The Impact of Criminal Records on Employment,

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Earnings, and Tax Filing ∗

Amanda Agan†

Andrew Garin‡

Dmitri Koustas§

Alexandre Mas¶

Crystal S. Yang‖

September 29, 2023

Abstract

This paper combines IRS tax return data with administrative court records from

several jurisdictions to measure the impact of criminal records on taxpayer earnings and

filing behavior. Our data construction allows us to examine a wide range of interactions

with the criminal justice system including charges that led to convictions and those

that did not. We document sharp and persistent declines in the propensities to have

W-2 reported earnings and to file a 1040 return around an initial criminal charge,

even in the case of charges that do not lead to convictions. We also find that selfemployment earnings of Schedule SE and 1099-reported nonemployee compensation

both fall in close proportion to W-2 employment in most cases. There is evidence that

criminal record remediation for individuals who have had records for multiple years

leads to increases in platform-mediated gig work reported on 1099 returns, although in

most cases those gig earnings are not reported on Schedule SE. Aside from gig work,

we do not find evidence that remediation is associated with increased filing rates or

earnings.

∗

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors

and do not necessarily reflect the views or the official positions of the U.S. Department of the Treasury or

the Internal Revenue Service. All results have been reviewed to ensure that no confidential information is

disclosed. We thank the San Joaquin Public Defender’s Office and District Attorney’s office for their help

and tireless work in making this project possible. We also thank the numerous interns who worked in San

Joaquin to help us gather data. Camilla Adams, Kaan Cankat, Sarah Frick, Jared Grogan, Bailey Palmer,

Kalie Pierce provided instrumental research assistance. We also thank Emma Rackstraw and J-PAL NA for

the initial conversations that allowed this project to happen.

†

Rutgers University and NBER (aagan@economics.rutgers.edu)

‡

Carnegie Mellon University, IZA, and NBER (agarin@andrew.cmu.edu)

University of Chicago (dkoustas@uchicago.edu)

Princeton University and NBER (amas@princeton.edu)

‖

Harvard University and NBER (cyang@law.harvard.edu)

Introduction

Millions of people across the United States are charged with crimes each year. The prevalence of criminal records could have important implications for tax administration. The

consequences of these records for income reporting and tax filing may be substantial. Employment rates are lower for individuals with a criminal record than the general population

(Mueller-Smith 2015; Looney and Turner 2018) and recent studies suggest that records may

be preventing about one third of working-age males from contributing to the formal economy, leading to substantial lost income and tax revenue (Looney and Turner, 2018). Without

access to standard jobs, individuals with a criminal history may rely more heavily on alternative work like independent contracting and self-employment, which could increase workers’

tax filing burden and reduce their compliance with tax filing requirements. Recent work by

Finlay et al. (2022) finds that earners with criminal histories are disproportionately likely

to report Schedule C income. Further, interactions with the criminal justice system might

directly impact filing behavior, credit take-up, and compliance with the tax code.

This paper combines IRS tax return data with publicly-available administrative court

records from several jurisdictions to measure the impact of criminal records on taxpayer

earnings and filing behavior. Our data construction allows us to examine a wide range of

interactions with the criminal justice system beyond imprisonment previously studied by

Looney and Turner (2018)—including charges that led to convictions and those that did

not. By observing charge-level data rather than imprisonment data, we are able to assess

the impact of a criminal record per se, apart from sentencing decisions—which might have

distinct effects on subsequent earnings and filing behavior. A complementary companion

paper by Garin et al. (2022) studies the direct impacts of incarceration (for a given charge)

on taxpayer behavior.

As a first look at the impacts of criminal history events on tax filing behavior, we conduct

event-study analyses that examine how taxpayer reporting and third-party-reported earnings

change around charges and convictions. Importantly, we document that individuals who have

interactions with the criminal justice system have low filing rates and earnings levels even

prior to their first criminal charge, particularly for those charged with felonies. Nonetheless,

we find that the propensity to file a tax return falls sharply around an initial criminal charge.

These declines in 1040 filing are associated with similar drops in the probability of having

W-2 reported earnings. Strikingly, we observe drops even in the case of charges that do not

lead to convictions, suggesting that the mere presence of a criminal record might impact

earnings prospects. In all cases, these declines persist through several years after the initial

charge.

However, we do not find that individuals with criminal records are more likely to be

self-employed.1 Conditional on participating in the workforce—i.e. earning labor income

reported on a W-2 or Schedule SE—we never observe self-employment rates above ten percent; this is below the self-employment rate in the broader workforce (Collins et al., 2018).

With the exception of misdemeanor convictions, the probability of reporting self-employment

earnings on Schedule SE declines in close proportion to W-2 reported wages around initial

criminal history events, such that the relative prevalence remains mostly constant.2 An

important caveat is that Schedule SE earnings are self-reported and therefore changes may

reflect reporting behavior rather than in underlying earnings. However, we see nearly identical changes in the probability of having third-party-reported payments for contract work

on 1099-MISC returns, suggesting the observed evolution in Schedule SE earnings reflect

changes in actual work and not changes in reporting or compliance.

These findings raise an important question: does the ongoing presence of a criminal

record itself drive the persistent declines in W-2 receipt and tax filing that we observe? To

isolate the impacts of an observable criminal record itself versus alternative mechanisms, we

make use of institutional features that alter the information visible to employers on criminal

background checks. In particular, we investigate the impact of retroactive changes in the

severity of a charge for eligible felonies in California under Proposition 47, the removal of

charges that did not lead to convictions from background checks conducted by consumer

reporting agencies (CRAs) under the Federal Credit Reporting Act (FCRA), and removal

of charges that did not lead to convictions following a Clean Slate law in Pennsylvania.

Our findings are consistent across these different analyses. Rather than finding that

individuals shift out of alternative work arrangements, like gig work, and into traditional

jobs when records are remediated, we find consistent evidence that remediation increases the

rate of electronically-mediated gig platform work, albeit from a very low base. In practice,

however, only a minority of the individuals entering platform work report their earnings on

a 1040 Schedule SE.

However, with the exception of very recent convictions, we find no evidence that a change

in how or whether a record is reported impacts the filing behavior or reported earnings of

a typical person with a record. We find little evidence that a reduction from a felony to a

misdemeanor changes non-gig outcomes, on average. We also find no evidence that removal

of records from criminal background checks, even for individuals with no other convictions

that would still be reported, increases the likelihood of having W-2 reported earnings. This

This finding contrasts with Finlay et al. (2022), in part because we do not condition on filing a tax

return when measuring the prevalence of self-employment.

We find no change in the propensity to report Schedule E earnings around an initial misdemeanor

conviction.

finding holds across almost every outcome, for both non-convictions and for convictions, and

for felonies and misdemeanors. These results are further supported by the analysis of the

Pennsylvania Clean Slate law where we also find no effect of having non-conviction records

sealed on average filing behaviors or earnings outcomes. The evidence presented strongly

points towards the conclusion that in the years after a criminal justice event, lower W-2

reporting is not due to the reporting of criminal records, but to other factors.

The paper is organized as follows. Section 2 describes the IRS data and the publiclyaccessible court records we collect from several jurisdictions. Section 3 examines the evolution

of taxpayer behavior around different types of initial criminal history events. Section 4

describes several interventions that remediated criminal records and presents our evaluation

of the impact of these interventions on taxpayer behavior. Section 5 offers some concluding

remarks.

Data

In this section, we describe the restricted-access tax data and publicly-available administrative court records used in our analysis.

2.1

IRS Tax Return Data

We study de-identified federal income tax records from the years 2000 to 2019. The tax

records include all individuals with a Social Security Number or Individual Taxpayer Identification Number. We draw both on Form 1040 individual income tax filings and third-party

reported information returns. Our primary wage and salary earnings and employment outcomes are drawn from W-2 returns issued by employers for each employee in each year.

Importantly, W-2 returns are sent by employers to the IRS irrespective of whether and how

the employee files their own individual tax return. We examine whether individuals have

any wage and salary earnings as well as whether earnings exceed specified threshold levels.

We supplement these earnings records with gross payments to non-employee independent

contractors and online platform “gig” workers reported by firms on 1099-MISC and 1099K

forms. Beginning in 2012, we break out payments from online platform companies following

the method in Collins et al. (2018).3

Unlike payments to independent contractors on 1099-MISC, which are subject to a $600 threshold, online

platform economy earnings reported on 1099-K are subject to a much higher $20,000 threshold. Garin et al.

(Forthcoming) reports that while that most major platforms issued 1099-Ks to all platform participants

through 2016 regardless of the earnings level, several large platforms announced that they would adhere to

the higher thresholds beginning in 2017. Thus, some smaller payments from these platforms after 2016 may

not be observed in the data.

We also examine outcomes directly reported by individual taxpayers themselves to the

IRS on Form 1040 tax returns. These include total gross self-employment revenues and net

profits after expenses reported on Schedule C, with self-employment net profits further broken

out for each individual within the filing unit (with net profits of at least $433) on Schedule

SE. Unlike information returns reported by third-parties, individual tax reporting on 1040

forms may be impacted by taxpayer non-compliance or strategic reporting. Nonetheless,

reporting behaviors such as filing any 1040 return are of interest to understand how criminal

record remediation impacts tax compliance.

2.2

Court Data for FCRA, Maryland, and Clean Slate Analysis

We collect publicly-available administrative court records from Maryland, New Jersey, Pennsylvania, and Bexar County, Texas. These data include information that make it possible

to link defendants across cases over time. They include all charges filed with the court

within the time period we have data for (both misdemeanor and felony, with the exception

of New Jersey, which just contains felonies), the date of the charges, the disposition date

and ultimate outcome (dismissed, convicted, etc.), as well as other defendant and case characteristics. We also have publicly-available data on Proposition 47 petitions in San Joaquin

County that were filed by the Public Defender’s Office in conjunction with the Office of the

District Attorney to reduce eligible felonies to misdemeanors.

2.2.1

Bexar County, Texas

The data from Bexar County include all charges filed between 1975 and 2018. The data are

publicly available for download via the court website, which began releasing them as part of

an effort to make court records more accessible. Full detailing of this data can be found in

Agan et al. (2021) and Freedman et al. (2018).

2.2.2

Maryland

The Maryland criminal records were drawn from several publicly-accessible tables hosted on

the Maryland Volunteer Lawyers Service (MVLS) database.4 The Maryland data cover all

cases filed between 1990 and 2018.

Access to the database was provided by Matthew Stubenberg, the Associate Director of Legal Technology

at the Access to Justice Lab (A2J Lab) at Harvard Law School. Stubenberg created the database for the

MVLS using publicly available public court records. Mr. Stubenberg may be contacted at mstubenberg@law.

harvard.edu. See https://a2jlab.org/ for more information on the A2J Lab. See https://mvlslaw.org/

for more information on the MVLS.

2.2.3

New Jersey

We obtained New Jersey court data from the Superior Court of New Jersey via a Public

Access Information Request.5 New Jersey does not use a “felony” versus “misdmeanor”

distinction, but rather distinguishes between “indictable offenses” and “disorderly person”

offenses. Indictable offenses fairly closely align with what would be felonies in other states,

and disorderly persons offenses with misdemeanors. On the criminal side, the Superior Court

hears cases for “indictable” offenses, and thus we only have data on these types of offenses

for New Jersey and throughout the paper we refer to these offenses as felonies to more closely

align with terminology from other states.6 These files contain records from January 1, 1980,

to May 30, 2018, which was the last day of the month of the request for the records.

2.2.4

Pennsylvania

The Pennsylvania court data was obtained from the Administrative Office of Pennsylvania

Courts (AOPC) via a Public Access request. The data cover all cases in the Magisterial District Court system (which handles misdemeanors) and Common Pleas Court system (which

handles felonies) filed between May 2008 and April 2018. We also obtained case numbers

that can be accessed through Public Access requests; the case numbers in the AOPC data

were current as of when we obtained them in December 2020.7 Merging this with our original

AOPC request, we can see all cases that no longer exist in the data and our conversations

with the DA’s office imply that these are all records that were sealed by Clean Slate between June 2019 and June 2020. Our matching indicates that 57% of the nearly 7.5 million

non-convictions charges in our AOPC data were sealed, with the ones that were not sealed

presumably due to individuals still owing fines and fees at the time of the Clean Slate implementation.8 This group will serve as our comparison group.

2.2.5

San Joaquin County, California

The data on Proposition 47 petitions filed in San Joaquin County, CA are available on the

county website. The data were made available to the public by the Office of the Public

Defender of San Joaquin (OPDSJ), CA. The dataset reports all Proposition 47 petitions

According to New Jersey Court Rule 1:38, names, date of births, and locations are not considered

confidential personal identifiers. These reports are available for sale to the general public.

To get data on “disorderly person”/misdemeanor offenses would require obtaining data from each of the

hundreds of municipal courts in NJ.

The Philadelphia DAO provided these data to us directly to facilitate a quicker turnaround, though all

records (in their most current version) are available through standard Public Access requests.

Our research team looked up a small, random subset of case information online for non-convictions that

were not sealed and were able to confirm that for that subset, each one owed fines and fees.

that were filed between December 2014 to December 2018 and were successfully reduced by

September 2019. We exclude all individuals who were currently serving sentences or under

supervision (parole/probation) at the time of the filing as these were generally prioritized

and done quickly soon after the law went into effect. With this restriction, we have data on

8,155 successful petitions in San Joaquin.

Most of these petitions were filed proactively on behalf of eligible defendants without the

need for the defendants to request the petitions, through a joint effort between the OPDSJ

and the San Joaquin County District Attorney’s Office. The OPDSJ reported that a small

fraction of these petitions were filed on behalf of people who contacted the office to request

a reduction, but the data do not directly identify self-petitioners with the exception of 96

individuals who were referred to the OPDSJ for reduction through a local “Justice Fair.”

OPDSJ reports that they started reductions for those who had eligible drug crimes (“Health

and Safety”or HS) as this list was the largest; they received the lists of eligible offenders

in alphabetical order by last name and worked through the lists in an alphabetical fashion.

Thus, we take a data-driven approach to identifying likely self-petitioners from the proactive

petitions by using this alphabetical nature of the order in which petitions were filed. Petitions

filed before the “surge” corresponding to the first letter of their last name are identified as

likely self-petitions. Appendix B describes how we identified those beginning dates for each

surge for each letter on the HS crime list. Throughout the paper, we refer to individuals

who had their reductions filed during or after a surge as those whose records were reclassified

by “proactive reductions.” We refer to individuals whose petitions were filed before these

clear surge periods as “possible self-petitioners.” While there are other idiosyncratic reasons

that individuals’ petitions would be filed before the office got to their letter of last name,

this group is likely to have the bulk of any potential self-petitioners in it. We confirm that

our approach is sensible using information we have on Justice Fair participants, who we

know were self-petitions and prioritized by the OPD. Among Justice Fair participants in our

analysis sample, 70% are accurately flagged as likely self-petitioners using our data-driven

methodology.

OPDSJ reported that the office started with the lists of eligible drug crime offenders.

Because alphabetical ordering was not preserved for other lists our main analysis sample

focuses on individuals with an drug crime: this represents 6,626 of the successful petitions

(81.3%). Among this analysis sample, we identify 4,978 as proactive petitions (89%) and

644 as likely self-petitions (11%).

2.3

Data Linkage

The IRS data are linked to the criminal records data using individual name, date of birth,

and geographic location and are subsequently de-identified. In the case of San Joaquin

County, we were able to match 84% of our main sample to the IRS data. We were able to

match 86%, 73%, 81%, and 91% of the data in Bexar County, Maryland, New Jersey, and

Pennsylvania, respectively. Further details on our matching algorithm to IRS records can be

found in Appendix C. For each jurisdiction, our match rate is comparable or higher than past

work matching to IRS or Unemployment Insurance data (Dobbie et al. 2018; Travis et al.

2014). Appendix C also compares characteristics of matched and non-matched individuals

in San Joaquin County, CA and the distribution of last criminal history events in the full

versus matched sample for our FCRA analysis.

Tax Reporting and Earnings Around Initial Criminal

History Events

We begin by documenting reporting behaviors and earnings before and after individuals’

initial criminal history events. For this analysis, we restrict the full sample to have been 18

by the time the first charge appears in the data for both non-convictions and convictions.

Table 1 Panels (a)-(d) examines mean outcomes around four distinct types of events, pooling

data across the jurisdictions with data on each event: (1) misdemeanor charges that do not

lead to a conviction, (2) felony charges that do not lead to a conviction, (3) misdemeanor

charges that do lead to a conviction, and (4) felony charges that do lead to a conviction;

each type of event is presented in a separate panel.

The first column in each panel presents means two years prior to the initial event. Prior

to their first event, those eventually charged with a misdemeanor start out more likely to file

tax returns and have earnings reported on information returns than those eventually charged

with a felony, and within each type of charge those who are ultimately not convicted are more

likely to file tax returns and have income reported than those who are eventually convicted.

Notably, only 55 percent of felony convicts are 1040 filers prior to their initial charge, and

nearly 30 percent have no earnings reported on either a W-2 return or on Schedule SE.

We document the change in earnings around these first events using an event-study

framework. We estimate the model:

yit =

βk I{Ei = t + k} + αi + αa(i,t) + αt + it

(1)

Event time is measured as time since charge for non-convictions, and time since disposition

for convictions. αi is an individual fixed-effect, αa(i,t) are age fixed effects, and αt is a

year fixed effect. Coefficients are relative to two years prior to the event. We plot the βk

coefficients for key outcomes in Figure 1. In the second and third columns of Table 1, we

add these estimates to the mean outcome levels two years prior to the event to estimate the

outcome levels one year and five years after the events, adjusted for life-cycle changes and

macroeconomic conditions.

Figure 1 shows that even non-convictions are associated with substantial and persistent

reductions in 1040 filing rates and W-2 earnings. For misdemeanor non-convictions, the

probabilities of filing a 1040 or receiving a W-2 decline are five percentage points lower five

years after the charge. While the changes in filing and W-2 earnings around misdemeanor

convictions are larger in the short run, the long run changes are nearly identical to what

we see for misdemeanor non-convictions—with the exception of 1099-reported contract work

and Schedule SE self-employment, which, interestingly, decline for those with misdemeanor

non-convictions but not those with misdemeanor convictions. We find that the impacts of

felony non-convictions are more severe than misdemeanor non-convictions in the short run,

but the effects converge in the long term.

