Friday, November 22, 2013

Comparing ACS Data: Economic Changes Over the Last Five Years in Oneida County

An earlier examination of the social changes in Oneida County suggested by the single year estimates of the American Communities Survey (ACS) data between 2008 and 2012, came with a considered warning about placing the data and trends found there within context. It is important to exercise due caution when looking at significant changes in the ACS over time such that the trends that seem to be present make some sense in the larger picture. This is just as true about the economic profiles found in the ACS as it was with the social profiles.

Income Measures: That being said, let's begin with what the data does NOT show us - or more accurately, what it shows us has not changed in the last five years. Specifically, that the income levels of the region have remained relatively stable over that time frame. Income can be measured several ways. Typically we measure income based on the family median income, the household median income or the per capita income of the county. Here's a look back at these measures in the 2011 Five Year ACS for every municipality in the region.

The graph below shows the single year ACS income data from 2008 to 2012. Based on the margins of error for each income measure, the reality is that income levels haven't changed significantly since 2008 for families, households or on a per capita basis.For families it remains around $60,000; for households it is about $47,000; and on a per capita basis it is around $25,000. The fluctuations seen across the last five years are not statistically significant.

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Poverty: At the same time as income has remained relative stable, however, we have seen a significant increase in the percentage of the population that has fallen into poverty.Looking at the population as a whole, the percent of the County's residents who live in poverty has climbed from around 14% in 2008 to about 16.4% in 2012. Similarly, there has been an increase in the percentage of families in poverty over this period - from 10% in 2008 to 12.5% of families in 2012.

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Health Insurance Coverage: The last piece of economic data noted here that has changed significantly since 2008 is the number of people covered by health insurance.  Health insurance is measured in two broad sectors - specifically as being provided by the private sector or as being provided by the public sector.

These two types of coverage are not mutually exclusive, but may be. In other words, you could have someone who receives private insurance, and also has some public/government insurance coverage as well. Keep that in mind as you look at the graphic below, in that the data is not intended to add up to 100% - the coverage by either a private vender or public resource is totally independent of one another.

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Statistically speaking we have seen a decrease in the percentage of the population with private insurance coverage in the last five years. It has fallen from around 72% of the population in 2008 to about 67% of the current population. In terms of public insurance coverage, we have seen an increase in the percentage of the county's population using public insurance options from about 35% in 2008 to roughly 39% as of 2012.

While other comparative data may show statistically significant changes between 2008 and 2012, these few items are the ones that seem to suggest decipherable trends worth noting.

Monday, November 18, 2013

Comparing ACS Data: Where We Have Come in the Last Five Years in Oneida County

I'd like to provide some insights into the utility of the American Communities Survey (ACS) data that has been collected for nearly a decade now. The resulting information we have gained about our region CAN now be looked at across time in ways that weren't possible in the past. After all, such data used to be collected through the decennial census, so we only saw it once every 10 years. With the ACS, areas with populations in excess of 65,000 people have data available on an annual basis. So, theoretically, we should be able to track how things have changed over time without having to wait 10 years for the next census to arrive.

The "benefits" of doing this are questionable however. Not so much because there is limited ability to make policy changes on the fly as new data comes along. Rather, the limitations of comparing the data across small incremental time periods is that the data is based on small samples that often have large margins of error. To learn more about the MOEs (or margins of error) you may want to revisit this post. So as I share information about changes we've seen in the last five years, please make note of the fact that ACS is best viewed with caution and in the context of other meaningful data sets.

So where have we come then? Well let's begin with a look at how we have changed in terms of our social profile.

While many data points could be examined, I will only talk about those that have changed in a statistically significant way over the last 4 or 5 years, and that also seem to show a pattern of change that makes intuitive sense as well. All of the following data concerns Oneida County from the year 2008 to 2012. 

Families, Nonfamilies, and the Elderly: Statistically, we have seen a decline in the number of family households in the county between the years 2008 and 2012 (64.5% versus 61,2%, respectively). This comes at the same time as we see a significant rise in non-family households, from 35.5% to 38.8% between 2008 and 2012. "Nonfamilies" are householders living alone, or with other non-related individuals. Within these nonfamilies, we have found an increase in the percentages of people who actually live by themselves, as well as among those who are age 65 or older. One third of all people living in households in the county are "loners" - they live alone with no one else present. And nearly one in seven households in the county (14%) are occupied by a lone senior citizen. In both these cases, the percentage of households that are occupied by either a person living alone, or in which an elderly person is living alone, has increased significantly since 2008.

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Marital Status For Males and Females: It appears as though fewer males and females are reporting that they are married and living together now than five years ago. While about 49% of males said that they were married and living with their spouse in 2009, that number has dropped significantly to 46% of males in the most recent ACS data. Among women, the percentage has gone from around 46% in 2009 to about 43% in 2012. The percentage of females indicating that they were divorced has increased as well over the same period.

