Methodology: City Economic Recovery Tracker


The aim of the City Economic Recovery Tracker is to create a practical, weekly pulse-check of the economic progress of major metropolitan areas in the U.S. Cities were chosen based on the availability of data and diversity of region. As we move into the next phase of the recovery, we hope to be able to add additional cities for comparison. 

We built the overall city recovery index from components that align with the lives of American communities:

  • Health
  • Transport and mobility
  • Jobs
  • Small businesses
  • Restaurant reservations

We required the following criteria for a dataset to be included in the overall index:

  • Availability: The data must be reported reliably, at least weekly, and available to the public. There are multiple measures that we would have liked to include, but are only reported monthly or quarterly, and often with a significant delay. 
  • Relevance: For each individual measure, a return to the baseline (accounting for seasonality) should reasonably reflect the normal functioning of the city. For example, a consistently crowded restaurant would suggest that residents had returned to their previous habits.

The City Economic Recovery Tracker is calculated from an un-weighted average of each of the five sub-indices. A reading of 100 is considered "normal”.

Data Collection & Analysis

COVID-19 Case Rate Index

The COVID-19 Case Rate Index represents the impact of the pandemic on the functioning of the city.


We chose to base our health index on new case rates for each city. While at the beginning of the pandemic this measure was negatively impacted by scarcity of available tests, testing data have become more reliable and we expect the detection of new cases to be tightly correlated with the spread of the disease. Bearing this in mind we believe that new case rates per 100,000 people are the best measures to track the recovery across cities.


New York Times COVID-19 Repository via Opportunity Insights


This is a complex index. The aim of this index is to understand where we are in the progress of containing the virus, as it relates to the functioning of U.S. Cities. Therefore, we built the index to reflect the following: When there are no new confirmed cases, the index will be 100.

The spread of COVID-19 was exponential before measures were taken to contain the spread, and further waves have progressed similarly. Therefore we are concerned with the rate of increase (whether or not new cases double week over week) as much as the total number. 

To construct the index, we use the trailing 7-day average of new confirmed cases per 100,000 people (new case rate) beginning on January 28, 2020. We based the index on the log of the new case rate to ensure the index directly tracks the rate of change of cases. By basing the index on the log of the number, it reflects that even with 30 new cases per day per 100,000 people the city is still not in a position to return to normal. This aligns with measures that health officials use to advise for social distancing practices and thus the viability of much economic activity in any city. For weeks early in the year where no data are available, we set the baseline ‘100’. 

Transit Mobility Index

Public transit is an indicator of the mobility of city residents, and an important component of the economic state of a city. Furthermore, the physical reality of public transportation also means ridership has been especially impacted by the pandemic.


We used Google’s Community Mobility Report to observe the impact of the pandemic across each city’s transportation network, relative to “normal”. These reports are based on Google users who visited/time spent in transit stations change compared to the baseline days. A baseline day represents a normal value for that day of the week. Google Community Mobility Reports calculates the baseline day is the median value from the 5‑week period between January 3rd and February 6th, 2020. Google Community Mobility Report defines transit stations to include: subway stations, sea ports, taxi stands, car rental agencies, and more.


Google Community Mobility Reports via Opportunity Insights


To construct the index, we take the 7-day trailing average of the index values reported by the Google Community Mobility Reports. These values are converted to whole numbers for the Transit Mobility Index. Google Community Mobility Reports make these daily transit numbers going back to March 1. Prior to March 1 the index is set at a value of “100” or normal.

Unemployment Claims Index

An unemployment claim is an application for cash benefits that an employee makes after being laid off or being unable to work for other covered reasons, such as the COVID-19 pandemic. As such, unemployment insurance claims are a vital measure of the economic state of a city. In our index, all positive changes are associated with positive outcomes. As with the COVID-19 Case Rate Index, a rise in initial unemployment claims is seen as a decrease in the index.


