Use and interpret Age Dependency Ratios

Age Dependency Ratios provide a quick and powerful measure to better understand the age composition of an area.

Esri's Data Development team produces demographic data (known as Updated Demographics) for the United States using a variety of sources to update small areas, beginning with the latest U.S. Census base along with a mixture of other private sources to capture demographic change. Alongside Updated Demographics, Esri provides U.S. Census Bureau and American Community Survey (ACS) demographics as a point of reference for understanding growth in an area and to provide additional community details. Data tutorials educate both the novice and the expert analyst to learn more about a topic to properly incorporate Esri Demographics that are accessible within various products. In this tutorial, you will learn about the following:

  • What Age Dependency is and how the data is developed
  • Why using Age Dependency data is so powerful
  • How to use and interpret Age Dependency data using a business scenario
  • Important data considerations
  • Additional resources

First, you'll learn what Age Dependency is and why using this measure can provide valuable insight for a community.


Age Dependency Ratios

Dependent populations are defined as the portion of the population that are not economically active and do not produce income. This population includes children and the elderly who typically rely on others for the goods and services they consume. The remainder of the population constitutes the working age population.

Age Dependency Ratio (ADR) is a relative measure of the working-age population supporting the non-working age population and the potential economic burden associated with such support. This measure is used to express the relationship between three age groups within a population:

Child Population age 0–17 years (economically dependent)

Working Population age 18–64 years (economically independent)

Senior Population age 65 years or older (economically dependent)

Age Dependency Ratios are often used to measure the financial pressure on the actively working population of a community. The higher the ratio, the greater the burden is carried by working-age people. Lower ratios indicate more people are working who can support the dependent population.


Why study Age Dependency

Being able to quickly identify age dependent populations and where age shifts occur is essential for governments, economists, universities, businesses, and any other major economic entity which benefits from understanding the impacts of population change.

Looking at how Age Dependency Ratios change over time reveals the future of an area's dependency on the working age population. The nature of these changes can have large implications for policy and planning. Using age dependency data helps communities to better plan, manage, and allocate resources effectively.

The power behind Age Dependency Ratio data is understanding where age structure is having a disproportional impact on communities; understanding the direction and pace of changes in ratios over time; and utilizing the comparative nature of age dependency data.

Here's what we can learn quickly from mapping U.S. Age Dependency Ratios:

U.S. Age Dependency

This map shows the current year relationship between senior and children age dependency. Click on the bottom left symbol to display the map legend.

Click on any county to reveal age dependency information.

U.S. Age Dependency

Areas with high concentrations of both seniors and children dependencies are symbolized in dark blue, mostly found in Middle America.

U.S. Age Dependency

Areas that experience both low senior and child dependency, shaded in light green, are scattered throughout the country.

U.S. Age Dependency

Areas shaded in the darker green and darker blue depict higher senior dependent ratios.

Over the next 5 years, the U.S. Senior Age Dependency is projected to rise to 30.8, an increase of 4.1 from 2019.

Age Dependency Ratio calculations

Besides an overall Age Dependency Ratio (ADR), Esri’s data team developed a Child Dependency Ratio (CDR), and a Senior Age Dependency Ratio (SDR). These ratios are available as a current year estimate and a 5-year forecast. Using Esri’s annual updated population by age data here’s how the data is calculated: 

Age Dependency Ratio formula

(Working Age Population is between the ages of 18 and 64.)

Child Dependency Ratio formula

(Child Population is between the ages of 0 and 17.)

Senior Dependency Ratio formula

(Senior Age Population is 65 years or older.)


Data access

You can access Esri Demographics using Esri software and through apps like ArcGIS  Business Analyst ,   ArcGIS for Excel , or ready to use maps from  ArcGIS Living Atlas of the World . For use outside of the Esri platform data files are available in CSV, dBase, Excel, shapefile, or file geodatabase formats.

Contact an Esri data sales specialist with data questions at 800-447-9778 or send an email your request to: datasales@esri.com.


