Most Americans haven’t a clue about the process of taking a national census, the cost and the decade long planning that goes into it. Important decisions on the process, questions, and use of technology to make it easier for all of us to respond are being made now – and Congress isn’t making wise decisions. Benjamin Chevat and Terri Ann Lowenthal follow census activities and the link below is a great report on the status of 2020 planning and the challenges confronting the Census Bureau.
Meeting the Challenges of the 2020 Census
The Census Bureau has announced their intention to eliminate the American Community Survey 3-year estimates products starting with the upcoming 2012-2014 estimates series. This is an important data produce that well impact small to medium sized communities nationwide.
If you aren’t familiar with the ACS product stream, annual period estimates are reported for 1-year, 3-year, and 5-year periods based on the population size of governmental and administrative geographies. This is because the ACS sample simply isn’t large enough to provide 1-year data for all areas. Areas of 65,000 or more can receive estimates based on one year of data collection. Areas between 20,000 and 64,999 receive estimates based on three years of data collection and all areas regardless of size receive estimates based on the full five year data collection period. The elimination of the 3-year estimates will impact a governmental and administrative units with greater than 20,000 population.
Here are a number of important issues that arise as a result of this decision and reasons why the Census Bureau should reconsider this cut of data products.
- Nationwide, 39 million people or 12.2 percent of the nation’s population live in counties between 20,000 and 64,999 and 55 million people or 17.5 percent live in medium sized cities, villages and Census Designated Places. In seven states, over 30% of the population live in counties in the 20,000-64,999 range. In Vermont, 72 percent of the state’s population lives in counties that would be affected. All of these areas would lose this valuable data source.
- The 3-year estimates are better than both the 1-year and 5-year data for time series comparisons. By the Census Bureau’s own recommendations, the use of non-overlapping periods for time series comparisons means that the 5-year estimates must span a 10 year period to allow for proper comparisons. The 3-year estimates allow for more frequent analysis and avoid much of the irregularity of the 1-year estimates. For example, the 3-year estimates allow for comparison of data from 2005-2007 to 2008-2010 and 2011-2013 (pre-recession, recession, and post-recession). Elimination of the 3-year estimates means such analysis has to rely on periods 2005-2009 and 2010-2014, none of which are useful for analyzing the impacts of the recent recession. This really provides the best example of the negative impact of reliance on the 5-year estimates. The 3-year estimates allow for analysis of the social and economic climate before and after the recession while the 5-year data would totally mask such sudden periods of economic distress. Analysis of any future recessionary periods would be greatly hampered by reliance on the 5-year estimates.
- The 3-year estimates provide needed stability in the estimates over the 1-year data while still reflecting a reasonable time period for analysis. A simple measure like the poverty rate for the City of Albany, New York (population 97,000) shows considerable year-to-year variation that is not statistically significant and can be very misleading to data users. The 3-year estimates show a much more stable level of poverty and avoid the mistake of placing too great an emphasis on the reported data without verifying the margins of error, which unfortunately, is a problem for too many users.
- Data suppression as a result of filtering is also an issue. Characteristic detail can be lost when suppression occurs and the 3-year data products have the advantage of larger sample sizes. Thus even the 1-year estimates, available for areas with higher population thresholds, can yield less data than the 3-year estimates. Again, resulting in a net loss of characteristic detail that the 3-year estimates provide.
- Changes in questionnaire content, definitions and wording can negatively impact comparisons over time. Reliance on the 5-year estimates means that any changes in content require a prolonged period of data collection before being able to use comparable data items.
- Just when data users have become comfortable with the ACS product stream and have learned how to use, and not use, the data, the Census Bureau is eliminating one of the most useful products. Part of the Census Bureau’s stated rationale for this cut is that the 3-year estimates were never intended to be a permanent part of the product stream. If that is the case, that position was never stated to the data user community. The 3-year estimates have become an important analytical tool for communities in that 20,000 to 65,000 range and the 5-year estimates simply don’t provide the needed portrait the ACS has provided for the past seven years.