The Unemployment Claims Index is constructed from unemployment insurance claims data from the Department of Labor (national and state-level) and numerous individual state agencies (county-level). We use the county-level initial claims reported by states, which sometimes vary in their exact definitions (e.g., including or excluding certain federal programs). In some cases, states only publish monthly data. For these cases, we estimate the weekly city values. We do this by taking the distribution of statewide claims to that city from the most recently available month, and apply it to the weekly state-level data release from the Department of Labor



The index takes the inverse of the year-over-year percentage change of unemployment claims. For example, if unemployment claims this year are 2X higher than last year (200% year-over-year), then the index is 50 (100/2). If initial claims are 4X higher than last year (400% year-over-year), the index is 25 (100/4). If initial claims are the same as last year, the index will be 100 (100/1). So as initial claim numbers trend back to the level seen in 2019, the index will increase back up to 100.

Our Index begins reporting on January 25th, 2020, but we do not have data for some cities until March 7, 2020. For those cities and dates we set the index to a value of 100.  

Restaurant Reservations Index

Restaurants are a key indicator of the economic and social life of a city. While not all restaurants take reservations, only states or metros with 50+ restaurants on the OpenTable network for 2019 or 2020 are included in the sample. 


The index is created from OpenTable data showing daily year-over-year seated diners. The data are collected from a sample of restaurants on the OpenTable network including online reservations, phone reservations, and walk-ins. Day-of-week fluctuations are accounted for by comparing the same day of the week from the same week in the previous year. OpenTable data reporting begins on February 18. A baseline index of 100 is assumed for January 1st-February 17th. 


Full OpenTable data set & methodology 


Year-over-year percentages are converted to whole numbers for the Restaurant Reservations Index.

Small Business Index

Small businesses are an important part of the local economy in any city. The small business index reflects the number of employees working in small businesses using Homebase, a free scheduling and time tracking tool used by 100,000+ local businesses. Homebase’s customers in the US primarily consist of restaurant, food & beverage, retail and services and are largely individually owned/operator-managed businesses. This data is of course not indicative of employment trends of the country as a whole, but rather highlights smaller, “Main Street” businesses that are particularly likely to be impacted by COVID-19.


To understand the impact of COVID-19 on a city, we use Homebase’s reporting of ‘employees working.’ This measure reflects the number of hourly workers with at least one clock-in for businesses utilizing Homebase. Data are collected from individual cities based on businesses in Metro Service Areas defined by Homebase.




The Small Business Index is created by taking a trailing seven-day average of the percent change of employees working in all Homebase managed firms for each city to a January 2020 baseline. The data are then seasonally adjusted using the expected percent change from January for the same date in 2019. Finally, the data are transformed from a negative percent change to a whole number by adding one (1) and then multiplying by 100 to give an index score ranging from 0 to 100.

Current Limitations and Areas for Future Exploration

There are countless ways of measuring the economic impact COVID-19. The City Economic Recovery Tracker is not meant to be a complete portrait of every economic aspect of each city. There were several measures not included that fit our objectives, but are not consistently or publicly available. Also, it is possible that the future economic landscape of each city differs from its past, and requires new indicators to better reflect that reality. 

As with the New York City Recovery Index, the City Economic Recovery Tracker does not reflect the economic recovery of any individual. The iniquitous distribution of wealth, as well as the disproportionate impact of the pandemic on Black and Latino residents for example results in a broad range of lived experiences not reflected in the index. We hope to be able to supplement our weekly reporting with richer data to be able to speak to these shortcomings. 

We further acknowledge that with each data set there are limitations. Initial Unemployment Insurance claims, for example, do not reflect the job losses of undocumented individuals who represent an important and especially vulnerable segment of the workforce. Additionally, the accuracy of initial COVID-19 case rates data were impacted by the availability of tests in the early days of the pandemic, among other factors.


This index was created by the Investopedia Data Journalism Team with direction from Jon Roberts, Ph.D., and in collaboration with Adrian Nesta, Alexandra Kerr, Elana Duré, and Caleb Silver. Special thanks to Opportunity Insights for their collaboration and for making so many datasets easily accessible. 

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