Opportunity Insights Economic Tracker Data Dictionary (last updated 2025-11-13)

Summary

The Opportunity Insights Economic Tracker Data Dictionary, last updated on 2025-11-13. The overview states that each data source and level of aggregation has a separate CSV named by data source, geographic level of aggregation and temporal level of aggregation, and that three GeoIDs files for State, County and City provide geographic crosswalks. It states that the City-level data, listed under Metro on the tracker site, associates the largest cities in the United States with a representative county, and that a Policy Milestones - State file collects key state-level policy dates. The document then describes the columns in each file, beginning with GeoIDs - State.csv and GeoIDs - County.csv.

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Full text

Opportunity Insights Economic Tracker
Data Dictionary
last updated on 2025-11-13

1 Overview

Each data source and level of aggregation has a separate CSV, named using the following
convention: Data source – Geographic Level of Aggregation – Temporal Level of Aggregation
Additionally, we have three files, GeoIDs – State and GeoIDs – County and GeoIDs –
City, that provide information on geographic crosswalks and aggregation. These can be merged
to any file sharing the same geographic level of aggregation using the geographic identifier.
Additionally, GeoIDs – County indicates the commuting zone (CZ) and state that each county
belongs to. The City-level data (listed under “Metro” on the tracker site) associates the largest
cities in the United States with a representative county one-to-one (except in the case of New
York City which includes the 5 boroughs).
Finally, we have gathered a collection of key state-level policy dates relevant for changes in other
series trends and values. These are contained in the Policy Milestones – State file.
A description of the columns in each file follows.

2 GeoID File Descriptions

2.1 GeoIDs - State.csv

Geographic identifier: statefips

• statename: The name of the state.
• stateabbrev: The 2-letter state abbreviation.
• state_pop2019: The population of the state in 2019, from Census Bureau estimates.

2.2 GeoIDs - County.csv

Geographic identifier: countyfips

• countyname: The name of the county.
• cityid: The city identifier that the county is assigned to.
• cityname: The name of the city that the county is assigned to.
• cz: The numeric identifier of the commuting zone (CZ) in which the county is contained.
• czname: The name of the commuting zone (CZ) in which the county is contained.
• statename: The name of the state in which the county is contained.

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• statefips: The FIPS code of the state in which the county is contained.
• stateabbrev: The 2-letter abbreviation of the state in which the county is contained.
• county_pop2019: The population of the county in 2019 according to Census Bureau
estimates.

2.3 GeoIDs - City.csv

Geographic identifier: cityid

• cityname: The name of the city.
• stateabbrev: The 2-letter abbreviation of the primary state in which the city is contained.
• statename: The name of the primary state in which the city is contained.
• statefips: The FIPS code of the primary state in which the city is contained.
• lat: Latitude of the city.
• lon: Longitude of the city.
• city_pop2019: The population of the city in 2019 according to Census Bureau estimates,
calculated as population of the county or counties assigned to the city.

3 Data File Descriptions

3.1 Affinity

Credit/debit card spending data from Affinity Solutions.

• spend_all: Spending in all merchant category codes (MCCs).
– spend_all_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
– spend_all_incmiddle: . . . by consumers living in ZIP codes with median income in
the middle two quartiles.
• spend_aap: Spending in apparel and accessories (AAP) MCCs.
– spend_aap_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_acf: Spending in accomodation and food service (ACF) MCCs.
– spend_acf_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_aer: Spending in arts, entertainment, and recreation (AER) MCCs.
– spend_aer_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_apg: Spending in general merchandise stores (GEN) and apparel and accessories
(AAP) MCCs.
– spend_apg_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_gen: Spending in general merchandise stores (GEN) MCCs.
– spend_gen_q#: . . . by consumers living in ZIP codes with median income in quartile
#.