These issues all reflect the importance of the 3-year estimates and their use in population analysis. The 3-year estimates are important for medium sized areas of 20,000 to 65,000 but they are also important for improved analysis in areas of over 65,000 where the 1-year data exhibits considerable variability and inconsistent trends.
You can comment on this proposed cut by directing correspondence to Mr. John Thompson, Director of the Census Bureau, Ms. Katherine Wallman, Chief Statistician at the Office of Management and Budget, and your local representatives.
Those of you in urban areas with high-speed broadband simply won’t relate to this but for us folks in rural areas, having access to 21st century technology is a challenge. We get Internet service off a satellite or now a Verizon cell tower. Both just marginally better than dial-up. Today I have an article in the Daily Yonder, a discussion of rural issues, that illustrates the frustration of about 20 million Americans and their communities who don’t have adequate Internet access.
The Census Bureau just released a new interactive application that illustrates voting patterns by state.
• Are registrations increasing or decreasing?
• Are people registered but don’t vote?
• What are the differences in voting by age, race, gender, and education?
The application illustrates the results of the Census Bureau’s Current Population Survey which interviews about 70,000 households nationwide on their voting experience. You can look at the patterns for your state and the nation for every congressional and presidential election from 1996 to 2010.
Check it out at: http://smpbff1.dsd.census.gov/TheDataWeb_HotReport/servlet/HotReportEngineServlet?reportid=767b1387bea22b8d3e8486924a69adcd&emailname=essb@boc&filename=0328_nata.hrmlk
A couple of things to pay attention to: to see data for a state, click the “See State” link; when a state is selected you get the same national map so scroll down the page for the state specific data.
Do you have price strategies for determining how much you should charge for your product or service? I received an interesting question from a business entrepreneur. “Where can you find buying statistics on what someone will pay for a product by demographic group?”
That’s a pretty unique request though really important when you are trying to figure out the best how much you should charge for your product. Maybe people in different age or income groups would be willing to pay more for your product and you don’t want to charge too little. But if you charge too much you’ll lose your customers. There’s likely an optimal price point for your product but the problem will be finding some standard data or formula that leads to your unique price strategy. So how can we approach the question of what people are willing to pay?
First, don’t bother looking for standard Census or consumer buying data for this. That type of data is much to general and most often provides standard budget expenditures for categories of products like groceries, housing, fuel, restaurants, and so on. We want to know what people age 30 to 49 will comfortably spend on the custom widgets that we import.
- One of the first things you need to be clear about is your target market? Do you know their demographics – age, income, education, type of area they live in? All those factors can affect demand and price points. Are we talking about the general national population or the population of your local metro?
- Price differences across different cities and states can be important also. You can find the official Consumer Price Index data for some large local metros from the federal government at www.bls.gov/data/#prices. There’s also cost of living data for more local areas at www.coli.org though this isn’t a free service. These sources still don’t answer the question of “how much should I charge” or, “what will consumers pay” for a particular product, just what the general price differences are in different areas. Your best price strategy for determining local costs may just be a window shopping trip through your local stores. This is likely a good indicator especially when you relate that to the demographic characteristics of the local market. Are you looking in an upscale area, middle income, or low-income.
- Another price strategy is “what would people pay on Amazon”. Say you’re marketing a tutorial package on how your high school student can improve their SAT scores. Look on Amazon and search for improving SAT scores. I found almost 300 results in book, video, and audio formats. This type of search does two things: gets you to a wide range of similar products and focuses on your target market which is high school students. And don’t forget to pay attention to the “used price” for your item. This is often a better indicator of what people are willing to pay.
- Finally, if we’re talking about marketing to your specific list or community, then your best option is to go straight to your list and do some split testing to answer the question “how much should I charge?”. That’s the only way you’ll know for sure how your community of followers will respond to price differences.
Bottom line, external data on buying habits of specific demographics simply may not get you the information you need. Nothing will substitute for your direct research and testing for your specific target market. Listening to your customers, whether through direct testing or simply paying attention to what they tell you they want will always be your best information. When you ask yourself the question “how much should I charge?”, use your price strategy to come up with an answer.
