Data Literacy: Navigating Sources for Employment and Unemployment Data

Employment and unemployment are important economic indicators – we use them as part of our set of indicators and include them on our Regional Dashboard – and the unemployment rate is an oft-cited statistic when it comes to a community’s economic well-being. But despite how widely discussed they are, employment and unemployment data can be tricky to interpret, and even the unemployment rate has a pitfall or two for the unwary data user. Also, the number of sources for this data can make it difficult to narrow down which is ideal for your project. So for our fourth data literacy post, we’re exploring these pieces of data, their common sources, and their common problems so you can read, discuss, and interpret them like a pro. Or at least like a savvy amateur.

How is the unemployment rate defined, and what exactly is the “labor force”?

The Bureau of Labor Statistics (BLS) defines the unemployment rate as the percentage of total labor force that is unemployed[1]. To break this down further, it’s important to understand exactly what’s meant by “labor force” and, for that matter, what’s meant by “unemployed.”

“Unemployed persons” also has a BLS definition: “All persons who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment some time during the 4 week-period [sic] ending with the reference week.”[2] To paraphrase this, the unemployed population is made up of individuals who currently have no job, but who want and are looking for a job.

The labor force includes individuals who are employed, and individuals who do not have a job but who are looking for one.[3] Individuals who do not have a job and are not currently looking for a job are not counted as among the labor force. This includes groups like full-time students choosing not to work while pursuing their degrees, stay-at-home parents and other family caregivers, retired persons, and individuals who may have been looking for a job previously, but who have since given up.

Where do different employment and unemployment rate statistics come from?

Part of what makes employment and unemployment statistics challenging to interpret is that they can be found from several sources, which can vary in timeframe, level of geography, and methodology, resulting in data that isn’t always comparable.

The American Community Survey

The U.S. Census Bureau offers employment and unemployment data as part of the American Community Survey (ACS), broken down by a variety of demographic and economic factors: age, race and ethnicity, sex, educational attainment, disability status, household or family type, poverty status, and more. Tables are available in 1-Year and 5-Year Estimates datasets, and geographic availability depends, as it always does with ACS data, on the dataset selected. Tables from the 1-Year Estimates dataset are available for areas with a population of 65,000 or greater; tables from the 5-Year Estimates dataset are available for all areas regardless of population. As we’ve discussed in previous blog posts, ACS data is comprised of rolling estimates: a statistic from the 2015 1-Year Estimates dataset is reflective of all of 2015, and a statistic from the 2011-2015 5-Year Estimates dataset is reflective of all five years between 2011 and 2015.

Employment status data is also available from the U.S. Census Bureau’s Decennial Census up to 2000, as part of the long-form Census questionnaires. It’s not available as part of the 2010 Decennial Census: by 2010, the long-form questionnaires had been replaced by the rolling ACS estimates.

For a more concrete comparison of the types of data offered by the various sources, we’ll take a look at the five most recent unemployment rate figures published via the ACS for Champaign County.

Unemployment in Champaign County: ACS Data

 Estimate (%)MOE
20154.5%1.1
20145.4%1.2
20136.5%1.1
20126.6%1.0
20119.4%1.6

Source: U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, 2014 American Community Survey 1-Year Estimates, 2013 American Community Survey 1-Year Estimates, 2012 American Community Survey 1-Year Estimates, 2011 American Community Survey 1-Year Estimates, Table S2301; generated by CCRPC staff; using American FactFinder;(4 August 2017).

As we see in the table above, the ACS offers estimates on an annual basis. And it’s a viable source for this example because we’re looking at all of Champaign County. However, if we were interested in an area that falls under the 65,000 population threshold for 1-Year Estimates (like the City of Urbana, for example), or if we wanted data updated even more frequently, we might want to look elsewhere.

The U.S Bureau of Labor Statistics

The U.S. Bureau of Labor Statistics (BLS) offers unemployment data via the Local Area Unemployment Statistics program. As one might assume from the name, this dataset primarily offers unemployment data, including unemployment rates, change in unemployment rates, and overall employment status. Unlike the ACS data, it doesn’t come associated with those other economic or demographic factors: you can find the unemployment rate of a given area at a given time, but you can’t connect it to the same area’s poverty rate or educational attainment, or find the unemployment rate for a certain age bracket.

The major advantages of LAUS data are its publication frequency, its historical range, and its wide variety of geographic areas. LAUS data is published on a monthly basis, and is available online back to 1994. It’s available for states, metropolitan areas, metropolitan divisions, micropolitan areas, combined statistical areas (CSAs), counties and county equivalents, and cities and towns with a population greater than 25,000.

There are also options for geographies that might otherwise be difficult to find data for. If your area of interest is a city (which meets or exceeds the minimum population of 25,000) that falls partly into two different counties, and you want the unemployment rate for only the part of the city that’s in one county, it’s available from the LAUS. If your area of interest is a metropolitan or micropolitan statistical area that falls into multiple states, and you’re interested only in the part of the area that’s in one of the states, that’s available too. For Illinois, the LAUS also offers data for the “Balance of Illinois” area: the remainder of the state without the Chicago area.

But for right now we’re interested, again, in Champaign County, for the purposes of comparing what’s available. Remember, we’re not trying to compare the published unemployment rates in the following table with those in the above table – for one thing, they’re not comparable, as we’ll discuss more later. We’re just looking at the different types of data offered by the different sources.

Unemployment in Champaign County: BLS Data

 Unemployment Rate (%)
June 20174.8% (preliminary)
May 20174.0%
April 20173.8%
March 20174.4%
February 20174.9%

Source: U.S. Department of Labor; Bureau of Labor Statistics; Local Area Unemployment Statistics, “Databases, Tables & Calculators by Subject,” Series ID LAUCN170190000000003; retrieved by CCRPC staff; from <https://data.bls.gov/cgi-bin/dsrv?la>; (4 August 2017).

Here, we again have the five most recent data points for the Champaign County unemployment rate from the source of interest, but this time going back five releases doesn’t even get us out of 2017. The level of granularity offered by monthly publications can be great for some projects, but may be more detail than is needed for others. Annual data – from other BLS tables, or from other sources – may be as much as you need.

The Illinois Department of Employment Security

The Illinois Department of Employment Security (IDES) offers a wide variety of data products. Some are available nationally from the BLS and Census Bureau, while others are exclusive to Illinois and IDES. Many of these data products and programs are organized into the Illinois Virtual Labor Market Information dashboard. They offer data in a variety of timeframes (monthly, quarterly, and annually), on a variety of topics (employment, unemployment, payroll, wages, commuting, and more), and at a variety of levels of geography (state, county, MSA, Economic Development Region, Local Workforce Investment Area, municipality, and legislative district). Not every program offers its data at all geographic levels or publishes at all frequencies, and most specialize by topic.

Below are the five most recent unemployment figures for Champaign County as available from IDES.

Unemployment in Champaign County: IDES Data

 Unemployment (%)
June 20174.8%
May 20174.0%
April 20173.8%
March 20174.4%
February 20174.9%

Source: Illinois Department of Employment Security; Economic Information and Analysis Division; U.S. Department of Labor, Local Area Unemployment Statistics, “Monthly LAUS Reports”; “Illinois Monthly Labor Force Report”; retrieved by CCRPC staff; (4 August 2017).