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• spend_grf: Spending in grocery and food store (GRF) MCCs.
– spend_grf_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_hcs: Spending in health care and social assistance (HCS) MCCs.
– spend_hcs_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_hic: Spending in home improvement centers (HIS) MCCs.
– spend_hic_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_sgh: Spending in sporting goods and hobby (SGH) MCCs.
– spend_sgh_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_tws: Spending in transportation and warehousing (TWS) MCCs.
– spend_tws_q#: . . . by consumers living in ZIP codes with median income in quartile
#.
• spend_retail_w_grocery: Spending in retail (BLD, CLO, ELC, FBS, FUR, GEN, SPO)
MCCs including grocery spending.
– spend_retail_w_grocery_q#: . . . by consumers living in ZIP codes with median
income in quartile #.
• spend_retail_no_grocery: Spending in retail (BLD, CLO, ELC, FUR, GEN, SPO)
MCCs excluding grocery spending.
– spend_retail_no_grocery_q#: . . . by consumers living in ZIP codes with median
income in quartile #.
• spend_durables: Spending in durable goods (BLD, ELC, FUR, SPO, TEL, VEH) MCCs.
– spend_durables_q#: . . . by consumers living in ZIP codes with median income in
quartile #.
• spend_nondurables: Spending in non-durable goods (CLO, FBS, GAS, GEN, HPC, MSC,
WHO) MCCs.
– spend_nondurables_q#: . . . by consumers living in ZIP codes with median income
in quartile #.
• spend_remoteservices: Spending in remote services (ADM, EDU, FIN, INF, NSR, PST,
PUB, UCM) MCCs.
– spend_remoteservices_q#: . . . by consumers living in ZIP codes with median income
in quartile #.
• spend_inperson: Spending in in-person services (ACF, HCS, AER, TWS, REN, REP,
PLS) MCCs.
– spend_inperson_q#: . . . by consumers living in ZIP codes with median income in
quartile #.
• spend_inpersonmisc: Spending in other in-person services (REN, REP, PLS) MCCs.

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– spend_inpersonmisc_q#: . . . by consumers living in ZIP codes with median income
in quartile #.
• provisional: Indicator to mark that the date is within the most recent three weeks of
data and is subject to non-negligible changes as new data is posted.
• freq: Marks whether the data represents a daily (“d”) or weekly (“w”) value.

All spending variables are measured relative to January 6 to February 2, 2020, seasonally
adjusted, and calculated as a 7 day moving average. When we subdivide by income using the
median income of the ZIP codes, q1 is the quartile with the lowest median income and q4 is
the quartile with the highest median income. At the national level, we release a variety of
breakdowns without seasonal adjustment in variables that begin with spend_s_ (relative to
January 2019 for 2019 data, relative to January 2020 for 2020 data onward) or spend_19_
(relative to Janurary 7 to February 3, 2019 for all data) instead of spend_.
The merchant category codes (MCC) making up the grouped spending categories are:

• Retail spending: CLO clothing and clothing accessories; BLD building materials, garden
equipment, and supplies; ELC electronics and appliances; FBS food and beverage stores;
FUR furniture and home furnishings; GEN general merchandise stores; and SPO sporting
goods, hobbies, musical instruments, and book stores.
• Durable goods: BLD building materials, gardening equipment, and supplies; ELC
electronics and appliances; FUR furniture and home furnishings; SPO sporting goods,
hobbies, musical instruments, and bookstores; TEL telecommunications; and VEH motor
vehicles and parts.
• Non-durable goods: CLO clothing and clothing accessories; FBS food and beverage
stores; GAS gasoline stations; GEN general merchandise; HPC health and personal care
stores; and WHO wholesale trade.
• Remote services: ADM administrative and support and waste management and re-
mediation services; EDU education; FIN finance and insurance; INF information; PST
professional, scientific, and technical; PUB public administration; and UCM utilities,
construction, and manufacturing.
• In-person services: ACF accomodation and food services; HCS healthcare and social
assistance; AER arts, entertainment, and recreation; TWS transportation and warehousing;
REN rental and leasing; REP repair and maintenance; and PLS personal and laundry
services.

