labor market flows and the covid-19 economypublish.illinois.edu/elizaforsythe/files/2020/04/... ·...

Labor Market Flows and The Covid-19 Economy

Eliza Forsythe, University of Illinois, Urbana-Champaign∗

April 13, 2020

Abstract

I examine labor market flows in the CPS through March 2020. Although there was

a dramatic single month increase in the unemployment rate and exit rate from em-

ployment, most cyclical flows (hiring, employer-to-employer flows, quits) have not yet

changed substantially. I show that increases in flows to non-employment are concen-

trated in a few sectors (hospitality, other services, and construction), suggesting that

the economic slow-down was not yet widespread in March of 2020. However, I show

that within-individual hours decreased in March 2020, and this reduction was broad

based across industries, occupations, and demographic groups.

1 Introduction and Methodology

In this note, I use matched data from the Current Population Survey to investigate how

labor market flows have adjusted in the face of the Coronavirus/Covid-19 labor market shut-

down. This note includes data through March 14th 2020, which was early in the shutdown

process. I will update as more data becomes available.

I use CPS monthly data from December 2018 through March 2020. Using a procedure

developed by Madrian and Lefgren (1999), I match individuals across adjacent months using

administrative IDs, and confirm matches using sex, race, and age. I restrict the sample

to non-institutionalized civilians of at least 16 years old. All estimates are weighted to be

consistent with the non-seasonally adjusted figures reported by the CPS.

∗School of Labor and Employment Relations and Department of Economics, Email:eforsyth@illinois.edu. See https://sites.google.com/site/elizaforsythe/ for a digital ver-sion.

4.63 4.65

3.483.75

January February March

% of Employed Exiting to Nonemployment

2019 2020

Figure 1

2 Aggregate Flows

I begin by examining aggregate flows. In Figure 1, I show the fraction of individuals who

were employed in the previous month who move to non-employment (either unemployed

or out of the labor force (NILF)). This flow increased sharply in March of 2020, rising

1.48 percentage points from February of 2020. In Figure 2, I separate this into flows to

unemployment and NILF. Here we see that the outflows from employment are evenly divided

into unemployment (0.73 pp increase) and NILF (0.76 pp increase). Thus, while the (non-

seasonally adjusted) unemployment rate increased by half of a percentage point between

February and March 2020, this only captures half of the job loss. See Figure 16.

As a measure of the intensive margin of employment, we can also look at how hours

change. I use the measure for the actual number of hours worked during the reference week.

In Figure 3, I calculate the change in hours for individuals who were employed both months.

For individuals who were employed and at work in both February and March 2020, hours

decrease by 0.46 per week, while hours increased between Feb-March 2019. This year-over-

year change is similar to what we see in the cross-section (see Figure 18) . Alternatively, if we

look at the fraction of employed workers with an hours cut, this rose 1.2 pp from February

to March 2020.

During recessions, employers reduce hiring, which is ultimately responsible for most of

1.241.28

0.840.90

% of Employed Exiting to Unemployment

2019 2020

3.39 3.37

2.642.84

% of Employed Exiting to NILF

2019 2020

Figure 2

-0.62-.6-.4

Change in Hours from Previous Month's Reference Week

2019 2020

25.68 26.0925.13

24.2123.16

% of Workers with an Hours Cut

2019 2020

Figure 3

2.792.94

2.66 2.672.48

Percent of Individuals Hired this Month

2019 2020

Figure 4

the rise in unemployment. In Figure 4, I show how the hiring rate has changed. This is

constructed using hires from non-employment as well as flows between employers to capture

the full hiring picture. In March 2020, we see what may be a small reduction in hiring from

February (0.18 pp). This suggests employers have not yet changed hiring behavior as of

March 14th. I expect to see a much larger drop in the April 2020 numbers.

Another way that labor market flows change during recessions is that employer-to-

employer (EE) flows drop dramatically. This is two sided– workers are less likely to seek out

new work during recessions, and the lower volume of openings make it harder to find new

positions. In Figure 5 we see there is no evidence that EE flows have yet been affected by

the crisis.

Next, during recessions, workers are less likely to quit and more likely to lose their jobs.