Notice anything about these numbers? Like how they’re completely identical to those shown in the table for the Bureau of Labor Statistics? There’s a good reason for that – the IDES data is also pulled from the LAUS. This means that even though it comes from a different source agency and webpage, it is exactly the same data, redistributed by IDES through a different channel and in a different report format.

It’s important to pay attention to where data comes from – not just the direct source, but any indirect ones, whether there’s a common program like LAUS or a citation that indicates that the person or agency doing the analysis is not the person or agency that collected the data. Knowing more about the origin of the data you’re discussing, presenting, or writing makes you a more credible source as you pass the data along.

How are they comparable and not comparable?

The range of data products and sources available present comparability challenges in both timeframe and level of geography.

When you’re comparing employment or any other data, it’s important that the timeframes do not overlap or nest. Just as it’s not appropriate to compare overlapping American Community Survey 5-Year Estimates datasets (e.g., the 2010-2014 unemployment rate with the 2011-2015 unemployment rate), it’s not appropriate to compare a statistic from a shorter timeframe (e.g., April 2015) to a statistic from a longer timeframe that it falls within (e.g. full year 2015), even if they’re from the same program and reporting agency.

Geographic comparability is just as important. As we discussed above, there are many levels of geography with associated data, and not all of them may be familiar to a beginning data user. Obviously, we all know that states are made up of counties, and counties are not shared across state lines. However, MSAs can fall into multiple counties, and can include multiple municipalities, some or all of which might meet the BLS minimum population of 25,000 and have their own, single-municipality data available. The other less familiar geographies, Economic Development Regions (EDRs) and Local Workforce Investment Areas (LWIAs), are both multi-county areas within Illinois, and, unlike with MSAs, counties are not shared between multiple EDRs or LWIAs. Although they share those characteristics, we can’t compare, for example, occupational wages from an EDR to occupational wages from an LWIA: they’re two separate geographic classifications, and in many cases they overlap with each other. Illinois has 10 EDRs, but 26 LWIAs, so the EDR that our county of interest falls within is highly unlikely to include exactly the same group of counties as its LWIA.

The important thing to remember when starting to work with employment data is to make sure you’re looking for the right dataset, time, and geography for your specific project, whether you know what that is at the outset or you have to do a little trial and error along the way. Not every data source will have every statistic that you want in your area of interest and for your period of study, but with the variety that’s available, there will almost certainly be a source or program that meets the needs of your project or research question. So we advise spending some time at the beginning of the process, winnowing down the above list of sources until you find the one that best fits your project, rather than cherry-picking data points from several sources and trying to force them to be comparable later on. If you start your data gathering process knowing what you’re looking for, and you can avoid being overwhelmed by the number of options available, your research process can be easier and, dare we say it, fun.

[1] United States Department of Labor. Bureau of Labor Statistics. 2016. “BLS Information: Frequently Asked Questions (FAQs). (Accessed 4 August 2017).

[2] Ibid.

[3] Ibid.

Measuring Tourism: The Economic and Amenities Impact on Champaign County

Whether you’re planning a vacation or only wish you were, summer is a major tourism season. But instead of thinking about tourism as going somewhere else, we decided to take a look at the economic impact of tourism here in Champaign County.

Part of using data well is knowing when you aren’t the best source for a given piece of data and it’s time to look elsewhere. All of the data in the table and charts below are courtesy of Visit Champaign County. These and other pieces of data can be found in their annual fiscal year reports.

Economic Impact of Tourism in Champaign County

   Champaign County     
Visitor Spending (million)Local Tax Receipts (million)Tourism JobsTourism Payroll (million)
($)% Change($)% ChangeEmployment% Change($)% Change
2010$266-$4.5-----
2011$2836.39%$4.643.11%2,460---
2012$298.55.48%$4.966.90%2,5001.63%--
2013$306.62.71%$5.072.22%2,5200.80%$63.04-
2014$323.55.51%$5.34.54%2,5701.98%$65.84.38%
2015$330.82.26%$5.65.66%2,6503.11%$70.57.14%
Total Change$64.824.36%$1.124.44%1907.72%$7.4611.83%
Source: Visit Champaign County, Annual Reports FY12, FY13, FY14, FY15, FY16.

Total direct visitor spending, or the cumulative amount of money that all visitors to the county spend on their visits, has increased between two and seven percent each year between 2010 and 2015; the change for its entire 2010-2015 study period is 24.36%, the second-highest growth shown by any of the four measures.

Source: Visit Champaign County, Annual Reports FY12, FY13, FY14, FY15, FY16.

The highest 2010-2015 growth, 24.44%, was in local tax receipts. Unsurprisingly, the 2010-2015 growth in local tax receipts corresponds closely with the 2010-2015 growth in visitor spending, although the two measures show different year to year growth percentages. Looking at their general slopes over the study period, they appear similar, if not exact matches.

Source: Visit Champaign County, Annual Reports FY12, FY13, FY14, FY15, FY16.

The study period growth of Champaign County’s tourism employment, 7.72%, was the smallest of any of the four measures, with 190 additional tourism jobs between 2011 and 2015. The shorter study period may have an effect on the overall percent change; tourism employment data is not available for 2010. If the trend was consistent through the previous year and the unknown 2010 figure was less than 2,460, the overall increase would be greater for 2010-2015 than 2011-2015, and the overall growth percentage would be higher.

(However, even assuming the extension of a consistent trend to a previous year, it is highly unlikely that it would reach or even approach the percentages visitor spending or local tax receipts. In order for the number of tourism jobs to have increased by 25% between 2010 and 2015, the number of jobs in 2010 would have had to be 2,120, over 300 fewer jobs than 2011, which would be a year-to-year increase over three times greater than that seen for tourism jobs in any other year.)

Source: Visit Champaign County, Annual Reports FY12, FY13, FY14, FY15, FY16.

The study period for tourism payroll is the shortest of the four measures: data is only available between 2013 and 2015. The year to year increase between 2014 and 2015 was 7.14%, the largest single year to year increase of any of the four measures. Like visitor spending and local tax receipts, the slope of the tourism jobs and tourism payroll are very similar, but are not exactly alike.

Source: Visit Champaign County, Annual Reports FY14, FY15, FY16.

The impression given by the four measures in the table and charts above is that the tourism industry in Champaign County is growing. Additional years of data will be helpful in continuing to assess this trend; earlier years would be helpful to gauge the trends of tourism before and during the recession.

Another way to look at tourism is by economic sector, using NAICS (North American Industry Classification System) codes. At the county level, the U.S. Census Bureau releases an annual dataset called County Business Patterns, which provides the number of establishments in a given industry, the number of employees of those establishments, and the establishments’ payroll. By selecting relevant NAICS codes – for tourism, we chose all codes under “71: Arts, entertainment, and recreation” and all codes under “72: Accommodation and food services” – you can access a wealth of detailed economic data.

However, trying to evaluate tourism in a given area only using County Business Patterns presents a few challenges. One is that the dataset includes only information on the establishments, not on the people visiting the establishments and why (obviously, because that would raise concerns both on the basis of privacy and feasibility). So, for example, according to the 2015 County Business Patterns for Champaign County, there were four establishments in Champaign County in 2015 categorized as “Golf courses and country clubs” (codes 71391 and 713910).[1] Their employment and payroll information are available, but we have no way to know how many people are visiting those establishments, and how the number of visitors breaks down into local residents and tourists. The same can be said about the 180 full-service restaurants (code 722511) in Champaign County.[2] Each one almost definitely serves a mix of both Champaign County residents and visitors from outside the county. The best we can do when evaluating tourism based on NAICS codes is to select promising-looking codes for establishments that could be attracting and serving tourists – we can’t be sure if, and to what degree, they actually are attracting serving tourists.

