used data from the 2009 population to map potential congressional district lines which is also shown below. The splitline map for Virginia is from: http:// https://rangevoting.org/SplitLR.html

In 2017 a team at FiveThirtyEight began The Gerrymandering Project to understand the effects gerrymandering had on people across the nation and to compare different ways people can lessen or increase the effects of gerrymandering. Galen Druke, a producer and reporter for FiveThirtyEight, traveled to Wisconsin, North Carolina, Arizona, and California interviewing professionals, politicians, civilians, and those affected by gerrymandering in their state. He discussed things from the Supreme Court case that started in Wisconsin, to a drop in the number of competitive elections in Arizona. Throughout his podcast series he looked into ways that helped minimize the effects of gerrymandering and ways that seemed to help at first, but may have disadvantaged other groups in the effort to fix the system.[16]

The Atlas of Redistricting is a resource created by the team at FiveThirtyEight that highlights the differences in voting results when district lines are created with a specific bias in mind. This atlas compares the effects of Democratic and Republican gerrymandering, the current districts, proportionally partisan districts, majority minority districts, highly competitive districts, compact (by county splits) districts, and compact (by a mathematical algorithm) districts. Viewers are able to take a closer look at most individual states or change the entire nation to each type of district setting. Each type of redistricting method is also summarized and ranked by category to determine which one has the highest compactness rank, country splits, majority-nonwhite districts, competitive districts, and efficiency gap. This atlas can be useful in trying to determine the best methodology for redistricting and which type of redistricting causes the most amount of harm. Below are some of the maps created by FiveThirtyEight to show the results of different settings.[17]

These maps were created by FiveThirtyEight to show the differences in election results when district lines are drawn to fit specific needs. The top two compare current districts with those drawn to increase the number of competitive elections while the bottom two maps show election results if Republicans and then Democrats had full power to gerrymander. These maps can be found on FiveThirtyEight’s website here: https://projects.fivethirtyeight.com/redistricting-maps/

So far, congressional districts have been the focus of discussion because those are the districts that affect members in the House of Representatives nationwide and have a notorious history of being gerrymandered. The gerrymandered map by Democrats designed above by FiveThiryEight may look like it still has a lot of Republican districts, but many of those districts have a lower population density and therefore have fewer electoral votes in the Presidential election. In the example below, state and county borders are observed instead to highlight the fact that these borders, as well as congressional districts, can sometimes be misleading when showing election results because populations within these borders are not taken into account.

Cartograms rescale the size of states by a specific feature (in this case population) and are therefore better at representing election results by the number of people who voted for the Democratic or Republican candidates. This is useful when looking at Democratic districts, because urban areas and districts are more likely to vote for Democrats than rural, less populated areas. According to the PEW Research Center, the difference between the percentage of registered Democrats and Republicans is a lot greater in urban counties than in rural counties with Democrats being favored 62 to 31 in urban counties and Republicans being favored 54 to 38 in rural counties.[18] Below are some maps and cartograms created by Mark Newman from the University of Michigan, representing 2016 Presidential election results according to different factors. [19]

The maps above show election results from the 2016 election. The first one shows current state borders divided by states that went blue or red. The second map changes the shape of the US to adjust to the number of electoral votes that went to each candidate. For example, Illinois has 20 electoral votes and the majority of the population voted for Clinton, so the state of Illinois is blue and is bigger in the cartogram than in the original map since it represents more electoral votes.

Research was also conducted on the county level to show the differences in county results assuming everyone in a blue county voted for Clinton and everyone in a red country voted for Trump. The cartograms above highlight how populous some small blue districts are. Focusing in on Florida, the counties of Palm Beach, Broward, and Miami-Dade are small counties compared to the rest of the state, but much larger counties according to population which explains its explosion in the cartogram.

It is also important to take into account the fact that everyone in a county that went blue did not vote for Clinton. Just because the majority of the county voted for a candidate does not mean these districts were not competitive. This last set of maps above recognize this fact and make more competitive districts purple instead of simply red or blue. The cartographer used a scale that makes counties that voted 70 percent or more for Republicans or Democrats red or blue respectively and a county that received less than 70 percent of the votes for a single candidate a different shade of purple according to its competitiveness.[20]

The point of these cartograms is to show how easily districts can be misinterpreted. Whether you are looking at counties or congressional districts, the underlying issues that affect our elections need to be understood before one jumps to conclusions about interpreting these results. Election results do not always reflect true opinions and values of American voters. These two examples highlight the significant problems within our current system of voting and until direct action is taken to remove partisan bias and restore the power of one person’s vote, the democratic system will remain in jeopardy of misrepresenting its people.

