Putting the Paycheck Protection Program into Perspective:

An Analysis Using Administrative and Survey Data

Michael Dalton

Bureau of Labor Statistics

10/25/22

First Draft November 2021

New Draft Very Soon!

Views expressed do not reflect those of the Bureau of Labor

Statistics

1 / 41
Paycheck Protection Program

• Created in March 2020 with $669 billion in funding and administered by the Small Business Admin (SBA)

• This amount is 85% of the estimated size of the *entire* American Recovery and Reinvestment Act of 2009

• There was a second round in 2021 - Not using that for this

analysis

2 / 41
Paycheck Protection Program

• Created in March 2020 with $669 billion in funding and administered by the Small Business Admin (SBA)

• This amount is 85% of the estimated size of the *entire* American Recovery and Reinvestment Act of 2009

• There was a second round in 2021 - Not using that for this

analysis

• Vast majority of employers in U.S. were eligible

• Loan can be converted to grant if specific payroll criteria met

• Otherwise, loan had 1% interest rate with 2 (or 5) year maturity

2 / 41
Paycheck Protection Program

• Created in March 2020 with $669 billion in funding and administered by the Small Business Admin (SBA)

• This amount is 85% of the estimated size of the *entire* American Recovery and Reinvestment Act of 2009

• There was a second round in 2021 - Not using that for this

analysis

• Vast majority of employers in U.S. were eligible

• Loan can be converted to grant if specific payroll criteria met

• Otherwise, loan had 1% interest rate with 2 (or 5) year maturity

• Purpose of program was for small business employers to maintain payroll by supplying direct payments of up to 10 weeks of

payroll costs (max $10 million)

Given the size and scope, what impacts can we actually

attribute to the program?

2 / 41
Assessing PPP Impact

• Regression discontinuity around eligibility - rather small effects

on employment

• Autor et al (2020), Chetty et al (2020), Hubbard and Strain

(2020)

• Focuses on largest loans and largest eligible employers (and

smallest ineligible employers)

3 / 41
Assessing PPP Impact

• Regression discontinuity around eligibility - rather small effects

on employment

• Autor et al (2020), Chetty et al (2020), Hubbard and Strain

(2020)

• Focuses on largest loans and largest eligible employers (and

smallest ineligible employers)

• Geographic differences in loan access as instrument

• Small-ish employment effects - Granja et al (2022)

• Larger employment effects - Bartik et al (2021), Doniger and

Kaye (2021), Bartlett and Morse (2020), Faulkender et al (2020)

• Kurmann et al (2021) and Bartik et al (2021) find evidence of

reduced closures

3 / 41
Assessing PPP Impact

• Regression discontinuity around eligibility - rather small effects

on employment

• Autor et al (2020), Chetty et al (2020), Hubbard and Strain

(2020)

• Focuses on largest loans and largest eligible employers (and

smallest ineligible employers)

• Geographic differences in loan access as instrument

• Small-ish employment effects - Granja et al (2022)

• Larger employment effects - Bartik et al (2021), Doniger and

Kaye (2021), Bartlett and Morse (2020), Faulkender et al (2020)

• Kurmann et al (2021) and Bartik et al (2021) find evidence of

reduced closures

• Difference-in-Difference

• Positive employment effects - Autor et al (2022)

• Most similar to what you’ll see here today

3 / 41
Where does this paper fit in?

• Using the full wage record database of employers

• Full employment and wage history and rich information about

establishments

• Not subject to sample churn

• Can easily match it to other survey data at the BLS

• Longer-term effects, in particular on closures

• Use wage data to assess pass-through of PPP to wages paid

• Dynamic diff-in-diff strategy using microdata

• More detailed heterogeneity analysis

4 / 41
Key Results

• One month after PPP approval, PPP has the effect of...

• 8.1% increase in employment

• 5.6% decline in probability of closure

• 12.2% increase in wages

5 / 41
Key Results

• One month after PPP approval, PPP has the effect of...

• 8.1% increase in employment

• 5.6% decline in probability of closure

• 12.2% increase in wages

• Effects fall after first month but are persistently positive up to

12 months after PPP approval

5 / 41
Key Results

• One month after PPP approval, PPP has the effect of...