In addition, for all industries, the amount of available data depends on the size of your area of interest. If there is only a single establishment in your area of interest with a particular NAICS code, the data on payroll and number of employees is withheld to protect the privacy of that establishment. For example, also according to the 2015 County Business Patterns for Champaign County, there was only one dance company (codes 71112 and 711120) in Champaign County in 2015.[3] We can view the NAICS codes, the study year, and the number of establishments, but no data on their employment or payroll.

We can’t wrap up without bringing the discussion back to Champaign County as a place to live, in addition to as a place to visit. As we can see from the Visit Champaign County data, tourism is an asset to the local economy, through money visitors spend at local businesses and venues, and through local tax receipts. Furthermore, the types of venues and events (recreational, educational, arts and culture, etc.) that lend themselves to bringing tourists to an area from elsewhere are also great quality of life amenities to people who live there year-round. Think of a place you’ve taken a trip to and enjoyed: how many of the things that you liked about going there are things that locals probably take advantage of and enjoy on a regular basis? We’d bet that it’s a lot of them.

There are certainly locations, venues, and events that are oriented more toward welcoming tourists than serving locals. Annual festivals, seasonal attractions like ski or beach resorts, and major recreation destinations may not be accessible to or geared toward year-round local use. But other things that are positive additions to a trip experience, like a favorite restaurant in a city you visit for an annual conference, a walkable shopping and entertainment district, or a user-friendly public transit system, are things that make a place great to live in, as well as great to visit.

[1] U.S. Census Bureau; Geography Area Series: County Business Patterns, 2015 Business Patterns, Table CB1500A11; generated by CCRPC staff; using American FactFinder; (26 June 2017).

[2] Ibid.

[3] Ibid.

Trails Month: Trail Events, Trail Mileage, and Residential Access

June is National Trails Month, and in honor of that (and National Trails Day, coming up on Saturday, June 3), we’re taking a look at trails in the Champaign County area: what the mileage and residential access are, what the local trail plans and jurisdictions are, and what local events are coming up on National Trails Day.

What is the trail mileage in our area? What is the residential access to trails? What additional data would be useful?

We’ve assessed both trail mileage and trail access at three levels of geography: all of Champaign County, the five largest municipalities in Champaign County (Champaign, Urbana, Rantoul, Savoy, and Mahomet), and Champaign-Urbana alone.

First, we’ll look at trail mileage.

Trail Mileage in Champaign County

AreaLength (Miles)% of Total Trail Mileage
Champaign County85.22100.00%
Champaign, Urbana, Rantoul, Savoy, Mahomet84.7299.41%
Champaign-Urbana67.6279.35%

Source: CUUATS Data Portal; Champaign County GIS Consortium.

As one would expect, the greatest trail mileage is in the largest area: all of Champaign County. But the difference between the trail mileage of the whole county and the trail mileage of the five largest municipalities within the county is very small, only half a mile. And over three-quarters of the county’s total trail mileage is within Champaign-Urbana.

Next, we’ll check out residential access.

Trails Access in Champaign County Areas

AreaTotal Residential Parcels Residential Parcels within 1/2-Mile Buffer Residential Parcels within 1/4-Mile Buffer 
Parcels%Parcels%Parcels%
Champaign County52,810100.00%32,61261.75%20,75639.30%
Champaign, Urbana, Rantoul, Savoy, Mahomet35,776100.00%29,89483.56%19,29853.94%
Champaign-Urbana28,280100.00%24,14585.38%15,65855.37%

Source: CUUATS Data Portal; Champaign County GIS Consortium.

We calculated trail access in the same way we calculate our Accessibility indicators (Access to Bicycle Infrastructure, Access to Parks, and Access to Transit). Based on parcel land use, we generated a total number of residential parcels within our three areas of interest. We then performed a geographic analysis to determine how many of the total parcels in each area fall within a half mile and a quarter mile of a trail, and presented that as a percentage.

This method does have a few shortfalls. For the sake of simplicity, the distance of any given parcel from a trail is measured as linear distance, rather than the walking distance that it would take for residents of that parcel to reach the trail. This method greatly increases the ease of analysis, but may not be an accurate representation of actual walking distance from some parcels.

Methodological disadvantages aside, the residential access data tells a similar story to the trail mileage data. Residential access to trails is greatest in Champaign-Urbana for both half-mile and quarter-mile distances. The difference between residential access in Champaign-Urbana and residential access in the five largest municipalities is relatively small, only a couple of percentage points. But residential access in all of Champaign County, which includes all of the strong residential access in the five largest municipalities, trails (pun absolutely intended) residential access of the other two areas by over 20 percentage points for the half-mile distance and almost 15 percentage points for the quarter-mile distance.

These percentages may change when the first phase of the Kickapoo Rail Trail opens, which is anticipated to happen late this summer. The almost seven miles of trail between Urbana and St. Joseph will add to the trail mileage and to the residential access statistics.

But there’s more to trails data than mileage and access. Data about trail usage, like bicycle and pedestrian counts, can be helpful in applying for grant funding and planning additional outreach. A local average cost per mile for trail construction would be another useful figure for trail planning. However, there are challenges associated with both of these data needs. Traffic counts, whether bicycle, pedestrian, or vehicle, are time-consuming when collected manually, and construction costs can differ based on circumstantial and site-based factors. So while these pieces of data would be useful to have, they are not immediately available.

What are the existing trails jurisdictions? What existing trails plans are there in our area?

Trails can be held by a variety of jurisdictions. Many are held by the Champaign Park District, Urbana Park District, and Champaign County Forest Preserve District. Others are held by municipalities: the City of Champaign, City of Urbana, Village of Mahomet, Village of Rantoul, and Village of Savoy. Several, all physically located within the Village of Mahomet, are held by IDOT. Some are institutionally held, by the University of Illinois or the Urbana School District, while others are private.

There are a number of existing trails plans in the Champaign County area: the Champaign Park District Trails Master Plan, the Urbana Park District Trails Master Plan, the Champaign County Greenways & Trails Plan, the Champaign Trails Plan, and the Savoy Bike & Pedestrian Plan. Most of these plans include not only the current state of trails in that jurisdiction, but also a vision for future expansions and improvements.

What are the different ways the public can interact with trails? What trails events are coming up in the Champaign-Urbana area?

The Urbana Park District will hold a National Trails Day event in Busey Woods. This Saturday, June 3rd, starting at 9 a.m., volunteers will be able to both enjoy and improve Busey Woods. Tasks and activities will include hiking, tree planting, and trash and invasive species removal. The National Hiking Society website also lists more information for this event.

Looking for a trail near you so you can get out and about for National Trails Month? Trail locations and information are available on the CUUATS Data Portal. So whether you’re trying to find an off-street bike route or planning a recreational outing, you can find the route that best suits your needs.