The observed changes are more pronounced for individuals who are convicted of felonies,

and may face incarceration. The probability of filing a 1040 return falls by over 10 percentage points after an initial conviction, amounting to more than a 20 percent decline

relative to the already low baseline rate. The decline in 1040 roughly mimics the decline in

employer-reported W-2 payments. Strikingly, in the short run, average W-2 reported earnings (including zeros) fall by nearly half. While 1040 filing rates rebound significantly in the

long run, W-2 wage earnings only recover slightly by seven years out.

We do not find that individuals with criminal records are more likely to be self-employed.

We see in Table 1 that both before and after criminal history events, no more than seven

percent of individuals have self-employment income reported on 1040 Schedule SE. Conditional on participating in the workforce—i.e. earning labor income reported on a W-2 or

Schedule SE—we never observe self-employment rates above ten percent; this is below the

self-employment rate in the broader workforce (Collins et al., 2018). With the exception

of misdemeanor convictions (where we observe no change in Schedule SE filing around an

initial event), the probability of reporting self-employment earnings on Schedule SE declines

in close proportion to W-2 reported wages, such that the relative prevalence remains mostly

constant. An important caveat is that Schedule SE earnings are self-reported and therefore

changes may reflect reporting behavior rather than changes in underlying earnings. However,

in this case, we see nearly identical changes in the probability of having third-party-reported

payments for contract work on 1099 returns; this suggests the observed changes in Schedule

SE earnings reflect changes in actual work and not changes in compliance.

The results in Figure 1 and Table 1 pool individuals across jurisdictions. However, we

see highly similar patterns across each jurisdiction, as shown in Appendix Figure A.1. We

examine sensitivity to studying changes around individuals’ most recent events in the data

instead of their first events in Appendix Table A.2 and Appendix Figure A.2—the primary

difference being that one’s most recent event may follow prior events.9 We find similar effects

when looking at latest events, though we see less long-term scarring in the case of felony

convictions; we interpret this result as suggesting that initial felony convictions lead to larger

long-run declines in earnings than subsequent convictions.10

We caution that these patterns in employment around the time of either the first or last

criminal history event do not necessarily reflect the causal impact of having a record. These

persistent trends could be the direct result of the criminal charge or conviction, or they could

also be the result of other unobservable events in an individual’s life that caused both the

criminal charge and a decline in employment, such as recent drug addiction or job loss. The

reduction in any wage employment for convictions could also be the result of incapacitation

effects stemming from incarceration, though this is not the case for non-convictions which

would not result in incarceration sentences.

In summary, these patterns show that formal labor sector engagement falls sharply and

persistently after a criminal charge or conviction (even when there may be other criminal

charges in the person’s past). A key finding is that changes in taxpayer filing behavior closely

follows changes in firm-reported earnings. In particular, the changes in 1040 filing generally

follow the evolution of W-2 earnings, and changes in Schedule SE filings generally follow

the evolution of 1099-MISC non-employee compensation. This finding suggests that changes

in taxpayer behavior around criminal history events largely reflect the persistent decline in

employment and earnings following those events, which we examine in more depth in the

next section.11

When examining non-convictions, we omit cases where individuals have prior convictions but not cases

where they have prior non-convictions, since these are the cases directly impacted by the FCRA law studied

below.

Using information on defendant race from the case records, we plot effects by race in Figures A.3 and

A.4 and find larger drops in employment for Black individuals around initial events. We also run separate

event studies by industry of the payer of the largest W-2 for non-convictions and convictions and report the

event study coefficient for the year of the conviction in Appendix Figures A.11 and A.12. We find that the

employment impact for non-convictions varies across industries. For convictions, a similar decline is seen

across nearly all industries.

The patterns also establish that the matching procedure between criminal records and the IRS data are

accurate in that they yield the expected patterns in the data post-event.

Tax Reporting Implications of Criminal Record Remediation

We find that taxpayer earnings and filing behavior rates fall persistently after criminal history

events—even charges that do not lead to convictions. There are several reasons employment

rates may be lower for individuals with criminal records. A criminal record itself can be

a direct barrier to employment. Some industries, such as healthcare or education, legally

prohibit the hiring of individuals with certain criminal records. Across several surveys, over

90 percent of employers state that they perform background checks for all or some of their

positions (Society for Human Resource Management 2012; HireRight 2015).

In this section, we use institutional features of the criminal justice system to examine

the implications of remediating criminal records on tax filing and compliance. We begin

by presenting results for the federal Fair Credit Reporting Act (FCRA), estimating the

impact of removing non-convictions from criminal background checks at seven years. We

then present estimates for California’s Proposition 47 in San Joaquin County where criminal

justice agencies proactively petitioned to have eligible felonies reclassified as misdemeanors

in cases covered by the new law. Finally, we present our findings for Pennsylvania Clean

Slate, which initially automated the sealing of all non-convictions for individuals that did

not owe fines and fees.

4.1

Evidence from the Federal Consumer Reporting Act

4.1.1

Research Design

FCRA prohibits reporting of criminal charges that did not lead to a conviction after seven

years for jobs that pay less than $75,000 a year. We study this policy using publicly-available

administrative criminal records data from Maryland, New Jersey, Pennsylvania, and Bexar

County, Texas. We provide additional detail on FCRA and related state laws in Appendix

We use the seven-year rule under FCRA to estimate the effect of having a non-conviction

record cleared from a CRA-run employment background check. Under this rule, for an

individual who has no convictions on their record, their criminal record should be completely

cleared seven years after the last criminal charge. This feature of FCRA allows for an eventstudy design where individuals do not select into the event in the relevant time horizon

for estimation. Accordingly, we examine the evolution of outcomes following a criminal

charge that is eventually dismissed with a focus on the sharp seven-year change in reporting,

comparing outcomes for individuals before and after this non-conviction charge is removed.

In the baseline analysis, our focus is on charges that did not lead to a conviction, when

that non-conviction charge is no longer reportable by a CRA. We therefore restrict our analysis of non-convictions to individuals with no other conviction in that jurisdiction because

nothing should be reported on these individuals’ CRA background checks at the seven year

mark. We define event-time in relation to the year in which the record is cleared (the FCRA

event). We run a fully saturated event-study specification, and balance the sample three

years prior to the FCRA event and one year post the event, so that the estimated coefficients

around the event are not driven by changes in sample composition. Our main analysis separately examines last events that are felony non-convictions, misdemeanor non-convictions,

and convictions. Our main specification is given by:

yit =

βk I{Ei = t + k} + αi + αa(i,t) + αt + it

(2)

Event time is measured as time since last criminal history event. αa(i,t) are age fixed effects,

and αt is a year fixed effect.

For the FCRA analysis, we use separate analysis samples to analyze the clearance of a

non-conviction and conviction. The FCRA rule allows us to estimate the effects of having

a non-conviction record cleared seven years after the last criminal history event. To study

non-convictions, we restrict our sample to individuals with a felony or misdemeanor nonconviction as their last event, limited to individuals with no other convictions on their record.

To reduce measurement error associated with measuring no past convictions, we limit the

sample to individuals who were 18 or younger as of the earliest year of data available in

the respective jurisdiction to ensure that we can accurately measure their adult criminal

record. We focus on the last event and limit to individuals with no other conviction in

that jurisdiction because nothing should be reported on these individuals’ CRA background

checks at the seven year mark, giving FCRA the best chance at improving outcomes for these

individuals. To study convictions, we restrict our sample to individuals whose last event was

either a felony or misdemeanor conviction. Reporting of convictions does not change after

seven years in Texas, New Jersey and Pennsylvania. In Maryland, convictions are removed

from employer criminal record searches for low-income workers after 7 years.

Appendix Table A.1 Panel (a) presents summary statistics for the FCRA analysis samples. Columns 1 and 2 present summary statistics for individuals with a felony or misdemeanor non-conviction as their last event, restricted to individuals with no other convictions

on their record. Columns 3 and 4 present summary statistics for individuals whose last event

was either a felony or misdemeanor conviction, respectively. Summary statistics on baseline

outcomes are presented at five years after the charge or disposition date (or alternatively, two

years before the potential FCRA removal event). Among the sample of individuals whose

last event is a non-conviction with no other convictions, the average age is approximately

31 years of age, and baseline measures of extensive and intensive employment are generally higher for those whose latest non-conviction was a misdemeanor versus a felony. For

example, among last-event misdemeanor non-convictions, 74 percent of individuals had any

wages at five years post-charge, with average wages of $19,647. In contrast, among last-event

felony non-convictions, 66 percent of individuals had any wages at five years post-charge,

with average wages of $14,564. Individuals are older among the sample of people whose last

event was a conviction. This sample also has relatively low baseline rates of employment

and average wages, particularly among individuals whose last event was a felony conviction.

4.1.2

Results

In Figures 2 and 3 Panels (a)–(b), we plot event-study coefficients for the removal of a

felony and misdemeanor non-conviction, respectively, which occurs seven years after the

original event. Figure 2 reports share with any 1040 filing and Figure 3 reports share with

any W-2 wages. Even though non-conviction events are associated with significant drops

in 1040 filing and W-2 reported employment, this figure shows that there is no evidence

that removing the last non-conviction from the record of someone with no other convictions

increases employment or tax filing.

Figures 2 and 3 Panels (c)–(d) show the same event-study plots for last felony and misdemeanor convictions on record. Recall that in Maryland these convictions are no longer

reportable seven years after their disposition, but in the other jurisdictions they are reportable indefinitely. And yet across jurisdictions we see similar patterns of no divergence

in the likelihood of having either a 1040 return or W-2 reported earnings around the seven

year mark.

Tables 2 and 3 report formal tests of whether the event study coefficients reported in

Figures 2 and 3 seven and eight years after the last event are different from a linear trend

implied by our event-study coefficients, for both 1040 reporting, any W-2 wages, and other

employment outcomes.12 Event-time coefficients will identify any trends around the event.

Because a criminal history event mechanically occurred seven years earlier than our FCRA

event, individuals could still be recovering from the initial event. For exposition purposes,

we pool the jurisdictions. Table 2 Panel (a) reports results for misdemeanor non-convictions

Specifically, we calculate: d+7 = 2 × β+4 + β+7 and d+8 = 3 × β+4 + β+8 and the standard errors on

these sums using the delta method. d+7 and d+8 report the deviation of our period 7 and 8 event-study

coefficients, respectively, from the linear trend implied by our period 4 event-study coefficient, β+4 . If β+4

is negative, this implies a positive pre-trend. In that case, a positive and statistically significant deviation

from trend in periods 7 or 8 would be suggestive that FCRA is having a positive impact.

and Panel (b) reports results for felony non-convictions. Table 3 Panel (a) reports analogous

tests of deviations from trend in year 7 and 8 for convictions in Bexar County, New Jersey,

Pennsylvania and Panel (b) reports results separately for Maryland, which has a state FCRA

law limiting the reporting of convictions after seven years.

Consistent with Figures 2 and 3 Panels (a) and (b), Table 2 shows that for non-convictions

(both felony and misdemeanor), we find no detectable deviation from the pre-trend trend

around the timing of the record removal. There is similarly no significant discontinuity

or deviation from trend for any type of earnings or reporting behavior. With respect to

convictions, Table 3 Panel (a) shows that individuals appear to be below trend seven to

eight years after the initial charge in states (NJ, PA, and TX) that do not prohibit the

reporting of convictions, suggesting the positive trend observed closer to the initial event

has slowed (see Figure 1). Table 3 Panel (b) shows results for convictions in Maryland.

We see evidence of positive trends before the event for any wages at various thresholds

(see Figure 3 and Appendix Figure A). If anything, these trends are slowing, not increasing

after the conviction is removed at seven years, as indicated by formal tests of deviation

from pre-trends. We see similar trends for tax filing, and little response on our measures of

self-employment or independent contracting.

These results are highly robust. Our finding of no discrete change in trend in W-2

reported earnings seven years after a criminal charge (or conviction) is removed holds across

subsamples by industry (see Appendix Figure A.13) and by crime type of the last event

(see Appendix Figure A.14). Both of these Appendix figures report whether the event

study coefficients seven years after last charge are different from the linear pre-trend. We

look separately at effects for defendants identified as Black versus all others (see Appendix

Figure A.7).13 We also look seperately at effects by gender and age in Appendix Figure A.8

and in Appendix Figure A.9. In Appendix Figure A.10 we also consider the impact of these

criminal history removals on employment in large firms (firms with 10,000 or more workers)

and small firms (firms with less than 100 workers). Finally, we find null effects at year seven

using an alternative differences-in-differences estimator following Sun and Abraham (2021)

(see Appendix Figure A.16). Overall, these results imply that removing criminal history from

CRA background checks after seven years does not result in positive impacts on employment

and tax-filing outcomes for affected individuals.14

Race data is available in public court records in Bexar County, Texas, Maryland, and Pennsylvania.

One explanation for the null result might be that employers are asking applicants about records, and

any information obtained in a background check is superfluous. To examine this mechanism, we estimate the

main FCRA specifications for New Jersey after the state Ban the Box (BTB) law went into effect in March

2015. Appendix Figure A.6 shows that even in post-BTB years, there remains a null effect after seven years

of record sealing.

An important exception to the pattern of null results is non employee compensation. For

non-convictions, we see increases in 1099 work that are above trend. For convictions, we see

an increase in online-platform mediated gig work. While this type of employment is a small

share of overall employment, gig jobs are interesting because, for the most part, gig platform

work does not involve an interviewing process or an evaluation of one’s work history. If

an applicant can pass the initial requirements necessary to be on the platform, they are

allowed to begin to earn money on the platform. Gig platform work, such as ride-sharing

and app-based delivery services, has increased dramatically in recent years, and may provide

opportunities that were not previously available. Because this type of gig work has only

been prevalent since circa 2012, for this part of our analysis, we restrict our sample to years

since 2012 and FCRA events beginning in 2015.

Event-study results pooling all criminal history events and jurisdictions over the period

since 2012 are plotted in Figure 4. The left-hand panel shows that for those with a criminal

history event, any gig platform work peaks in the year before the initial criminal history

event, and then falls in subsequent years. The time pattern is similar to the patterns around

the initial criminal history event for wage employment, and is suggestive that the criminal

record limits employment opportunities in gig work. The right hand panel of Figure 4

plots the event study coefficients around the FCRA event pooling all the data. We find

gig employment increases discretely at seven years. The increase is small in percentage

points (0.4 percentage points one year after the FCRA removal event), but quite large in

percentage terms (an over 100% increase relative to the baseline mean of 0.003 two years

before the FCRA event).

While gig work is a new form of work activity, we find evidence that removal of a criminal

record via FCRA has a large (in percent terms) impact on gig work for this particularly

disadvantaged group, many of whom are likely entering self-employment for the first time.

The increase in Schedule SE filing is only about one-quarter the size, suggesting low earnings

after expenses or compliance issues15 .

4.2

Evidence from Proactive Felony Reductions After California

Proposition 47

4.2.1

Research Design

We next study the labor market impacts of criminal record remediation efforts under California’s 2014 Proposition 47, which reclassified certain theft and drug possession felonies to

Collins et al. (2018) document that only a small share of platform-based gig workers in the broader

population file a Schedule SE and discuss several potential explanations.

misdemeanors. While largely prospective in nature, Proposition 47 also allowed individuals

with eligible offenses to petition to have their previous felonies reclassified as misdemeanors,

with an estimated one million Californians eligible for a record reduction under the law.

We focus our analysis in San Joaquin County, CA, where criminal justice agencies worked

to proactively reduce tens of thousands of eligible felonies to misdemeanors without involvement or notification to eligible individuals. Starting in December 2014, the Office of the

Public Defender of San Joaquin (OPD) and the San Joaquin County District Attorney’s Office (DAO) coordinated to file petitions on behalf of all eligible defendants without requiring

effort, intervention, or even knowledge from the defendant. As of September 2019, this effort

resulted in the reduction of approximately 10,000 felony convictions under Proposition 47.

They have posted information about these reductions on their website publicly. For a large

subset of defendants, the order in which their proactive reductions were filed was unsystematic, based on the first initial of their surname, giving rise to plausibly exogenous variation in

the timing of record reductions among a sample that did not self-select into treatment. We

use this quasi-experimental variation to estimate the causal effect of these reductions on labor market and tax outcomes. We describe Proposition 47 and the reductions in San Joaquin

County in detail in Appendix B. As described in Section 2.2.5, we also take a data-driven

approach to identify individuals who were more likely to have self-requested a petition rather

than have their petitions filed proactively, leveraging this alphabetical ordering, a group we

call likely self-petitioners.

For this analysis, we estimate conventional event-study models for our labor market and

tax outcomes. We define the event as the year a person in the estimation sample obtains a

Proposition 47 reduction of their felony to a misdemeanor. Our focus will be on “proactive”

reductions, that is, those reductions that were initiated by the Public Defender and District

Attorney without knowledge or involvement by the affected individuals, but we also make

comparisons to the sample of possible self-petitioners as well as the pooled sample that

combines the two groups. In the main Proposition 47 analysis, we estimate the following

event-study specification:

yit =

β k 1{Ei = t + k} + Xit γ + αi + αt + εit

(3)

where yit is the outcome of interest (e.g. any wages, any self-employment, etc.) for individual

i in year t. 1{Ei = t + k} is an indicator for the Proposition 47 reduction occurring k periods

from t, with negative k indicating a future event date, and positive k indicating the event

occurred k years in the past. αi are individual fixed effects, αt are year fixed effects, and

Xit includes a quintic in age. Standard errors are clustered at the individual level. The

coefficients of interest are β k , which trace out the labor market impact of a Proposition

47 reduction. For consistency across policies, we omit k = −2 so that the estimated β k

coefficients are relative to two years before the reduction.

Summary statistics on individuals matched to the IRS data are presented in Appendix

Table A.1. Table A.1 Panel (b) presents summary statistics for our estimation sample of

individuals who received reductions under Proposition 47 in San Joaquin County, CA, pooled

(columns 1-2) and separated by individuals who we identify as likely self-petitioners using the

procedure described above (column 3) and those who received proactive reductions without

their knowledge or involvement (column 4). Compared to those who received proactive

reductions, likely self-petitioners are much more likely to receive reductions within seven

years of the original conviction (18.0 percent versus 6.1 percent).16 In terms of baseline

outcomes measured at two years prior to the Proposition 47 reduction, likely self-petitioners

are slightly younger and are negatively selected in terms of wages, with annual baseline

earnings of $6,003 compared to $7,920 for those who received proactive reductions. These

differences indicate that likely self-petitioners are not a random subset of eligible individuals.

