Municipal Level Income Comparisons in Herkimer County: 2014 Versus 2009 Five Year Estimates

The three primary ways that the Census Bureau measures income is by median household income, median family income, and per capita income. Median household income is basically the "middle income value" of all households in a given municipality. Households include anyone sharing a housing unit and living together regardless of their relationships. Median family incomes are the "middle income value" of all families, meaning related people living in a household. Measuring income on a per capita basis is taking income and dividing it by the total number of people living in a municipality, regardless of age.

Below is a chart showing the 2014 and the inflation-adjusted 2009 income measures for households, families and per capita within each municipality in Herkimer County.

Please note that NONE of the municipalities show any SIGNIFICANT changes from 2009 when it comes to any of the income measures. Any differences in income (whether large or small, positive or negative) between 2009 and 2014 are within the margins of error for the data.

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By Income, the Percent of Regional Households With Broadband

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Tracking Income Equity: Use of the Gini Index 2006-2014

With the ACS, income inequity can now been seen longitudinally. The main measure of income inequality available through the census is the use of the Gini Index. The Gini Index is a summary measure of income inequality. As an index, it only has a value of between 0 and 1. A value of "0" would mean that every household had the same exact income; a value of "1" would mean that income was concentrated solely in a single household.

The index does NOT speak about the absolute levels of income - in other words, it doesn't measure how much income exists in a household. It measures the relative distribution of income across all households in an area. So when one area has a higher Gini index than another, nothing can be said about the income levels between the areas. Rather, it would tell you about the distribution of incomes within both areas.

That being understood, what are the regional, state and nation Gini levels over the last decade or so? The chart below shows them for the period 2006 to 2014. To enlarge it, click on the graph.

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Note a few things:

• Each of the three lines show an increase in income disparity - the higher the Gini index number, the less "evenly" that income is spread among households.
• There is more fluctuation in the Gini index numbers for the regional and state data than the national data - this has to do with the number of households in each area. Fewer households (or a smaller sample size) results in more variability.
• Despite this variability, the Gini index for all three levels of data (nation, state and region) vary significantly from each other (for example in 2014 the state has the a significantly higher Gini index than the nation, which has a significantly higher Gini index score than the region) and over time within each geography (for the region, the Gini score in 2014 is significantly higher than the score from 2006).

Given a basic look at the trends,  regional income inequity has grown at a rate of more than twice that of the state and one and a half times that of the nation. Don't forget, however, that our overall income inequity score is significantly lower than either the state or nation.

What Percent Are You?

From the New York Times comes an interactive map that allows you to see what "percent" you are when it comes to household income. Simply put in your household income and then zoom to the region to see how you compare. Below is a screen shot for a household with an income of $50,000. Click the graphic to enlarge it.

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Percent of Regional Renters Using 30% or More of Their Income to Pay Rent In The Last Decade

The U.S. Housing and Urban Development office suggests that "no more than 30% of a household's income" should be used toward rent. Below is a look at basically the last decade and the percent of renters in the region using 30% or more of their income to pay for their apartment. Click to enlarge the graph.

Herkimer County Household Size and Income 1980 to 2013

Here's a comparison of income by household size stretching from the 1980 Census to the 2013 Three Year ACS Estimates. The income levels have all been adjusted to 2013 dollars to account for inflation. The only two household sizes showing any true statistical change in income level over that period of time are the 2 person and 4 person households. This is based on the margins of error from  he 2013 ACS data.

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Middle Income Households in Herkimer County

Based on the 2013 ACS data for Herkimer County, the graphic below provides some insight into income quintiles for household in the county. By breaking the income levels into quintiles (or five equal parts, each representing 20% of the households in the county), you can see that the middle quintile ranges from roughly $36,000 to just over $59,000.




Don't make the mistake of equating middle income with middle class ! Class definitions generally incorporate income levels but are rarely limited to ONLY a household's earnings.

Preparing for Adequate Retirement Income: How Close Are Retirees in Herkimer County to the 70% Rule?

A recent article by interest.com dealt with measuring people's fiscal preparedness for retirement. The study used income statistics from the Census Bureau’s American Community Survey to compare how those of residents age 65 and over are faring against pre-retirement households led by those 45 to 64.

Interest.com chose that comparison because, as they put it, "a rough rule of thumb is that you’ll need at least 70% of your pre-retirement income once you stop working. Some people will need more, of course, but few will be able to get by on less."