• 8.1% increase in employment

• 5.6% decline in probability of closure

• 12.2% increase in wages

• Effects fall after first month but are persistently positive up to

12 months after PPP approval

• Estimate of $27,690 of PPP loans per employee-month retained

after 6 months

• Estimate goes to $16,199 after 12 months - long term results

because of reduced closures

• $3.12 of PPP spent per dollar of wages retained after 12 months

5 / 41
Paycheck Protection Program

• Using published PPP microdata with employer name, address,

date of approval, and amount of loan

• Focusing only on first round of PPP from April 2020 through

August 2020

• Remove some loans that are out of scope for the wage records

• Self-employed

• Independent Contractors

• Sole proprietorship

• Non-profit Religious Organizations

• Remaining: 3.8 million loans worth $483 billion

6 / 41
Quarterly Census of Employment and Wages

• Covers all establishments that pay into the Unemployment Insurance (UI) system nationwide

• Covers more than 95% of all jobs

• Gives monthly employment and quarterly wages

• Employer names and addresses

• Can track establishment over time

• Before pandemic and after receipt of PPP loan

• Using data through September 2021

• Can also partially map establishments by firm

• Relies on Employment Identification Number (EIN)

• Though, this is an imperfect measure of firms

• Important for thinking about who the PPP loan recipient actually is

7 / 41
Record Linking

• Fuzzy text match on employer name and address between PPP

and QCEW

• Finds closest name and geography match

• This linkage allows analysis of those that did - and did not -

get approved for PPP loan and when that approval happened

8 / 41
Record Linking

• Fuzzy text match on employer name and address between PPP

and QCEW

• Finds closest name and geography match

• This linkage allows analysis of those that did - and did not -

get approved for PPP loan and when that approval happened

• Employment, wages, and closure status before and after PPP

approval

8 / 41
Match Rate of PPP to QCEW

Descriptor

Total

Number of

Loans

(millions)

Total Dollar

Amount

($billions)

% of Loans

Matched

% of Loan $

Amount

Matched

All Loans 3.84 483.5 76.3 87.9

Removing Loans Reporting Only 1 Job 3.3 476.7 80.5 88.5

Removing Loans Reporting 0 or 1 Job 2.7 412.2 82.5 88.8

Match Rate Verification

9 / 41
The True Effect of PPP

• We want to estimate the true treatment effect of receiving

PPP

• A particularly thorny econometric question

• PPP approval is not random - based on choice on behalf of

establishment - ”selection effects”

10 / 41
The True Effect of PPP

• We want to estimate the true treatment effect of receiving

PPP

• A particularly thorny econometric question

• PPP approval is not random - based on choice on behalf of

establishment - ”selection effects”

• Beyond selection effects, standard difference-in-difference has

problems in this context

10 / 41
The True Effect of PPP

• We want to estimate the true treatment effect of receiving

PPP

• A particularly thorny econometric question

• PPP approval is not random - based on choice on behalf of

establishment - ”selection effects”

• Beyond selection effects, standard difference-in-difference has

problems in this context

• PPP approved over the course of 4 months - multi-period treatment means two-way fixed effects do not give true causal parameters without additional strong assumptions

• Those who got PPP earlier look different than those who got

it later - treatment heterogeneity

10 / 41
The True Effect of PPP

• We want to estimate the true treatment effect of receiving

PPP

• A particularly thorny econometric question

• PPP approval is not random - based on choice on behalf of

establishment - ”selection effects”

• Beyond selection effects, standard difference-in-difference has

problems in this context

• PPP approved over the course of 4 months - multi-period treatment means two-way fixed effects do not give true causal parameters without additional strong assumptions

• Those who got PPP earlier look different than those who got

it later - treatment heterogeneity

• Want to look at effect for full sample - not just establishments

in a particular size range

10 / 41
Callaway and Sant’anna (2021)

• Related to Goodman-Bacon (2021) and Sun and Abraham (2020)

• Dynamic diff-in-diff

• Allows for estimating average treatment on the treated

(ATT) effect of policy change

• Handles treatment heterogeneity

• Handles differential treatment timing

• Controls for time invariant characteristics to better deal with

selection effects - makes use of rich QCEW data!