How to use and interpret Age Dependency Ratios

Age Dependency ratios provide you with the ability to gain insights into the age structure of an area. Higher ratios indicate a greater level of dependency on the working-age population. The U.S. ADR is 62.5 for 2019, or roughly 62 dependents for every 100 workers. Likewise, the U.S. CDR and SDR are 35.8 and 26.7, respectively. Age Dependency measures can help your analysis in multiple ways such as ensuring adequate services and programs are meeting specific population needs such as care for aging seniors, or schools for children. To demonstrate a typical analysis that uses Age Dependency ratios let’s look at a “What if” business scenario:

What if a policy decision maker in the state of West Virginia wants to assess and plan for age structure changes? Knowing that Baby Boomers will become 65 or older by 2030, they want to determine which communities experience the highest ratio of senior age dependency to decide if and where additional retirement facilities should be opened.

One of the best ways to begin an analysis is to run a demographic report. In this example, the Age by Sex Profile report was generated for the state of West Virginia, using the ArcGIS Business Analyst Web App. (It's also helpful to run demographic reports at the U.S. level so that national ratios can be used for area comparison.)

The Age by Sex Profile report allows you to gain a quick understanding of the area, revealing its total population by age distribution, age by sex distribution, and age dependencies for Census 2010, current year, and a 5-year forecast. In total, the Age by Sex Profile produces 3 pages of informative data about the age and age by sex of a population. Let’s look at each page to demonstrate how to interpret the data so you can gain a better understanding of what the numbers mean and how these numbers can affect your decision-making process.

Age by Sex Profile Report - Page 1

What you learn

Page 1 of the report reveals the current median age is just under 49 years. However, more than 387,000 people are considered "seniors" (population age 65 or older) in the state of West Virginia. The state's Senior Age Dependency is slightly higher than its Child Dependency Ratio.

West Virginia's Age Dependency Ratio of 65.5 means that there is a total combined of 65 children plus seniors for every 100 working age adults. The ADR is 3 points or 4.8% higher in West Virginia than for the U.S. By 2024, West Virginia's ADR is expected to be 6 points higher than the 2024 projected U.S. ADR of 67.2.

West Virginia's Senior Dependency Ratio of 33.8 means there are nearly 34 seniors for every 100 working age adults. This means that West Virginia's senior age dependency is 7.1 points higher than the U.S. national average. By 2024, West Virginia's ADR and SDR is expected to rise to 73.2 and 39.8 respectfully, resulting in dependency ratios that will be nearly 8 and 9 points higher than the U.S. national dependency ratios.

West Virginia's Child Dependency Ratio of 31.7 means there are nearly 32 children for every 100 working age adults. The U.S. level is at 35.8, therefore West Virginia is slightly less child dependent than the U.S. in 2019. However, by 2024, the CDR for West Virginia is expected to be 3 points higher than the 2024 projected national CDR.

From these three dependency ratios we can tell that West Virginia is more dependent than the U.S. as a whole, with seniors being the largest driver of the increased dependence when compared with the U.S.

Page 2

Page 2 of the report breaks out the population by sex and age.

When comparing male and female populations, the percentage of senior women age 65 years or older are consistently larger than men age 65 years or older for 2010, 2019, and 2024. Looking at the estimates of males vs. females confirms that there are more senior age women living in West Virginia compared to senior age men.

After summing up the 5-year age groups in 2024, there will be around 3.5% more women age 65 or older than men age 65 or older. Knowing a population’s characteristics, such as sex and age can help decision makers understand how to market, plan and provide specific programs or services to consumer demand; like in this case for senior facilities.

Page 3

Page 3 of the report shows the same statistics listed on page 2, but in graph format.


Next, you learn the relationship between the SDR measure and location data to help answer the business scenario question of "do we need to establish additional senior retirement facilities?"

West Virginia by County

The map image shows where current Senior Age Dependency is high.

Adding senior retirement locations from Infogroup's Business Location database sheds light on the proximity of facilities in relationship to the SDR of each area.

West Virginia by County

Areas shaded in blues depict above average Senior Age Dependency (greater than the national average of 26.7).

Retirement locations are indicated with blue symbol markers. Red label markers indicate number of facilities.

Northern West Virginia

Ten of the top 20 counties with the highest Senior Age Dependency ratios are located in northern West Virginia.

Hancock County, with a Senior Age Dependency of 40, is expected to increase to 47.2 by 2024.

Southwest, West Virginia

Majority of the senior facilities located in this part of the state are also some of the state's most populous counties such as Cabell, Putnam, Kanawha, Greenbrier, and Monroe.

Pendleton County

Pendleton County ranks the highest across all West Virginia counties for Senior Age Dependency (SDR).

With a SDR of 45.1, one-quarter of its population is comprised of seniors.

