Unemployment in Connecticut During COVID-19 Crisis
Since the beginning of the pandemic, 512,000 Connecticut residents applied for unemployment benefits.
Where in Connecticut are people applying?
In absolute terms, it is not surprising that big Connecticut cities see the largest number of initial claims. The most claims were made in:
Bridgeport—22,448
Hartford—19,911
Waterbury—17,237
Stamford—17,170
New Haven—16,230
The picture changes significantly when looking at per-capita rates. New London (185 per 1,000), Norwich (234) and Canaan (288) saw much higher application rates than New Haven (124), Stamford (133), Bridgeport (153), or Hartford (161).
The map below shows the number of initials claims made during 21 weeks, from 03/15 to 08/02 by town per 1,000 residents.
The top-20 towns with the most claims per 1,000 residents are labeled. You can see that of 20 most affected towns per capita, 8 are in New London County. Click the map to enlarge.
What is this data?
The Connecticut Department of Labor’s (DOL) Initial Claims data is an important resource to understand the economic impact of the COVID-19 crisis on the state of Connecticut.
Initial claims are applications for unemployment benefits, which may or may not result in an applicant receiving them. People who aren’t covered by the Unemployment program include federal workers, railroad & religious workers, and self-employed, although they might be able to access benefits through the Pandemic Unemployment Compensation (PUA) scheme.
Due to a reduced capacity of the department to process claims, as well as an unprecedented surge in the number of applications (initial claims), the complete (non-preliminary) data released by the DoL is lagging by about a month. As of August 13, 2020, the latest non-preliminary data we have is for the week of 07/12.
Accommodation and food services, arts & entertainment, and the Self-Employed are hit hardest
How did we calculate these numbers?
We used DOL’s industry employment counts for February 2020 (the latest full month not affected by COVID-19) to calculate the percentage of employees by industry who applied for unemployment benefits. The bar chart shows both percentage (x-axis, represents how much each industry was impacted) and number of workers who applied for unemployment benefits (labels).
Women and lower-income workers apply more often
Nationwide, women are more affected by layoffs than men. The chart below shows that the same is true for Connecticut.
See Coronavirus Unemployment Tsunami Hitting Women Harder—And It Could Cause ‘Prolonged Damage’ by Forbes and The Economic Devastation Of COVID-19 Is Hitting Women Particularly Hard by HuffPost to read more about women employment & Covid-19.
Nearly half of first-time claimers declared either no earnings, or earnings below $20,000 in 12 months prior to claim.
Which Demographic Groups are Impacted the Most?
We looked at UI applicants’ age, race, and education to understand which subgroups got impacted the most. We divided the number of claims in each subgroup by the respective labor force size to calculate what percentage of that demographics got impacted. Conclusion: the young, the ones with fewer years of education, and workers of color were more likely to apply for UI benefits.
We used 2018 ACS 1-year data for Connecticut to estimate % of labor force that got impacted.
Just under a third of those aged 20-29 in the labor force applied for UI benefits
People in the middle of their career, aged 40–60, were less likely to lose jobs than those at the beginning or the end of their careers.
Workers of color are worse off than white workers.
30.3% of Black workers claimed benefits, compared to 20.7% of white workers.
A worker with a high school diploma was twice more likely to apply for UI benefits than a worker with a Bachelor’s degree*.
Workers with more years of education were less likely to apply for UI benefits.
*Note: Education data is not available for 40.3% of first-time claimers for the 21-week period.
Data
We will monitor new releases of Initial Claims data by DOL and update this page. Charts and the map are created in Python (with matplotlib
, Seaborn
, and geopandas
).