In the CPS, we can only observe these flows for individuals who are classified as unemployed

(e.g. actively looking for work), but this also gives an indicator for the state of the labor

market. In Figure 6, we see both quits and job losses increased very slightly in March of

2020. In April I expect to see more movement. Further, given the nature of the crisis in

which work environments may be unsafe or individuals may have more caregiving demands

on their time, we may see individuals are more likely to quit compared to typical recessions.

1.791.86 1.87

1.811.73

% of Employed Changing Firms

2019 2020

Figure 5

0.130.13

% of Employed Quiting to Unemployment

2019 2020

0.33 0.34

% of Employed Losing Job (to Unemployment)

2019 2020

Figure 6

1.911.75

7.845.28

10.115.84

4.783.02

4.163.67

3.572.28

3.543.78

4.404.19

4.494.40

2.812.67

6.004.44

8.398.71

0 2 4 6 8 10

Public AdminOther Services

HospitalityEd and Health

Prof.Financial

InfoTranspTrade

MfgConstr

% of Employed Exiting to Nonemp. by Industry

March 2019 March 2020

Figure 7

3 Flows by Industry

I next examine how these flows vary by industry. In all figures, I compare flow rates

from March 2020 to March 2019. In Figure 7, I show a dramatic increase in exits to non-

employment from leisure and hospitality- rising from 5.84 in March of 2019 to 10.11 in

March of 2020. Other industries with 2-3 pp increases in exit rates include other services,

construction, and education and health.

In Figure 8, I look at the change in reference week hours for individuals who were em-

ployed in both February and March. Here we see the same industries with increased flows

to non-employment also have the largest reduction in hours. Leisure and hospitality workers

experienced a 1.59 average reduction in hours, while other services workers experienced a 1.08

reduction, construction experienced a 0.81 reduction, and manufacturing experienced 0.87

reduction. Nonetheless, we see reductions in hours across industries that are substantially

larger than the comparison of February to March 2019, with the exception of agriculture and

public administration. Thus, hours reductions appear to be much more broad based then

the increase in exits.

In Figure 9, I show that the same industries with high outflow rates do not yet have a

substantial reduction in their hiring rate. This is measured by the share of all working aged

0.00-0.11

-1.080.11

-1.590.09

-0.41-0.06

-0.750.48

-0.59-0.10

-0.410.32

-0.39-0.31

-0.24-0.02

-0.87-0.03

-0.810.32

0.391.91

-2 -1 0 1 2

Prof.Financial

InfoTranspTrade

MfgConstr

Change in Weekly Hours by Industry

March - Feb 2019 March - Feb 2020

Figure 8

individuals who are hired into that particular industry each month. Leisure and hospitality

shows no decrease in the hiring rate. However, both construction and other services show

sizeable decreases. I expect to see a reduction in hiring across industries in the April 2020

0.090.09

0.110.16

0.410.41

0.500.50

0.270.31

0.120.13

0.030.03

0.130.13

0.360.39

0.220.22

0.170.21

0.040.06

0 .1 .2 .3 .4 .5

Prof.Financial

InfoTranspTrade

MfgConstr

% Hires by Industry

Figure 9

4 Flows by Occupation

Next I examine heterogeneity by occupation. In Figure 10, results are consistent with

industry flows. Service occupations show the largest increase in exit rates compared with

March of 2019, while construction also shows a sizable increase. However, workers in all

occupations reported exiting employment at a higher rate than in March of 2019. In Figure

11, I show there are large reductions in hours for service occupations (1.02), and installation

(1.07), however all occupations show substantial decreases in weekly hours, again indicating

that the hours reductions are affecting a segments of the labor market.

6.425.19

4.383.82

3.102.01

6.764.86

4.433.78

4.714.27

8.655.50

3.782.70

2.962.47

0 2 4 6 8

Transport

Install

Constr

Office Support

Service

% of Employed Exiting to Nonemp. by Occupation

Figure 10

In Figure 12, similar to Figure 9, service occupations do not yet reveal a decrease in

hiring. However construction, sales, and installation all indicate a reduction in hiring may

have already begun.