West Virginia 5 years from now

All but 4 counties are expected to see Senior Age Dependency ratios rise above the national average of 30.8 by 2024.

Northern West Virginia

By 2024, Tyler County's SDR is expected to increase by 8.4, (a projected SDR of 48.6).

This makes Tyler County one of the top 3 highest ratio increases in West Virginia (next to Wirt and Ritchie County).

Southern West Virginia

Counties like Wyoming, Pocahontas, Greenbrier and Logan are also projected to see significant SDR increases in the next 5 years --- at least 8 points or more.

Upon review of the Age by Sex Profile report and the Senior Age Dependency/Retirement locations map, should policy makers consider adding additional senior facilities in West Virginia?

In this example, population by age characteristics, along with the Senior Age Dependency Ratio has taught you that the people in this state are aging, and that within West Virginia there are more senior age women than men. In addition, the majority of West Virginia counties show SDRs greater than the U.S. county average of 26.7. And although senior retirement and assisted living facilities are present across the state, several counties, particularly in central West Virginia, have either very few or no retirement facilities at all. These counties, such as Pendleton County, that show a high current year SDR and an significant ratio increase to 52.6 by 2024, may benefit by increasing senior programs and facilities.

An area’s demographic composition, while a critical element in the analysis, is just one of many components that should be considered with this type of analysis. With that said, using measures such as Age Dependency Ratios, proves to be a valuable tool for easily understanding the age distribution within an area. These measures are also powerful comparative tools that enable the ability to not only assess what type of dependency (child or senior) an area is experiencing, but where the largest impact may be happening in a community.


Data review and considerations

Traditionally defined measures are approximations based solely on age and not adjusted by labor force participation by age. Dependency ratios assume the entire working-age population (age 18-64) participate in the civilian labor force. A portion of dependent child and senior populations are participating in the labor force, but the ratio assumes they are not.

When analyzing age populations, it's tempting to use median age. However, median age is not always the best single number to use when reviewing an age structure. Using Age Dependency Ratios provide a quick view of the age structure in an area that can immediately show where the age distribution might be drastically different (more seniors vs. children).

When using Age Dependency data, ratios that are considered “high” (ratios that exceed the current U.S. ratio of 62.5), may indicate an area is experiencing high financial stress or low economic growth due to large numbers of dependents that pay little or no taxes. 

Areas that reveal a high senior age dependency ratio (greater than 26.7), could drive social or economic agendas that lead to critical decisions such as adding long-term care facilities to help a community’s aging population.

The fewer number of working age people, the fewer number of people who can support  schools , retirement/ disability pensions , and other assistances to the youngest and oldest members of a population--the most  vulnerable  members of  society .

Dependency ratios that are “low” (below the U.S. ratio of 62.5) may indicate an area is experiencing economic growth. Factors attributing to low dependency ratios may include: increased number of women in the work force adding to the total working-age population, decreased fertility rates as women delay starting families, or simply smaller family sizes.


Next steps

In this tutorial, you learned about the basics of Age Dependency Ratios, how to interpret the data, and the significant impact it has on communities. Additional data tutorials in two series are available. Click on the links below for continued data exploration, learning, and ways to access the data.


Learn more

Data methodologies

Age Dependency Ratios are developed using Esri’s estimates and forecasts of age from the  Esri U.S. Updated Demographics portfolio . Represented as point-in-time estimates as of July 1, the data is available for Esri’s standard geographic areas and for any user-defined polygon such as a ring or drive time.  Read the Esri Dependency Ratios Methodology Statement for more information 

Frequently asked questions

Use our  data reference page  to help answer additional questions about Esri Demographics.

Helpful links


Connect with us

If you have a topic you would like covered in a data tutorial to help you better understand U.S. data, send us an e-mail with your topic idea.

About this story

This story was created by Donna Fancher in collaboration with the Esri Data Development team. To start working with the U.S. data collection, visit the  Esri Location Data Resources  page.

Led by chief demographer Kyle Cassal and economist Douglas Skuta, Esri's Data Development team uses sophisticated quantitative methods to produce small area demographic and socioeconomic data to support informed decision-making. The team builds on a rich history of market intelligence to produce trusted independent estimates and forecasts for the United States based on innovative methodologies that use public and private data sources with the power of ArcGIS. Esri's Data Development team provides more than 7,000 proprietary data items to better understand the characteristics of people and places across multiple statistical and administrative boundaries and custom trade areas.

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