Evaluate COVID-19 financial vulnerability

State and local governments have taken unprecedented actions to slow the transmission of coronavirus disease 2019 (COVID-19), including mandatory business closures and safer-at-home policies. These actions, while necessary to protect public health, have significantly impacted the economy.

As transmission rate slows, policymakers must make challenging decisions about when and where to reopen businesses and relax social distancing requirements. Economic impacts have not been uniform across the United States, and not everyone is equally equipped to weather the financial challenges of the pandemic. Understanding a community's ability to cope with the financial consequences of COVID-19 can inform decisions of where and when to reopen the economy.

This tutorial outlines a proposed data-driven approach for mapping the level of financial vulnerability across a region. Financial vulnerability occurs when income and liquid financial assets are insufficient to cover debt payments and basic living costs. Just as you can't measure physical health by only looking at blood pressure, you can't measure financial health by only looking at a single variable. For this tutorial, you'll model financial vulnerability as a function of income, expenditures, and external factors that can negatively impact income.

This tutorial was last tested on May 10, 2023, using ArcGIS Pro 3.1. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• ArcGIS Pro (see options for software access)
• ArcGIS Business Analyst Pro
• Optional: 27 credits

Evaluate COVID-19 financial vulnerability

Download data

First, you'll download the ArcGIS Pro package and confirm that you have the necessary extension for completing the workflow.

1. Download the COVID-19 Financial Vulnerability project package.
2. Double-click the downloaded COVID19FinancialVulnerability.ppkx to open the project in ArcGIS Pro. If prompted, sign in to your ArcGIS organizational account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

   The project opens in ArcGIS Pro.

   The map shows census tracts for Summit County, Ohio, the location of Akron, a moderate-sized city. This workflow can be applied to any county, state, or even the whole country. A small area is used to simplify the analysis .

   

3. On the ArcGIS Pro ribbon, click Project. Choose Licensing.
4. Under Esri Extensions, confirm that you have a ArcGIS Business Analyst license, required to complete this tutorial.

   Note:

   To learn more about the ArcGIS Business Analyst license and how to acquire it, check out the ArcGIS Business Analyst product page.

5. Click the back button to return to the map.

Enrich data with vulnerability variables

Next, you'll acquire the data about income, expenditures, and other variables that you'll use to assess financial vulnerability. You'll find these variables using the Enrich Layer geoprocessing tool, which adds demographic data to a layer based on its geographic location.

Note:

Using the Enrich Layer tool consumes credits. The number of credits consumed depends on the number of variables and the size of the geographic area. For this tutorial, the tool consumes 27 credits.

If you don't want to spend any credits, running the tool is optional. The project package includes the Summit_County_Data layer, which is enriched with the appropriate data variables. Even if you don't run the tool, it's recommended that you read the following steps to learn how to configure the tool parameters.

1. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Tools.

   

   The Geoprocessing pane appears.

2. In the Geoprocessing pane, search for and open the Enrich Layer tool.

   

3. For Input Features, choose Summit County. For Output Feature Class, type Summit_County_Data.

   

4. Click the Variables add button.

   

   The Data Browser window appears.

5. In the Data Browser window, search for and add the following variables for the most recent available year. (For variables followed by Percent, select the percent sign and deselect the number sign):

   Note:

   You can copy the variable names from the list and paste them into the search box in the Data Browser. For variables that use the percent, only copy the variable name, not the : Percent text.

   Also, be sure that you are on the home page of the Data Browser window when you search.

   • Median Household Income
   • Median Disposable Income
   • Per Capita Income
   • HHs: Inc Below Poverty Level (ACS 5-Yr): Percent
   • HHs w/Food Stamps/SNAP (ACS 5-Yr): Percent
   • Pct of Income for Mortgage
   • HHs/Gross Rent 30-34.9% of Income (ACS 5-Yr): Percent
   • HHs/Gross Rent 35-39.9% of Income (ACS 5-Yr): Percent
   • HHs/Gross Rent 40-49.9% of Income (ACS 5-Yr): Percent
   • HHs/Gross Rent 50+% of Income (ACS 5-Yr): Percent
   • Pop <19: No Health Insurance (ACS 5-Yr): Percent
   • Pop 19-34: No Health Insurance (ACS 5-Yr): Percent
   • Pop 35-64: No Health Insurance (ACS 5-Yr): Percent
   • Food Service/Drinking Estab Emp (NAICS): Percent
   • General Merchandise Employees (NAICS): Percent
   • Motor Vehicles/Parts Dealers Emp (NAICS): Percent
   • Arts/Entertainment/Rec Emp (NAICS): Percent
   • Occupation: Personal Care: Percent
   • HHs w/No Internet Access (ACS 5-Yr): Percent
   • Owner HHs with 0 Vehicles (ACS 5-Yr): Percent
6. In the Data Browser window, confirm that 20 variables are selected.