What they found was that "Older Americans are making slow but steady gains in income compared with their younger counterparts, but they are still falling short of the levels needed for a healthy retirement in all but one state. It's clear that, nearly everywhere in the country, older Americans still don’t have the kind of money coming in they need for a secure and comfortable retirement,” says Mike Sante, managing editor of Interest.com.

Below is a map of the US that they presented showing the "replacement income ratio" of each state. This replacement ratio is basically the median income of households where the householder is age 65 and over, divided by the median income of households age 45 to 64.Ideally it should be 70% or higher based on the 70% rule mentioned above. New York happens to come in at 54.98%.

Here is a table showing the Replacement Rate for Herkimer County based on the 2012 Five Year ACS Estimates showing the median household incomes for both types of householders (65+, and 44 to 64 years of age) as well as their replacement income ratio. Again, remember that the supposed goal is for older householders to make 70% or more of their slightly younger counterparts. The closer the rank to "1" the higher the county replacement rate. None of the 62 counties reached the 70% rate using the 2012 Five Year ACS Estimates data.

New Zip Code Data from the IRS

The IRS recently released ZIP Code data showing selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. These data are based on individual income tax returns filed with the IRS and are available for Tax Years 1998, 2001, 2004 through 2012.

 

The map on the IRS page allows you to select a set of state ZIP Codes and peruse their data. 

 

 

The data include items such as:

• Number of returns, which approximates the number of households
• Number of personal exemptions, which approximates the population
• Adjusted gross income 
• Wages and salaries
• Dividends before exclusion
• Interest received  

So pick a year below and take a look at your local ZIP code !

ZIP Code Data

2011  2012

ZIP Code Data 1998–2010

1998  2001  2004  2005  2006  2007  2008  2009  2010

Regional Income Variations By Race, Sex and Education Levels

Recently there have been several news articles locally and nationally drawing attention to the variations in income and employment between minorities and non-minorities. Taking a quick look at the regional data for Herkimer and Oneida Counties in the PUMS files, some interesting numbers can be found. Below is a chart showing the mean income over the last 12 months when respondents were surveyed through the American Communities Survey. These data are part of the ACS Five Year estimates for 2012.

As seen below, white males make the most when compared to their male counterparts among blacks, Asians and Hispanics. While white females generally make more annually than do their black cohorts, Asian and Hispanic females appear to generally average more income than do white females.

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The one thing that clearly stands out when looking at that graphic, as well as the table below, is how poorly black males and females perform financially compared to their cohorts, regardless of their educational levels. Black men and women are consistently paid less than almost every other similarly educated group. The table below shows the overall ranking of each of the 32 groups examined, and provides some break out of women, blacks, Asians and Hispanics for comparison purposes.

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2010 Oneida County Retrospective: Income and Poverty (Part 2)

Income Measures: Changes in income on the family, household and per capita levels have been considerable over the last 50 years. Income, in all of its various measures, has grown dramatically, increasing by more than 8  times over what it was in 1960. Per capita income, for example, has gone from a level of about $2,000 in 1960 to more than $25,000 in 2010.

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Family Income Quintiles: Income growth over the last fifty years wasn’t necessarily uniform among all members of the county.  Looking at changes in the growth of family income by quintile can shed some light onto some of these disparities. Even for the lowest quintile, income growth has been significant since 1960. While the mid-value of the lowest family income quintile was around $2,400 in 1960, by the year 2010 it had risen to nearly $17,000. 

There has been, in fact, substantial income growth for all of the county’s families since 1960. As suggested above, Oneida County families in the lowest quintile have seen their income grow by more than 700% over the last 50 years. In comparison those families comprising the top two quintiles have had income growth of 1100% over the same period. 

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2010 Oneida County Retrospective: Income and Poverty (Part 1)

Income and Poverty in Oneida County 2010

Of the many measures of how well a community is doing economically, income and poverty are two of the most common indicators of economic health.

Income Measures: Income is generally measured in three ways within the census data: household income, family income, and per capita income. Each measures three very different things. Household data measures the cumulative income of all those people within a housing unit; family income reflects the income earned by a family unit’s members; and per capita income is the total cumulative income of a geographical area divided evenly by the total number of all the persons, regardless of age, living there.(Note: For purposes of this report, income levels for 2010 are based on the 2012 Five Year ACS data, using the 2010 year as a midpoint of this grouped data set).

As a general matter, median family income tends to be higher than median household income. In comparison, per capita income is lower than either family or household income. This is understandable given that every person is included in its calculation, including those not earning income such as children, the elderly, or the infirmed. According to the ACS 2012 Five Year Estimates, median household income was about $49,148 in Oneida County. Median family income, on the other hand, was $62,232. Per capita income was around $24,890.