Bike Month: Celebrating and Educating

May is here, bringing warm weather, the end of the 2016-17 academic year, and National Bike Month. To celebrate Bike Month, in this post, we’ll be talking about the prevalence of bicycling in Champaign County, the pros and cons of the data available, and the BikeMoves Illinois app. Finally, we’ll present some upcoming events happening during Bike Month here in Champaign-Urbana.

To assess how prevalent bicycling is in Champaign County and selected Champaign County municipalities, a good place to start is commuter mode share. (“Mode share” is generally defined as the percentage of travelers that opt for a given mode of transportation.)

 Bicycle Mode Share (%), Workers 16 and Over 
PlaceEstimateMOE
Champaign County2.60.3
City of Champaign2.70.5
City of Urbana6.21.1
Village of Mahomet0.00.7
Village of Rantoul1.31.0
Village of St. Joseph0.50.8
Village of Savoy0.40.6

Source: U.S. Census Bureau; American Community Survey, 2011-2015 American Community Survey 5-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (27 March 2017).

The data in the table above is from the U.S. Census Bureau’s 2011-2015 American Community Survey (ACS) 5-Year Estimates. You can find data from the ACS 1-Year Estimates datasets at the county level discussed as part of our Commuter Mode Share indicator, and at the municipal and township level under the Transportation section of our Regional Dashboard. For additional data, the League of American Bicyclists website has more resources on commuting by bike.

We can see that a relatively small percentage of workers commute by bicycle in the Champaign County area (under 10% in all areas, and under 5% in most), but that there is also some variance from municipality to municipality. The Village of Mahomet is estimated to have no bicycle commuters, while the City of Urbana’s estimated bicycle commuter mode share, the highest in the county, is 6.2%.

The primary drawback of commuter mode share data is just that: that it measures commuter mode share. While it’s sound data for what it measures, it does not offer a complete picture of transportation behaviors in a given area. Commuter mode share data does not capture trips that are not work-related. It provides no insight on how residents of a given area run errands or make recreational trips, or how students get to school. Although it is helpful for the topic it covers, it is by no means comprehensive.

CCRPC/CUUATS is working on other resources to provide a more complete data set, including the BikeMoves Illinois app.

What’s the BikeMoves Illinois app, and where can I find it?

Released in August 2016, BikeMoves Illinois is a mobile app for Android and iOS that allows cyclists to record and share their routes with local planners. It also provides cyclists with information such as the locations of bicycle facilities and bike parking. BikeMoves Illinois was developed by the Champaign County Regional Planning Commission in partnership with the Illinois Department of Transportation and has been used to record and share more than 500 bicycle trips. Download the app from the App Store and Google Play Store, and look for a new version with improved mapping and automatic recording this summer!

What Bike Month events are coming up in the Champaign-Urbana area?

There is a full calendar of events going on in our area throughout the month of May to celebrate, and educate about, bicycling. A run-down of some events is below; even more events, additional details, and registration information can be found on the C-U Bike Month 2017 website, event calendar, and Facebook page.

  • Tuesday, May 2: Bike to Work Day
    Registration is still open for Champaign County Bike to Work Day, and welcome stations will be located around the community to offer free breakfast to participating cyclists.
  • Wednesday, May 10: C-U Bike to School Day
    Registration is open on the C-U Bike Month 2017 website.
  • Wednesday, May 10, 6 p.m.-dusk: Community Full Moon Night Ride
    The Community Full Moon Night Ride, to be held at Meadowbrook Park in the Windsor Road parking lot, includes activities for all ages, from live music, bike check-ups, and a raffle to guided bike rides and the optional night ride to Sidney Dairy Barn.
  • Saturday, May 20, 9 a.m.-12 p.m.: Playing It Safe Bike Rodeo
    This family safety fair, with opportunities for kids to learn and practice biking skills, will be held at Leonhard Recreation Center (2307 Sangamon Drive, Champaign).
  • Saturday, May 20, 2 p.m.-dusk: C-U Food Cruise
    Participants can join in any of seven guided tours through the Champaign, Urbana, and Savoy area, with stops at a variety of local eateries. All tours begin at Hessel Park and end at Jarling’s Custard Cup (309 W. Kirby Avenue, Champaign). Registration is required.
  • Monday, May 22, 5-7 p.m.: Spring Fling Bike Rodeo
    This community resource fair, another chance for kids to learn and practice biking skills, will be held at Garden Hills Park in Champaign.

So whether bicycling is your commuting mode of choice, a healthy way to run errands, or just a way to have fun, keep an eye out for opportunities to celebrate Bike Month here in Champaign County.

 

Data Literacy: Testing for Statistically Significant Difference Between Estimates

It’s time for another data literacy blog post, and this month we’ll be discussing how to work with the concept of statistical significance. But to be clear from the outset, what we’ll be talking about is understanding statistical significance as a data user, rather than as a statistician: interpreting published figures, not calculating or publishing your own figures.

For starters, let’s establish what we mean by “statistical significance” for the purposes of this discussion. What we’ll be talking about is determining whether the difference between two estimates is statistically significant. This means, basically, whether the two estimates can be viewed as really different, or if the difference between them may just be due to some chance or statistical reason[1].

The U.S. Census Bureau offers free training presentations on a variety of topics, and one of them, “Using ACS Estimates and Margins of Error,” introduces the following formula as a basic way to test whether two estimates are statistically different from each other[2]:

|Est1 – Est2|
(√(MOEest12+MOEest22))

It’s much easier than it looks. Don’t worry – we’ll break it down and provide examples. But to make a long story short, if the number produced by this formula is greater than one, the difference between the two estimates is significant. If it’s less than one, it’s not[3].

To demonstrate how this formula works, we’re going to walk through two related examples: the estimated percentage of the population over 25 years of age with a bachelor’s degree in the years 2014 and 2015, and the estimated percentage of the population over 25 years of age with a graduate or professional degree in the years 2014 and 2015. (In both cases, these estimated percentages are from Champaign County. All the estimates we’re about to discuss are from the U.S. Census Bureau’s American Community Survey 1-Year Estimates datasets. Something else to keep in mind is that all ACS estimates are at a 90% confidence interval – meaning that it is 90% certain that the actual value lies within the range denoted by the margin of error.[4])

Example 1

 % of Population Aged 25 Years or Older with a Bachelor's Degree Statistical Significance Test Result
YearEstimateMOE
201421.8%1.9
201522.0%2.10.07062

|Est1 – Est2|
(√(MOEest12+MOEest22))

|21.8 – 22.0|
(√(1.92+2.12))

|-0.2|
(√(3.61+4.41))

_0.2_
(√(8.02))

_0.2_
2.83196

= 0.07062

As we see here, the resulting figure is less than one, so the two estimates are not statistically different from one another. What this means is that, even though the two years’ estimates appear different, the difference is small enough and the margins of error are large enough that we can’t be sure that this apparent increase reflects an actual increase.

This really becomes relevant when we’re trying to describe trends: we can’t describe the years 2014-2015 as part of an increasing trend in the percentage of Champaign County residents aged 25 and over with bachelor’s degrees. All we can say is that, between 2014 and 2015, the percentage of the given population with a bachelor’s degree remained roughly the same.

Let’s take a look at a related statistic: the percentage of Champaign County residents aged 25 and over with a graduate or professional degree.