   

7. Click OK.

   The variables are added to the Enrich Layer tool pane.

   

   Rather than running the tool and expending credits, you'll close the tool and review the Summit_County_Data layer provided with the project, which contains the enriched data.

   Tip:

   On the Enrich Layers tool pane, you can click the estimate credits link to view how many credits running the tool will consume.

8. In the Geoprocessing pane, click the Back button.

   

9. In the Contents pane, right-click the Summit_County_Data layer and choose Attribute Table.

   The fields have been added to the original Summit County layer. Each of the variables, along with the rationale for including them, is provided at the end of the tutorial. These variables capture the five components of financial vulnerability. You can, and should, add additional variables you feel would be useful.

10. Close the attribute table.

Determine vulnerability

Now that you've acquired the variables, you'll use tools from the Business Analyst extension to determine the vulnerability of each census tract in the county.

1. In the Geoprocessing pane, search for and open the Make Suitability Analysis Layer tool.

   This tool creates a layer in the Contents pane to store your analysis. Although the tool name includes suitability analysis, you can use it for any type of overlay analysis.

2. For Input Features, choose Summit_County_Data. For Layer Name, type Financial Vulnerability.

   

3. Click Run.

   The tool runs and the layer is created. Next, you'll choose the fields that will be analyzed. In this case, you'll choose the variables you added with the Enrich Layer tool.

4. In the Geoprocessing pane, click the Back button. Search for and open the Add Field Based Suitability Criteria tool.
5. For Input Suitability Analysis Layer, choose Financial Vulnerability.
6. For Fields, click the Add Many button.

   

   A list of all of the fields in the layer is added. Most of these fields are the variables you added by enriching the layer.

7. At the top of the Fields window, next to the search box, click the Toggle All Checkboxes button.

   

   All fields are now selected.

8. At the bottom of the list of fields, uncheck Shape_Area and Shape_Length. Click Add.

   

   The twenty variable fields are listed.

9. Click Run.

   The tool runs. No new layer is created, and nothing changes on the map. However, the fields you chose will be made available in subsequent analysis tools. Next, you'll set the properties of each criterion.

10. In the Geoprocessing pane, click the Back button. Search for and open the Set Criteria Properties tool.
11. For Input Suitability Analysis Layer, choose Financial Vulnerability.

    All of the variables (criteria) you specified are added to the Criteria Properties parameter. Each criterion has several parameters associated with it.

    

    By default, the Weight parameter for each variable is 5 (all variables are given equal weighting). You can decrease the weight for less important variables and increase it for more important variables. For example, you have several variables reflecting households paying more than 30 percent of their total income for rent. You can set the weight for households paying 30–34.9 percent of their income for rent to 2, those paying 35–39.9 percent to 4, those paying 40–44.9 percent to 6, and those paying more than 50 percent to 10. Similarly, you can determine that children and seniors without health insurance should have a higher weight than adults aged 19–34 or 35–64. The weights can be any values, but they must add up to 100. (You have 20 variables total, which is why the default weight is 5.)

    The Influence parameter indicates if a high value has a positive or negative influence on the scoring. For median income, for example, tracts with small values (less income) are more vulnerable. If small values are more vulnerable, the criterion has an inverse relationship, and if high values are more vulnerable, the criterion has a positive relationship.

    You won't use the Minimum Value or Maximum Value parameters. For suitability analyses, these can be used to exclude features outside the minimum to maximum range from the analysis.

12. For the following variables, change the Influence to Inverse (you may need to scroll through the list of variables to find them):
    • Median Disposable Income
    • Median Household Income
    • Per Capita Income

    

    For now, you'll leave the weights unchanged.

13. Click Run.

    The tool runs. Again, no new layer is created. You've defined how each variable impacts the vulnerability score. The next tool you run will calculate a suitability score for each census tract based on the variables you've configured.