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Family Income Quintiles: Income quintiles are another way to look at family income. They provide information about the lowest, as well as the highest, income brackets within Oneida County. Each grouping represents a fifth of the families in the county. Looking at the income ranges of each quintile provides insight into the income level needed for a family to move upward to a higher income group. For example, among those in the bottom quintile, a family would have to earn in excess of $28,662 to move from the “low” income class to a “lower-middle” income class. “Middle” income class families, in comparison in Oneida County, earn basically from $51,729 to as much as $84,729. Families in the middle class would need to earn close to $85,000 to move up a bracket. To be part of the upper most quintile, or “upper” income class, a family would have to earn in excess of $112,000. 

Back to the Future2: The Release of the 2010 County Retrospectives

Often times data lays dormant for years, or in some cases decades, with little or no comprehensive review being undertaken to place current information in any historical context. One of the most sought after but often underutilized resources of such time series data is the decennial census. In December of 2004, the Herkimer Oneida Counties Comprehensive Planning Program released a report on the last 50 years of census related data for both Herkimer and Oneida Counties.

The time has come for the re-release of this series, updated with data from 2010. Given changes to the decennial census, the data source for much of this comparison now comes from the American Communities Survey, or the ACS. The ACS is NOT the equivalent of the decennial census. It is a very different vehicle for data collection. However, to the degree possible, comparative data will be offered.

Such a review of data is, by the very nature of such time constrained data collection, and the introduction of a new source of data (the ACS), of fraught with pitfalls. Changing definitions, as well as the nature of the data collection process itself, often conspire to make many comparisons the equivalent of mixing apples with oranges. As the social constructs of race, poverty, aging, etc., all evolve to better, or perhaps at least different, levels of understanding over the years, the ability to make comparisons with past data becomes tricky, perhaps difficult, and even impossible at times.

These report will attempt to recognize those potential issues and bring them to light. They will involve data collected since the 1950 census through the 2012 ACS Five Year Estimates. In many cases, in terms of the historical sections of each chapter, the data may only extend back to 1960 or 1970 until the 2012 ACS. Much of that is due to the introduction of new concepts (such as poverty) or a change in the basic definitions and collection of data on an issue, such as race. Sometimes, issues are only able to be examined in a broad context, such as white versus non-white populations. But there are many topics in which the data does allow for direct comparison over several decades with little change in how the data was collected or coded.

Still, it is important to strongly urge that each of the data sets be examined in terms of the subtleties of the definitions for each topical issue. Efforts have been made to be sure to compare like items when possible, and to note potential problem areas. All of the included analysis provides at least a loose sketch of the immediate historical past, and, at best, a more thorough review of some of the changes being experienced within Oneida County over the last fifty to sixty years. 

These reports are not intended as an assessment of demographic trends in the last half of the 20^th century in either county. Rather they are more of a simple review of what has occurred. While a plethora of other topics could have been included, few have enough historical context (i.e. data available) to make them readily reviewable. As a result, this report focuses on five topics: aging, families, income/poverty, nativity/race, and employment.

Expect to see these chapters released here in the next several weeks (one will be later today!). Oneida County's chapters will be released first and then Herkimer County's will follow. 

2011 IRS Federal Tax Return Data Summaries for Herkimer and Oneida Counties

The Missouri State Census Data Center has recently released a tool for extracting IRS data from the 2011 IRS Returns. The returns data are aggregated summaries of U.S. federal tax returns (form 1040 et.al) for specific tax years and geographic areas. In this case, they are the 2011 returns and have been disaggregated into State, County and Zip Code geographies. Regional data has been downloaded for further analysis, but I wanted to offer the following sort of first cut look at some of the basics when it comes to those that filed taxes from either Herkimer or Oneida Counties.

Below is a table providing insight into the number of returns, whether they were filed jointly, were filed with a paid preparer, and the number of exemptions and dependents declared by filers. Each of these is broken out by 7 basic adjusted gross income (AGI) categories - those with no income, those between a $1 and $24,999, those from $25,000 to $49,999, those from $50,000 to $74,999, those from $75,000 to $99,999, those from $100,000 to $199,999, and those at $200,000 and above.

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As I get other data disaggregated I will post more in the coming days !

2012 Income and Poverty Data for Municipalities in Herkimer and Oneida Counties

With the release of the Five Year ACS Estimates in December, it is now possible to get data for every municipality in the region, regardless of size. Below are two tables (click to enlarge them) which have income and poverty data for each municipality in Herkimer and Oneida Counties. If you'd like to see other data for either county as a whole, visit the ACS matrix link.