In addition, four supplemental files are included (see the documentation for more details on
these files):

• Affinity Income Shares - National - 2019.csv
• Affinity Income Shares - National - 2020.csv
• Affinity Industry Composition - National - 2020.csv
• Affinity Daily Total Spending - National - Daily.csv

3.2 Job Postings

Job postings data from Lightcast (formerly known as Burning Glass Technologies).

• bg_posts: Level of new job postings relative to January 4 to 31, 2020.
• bg_posts_ss30: Level of new job postings relative to January 4 to 31, 2020 in manufac-
turing (NAICS supersector 30).

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• bg_posts_ss55: Level of new job postings relative to January 4 to 31, 2020 in financial
activities (NAICS supersector 55).
• bg_posts_ss60: Level of new job postings relative to January 4 to 31, 2020 in professional
and business services (NAICS supersector 60).
• bg_posts_ss65: Level of new job postings relative to January 4 to 31, 2020 in education
and health services (NAICS supersector 65).
• bg_posts_ss70: Level of new job postings relative to January 4 to 31, 2020 in leisure and
hospitality (NAICS supersector 70).
• bg_posts_jz1: Level of new job postings relative to January 4 to 31, 2020 requiring
little/no preparation (ONET jobzone level 1).
• bg_posts_jz2: Level of new job postings relative to January 4 to 31, 2020 requiring some
preparation (ONET jobzone level 2).
• bg_posts_jz3: Level of new job postings relative to January 4 to 31, 2020 requiring
medium preparation (ONET jobzone level 3).
• bg_posts_jz4: Level of new job postings relative to January 4 to 31, 2020 requiring
considerable preparation (ONET jobzone level 4).
• bg_posts_jz5: Level of new job postings relative to January 4 to 31, 2020 requiring
extensive preparation (ONET jobzone level 5).
• bg_posts_jzgrp12: Level of new job postings relative to January 4 to 31, 2020 requiring
low preparation (ONET jobzone levels 1 and 2).
• bg_posts_jzgrp345: Level of new job postings relative to January 4 to 31, 2020 requiring
high preparation (ONET jobzone levels 3, 4 and 5).

In addition, the following supplemental file is included (see the documentation for more details
on this file):

• Job Postings Industry Shares - National - 2020.csv

3.3 COVID

COVID cases and deaths numbers are from the New York Times and the Centers for Disease
Control and Prevention, hospitalizations numbers are from the U.S. Department of Health and
Human Services, tests numbers are from Johns Hopkins University, and vaccination numbers
are from the Centers for Disease Control and Prevention.

• case_rate: Confirmed COVID-19 cases per 100,000 people, seven day moving average.
– case_count: Confirmed COVID-19 cases, seven day moving average.
• new_case_rate: New confirmed COVID-19 cases per 100,000 people, seven day rolling
sum.
– new_case_count: New confirmed COVID-19 cases, seven day rolling sum.
• death_rate: Confirmed COVID-19 deaths per 100,000 people, seven day moving average.
– death_count: Confirmed COVID-19 deaths, seven day moving average.
• new_death_rate: New confirmed COVID-19 deaths per 100,000 people, seven day rolling
sum.
– new_death_count: New confirmed COVID-19 deaths, seven day rolling sum.
• test_rate: Confirmed COVID-19 tests per 100,000 people, seven day moving average.
– test_count: Confirmed COVID-19 tests, seven day moving average.