14. Click the Back button. Search for and open the Calculate Suitability Score tool.
15. For Input Suitability Analysis Layer, choose Financial Vulnerability and click Run.

    The Financial Vulnerability layer is updated with the vulnerability score for each census tract. The map is symbolized so that tracts with lowest vulnerability are in yellow and tracts with highest vulnerability are in red.

    

    The map shows that the urban areas of Akron have a higher level of financial vulnerability than the surrounding suburbs and the rural areas of the county.

Understand drivers of vulnerability

You know where vulnerability is high or low. But which variables are increasing vulnerability the most? To find out, you'll sort the 20 vulnerability variables into five categories (Income, Housing, Health Insurance Burden, Employment Volatility, and Adaptability). Then, you'll compute the average score for each category.

When you calculate the average, you'll multiply the value (which will be between 0.0 and 1.0) by 1,000,000 to convert them into integers. You'll perform this conversion because subsequent analysis will involve testing for equality. Testing for equality using decimals can lead to false negatives due to rounding differences, so it's important to use integers instead.

1. In the Geoprocessing pane, click the Back button. Search for and open the Calculate Field tool.
2. In the Calculate Field tool, enter the following parameters:.
   • For Input Table, choose Financial Vulnerability.
   • For Field Name, type IncomeAve.
   • For Field Type, choose Long (32-bit integer).

   

   When you provide a field name that doesn't already exist in the input table, the tool automatically creates it for you. For the expression, you'll add together the five income category variables and divide by five to find the average. Then, you'll multiply the result by 1,000,000 to convert it to an integer.

3. For IncomeAve =, create (or copy and paste) the following expression:

   ((!Weighted_score_of_foodstampssnap_acssnap_p! + !Weighted_score_of_atrisk_acshhbpov_p! + !Weighted_score_of_wealth_meddi_cy! + !Weighted_score_of_wealth_medhinc_cy! +!Weighted_score_of_wealth_pci_cy!)/5) * 1000000

   

4. Click Run.

   The tool runs and the field is calculated. You'll repeat the process four more times for the other categories.

5. In the Calculate Field tool, change Field Name to HousingAve and replace the expression with the following expression:

   ((!Weighted_score_of_populationtotals_incmort_cy! + !Weighted_score_of_housingcosts_acsgrnti30_p!+ !Weighted_score_of_housingcosts_acsgrnti35_p! + !Weighted_score_of_housingcosts_acsgrnti40_p! +!Weighted_score_of_housingcosts_acsgrnti50_p!) / 5) * 1000000

6. Click Run.
7. Change Field Name to InsuranceAve and replace the expression with the following expression:

   ((!Weighted_score_of_health_acs0nohi_p! + !Weighted_score_of_health_acs19nohi_p! + !Weighted_score_of_health_acs35nohi_p!) / 3) * 1000000

   There are only three variables for this component, so you'll divide by three instead of five to get the average.

8. Click Run.
9. Run the tool again to create a field named EmploymentAve using the following expression:

   ((!Weighted_score_of_employees_n37_emp_p! + !Weighted_score_of_employees_n18_emp_p! + !Weighted_score_of_employees_n09_emp_p! + !Weighted_score_of_employees_n34_emp_p! + !Weighted_score_of_occupation_occpers_cy_p!) / 5) * 1000000

10. Run the tool again to create a field named AdaptabilityAve using the following expression:

    ((!Weighted_score_of_internetcomputerusage_acsnonet_p! + !Weighted_score_of_vehiclesavailable_acsoveh0_p!) / 2) * 1000000

    You've calculated and converted the average values for all five categories. Next, you'll create a field to determine which category has the highest average value for each census tract.

11. In the Calculate Field tool, change Field Name to MaxValue. Create the following expression:

    (max(!IncomeAve!, !HousingAve!, !InsuranceAve!, !EmploymentAve!, !AdaptabilityAve!))

12. Click Run.

    You have calculated the average score for each of the five categories of vulnerability. Next, you'll create a text field to store the highest score for all five vulnerability components.

13. In the Geoprocessing pane, click the Back button. Search for and open the Add Field tool.
14. In the Add Field pane, set the following parameters:
    • For Input Table, choose Financial Vulnerability.
    • For Field Name, type Predominance.
    • For Field Type, choose Text.
    • For Field Length, type 30.
    • For Field Alias, type Predominant Vulnerability Component.