4.2.2

Results

Figure 5 Panel (a) plots event-study coefficients around the Proposition 47 reduction for

all proactive drug offense petitions (N = 4,978), along with 90 and 95 percent confidence

intervals. We do not find any statistically significant increase in W-2 wage earnings in the

years after the reduction occurs. This null effect is precisely estimated and a 90 percent

confidence interval rules out effect sizes larger than a 3.6 percentage point increase in the

year of reduction. Consistent with the unsystematic ordering of these proactive reductions,

there are parallel trends between the treated and comparison group in the pre-reduction

periods. Figure 5 Panel (b) reports analogous event-study coefficients for the subsample we

identified as likely self-petitioners (N = 644). In contrast to Panel (a), we find that in the

year of the reduction, any wage employment is 3.7 percentage points higher than in the year

prior to the reduction (a nearly 10% increase, p < .10). This effect can be ruled out by the 90

percent confidence interval of the estimates from Panel (a) for proactive reductions. By two

years after the reduction, this increase relative to the year prior to the reduction drops to 1.7

percentage points. However, consistent with selection into treatment, Panel (b) documents

increases in any wage employment that begin in the several years before the reduction.

To formally compare our findings for those who received proactive reductions to likely

Panel(c) provides comparisons of the standard deviations of baseline characteristics in both of these two

groups.

self-petitioners, Figure 5 Panel (c) plots event-study estimates where we interact time since

event with an indicator for being a likely self-petitioner. The reported coefficients estimate

the differential effect of the reduction for self-petitioners versus proactive reductions by year

since event. The findings document notable and statistically significant differences in both

pre-trends and post-treatment effects among these two groups.

Figure 5 Panel (d) presents results combining the proactive reductions and likely selfpetitioners. As can be seen, there is an uptick in any wage employment in the one to two

years following the Proposition 47 reduction. This increase is significant at the 10% level.

Notably, even a small number of observations that were self-selected into treatment can

be influential for the conclusions, highlighting the importance of accounting for selection.

Moreover, when we focus further on the subset of likely self-petitioners whose reductions

were obtained more than seven years after the original conviction—at which point their

convictions can no longer be reported by CRAs under California’s ICRAA law—we continue

to observe an increase in employment despite the fact that their charges would have largely

been hidden for most purposes regardless of their Proposition 47 reduction (panel e). There

also remain large pre-trends, with significant rises in employment several years preceding

the reductions. While the post-treatment employment increase could be due to these selfpetitioners specifically working in jobs that require occupational licenses, these results are

also consistent with selection driving the results for likely self-petitioners, rather than real

treatment effects of the reduction.

To examine the broader set of tax reporting behavior, Table 4 collapses the event-studies

estimates for proactive reductions to a single “treated” coefficient. This specification is given

as follows:

yit = βTreatedit +

δ k 1{Ei = t + k} + Xit γ + αi + αt + εit

(4)

k∈K≤−2

Under this specification, the coefficient of interest is β, which estimates the average impact

of a Proposition 47 reduction in all observed post-treatment years.17 We report impacts

of the proactive reductions for a range of employment outcomes, including any wage employment and wage employment across certain income thresholds, as well as measures of

self-employment.

Consistent with Figure 5, we cannot rule out a null effect of PD-initiated reductions on

any wage employment in Panel (a). There are also no statistically significant effects for other

employment outcomes, including earning wages above $7,500 or $15,000 or any other 1099

work. The results in Column (3) show this finding holds even among individuals with only

Tabulate the full δ k coefficients in Appendix Table ??.

one felony.18

We further explore whether tax reporting effects are different depending on the time

since original conviction. In California, a criminal conviction can only be reported on an

employment background check for seven years after the latter of disposition date, release

date, or parole violation, unless another law requires employers to look more deeply into the

employee’s background. To capture potential dynamics in treatment effects, in Table 4 Panel

(a) Column 4 we interact our Post indicator with the number of years since conviction at the

time of proactive reduction.19 For the main employment outcomes, we find evidence consistent with the hypothesized relationship. The estimated interaction term is indeed negative,

and strongly significant implying that the benefits of a felony reduction are diminishing with

time since initial disposition.

As in the FCRA analysis, one exception to the pattern of null average effects is gig

platform taxable income. In Table 4 column (5) of Panel (a), we see that a proactive

Proposition 47 reduction is associated with a 0.4 percentage point increase in the rate of gig

work that is statistically significant at the 5% level. This increase doubles the rate of gig

work prior to reduction.

Panels (b) and (c) report show complementary results for likely self-initiated petitioners

and for the pooled sample. The tabulated results consistent with our findings in Figure 5.

We present effects on additional outcomes in Appendix Figure .

To understand whether part of the reason we see no impact of felony reductions is due

to lack of knowledge on behalf of impacted individuals, we analyze the impacts of an effort

by the Public Defender’s office (along with resources from the District Attorney’s office)

in San Joaquin county to notify individuals about these reductions. The notifications took

place in randomized waves, with 4086 individuals with reductions being chosen to be notified

at the time of the analysis (notifications are ongoing). The notifications we analyze took

place between June 2019 and March 2020. Of the 4610 individuals randomly chosen to be

contacted in this first-wave, contact information could be located for 3982 (86.3%). Between

June 2019 and March 2020, SJOPD personnel with carefully written scripts attempted to

call these 3982 individuals; in January 2020 letter were mailed to individual homes (with

self-addressed postcards included to return upon receipt); and in January 2020 e-mails were

In Appendix Figure A.15, we examine whether our event-study estimates are biased due to treatment

effect heterogeneity by constructing alternative estimators following Sun and Abraham 2021. In Appendix

Table ?? we display results from specifications with alternate age controls. In both cases continue to find null

effects of the reductions on any wage employment for those that received proactive reductions. In Appendix

Table , we explore an alternative approach where the year of reduction is instrumented with the year of the

Public Defenders surge for last names beginning with the same letter in the pooled sample, but we obtain

imprecise estimates.

We do not have data on release dates.

sent as well.20

In Appendix Figure A.17, we present raw trends in outcomes for the group that was

notified as well as the group that was not.21 We also present intention-to-treat (ITT) estimates of employment outcomes in 2019 and 2020 between the notified and not notified

groups.22 We present results for any wage employment and wages > $15,000. This figure

reveals remarkably similar raw trends in any wage employment and wages exceeding $15,000

in the years before and after notification. The COVID-19 pandemic began in the second

year of the post-treatment period, but only small dips in employment rates are observed in

that year and the notification and comparison groups respond similarly. Our intent-to-treat

(ITT) estimates confirm that individuals chosen for notification did not experience detectable

improvements in labor market outcomes compared to those not chosen for notification. Appendix Table A.8 presents the full set of employment outcomes associated with notification.

We find null effects across all outcomes. In Appendix Table A.9, we present estimates of the

average treatment effect of successful notification on the treated (TOT) using notifications

assignment as an instrument for successful contact, but continue to find no evidence of an

effect. In sum, these results imply that lack of knowledge about a Proposition 47 reduction

is unlikely to be the main driver of our null result among individuals who received proactive

reductions.

4.3

PA Clean Slate Law

4.3.1

Research Design

The last institutional feature we study is Pennsylvania’s Clean Slate Law of 2018, which

legislated automated sealing of all non-convictions and some older low-level convictions.

PA Clean Slate automated the sealing of non-conviction records with no waiting period for

individuals who did not owe fines and fees to the court at the time of the initial set of sealings.

We provide additional details about the Clean Slate Law in Appendix B. We use individuals

that owed fines and fees as a comparison group for individuals who received automatic

In Appendix figure A.7 we show covariate balance across people who were notified in the first wave and

those who were not.

We downloaded the list of contacted defendants from the public link at the website of the San Joaquin County public defender’s office (Last accessed 6/20/22).

See:

https://www.sjgov.org/department/pubdef/programs- services/proposition-47.

We estimate: yit = β Notifiedit + Xit γ+ OneFelonyi + εit where yit is the outcome of interest for

individual i in year t, where we run separate regressions for each year of interest. N otif iedi is an indicator

treatment, i.e. being notified in the first wave. Xit includes a quintic in age, and OneF elonyi is an indicator

for being on the one felony list. We control for an indicator for one felony because the randomization was

stratified in this dimension. Standard errors are clustered at the individual level. β captures the causal effect

of notification of Proposition 47 reduction on labor market outcomes.

sealings to difference out any trends in employment for people with criminal system contact

during this time period. Summary statistics on individuals matched to the IRS data in both

groups are presented in Panel (d) of Appendix Table A.1.

Our analysis focuses on the subset of individuals who only have non-convictions on their

records, as these individuals’ entire criminal histories are cleared by PA Clean Slate (if they

do not owe fines and fees). We compare the former group to individuals who also only have

non-convictions on their records but whose charges were not cleared between June 2019 and

June 2020 due to the fines and fees. Our main specification is given as follows:

yit = βClearedi × 1{Y eart ∈ 2019-2020} +

δk Clearedi × 1{Y eart = k}

k∈2016,2017

+ Xit γ + αi + αt + εit

(5)

where Clearedi is an indicator for an individual having their record cleared (i.e. an evertreated indicator), and 1{Y eart ∈ 2019-2020} is an indicator for being in the period after records were cleared. The interpretation of β is the difference-in-difference estimator,

comparing the change in the outcome in the post period between those who had all their

misdemeanor charges cleared with those who did not because they owed fines and fees. The

interactions between Clearedi and earlier years provide a test for pretrends.23

To examine if the effect varies with the time elapsed since the last charge, we also estimate

a triple difference specification including additional interations with months since the latest

charge (calculated as months since June 2019):

yit = β1 Clearedi × 1{Y eart ∈ 2019-2020} + β2 1{Y eart ∈ 2019-2020} × Months since chargei

+ β3 Clearedi × 1{Y eart ∈ 2019-2020} × Months since chargei

δk Clearedi × 1{Y eart = k} + Xit γ + αi + αt + εit

(6)

k∈2016,2017

The interpretation of the coefficient on Clearedi × 1{Y eart ∈ 2019-2020} is now the out of

sample predicted effect at 0 months since the initial charge. The earliest charge reductions

we are able to see in our data occur approximately 18 months after the charge, as we have

data through 2018 and the sealings took place June 2019–June 2020.24

We use data since 2016 for this analysis and restrict our analysis sample to individuals aged 18 to 25 to

ensure that they had no other prior convictions by the start of our charge data, which begins in 2008.

We do not know the exact date the sealing took place within this date interval.

4.3.2

Results

Table 5 Panel (a) presents these differences-in-differences results, where we interact a Post

(2019-2020) indicator with a treatment indicator for individuals whose records were cleared.

Panel (b) presents estimates including a triple interaction with months since original charge

to test whether more recently cleared charges had a differential effect on employment.

We present graphical event-study estimates in Appendix Figure A.18. While these nonconviction records were generally cleared less than seven years before the original event (on

average cleared 5-6 years after the charge), we find no detectable effect of the automated

record clearance for treated versus control individuals for a range of employment outcomes,

although we do find a marginally significant effect for gig platform work for those with more

recent charges.25

Discussion and Conclusion

Our paper documents that initial criminal history events are associated with sharp and

persistent declines in the propensities to have W-2 reported earnings and to file a 1040

return, even in cases where charges did not lead to convictions. We find no evidence that

remediation for individuals who have had criminal records for multiple years improves filing

rates or earnings. Our findings point to the conclusion that older records do not directly

suppress individuals’ earnings and filing rates, which could be because the initial short-term

impacts of a record—for example, resume gaps and loss of experience—lead to longer term

labor-market scarring that can be difficult to undo.

An initial motivation for this work was our hypothesis that individuals with criminal

histories facing barriers to traditional employment might shift towards alternative work arrangements like gig work and other self-employment where they bear the full burden of

tax compliance. In practice, we do not find this to be true. Rather, in most cases selfemployment earnings of Schedule SE and 1099-reported nonemployee compensation both

decline in close proportion to W-2 employment after most types of criminal history events.

Contrary to our expectations, we find that clearing records leads to increases in platformmediate gig work reported on 1099 returns, though in most cases those gig earnings are not

reported on Schedule SE. These findings suggest that non-traditional work arrangements,

which have typically been unstudied, may be an increasingly important avenue of work for

To ensure our results are not driven by Philadelphia, which generally does not levy fines and fees for

non-convictions and thus had very few defendants in the control group, in Appendix Table A.10 we exclude

Philadelphia and find similar results, although the result for gig platform work is attenuated from removing

this large urban county where gig work would be concentrated. We also estimate effects separately by race

in Appendix Tables A.11 and A.12.

those who have previously had a criminal record–but an avenue that places the burden of

tax compliance on the individual, potentially leading to compliance challenges.

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Figures and Tables

Figures

Figure 1: Reductions in Tax Filing After First Event

(a) Files 1040

(b) Has W2 Wages

-.05

-.1

-.15

-.2

Percentage Points, Relative to -2

.01

-.05

-.1

-.15

-2

-1

Years since last criminal history event

Conviction

Non-conviction

Misdemeanor

-3

-1

Years since last criminal history event

Conviction

Non-conviction

Misdemeanor

-.01

-.02

-.03

-.04

-.05

-1

Years since last criminal history event

Misdemeanor

-3

Felony

-2

Felony

-1

Years since last criminal history event

Conviction

Non-conviction

Misdemeanor

Felony

(f) Wage Earnings (Dollars)

.01

1000

-.01

-.02

-.03

-.04

-1000

-2000

-3000

-4000

-5000

-.05

Conviction

Non-conviction

-.04

Dollars, Relative to -2

Percentage Points, Relative to -2

-2

-.03

(e) Has 1099 NEC

.01

Percentage Points, Relative to -2

-2

Felony

(d) Has SE Earnings

-3

-.02

-.05

-.2

-3

-.01

-6000

-3

-2

-1

Years since last criminal history event

Conviction

Non-conviction

Misdemeanor

Felony

-3

-2

-1

Years since last criminal history event

Conviction

Non-conviction

Misdemeanor

Felony

Notes: Each panel plots selected event study coefficients for the specified outcome after an initial criminal history event following specification 1 in

the text, where the type of event is as specified in the legend. For this analysis, we restrict the full sample to have been 18 by the time the first charge

appears in the data for both non-convictions and conviction. This event is the charge date for non-convictions and disposition date for convictions.

Coefficients are relative to 2 years before the event. We run separate event studies for each event type and outcome. Data from 2000-2020. The

sample is restricted to events occurring between 1996-2011 to ensure the regression is balanced in the event window.

Figure 2: FCRA Event Study of Any 1040 Around Removal (Year 7)

Note: MD has State FCRA for Convictions

Percentage Points, Relative to +5

(a) Felony Non-Convictions, no other convictions

(b) Mis. Non-Convictions, no other convictions

.05

.025

-.025

-.05

Years since FCRA criminal history event

Bexar, TX

-.05

Bexar, TX

Bexar, TX: N= 10,217, NxT= 202,127, Dep. Mean in +5: 0.731

MD: N= 14,359, NxT= 276,553, Dep. Mean in +5: 0.621

NJ: N= 15,976, NxT= 317,267, Dep. Mean in +5: 0.597

Bexar, TX: N= 68,694, NxT=1,345,682, Dep. Mean in +5: 0.779

MD: N= 90,519, NxT=1,722,568, Dep. Mean in +5: 0.724

Percentage Points, Relative to +5

Years since FCRA criminal history event

(d) Mis. Convictions

.05

.025

-.025

-.05

Years since FCRA criminal history event

Bexar, TX

Bexar, TX: N= 28,786, NxT= 576,704, Dep. Mean in +5: 0.427

MD: N= 23,719, NxT= 473,663, Dep. Mean in +5: 0.428

PA: N= 34,622, NxT= 665,843, Dep. Mean in +5: 0.451

NJ: N= 194,068, NxT=3,894,497, Dep. Mean in +5: 0.498

-.05

Years since FCRA criminal history event

Bexar, TX

Bexar, TX: N= 68,336, NxT=1,362,099, Dep. Mean in +5: 0.621

MD: N= 86,508, NxT=1,733,092, Dep. Mean in +5: 0.543

PA: N= 106,967, NxT=2,091,048, Dep. Mean in +5: 0.604

Notes: Each panel plots selected event study coefficients for the share with any 1040 filing around 7 years after the event, following specification 2 in

the text. Timing from the event is based on the charge date for non-convictions and disposition date for convictions. Coefficients are relative to +5

periods after the event (2 years prior to the year 7 FCRA event, if applicable). We run separate event studies for each state in each panel. Data from

2000-2020. The sample is restricted to events occurring between 1996-2011 to ensure the regression is balanced in the event window.

Figure 3: FCRA Event Study of Any Wages Around Removal (Year 7)

Note: MD has State FCRA for Convictions

Percentage Points, Relative to +5

(a) Felony Non-Convictions, no other convictions

(b) Mis. Non-Convictions, no other convictions

.05

.025

-.025

-.05

Years since FCRA criminal history event

Bexar, TX

-.05

Bexar, TX

Bexar, TX: N= 10,217, NxT= 202,127, Dep. Mean in +5: 0.696

MD: N= 14,359, NxT= 276,553, Dep. Mean in +5: 0.661

NJ: N= 15,976, NxT= 317,267, Dep. Mean in +5: 0.633

Bexar, TX: N= 68,694, NxT=1,345,682, Dep. Mean in +5: 0.754

MD: N= 90,519, NxT=1,722,568, Dep. Mean in +5: 0.747

Percentage Points, Relative to +5

Years since FCRA criminal history event

(d) Mis. Convictions

.05

.025

-.025

-.05

Years since FCRA criminal history event

Bexar, TX

Bexar, TX: N= 28,786, NxT= 576,704, Dep. Mean in +5: 0.480

MD: N= 23,719, NxT= 473,663, Dep. Mean in +5: 0.465

PA: N= 35,163, NxT= 676,163, Dep. Mean in +5: 0.483

NJ: N= 194,068, NxT=3,894,497, Dep. Mean in +5: 0.547

-.05

Years since FCRA criminal history event

Bexar, TX

Bexar, TX: N= 68,336, NxT=1,362,099, Dep. Mean in +5: 0.643

MD: N= 86,508, NxT=1,733,092, Dep. Mean in +5: 0.569

PA: N= 106,080, NxT=2,073,847, Dep. Mean in +5: 0.618

Notes: Each panel plots selected event study coefficients for the share with any wages > $0 around 7 years after the event, following specification 2

in the text. Timing from the event is based on the charge date for non-convictions and disposition date for convictions. Coefficients are relative to

+5 periods after the event (2 years prior to the year 7 FCRA event, if applicable). We run separate event studies for each state in each panel. Data

from 2000-2020. The sample is restricted to events occurring between 1996-2011 to ensure the regression is balanced in the event window.