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• new_test_rate: New confirmed COVID-19 tests per 100,000 people, seven day moving
average.
– new_test_count: New confirmed COVID-19 tests, seven day moving average.
• vaccine_rate: First vaccine doses administered per 100 people.
– vaccine_count: First vaccine doses administered.
• new_vaccine_rate: New first vaccine doses administered per 100 people, seven day moving
average.
– new_vaccine_count: New first vaccine doses administered, seven day moving average.
• fullvaccine_rate: Vaccine series completed per 100 people.
– fullvaccine_count: Vaccine series completed.
• new_fullvaccine_rate: New vaccine series completed per 100 people, seven day moving
average.
– new_fullvaccine_count: New vaccine series completed, seven day moving average.
• booster_first_rate: First booster doses administered per 100 people.
– booster_first_count: First booster doses administered.
• new_booster_first_rate: New first booster doses administered per 100 people, seven
day moving average.
– new_booster_first_count: New first booster doses administered, seven day moving
average.
• hospitalized_rate: New patients currently hospitalized in an inpatient bed who have
suspected or confirmed COVID-19 per 100,000 people, seven day moving average.
– hospitalized_count: Newly patients currently hospitalized in an inpatient bed who
have suspected or confirmed COVID-19, seven day moving average.

3.4 Google Mobility

GPS mobility data indexed to January 3 to February 6, 2020 from Google COVID-19 Community
Mobility Reports.

• gps_away_from_home: Time spent outside of residential locations.
• gps_retail_and_recreation: Time spent at retail and recreation locations.
• gps_grocery_and_pharmacy: Time spent at grocery and pharmacy locations.
• gps_parks: Time spent at parks.
• gps_transit_stations: Time at inside transit stations.
• gps_workplaces: Time spent at work places.
• gps_residential: Time spent at residential locations.

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3.5 Employment

Please note we are planning the release of a new, updated, and overhauled employment series
in the coming weeks. This series will much better handle entry and exit from the underlying
sample as well as improve on other biases in the current series. As such we advise that users
wait for the release of the new and updated data in order before using the Employment series so
that users have the most accurate information available.
Employment levels relative to January 4 to 31, 2020 from Paychex and Intuit.

• emp: Employment level for all workers.
• emp_incq1: Employment level for workers in the bottom quartile of the wage distribution
(annualized wage lower than the federal poverty line).
• emp_incq2: Employment level for workers in the second quartile of the wage distribution
(annualized wage between 1x and 1.5x the federal poverty line).
• emp_incmiddle: Employment level for workers in the middle two quartiles of the wage
distribution (annualized wage between 1x and 2.5x the federal poverty line).
• emp_incq3: Employment level for workers in the third quartile of the wage distribution
(annualized wage between 1.5x and 2.5x the federal poverty line).
• emp_incq4: Employment level for workers in the top quartile of the wage distribution
(annualized wage greater than 2.5x the federal poverty line).
• emp_incbelowmed: Employment level for workers in the bottom half of the wage distribu-
tion (annualized wage less than 1.5x the federal poverty line).
• emp_incabovemed: Employment level for workers in the top half of the wage distribution
(annualized wage greater than 1.5x the federal poverty line).
• emp_ss40: Employment level for workers in trade, transportation and utilities (NAICS
supersector 40).
• emp_ss60: Employment level for workers in professional and business services (NAICS
supersector 60).
• emp_ss65: Employment level for workers in education and health services (NAICS super-
sector 65).
• emp_ss70: Employment level for workers in leisure and hospitality (NAICS supersector
70).
• emp_retail: Employment level for workers in retail (NAICS sector 44-45).
• emp_retail_inclow: Employment level for workers in retail (NAICS sector 44-45) and
in the bottom quartile of the wage distribution (annualized wage lower than the federal
poverty line).
• emp_retail_incmiddle: Employment level for workers in retail (NAICS sector 44-45) and
in the middle two quartiles of the wage distribution (annualized wage between 1x and 2.5x
the federal poverty line).
• emp_retail_inchigh: Employment level for workers in retail (NAICS sector 44-45) and
in the top quartile of the wage distribution (annualized wage greater than 2.5x the federal
poverty line).
• emp_s72: Employment level for workers in accommodation and food services (NAICS
sector 72).
• emp_subset_unweighted_q1: Employment level for workers in the bottom quartile of
the wage distribution (annualized wage lower than the federal poverty line) in county x
industry (2-digit NAICS code) cells with nonzero employment for all four wage quartiles.
• emp_subset_unweighted_q2: Employment level for workers in the second quartile of the
wage distribution (annualized wage between 1x and 1.5x the federal poverty line) in county
x industry (2-digit NAICS code) cells with nonzero employment for all four wage quartiles.