    

15. Click Run.

    The MaxValue field contains only the highest numerical value among the five average values. It doesn't tell you whether the maximum value is an income value, a housing value, or so on. To determine that, you'll select census tracts where MaxValue matches IncomeAve. For the selected tracts, you'll set Predominant Vulnerability Component to Income. Then, you'll repeat the process for the other four categories.

16. Click the Back button and search for and open the Select Layer By Attribute tool. Set the following parameters:
    • For Input Rows, choose Financial Vulnerability.
    • For Selection type, choose New selection.
    • For Expression, create the expression Where MaxValue is equal to IncomeAve.

    Note:

    For the third drop-down menu, click Fields to select the IncomeAve field.

    

17. Click Run.

    All of the tracts for which the average income variable scores are largest compared to the other scores are now selected.

    

    Note:

    The selected regions may slightly differ depending on the year of the variables used.

    Next, you'll set the predominance value label to Income.

Calculate predominance value for vulnerability

You will use the Calculate Field tool to label the selected census tracts Income for the field Predominant Vulnerability Component.

1. Click the Back button and search for and open the Calculate Field tool. Set the following parameters:
   • For Input Table, choose Financial Vulnerability
   • For Field Name, choose Predominant Vulnerability Component
   • Under Expression, for Predominance =, type "Income". (Include the quotation marks.)

   

2. Click Run.

   You'll repeat the process for the other four categories. To avoid entering the same tool parameters repeatedly, you'll open the History pane so you can reopen tools you've used with the same parameters you set earlier.

3. On the ribbon, on the Analysis tab, in the Geoprocessing group, click the History button.

   

4. In the History pane, double-click Select Layer By Attribute.

   The tool appears with the parameters filled in. All you need to change is the expression.

5. Change the expression to Where MaxValue is equal to HousingAve and click Run.

   The tracts matching the expression are selected on the map.

   

6. In the History pane, double-click the most recent Calculate Field entry. In the Calculate Field tool, change Predominance = to "Housing" and click Run.
7. In the History pane, double-click the most recent Select Layer By Attribute entry. Change the expression to Where MaxValue is equal to InsuranceAve and click Run.
8. In the History pane, double-click the most recent Calculate Field entry. In the Calculate Field tool, change Predominance = to "Insurance" and click Run.
9. Reopen and run the Select Layer By Attribute tool with the expression changed to Where MaxValue is equal to EmploymentAve.
10. Reopen and run the Calculate Field tool with Expression changed to "Employment".
11. Reopen and run the Select Layer By Attribute tool with the expression changed to Where MaxValue is equal to AdaptabilityAve.

    There are no census tracts where AdaptabilityAve is the highest value, so you don't need to run the Calculate Field tool again. You've found the predominant vulnerability component for every tract.

Symbolize predominance in the map

Lastly, you'll symbolize the map based on the Predominant Vulnerability Component field values.

1. In the Contents pane, right-click the Financial Vulnerability layer and choose Symbology.

   The Symbology pane appears.

2. For Primary symbology, choose Unique Values. For Field 1, choose Predominant Vulnerability Component.

   

   Even though no census tracts have Adaptability as their highest vulnerability category, you'll add it to the legend so users will understand that it is one of the possible categories.

3. For Classes, click the Add unlisted values button.

   

   The Select values to add pane appears.

4. Click Options and choose Add new value.

   

   A new value is added to the list.

5. For the new value, for Value, type Adaptability and press Enter.

   The Label option is also set to Adaptability.

6. At the bottom of the pane, click OK.

   Now, the legend includes all five vulnerability components.

7. For Color scheme, choose the Muted Pastels color scheme.

   Tip:

   Point to a color scheme to see the name of each color scheme.

   

   The symbols change on the map.

   Note:

   The colors on your map may not match those in the example image.

   

   The map shows that income (blue in the example image) is the primary driver of financial vulnerability for most tracts in Summit County. However, in several border tracts, housing (purple) is the primary driver. Employment (beige) is the primary driver in three of the tracts. Exploring the attributes of these tracts shows that more than 25 percent of the employees work in either food service or arts and entertainment, two of the at-risk employment categories. A single tract has insurance (brown) as the dominant category. Exploring the attribute table for this tract shows that almost 20 percent of the non-Medicare age population is uninsured.