• Allows for event study parameters

• Estimation strategy relying on *full* sample, not restricted

to subpopulation

Bottom line: This gives a credible estimation strategy for

assessing PPP

Detailed Equations

11 / 41
Defining the Dependent Variable

E

∗

imyjc =

Employment of Establishment i

z }| {

eimyjc

P2019

t=2017 eimtjc

3

−

P

k6=i∈j,c P

ekmyjc

k6=i∈j,z

ekm2019jc

• E

∗

imyjz is the dependent variable of interest

• eimyjz is employment

• for establishment i

• Month m and year y [post-pandemic]

• establishment i’s 4-digit industry j

• establishment i’s physical location county c

12 / 41
Defining the Dependent Variable

E

∗

imyjc =

eimyjc

P2019

t=2017 eimtjc

3

| {z }

Avg. Employment Prior to Pandemic

−

P

k6=i∈j,c P

ekmyjc

k6=i∈j,c

ekm2019jc

• Employment relative to average employment in 3 years prior to

pandemic

• Same calendar month for the baseline deals with seasonality

• Uses information prior to any pandemic effects

13 / 41
Defining the Dependent Variable

E

∗

imyjc =

eimyjc

P2019

t=2017 eimtjc

3

−

Employment Change in County - Industry

z P }| {

k6=i∈j,c

ekmyjc

P

k6=i∈j,c

ekm2019jc

• Subtract employment change in establishment i’s county - industry

• Geography specific effects

• Bank access

• Local COVID policies

• Local COVID incidence

• Industry specific effects

• COVID-specific impacts

14 / 41
Defining the Dependent Variable

E

∗

imyjc =

eimyjc

P2019

t=2017 eimtjc

3

−

P

k6=i∈j,c P

ekmyjc

k6=i∈j,c

ekm2019jc

Example:

• Establishment reports employment of 11 in June 2020

• Average June employment for establishment from 2017-2019:

10

• County-4-digit industry of establishment has lost 5% of 2019

employment as of June 2020

• E

∗

imyjc = 100 ∗ [

11

10 − .95] = 15

Control Variables

15 / 41
ATT on Employment

• Pre-treatment months

• Statistically null or economically close to zero

• Evidence of satisfying key assumption: parallel trends

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

Effect on Employment %

 Relative to Pre-Pandemic Employment Baseline

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Employment Relative to Pre-Pandemic Employment Baseline

95% Simultaneous Confidence Band

ATT of PPP Loan

16 / 41
ATT on Employment

• 8.1% increase in employment due to approval in month 1

• Effect falls but still significant 12 months after PPP approval

• 4.9% higher employment 12 months after approval

• Effect increases after second COVID wave

• Extended effects due to avoiding closure?

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

Effect on Employment %

 Relative to Pre-Pandemic Employment Baseline

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Employment Relative to Pre-Pandemic Employment Baseline

95% Simultaneous Confidence Band

ATT of PPP Loan

17 / 41
Placebo Test

• Think back to happier times, when the word ”pandemic” instead elicited thoughts of the ”Antonine Plague” and ”The

Black Death”

• 2018-2019 employment change relative to pre-2018 employment

• Do the same establishments that receive PPP in 2020 have any

different trajectory pre-pandemic compared to non-receiving establishments?

18 / 41
Placebo Test

• Think back to happier times, when the word ”pandemic” instead elicited thoughts of the ”Antonine Plague” and ”The

Black Death”

• 2018-2019 employment change relative to pre-2018 employment

• Do the same establishments that receive PPP in 2020 have any

different trajectory pre-pandemic compared to non-receiving establishments?

-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Months Until/Since PPP Approval

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

Effect on Employment %

 Relative to Pre-Pandemic Employment Baseline

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Employment Relative to Pre-2018 Employment Baseline

Pre-Pandemic Placebo Test

95% Simultaneous Confidence Band

ATT of PPP Loan

18 / 41
Imputing Audience Comments

19 / 41
Imputing Audience Comments

”Mike, you rube! Establishments struggling the most because of

COVID are going to apply for PPP, and that will dampen the

effects you find compared to the true ATT!”

• Bartik et al (2021) find those with less cash-on-hand and more

impacted by COVID were more likely to apply, but...

• Also report that less cash on hand was less likely to get approval

19 / 41
Imputing Audience Comments

”Mike, you rube! Establishments struggling the most because of

COVID are going to apply for PPP, and that will dampen the

effects you find compared to the true ATT!”

• Bartik et al (2021) find those with less cash-on-hand and more

impacted by COVID were more likely to apply, but...