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• emp_subset_unweighted_q3: Employment level for workers in the third quartile of the
wage distribution (annualized wage between 1.5x and 2.5x the federal poverty line) in
county x industry (2-digit NAICS code) cells with nonzero employment for all four wage
quartiles.
• emp_subset_unweighted_q4: Employment level for workers in the top quartile of the
wage distribution (annualized wage greater than 2.5x the federal poverty line) in county x
industry (2-digit NAICS code) cells with nonzero employment for all four wage quartiles.
• emp_subset_reweighted_q1: Employment level for workers in the bottom quartile of
the wage distribution (annualized wage lower than the federal poverty line) in county x
industry cells with nonzero employment for all four wage quartiles, reweighting to match
the county x industry (2-digit NAICS code) distribution of workers in the top quartile of
the wage distribution.
• emp_subset_reweighted_q2: Employment level for workers in the second quartile of the
wage distribution (annualized wage between 1x and 1.5x the federal poverty line) in county
x industry cells with nonzero employment for all four wage quartiles, reweighting to match
the county x industry (2-digit NAICS code) distribution of workers in the top quartile of
the wage distribution.
• emp_subset_reweighted_q3: Employment level for workers in the third quartile of the
wage distribution (annualized wage between 1.5x and 2.5x the federal poverty line) in
county x industry cells with nonzero employment for all four wage quartiles, reweighting
to match the county x industry (2-digit NAICS code) distribution of workers in the top
quartile of the wage distribution.
• emp_subset_reweighted_q4: Employment level for workers in the top quartile of the
wage distribution (annualized wage greater than 2.5x the federal poverty line) in county x
industry cells with nonzero employment for all four wage quartiles, reweighting to match
the county x industry (2-digit NAICS code) distribution of workers in the top quartile of
the wage distribution.

In addition, the following supplemental file is included (see the documentation for more details
on this file):

• Earnin - ZCTA - 2020.csv

3.6 UI Claims

Unemployment insurance claims data from the Department of Labor (national and state-level)
and numerous individual state agencies (county-level).

• initclaims_rate_regular: Number of initial claims per 100 people in the 2019 labor
force, Regular UI only
– initclaims_count_regular: Count of initial claims, Regular UI only
• initclaims_rate_pua: Number of initial claims per 100 people in the 2019 labor force,
PUA (Pandemic Unemployment Assistance) only
– initclaims_count_pua: Count of initial claims, PUA (Pandemic Unemployment
Assistance) only
• initclaims_rate_combined: Number of initial claims per 100 people in the 2019 labor
force, combining Regular and PUA claims
– initclaims_count_combined: Count of initial claims, combining Regular and PUA
claims

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• contclaims_rate_regular: Number of continued claims per 100 people in the 2019 labor
force, Regular UI only
– contclaims_count_regular: Count of continued claims, Regular UI only
• contclaims_rate_pua: Number of continued claims per 100 people in the 2019 labor
force, PUA (Pandemic Unemployment Assistance) only
– contclaims_count_pua: Count of continued claims, PUA (Pandemic Unemployment
Assistance) only
• contclaims_rate_peuc: Number of continued claims per 100 people in the 2019 labor
force, PEUC (Pandemic Emergency Unemployment Compensation) only
– contclaims_count_peuc: Count of continued claims, PEUC (Pandemic Emergency
Unemployment Compensation) only
• contclaims_rate_combined: Number of continued claims per 100 people in the 2019
labor force, combining Regular, PUA and PEUC claims
– contclaims_count_combined: Count of continued claims, combining Regular, PUA
and PEUC claims

3.7 Womply

Small business openings and revenue data from Womply.