Figure 4: FCRA Event-Study Estimates: Any Gig Platform Work

0.06

.004

0.04

.002

0.02

0.00

-.002

-0.02

-.004

-0.04

-.006

-0.06

-3

-2

-1

Years since last criminal history event

Left axis:

Right axis:

Platform gig work

Wage work

...and SE filer

Platform gig work: N= 779,515, NxT=7,014,395, Dep. Mean in -2: 0.003

...and SE filer: N= 779,515, NxT=7,014,395, Dep. Mean in -2: 0.001

Wage work: N= 779,515, NxT=7,014,395, Dep. Mean in -2: 0.632

.006

0.06

.004

0.04

.002

0.02

0.00

-.002

-0.02

-.004

-0.04

-.006

Percentage Point, Relative to +5

.006

Percentage Point, Relative to +5

FCRA “Time 7” Event

Percentage Point, Relative to -2

Percent, Relative to -2

“Time 0” Event

-0.06

Years since last criminal history event

Left axis:

Right axis:

Platform gig work

Wage work

...and SE filer

Platform gig work: N= 515,957, NxT=4,653,837, Dep. Mean in +5: 0.004

...and SE filer: N= 515,957, NxT=4,653,837, Dep. Mean in +5: 0.001

Wage work: N= 515,957, NxT=4,653,837, Dep. Mean in +5: 0.596

Notes: Each panel plots selected event study coefficients for the share with any gig income > $0 in different windows around the latest event. Events

are defined as charge dates for non-convictions and disposition dates for convictions. The left panel is restricted to latest events from 2015-2018 and

focuses on the period around the initial event. The right panel is restricted to latest events from 2008-2011 and focuses on the period 7 years after

the latest event. The data are restricted to the period since 2012. We pool all charges for this figure.

Figure 5: Any Wage Employment Around CA Prop 47 Reductions in SJ County

(a) Proactive Reductions

(b) Likely Self-Petitioners

0.20

0.16

0.12

0.08

0.04

0.00

-0.04

-0.08

-0.12

-0.16

-0.20

-6

-5

-4

-3

-2

-1

Years Since Reduction

-6

-5

-4

-3

-2

-1

Years Since Reduction

(d) Pooled Estimates

0.10

0.08

0.06

0.04

0.02

0.00

-0.02

-0.04

-0.06

-0.08

-0.10

≤6

-5

-4

-3

-2

-1

Years Since Reduction

≤6

-5

-4

-3

-2

-1

Years Since Reduction

(e) Likely Self-Petitioners >7 Yrs Post-Conviction

0.20

0.16

0.12

0.08

0.04

0.00

-0.04

-0.08

-0.12

-0.16

-0.20

-6

-5

-4

-3

-2

-1

Years Since Reduction

Notes: Figure shows event-study coefficients for having any wage employment around Proposition 47 felony

reductions in San Joaquin County, CA. Panels A and B report event-study coefficients from separate regressions following equation 3 in the text for proactive reductions and likely self-petitioners, respectively. Panel

C plots event-study coefficients from an interaction of post-reduction and likely self-petitioner, representing the differential labor market impacts for likely self-petitioners compared with individuals who received

proactive reductions. Panel D pools all individuals (both likely self-petitioners and proactive reductions) in

our main analysis sample of individuals with HS charges. Panel (E) plots event-study coefficients for likely

self-petitioners who received reductions more than seven years post-conviction. Darker shading shows 90

percent confidence intervals, and lighter shading extends out to 95 percent confidence intervals.

Tables

Table 1: Tax Reporting Behavior Before and After First Criminal History Events

(a) Misdemeanor Non-convictions , MD and Bexar, TX

Two Years Before

0.711

Year After

0.682

(0.002)

Five Years After

0.659

(0.003)

Has Labor Earnings

0.804

0.780

(0.001)

0.746

(0.002)

Has W2 Earnings

0.768

0.745

(0.001)

0.716

(0.002)

W2 Earnings (1000 $)

14.594

13.147

(0.067)

12.794

(0.139)

Has SE Earnings

0.068

0.065

(0.001)

0.058

(0.002)

SE if Has Earnings

0.084

(0.001)

0.082

(0.002)

Has 1099 NEC

0.076

0.077

(0.001)

0.067

(0.002)

EITC Claimant

0.247

0.240

(0.001)

0.233

(0.002)

160072

Files 1040

(b) Felony Non-convictions, MD, NJ, and Bexar, TX

Two Years Before

0.635

Year After

0.574

(0.003)

Five Years After

0.592

(0.006)

Has Labor Earnings

0.754

0.691

(0.003)

0.682

(0.005)

Has W2 Earnings

0.719

0.657

(0.003)

0.652

(0.005)

W2 Earnings (1000 $)

11.593

8.639

(0.122)

9.564

(0.241)

Has SE Earnings

0.067

0.063

(0.002)

0.057

(0.003)

SE if Has Earnings

0.089

0.095

(0.003)

0.089

(0.004)

Has 1099 NEC

0.061

0.054

(0.002)

0.051

(0.003)

EITC Claimant

0.266

0.248

(0.003)

0.254

(0.005)

37891

Files 1040

Two Years Before

0.654

Year After

0.593

(0.002)

Five Years After

0.598

(0.004)

Has Labor Earnings

0.792

0.749

(0.002)

0.725

(0.004)

Has W2 Earnings

0.770

0.728

(0.002)

0.703

(0.004)

W2 Earnings (1000 $)

11.084

9.125

(0.082)

9.419

(0.172)

Has SE Earnings

0.046

0.045

(0.001)

0.046

(0.002)

SE if Has Earnings

0.058

0.062

(0.001)

0.068

(0.003)

Has 1099 NEC

0.062

0.061

(0.001)

0.062

(0.002)

EITC Claimant

0.187

0.166

(0.002)

0.177

(0.003)

108397

Files 1040

(d) Felony Convictions, MD, PA, and Bexar, TX

Two Years Before

0.550

Year After

0.427

(0.002)

Five Years After

0.478

(0.003)

Has Labor Earnings

0.714

0.584

(0.002)

0.607

(0.003)

Has W2 Earnings

0.690

0.565

(0.002)

0.587

(0.003)

W2 Earnings (1000 $)

9.358

4.891

(0.064)

5.765

(0.121)

Has SE Earnings

0.045

0.034

(0.001)

0.036

(0.002)

SE if Has Earnings

0.063

0.062

(0.001)

0.067

(0.002)

Has 1099 NEC

0.046

0.034

(0.001)

0.036

(0.001)

EITC Claimant

0.194

0.145

(0.002)

0.161

(0.003)

123429

Files 1040

Notes: Table displays mean outcome levels two years prior to an initial criminal history event, where the type

of event differs across panels. Table also presents mean outcomes one and five years after the specified event

implied by our event study estimates of 1, which estimate the change relative to period -2 controlling for

aging and macroeconomic conditions. Specifically, we add our estimates of β 1 and β 5 from 1 for each event

type (displayed in Figure 1) to the means two years prior to the event. Standard errors reflect estimation

of event-study coefficients but not estimation of sample means in period -2. For this analysis, we restrict

the full sample to have been 18 by the time the first charge appears in the data for both non-convictions

and conviction. Data from 2000-2020. The sample is restricted to events occurring between 1996-2011 to

correspond to the event-study sample used to estimate Equation 1. W2 earnings are winsorized at the 99th

percentile.

Table 2: Test for Deviations from Trend Around “Year 7” FCRA Event

(a) Mis. non-convictions and no other convictions, MD and Bexar, TX

+7 Trend Deviation

(S.E.)

+8 Trend Deviation

(S.E.)

NxT

(1)

Any Wages>$0

-0.002

(0.002)

-0.002

(0.003)

159,194

3,068,250

(2)

...>$7,500

-0.004ª

(0.003)

-0.007ª

(0.003)

159,194

3,068,250

(3)

...>$15,000

-0.004

(0.003)

-0.004

(0.003)

159,194

3,068,250

(4)

Any Gig

-0.001

(0.001)

-0.001

(0.002)

57,246

515,235

(5)

Any Other 1099

0.004∗

(0.002)

0.006∗

(0.003)

159,194

3,068,250

(6)

Files 1040

-0.001

(0.002)

-0.000

(0.003)

159,194

3,068,250

(7)

Files SE

0.001

(0.002)

0.002

(0.002)

159,194

3,068,250

(b) Felony non-convictions and no other convictions, MD, NJ, and Bexar, TX

+7 Trend Deviation

(S.E.)

+8 Trend Deviation

(S.E.)

NxT

(1)

Any Wages>$0

0.001

(0.005)

0.002

(0.007)

40,540

795,947

(2)

...>$7,500

0.006

(0.005)

0.008

(0.007)

40,540

795,947

(3)

...>$15,000

-0.009ª

(0.005)

-0.013ª

(0.007)

40,540

795,947

(4)

Any Gig

-0.001

(0.002)

0.000

(0.003)

13,480

121,324

(5)

Any Other 1099

0.006ª

(0.003)

0.006ª

(0.005)

40,540

795,947

(6)

Files 1040

-0.004

(0.005)

-0.003

(0.007)

40,540

795,947

(7)

Files SE

0.001

(0.003)

-0.000

(0.004)

40,540

795,947

Notes: Table reports results from a test of whether the event study coefficients 7-8 years after the last charge are different from a linear trend.

Specifically, “+7 Trend Deviation” reports 2 × β+4 + β+7 , and “+8 Trend Deviation” reports 3 × β+4 + β+8 . With the exception of Column (4), data

is from 2000-2020, and the sample is restricted to events occurring between 1996-2011 to ensure the regression is balanced in the window around the

event. Column (4) restricts to data from 2012 and events from 2015-2018. Standard errors clustered on individual are reported in parentheses. ª

p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Table 3: Test for Deviations from Trend Around “Year 7” in MD v All Other States

(a) Convictions in NJ, PA and Bexar, TX [No state FCRA]

+7 Trend Deviation

(S.E.)

+8 Trend Deviation

(S.E.)

NxT

(1)

Any Wages>$0

-0.006∗∗∗

(0.002)

-0.011∗∗∗

(0.002)

430,324

8,584,190

(2)

...>$7,500

-0.006∗∗∗

(0.001)

-0.010∗∗∗

(0.002)

430,324

8,584,190

(3)

...>$15,000

-0.003∗

(0.001)

-0.006∗

(0.002)

430,324

8,584,190

(4)

Any Gig

0.002∗∗∗

(0.000)

0.004∗∗∗

(0.000)

227,737

2,050,582

(5)

Any Other 1099

-0.001

(0.001)

-0.002

(0.001)

430,324

8,584,190

(6)

Files 1040

-0.010∗∗∗

(0.001)

-0.012∗∗∗

(0.002)

430,324

8,584,190

(7)

Files SE

-0.001

(0.001)

-0.001

(0.001)

430,324

8,584,190

(6)

Files 1040

-0.008∗∗

(0.003)

-0.009∗∗

(0.004)

110,227

2,206,755

(7)

Files SE

0.001

(0.002)

0.000

(0.002)

110,227

2,206,755

(b) Convictions in MD [State FCRA]

+7 Trend Deviation

(S.E.)

+8 Trend Deviation

(S.E.)

NxT

(1)

Any Wages>$0

-0.003

(0.003)

-0.004

(0.004)

110,227

2,206,755

(2)

...>$7,500

-0.008∗∗

(0.003)

-0.012∗∗

(0.004)

110,227

2,206,755

(3)

...>$15,000

-0.010∗∗∗

(0.003)

-0.015∗∗∗

(0.004)

110,227

2,206,755

(4)

Any Gig

0.002

(0.001)

0.004∗∗

(0.001)

34,881

312,769

(5)

Any Other 1099

0.000

(0.002)

0.002

(0.003)

110,227

2,206,755

Notes: Table reports results from a test of whether the event study coefficients 7-8 years after the disposition are different from a linear trend.

Specifically, “+7 Trend Deviation” reports 2 × β+4 + β+7 , and “+8 Trend Deviation” reports 3 × β+4 + β+8 . With the exception of column (4), data

is from 2000-2020, and the sample is restricted to events occurring between 1996-2011 to ensure the regression is balanced in the window around the

event. Column (4) restricts to data from 2012 and events from 2015-2018. Standard errors clustered on individual are reported in parentheses. ª

p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Table 4: Impact of Proposition 47 Reductions on Employment Outcomes

(a) Public Defender Initiated Petitioners

(1)

Any Wages>$0

0.003

(0.014)

Prop 47 Reduction

(2)

...>$15,000

0.002

(0.012)

Prop 47 Reduction × 1 Felony

(3)

...>$0

0.003

(0.015)

(4)

...>$0

0.054*

(0.026)

(5)

Any Gig

0.004*

(0.002)

(6)

Files SE

0.003

(0.006)

0.338

4,967

94,373

-0.004**

(0.001)

0.335

4,336

82,384

0.002

4,967

94,373

0.029

4,967

94,373

(3)

...>$0

0.036

(0.036)

(4)

...>$0

0.024

(0.051)

(5)

Any Gig

-0.004

(0.006)

(6)

Files SE

0.019

(0.013)

-0.000

(0.003)

0.330

615

11,685

0.005

655

12,445

0.031

655

12,445

-0.006

(0.026)

Prop 47 Reduction × Years Since Crime

Dep. Mean (-1)

0.338

4,967

NxT

94,373

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

0.195

4,967

94,373

(b) Likely Self-Initiated Petitioner

Prop 47 Reduction

(1)

Any Wages>$0

0.034

(0.036)

(2)

...>$15,000

0.019

(0.029)

Prop 47 Reduction × 1 Felony

-0.018

(0.054)

Prop 47 Reduction × Years Since Crime

Dep. Mean (-1)

0.334

655

NxT

12,445

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

0.160

655

12,445

0.334

655

12,445

Prop 47 Reduction

(1)

Any Wages>$0

0.024ª

(0.013)

(2)

...>$15,000

0.006

(0.010)

Prop 47 Reduction × 1 Felony

(3)

...>$0

0.024ª

(0.013)

(5)

Any Gig

0.004*

(0.002)

(6)

Files SE

0.006

(0.005)

-0.004***

(0.001)

0.334

4,951

94,069

0.002

5,622

106,818

0.029

5,622

106,818

-0.002

(0.024)

Prop 47 Reduction × Years Since Crime

Dep. Mean (-1)

0.338

5,622

NxT

106,818

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

(4)

...>$0

0.078***

(0.022)

0.191

5,622

106,818

0.338

5,622

106,818

Notes: This table reports coefficients on receiving a Prop 47 reduction indicator following Equation 4. Panel (a) presents results for Public Defender

Initiated Petitioners, Panel (b) presents results for likely self-initiated petitioners, and Panel (c) presents results for the pooled sample.

Table 5: Impact of PA Clean Slate Reductions on Employment Outcomes

(a) DD Estimates

(1)

Any Wages>$0

0.00424

(0.00326)

(2)

...>$7,500

-0.00131

(0.00401)

(3)

...>$15,000

0.00123

(0.00407)

(4)

Any Gig

0.000686

(0.00150)

(5)

Any Other 1099

0.000542

(0.00227)

(6)

Files 1040

0.00767ª

(0.00401)

(7)

Files SE

-0.00415ª

(0.00214)

2017 × Cleared

-0.00275

(0.00341)

-0.000528

(0.00426)

0.0113**

(0.00419)

-0.000974

(0.00123)

-0.00657**

(0.00249)

-0.00575

(0.00407)

-0.00515*

(0.00225)

2016 × Cleared

-0.00350

(0.00405)

0.809

45,877

275,634

-0.000584

(0.00501)

0.618

45,877

275,634

0.00755

(0.00490)

0.480

45,877

275,634

0.00155

(0.00129)

0.011

45,877

275,634

-0.00393

(0.00277)

0.056

45,877

275,634

-0.00545

(0.00468)

0.713

45,877

275,634

-0.00234

(0.00244)

0.051

45,877

275,634

Post (2019-2021) × Cleared

Dep. Mean (2018)

NxT

Age Controls

Indiv. FE

Year FE

(b) By months since charge

(1)

Any Wages>$0

0.00249

(0.00779)

(2)

...>$7,500

-0.00830

(0.00970)

(3)

...>$15,000

-0.0233*

(0.00953)

(4)

Any Gig

0.00724*

(0.00355)

(5)

Any Other 1099

-0.00253

(0.00472)

(6)

Files 1040

0.0212*

(0.00924)

(7)

Files SE

-0.00202

(0.00449)

-0.00000386

(0.000121)

0.000126

(0.000147)

0.000450**

(0.000146)

-0.000115*

(0.0000548)

0.0000561

(0.0000750)

-0.000226

(0.000141)

-0.0000413

(0.0000736)

0.000340**

(0.000109)

-0.000112

(0.000134)

-0.000474***

(0.000134)

0.0000673

(0.0000495)

-0.0000542

(0.0000694)

0.0000323

(0.000130)

0.0000627

(0.0000675)

2017 × Cleared

-0.00341

(0.00341)

-0.000483

(0.00426)

0.0116**

(0.00420)

-0.000948

(0.00124)

-0.00654**

(0.00249)

-0.00550

(0.00408)

-0.00522*

(0.00225)

2016 × Cleared

-0.00482

(0.00406)

-0.000488

(0.00502)

0.00818ª

(0.00491)

0.00160

(0.00130)

-0.00387

(0.00277)

-0.00496

(0.00469)

-0.00247

(0.00244)

Post (2019-2021) × Cleared

× Months since charge

Post (2019-2021)

× Months since charge

Notes: Table reports difference-in-differences results comparing outcomes for individuals who had all their non-convictions cleared by PA’s Clean Slate law by 2020, compared

with those who did not. Data from 2016-2021. Sample is restricted to ages 18-25 to ensure they had no other prior convictions by the start of our charge data, which begins in

2008. Standard errors clustered on individual are reported in parentheses. ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Appendix

Additional Figures and Tables

Table A.1: Summary Statistics

(a) Last-Event Analysis Estimation Sample

(1)

(2)

Last Event is Non-Conv &

No Other Conv

Felony

Misdemeanor

Male

0.648

0.643

5 Years After Charge/Disposition:

Age

30.95

30.61

Any Wages

0.659

0.743

Wages>$15k

0.390

0.499

Avg. Wages

14,564

19,647

Any 1099 NEC

0.067

0.086

Filed Taxes

0.639

0.739

Any SE Income

0.073

0.078

Total Obs

40,552

171,725

(3)

(4)

Last Event is Conv.