• Also report that less cash on hand was less likely to get approval

Another point - treatment misclassification

ATT∗ =

ATTˆ

P(PPP∗=0|PPP=0)+P(PPP∗=1|PPP=1)−1

This gives an ATTˆ that undershoots the true ATT∗ by about

22%

Almost entirely driven by false negatives - can I improve the

match?

19 / 41
Imputing Audience Comments, pt. 2

20 / 41
Imputing Audience Comments, pt. 2

”Mike, you knucklehead! Businesses that were going to fail weren’t

going to apply for PPP, so you are *overestimating* the true

ATT!”

• Loans were forgivable if the establishment met certain payroll

criteria, but...

• Even if the criteria couldn’t be met, loans had a 1% interest rate

with a maturity of 2 years (changed to 5 years for later-stage

loans)

• Most loans are under $25k with no collateral requirement -

these would be forgivable in the event of business closure

20 / 41
Imputing Audience Comments, pt. 2

”Mike, you knucklehead! Businesses that were going to fail weren’t

going to apply for PPP, so you are *overestimating* the true

ATT!”

• Loans were forgivable if the establishment met certain payroll

criteria, but...

• Even if the criteria couldn’t be met, loans had a 1% interest rate

with a maturity of 2 years (changed to 5 years for later-stage

loans)

• Most loans are under $25k with no collateral requirement -

these would be forgivable in the event of business closure

Can try to bound potential violations of parallel trends

20 / 41
Interpreting Medium-Term Effects

• The immediate effect of PPP (t=0, t=1, t=2) make sense,

but...

• What to make of t > 2 effects?

21 / 41
Interpreting Medium-Term Effects

• The immediate effect of PPP (t=0, t=1, t=2) make sense,

but...

• What to make of t > 2 effects?

• Are establishments who get PPP more likely to participate in

other programs?

• If so, some of the medium-term effects may be due to participating in more programs

• I am controlling for EIDL participation or PPP 2021, plus this

was a smaller set of programs

• Is this picking up longer-term avoidance of closures?

21 / 41
ATT on Probability of Closures

• 5.6% decrease in probability of closure in first month

• Effect falls initially but still statistically significant 12 months

after PPP approval

• 3.9% decline in closure probability 12 months out

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-7.0%

-6.0%

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

Effect on Probability of Closing

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Probability of Establishment Closure

95% Simultaneous Confidence Band

ATT of PPP Loan

22 / 41
Effect on Closures

• For those businesses open in 2019, about 15% reported being

closed in September 2021.

• Estimates suggest without PPP 16.7% of businesses would

have closed

• 11% increase in permanent business closures without PPP

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-7.0%

-6.0%

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

Effect on Probability of Closing

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Probability of Establishment Closure

95% Simultaneous Confidence Band

ATT of PPP Loan

23 / 41
Comparison to Previous Results

• Full sample estimates in line with Autor et al (2022)

• Why were previous results relying on size cutoffs so much smaller?

• Chetty et al (2020); Autor et al (2020); Hubbard and Strain

(2020)

CES Hours

24 / 41
Alternative Results - By Size

• Biggest employment effects are for smallest size class

• Establishments of size 100+ have an imprecisely estimated null

effect

25 / 41
Alternative Results - By Size

• Biggest employment effects are for smallest size class

• Establishments of size 100+ have an imprecisely estimated null

effect

•

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

Effect on Employment %

 Relative to Pre-Pandemic Employment Baseline

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Employment Relative to Pre-Pandemic Employment Baseline

by Establishment Size Class : 1-10 Employment

95% Simultaneous Confidence Band

ATT of PPP Loan

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

Effect on Employment %

 Relative to Pre-Pandemic Employment Baseline

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Employment Relative to Pre-Pandemic Employment Baseline

by Establishment Size Class : 10-50 Employment

95% Simultaneous Confidence Band

ATT of PPP Loan

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

Effect on Employment %

 Relative to Pre-Pandemic Employment Baseline

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Employment Relative to Pre-Pandemic Employment Baseline

by Establishment Size Class : 50-100 Employment

95% Simultaneous Confidence Band

ATT of PPP Loan

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

Effect on Employment %

 Relative to Pre-Pandemic Employment Baseline

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Employment Relative to Pre-Pandemic Employment Baseline

by Establishment Size Class : 100 + Employment

95% Simultaneous Confidence Band

ATT of PPP Loan

25 / 41
ATT on Wages

• 9.9% increase in wages in first month

• Effect falls but still statistically significant 12 months after

PPP approval

• 6% increase in wages 12 months out

-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Months Until/Since PPP Approval