-0.66-0.08

-0.64-0.11

-1.07-0.40

-0.660.12

-0.230.15

-0.53-0.15

-1.020.31

-0.590.02

-0.470.06

-1 -.5 0 .5

Transport

Install

Constr

Office Support

Service

Change in Weekly Hours by Occupation

Figure 11

0.220.22

0.130.15

0.050.09

0.140.19

0.300.31

0.240.31

0.680.69

0.430.39

0.230.25

0 .2 .4 .6 .8

Transport

Install

Constr

Office Support

Service

% Hires by Occupation

Figure 12

5 Flows by Demographics

Next, I examine how labor market flows differ by demographic groups. Recessions are

typically more severe for disadvantaged groups, including non-white, those without college

degrees, and younger workers (see Forsythe and Wu (2019)). However, given the variety of

differences between the Covid-19 shock and typical recessions, the demographic impact may

differ. One key difference is the large shock to caregiving responsibilities with daycare and

schools closing. Further, closure of restaurants and other services likely increase time spent

on home production. Given the gendered division of childcare and home production, this

may lead to a larger negative effect on female labor force participation. This will be more

evident in future months.

Across demographics, the groups that had higher exit rates in March of 2019 also have

larger increases in exit rates from March 2019 to March 2020. Thus, the increase in exit

rates is larger for non-white individuals, individuals without a college degree, and individuals

under 30. Women also see a substantially larger increase in exits then men.

We see somewhat different patterns when we look at the change in weekly hours, which are

widespread across demographic groups. Men and college educated individuals have slightly

larger decreases in hours than women and those without a college degree. Finally, hours

decreases are similar for young and prime-aged workers, but older workers have a smaller

decrease in hours.

White Non-White

% of Employed Exiting to Nonemployment by Race

4.324.11

Male Female

% of Employed Exiting to Nonemployment by Gender

No College College

% of Employed Exiting to Nonemployment by Ed.

3.593.99

30 and Under 30 to 50 Over 50

% of Employed Exiting to Nonemployment by Age

Figure 13

-.8-.6

-.4-.2

White Non-White

Change in Weekly Hours by Race

-0.51-.8

-.6-.4

Male Female

Change in Weekly Hours by Gender

-0.67-.6-.4

No College College

Change in Weekly Hours by Ed.

-.8-.6

-.4-.2

Change in Weekly Hours by Age

Figure 14

2.702.80

White Non-White

Percent of Individuals Hired this Month by Race

2.58 2.502.38

Male Female

Percent of Individuals Hired this Month by Gender

2.051.94

No College College

Percent of Individuals Hired this Month by Ed.

2.64 2.54

1.49 1.42

Percent of Individuals Hired this Month by Age

Figure 15

3.98 4.073.79 3.92

Unemployment Rate

2019 2020

62.77 63.00 63.00 63.26 62.98 62.57

Labor Force Participation Rate

2019 2020

Figure 16

6 Cross-Sectional Facts

In this section, I use the unmatched monthly data to show several facts about the labor

market. In Figure 16, I show how the unemployment and participation rates have changed

(both seasonally unadjusted). In Figure 17, I show the increase in the share of employed

individuals who were absent from work in March 2020. Compared with 3.27% in March of

2019, 4.15% were absent in March of 2020. This will be an indicator to watch as the crisis

unfolds.

In Figure 18, I report the year-over-year change in average hours for employed individual

for each of January, February, and March. Here we see that average hours down 0.48 in

March of 2020 compared with March of 2019.

References

Forsythe, E., & Wu, J.-C. (2019). Explaining Demographic Heterogeneity in Cyclical Unem-

ployment.

Madrian, B. C., & Lefgren, L. J. (1999). A Note on Longitudinally Matching Current

Population Survey (CPS) Respondents. NBER Working Paper No. t0247..

2.79 2.842.63

Percent of Employees Absent

2019 2020

Figure 17

-.4-.3

-.2-.1

Year over Year Change in Hours (2020-2019)

Figure 18

labor market flows and the covid-19 economypublish.illinois.edu/elizaforsythe/files/2020/04/... ·...

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