Felony

0.793

Misdemeanor

0.765

38.18

0.525

0.279

10,803

0.053

0.479

0.049

280,854

39.69

0.608

0.399

16,533

0.079

0.589

0.061

260,515

(b) San Joaquin Felony Reductions Estimation Sample

Male

Reduction<7 Years from Conviction

2 Years Prior to Reduction:

Age

Any Wages

Wages>$15k

Avg. Wages

Any 1099 NEC

Filed Taxes

Any SE Income

Total Obs

(1)

All

Crimes

All

0.735

0.087

0.751

0.075

(3)

Has HS Crime

Likely

SelfInitiated

Petitioner

0.766

0.177

47.63

0.331

0.179

7,674

0.033

0.303

0.031

6,729

47.55

0.332

0.181

7,697

0.032

0.310

0.030

5,622

45.09

0.296

0.139

6,003

0.020

0.301

0.029

655

(2)

(4)

Initiated

Petitioner

0.749

0.061

47.88

0.337

0.187

7,920

0.034

0.311

0.030

4,967

Male

Reduction<7 Years from Conviction

2 Years Prior to Reduction:

Age

Years Since Conviction

1 Felony

Any Wages

Wages>$15k

Any Platform Gig

Any Filed SE Income

Total Obs

(1)

(2)

Likely

Self-Initiated

Petitioner

0.766

(0.424)

0.177

(0.382)

PD Initiated

Petitioner

45.09

(10.79)

12.176

(5.668)

0.095

(0.293)

0.296

(0.457)

0.139

(0.346)

0.002

(0.039)

0.029

(0.168)

655

47.88

(10.55)

14.474

(5.322)

0.073

(0.260)

0.337

(0.473)

0.187

(0.390)

0.001

(0.032)

0.030

(0.172)

4,967

0.749

(0.434)

0.061

(0.239)

(d) PA Clean Slate Estimation Sample, Summary Statistics in 2018

Pooled

Male

0.645

(0.478)

Black

0.303

(0.460)

Age

26.39

(3.677)

Years Since Charge

4.781

(2.958)

Any Wages

0.809

(0.393)

Wages>$15k

0.480

(0.500)

Avg. Wages

19,531

(20,747)

Any 1099 NEC, non platform

0.056

(0.229)

Any platform gig

0.011

(0.104)

Filed Taxes

0.713

(0.452)

Any SE Income

0.051

(0.221)

Total Obs

45,877

Treated

0.631

(0.483)

0.314

(0.464)

26.77

(3.674)

5.122

(2.976)

0.798

(0.402)

0.478

(0.500)

19,566

(20,950)

0.056

(0.229)

0.011

(0.106)

0.706

(0.456)

0.053

(0.225)

32,677

Control

0.682

(0.466)

0.276

(0.447)

25.45

(3.512)

3.941

(2.738)

0.835

(0.371)

0.484

(0.500)

19,445

(20,235)

0.056

(0.230)

0.010

(0.100)

0.731

(0.443)

0.047

(0.211)

13,221

Notes: Panel (a) uses charges in the case of non-convictions, or disposition dates in the case of convictions.

Sample is restricted to charges or dispositions from 1996 to 2011. Panel (b) reports summary statistics

for our main estimation sample in San Joaquin County, CA. Likely self-initiated petitions are those whose

petitions were filed before the “surge” for the first letter of their last name, the rest are classified as PD

initiated petitions (see text for more detail). Panel (c) provides additioan information on the focal subsample of individuals with HS (“health & safety”) crime as those were the petition list the DPD started

with and make up a majority of petitions (84%). In panel (c), standard deviations are in parenthesis. Panel

(d) presents summary statistics for PA estimation sample. This sample had charges that were dismissed or

withdrawn, and were 18-25 at time of the charge.

Figure A.1: Reduction in W2 Employment after First Charge, By State

Percentage Points, Relative to -2

(a) Felony Non-Convictions

(b) Misdemeanor Non-Convictions

-.02

-.05

-.04

-.06

-.1

-3

-2

-1

Years since first criminal history event

Bexar, TX

-.08

-3

Percentage Points, Relative to -2

-.05

-.1

-.15

-.2

Bexar, TX

Bexar, TX: N= 9,885, NxT= 192,419, Dep. Mean in -2: 0.709

MD: N= 5,238, NxT= 92,699, Dep. Mean in -2: 0.729

NJ: N= 99,228, NxT=1,820,135, Dep. Mean in -2: 0.685

(d) Misdemeanor Convictions

-1

Years since first criminal history event

Bexar, TX: N= 59,737, NxT=1,140,081, Dep. Mean in -2: 0.800

MD: N= 100,356, NxT=1,741,007, Dep. Mean in -2: 0.749

-2

-1

Bexar, TX

Bexar, TX: N= 7,363, NxT= 142,661, Dep. Mean in -2: 0.763

MD: N= 16,781, NxT= 285,610, Dep. Mean in -2: 0.726

NJ: N= 13,762, NxT= 251,551, Dep. Mean in -2: 0.686

-3

-2

-.05

-.1

-.15

-.2

-3

-2

-1

Years since first criminal history event

Bexar, TX

Bexar, TX: N= 43,100, NxT= 824,407, Dep. Mean in -2: 0.770

MD: N= 28,334, NxT= 506,063, Dep. Mean in -2: 0.736

Notes: Each panel plots selected event study coefficients for the share with any wages around an individual’s first charge,

following specification 1 in the text. For this analysis, we restrict the full sample to have been 18 by the time the first charge

appears in the data for both non-convictions and convictions. Coefficients are relative to 2 periods before the first charge. We

run separate event studies for each state in each panel. Data from 2000-2020. The sample is restricted to events occurring

between 1996-2011 to ensure the regression is balanced in the event window.

Table A.2: Summary Statistics: Last-Event Analysis Estimation Sample, Two Years Before

Last Charge/Disposition.

(a) Last-Event Analysis Estimation Sample

(1)

(2)

Last Event is Non-Conv &

No Other Conv

Misdemeanor

Felony

2 Years Before Charge/Disposition:

Any Wages

0.781

0.725

Wages>$15k

0.399

0.323

Any 1099 NEC

0.079

0.065

Filed Taxes

0.716

0.639

Any SE Income

0.059

0.061

Total Obs

87,681

21,607

(3)

(4)

Last Event is Conv.

Misdemeanor

Felony

0.693

0.360

0.074

0.627

0.050

204,084

0.602

0.239

0.051

0.475

0.040

183,069

Notes: Table displays summary statics for sample in Figure A.2, pooling individuals across the states presented within each panel of Figure A.2.

Figure A.2: Reductions in W2 Employment After Latest Criminal History Event

Percentage Points, Relative to -2

(a) Fel. Non-Convictions, no earlier convictions

.025

-.025

-.05

-.075

-.1

-3

-2

-1

Years since last criminal history event

Bexar, TX

-.1

-3

-2

-1

Years since last criminal history event

Bexar, TX

Bexar, TX: N= 37,906, NxT= 764,632, Dep. Mean in -2: 0.801

MD: N= 49,775, NxT= 968,558, Dep. Mean in -2: 0.765

(d) Mis. Convictions

-.05

-.1

-.15

Bexar, TX: N= 6,291, NxT= 126,933, Dep. Mean in -2: 0.756

MD: N= 7,331, NxT= 143,700, Dep. Mean in -2: 0.725

NJ: N= 7,985, NxT= 158,953, Dep. Mean in -2: 0.700

Percentage Points, Relative to -2

(b) Mis. Non-Convictions, no earlier convictions

-.15

-.2

-3

-2

-1

Years since last criminal history event

Bexar, TX

Bexar, TX: N= 20,025, NxT= 401,047, Dep. Mean in -2: 0.632

MD: N= 14,160, NxT= 283,155, Dep. Mean in -2: 0.569

PA: N= 34,135, NxT= 668,569, Dep. Mean in -2: 0.569

NJ: N= 114,749, NxT=2,303,491, Dep. Mean in -2: 0.610

-.2

-3

-2

-1

Years since last criminal history event

Bexar, TX

Bexar, TX: N= 42,344, NxT= 846,248, Dep. Mean in -2: 0.728

MD: N= 55,052, NxT=1,100,163, Dep. Mean in -2: 0.648

PA: N= 106,688, NxT=2,102,658, Dep. Mean in -2: 0.703

Notes: Each panel plots selected event study coefficients for the share with any wages > $0 around the latest event, following specification 1 in the

text. This event is the charge date for non-convictions and disposition date for convictions. Coefficients are relative to 2 years before the event. We

run separate event studies for each state in each panel. Data from 2000-2020. The sample is restricted to events occurring between 2003-2011.

Figure A.3: Event Study of Any Wages Around First Criminal History Event, By Race

Any Wage/Salary Employment, Relative to -2

(a) Non-Convictions

-.02

-.04

-.06

-.08

-3

-2

-1

Years Since First Criminal History Event

Black

All Others

Black: N= 72,546, NxT=1,245,081, Dep. Mean in +5: 0.791

All Others: N= 111,664, NxT=2,064,378, Dep. Mean in +5: 0.746

Any Wage/Salary Employment, Relative to -2

(b) Convictions

-.05

-.1

-.15

-3

-2

-1

Years Since First Criminal History Event

Black

All Others

Black: N= 33,746, NxT= 518,918, Dep. Mean in +5: 0.738

All Others: N= 98,960, NxT=1,607,249, Dep. Mean in +5: 0.766

Notes: Each panel plots selected event study coefficients for the share with any wages around the first

criminal history event, following specification 2 in the text. Timing from the event is based on the charge

date for non-convictions and disposition date for convictions. Coefficients are relative to 2 periods before

the event. We run separate event studies for each state in each panel. For this analysis, we restrict the

full sample to have been 18 by the time the first charge appears in the data for both non-convictions and

conviction. Data from 2000-2020. The sample is restricted to events occurring between 2003-2018. We run

separate event studies for Black individuals and all those of all other racial identities.

Figure A.4: Event Study of Any Wages Around Latest Criminal History Event, By Race

Any Wage/Salary Employment, Relative to -2

(a) Non-Convictions

.02

-.02

-.04

-3

-2

-1

Years Since Last Criminal History Event

Black

All Others

Black: N= 35,246, NxT= 690,280, Dep. Mean in +5: 0.804

All Others: N= 66,047, NxT=1,313,765, Dep. Mean in +5: 0.759

Any Wage/Salary Employment, Relative to -2

(b) Convictions

-.02

-.04

-.06

-.08

-3

-2

-1

Years Since Last Criminal History Event

Black

All Others

Black: N= 77,823, NxT=1,548,288, Dep. Mean in +5: 0.600

All Others: N= 194,481, NxT=3,864,358, Dep. Mean in +5: 0.693

Notes: Each panel plots selected event study coefficients for the share with any wages around the last criminal

history event, following specification 2 in the text. Timing from the event is based on the charge date for

non-convictions and disposition date for convictions. Coefficients are relative to 2 periods before the event.

We run separate event studies for each state in each panel. Data from 2000-2020. The sample is restricted

to events occurring between 2003-2018 to ensure the regression is balanced in the event window. We run

separate event studies for Black individuals and all those of all other racial identities.

Table A.3: Impact of Proposition 47 Reductions on Employment Outcomes: Including Pre-Trend Estimates

(a) Main Results-Proactive Reductions

(1)

Any Wages>$0

0.00270

(0.0145)

(2)

...>$7,500

-0.00395

(0.0128)

(3)

...>$15,000

0.00234

(0.0119)

(4)

Any Gig

0.00380*

(0.00167)

(5)

Any Other 1099

0.00117

(0.00653)

(6)

Files 1040

-0.0133

(0.0142)

(7)

Files SE

0.00270

(0.00586)

-2

0.00471

(0.00909)

-0.00164

(0.00794)

-0.00624

(0.00735)

-0.00129

(0.00137)

-0.00647

(0.00415)

0.00607

(0.00883)

0.00124

(0.00404)

-3

0.00470

(0.0155)

-0.00800

(0.0134)

-0.0167

(0.0124)

-0.000557

(0.00160)

-0.00842

(0.00704)

0.00628

(0.0151)

0.00546

(0.00657)

-4

-0.00948

(0.0213)

-0.0183

(0.0187)

-0.0226

(0.0169)

-0.000203

(0.00165)

-0.00672

(0.00932)

0.00499

(0.0212)

0.00971

(0.00910)

-5

-0.0143

(0.0267)

-0.0154

(0.0229)

-0.0208

(0.0209)

-0.000200

(0.00163)

-0.0118

(0.0114)

0.0126

(0.0267)

0.0122

(0.0111)

-0.0189

-0.0134

-0.0190

(0.0319)

(0.0273)

(0.0248)

Dep. Mean (-1)

0.338

0.243

0.195

4,967

NxT

94,373

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

-0.0000890

(0.00167)

0.002

4,967

94,373

-0.0160

(0.0133)

0.038

4,967

94,373

0.00329

(0.0315)

0.304

4,967

94,373

0.0136

(0.0129)

0.029

4,967

94,373

Treated

≤ -6

(b) By Years Since Conviction

(1)

Any Wages>$0

0.054*

(0.026)

(2)

...>$7,500

0.050*

(0.022)

(3)

...>$15,000

0.052**

(0.020)

(4)

Any Gig

-0.000

(0.003)

(5)

Any Other 1099

0.008

(0.012)

(6)

Files 1040

0.002

(0.025)

(7)

Files SE

0.012

(0.010)

Treated × Years Since Crime

-0.004**

(0.001)

-0.005***

(0.001)

-0.005***

(0.001)

0.000

(0.000)

-0.001

(0.001)

-0.002

(0.001)

-0.000

(0.001)

-2

0.011

(0.010)

0.005

(0.009)

0.000

(0.008)

-0.002ª

(0.001)

-0.003

(0.005)

0.016

(0.010)

0.002

(0.005)

-3

0.016

(0.018)

0.003

(0.016)

-0.004

(0.014)

-0.002

(0.001)

-0.006

(0.008)

0.022

(0.018)

0.003

(0.008)

-4

0.006

(0.025)

-0.002

(0.022)

-0.004

(0.020)

-0.001

(0.002)

-0.004

(0.011)

0.025

(0.025)

0.008

(0.011)

-5

-0.001

(0.031)

0.003

(0.027)

-0.004

(0.024)

-0.001

(0.002)

-0.012

(0.013)

0.041

(0.032)

0.009

(0.013)

-0.006

0.007

(0.037)

(0.032)

Dep. Mean (-1)

0.335

0.243

4,336

NxT

82,384

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

0.001

(0.029)

0.196

4,336

82,384

-0.001

(0.002)

0.002

4,336

82,384

-0.014

(0.015)

0.039

4,336

82,384

0.035

(0.037)

0.303

4,336

82,384

0.009

(0.015)

0.028

4,336

82,384

Treated

≤ -6

(1)

Any Wages>$0

0.003

(0.015)

(2)

...>$7,500

-0.004

(0.013)

(3)

...>$15,000

0.001

(0.012)

(4)

Any Gig

0.004*

(0.002)

(5)

Any Other 1099

0.001

(0.006)

(6)

Files 1040

-0.014

(0.014)

(7)

Files SE

0.002

(0.006)

Treated × 1 Felony

-0.006

(0.026)

0.006

(0.025)

0.024

(0.022)

-0.000

(0.005)

0.008

(0.015)

0.012

(0.028)

0.020

(0.016)

-2

0.005

(0.009)

-0.002

(0.008)

-0.006

(0.007)

-0.001

(0.001)

-0.006

(0.004)

0.006

(0.009)

0.001

(0.004)

-3

0.005

(0.016)

-0.008

(0.013)

-0.017

(0.012)

-0.001

(0.002)

-0.008

(0.007)

0.006

(0.015)

0.006

(0.007)

-4

-0.010

(0.021)

-0.018

(0.019)

-0.022

(0.017)

-0.000

(0.002)

-0.007

(0.009)

0.005

(0.021)

0.010

(0.009)

-5

-0.014

(0.027)

-0.015

(0.023)

-0.021

(0.021)

-0.000

(0.002)

-0.012

(0.011)

0.013

(0.027)

0.012

(0.011)

≤ -6

-0.019

(0.032)

-0.013

(0.027)

-0.019

(0.025)

-0.000

(0.002)

-0.016

(0.013)

0.003

(0.032)

0.014

(0.013)

0.000

362

6,878

0.041

362

6,878

0.362

362

6,878

0.028

362

6,878

0.002

4,605

87,495

0.038

4,605

87,495

0.299

4,605

87,495

0.029

4,605

87,495

Treated

1 Felony

Dep. Mean (-1)

0.387

0.276

0.240

362

NxT

6,878

>1 Felony

Dep. Mean (-1)

0.334

0.240

0.192

4,605

NxT

87,495

Age Controls

Indiv. FE

YearXGroup FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

(d) Likely Self-Initiated Petitioners

(1)

Any Wages>$0

0.0344

(0.0364)

(2)

...>$7,500

0.0331

(0.0320)

(3)

...>$15,000

0.0185

(0.0293)

(4)

Any Gig

-0.00375

(0.00629)

(5)

Any Other 1099

0.00792

(0.0106)

(6)

Files 1040

0.00708

(0.0352)

(7)

Files SE

0.0194

(0.0134)

-2

-0.0342

(0.0228)

-0.0181

(0.0183)

-0.00876

(0.0150)

-0.00101

(0.00145)