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Effect on Wages %

 Relative to Pre-Pandemic Wage Baseline

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Wages Relative to Pre-Pandemic Wage Baseline

95% Simultaneous Confidence Band

ATT of PPP Loan

26 / 41
Interpreting Money Spent Relative to Outcomes

ATTt Retained due to PPP

Month t

Employment

%

Wages % Employee

Months Monthly Wages ($)

(1) (2) (3) (4)

ATT0 7.24 10.28 3,234,634 19,641,710,192

ATT1 8.11 12.18 3,626,237 23,276,331,554

ATT2 5.63 7.08 2,517,038 13,522,847,969

ATT3 4.45 5.06 1,989,255 9,673,784,520

ATT4 3.62 4.36 1,618,702 8,337,439,536

ATT5 3.41 3.38 1,524,265 6,466,518,334

ATT6 3.01 2.7 1,343,794 5,165,913,697

ATT7 2.82 2.46 1,262,541 4,700,333,551

ATT8 3.71 3.95 1,657,407 7,540,448,892

ATT9 4.23 4.68 1,892,717 8,953,817,193

ATT10 4.59 5.46 2,050,797 10,435,225,941

ATT11 4.94 6.17 2,210,040 11,783,229,541

ATT12 4.9 6.01 2,188,050 11,478,576,523

Columns (1) and (2) are estimates previously shown in figures

27 / 41
Interpreting Money Spent Relative to Outcomes

ATTt Retained due to PPP

Month t

Employment

%

Wages % Employee

Months Monthly Wages ($)

(1) (2) (3) (4)

ATT0 7.24 10.28 3,234,634 19,641,710,192

ATT1 8.11 12.18 3,626,237 23,276,331,554

ATT2 5.63 7.08 2,517,038 13,522,847,969

ATT3 4.45 5.06 1,989,255 9,673,784,520

ATT4 3.62 4.36 1,618,702 8,337,439,536

ATT5 3.41 3.38 1,524,265 6,466,518,334

ATT6 3.01 2.7 1,343,794 5,165,913,697

ATT7 2.82 2.46 1,262,541 4,700,333,551

ATT8 3.71 3.95 1,657,407 7,540,448,892

ATT9 4.23 4.68 1,892,717 8,953,817,193

ATT10 4.59 5.46 2,050,797 10,435,225,941

ATT11 4.94 6.17 2,210,040 11,783,229,541

ATT12 4.9 6.01 2,188,050 11,478,576,523

Columns (1) and (2) are estimates previously shown in figures

Columns (3) and (4) are solved from

(3) = (1) x 45million / 100

(4) = (2) x 191billion / 100

where 45 million is the 2019 monthly employment at establishments receiving PPP

and $191 billion is the 2019 monthly wages at establishments receiving PPP

27 / 41
$ of PPP Relative to Jobs Saved After 12 Months

Total Retained due to PPP $ of PPP Loans per...

Employee Months Monthly Wages ($)

Employee-Month

Retained

Dollar-Wage

Retained

(3) (4) (5) (6)

27,115,477 140,976,177,444 $16,199 $3.12

Columns (5) and (6) are

(5) = 439billion / (3)

(6) = 439billion / (4)

where $439 billion is the total matched $ of approved PPP loans

28 / 41
$ of PPP Relative to Jobs Saved After 12 Months

Total Retained due to PPP $ of PPP Loans per...

Employee Months Monthly Wages ($)

Employee-Month

Retained

Dollar-Wage

Retained

(3) (4) (5) (6)

27,115,477 140,976,177,444 $16,199 $3.12

Columns (5) and (6) are

(5) = 439billion / (3)

(6) = 439billion / (4)

where $439 billion is the total matched $ of approved PPP loans

• Can invert (6) to get percent of PPP dollars going to wage retention: 32.1%

28 / 41
$ of PPP Relative to Jobs Saved After 12 Months

Total Retained due to PPP $ of PPP Loans per...