Needless to say, improvements need to be made to our system of redistricting to ensure that politicians do not wield their political power to game the system. There is still a lot of work to be done to ensure that district lines are fair and to give everyone an equal chance of winning. The independent commissions are a step-up from previous practices, and systems rooted in mathematical proofs or technological advancements that enable computer systems to calculate fair maps are another progressive way to improve the current system. Taking cases of gerrymandering to the Supreme Court is a quick way to make drastic changes and to create precedents for all other states to follow, but this should only be the beginning. Recognizing that partisan politics needs to be taken out of the redistricting process, public citizens, legislators, and researchers must come together to push for immediate change using fact based methods to prove the legitimacy of gerrymandering as well as the legitimacy of alternatives to contemporary redistricting methods. To do so requires a greater study of gerrymandering and the current suggestions for new redistricting methods. Until everyone has a full understanding of the problem that citizens and legislators truly face when a district is gerrymandered, it will be impossible to find the perfect solution. With the integrity of our nation’s elections at stake, investing time, energy, and resources into new ways to redistrict should be a top priority.


Extra Links Addressing Other Issues and Explaining Other Pieces of the Gerrymandering Problem

Awareness for gerrymandering has improved following its immediate effects on recent elections, and a multitude of resources and videos have been produced. One of the better explanatory videos is from CP Grey here: https://www.youtube.com/watch?v=Mky11UJb9AY . The Washington Post also does a good job at explaining how it works here: https://www.washingtonpost.com/video/business/gerrymandering-explained/2016/04/21/e447f5c2-07fe-11e6-bfed-ef65dff5970d_video.html

Another fun piece of evidence is the Gerrymandering Gallery which has a few pieces of “art” (severely gerrymandered districts): https://rangevoting.org/GerryGal.html

Turning gerrymandering into a game is a perfect way to grab people’s attention. The USC Game Innovation Lab created the Gerrymandering Game in which you are tasked with redrawing the lines while pleasing a multitude of different bureaucrats on all sides of the political spectrum. Not only do you learn how redistricting works, but you also get a better sense of the political pressure people face when trying to redraw lines. Play here: http://www.redistrictinggame.org/

Another way state legislatures tilt the scales in their favor is to use their power to gerrymander areas with prisons. Prison gerrymandering is when districts are drawn around prisons in states that do not allow prisoners to vote, but count them in the district as though they have the ability to be a part of the electorate. This takes away the power of a persons’ vote because the district of eligible voters will be incredibly small since the district accounts for the number of people imprisoned. You can learn more about this problem here: https://www.prisonersofthecensus.org/toobig/exec_sum.html


Bibliography

[1] Trickey, Erick.(2017, 20 July). Smithsonian. Where Did the Term “Gerrymander” Come From? Retrieved 2019, February. https://www.smithsonianmag.com/history/where-did-term-gerrymander-come-180964118/

[2] N/A. Wikipedia. Gerrymandering. Retrieved 2019, February. https://en.wikipedia.org/wiki/Gerrymandering

[3] Knudson, Kevin. (2015, August 3). The Conversation. Can math solve the congressional districting problem? Retrieved 2019, February. https://theconversation.com/can-math-solve-the-congressional-districting-problem-44963

[4]N/A. (2019, February 26). National Conference of State Legislatures. 2020 Census Resources And Legislation. Retrieved 2019, February. http://www.ncsl.org/research/redistricting/2020-census-resources-and-legislation.aspx

[5] Neely, Brett and McMinn, Sean. (2018, December 28). National Public Radio.Voters Rejected Gerrymandering in 2018, But Some Lawmakers Try to Hold Power. Retrieved 2019, February. https://www.npr.org/2018/12/28/675763553/voters-rejected-gerrymandering-in-2018-but-some-lawmakers-try-to-hold-power

[6] Druke, Galen, host. (2017, November 30). FiveThirtyEight. Why Can’t We Just Burn Gerrymandering To The Ground? Retrieved 2019, February. https://fivethirtyeight.com/features/why-cant-we-just-burn-gerrymandering-to-the-ground/

[7] N/A. Ballotpedia. Party control of Wisconsin state government. Retrieved 2019, February. https://ballotpedia.org/Party_control_of_Wisconsin_state_government

[8] Cameron, Darla. (2017, October 4). The Washington Post. Here’s how the Supreme Court could decide whether your vote will count. Retrieved 2019, February.
https://www.washingtonpost.com/graphics/2017/politics/courts-law/gerrymander/?utm_term=.d34a307a5c46