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Highest Level of Education: The population appears to be more well educated now that it was five years ago. Fewer people reported their highest level of education as being a high school diploma (it has dropped from about 35% in 2008 to around 32% in 2012), while more people said that they had attained a bachelors degree or higher (from a low of 19% in 2009 to as many as 24% in 2012).
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Foreign Born Populations and Language Spoken At Home: More foreign-born people now live in the county than did five years ago. In 2008 about 6.4% of the population reported being foreign born. Now, around 8% of the county's population claims birth in a foreign country. Along the same lines, more people are speaking a language other than English at home as well. In 2008, about 10% of the population reported that English wasn't the primary language that they spoke while at home. In 2012, 13% of county residents speak something other than English while at home.

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Future Comparative Posts: Statistically speaking, these are the main social changes we have seen in the last five years within Oneida County, based on the ACS data. Still to come if future posts will be and examination of the changes we have seen economically and in terms of housing data. Again, be careful to recognize that the limitation of the ACS data lies in the margins of error that come with relative small sampling done with the survey instrument. Such data as provided here should be used as a springboard for looking at other data sets that may address the social, economic or housing issues brought forth by the ACS comparison files.

Friday, November 15, 2013

2012 ACS Profiles for the Cities of Utica and Rome, And Town of New Hartford

With the recent release of the Three Year Estimates of the 2012 American Communities Survey, communities with populations of 20,000 or greater now have demographic, economic, social and housing profiles available. Below are the three municipalities in our region that meet this population threshold and their ACS profiles.

Municipality Profile 1 Profile 2 Profile 3 Profile 4
Utica Demographics Economics Social Housing
Rome Demographics Economics Social Housing
New Hartford (T) Demographics Economics Social Housing

2012 Three Year Estimates Released !

Each year, the Census Bureau releases three versions of the American Communities Survey (ACS). These are the 1-Year, the 3-Year, and the 5-Year ACS Estimates. These are released based on the population size of the municipality. Five year estimates are released for EVERY level of census geography (down to the block group level) as well as every municipality (think town, city, and village). Three year estimates are only released for geographies or municipalities with a MINIMUM population of 20,000 persons. So many smaller villages and towns are excluded. One year estimates are released for municipalities with a MINIMUM population of 65,000. This means they are mostly for counties and larger cities. For our region, ONLY ONEIDA COUNTY HAS SINGLE YEAR ESTIMATES AVAILABLE.

The most recent 3-Year Estimates are now a permanent part of this blog in the linked area just below the title above. A permanent link will take you to the most recent 3 year estimates post so you can always easily find this important data for both Oneida Counties !

Below are the individual links to the Demographic, Social, Economic and Housing Profiles for the 2012 Three Year ACS Estimates for Herkimer and Oneida Counties.

2012 OC ACS 3 Year Estimate Demographic Profile
2012 OC ACS 3 Year Estimate Social Profile
2012 OC ACS 3 Year Estimate Economic Profile
2012 OC ACS 3 Year Estimate Housing Profile

2012 HC ACS 3 Year Estimate Demographic Profile
2012 HC ACS 3 Year Estimate Social Profile
2012 HC ACS 3 Year Estimate Economic Profile
2012 HC ACS 3 Year Estimate Housing Profile

Friday, November 8, 2013

Driving Boom or Bust: The Decline of Annual Driving Miles Per Capita

According to a report recently released by, after sixty years of almost constant increases in the annual number of miles Americans drive, Americans have decreased their driving per-capita for eight years in a row. Since 2004, driving miles per person are down especially sharply among Millennials, America’s largest generation that will increasingly dominate national transportation trends.


Some skeptics have suggested that the apparent end of the Driving Boom might be just a temporary hiccup in the trend toward more driving for Americans. By the time Americans took notice of the decline in driving, the economy was in deep recession. For some, the culprit behind this decline was the poor economy of the last several years.

So the question became, would economic growth bring back rapid increases in driving?

This study finds that declining rates of driving do not correspond with how badly states suffered economically in recent years. On the contrary:

  • Among the 23 states in which driving miles per person declined faster than the national average, only six saw unemployment increase faster than the nation as a whole.
  • Among the 10 states with the largest declines in driving per person, only two rank among the ten with largest increases in unemployment.
  • Among the 23 states where driving declined faster than the national average, only 11 saw faster-than-average declines in the employed share of their working-age population.
  • Among the 10 states with the greatest reductions in the employed share of population, only two were also among the ten states with the largest reductions of driving (Georgia and the District of Columbia)

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Their conclusion then? In the view of USPIRG researchers, the evidence suggests that the nation’s per capita decline in driving cannot be dismissed as a temporary side effect of the recession. While certainly a contributing factor and an economic rebound could be expected to have some upward lift on driving, the recession does not appear to be the prime cause of the fall off in driving over the past eight years. Nor is it clear that future economic growth would lead to a resumption of the postwar Driving Boom. Policy makers can stop wondering whether American driving trends are changing. They should focus carefully on these trends, and start adapting policies to match them.