Example 2

 % of Population Aged 25 Years and Over with a Graduate or Professional Degree Statistical Significance Test Result
YearEstimateMOE
201420.5%2.0
201523.9%2.01.20216

|Est1 – Est2|
(√(MOEest12+MOEest22))

|20.5 – 23.9|
(√(2.02+2.02))

|-3.4|
(√(4.0+4.0))

_3.4_
(√(8.0))

_3.4_
2.82843

= 1.20216

Unlike the result from the first example, 1.20216 is greater than one: this means that the difference between the two years’ estimates is statistically different. In other words, the increase from 20.5% to 23.9% isn’t due to statistical error or chance; it reflects an actual, albeit small, increase in the real percentage of the given population with a graduate or professional degree in Champaign County. If the differences between these and other previous or subsequent years are also significant, we can start discussing concrete trends.

With what types of data is this formula useful?

As we said earlier, this formula should only be used to test the statistical significance of the difference between estimates. Exact counts are exact counts – they don’t have margins of error, and you don’t need to test whether the pile of seven hats you counted today is really greater than the pile of five hats you counted yesterday. The question just isn’t relevant. (For more information about the difference between estimates and counts, check out our August data literacy post, about margins of error, and our December data literacy post, about the U.S. Census Bureau’s American Community Survey.)

Obviously, you’ll need margins of error to work with in order to carry out these calculations. Ideally, these will be available with the estimates in question.

What about the shortcut method?

You may also be aware of a bit of a shortcut: using the given margin of error for each estimate to calculate a possible range for that estimate, then comparing the ranges of two estimates to see if they overlap, with the assumption that estimates with overlapping ranges are not statistically different from each other, and estimates with non-overlapping ranges are.

We’re sorry to have to tell you this, but it doesn’t work like that. Let’s go through those two examples again with this method.

If the 2014 percentage of the population aged 25 and older with a bachelor’s degree in Champaign County is 21.8 +/- 1.9, we have a range of 19.9-23.7. Similarly, the same estimate from 2015, 22.0 +/- 2.1, has a range of 19.9-24.1. Clearly, these ranges overlap, and the difference between the estimates is not statistically significant.

If the 2014 percentage of the population aged 25 and older with a graduate or professional degree in Champaign County is 20.5 +/- 2.0, we have a range of 18.5-22.5. The same estimate from 2015, 23.9 +/- 2.0, has a range of 21.9-25.9. These ranges also overlap – but as we demonstrated above, the difference between the estimates is statistically significant.

What we can learn from this is that the assumption that all estimates with overlapping ranges are not statistically different from each other is pretty flawed.

The shortcut method does have its uses: if you’re asked a question about this in person, or if you otherwise really do not have time to do the full calculation, it can allow you to provide a guess. If the ranges don’t overlap, you can say that the estimates are probably, but not definitely, statistically different. If the ranges do overlap, the best thing to say is that it’s possible that the estimates are not statistically different – but that it’s also possible that they are. Be upfront about the fact that all you can do is hazard a guess. Even better, offer to follow up with the person who asked the question later, and get back to them with a solid answer after you have time to run the calculation. It’s better to give a correct answer a day later than a wrong answer immediately.

What does all of this mean about the data visualizations elsewhere on the site?

If you’ve spent time looking at different pages around this site, especially in the Indicators section, you’ll notice that many of the pages include charts and graphs plotting the year-to-year data. Some of these year-to-year data points are not significantly different from each other. In those cases, graphs may look like they show an upward or downward trend, but with a quick test of the data, it’s clear that, like in Example 1 above, we can’t actually assign that trend to those years.

Does that mean that these graphs are meaningless? No – no more so than the tables of data associated with them. Each data point is the estimate for its period of time. That much is solid. The only thing we can’t get concrete about is exactly what’s going on between one data point and the next.

Every graph on this website has an associated table of data with it, and all estimates include their MOEs, so you can run the formula for yourself and check the statistical difference between any years you’re interested in. And where we can’t assign a year-to-year trend, we’ve usually noted this in the analysis.

 

Thanks for sticking with us through 1,500 words of statistics! We hope this post cleared things up a little, and gave you one more skill for being an informed, savvy data user.

 

 

[1] U.S. Census Bureau, Sirius Fuller. (April 6, 2016). “Using ACS Estimates and Margins of Error.” (Retrieved 14 March 2017).

[2] Ibid.

[3] Ibid.

[4] U.S. Census Bureau; Census Glossary; “Confidence Interval (American Community Survey”; (Retrieved 23 March 2017).

Table sources: U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; <http://factfinder2.census.gov>; (19 September 2016). U.S. Census Bureau; American Community Survey; 2014 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Library Patronage and Community Services: The Quantifiable and Non-Quantifiable Benefits of Public Libraries

March is National Reading Month, so we’re taking this opportunity to talk about public libraries, measuring library patronage, and the community, recreational, and informational benefits of public libraries in general.

Champaign County is home to 10 library districts: the Champaign Public Library, the Homer Community Library, the Mahomet Public Library District, the Ogden Rose Public Library, the Philo Public Library District, the Rantoul Public Library, the St. Joseph Township-Swearingen Memorial Library, the Sidney Community Library, the Tolono Public Library District, and the Urbana Free Library. For each one, we’ve calculated the library patronage percentage: the percent of residents in the library’s area that have an unexpired library card.

Library Patronage

  2015 2016 
LibraryPopulationNumber of Unexpired Resident Users Cards% of Population with Library CardsNumber of Unexpired Resident Users Cards% of Population with Library Cards
Champaign Public Library81,05531,55538.9%29,13035.9%
Homer Community Library1,19359549.9%53845.1%
Mahomet Public Library District12,6236,08948.2%7,00455.5%
Ogden Rose Public Library81017822.0%17021.0%
Philo Public Library District1,95463932.7%58029.7%
Rantoul Public Library12,9414,33833.5%3,75029.0%
St. Joseph Township-Swearingen Memorial Library5,8761,87631.9%1,56526.6%
Sidney Community Library1,23336029.2%35729.0%
Tolono Public Library District11,6753,79732.5%3,50530.0%
Urbana Free Library41,25012,59230.5%12,45430.2%

Source: Illinois State Library, Office of the Illinois Secretary of State

You can see that there is a wide range of resident populations among the library districts, but the patronage rates tend to be more consistent. The total range (roughly 20%-50%) is not small, but six of the 10 libraries have patronage rates clustered within a few percentage points of 30%. More analysis on this data can be found on our Library Patronage page – it’s one of the indicators in our Engagement category.

You may notice that there’s only one year of data in the table above, and that’s 2015. This is because CCRPC was not looking at library patronage before the revision of our indicators set in summer 2016 (and you can read more about that process in our April 2016 and July 2016 blog posts). We’re planning to update the Library Patronage page with newly-released 2016 data and additional analysis later this spring.

One item to note is that, for the libraries with outlying patronage rates, there does not seem to be any correlation between the number of residents in the area and the percentage with an unexpired library card. The library with the lowest participation rate, Ogden Rose Public Library (22.0%) also has the smallest number of residents in its area (810). However, the library with the highest participation rate, Homer Community Library (49.9%), has the second-smallest number of area residents (1,193). The Mahomet Public Library District comes in right behind the Homer Community Library with a patronage rate of 48.2%, but has a much larger population (12,623).