Employee Months Monthly Wages ($)

Employee-Month

Retained

Dollar-Wage

Retained

(3) (4) (5) (6)

27,115,477 140,976,177,444 $16,199 $3.12

Columns (5) and (6) are

(5) = 439billion / (3)

(6) = 439billion / (4)

where $439 billion is the total matched $ of approved PPP loans

• Can invert (6) to get percent of PPP dollars going to wage retention: 32.1%

• Boushey and Glenn (2012) estimate that the cost to an employer of turnover is

about 20% of lost employee salary

• Difficult to put $ value on keeping businesses from closing

• Long-term costs to an employee of becoming unemployed

• Employee would move to unemployment insurance if they had lost their job - that

cost is saved by being retained

• Ignores general equilibrium effects - what happens to new businesses?

28 / 41
Conclusion

• Presented credible estimation strategy that reconciles prior research results

• PPP loans lead to improvement in employment, ability to stay

open, and wages for up to 12 months post-PPP approval

• 12 months post-PPP, establishments are 3.9% less likely to have

closed

• PPP loans measure to $16,199 per employee-month retained

after 12 months

• PPP loans measure to $3.12 per dollar of wage retained after

12 months

• Lowest wage establishments, high poverty areas, smallest establishments, and youngest establishments show the lowest cost of

retaining employment and wages per PPP $

• These are average effects, not marginal effects, so some caution

should be given to interpreting the estimates

• Noise in matching - dampens effects

• What has happened to new businesses?

29 / 41
Michael Dalton

Research Economist

ERPDS

www.bls.gov

202-691-7403

dalton.michael@bls.gov

30 / 41
Controls Included

• Employment Growth from 2018 to 2019

• Sector

• Size x multiunit status

• Monthly closure status for each calendar month in 2019 Bins

for age

• Bins for wage class

• Franchise dummy

• Urban classification

• Receipt of EIDL Grant or Loan

• PPP eligibility status

Return

31 / 41
Pay per Hour in CES

• Make estimate of ”pay per hour”

32 / 41
Pay per Hour in CES

• Make estimate of ”pay per hour”

• 1.2% increase in pay per hour in first month

• Overall ATT of .73%

-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Months Until/Since PPP Approval

4.0%

2.0%

0.0%

2.0%

4.0%

Effect on Pay per Hour %

 Relative to January 2020 Pay per Hour

[Average Treatment on Treated]

Effects of Receiving a PPP Loan on 

Pay per Hour Relative to January 2020

95% Simultaneous Confidence Band

ATT of PPP Loan

Return

32 / 41
Estimation Methodology

Semi-parametric estimation of an ATT for each

• month t and

• for each group, defined by month of PPP receipt, p

• PPPp = 1 for every month t for establishments receiving PPP

in month p, zero otherwise

• Dt = 1 for every month where t ≥ p and PPPp = 1

• Yt

is the outcome variable at month t

• δ is the 1 month anticipation term

• X are the time invariant control variables

33 / 41
Estimation Methodology

ATTp,t =

E















Inverse Probability Weight

z }| {



PPPp

E(PPPp)

−

Probp,t+δ(X)(1−Dt+δ)

1−Probp,t+δ(X)

E[

Probp,t+δ(X)(1−Dt+δ)

1−Probp,t+δ(X)

]





Outcome Regression

z }| {

(Yt − Yp−δ−1 − cp,t,δ(X))















,

34 / 41
Estimation Methodology

ATTp,t =

E















Inverse Probability Weight

z }| {



PPPp

E(PPPp)

−

Probp,t+δ(X)(1−Dt+δ)

1−Probp,t+δ(X)

E[

Probp,t+δ(X)(1−Dt+δ)

1−Probp,t+δ(X)

]





Outcome Regression

z }| {

(Yt − Yp−δ−1 − cp,t,δ(X))















,

where Probp,t+δ(X) = E(PPPp|X, PPPp + (1 − Dt+δ) = 1)

Inverse Probability Weight is basically a matching score estimator

(Abadie (2005))

Higher weight goes to control group observations that have X

similar to employers receiving PPP in month p

35 / 41
Estimation Methodology

ATTp,t =

E















Inverse Probability Weight

z }| {



PPPp

E(PPPp)

−

Probp,t+δ(X)(1−Dt+δ)

1−Probp,t+δ(X)

E[

Probp,t+δ(X)(1−Dt+δ)

1−Probp,t+δ(X)

]





Outcome Regression

z }| {

(Yt − Yp−δ−1 − cp,t,δ(X))















,

where cp,t,δ(X) = E[Yt − Yp−δ−1|X, Dt+δ + PPPp = 0]

Outcome Regressions predict are similar to diff-in-diff (Heckman et

al (1998))

Return

36 / 41
Verifying Record Linking Using BRS

• BRS is online survey of 160,000 employers conducted July 2020

- September 2020

• Asked about receiving *any* loan/grant from government

37 / 41
Verifying Record Linking Using BRS

• BRS is online survey of 160,000 employers conducted July 2020

- September 2020

• Asked about receiving *any* loan/grant from government

• Takeaway: High correlation between PPP match and reporting received loan or grant. Good news.