OH SNAP! Part Deux: Looking Back on a Decade of SNAP Data

My most recent post on the Supplemental Nutrition Assistance Program (SNAP) provided some data on SNAP, or Food Stamp Program, looking at how it has changed over the last 40 years. Below is a chart that shows SNAP Data for each NYS county over the last decade (2000 to 2010). It includes changes in the annual number of participants as well as changes in the total amount of funding coming to the SNAP recipients in each county. As you can see, the program dollars have grown considerably over the first decade of this century, as has the number of people participating in the program. Click on the table to enlarge it !

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Tuesday, November 5, 2013

NYS Counties SNAP (Food Stamps) Historical Benefits 1970-2010

With cuts to the Supplemental Nutrition Assistance Program (SNAP or Food Stamp Program) taking place this past week, I thought it would be a good thing to provide some insight into how large the program is in New York State counties.

According to the Center on Budget and Policy Priorities  the American Recovery and Reinvestment Act of 2009 (ARRA) increased SNAP benefits across the board as a way of delivering high “bang-for-the-buck” economic stimulus and easing hardship in response to the economic downturn.  ARRA increased SNAP maximum monthly benefits by 13.6 percent beginning in April 2009.

ARRA provided that SNAP benefit levels would continue at the new higher amount until the program’s regular annual inflation adjustments to the maximum SNAP benefit exceeded those set by ARRA.  The maximum SNAP benefit levels for each household size, which are set each October 1, are equal to the cost of the Thrifty Food Plan (TFP) from the preceding June scaled to each household size.  The TFP is the cost of U.S. Department of Agriculture’s (USDA) food plan for a family of four to purchase and prepare a bare-bones diet at home.  At the time ARRA was enacted, food price inflation was expected to be high and the TFP cost was expected to exceed the ARRA level in fiscal year 2014.  Food price inflation, however, turned out to be lower than expected over the 2009 to 2013 period, resulting in the pushing out of the date that the TFP was expected to exceed the ARRA level.

In August 2010, Congress passed and the President signed P.L. 111-226, which accelerated the sunset of the ARRA benefit increase to April 2014 and used the estimated savings for state fiscal relief through additional federal funding for school districts to maintain teachers’ jobs and maintaining a higher federal match for Medicaid costs.  Four months later, the Healthy Hunger-Free Kids Act (P.L. 111-296), which reauthorized Child Nutrition programs, further accelerated the sunset date of ARRA to October 31, 2013, to offset the cost of the legislation.  As a result, beginning on November 1, 2013, SNAP benefit levels will be based on the cost of the June 2013 TFP, which is lower than the ARRA levels.

The impact of this change will be seen as the country heads forward. In the meantime here are annual data for1970, 1980, 1990, 2000 and 2010 on how much program funding was provided to the total recipients in each county in New York State. This data comes from the USDA website on SNAP .

Monday, November 4, 2013

Veterans of Herkimer and Oneida County

Veterans Day originated as "Armistice Day" on Nov. 11, 1919, the first anniversary of the end of World War I. Congress passed a resolution in 1926 for an annual observance, and Nov. 11 became a national holiday beginning in 1938. President Dwight D. Eisenhower signed legislation in 1954 to change the name to Veterans Day as a way to honor those who served in all American wars.

According to this release from the US Census Bureau:

There were 21.2 million military veterans in the United States in 2012.
  • Of those, 1.6 million were females.
  • About 11.3% of 2012 veterans were black. Additionally, 5.7 percent were Hispanic; 1.3 percent were Asian; 0.8 percent were American Indian or Alaska Native; 0.2 percent were Native Hawaiian or Other Pacific Islander; and 79.6 percent were non-Hispanic white.
  • There were 9.6 million veterans age 65 and older in 2012. At the other end of the age spectrum, 1.8 million were younger than 35.
  • Vietnam-era veterans numbered 7.4 million in 2012. Moreover, there were 5.4 million who served during the Gulf Wars (representing service from August 1990 to present); 1.6 million who served in World War II (1941-1945); 2.3 million who served in the Korean War (1950-1953); and 5.3 million who served in peacetime only.
  • The number of veterans that served during World War II, the Korean War and the Vietnam era, totaled about 50,000.
  • Three states were home to more than a million veterans in 2012: California (1.9 million), Texas (1.6 million) and Florida (1.6 million).
  • The annual median income of veterans in 2012 was $36,264, compared with $26,278 for the population as a whole
  • There were nearly 9 million veterans aged 18 to 64 in the labor force in 2012.
  • About 3.6 million veterans had a service-connected disability rating in 2012. Of this number, 881,981 had a rating of 70 percent or higher. A "service-connected" disability is one that was a result of a disease or injury incurred or aggravated during active military service. Severity of one's disability is scaled from 0 to 100 percent, and eligibility for compensation depends on one's rating.

Below is a table showing a range of data about our region's veterans.
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