{ "id": "2004-july-august-targeting-voting-wrongs", "slug": "2004-july-august-targeting-voting-wrongs", "url": "https://dwillis.github.io/uplink/article/2004-july-august-targeting-voting-wrongs.html", "year": 2004, "month": "July-August", "monthNum": 7, "headline": "Targeting voting wrongs", "kicker": "ELECTIONS", "author_name": "Benjamin Lesser", "author_title": "Reporter", "affiliation": "The Record", "summary": "An investigation into voting rights violations in Teaneck, New Jersey, revealing racially polarized voting patterns and the potential for federal intervention.", "keywords": [ "voting rights", "Teaneck", "investigation" ], "topics": [ "Government & Politics", "Investigative Methods", "Demographics & Census" ], "technologies": [ "ArcView", "Excel" ], "full_text": "Last year reporters at our news- paper learned that the U.S. Department of Justice was in- vestigating possible voting rights violations in Teaneck, a small New Jersey suburb of New York. This January one of our reporters, Scott Fallon, ap- proached me about doing something more substantive than the typical he said-she said story about the probe. After some discussions with our editors we decided to give it a go. Our plan was to conduct a parallel investigation to the Jus- tice Department's. A few months later we published a story that detailed for our read- ers what the Justice Depart- ment investigation involved, what conclusions the investiga- tors may draw and ultimately what the investigation could mean for the town. The Justice Department began its investigation after receiving an anonymous complaint from a local resident. Investigators were try- ing to determine whether Teaneck's at- large voting system should be replaced with a ward system that would help promote the election of black candi- dates to the town council and school board. Teaneck was 28 percent black but none of the members of the town council and only one member of the school board were black. In fact, there hadn't been a black resident on the town council since 2000. We worked the story on two tracks. Fallon, through his sources within the town, began retracing the steps of the investigators from the Department of Justice. He requested the same records from the town and interviewed the same town officials and residents. Meanwhile, I began to work on the sta- tistical analyses the investigators were likely to conduct. We contacted Edward Still, an Ala- bama attorney who specializes in vot- ing rights cases. Still explained what the investigators would be looking at and how we could mimic their analy- sis. I also asked two university profes- sors to act as expert advisers. We learned that the investigators would be seeking to answer three key questions. First, was there racially polarized voting? Put simply, do whites vote predominately for white candidates and blacks vote predomi- nately for black candidates? Second, do black candidates routinely run for office and lose? And, third, can a ward system be created that includes at least one ward in which minorities would be dominant? So, while Fallon did the shoe-leather reporting, I dug into the numbers. To answer the first question I needed to conduct a regression analysis. Sounds frightening, doesn't it? It is. At least that was my initial thought. But just before I ran away screaming, I pulled out notes from a weeklong IRE and NICAR statistics boot camp that I had attended in Chapel Hill, N.C., where I learned statistical analysis. I said to myself, \"You can do this.\" So what is regression analysis? It is simply an attempt to determine the pre- dictive value of the relationship that exists between two variables. In this case, our variables were the demo- graphics of the voting age population and voting results for each of the 21 voting districts within Teaneck from several recent elections. My first step was to collect the data. Determining the racial makeup for each of the voting districts became a long and tedious process. First, I grabbed block level race data from the 2000 Census and then mapped that informa- tion out using ESRI's ArcView 3.2a. That sounds simple enough, but here's where the real work started. Teaneck did not maintain an electronic version of the voting districts map. Fortunately, the borders of the voting districts and those of the Census blocks were, in all but one case, identical, So I placed a street map of the town over my map of the Census blocks and fol- lowed the streets to determine which blocks were within which voting district. I then created mapping shapefiles for each of the 21 voting districts and also exported the underlying data to a Microsoft Excel spreadsheet. Now that I had the demographic data and the voting results data I was ready to conduct a regression analysis. (NOTE: It would have been better to use the racial breakdown of registered voters rather than the racial break- downs in the Census voting age popu- lation data but that information was un- available.) I conducted the initial regression analy- sis in Microsoft Excel after my expert advisers, Bernard Grofman at the Uni- versity of California-Irvine and David Epstein at Columbia University, recom- mended the program. I built a spread- sheet containing both the racial break- down and voting results for each of the 21 voting districts. Then, with a few clicks, Excel produced a scatter plot graph and several statistical measures of the data to indicate what - if any - relationship existed between the per- cent of the population that was black and the performance of black candi- dates within each district. Excel's regression routine does not report statistics that tell whether the any relationship is meaningful. I repli- cated the analysis in SPSS, a statisti- cal program that has the tools, and also ran the results by Grofman and Epstein. I analyzed five recent Teaneck elec- tions in which a black candidate ran and found at least some level of ra- cially polarized voting in four of those. I then asked Epstein to double-check my analysis, and he came to the same conclusions. However, Epstein was quick to point out that the pres- ence of racially polarized voting is not uncommon. Complicating matters further was the success of Gordon Johnson, a black state assemblyman, who had great success within Teaneck during primary and general elections. So, while it appeared that the town did suffer from racially polarized voting that didn't necessarily mean the at-large voting system needed to be replaced. But it did indicate that the Justice De- partment investigation was not a waste of time, and that the town had some difficulties. The second and third questions were much easier to answer. We found that since 1990, seven of the 22 black can- didates (32 percent) for the township council and school board won while 56 of 90 white candidates (62 percent) ran successfully. So black candidates did have a history of running and losing. By using ArcView and Excel I was able to determine that the population in the northeast section of the town was 65 percent black so the creation of a ward where minority group members would dominate would not be difficult. At the end of the analysis we were able to paint a statistical portrait of Teaneck for our readers that helped explain what had happened in recent elections and also gave them insight into what was probably happening inside the federal investigation. One important piece of advice I might offer is that you don't need a federal investigation to do a story like this. Many elected bodies in towns and cit- ies all across the country look nothing like the communities they represent. Does that mean those towns or cities are violating federal law? No, but it is worth the time and effort of reporters to find out why and, in the process, perhaps begin a constructive discus- sion within those communities about how best to ensure elected officials represent all segments of a community. Contact Benjamin Lesser by e-mail at lesser@northjersey.com.", "provenance": { "extracted_by": "pre-v2", "extracted_at": "", "verified": true }, "similar": [ { "slug": "1994-october-voters-cast-ballots-from-grave", "score": 0.267 }, { "slug": "2004-july-august-readmetxt-2", "score": 0.248 }, { "slug": "2000-september-a-beginners-lessons", "score": 0.219 }, { "slug": "1998-april-ire-resource-center-voter-fraud-stories", "score": 0.209 }, { "slug": "2001-marchapril-car-in-election-2000", "score": 0.167 } ], "html_url": "https://dwillis.github.io/uplink/article/2004-july-august-targeting-voting-wrongs.html", "json_url": "https://dwillis.github.io/uplink/article/2004-july-august-targeting-voting-wrongs.json" }