This data comes from the Illinois Public Library Annual Report (IPLAR), a resource that is compiled by the Illinois State Library and comprised of self-reported data from libraries throughout the state. Some, but not all, of this data is available online, and the full data set is requestable.

Library patronage data doesn’t give us a complete picture of the role of libraries in our communities. To try to create a fully quantified measure of a library, we would need to account for their other services. The prospect of rolling library patronage together with things like event attendance, sessions on public computers, and resources lent (among others), while accounting for the size of the library, its funding, its staffing, and its other resources, is a daunting one. And even if that were feasible, it would still miss residents that come in to pick up a tax form, ask a question at the information desk, page through a book or a newspaper, or use a library computer as a guest – all services that may not require the resident to be a cardholder. But just because something can’t be accurately quantified does not mean it’s unimportant. (Obviously.) Libraries in Champaign County and elsewhere provide their communities with services that may not be available anywhere else in town. They act as community centers, by holding events and by providing public space for informal gathering. They offer free Internet and computer access that patrons may not have at home. And that’s in addition to their information and lending services, which offer enormous educational, informational, and recreational benefits.

What library patronage data can tell us is something about community engagement as a whole, which is why we use it as one of our Engagement indicators. As we’ve pointed out, libraries act as both formal and informal community centers. We posit that residents that are engaged at and invested in their local library may be more engaged and invested, socially, economically, and politically, in the community as a whole. This is not the result of a long study, but a shorthand assumption: involvement at the library implies involvement in the community.

If you’re part of the percentage of residents with an unexpired library card, celebrate Reading Month and keep enjoying your library! If you’re not, this could be the perfect time of year to go check out a library in your area – you’ll probably find something interesting.

Source: Illinois State Library, Office of the Illinois Secretary of State

The Statistics of Valentine’s Day: Measuring Relationships with Census Data

It’s February. With Valentine’s Day on the horizon, we’re matching up a sentimental holiday with statistical rationality to address the following question: can we determine, by numbers or percentages, an accurate, basic relationship status breakdown in Champaign County?

The short answer is yes and no. There is data on marital status and household type available from the U.S. Census Bureau’s American Community Survey, which gets us some of the information we’re looking for. However, the categories that this data is broken down into are highly detailed in some respects, but not at all detailed in others, making it difficult to arrive at a concrete answer.

Marital Status – Categories and Limitations

The American Community Survey includes questions on respondents’ marital status, and offers five response options: “Now married (except separated),” “Widowed,” “Divorced,” “Separated,” and “Never married.” For the purpose of our discussion, we’ll refer to the first four categories as the “marriage and post-marriage” categories.

 

Champaign CountyNow married (except separated) Widowed Divorced Separated Never married 
EstimateMOEEstimateMOEEstimateMOEEstimateMOEEstimateMOE
Population 15 years and over39.3%0.94.1%0.38.7%0.51.5%0.246.4%0.7

U.S. Census Bureau; American Community Survey, 2011-2015 American Community Survey 5-Year Estimates, Table S1201; generated by CCRPC staff; using American FactFinder; (7 February 2017).

 

This table offers a very detailed and useful breakdown for answering questions about populations who are married, or who have been married and are now in one of the three post-marriage categories (“Widowed,” “Divorced,” and “Separated”).

It does bear keeping in mind that the American Community Survey is a point-in-time study, with respondents selecting the option that best reflects their marital status at the time of their response. This means that we can’t interpret the four marriage and post-marriage categories as mutually exclusive – an individual who is “now married” may not be on his or her first marriage, meaning that he or she may also have been widowed, divorced, or separated at some point in the past. However, for anyone concerned with present marital status of individuals who are or have been married, the marriage and post-marriage categories provide sound and helpful data.

It’s the monolithic “Never married” category where this level of detail starts to break down – because we can’t interpret “never married” as “single.” Some of the 46.4% of the surveyed population who have never been married may be in a relationship, but based on these categories, we can draw no sound conclusions about what that percentage might be.

Households and Families – Categories and Limitations

Another table, Households and Families, can shed a little light on this question, but also does not provide an easy breakdown into categories of “single” and “not single.” It uses a few categories that cover family households, broken down into households made up of married couples and householders with no spouse present, as well as a category for non-family households (U.S. Census Bureau; American Community Survey, 2011-2015 American Community Survey, Table S1101). Households made up of married couples can be assumed to correspond pretty strongly to the “Now married (except separated)” category of the Marital Status table, while householders with no spouse present could fall into any of the Marital Status categories other than “Now married (except separated).” This table adds no detail, and in fact can confuse the conversation, when discussing the marriage and post-marriage categories of the first table.

Where this table does add some detail is to that “Never married” group identified in the Marital Status table, because it includes lines for “Unmarried-partner households.” While we can’t directly compare these two tables, because the Marital Status table surveys individuals while the Households and Families table surveys households, this at least provides a little information on the population who are in a relationship, but are not married.

But even this brings up caveats and limitations. Some householders of “Unmarried-partner households” may fall into the “Never married” marital status category, but others may be “Widowed,” “Divorced,” or “Separated,” per the categories of the Marital Status table, and cohabitating with a partner they are not married to. As we saw, the Marital Status table calls for the respondent’s present status, and “Dating” and “Cohabitating” aren’t options (and forestalling an even more complex analytical nightmare, neither is “It’s Complicated”). Furthermore, while the “Unmarried-partner households” category provides some help in measuring non-married relationships, it offers no information on relationships where the individuals are neither married nor cohabitating.

 

Given the available Census data on the topic, we can’t arrive at a comprehensive categorization of “single” or “not single” – the categories of the data available don’t line up in a way that’s conducive to that much simplification. So 750 words later, we’re forced to revise our short answer: “almost, but actually no.”

Quantifying Winter: Climate and Weather Data Resources

It may be a new year, but for better or for worse, we have over two months of winter left. Whether you love snow or hate it, and whether you can’t get enough cold weather or can’t wait to move somewhere warm, you can always find detailed data on what the weather’s been doing where you are (or where you wish you were).

What do we measure about winter weather?

Two of the major dimensions when talking about any weather or climate topics are temperature and precipitation. Other common factors include wind speed and wind chill. Items that do not usually make their way into neatly aggregated datasets, but that could provide additional detail on the on-the-ground, so to speak, impacts of winter weather include tonnage of road salt and other de-icing agents in a given season, or the number of winter weather advisories and warnings in a given month.

How are these measurements usually taken, and by whom? Who is the source for the weather data that RPC uses?

The highly specific weather factors (temperature, precipitation, wind speed, etc.) are measured by agencies like state surveys, and are often released on a monthly basis in datasets associated with different sites.

Locally, the Illinois State Water Survey’s Illinois Climate Network releases their Monthly Summary Data reports online. This is the data that we reference for our Regional Dashboard’s Natural Resources page, and each report includes much more information than the basics in the Natural Resources page’s Weather Summary table. If you’re looking for data from other states, we recommend starting with the websites of similar agencies, like state water and geological surveys, and extending your search from there if necessary.

Two more great resources are the National Oceanic and Atmospheric Administration (NOAA) and NOAA’s National Centers for Environmental Information (NCEI). These websites offer data, analysis, news items, and more, for the nation as a whole, categorized by topic, and broken down by region.

How does CCRPC use environmental data?