• merchants_all: Percent change in number of small businesses open, calculated as a
seven-day moving average, seasonally adjusted, and indexed to January 4 to 31, 2020.
– merchants_inchigh: . . . in high income (quartile 4 of median income) ZIP codes.
– merchants_incmiddle: . . . in middle income (quartiles 2 & 3 of median income)
ZIP codes.
– merchants_inclow: . . . in low income (quartile 1 of median income) ZIP codes.
– merchants_retail: . . . in retail businesses (NAICS 2-digit codes 44-45).
– merchants_food_accommodation: . . . in food and accommodation businesses
(NAICS 2-digit code 72)
– merchants_professional: . . . in professional services businesses (NAICS 2-digit
code 54)
– merchants_other_services: . . . in other services businesses (NAICS 2-digit code
81)
– merchants_health: . . . in health & social service businesses (NAICS 2-digit code
62)
• revenue_all: Percent change in net revenue for small businesses, calculated as a seven-day
moving average, seasonally adjusted, and indexed to January 4 to 31, 2020.
– revenue_inchigh: . . . in high income (quartile 4 of median income) zipcodes.
– revenue_incmiddle: . . . in middle income (quartiles 2 & 3 of median income)
zipcodes.
– revenue_inclow: . . . in low income (quartile 1 of median income) zipcodes.
– revenue_retail: . . . in retail businesses (NAICS 2-digit codes 44-45).
– revenue_food_accommodation: . . . in food and accommodation businesses (NAICS
2-digit code 72).
– revenue_professional: . . . in professional services businesses (NAICS 2-digit code
54).

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– revenue_other_services: . . . in other services businesses (NAICS 2-digit code 81)
– revenue_health: . . . in health & social service businesses (NAICS 2-digit code 62)

In addition, the following supplemental file is included (see the documentation for more details
on this file):

• Womply - ZCTA - 2020.csv

3.8 Zearn

Online math learning data from Zearn.

• engagement: Average level of students using platform relative to January 6 to February
21, 2020.
• engagement_inclow: Average level of students using platform relative to January 6 to
February 21, 2020 for schools in the 25% of ZIP codes with the lowest median income.
• engagement_incmiddle: Average level of students using platform relative to January 6
to February 21, 2020 for schools in ZIP codes with median income between the 25th and
75th percentiles.
• engagement_inchigh: Average level of students using platform relative to January 6 to
February 21, 2020 for schools in the 25% of ZIP codes with the highest median income.
• badges: Average level of student achievements earned (badges) on platform relative to
January 6 to February 21, 2020.
• badges_inclow: Average level of student achievements earned (badges) on platform relative
to January 6 to February 21, 2020 for schools in the 25% of ZIP codes with the lowest
median income.
• badges_incmiddle: Average level of student achievements earned (badges) on platform
relative to January 6 to February 21, 2020 for schools in ZIP codes with median income
between the 25th and 75th percentiles.
• badges_inchigh: Average level of student achievements earned (badges) on platform
relative to January 6 to February 21, 2020 for schools in the 25% of ZIP codes with the
highest median income.

Note that for every variable listed here, there is a corresponding variable with the prefix break_
(for example, break_engagement). During the period in which schools are on summer or winter
break, we record the outcomes in these break_ variables instead of the usual variables. These
numbers are not displayed on the Economic Tracker because they do not reliably measure
differences in student learning across geography and income groups when many schools are on
break.
To ensure privacy, the results for some counties are masked. Where possible, masked county
levels values are replaced by commuting zone means, as indicated by the imputed_from_cz
variable. The masking criteria are explained in further detail in our data documentation.

4 Policy Milestones

Key state-level policy dates relevant for changes in other series trends and values.

• statefips: 2-digit FIPS code of the U.S. state a milestone was recorded in
• date: Date the policy milestone occurred
• policy_description: Description of policy milestone

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• schools_first_closed: Whether a milestone is the date at which the state first ordered
all public K-12 schools statewide to physically close
• nonessential_biz_first_closed: Whether a milestone is the date on which the state
government first ordered all nonessential businesses to close statewide
• stayathome_first_start: Whether a milestone is the date on which the state government
first told residents to stay home, save for excepted activities statewide

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