Herkimer County Data

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Oneida County Data

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Revisiting MOE:A Lesson About Margins of Error in the ACS for Median Household Income

A while back I posted about understanding the margins of error (MOEs) in the American Community Survey estimates. As you may recall, ALL estimates implicitly come with a margin of error - that is to say some degree to which the provided estimate may be close to the actual number for characteristic "X". Because the estimates are based on fairly small samples for the ACS, statisticians like to build in some "fudge factor" and recognize that the estimate is probably right "plus or minus some margin of error". So we all recognize that the resulting sample data is probably not dead on the actual number if we surveyed every single person. Instead we build a "confidence interval" around the sample estimate which we are willing to say (typically) we're 90% confident that the actual number, if we surveyed the entire population, would be within.

Perhaps a good example would be something like median household income.

The 2012 ACS Five Year Estimate shows that the median household income for Oneida County is $49,148. Now this is based on a sample in which about one in 50 households were surveyed. If we surveyed ALL of the other households in the county would we get the same median income number? Possibly, but very, VERY unlikely. So instead, what demographers and statisticians like to go is take the margin of error (MOE) for this piece of data and construct a 90% confidence interval around this data point. this is done by going one MOE above and one MOE below the estimated number.

In the income case, we would add and subtract the MOE (which is +/- $999) and now say that we are 90% confident that the ACTUAL value of the median household income for the county lies between  $50,147 and $48,149.

Let's take this a step further and look at this visually. Here's the Oneida County Median Household income on a graph; the green box represents the estimated value, and the vertical line shows the range of 90% confidence interval/ We are 90% confident that the actual median household income lies between $50,147 and $48,149.

Now, how do the various towns compare to the county estimated? The way to tell this is to plot out the median household income estimates AND their margins of error and see where they overlap. When the county's overlaps with a towns, that means that they are, essentially no different. Technically it is safe to say that statistically we see no significant difference between the town and the county median household income.

On the other hand, where they do NOT overlap, that means that a town's median household income is either significantly higher, or lower, than the rest of county's as a whole. The graph below shows all of the town median household income estimates and their 90% confidence intervals.

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To make this a bit more understandable, I've drawn in red lines showing the 90% confidence interval for the county so you can more easily see how it overlaps, or doesn't overlap, each towns median household income 90% confidence intervals.

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Looking at the right side of the graph, note how the 90% confidence intervals for Lee, Deerfield, Marshall, Trenton, Marcy and Westmoreland are all above the red line depicting the top edge of the county's MOE. However for the twon of Western, while the estimated value of their median household income (the green box for Western) is above the county's, the confidence intervals overlap. This means it's possible that the numbers, in fact, could be identical ! So you'd have to say statistically that they are not significantly different !

On the left hand side you can see that Utica,  Annsville and Rome are all below the county median household income - they do not overlap, so therefore they are significantly lower than the county when it comes to median household income.

This same process could be done with any of the towns in order to compare them to other towns in the county. For example, what could you say about the Town of Trenton? What towns are not significantly different when it comes to median household income? Which are below them?

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Whenever you are looking at ACS data, you need to be aware of these margins of error and what they say about the estimates, especially in comparison to other geographies !

Upward Mobility May Depend on Where You Live

The New York Times has an online article about a study that examined the likelihood that children of parents whose income is in the bottom quintile of an area rising over his or her lifetime as they grow older. In essence, how likely is it that your kids will earn more than you in the future in your area?

The study — based on millions of anonymous earnings records and being released this week by a team of top academic economists — is the first with enough data to compare upward mobility across metropolitan areas. These comparisons provide some of the most powerful evidence so far about the factors that seem to drive people’s chances of rising beyond the station of their birth, including education, family structure and the economic layout of metropolitan areas.

The resulting map, which appears below, is broken into a variety of metropolitan areas - for us this upwards mobility map lumps us into the Syracuse area.

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In terms of the actual numbers, here is what the study by Harvard predicts for children int he lowest quintile in our region:

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To see how other areas fare, visit New York Times article and the play with the interactive piece about halfway down the page.

Median Household Income: New Maps Now Available!

Thanks to our GIS group for getting these maps together showing the median household incomes by block group within Herkimer and Oneida Counties ! Remember that block groups are as small as we can go geographically with most data. Block groups are basically sub-areas of Census tracts, so a tract might typically have any where from three to six block groups within it.