[9] N/A. (2019, February 4). The Brennan Center. Gill v. Whitford. Retrieved 2019, February. https://www.brennancenter.org/legal-work/whitford-v-gill

[10] N/A. (2018, October 29). The Brennan Center. League of Women Voters of Pennsylvania v Commonwealth of Pennsylvania. Retrieved 2019, February. https://www.brennancenter.org/legal-work/league-women-voters-v-pennslyvania

[11] N/A. (2019, March 7). The Brennan Center. Rucho v Common Cause. Retrieved 2019, March. http://www.brennancenter.org/legal-work/common-cause-v-rucho

[12] Knudson, Kevin. (2015, August, 3). The Conversation. Can math solve the congressional districting problem? Retrieved 2019, February. https://theconversation.com/can-math-solve-the-congressional-districting-problem-44963

[13] Underhill, Wendy. (2019, January 2019). National Conference of State Legislatures. Redistricting Commissions: Congressional Plan. Retrieved 2019, February. http://http://www.ncsl.org/research/redistricting/redistricting-commissions-congressional-plans.aspx#Other

[14] N/A (2018, April 6). National Conference of State Legislatures. The “Iowa Model” for Redistricting. Retrieved 2019, February. http://www.ncsl.org/research/redistricting/the-iowa-model-for-redistricting.aspx

[15] Written by the Center for Range Voting; algorithm invented by Smith, Warren; program to produce the images by Ryan, Ivan; data sourced from the US Census Bureau. “Splitline Districting of all 50 States + DC + Puerto Rico.” https://rangevoting.org/SplitLR.html

[16] Druke, Galen, host. (2017, November 30). FiveThirtyEight. Why Can’t We Just Burn Gerrymandering To The Ground? Retrieved 2019, February. https://fivethirtyeight.com/features/why-cant-we-just-burn-gerrymandering-to-the-ground/

[17] Bycoffe, Aaron; Koeze, Ella; Wasserman, David; Wolfe, Julia. (2018, January 25). FiveThirtyEight. The Atlas of Redistricting. Retrieved 2019, February. https://projects.fivethirtyeight.com/redistricting-maps/

[18] Parker, Kim; Horowitz, Juliana; Brown, Anna; Fry, Richard; Cohn, D’Vera; Igielnik, Ruth. (2018, May 22). Urban, Suburban and Rural Residents’ Views on Key Social and Political Issues. Retrieved 2019, February.http://www.pewsocialtrends.org/2018/05/22/urban-suburban-and-rural-residents-views-on-key-social-and-political-issues/

[19] Newman, Mark. (2016, December 2).
Department of Physics and Center for the Study of Complex Systems. Maps of the 2016 US Presidential Election Results. Retrieved 2019, February. http://www-personal.umich.edu/~mejn/election/2016/

[20] Newman, Mark. (2016, December 2).
Department of Physics and Center for the Study of Complex Systems. Maps of the 2016 US Presidential Election Results. Retrieved 2019, February. http://www-personal.umich.edu/~mejn/election/2016/

Introduction: Undergraduate Research Assistant, Sal Balkus

Hi everyone! My name is Sal Balkus, and I am an Undergraduate Research Assistant at the Public Policy Center. I am from Franklin, Massachusetts, and I am currently a freshman at UMass Dartmouth, majoring in Data Science. I also serve on the university’s Honors Council, and I enjoy rock climbing and hiking with the Outdoor Club.

Working at the PPC provides an exciting opportunity for me to do social science research and apply my data analysis and statistics skills to a variety of data. I am passionate about all things data science and I hope to pursue graduate study, as well as a career in the field. As such, I am very glad that I am able to work a job on campus that is relevant to my future career goals and yields valuable experience that will aid me in the future.

The 2020 Census and The Importance of the Hard-to-Count Population

By Robert Stickles

Every decade, when an updated version of the U.S. Census is published, questions regarding the accuracy of the information arise – and for good reason. The U.S. Census Bureau has the monumental, overwhelming task of counting every person in the United States and recording basic information such as race, sex, and age. But how can the Bureau accomplish this without making any errors? Well, it is almost impossible to collect perfect data without any mistakes, especially because many populations throughout the country are considered “Hard-to-Count.”