In 1948 the Annie E. Casey Foundation was established to help build better futures for disadvantaged children in the United States. Among their many projects and services is the annual publication of the Kids Count Data Book and the on-line system that allows you to explore a variety of demographic and health indicators to monitor child well-being. You should check it out if your work in any way involves the well-being of children or if you simply are interested in economic, demographic, and health related data.
The Kids Count Data Center allows for on-line access to state and community level data and allows for comparisons across states. For example, the percent of children without health insurance in 2009 varied from a low of 3 percent in Massachusetts to a high of 17% in both Nevada and Texas. The percent of children in poverty in 2010 was lowest in the states of New Hampshire (10 percent) and highest in Mississippi at 33 percent.
Here’s an example of a map you can generate on the Kids Count Data Center site showing the state by state distribution in the rate of children 0 to 17 years old in foster care per 1,000 population in 2009.
Source: The Annie E. Casey Foundation, KIDS COUNT Data Center, datacenter.kidscount.org.
Now it’s not always easy to interpret these differences because of potential variations in how individual state’s foster care programs operate. However, the Kids Count Data Center does a good job of presenting source information and definitions. It’s always desirable to do some extra research to make sure you’re not comparing apples to oranges but the Kids Count Data Center is a reliable source of data on the well-being of children and a great resource for anyone monitoring those trends.
Today the U.S. Bureau of Economic Analysis (BEA) released their monthly figures on personal income for December. I think they could use a lesson in making their data understandable to the general public. I’ve worked with these data for years and still have to read their releases a few times to make sense of the basics.
Personal income includes all types of incomes that individuals can receive: wages and salary from your job(s); interest and dividends from your banking/investment accounts; rental income; self-employment income; and percent transfer income like food stamps and other benefit income.
So, personal income nationwide went up by about $61 billion between November and December. Seems like a lot but that’s out of $13.1 TRILLION in total personal income or only 0.5 percent. Still, that’s a pretty good increase considering the November to December increase was only 0.1 percent.
In addition to personal income, the BEA tracks Personal Consumption Expenditures (sorry, spending) too. In December, spending was up only 0.1 percent from November which means Americans actually had an increase in disposable income but chose to save it instead of spend it. Surprising in the holiday season.
You should be feeling better knowing that you have more money in your pocket! But do you really? Over the entire year of 2011, income and spending pretty much kept pace with each other. So that increase in December probably doesn’t feel like it’s putting more income in your pocket. Besides, unemployment, while falling, remains stubbornly high, state and local governments are holding back and cutting jobs, and housing in many areas of the country is still lagging if not still falling.
There are some bright spots on the U.S. economic horizon but it’s still a long climb out of the recession. We tend to always expect quick fixes to our economic woes but the pervasiveness of the housing decline and financial market debacle means there are NO quick fixes.
It’s no secret that the advertising and marketing world are heavy consumers of demographic data and track population statistics. Knowing who your customer is and implementing segmentation strategies are critical to most businesses. It’s easy to see how advertisers will pick specific programming for TV or other media advertising based on the demographics of the audience for the show or production. comScore, a global leader in digital market intelligence data, recently released a report “Next-Generation Strategies for Advertising to Millennials”.
As the Baby Boom generation ages, their market needs change with them. There’s a shift from big houses to small, cars for comfort instead of hauling kids, less need for so many electronic gadgets, and what about making that retirement destination decision. Advertisers look to unique characteristics of generations to focus their messages and products to the largest groups of consumers. The Millennials fit that model with an estimated $170 billion in purchasing power. While a dollar is the same regardless of who spends it, the characteristics of each generation mean those dollars get spent in different ways.
So, how different are the Millenials? Advertisers need to answer some key questions: are there common grounds between generations; how effective is television or radio advertising for different age groups; are racial differences important in the use of a particular product or service; how to maximize use of digital media; and what are the best means of persuasion?