The plans, reports, and studies that CCRPC produces often have an environmental component, whether it’s an impact review, a sustainability chapter, or an appendix of data and analysis. Weather and climate resources like those discussed above help us create better documents based on sound data.

What about measuring other potentially winter-related occurrences, such as traffic incidents?

It’s easy to hypothesize about incidents and occurrences to measure and analyze that could be correlated to winter weather. A prime example of this is traffic. That a greater number of traffic incidents occur either in winter months or on days with inclement weather are both reasonable hypotheses.

There is data available on traffic incidents in our area: CUUATS produces the Selected Crash Intersection Location (SCIL) reports, which include facts about traffic incidents at various intersections in Champaign-Urbana. These reports cover overlapping five-year periods. The earliest report available covers 1993-1998, while the most recent report, published in 2015, covers 2009-2013. All reports dating back to the 2001-2005 edition include some analysis on weather and road surface conditions.

In addition to the SCIL reports, data on traffic crashes that resulted in severe injuries or fatalities between 1999 and 2014, including details on weather, road surface, and other conditions and associated spatial data are available on the CUUATS Data Portal.

What about information on winter preparedness and safety?

Of course, there’s more to winter weather information than knowing this month’s total precipitation or minimum temperature: winter safety is important in any temperate climate. You can usually find winter weather advisories wherever you find your forecast information, which will keep you up to date on major weather events coming up in the East Central Illinois area.

In addition to knowing what weather is on the horizon, it’s important to know how to prepare for winter storm events in general. The National Weather Service and National Oceanic and Atmospheric Administration have a Winter Safety webpage full of tips and advice for preparing for winter weather and staying safe through the season.

 

We talk a lot on this blog about the importance of good data – that’s true regardless of the topic. Whether we’re using this data as part of our Regional Dashboard or as an input in an environmental impact review, or whether we just want complain about the weather in a quantifiable way, these climate and weather data resources are important to be familiar with.

 

Data Literacy: Using the American Community Survey

It’s already December, and that means it’s time for another data literacy post: this time, in honor of the upcoming American Community Survey (ACS) 2011-2015 5-Year Estimates release on Thursday, December 8th, we’ll be talking about what the ACS is, what topics it covers, how it can be used by members of the public, and what we use it for here at CCRPC.

 

What is the ACS?

The ACS is a data product from the U.S. Census Bureau. It is a series of estimates that cover a comprehensive range of topics and geographic areas, from the general to the highly specific. ACS data is available for free in detailed tables, area summary reports, and maps.

 

What data topics are included in the ACS?

The ACS covers a wide range of topics. Two of the major categories are demographics (e.g., age, sex, race, ethnicity, etc.) and economics (e.g., income, poverty, employment). Beyond those areas, there is also data on education, transportation behaviors, native and foreign-born populations, household and family topics, veteran status, populations having a disability, housing topics, and more.

 

Who can access and use ACS data, and where can I find ACS data?

Everyone! Data from the ACS, like data from other Census products, is public and free to use. Obviously, participants’ full responses are not released, since those include personal information like names and addresses. But once estimates are compiled into tables and published, anyone can go to Census.gov and access the data, and anyone can use the data as a source for projects, papers, presentations, or just general curiosity.

The user interface that allows access to most Census data is American FactFinder: users define their geographies of interest, pick their years of interest, and then get down to searching for their topic. If you’re not sure how to get started, the Census Bureau offers a number of tutorials on how to use American FactFinder, covering tasks from the basic to the advanced.

It should go without saying that, if you’re going to use ACS or other Census data, you should make sure you cite it. There is a recommended citation format for tables from American FactFinder outlined in a U.S. Census FAQ (and also, handily, at the bottom of this page, where we footnote some income figures); that’s what we use here at CCRPC when we use Census data, and we strongly encourage other data users to take the time to cite it right.

 

How often is the ACS produced and released?

The ACS is conducted on a rolling basis. Unlike the Decennial Census, where every household in the nation is asked to respond, only some households receive materials to respond to the ACS each month. Data collected from these sample populations is then extrapolated into the estimates about the total population.

Both datasets are released every year. In general, the new 1-Year Estimates are released in September of every year, and the new 5-Year Estimates are released in December.

 

What’s the difference between 1-Year Estimates and 5-Year Estimates?

There are actually several. The primary difference is the timeframe that the data covers. The 1-Year Estimates are applicable to the year named in their title: for example, the 2015 1-Year Estimates that were released in September are applicable to 2015. The 5-Year Estimates are applicable to the five-year period named in their title: for example, the 2011-2015 5-Year Estimates that will be released on December 8th cover the entire five-year period. Keep in mind that figures from the 5-Year Estimates are not the same as the figures from single years within that five-year period, and cannot be presented as such. Put simply, the 5-Year Estimates are a five-year average, while the 1-Year Estimates cover their single title year.

To offer a concrete example, let’s say that you’re looking for data on income, and you specifically want to know what percentage of households in Champaign County made between $25,000 and $34,999 in 2014. In that case, you should use data from the 2014 1-Year Estimates, and the figure you would find is 10.3% (+/-1.6)[1]. If you use the 2010-2014 5-Year Estimates, you’ll find a figure of 10.5% (+/-0.7)[2]. The figure from the 5-Year Estimates is slightly different, and it cannot be used to accurately represent the percentage of households in that income bracket in 2014. Both of these things are because the 5-Year Estimates include not only data collected in 2014, but also data collected in 2013, 2012, 2011, and 2010.

Also for this reason, you shouldn’t compare data from 5-Year Estimates datasets with overlapping years: for example, when the 2011-2015 5-Year Estimates dataset is released next week, you can’t compare its data with data from the 2010-2014 5-Year Estimates, because both datasets include the data collected in 2011, 2012, 2013, and 2014. If you want to compare 5-Year Estimates datasets, it’s only really doable with datasets with non-overlapping periods (e.g., comparing the 2005-2009 5-Year Estimates and the 2010-2014 5-Year Estimates, or comparing the 2006-2010 5-Year Estimates and the 2011-2015 5-Year Estimates).

Another major difference between the 1- and 5-Year Estimates datasets is the geographies they’re available in. The 1-Year Estimates include data from geographies with populations of 65,000 or more, while the 5-Year Estimates include data from all geographies in the United States. The limited number of geographic areas means that, even if you’re looking for data from a single year, it may be necessary to use 5-Year Estimates because your geography has a population of below 65,000, and has no 1-Year Estimates available. In that case, it’s important to know how to work with the 5-Year Estimates, and how to interpret and talk about data from that resource.

One more thing to keep in mind is that the margins of error of the 5-Year Estimates datasets tend to be smaller than the margins of error of the 1-Year Estimates datasets, because the 5-Year Estimates include more responses. In general, the larger the input dataset, the smaller the margin of error. This may be a reason to opt for using a figure from the 5-Year Estimates even when data is available from your geography of interest in the 1-Year Estimates.

Finally, data from 1-Year Estimates can’t be compared to data from 5-Year Estimates, even if they’re the same figure drawn from the same table and covering the same geography – the difference in their respective time periods makes the datasets not comparable.

 

Why does ACS data include margins of error when Decennial Census data doesn’t?