According to the Census Bureau, the groups that are especially difficult to gather data for are racial/ethnic minorities, linguistic minorities, lower income persons, homeless persons, undocumented immigrants, young mobile persons, and children. The government reported that in 2010 alone, the U.S. Census missed more than 1.5 million minorities nationwide after experiencing difficulty in counting black Americans, Hispanics, renters and young men. On the other hand, it was also reported that parts of the U.S. population had been over-counted, largely due to duplicate counts of affluent whites owning more than one home.

So, why is it crucial for U.S. Census to collect accurate data? To examine this topic, it is important to understand what the Census is used for. For the most part, the U.S. Census is used for population and demographic information. Population counts plays a large role in the way the government is run, as the correct population figures ensure that every community is given full representation in the halls of government. On top of that, the Census also assists in making the decisions regarding the distribution of public funds when it comes to educational programs, healthcare, law enforcement, and highways. If up-to-date population data are not available, areas of the country might not get their fair share of state Representatives or public funds.

The Hard-To-Count Hot Spots in Massachusetts and Greater Boston

Source: The Census 2020 HTC Map developed by the CUNY Mapping Service at the City University of New York’s Graduate Center.

In Massachusetts, many of the hard-to-count populations appear to be located in or around the larger cities such as Boston, Worcester, New Bedford, Fall River, Taunton, and Brockton. Boston, the largest city in Massachusetts, faces the largest challenge in obtaining data for every person. In 2010, there were many tracts in Boston where fewer than 60 percent of households mailed back their 2010 Census questionnaire.

For Massachusetts, this means that anywhere that there is a large population of “Hard-to-Count” individuals, entire communities may not get the funding or the political representation that they need to fairly serve and provide for their citizens.

Introduction: Undergraduate Research Assistant, Robert Stickles

Hello Everyone,

My name is Robert Stickles and I am an Undergraduate Research Assistant here at the Public Policy Center. I am currently a sophomore at Umass Dartmouth, where I am majoring in Finance and minoring in Accounting. Before attending Umass Dartmouth, I went to Tabor Academy for four years and also attended Stonehill College for one year, where I studied Business and played on the men’s ice hockey team. I grew up around the Cape Cod/Buzzards Bay area and in my downtime, I can usually be found at one of the local beaches or partaking in other outdoor activities. I enjoy assisting in the gathering of research that will help to strengthen towns and communities. The team here has been extremely welcoming and I am eager to contribute to the Center.

Introduction: Graduate Research Assistant, Jim DeArruda

Hello, I’m Jim DeArruda, a Graduate Research Assistant at the UMass Dartmouth Public Policy Center. I just began in the Fall 2017 semester. I began matriculation toward a Master’s in Public Policy in Fall 2015 by starting with the Graduate Certificate in Environmental Policy.

I came to the PPC after a 25-year career in newspapers (the last 20 at the same one), and the transition feels just right. The completion of my BS in Business Management plus my graduate studies wonderfully informed my past few years as an editorial writer, but I’m ready to put my effort into assisting the great academic research done at the PPC. In my first few weeks, my expectations have been well met, thanks to my new colleagues, some of whom have been my instructors. I consider myself very fortunate to be in this place right now.

I live in Dighton, Mass., in the home my father grew up in, and in which I raised my three children. I like doing things around my house by myself, whether it’s crafting tools, making home repairs or making jellies or other foodstuffs from the land around my home.

In Dighton, I’m the chairman of the Historical Commission, the secretary of the Council on Aging, and the Historical Commission representative to the Community Preservation Committee. My participation in town government has been rewarding and challenging, and is another example of how my graduate education has made other parts of my life richer.

I have spent most of my life living around and working in Southeastern Massachusetts. Generations of my family have worked on farms and in textile factories of Taunton, Fall River and New Bedford, so the work done at the PPC is very important to me. I consider it a privilege to be able to contribute to it.

Introduction: Undergraduate Research Assistant, Nathaniel Roberts

My name is Nathaniel Roberts. I am sophomore Political Science/Economics Double Major at UMass Dartmouth. I am a lifelong resident of Fall River. Fall River is a community that has been often overlooked and ignored due to a poor economic situation. The degrees I am working towards will help me put Fall River back on the map, hopefully one day as its political leader.

Interning here at the Public Policy Center is a step forward towards that goal. I am looking forward to having a deeper understanding of data analysis, something I think all future policy makers should have, and something I have wanted to attain since taking AP Statistics in high school.

I am most excited to be able to work in a field more closely related to my future goals and interests, since last summer I worked in a bread factory, and the summer before that in a kids’ youth camp.