-0.00671

(0.00892)

-0.0169

(0.0216)

-0.00728

(0.00734)

-3

-0.0352

(0.0365)

-0.0551ª

(0.0293)

-0.0344

(0.0242)

-0.00228

(0.00203)

-0.00675

(0.0147)

-0.0187

(0.0365)

-0.0121

(0.0111)

-4

-0.0402

(0.0485)

-0.0858*

(0.0391)

-0.0425

(0.0334)

-0.00249

(0.00248)

-0.0122

(0.0185)

-0.0506

(0.0509)

-0.0158

(0.0164)

-5

-0.0269

(0.0593)

-0.0790

(0.0486)

-0.0335

(0.0407)

-0.00285

(0.00297)

-0.0122

(0.0238)

-0.0628

(0.0643)

-0.00773

(0.0207)

-0.00944

-0.0747

-0.0385

(0.0713)

(0.0586)

(0.0486)

Dep. Mean (-1)

0.334

0.217

0.160

655

NxT

12,445

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

-0.00332

(0.00361)

0.005

655

12,445

-0.0216

(0.0274)

0.021

655

12,445

-0.0960

(0.0772)

0.299

655

12,445

-0.00509

(0.0231)

0.031

655

12,445

Treated

≤ -6

(e) Pooled

(1)

Any Wages>$0

0.0242ª

(0.0128)

(2)

...>$7,500

0.00892

(0.0113)

(3)

...>$15,000

0.00623

(0.0103)

(4)

Any Gig

0.00388*

(0.00157)

(5)

Any Other 1099

-0.000254

(0.00526)

(6)

Files 1040

-0.000715

(0.0125)

(7)

Files SE

0.00631

(0.00503)

-2

-0.00526

(0.00790)

-0.00550

(0.00677)

-0.00438

(0.00614)

-0.00110

(0.00107)

-0.00454

(0.00343)

-0.00154

(0.00751)

-0.000568

(0.00336)

-3

-0.00673

(0.0132)

-0.0132

(0.0112)

-0.0115

(0.0101)

-0.000697

(0.00131)

-0.00471

(0.00561)

-0.00259

(0.0127)

0.00224

(0.00532)

-4

-0.0147

(0.0180)

-0.0225

(0.0155)

-0.0141

(0.0137)

-0.000470

(0.00135)

-0.00306

(0.00734)

-0.0104

(0.0177)

0.00357

(0.00729)

-5

-0.00995

(0.0224)

-0.0137

(0.0189)

-0.00753

(0.0168)

-0.000475

(0.00134)

-0.00644

(0.00906)

-0.0104

(0.0224)

0.00350

(0.00887)

-0.000674

-0.00389

-0.00188

(0.0268)

(0.0225)

(0.0200)

Dep. Mean (-1)

0.338

0.240

0.191

5,622

NxT

106,818

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

-0.000394

(0.00139)

0.002

5,622

106,818

-0.0104

(0.0106)

0.036

5,622

106,818

-0.0328

(0.0268)

0.303

5,622

106,818

0.00323

(0.0104)

0.029

5,622

106,818

Treated

≤ -6

Notes: This table reports results from specifications in Table 4 but fully displays year-specific coefficients for leads of the events.

Table A.4: Impact of Proposition 47 Reductions: Alternative Age Controls

(a) PD Initiated Petitioners-No Age Controls

(1)

Any Wages>$0

-0.003

(0.015)

PD Initiated Petitioner

(2)

...>$15,000

-0.002

(0.012)

PD Initiated Petitioner × 1 Felony

(3)

...>$0

-0.002

(0.015)

(5)

Any Gig

0.004*

(0.002)

(6)

Files SE

0.002

(0.006)

0.002

4,967

94,373

0.029

4,967

94,373

-0.012

(0.027)

PD Initiated Petitioner × Years Since Crime

Dep. Mean (-1)

NxT

Age Controls

Indiv. FE

Year FE

(4)

...>$0

0.112***

(0.026)

0.338

4,967

94,373

0.195

4,967

94,373

0.338

4,967

94,373

-0.009***

(0.001)

0.335

4,336

82,384

(4)

...>$0

0.063*

(0.026)

(5)

Any Gig

0.004*

(0.002)

(6)

Files SE

0.003

(0.006)

-0.005***

(0.001)

0.335

4,336

82,384

0.002

4,967

94,373

0.029

4,967

94,373

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

(b) PD Initiated Petitioners-5 Year Age Bins

PD Initiated Petitioner

(1)

Any Wages>$0

0.001

(0.015)

(2)

...>$15,000

0.002

(0.012)

PD Initiated Petitioner × 1 Felony

(3)

...>$0

0.001

(0.015)

-0.007

(0.026)

PD Initiated Petitioner × Years Since Crime

Dep. Mean (-1)

NxT

Age Controls

Indiv. FE

Year FE

0.338

4,967

94,373

0.195

4,967

94,373

0.338

4,967

94,373

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Notes: This table replicates specifications in Table 4 with either no age controls or with age controlled using

5-year bins instead of the quintic in age included in the benchmark specification.

Table A.5: IV Estimates Using Letter-Specific Surge

Post Reduction

Post Surge

(1)

Any Wages>$0

0.025*

(0.012)

(2)

Post Reduction

(3)

Any Wages>$0

0.379***

(0.012)

0.012

(0.016)

KP Fstat

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

(4)

Any Wages>$0

0.031

(0.041)

1253.993

Notes: This presents IV estimates for the pooled San Joaquin sample where the Post-Reduction indicator

(treatment) is instrumented with an indicator of whether the current year is during or after the year of the

initial surge of petitions filed by the Public Defender’s office for individuals with the same last name. Column

1 presents OLS estimates as in 4 estimated on the sample with valid surge indicators, Column 2 presetns the

first stage estimates, Column 3 presents reduced-form effects of the surge indicator, and Column 4 presents

IV estimates.

Table A.6: Impacts of Proposition 47: Additional Outcomes

(a) Likely Self-Initiated Petitioner

(1)

(2)

(3)

Any Wages>$0 ...>$7,500 ...>$15,000

Treated

0.0344

0.0331

0.0185

(0.0364)

(0.0320)

(0.0293)

Dep. Mean (-1)

0.334

0.217

0.160

655

NxT

12,445

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

(4)

Wages

-295.1

(1148.5)

6815.554

655

12,445

(5)

Any Gig

-0.00375

(0.00629)

0.005

655

12,445

(6)

Any Other 1099

0.00792

(0.0106)

0.021

655

12,445

(7)

Files 1040

0.00708

(0.0352)

0.299

655

12,445

(8)

Files SE

0.0194

(0.0134)

0.031

655

12,445

Notes: This table reports coefficients on a “treated” reduction indicator following Equation 4, for only those identified as likely self-petitioners, for

a variety of outcomes.

(b) PD Initiated Petitioner

(1)

(2)

(3)

Any Wages>$0 ...>$7,500 ...>$15,000

Treated

0.00270

-0.00395

0.00234

(0.0145)

(0.0128)

(0.0119)

Dep. Mean (-1)

0.338

0.243

0.195

4,967

NxT

94,373

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

(4)

Wages

795.7

(535.7)

8460.821

4,967

94,373

(5)

Any Gig

0.00380*

(0.00167)

0.002

4,967

94,373

(6)

Any Other 1099

0.00117

(0.00653)

0.038

4,967

94,373

(7)

Files 1040

-0.0133

(0.0142)

0.304

4,967

94,373

(8)

Files SE

0.00270

(0.00586)

0.029

4,967

94,373

Notes: This table reports coefficients on a “treated” reduction indicator following Equation 4, for only those that received proactive reductions, for

a variety of outcomes.

(2)

...>$7,500

0.050*

(0.022)

(3)

...>$15,000

0.052**

(0.020)

(4)

Wages

617.516

(939.489)

(5)

Any Gig

-0.000

(0.003)

(6)

Any Other 1099

0.008

(0.012)

(7)

Files 1040

0.002

(0.025)

(8)

Files SE

0.012

(0.010)

-0.004**

-0.005***

(0.001)

Dep. Mean (-1)

0.335

0.243

4,336

NxT

82,384

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

-0.005***

(0.001)

0.196

4,336

82,384

-40.145

(48.801)

8470.711

4,336

82,384

0.000

(0.000)

0.002

4,336

82,384

-0.001

(0.001)

0.039

4,336

82,384

-0.002

(0.001)

0.303

4,336

82,384

-0.000

(0.001)

0.028

4,336

82,384

Treated

Treated × Years Since Crime

(1)

Any Wages>$0

0.054*

(0.026)

Notes: This table presents differential impacts for individuals who received proactive Proposition 47 reductions based on years since original conviction

for a variety of outcomes.

(d) PD Initiated Petitioner-< 7 Years Indicator

Treated

< 7 Years × Treated

(1)

Any Wages>$0

0.002

(0.014)

(2)

...>$7,500

-0.006

(0.013)

0.014

0.023

(0.027)

(0.024)

Dep. Mean (-1)

0.338

0.243

4,967

NxT

94,373

Age Controls

Indiv. FE

Year FE

Standard errors clustered on individual in parentheses

ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

(3)

...>$15,000

0.002

(0.012)

(4)

Wages

820.429

(535.615)

(5)

Any Gig

0.004*

0.002

(6)

Any Other 1099

0.001

(0.007)

(7)

Files 1040

-0.012

(0.014)

(8)

Files SE

0.003

(0.006)

0.004

(0.021)

0.195

4,967

94,373

-330.894

(972.513)

8460.821

4,967

94,373

-0.003

(0.003)

0.002

4,967

94,373

-0.002

(0.012)

0.038

4,967

94,373

-0.021

(0.026)

0.304

4,967

94,373

0.000

(0.012)

0.029

4,967

94,373

Notes: This table presents differential impacts for individuals who received proactive Proposition 47 reductions. An indicator is included for being

< 7 years from the charge.

Table A.7: Notification Analysis Balance Table

(1)

(2)

(5)

(6)

Difference (p-value)

1 Felony

All Others

-0.018

-0.019

(0.665)

(0.066)

Outcomes in 2018:

Age

49.70

49.07

49.46

49.08

Any Wages

0.364

0.332

0.382

0.330

Wages>$15k

0.223

0.197

0.231

0.185

Any SE Income

0.056

0.021

0.022

0.028

10,322.01

8,710.60

9,810.20

8,592.98

0.233

(0.820)

-0.018

(0.683)

-0.008

(0.832)

0.034

(0.051)

511.81

(0.769)

269

3,486

225

3,175

Male

Notified

1 Felony

All Others

0.669

0.742

(3)

(4)

Not Notified

1 Felony

All Others

0.688

0.762

Wages

Total Obs

Note: This table reports balance tests for the notified and no notified groups.

–0.005

(0.984)

0.002

(0.896)

0.012

(0.228)

-0.007

(0.074)

117.62

(0.798)

Table A.8: Notification Analysis, Additional Employment Outcomes

(a) 2019 Outcomes

(1)

Any Wages>$0

0.0110

(0.0116)

(2)

...>$7,500

0.00248

(0.0105)

(3)

...>$15,000

0.00669

(0.00966)

(4)

Any Gig

0.00130

(0.00179)

(5)

Any Other 1099

0.000384

(0.00357)

(6)

Files 1040

0.00186

(0.0123)

(7)

Files SE

-0.00855*

(0.00377)

Notified × 1 Felony

-0.0460

(0.0453)

0.000128

(0.0428)

0.0284

(0.0406)

-0.00203

(0.00605)

-0.00846

(0.0140)

-0.0475

(0.0468)

0.0176

(0.0180)

1 Felony

0.0623ª

(0.0337)

0.0632*

(0.0315)

0.0513ª

(0.0293)

-0.000280

(0.00460)

0.00525

(0.0110)

0.0256

(0.0344)

0.00721

(0.0127)

Constant

0.333***

(0.00837)

7155

0.239***

(0.00757)

7155

0.189***

(0.00695)

7155

0.00472***

(0.00122)

7155

0.0214***

(0.00257)

7155

0.503***

(0.00888)

7155

0.0283***

(0.00295)

7155

Notified

(b) 2020 Outcomes

(1)

Any Wages>$0

-0.00433

(0.0114)

(2)

...>$7,500

0.00834

(0.0104)

(3)

...>$15,000

0.00884

(0.00965)

(4)

Any Gig

-0.00144

(0.00233)

(5)

Any Other 1099

0.00247

(0.00331)

(6)

Files 1040

-0.00255

(0.0117)

(7)

Files SE

-0.00202

(0.00382)

Notified × 1 Felony

-0.0121

(0.0450)

-0.00137

(0.0418)

-0.0132

(0.0392)

-0.00300

(0.00501)

-0.00311

(0.0149)

-0.0176

(0.0450)

0.00137

(0.0150)

1 Felony

0.0514

(0.0333)

0.0475

(0.0307)

0.0440

(0.0290)

-0.00532

(0.00477)

0.00934

(0.0110)

0.0168

(0.0334)

0.000840

(0.0111)

Constant

0.322***

(0.00829)

7155

0.228***

(0.00745)

7155

0.187***

(0.00692)

7155

0.00976***

(0.00175)

7155

0.0173***

(0.00232)

7155

0.357***

(0.00850)

7155

0.0258***

(0.00282)

7155

Notified

Note: This table reports additional employment outcomes for the effect of notification. We separately report outcomes in 2019 and 2020.

Table A.9: Notification Experiment: ITT v IV estimates, 2020

Notified

Notified × 1 Felony

(1)

Any Wages>$0

-0.00433

(0.0114)

(2)

>$0

-0.0121

(0.0450)

(3)

...>$15,000

0.00884

(0.00965)

(4)

...>$15,000

-0.0132

(0.0392)

Success

-0.0151

(0.0400)

0.0308

(0.0336)

Success × 1 Felony

-0.0551

(0.191)

-0.0494

(0.166)

1 Felony

0.0514

(0.0333)

0.0514

(0.0333)

0.0440

(0.0290)

0.0440

(0.0289)

Constant

0.322***

(0.00829)

7155

OLS

0.322***

(0.00829)

7155

41.110

0.187***

(0.00692)

7155

OLS

0.187***

(0.00692)

7155

41.110

OLS/IV

KP F-stat

Note: This table reports IV estimates of the effects of successful notifications on 2020 outcomes, using

sent notifications (used to estimate ITT effects in Table A.8a) as an instrument for successful contact with

individuals.

Figure A.5: FCRA Event Study of Any Wages >$15,000 Around Removal (Year 7)

Note: MD has State FCRA for Convictions

Percentage Points, Relative to +5

(a) Felony Non-Convictions, no other convictions

(b) Mis. Non-Convictions, no other convictions

.05

.025

-.025

-.05

Years since FCRA criminal history event

Bexar, TX

-.05

Bexar, TX

Bexar, TX: N= 10,217, NxT= 202,127, Dep. Mean in +5: 0.450

MD: N= 14,359, NxT= 276,553, Dep. Mean in +5: 0.389

NJ: N= 15,976, NxT= 317,267, Dep. Mean in +5: 0.353

Bexar, TX: N= 68,694, NxT=1,345,682, Dep. Mean in +5: 0.514

MD: N= 90,519, NxT=1,722,568, Dep. Mean in +5: 0.503

(d) Misdemeanor Convictions

Percentage Points, Relative to +5

Years since FCRA criminal history event

.05

.025

-.025

-.05

Years since FCRA criminal history event

Bexar, TX

Bexar, TX: N= 28,786, NxT= 576,704, Dep. Mean in +5: 0.270

MD: N= 23,719, NxT= 473,663, Dep. Mean in +5: 0.275

PA: N= 35,163, NxT= 676,163, Dep. Mean in +5: 0.249

NJ: N= 194,068, NxT=3,894,497, Dep. Mean in +5: 0.286

-.05

Years since FCRA criminal history event

Bexar, TX

Bexar, TX: N= 68,336, NxT=1,362,099, Dep. Mean in +5: 0.427

MD: N= 86,508, NxT=1,733,092, Dep. Mean in +5: 0.373

PA: N= 106,080, NxT=2,073,847, Dep. Mean in +5: 0.403

Notes: Each panel plots selected event study coefficients for the share with any wages > $15,000 around 7 years after the event, following specification

2 in the text. Timing from the event is based on the charge date for non-convictions and disposition date for convictions. Coefficients are relative to

+5 periods after the event (2 years prior to the year 7 FCRA event, if applicable). We run separate event studies for each state in each panel. Data

from 2000-2020. The sample is restricted to events occurring between 1996-2011 to ensure the regression is balanced in the event window.

Figure A.6: FCRA Event Study of Any Wages Around Removal (Year 7) occurring between 2015-2018

Note: MD has State FCRA for Convictions

(a) Felony Non-Convictions, no other convictions

(b) Mis. Non-Convictions, no other convictions

Percentage Points, Relative to +5

.05

.025

-.025

-.05

Years since FCRA criminal history event

-.05

Years since FCRA criminal history event

MD: N= 4,981, NxT= 44,818, Dep. Mean in +5: 0.640

NJ: N= 4,492, NxT= 40,425, Dep. Mean in +5: 0.618

MD: N= 35,229, NxT= 317,056, Dep. Mean in +5: 0.727

(d) Misdemeanor Convictions

Percentage Points, Relative to +5

.05

.025

-.025

-.05

Years since FCRA criminal history event

MD: N= 7,319, NxT= 65,708, Dep. Mean in +5: 0.417

PA: N= 35,146, NxT= 315,608, Dep. Mean in +5: 0.483

NJ: N= 55,341, NxT= 496,820, Dep. Mean in +5: 0.517

-.05

Years since FCRA criminal history event

MD: N= 27,562, NxT= 247,060, Dep. Mean in +5: 0.534

PA: N= 106,043, NxT= 951,429, Dep. Mean in +5: 0.618

Notes: For this figure, the sample is restricted to events occurring between 2008-2011 (removal (Year 7) occurring between 2015-2018).