“Share of Choice” is a term used to indicate the response to advertising where a higher number indicates the ad is more persuasive in moving someone to a purchase decision while a lower number indicates less effectiveness. This quick graphic from the comScore study shows that TV advertising doesn’t do as well a job of persuasion for younger ages as it does for older ages.
The report presents some interesting stats but one thing I find lacking is a description of the data collection itself. The study reportedly includes more than 500,000 women across all ages yet the graphic presentation of some results are based on less than 100 cases and given only in percentages. And these are the basis for many of the conclusions about effectiveness of TV versus digital advertising media. I find it difficult to interpret such results and would be surprised that advertisers are making budget decisions on such few cases.
Just received a google alert about the release of data from Pingdom on the growth of the Internet in 2011. Fascinating data but there’s something missing – it doesn’t report on who we are. Email usage – huge. Website growth – no stopping it. Internet usage around the world – the US is big but certainly not the biggest. The only data reported on demographic characteristics is age – 45 percent of Internet users are under age 25.
There are 2.1 BILLION, yes billion, Internet users worldwide. That’s almost one-third of every human being on earth! But is it really? How do they count them? If I access the Internet at my home, and on a different machine at the library, and a different machine at work, and a different machine at school, am I one user or four? I hate it when data is reported without any information about how it’s derived. I teach my students to always source their information and talk about how they get it.
I find it really hard to put these data to use helping clients understand how to effectively use the Internet and reach their ideal customer. It’s great to know the Internet world is expanding at a rapid pace but I need to know who the users are and how best to reach them. Guess that’s the price of being a demographer and always wanting more out of the data. After all, how does anyone know the age of Internet users unless they do a sample survey or rely on Facebook users giving their birth dates?
So much for the griping about more data. Here’s some of the other interesting stuff coming from Pingdom:
- There were an estimated 100 billion photos on Facebook by mid-2011 (wonder how they “estimated” it – sorry, can’t help myself).
- 1 Trillion video playbacks on YouTube – can you imagine the servers required to handle that volume?
- 82.5 percent of the U.S. audience that viewed video online
- 800 million Facebook users at the end of 2011
- 100 million active Twitter users
- Asia tops the list of Internet users at almost 925 million. China is the nation with the largest number of users at 485 million.
- Also 600 million fixed (wired) broadband subscriptions worldwide – and even in New York, I can’t get one!
- 71 percent of worldwide email traffic was spam – can definitely believe that!
I’m used to a world of demographic data that is location based like the Census. It’s relatively easy to interpret population numbers for a community or county. But this new world of Internet and social media use – worldwide – is a completely different animal and has some many different applications in the business world.
A new horizon to explore!!
We hear a lot in the media about corporate America sitting on trillions of dollars of cash while the economy is still hurting and unemployment is high. I don’t think many will find an argument there and wonder what it takes to open the pocketbook and bring on new workers.
I saw some answers to that question in a news item from the Business Insider and their Money Game Chart of the Day. You might want to subscribe to their news feed. They were reporting on the results of the Philadelphia Federal Reserve Bank manufacturing survey and questions that asked why they were either hiring or not.
This interesting chart shows that some 30 percent of firms are most influenced in their hiring decisions by growth in sales. Not surprising is it? You expect growth in sales and you’re more inclined to increase employment. Note that less than 10 percent indicate that an overworked current staff is the most important reason! Of course the other side of the coin is, what factors restrain hiring and no surprise again that “expected growth in sales is low” is the most important factor for 30 percent of firms. Sort of suspect it may be the same guys focused on sales!
Politicians like to talk about uncertainty of regulations, taxes, and costs and the standstill of the bureaucracy as the big obstacle but clearly, when you get the scoop directly from businesses, it’s the business that matters – keep costs low and only hire when sales are high.
There’s another response here that shouldn’t be glossed over. Twenty percent of firms indicate that not finding workers with the required skills is an important restraint on hiring. That’s a lesson for education and those trying to cure the unemployment situation. Workers need to have the skills that employers are looking for and the mismatch between those two may be bigger than we think.