Anna Marini – introduction

Hello my name is Anna Marini and I have just started as a Graduate Research Assistant at the Public Policy Center. I am at the final stages of acquiring my Master’s of Public Policy at UMass Dartmouth, and have really loved the program – courses and professors (2017 graduation!). I’m thrilled to be working at the PPC and to participate in detailed and meaningful data analysis, studies, and evaluations; putting to work all that I’ve learned over the years. The staff here are great and I’m looking forward to learning from them.

I come to the PPC with a Master’s in Health Administration and years of experience in hospital management in Boston teaching hospitals (Brigham and Women’s, Children’s and Tufts). I’ve also worked as a consultant doing business development in health care and have managed and sold a small manufacturing business. I have maintained a deep connection to all things health care related through the years, and currently serve on the Patient and Family Advisory Committee at Beth Israel Deaconess-Plymouth. I’m looking forward to working on some health care related projects at the PPC.

I live in Cape Cod (Bourne), but over the last few years have become more knowledgeable about the SouthCoast region. First, during travels here (my daughter attends Bishop Stang High School) and second, from participating in the Leadership SouthCoast program (2015 graduate). I’ve grown to really love the region. I’m really looking forward to the opportunities presented by the PPC and to contributing to its work.

Joy Smith- Introduction

Hello everyone, I’m Joy, one of the new Graduate Research Assistants at the Public Policy Center. I just recently graduated from UMass Dartmouth, where I earned my Bachelor of Science in Marine Biology. I decided to continue my education at UMass Dartmouth to pursue a Master’s of Public Policy with a concentration in Environmental Policy.

This past summer, I interned at the Westport Land Conservation Trust as the Stewardship and Special Projects Intern. There, the idea of having a career protecting land, and ultimately benefiting our environment, became a passion. The summer prior, I interned at the Westport River Watershed Alliance where I assisted the Education Director with summer programs. We taught children about the different types of habitat around Westport and the types of animals and insects who reside in these areas. With my background in land management and outreach, I’ve got a lot to learn about policy and data analysis. I’m extremely excited to work at the PPC and alongside such an amazing group of individuals.

Census Publishes Commuting Data for Massachusetts!

We are super excited at The Public Policy Center that Massachusetts has finally joined the rest of the country in releasing the data that is used in the production of two important Census Bureau data products: Longitudinal Employer-Household Dynamics (LEHD) and LEHD Origin-Destination Employment Statistics (LODES). The LODES data will be instrumental in a recently launched study of the economic connections between southeastern Massachusetts and Rhode Island by allowing us to look at commuting patterns at the city and town level (formal announcement forthcoming). This data was just released yesterday afternoon and I’ve had a lot of fun playing around with the data via the Census’s OnTheMap tool. Here are a couple of the data visualizations that I produced using OnTheMap:

This first image shows the inflows and outflows for the Southcoast of Massachusetts in 2014 (defined here to include Swansea, Somerset, Fall River, Westport, Dartmouth, Freetown, New Bedford, Acushnet, Fairhaven, Mattapoisett, Rochester, Marion, and Wareham). According to the LODES data, 41,463 people who work on the Southcoast live elsewhere, 66,613 people who live on the Southcoast work elsewhere, and 76,289 people both live and work on the Southcoast.

This next image shows the distance, direction, and volume of commutes into the Boston Workforce Investment Area. The color indicates distance and the size of each slice indicates volume. This graph suggests that the greatest number of people commute from South of Boston, while people to the West and Southwest travel the longest distances. In 2014, an estimated 6,844 people commuted from the Southcoast to the Boston Workforce Investment Area. Meanwhile, 8,192 commuted from the Southcoast to the workforce investment area for Greater Rhode Island.

chart

Now it’s time to learn how to work with the raw data so I can develop customized data processing algorithms. Fun!

Introduction – Holly Stickles

Hello Everyone,

My name is Holly Stickles and I have recently joined UMASS – Dartmouth’s Public Policy Center as a Graduate Research Assistant. While I am quite new to the program, I am no stranger to UMASS – Dartmouth where I recently earned a B.S. in Finance. During my undergraduate studies, I was fortunate enough to have interned at the Greater New Bedford Workforce Investment Board. There, I assisted in the creation of models for workforce development and helped to market programs to local stakeholders. I also interned at Community and Economic Development Authority of Wareham, where I helped reconcile funding budgets while completely redeveloping the organization’s policies and procedures manuals to maximize efficiency. That being said, I am excited to put my acquired skills set to use within the parameters of this new role and, all the while, I am hoping to develop more knowledge in the field while pursuing an advanced degree in Public Policy.