Figure A.7: FCRA Event Study of Any Wages Around Removal (Year 7), By Race

(b) Convictions in PA, and TX

.02

Any Wage/Salary Employment, Relative to +5

(a) Non-Convictions

.01

-.01

-.02

Years Since Last Criminal History Event

Black

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Black

All Others

Black: N= 44,539, NxT= 870,174, Dep. Mean in +5: 0.531

All Others: N= 193,809, NxT=3,818,639, Dep. Mean in +5: 0.602

Black: N= 61,732, NxT=1,179,893, Dep. Mean in +5: 0.750

All Others: N= 122,034, NxT=2,367,037, Dep. Mean in +5: 0.735

Any Wage/Salary Employment, Relative to +5

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Black

All Others

Black: N= 58,136, NxT=1,164,786, Dep. Mean in +5: 0.532

All Others: N= 52,091, NxT=1,041,969, Dep. Mean in +5: 0.562

Notes: Race data is available in public court records in Bexar County, Texas, Maryland, and Pennsylvania.

Each panel plots selected event study coefficients for the share with any wages around 7 years after the event,

following specification 2 in the text. Timing from the event is based on the charge date for non-convictions

and disposition date for convictions. Coefficients are relative to +5 periods after the event (2 years prior to

the year 7 FCRA event, if applicable). We run separate event studies for each state in each panel. Data

from 2000-2020. The sample is restricted to events occurring between 1996-2011 to ensure the regression is

balanced in the event window. We run separate event studies for Black individuals and all other races.

Figure A.8: FCRA Event Study of Any Wages Around Removal (Year 7), By Gender

(b) Convictions in PA, NJ and TX

.02

Any Wage/Salary Employment, Relative to +5

(a) Non-Convictions

.01

-.01

-.02

Years Since Last Criminal History Event

Men

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Men

Women

Men: N= 335,832, NxT=6,694,948, Dep. Mean in +5: 0.566

Women: N= 94,799, NxT=1,895,563, Dep. Mean in +5: 0.586

Men: N= 128,181, NxT=2,483,506, Dep. Mean in +5: 0.725

Women: N= 71,518, NxT=1,380,523, Dep. Mean in +5: 0.742

Any Wage/Salary Employment, Relative to +5

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Men

Women

Men: N= 85,842, NxT=1,716,545, Dep. Mean in +5: 0.542

Women: N= 24,383, NxT= 490,168, Dep. Mean in +5: 0.563

Notes: Each panel plots selected event study coefficients for the share with any wages around 7 years after

the event, following specification 2 in the text. Timing from the event is based on the charge date for

non-convictions and disposition date for convictions. Coefficients are relative to +5 periods after the event

(2 years prior to the year 7 FCRA event, if applicable). We run separate event studies for each state in

each panel. Data from 2000-2020. The sample is restricted to events occurring between 1996-2011 to ensure

the regression is balanced in the event window. We run separate event studies for men and women (gender

based on SSA records).

Figure A.9: FCRA Event Study of Any Wages Around Removal (Year 7), By Age

(b) Convictions in PA, NJ and TX

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Any Wage/Salary Employment, Relative to +5

(a) Non-Convictions

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Age at FCRA event:

Under 30

30 and older

Age at FCRA event:

Under 30

30 and older

Under 30: N= 149,561, NxT=2,771,520, Dep. Mean in +5: 0.647

30 and older: N= 222,869, NxT=4,655,608, Dep. Mean in +5: 0.502

Under 30: N= 127,107, NxT=2,339,339, Dep. Mean in +5: 0.749

30 and older: N= 43,072, NxT= 904,764, Dep. Mean in +5: 0.649

Any Wage/Salary Employment, Relative to +5

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Age at FCRA event:

Under 30

30 and older

Under 30: N= 32,872, NxT= 627,473, Dep. Mean in +5: 0.615

30 and older: N= 56,419, NxT=1,161,493, Dep. Mean in +5: 0.476

Notes: Each panel plots selected event study coefficients for the share with any wages around 7 years after

the event, following specification 2 in the text. Timing from the event is based on the charge date for nonconvictions and disposition date for convictions. Coefficients are relative to +5 periods after the event (2

years prior to the year 7 FCRA event, if applicable). We run separate event studies for each state in each

panel. Data from 2000-2020. The sample is restricted to events occurring between 1996-2011 to ensure the

regression is balanced in the event window. We run separate event studies for individuals under 30 and for

individuals 40 and over.

Figure A.10: FCRA Event Study of Any Wages Around Removal (Year 7), by Firm Size

(b) Convictions in PA, NJ and TX

.02

Any Wage/Salary Employment, Relative to +5

(a) Non-Convictions

.01

-.01

-.02

Years Since Last Criminal History Event

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Large Firm with 10,000 or more workers

Small firm with <100 workers

Large Firm with 10,000 or more workers

Small firm with <100 workers

Dep. Mean in +5: Large: 0.120 Small: 0.192

Dep. Mean in +5: Large: 0.223 Small: 0.186

Any Wage/Salary Employment, Relative to +5

.02

.01

-.01

-.02

Years Since Last Criminal History Event

Large Firm with 10,000 or more workers

Small firm with <100 workers

Dep. Mean in +5: Large: 0.132 Small: 0.177

Notes: Each panel plots selected event study coefficients for the share with any wages from a large or small

firm around 7 years after the event, following specification 2 in the text. Timing from the event is based

on the charge date for non-convictions and disposition date for convictions. Coefficients are relative to +5

periods after the event (2 years prior to the year 7 FCRA event, if applicable). We run separate event studies

for each state in each panel. Data from 2000-2020. The sample is restricted to events occurring between

1996-2011 to ensure the regression is balanced in the event window.

Figure A.11: Event Study of Any Wages Around First Event, By 2-digit NAICS Industry

(a) Non-Convictions

% Change

Time 0

-5

-10

71: Arts/Entertainment

72: Accomodations/Food

81: Other Services

71: Arts/Entertainment

72: Accomodations/Food

81: Other Services

62: Health Care

61: Education

56: Temp Agencies

54: Professional Service

53: Real Estate

52: Finance/Insurance

48-49: Transport/Warehouse

44-45: Retail

42: Wholesale

31-33: Manufacturing

23: Construction

11: Agriculture

0: Unknown

-15

2-digit NAICS

Dep. Mean in -2: 0: 0.08, 11: 0.00, 23: 0.05, 31: 0.03, 42: 0.02, 44: 0.12, 48: 0.02, 52: 0.02

53: 0.02, 54: 0.05, 56: 0.09, 61: 0.01, 62: 0.06, 71: 0.01, 72: 0.10, 81: 0.03

(b) Convictions

% Change

Time 0

-10

-20

62: Health Care

61: Education

56: Temp Agencies

54: Professional Service

53: Real Estate

52: Finance/Insurance

48-49: Transport/Warehouse

44-45: Retail

42: Wholesale

31-33: Manufacturing

23: Construction

11: Agriculture

0: Unknown

-30

2-digit NAICS

Dep. Mean in -2: 0: 0.06, 11: 0.00, 23: 0.05, 31: 0.04, 42: 0.02, 44: 0.12, 48: 0.03, 52: 0.01

53: 0.02, 54: 0.04, 56: 0.10, 61: 0.01, 62: 0.04, 71: 0.01, 72: 0.11, 81: 0.03

Figure reports event study estimates in the year of someone’s first criminal history event of a W-2 issued

by a payer firm in the specified 2-digit NAICS code based on the firms’ tax return in that year. For this

analysis, we restrict the full sample to have been 18 by the time the first charge appears in the data for both

non-convictions and conviction. Data from 2000-2020. The sample is restricted to events occurring between

2003-2018. We run separate event studies by industry around the criminal history event, and divide by the

mean share in the in the industry in -2 and multiply by 100 to convert to a percent change.

Figure A.12: Event Study of Any Wages Around Last Criminal History Event, By 2-digit

NAICS Industry

(a) Non-Convictions

% Change

Time 0

0.2

0.1

0.0

-0.1

81: Other Services

72: Accomodations/Food

71: Arts/Entertainment

62: Health Care

61: Education

56: Temp Agencies

54: Professional Service

53: Real Estate

52: Finance/Insurance

48-49: Transport/Warehouse

44-45: Retail

42: Wholesale

31-33: Manufacturing

23: Construction

11: Agriculture

0: Unknown

-0.2

2-digit NAICS

Dep. Mean in -2: 0: 0.09, 11: 0.00, 23: 0.05, 31: 0.04, 42: 0.02, 44: 0.12, 48: 0.02, 52: 0.02

53: 0.02, 54: 0.05, 56: 0.08, 61: 0.01, 62: 0.06, 71: 0.01, 72: 0.10, 81: 0.03

(b) Convictions

% Change

Time 0

0.05

0.00

-0.05

-0.10

-0.15

81: Other Services

72: Accomodations/Food

71: Arts/Entertainment

62: Health Care

61: Education

56: Temp Agencies

54: Professional Service

53: Real Estate

52: Finance/Insurance

48-49: Transport/Warehouse

44-45: Retail

42: Wholesale

31-33: Manufacturing

23: Construction

11: Agriculture

0: Unknown

-0.20

2-digit NAICS

Dep. Mean in -2: 0: 0.05, 11: 0.00, 23: 0.07, 31: 0.06, 42: 0.02, 44: 0.08, 48: 0.02, 52: 0.01

53: 0.02, 54: 0.04, 56: 0.09, 61: 0.01, 62: 0.03, 71: 0.01, 72: 0.07, 81: 0.03

Notes: Figure reports event study estimates at time 0 of W-2 issued by a payer firm in the specified 2-digit

NAICS code based on the firms’ tax return in that year. We run separate event studies by industry around

the criminal history event, and divide by the mean share in the in the industry in -2 to convert to percent.

Figure A.13: FCRA Event Study of Any Wages Around Removal (Year 7), Deviation from

Trend, By 2-digit NAICS Industry

(b) Convictions in PA, NJ and TX

81: Other Services

71: Arts/Entertainment

72: Accomodations/Food

61: Education

62: Health Care

56: Temp Agencies

53: Real Estate

54: Professional Service

52: Finance/Insurance

44-45: Retail

48-49: Transport/Warehouse

42: Wholesale

0: Unknown

23: Construction

-.01

81: Other Services

72: Accomodations/Food

71: Arts/Entertainment

61: Education

62: Health Care

56: Temp Agencies

53: Real Estate

54: Professional Service

52: Finance/Insurance

44-45: Retail

48-49: Transport/Warehouse

42: Wholesale

31-33: Manufacturing

11: Agriculture

23: Construction

-.01

31-33: Manufacturing

.01

11: Agriculture

Deviation from Trend

.01

0: Unknown

Deviation from Trend

(a) Non-Convictions

2-digit NAICS

Dep. Mean in 5 0: 0.04, 11: 0.00, 23: 0.06, 31: 0.06, 42: 0.02, 44: 0.06, 48: 0.02, 52: 0.01

53: 0.01, 54: 0.04, 56: 0.08, 61: 0.01, 62: 0.03, 71: 0.01, 72: 0.07, 81: 0.03

Dep. Mean in 5 0: 0.08, 11: 0.00, 23: 0.05, 31: 0.04, 42: 0.02, 44: 0.09, 48: 0.02, 52: 0.02

53: 0.02, 54: 0.06, 56: 0.08, 61: 0.01, 62: 0.06, 71: 0.01, 72: 0.08, 81: 0.03

.01

81: Other Services

72: Accomodations/Food

62: Health Care

71: Arts/Entertainment

61: Education

56: Temp Agencies

54: Professional Service

53: Real Estate

52: Finance/Insurance

44-45: Retail

48-49: Transport/Warehouse

42: Wholesale

23: Construction

31-33: Manufacturing

11: Agriculture

-.01

0: Unknown

Deviation from Trend

2-digit NAICS

Dep. Mean in 5 0: 0.04, 11: 0.00, 23: 0.07, 31: 0.04, 42: 0.02, 44: 0.06, 48: 0.02, 52: 0.01

53: 0.02, 54: 0.04, 56: 0.06, 61: 0.01, 62: 0.04, 71: 0.01, 72: 0.06, 81: 0.03

Notes: Figure reports results from a test of whether the event study coefficients 7 years after the last charge

are different from a linear trend. Specifically, figure reports 2 × β+4 + β+7 .

Figure A.14: FCRA Event Study of Any Wages Around Removal (Year 7), Deviation from

Trend, By Crime-Type of Last Conviction

(a) Non-Convictions

(b) Convictions in PA, NJ and TX

.02

.01

Deviation from Trend

-.01

-.02

-.03

-.04

.01

-.01

-.02

-.03

Other

Property/White Collar

Drug

Traffic/DWI

Other

Property/White Collar

Drug

Traffic/DWI

Violent

-.04

Violent

Deviation from Trend

.02

Crime Type for Last Charge

Dep. Mean in 5 Violent: 0.52, Traffic: 0.63, Drug: 0.55, Property: 0.53, Other: 0.53

Dep. Mean in 5 Violent: 0.71, Traffic: 0.75, Drug: 0.72, Property: 0.71, Other: 0.71

Deviation from Trend

.02

.01

-.01

-.02

-.03

Other

Property/White Collar

Drug

Traffic/DWI

Violent

-.04

Crime Type for Last Charge

Dep. Mean in 5 Violent: 0.48, Traffic: 0.55, Drug: 0.50, Property: 0.49, Other: 0.50

Notes: Figure reports results from a test of whether the event study coefficients 7 years after the last charge

are different from a linear trend. Specifically, figure reports 2 × β+4 + β+7 .

Figure A.15: Robustness to Alternative DD-Estimators: San Joaquin Analysis

(a) Pooled

(b) Likely Self-Petitioners

.16

.12

.08

.04

-.04

-.08

-.12

-.16

-.2

-5

-4

-3

-2

-1

Years Since Last Criminal History Event

Baseline

-5

-4

-3

-2

-1

Years Since Last Criminal History Event

Sun-Abraham

Baseline

N= 5,622, NxT=106,818, Dep. Mean in -1: 0.338

Sun-Abraham

N= 655, NxT= 12,445, Dep. Mean in -1: 0.334

.16

.12

.08

.04

-.04

-.08

-.12

-.16

-.2

-5

-4

-3

-2

-1

Years Since Last Criminal History Event

Baseline

Sun-Abraham

N= 4,967, NxT= 94,373, Dep. Mean in -1: 0.338

Notes: Figure shows event-study coefficients for having any wage employment around Proposition 47 felony

reductions in San Joaquin County, CA. We report our baseline estimates alongside event-study coefficients

using the estimator proposed by Sun-Abraham (2020). The error bars report ninety percent confidence

intervals.

Figure A.16: Robustness to Alternative DD-Estimators: FCRA Event Study of Any Wages Around Removal (Year 7)

Note: MD has State FCRA for Convictions

Percentage Points, Relative to +5

(a) Felony Non-Convictions, no other convictions

(b) Mis. Non-Convictions, no other convictions

.05

.025

-.025

-.05

Years since FCRA criminal history event

Bexar, TX

(d) Mis. Convictions

.05

Percentage Points, Relative to +5

Years since FCRA criminal history event

.05

.025

-.025

-.05

Years since FCRA criminal history event

Bexar, TX

Years since FCRA criminal history event

Bexar, TX

Notes: Each panel plots selected event study coefficients for the share with any wages > $0 around 7 years after the event, from event-study estimation

following Sun and Abraham (2020). Year 7 events occurring in 2021 are used as the last treated group. Timing from the event is based on the charge

date for non-convictions and disposition date for convictions. Coefficients are relative to +5 periods after the event (2 years prior to the year 7 FCRA

event, if applicable). We run separate event studies for each state in each panel. Data from 2000-2020. The sample is restricted to events occurring

between 1996-2014.

Figure A.17: CA Prop 47 Reductions in SJ County: Effect of Notifications

Any Wages > $0

(a) Levels

(b) Differences

.36

0.04

0.02

.34

0.00

.32

-0.02

-0.04

.28

-0.06

2014

2015

2016

2017

Control

2018

2019

2020

2021

2014

2015

2016

Treated

2017

2018

Placebo

Treated

2019

2020

2021

2019

2020

2021

Any Wages > $15,000

(d) Differences

.21

0.04

0.02

.19

0.00

.17

-0.02

.15

-0.04

.13

-0.06

2014

2015

2016

2017

Control

2018

2019

2020

2021

2014

Treated

2015

2016

2017

2018

Placebo

Treated

Notes: The treatment group are those who the PD’s office attempted to notify about their reduction

(N=3,755), control received no attempted notification (N=3,400). Figures (a) and (c) show raw probability of wages > $0 and wages > $15, 000 for treatment (attempted notification) and control groups for each

year. Figures (b) and (d) show ITT regression coefficients of the effect of notification from Equation ?? in

the text run separately for each year. Notifications took place in 2019 and 2020, the dashed red line indicates

the end of the pre-period, before any notifications took place.

Figure A.18: Impact of PA Clean Slate Reductions on Employment Outcomes

Any Wages > $0

(a) Raw Data

(b) Event-Study Estimates

.84

.04

.03

.82

.02

.01

.78

-.01

.76

-.02

.74

2016

2017

2018

2019

2020

2021

-.03

-.04

Treated in 2019

Untreated in 2019 due to Fines/Fees

2016

2017

2018

2019

2020

2021

2020

2021

Any Wages > $15,000

(d) Event-Study Estimates

.55

.04

.03

.02

.01

.45

-.01

-.02

.35

2016

2017

2018

2019

2020

2021

-.03

-.04

Treated in 2019

Untreated in 2019 due to Fines/Fees

2016

2017

2018

2019

Notes: Figure reports raw means and event-study estimates for those who had their non-convictions cleared

by PA’s Clean Slate law by 2020, compared with those who did not. Data from 2016-2021. Sample is

restricted to ages 18-25 to ensure they had no other prior convictions by the start of our charge data, which

begins in 2008.