You can see the tract maps for both counties, as well as these many other types of maps (these median household income maps have just been added) on our MAPS page. If you're interested in some other income data, you might want to visit a previous post showing income and poverty measures by municipality for each county. You could also simply search using the search engine at the upper right of the Census Affiliate blog for all "income" related posts.

In the meantime, here are the maps for your perusal !

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Calculating the Cost of Living: The Bureau of Labor Statistics

Occasionally there is a need to compare prior income or costs of living with more recent ones. So how exactly do you account for changes in the cost of living ? The easy answer is you turn to the Bureau of Labor Statistics (BLS). The BLS is a government office that, among other things, is responsible for the CPI - or the Consumer Price Index. The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Quite literally it represents an actual shopping list of goods and services and checks them, on a monthly basis, for changes in pricing.

So what goods and services are we talking about ? The major groups and examples of categories in each are as follows:

• FOOD AND BEVERAGES (breakfast cereal, milk, coffee, chicken, wine, full service meals, snacks)
• HOUSING (rent of primary residence, owners' equivalent rent, fuel oil, bedroom furniture)
• APPAREL (men's shirts and sweaters, women's dresses, jewelry)
• TRANSPORTATION (new vehicles, airline fares, gasoline, motor vehicle insurance)
• MEDICAL CARE (prescription drugs and medical supplies, physicians' services, eyeglasses and eye care, hospital services)
• RECREATION (televisions, toys, pets and pet products, sports equipment, admissions);
• EDUCATION AND COMMUNICATION (college tuition, postage, telephone services, computer software and accessories);
• OTHER GOODS AND SERVICES (tobacco and smoking products, haircuts and other personal services, funeral expenses).

Also included within these major groups are various government-charged user fees, such as water and sewerage charges, auto registration fees, and vehicle tolls. In addition, the CPI includes taxes (such as sales and excise taxes) that are directly associated with the prices of specific goods and services. However, the CPI excludes taxes (such as income and Social Security taxes) not directly associated with the purchase of consumer goods and services. The CPI does not include investment items, such as stocks, bonds, real estate, and life insurance. (These items relate to savings and not to day-to-day consumption expenses.)

For each of the more than 200 item categories, using scientific statistical procedures, the Bureau has chosen samples of several hundred specific items within selected business establishments frequented by consumers to represent the thousands of varieties available in the marketplace. For example, in a given supermarket, the Bureau may choose a plastic bag of golden delicious apples, U.S. extra fancy grade, weighing 4.4 pounds to represent the Apples category. For more information about the CPI and how it is used visit the BLS CPI web page.

The BLS also has an Inflation Calculator which allows you to put in a dollar amount, attached to a specific year all the way back to 1913, and then ask what that amount would be in some other year based on the impact of inflation. So for example, my father had an annual salary of roughly $9,000 in 1962. According to the BLS Inflation Calculator that would be the equivalent of an annual salary of $69,297 today.

To see inflation adjusted income measures for Herkimer and Oneida Counties from 1970 to the most recently available data, click on the table below.

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Previous 12 Month Earnings of Males and Females in Herkimer and Oneida Counties

Census data recently analyzed by the Pew Research Center show that a quarter of all moms now bring home more money than their male counterparts. According to an NBC News article  "Overall, women -- including those who are unmarried -- are now the leading or solo breadwinners in 40 percent of U.S. households, compared with just 11 percent in 1960.That’s both good news and bad news, depending on which end of the scale you examine. At the top level, educated women are catching up with men in the workforce. But at the bottom rungs, there are more single mothers than ever and most of them are living near the poverty line."

While Pew was able to get special runs done for them in analyzing the American Communities Survey (ACS) data from the Census Bureau, we unfortunately don't have that type of access. However there are some interesting data in the ACS that tells us about the earning power of local males and females.

The graph below shows the cumulative percentages of males and females in Herkimer and Oneida Counties who worked full time in the past 12 months by how much they earned. It is set up to show the percentage that earn less than several benchmark amounts. 

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So for example,  looking at the graph you can see that 84% of all Herkimer County females who worked full time in the past year earned less than $50,000 annually; in comparison, only 66.3% of Herkimer County males earned $50,000. What this shows, then, is that females in Herkimer County are more likely to earn lower salaries than males - or more specifically more of them are paid at a lower rate than their male counterparts. Another way to think of that same data is to say that only 16% of Herkimer County females make MORE than $50,000, versus 34% of Herkimer County males. So which group would you rather be a part of ?

Here's the data the above chart comes from.

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You can visit the Census Bureau's American Fact Finder to explore other income related data broken down by gender or race !