Match Type Geography

Fuzzy

Match

Score

Number of

BRS Respondents

Percent Reporting in

BRS Received

Loan/Grant of

Any Type

Exact Address Match City Exact 45714 97.7%

Address Match City - 11024 94.6%

Exact Match City Exact 6485 97.0%

Fuzzy Match City High 341 92.8%

Fuzzy Match City Medium 471 91.4%

Fuzzy Match City Low 531 86.4%

Fuzzy Match City Lowest 235 80.0%

Exact Match County Exact 1126 96.4%

Fuzzy Match County High 64 95.3%

Fuzzy Match County Medium 58 91.4%

Exact Match State Exact 997 92.3%

BRS Respondents with no PPP Match 95338 39.0%

37 / 41
Ratio of Loan Amount to 2019 Wages

Less noisy measure: Take ratio of PPP Loan Amount to Total

Wages in 2019 in QCEW

38 / 41
Ratio of Loan Amount to 2019 Wages

Less noisy measure: Take ratio of PPP Loan Amount to Total

Wages in 2019 in QCEW

• Ratio of .19 = 10 weeks of salary

• Some variation, but interquartile range stays close to .19

Plot for Employment 

38 / 41
Ratio of Loan Amount to 2019 Wages

Less noisy measure: Take ratio of PPP Loan Amount to Total

Wages in 2019 in QCEW

• Ratio of .19 = 10 weeks of salary

• Some variation, but interquartile range stays close to .19

Plot for Employment Exact Address Match

Address Match

Exact Match,

City

Exact Match,

County

Exact Match,

State

Fuzzy Match,

City

Fuzzy Match,

County

Fuzzy Match,

State

Type of Match

0.0

0.1

0.2

0.3

0.4

0.5

Box Plot of Ratio of Loan Amount

 in PPP to Annual Wages in LDB

10 Weeks of Wages

38 / 41
Match Rate by Reported Sector on PPP Application

Worst match rate

for Educational

Services - but not

that much

variation

0.0 0.2 0.4 0.6 0.8

Proportion

Utilities

[0% of All $]

Public Admin

[0% of All $]

Mining

[1% of All $]

Agriculture

[2% of All $]

Information

[2% of All $]

Educational Serv.

[2% of All $]

Financial / Insurance

[2% of All $]

Real Estate

[3% of All $]

Wholesale Trade

[5% of All $]

Management Serv.

[5% of All $]

Other services

[6% of All $]

Retail Trade

[8% of All $]

Leisure / Hospitality

[10% of All $]

Manufacturing

[10% of All $]

Construction

[13% of All $]

Professional Serv.

[13% of All $]

Health Care

[13% of All $]

Sector

Proportion of Loan Amount Matched,

 Based on PPP NAICS

39 / 41
Box plot of Reported Retained Jobs to Employment

Take ratio of Retained Jobs to Average Employment in 2019 in

QCEW

40 / 41
Box plot of Reported Retained Jobs to Employment

Take ratio of Retained Jobs to Average Employment in 2019 in

QCEW

• Ratio of 1 = Expected

• Some variation, but interquartile range stays close to 1

Return

40 / 41
Box plot of Reported Retained Jobs to Employment

Take ratio of Retained Jobs to Average Employment in 2019 in

QCEW

• Ratio of 1 = Expected

• Some variation, but interquartile range stays close to 1

Return

Exact Address Match

Address Match

Exact Match,

City

Exact Match,

County

Exact Match,

State

Fuzzy Match,

City

Fuzzy Match,

County

Fuzzy Match,

State

Type of Match

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Box Plot of Ratio of Jobs Reported

 in PPP to Avg. Ann. Emp. in LDB

PPP Jobs Saved = 2019 Employment

40 / 41
Record Linking

Match looks good!

Let’s carry on with the real results.

Return

41 / 41