   Understanding the spatial distribution of financial vulnerability is the first step in designing targeted remediation plans. For example, in areas where the employment burden is the predominant driver of financial vulnerability, policymakers may want to consider targeted job training programs. In areas where housing burden is predominant, they may want to consider establishing or extending rent freeze and eviction restrictions.

8. Optionally, on the Quick Access Toolbar, click the Save button to save the project.

In this tutorial, you created a financial vulnerability map combining 20 variables that represented five different components of people's financial lives. Keep in mind that there are many ways to model vulnerability. When applying this workflow to your own region, use variables that best describe your local economy and choose weights that differentiate which variables are most and least important.

You can find more tutorials in the tutorial gallery.

Variables used

Income

Variable	Rationale
2021 Median Household Income	Wage or salary is the primary way most United States households generate income.
2021 Median Disposable Income	Households with higher levels of disposable income are more likely to meet basic living expenses, even if incomes are reduced.
2021 Per Capita Income	Households are more vulnerable if income must support more people.
2015-2019 ACS HHs: Inc Below Poverty Level: Percent	Households already below the poverty level likely lack liquid assets to weather any financial shock.
2015-2019 ACS HHs w/Food Stamps/SNAP: Percent	Households already experiencing food insecurity before the pandemic likely lack liquid assets to weather any financial shock.

Housing

Variable	Rationale
2021 Pct of Income for Mortgage	The general rule of thumb is that housing costs should be no more than 28–33 percent of income. Households with high mortgage burden are less likely to weather income reduction or job loss. Payment deferral for federally backed mortgages is currently permitted for only 180 days under the CARES Act.
2015-2019 ACS HHs/Gross Rent 30-34.9% of Income: Percent	While a few cities and states have enacted moratoriums on evictions and established rent assistance programs, these policies have not been enacted uniformly. Households with a high rent burden are less likely to weather income reduction or job loss.
2015-2019 ACS HHs/Gross Rent 35-39.9% of Income: Percent
2015-2019 ACS HHs/Gross Rent 40-49.9% of Income: Percent
2015-2019 ACS HHs/Gross Rent 50+% of Income: Percent

Health insurance burden

Variable	Rationale
2015-2019 ACS Pop <19: No Health Insurance: Percent	Households without health insurance are likely already experiencing financial stress and may be less likely to seek medical attention if experiencing COVID-19 symptoms. According to a 2018 Gallup poll, 61 percent of those surveyed indicated that having to pay higher premiums is a "major concern."
2015-2019 ACS Pop 19-34: No Health Insurance: Percent
2015-2019 ACS Pop 35-64: No Health Insurance: Percent

Employment volatility

Variable	Rationale
Food Service/Drinking Estab Emp (NAICS): Percent	An analysis by the Brookings Institute identified the 10 largest industries most at risk of being impacted by COVID-19 interventions. As the percentage of employees in an area increases, the overall financial vulnerability increases.
General Merchandise Employees (NAICS): Percent
Motor Vehicles/Parts Dealers Emp (NAICS): Percent
Arts/Entertainment/Rec Emp (NAICS)
2021 Occupation: Personal Care: Percent	A proxy for the 821 NAICS code for "Personal and Laundry Services" in the Brookings report.

Adaptability

Variable	Rationale
2015-2019 ACS HHs w/No Internet Access: Percent	Households without internet access find it difficult to apply for alternate employment.

Acknowledgements

• Summit County census tract data provided by the U.S. Census Bureau and Esri.
• Demographic variables provided by esri.
• Berube, A. & Bateman, A. (2020, April 3). Report: "Who are the workers already impacted by the COVID-19 recession?" The Brookings Institution.
• McCarthy, Justin (2018, December 10). Gallup News: "Six in 10 Americans Worry About Healthcare Premiums."
• McCarthy-Alfano, M., Glickman, A., Wikelius, K. & Janet Weiner (2019, April 2). Health Affairs Blog: "Measuring The Burden Of Health Care Costs For Working Families."
• Gallery image by fernando zhiminaicela from Pixabay.
• Banner image courtesy of the Defense Visual Information Distribution Service (DVIDs). The appearance of United States Department of Defense (DoD) visual information does not imply or constitute DoD endorsement.
• Special thanks to Charlie Frye and Jim Herries for their feedback on this tutorial.