As we noted above, and have mentioned in previous blog posts, ACS data are estimates, and estimates can always be off, either by a small amount or by a massive amount. The margins of error reflect that range of possibilities: that the estimate is X, but that the actual number could be as high as X+Y, or as low as X-Y. For more information on margins of error, check out our last data literacy post, which covers them in detail.

On the other hand, Decennial Census data are exact counts. Decennial Census surveys are now much shorter and have much less detail than the ACS, but they are point-in-time counts instead of estimates based on a population sample. Counts don’t have margins of error because they’re assumed to be absolutely correct (give or take some non-responding households) – they don’t need them.

 

How does CCRPC use ACS data?

The ACS is a great resource for public agencies as much as it is for individuals. Having the wide variety of topics available on a consistent basis is a big part of what allows us to develop and maintain this website, its indicators and analysis, and the regional dashboard. We use ACS data in our own reports, like the Long Range Transportation Plan. We also use it to create reports for other agencies and organizations, and to respond to data requests from private individuals. Not every request or report uses ACS or Census data, but many of them do.

ACS data is necessary for our work beyond any specific plans, reports, and data requests. CCRPC is one of many public, service-providing agencies in Champaign County and around the country; for organizations like ours, knowing about the population we serve and represent, both in general and in detail, is fundamental to our operations. The ACS may not be a perfect resource, and may not be suited to every data need or project, but in many cases, it is the best and only data that is reliable, released on a regular schedule, and free to use. It has enormous value, to CCRPC and as a public resource.

For even more information on the ACS, check out the ACS homepage on the Census Bureau website. In closing, we’d just like to say that we love our Census data, and that the ACS is a great resource for a wide range of users and topics. So check it out! You’ll probably find something interesting, too.

 

[1] U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1901; generated by CCRPC staff; using American FactFinder; (28 November 2016).

[2] U.S. Census Bureau; American Community Survey, 2010-2014 American Community Survey 5-Year Estimates, Table S1901; generated by CCRPC staff; using American FactFinder; (28 November 2016).

Voter Participation Statistics: Methods and Sources

You may already be aware of this, but Election Day is right around the corner. And although there are a lot of polarizing issues under discussion this season, we can all at least agree that we love statistics. (Right? Right?!) So here’s the rundown on voter participation rate statistics, how they’re calculated, and where to find them.

 

Compared Voter Participation Methods

Voter participation statistics can be calculated in two different ways, but both center on comparing the number of ballots cast with a larger population.

For bonus points, using the same data needed for these calculations, you can also work out the voter registration rate by comparing the number of registered voters with the population that is eligible to vote.

Method 1: Compares the number of ballots cast with the total population that is eligible to vote

Voter Participation Rate (%) = (Number of Ballots Cast)/(Eligible Population) x 100

Method 2: Compares the number of ballots cast with the number of registered voters.

Voter Participation Rate (%) = (Number of Ballots Cast)/Registered Voters) x 100

Both methods are in use, and both are sometimes referred to as “voter participation rate,” so it’s good to be familiar with both. And neither method is categorically better than the other one. All this also makes it important, when you’re reading voter turnout analyses, to check how they’re doing their calculations. As an example of how the two different methods return different results, let’s take a look at some Champaign County data from past elections.

Table 1: Voter Participation Rate Methods[1]

Eligible Population Registered Voters Ballots Cast Participation Rate: Method 1 Participation Rate: Method 2
2006 137,209 113,905 53,869 39.3% 47.3%
2008 141,979 123,150 84,804 59.7% 68.9%
2010 146,955 122,441 54,818 37.3% 44.8%
2012 150,055 112,933 78,939 52.6% 69.9%
2014 153,959 113,122 55,434 36.0% 49.0%

 

As you can see in the tables above, comparing ballots cast with the eligible population will almost invariably return a lower participation rate than comparing ballots cast with the number of registered voters. The only exception is in cases of complete voter registration saturation, where every person that is eligible to vote is registered to vote: in that case, the participation rate figures would be equal. Either method is viable and neither is the preferable option across the board, but it’s important to know which one is being used for the analysis you’re reading: ask the right questions, and your interpretation of the data will be sounder than assuming one method or the other. (This is a good rule to go by in most data analysis situations: read the methodology, be a more informed data consumer.) Also, if you’re calculating or compiling voter participation rate statistics for a study or a project that you’re working on, it’s important to check the methodologies and make sure that the different datasets are actually comparable. If the rate in one state was calculated with Method 1, and the rate in another state was calculated using Method 2, these two numbers can’t give you an apples to apples comparison: you either need to select new regions to analyze or get the raw data and recalculate the rates yourself, using the same method for every region.


Historic Voter Participation Statistics

If you’re looking for historic voter participation statistics, you can find Champaign County’s on the Champaign County Clerk website. The results available go back to 1968, and many years include results from both primary and general elections, as applicable, so you can go forth and do historical analyses of the county’s voting record to your heart’s content. At the national level, the U.S. Census Bureau produces some voting and registration products based on the Current Population Survey. If you’re looking for data only for a particular state, we recommend starting with that state’s board or department of elections and going from there; for Illinois, that’s the Illinois State Board of Elections, which provides historic election results at the state and county level. And finally, if you’ll excuse a moment of shameless self-promotion, we’ve compiled some historic Champaign County voter turnout data for our Voter Participation indicator.

Champaign County’s voter participation statistics have two staggered release dates. Unofficial statistics will be available on the Champaign County Clerk website after polls close on November 8th. The full reports will be available on the Champaign County Clerk website two weeks after Election Day.


Voter Participation Statistics, Additional Analysis, and Why This Data is Important

If you’re looking for analysis in addition to just the raw numbers, the U.S. Census Bureau has a “Voting and Registration” topics page where you can find the voting and registration products mentioned above, as well as resources like state voting profiles; topical reports; national, state, and regional-level data; and voting data broken down by age, sex, race, ethnicity, marital status, and other demographic dimensions.

Being able to reliably measure and compare voter participation statistics is important for several reasons. Working with this data gives the average data user a thorough grounding in election statistics. The same datasets that provide the raw data for voter participation rate can also include candidate- or party-based breakdowns of the election results, which, depending on what level of geography you’re working with, can provide a county-level view of election results. Finally, comparing the participation and registration rates at the county level, both among counties and from year to year, can provide information on how much of the population in that area is – or isn’t – voting. This can provide insight into concepts that are harder to pin down with data, like civic engagement, and also suggest when and where there might be a need for greater outreach in order to encourage residents of that area to vote or otherwise get involved in the local political process.

This all brings us to our last note before we close out the entry on this topic: read on for the obligatory (but important) public service announcement.


Obligatory Public Service Announcement

To wrap up, we need to take a moment to be serious and encourage you to go out and vote this election season.  Early voting is open today, this weekend, and Monday at locations throughout Champaign County, and if you do vote early, you can vote at any open early voting location, instead of only at your assigned polling place. To find early voting locations, check the Early Voting Schedule on the Champaign County Clerk website. To get your personal voter information, including checking your voter registration status and assigned polling place, go to the Voter Information Lookup page on the Champaign County Clerk website. If you’re planning to vote on Election Day but don’t know how you’re going to get to your polling place, CUMTD has you covered: MTD is offering free bus rides on November 8th to help people get out and vote.

 

[1] Sources: Champaign County Clerk Election History, Illinois State Board of Elections Publications (Vote Totals), U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016), U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016), U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016), U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016), U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).