Table A.10: Impact of PA Clean Slate Reductions on Employment Outcomes - Excluding Philadelphia

(a) DD Estimates

(1)

Any Wages>$0

0.00448

(0.00338)

(2)

...>$7,500

-0.00285

(0.00419)

(3)

...>$15,000

0.00244

(0.00427)

(4)

Any Gig

-0.000251

(0.00151)

(5)

Any Other 1099

0.000949

(0.00236)

(6)

Files 1040

-0.00115

(0.00415)

(7)

Files SE

-0.00264

(0.00224)

2017 × Cleared

-0.00251

(0.00353)

-0.00210

(0.00445)

0.0101*

(0.00441)

-0.00179

(0.00121)

-0.00551*

(0.00259)

-0.00593

(0.00422)

-0.00589*

(0.00233)

2016 × Cleared

0.000720

(0.00418)

0.824

38,268

229,872

-0.00159

(0.00524)

0.638

38,268

229,872

0.00477

(0.00514)

0.498

38,268

229,872

0.00146

(0.00127)

0.009

38,268

229,872

-0.00402

(0.00288)

0.055

38,268

229,872

-0.00361

(0.00484)

0.739

38,268

229,872

-0.00253

(0.00254)

0.050

38,268

229,872

Post (2019-2021) × Cleared

Dep. Mean (2018)

NxT

Age Controls

Indiv. FE

Year FE

(b) By months since charge

(1)

Any Wages>$0

0.00282

(0.00804)

(2)

...>$7,500

-0.00349

(0.0102)

(3)

...>$15,000

-0.00873

(0.0101)

(4)

Any Gig

0.00397

(0.00362)

(5)

Any Other 1099

-0.000909

(0.00489)

(6)

Files 1040

0.00232

(0.00957)

(7)

Files SE

-0.000211

(0.00471)

-0.00000208

(0.000125)

0.0000133

(0.000154)

0.000218

(0.000154)

-0.0000752

(0.0000560)

0.0000358

(0.0000778)

-0.0000575

(0.000146)

-0.0000465

(0.0000771)

0.000334**

(0.000111)

-0.0000298

(0.000137)

-0.000357**

(0.000137)

0.0000541

(0.0000500)

-0.0000538

(0.0000708)

-0.00000542

(0.000132)

0.0000666

(0.0000691)

2017 × Cleared

-0.00305

(0.00353)

-0.00206

(0.00445)

0.0105*

(0.00442)

-0.00179

(0.00122)

-0.00546*

(0.00259)

-0.00586

(0.00422)

-0.00595*

(0.00234)

2016 × Cleared

-0.000344

(0.00418)

-0.00152

(0.00524)

0.00546

(0.00515)

0.00144

(0.00128)

-0.00392

(0.00288)

-0.00347

(0.00485)

-0.00265

(0.00254)

Post (2019-2021) × Cleared

× Months since charge

Post (2019-2021)

× Months since charge

Notes: Table reports difference-in-differences results comparing outcomes for individuals who had all their non-convictions cleared by PA’s Clean Slate law by 2020, compared

with those who did not. Data from 2016-2021. Sample is restricted to ages 18-25 to ensure they had no other prior convictions by the start of our charge data, which begins in

2008. Standard errors clustered on individual are reported in parentheses. ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Table A.11: Impact of PA Clean Slate Reductions on Employment Outcomes - Black Individuals

(a) DD Estimates

(1)

Any Wages>$0

0.00464

(0.00615)

(2)

...>$7,500

0.00672

(0.00803)

(3)

...>$15,000

0.00656

(0.00782)

(4)

Any Gig

0.00147

(0.00357)

(5)

Any Other 1099

-0.000635

(0.00414)

(6)

Files 1040

0.0137ª

(0.00832)

(7)

Files SE

-0.00539

(0.00411)

2017 × Cleared

-0.00914

(0.00621)

0.00222

(0.00848)

0.0181*

(0.00796)

-0.000928

(0.00306)

-0.00922*

(0.00454)

-0.0182*

(0.00849)

-0.00520

(0.00422)

2016 × Cleared

0.00269

(0.00757)

0.813

13,889

83,538

0.00317

(0.00957)

0.569

13,889

83,538

0.00925

(0.00910)

0.409

13,889

83,538

0.00493

(0.00319)

0.019

13,889

83,538

-0.00429

(0.00499)

0.049

13,889

83,538

-0.0110

(0.00945)

0.645

13,889

83,538

-0.00241

(0.00456)

0.053

13,889

83,538

Post (2019-2021) × Cleared

Dep. Mean (2018)

NxT

Age Controls

Indiv. FE

Year FE

(b) By months since charge

(1)

Any Wages>$0

0.0270ª

(0.0151)

(2)

...>$7,500

0.0217

(0.0194)

(3)

...>$15,000

0.00419

(0.0187)

(4)

Any Gig

0.00762

(0.00806)

(5)

Any Other 1099

-0.00349

(0.00938)

(6)

Files 1040

0.0317ª

(0.0192)

(7)

Files SE

-0.0156ª

(0.00898)

-0.000520*

(0.000244)

-0.000400

(0.000311)

0.000117

(0.000305)

-0.000130

(0.000137)

0.0000570

(0.000152)

-0.000595ª

(0.000305)

0.000179

(0.000152)

0.000626**

(0.000212)

0.000243

(0.000275)

-0.000474ª

(0.000273)

0.000260*

(0.000123)

-0.0000506

(0.000139)

0.000398

(0.000273)

-0.000165

(0.000135)

2017 × Cleared

-0.0128ª

(0.00665)

0.00197

(0.00918)

0.0177*

(0.00874)

-0.00280

(0.00324)

-0.00956ª

(0.00495)

-0.0223*

(0.00913)

-0.00952*

(0.00461)

2016 × Cleared

0.00937

(0.00798)

0.00159

(0.0104)

0.00406

(0.00997)

0.00386

(0.00336)

-0.00656

(0.00540)

-0.0111

(0.0101)

-0.00480

(0.00500)

Post (2019-2021) × Cleared

× Months since charge

Post (2019-2021)

× Months since charge

Notes: Table reports difference-in-differences results comparing outcomes for individuals who had all their non-convictions cleared by PA’s Clean Slate law by 2020, compared

with those who did not. Data from 2016-2021. Sample is restricted to ages 18-25 to ensure they had no other prior convictions by the start of our charge data, which begins in

2008. Standard errors clustered on individual are reported in parentheses. ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Table A.12: Impact of PA Clean Slate Reductions on Employment Outcomes - All Others

(a) DD Estimates

(1)

Any Wages>$0

0.00464

(0.00385)

(2)

...>$7,500

-0.00349

(0.00462)

(3)

...>$15,000

0.0000691

(0.00476)

(4)

Any Gig

-0.000599

(0.00153)

(5)

Any Other 1099

0.000459

(0.00272)

(6)

Files 1040

0.00361

(0.00453)

(7)

Files SE

-0.00337

(0.00251)

2017 × Cleared

0.000241

(0.00407)

-0.00146

(0.00491)

0.00878ª

(0.00494)

-0.000956

(0.00121)

-0.00550ª

(0.00297)

-0.000759

(0.00459)

-0.00514ª

(0.00266)

2016 × Cleared

-0.00515

(0.00480)

0.807

31,996

192,096

-0.00138

(0.00589)

0.639

31,996

192,096

0.00698

(0.00581)

0.511

31,996

192,096

0.000429

(0.00128)

0.008

31,996

192,096

-0.00370

(0.00333)

0.059

31,996

192,096

-0.00240

(0.00536)

0.742

31,996

192,096

-0.00232

(0.00289)

0.051

31,996

192,096

Post (2019-2021) × Cleared

Dep. Mean (2018)

NxT

Age Controls

Indiv. FE

Year FE

(b) By months since charge

(1)

Any Wages>$0

-0.00672

(0.00953)

(2)

...>$7,500

-0.0148

(0.0119)

(3)

...>$15,000

-0.0157

(0.0120)

(4)

Any Gig

0.00318

(0.00392)

(5)

Any Other 1099

0.000498

(0.00573)

(6)

Files 1040

-0.00881

(0.0110)

(7)

Files SE

0.00577

(0.00554)

0.000183

(0.000146)

0.000185

(0.000178)

0.000286

(0.000180)

-0.0000660

(0.0000590)

0.0000214

(0.0000905)

0.000134

(0.000166)

-0.000129

(0.0000897)

0.000222ª

(0.000131)

-0.000171

(0.000158)

-0.000351*

(0.000160)

0.00000519

(0.0000521)

-0.0000344

(0.0000822)

-0.000124

(0.000151)

0.000150ª

(0.0000804)

2017 × Cleared

0.000312

(0.00417)

-0.00352

(0.00507)

0.00781

(0.00512)

-0.00142

(0.00117)

-0.00400

(0.00304)

0.000107

(0.00470)

-0.00472ª

(0.00271)

2016 × Cleared

-0.00399

(0.00491)

-0.00285

(0.00607)

0.00596

(0.00601)

0.000525

(0.00125)

-0.00304

(0.00341)

-0.000838

(0.00549)

-0.00194

(0.00295)

Post (2019-2021) × Cleared

× Months since charge

Post (2019-2021)

× Months since charge

Notes: Table reports difference-in-differences results comparing outcomes for individuals who had all their non-convictions cleared by PA’s Clean Slate law by 2020, compared

with those who did not. Data from 2016-2021. Sample is restricted to ages 18-25 to ensure they had no other prior convictions by the start of our charge data, which begins in

2008. Standard errors clustered on individual are reported in parentheses. ª p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Criminal Record Remediation Policies

In this appendix, we describe the policies that we study with a focus on the details of the

institutional background and setting that we use to formulate the research designs.

B.1

Fair Credit Report Act

The Fair Credit Report Act (FCRA) is a federal law that governs the type of information

reported by consumer reporting agencies (CRAs) to potential employers. CRAs include

the major credit bureaus and also employment background check companies including those

that provide criminal background checks to employers.26 FCRA only applies to criminal

background checks performed by CRAs—a firm that performs an in-house criminal background check is not subject to FCRA requirements. Relevant to our context, under FCRA,

non-convictions are only reportable for seven years for jobs with annual expected salary

<$75,000; in contrast, convictions are always reportable regardless of the age of conviction.

The seven-year clock for non-convictions starts at the date the charge is filed.27

We use the seven-year rule under FCRA to estimate the effect of having a non-conviction

record cleared from an employment background check. Under this rule, a record should be

cleared seven years after the last criminal history event/charge among individuals who have

no convictions on record. This feature of FCRA allows for an event-study design where

individuals do not select into the event in the relevant time horizon for estimation.

B.2

Maryland Credit Report Law

Nine states—California, Kansas, Maryland, Massachusetts, Montana, New Hampshire, New

Mexico, New York, and Washington—have a seven-year limit on reporting convictions, with

exceptions.28 Our study includes Maryland which, in 1976, passed a law which states that

employers cannot request arrest or conviction records that are more than seven years old

(starting from the date of disposition) for any job that pays less than $20,000 per annum.29

While this income threshold is low, it binds for the majority of individuals with criminal

records. Over our sample period of 1999 to 2018, $20,000 is approximately the median

annual labor income for employed men without college education in Maryland according

to the March Current Population Survey. In our administrative tax data, in Maryland,

There are two types of background checks an employer can choose to run: fingerprint checks and name

searches. When an individual is booked by police (e.g. arrested), they are fingerprinted. In a fingerprintbased search, these arrest records will appear regardless of whether the arrest leads to a formal charge. A

name-based search queries court records either via an online system or in-person at a court house. This kind

of search will turn up court charges even if they did not lead to conviction, although is unlikely to uncover

arrests that did not lead to a court charge. Name-based searches are more common for most mainstream

types of employment.

In the context of recent litigation, courts have upheld the Federal Trade Commission and Consumer

Financial Protection Bureau’s interpretation that the FCRA look-back window for non-convictions starts at

the date of filing and NOT the date of dismissal.

For example, California allows full look-backs for convictions for Transportation Network Companies

(e.g. Uber, Lyft). In New York, continued reporting of criminal convictions is allowed when the employer is

hiring the individual for an annual salary of $25,000 or more.

MD. CODE. ANN., COM. LAW §14-1203(b)(3) (2010).

the average W-2 earnings for individuals matched to criminal records data and who are

working is $14,000 per year (approximately $7,000 per year median, rounded to the nearest

thousand). These numbers suggest that part-time jobs and likely even full-time jobs will

be largely under this threshold in our population of individuals with records. In addition,

our conversations with a major CRA (Checkr) indicate that agency’s default policy is to not

provide prior convictions more than seven years old from date of disposition to any employer

in Maryland, and the vast majority of employers do not opt out of the default.

B.3

California’s Proposition 47

In November 2014, California voters passed a law through referendum known as Proposition

47: The Safe Neighborhoods and Schools Act. Proposition 47 implemented three broad

changes to felony sentencing laws within California. First, it prospectively reclassified certain

theft and drug possession offenses from felonies to misdemeanors. Broadly speaking, these

eligible offenses include theft offenses where the value of property stolen does not exceed $950,

such as shoplifting, grand theft, receiving stolen property, forgery, fraud, and drug offenses

including the personal use of most illegal drugs.30 Second, it authorizes defendants currently

serving sentences for felony offenses that would have qualified as misdemeanors under the

proposition to petition courts for resentencing under the new misdemeanor provisions. Third,

it authorizes defendants who have completed their sentences for felony convictions that

would have qualified as misdemeanors under the proposition to petition to reclassify those

convictions to misdemeanors.31 For the purposes of our study, we focus on this third change

under Proposition 47—the retroactive reclassification/reduction for individuals who have

completed their sentences.

Like many criminal record remediation efforts in the United States, retroactive reclassification under Proposition 47 is mainly available by petition to an appropriate court, where

the petitioner must establish that he or she committed a crime which, had Proposition 47

been in effect when committed, would be a misdemeanor. If the court grants the request to

reclassify the offense as a misdemeanor, the crime will be treated as a misdemeanor for all

purposes except for the right to own or possess firearms.32

Eligible offenses were communicated to us by a Deputy Public Defender in San Joaquin County, and

verified via online sources to the extent possible. For theft offenses, value taken or intended to be taken must

be <$950: Commercial burglary during business hours (Penal Code 459); Theft (PC 484(e)(a),484(e)(b),

484(e)(d), 484(g), 484(h), 487(a), 487(b), 487(c), 487(d)(1),487(d)(2), 487(e), 487(g)-487(i)); Receiving

stolen property (PC 496(a)); Forgery (PC 470; 471; 472; 475; 476; 484(f); 484(i)(b)); Insufficient Funds

[unless previously convicted of 3 or more other crimes] (PC 476a); Petty theft with a prior conviction (PC

666); Grand Theft (PC 489); Auto Theft (Vehicular Code 10851). Eligible drug offenses include possession

of methamphetamine (Health & Safety Code Section 11377);possession of controlled substance (H&S 11350);

possession of Concentrated Cannabis (H&S 11357(a)).

Some individuals, such as those who had previous convictions for sexually violent offenses, murder, or

sex offenses that require registration, were not eligible for the new resentencing, or reclassification provisions

of Proposition 47.

While Proposition 47 does not completely clear a person’s record, an employer conducting a criminal

background check will no longer be able to see the original felony conviction after a reduction. Instead, only a

misdemeanor conviction remains. Appendix Figure 1 shows four redacted examples of official court criminal

record searches after Proposition 47 reductions. This figure shows that following reduction, Proposition 47

charges are listed as misdemeanors by statute and dispositions denote that the eligible charge was “Reduced

Proposition 47 has been regularly argued to give eligible Californians an opportunity

to remove barriers to employment (in addition to housing and other outcomes) through

reclassification of a felony to misdemeanor.33 For example, advocates who helped draft

Proposition 47 argue that “[w]e created a system where there’s so many collateral impacts

to having a felony conviction on your record that you cannot sustain yourself....You cannot

find employment. You cannot find housing. You cannot integrate back with your family.

These are all things that lead to recidivism.”34

A Proposition 47 reduction can also allow individuals to obtain certain occupational

licenses that previously excluded those with felony convictions. In California, licensing laws

can categorically exclude the hiring of individuals with certain criminal records in hundreds

of professions, such as healthcare and education, regardless of whether the offense is relevant

to the practice of the occupation or poses a substantive risk to public safety, and regardless of

the age of the record.35 Even individuals who receive job-specific training while incarcerated

are excluded by licensing restrictions in these occupations. As one example, individuals with

a felony conviction are barred from obtaining a Californian alcoholic beverage license.36

The interaction of Proposition 47 and another law, the California Investigative Consumer

Reporting Agencies Act (ICRAA), implies that the any benefit of retroactive reductions

under Proposition 47 should be declining in the time since conviction. This is because under

the ICRAA, criminal convictions can be reported for only seven years from the latest of

the date of disposition, date of release, or date of violation of parole from the original case

(versus indefinitely under federal law), unless another law requires employers to look more

deeply into the employee’s background.37

B.3.1

San Joaquin County, CA and Research Design

Starting in December 2014, the Office of the Public Defender of San Joaquin (OPD) and

the San Joaquin County District Attorney’s Office (DAO) coordinated to proactively file

petitions on behalf of all eligible defendants without requiring effort, intervention, or even

knowledge from the defendant. As of September 2019, this effort has resulted in the reduction

of approximately 10,000 felony convictions under Proposition 47. As we discuss below, the

timing of these reductions was unsystematic and, crucially, most of the reductions were not

from individuals who self-selected into treatment, facilitating evaluation.

to Misd” on a particular date. We have verified that this is the underlying data and process that criminal

background check companies use when running a background check.

See, for example, “Finding a Job with a Felony Conviction is Hard.

California

May

Make

Easier,”

available

https://fivethirtyeight.com/features/

finding-a-job-with-a-felony-conviction-is-hard-california-may-make-it-easier/.

See

https://www.desertsun.com/story/news/crime_courts/2016/12/14/

prop-47-former-felons-new-jobs/94636088/.

Compared to just 5 percent in the 1950s, occupational licensing now covers over one quarter of the

U.S. workforce nationally and it is estimated that nearly 30 percent of California jobs require licensure,

certification, or clearance by an oversight board or agency for approximately 1,773 different occupations.

See https://www.bot.ca.gov/board_activity/meetings/20180524_material_3d_3e.pdf.

See California Business and Professions Code Section 23952.

see https://help.checkr.com/hc/en-us/articles/360000725967-Lookback-periods-How-far-back-are-criminal

These exceptions under the ICRAA would apply for certain types of jobs such as in the health industry, or

any job requiring an occupational license.

Petition timing: The county’s agencies took a multi-step approach to implementing

Proposition 47. First, the agencies focused on resentencing for individuals currently serving

sentences or under supervision (parole/probation) for eligible felony offenses. Since there is

no exogenous variation in when reductions occurred across these individuals, these individuals will not be the focus of our analysis. Second, after reducing records for those currently

serving sentences or under supervision, the OPD compiled a comprehensive list of all people

in the county with eligible criminal charges who had already completed their sentences from

relevant state agencies.38 There were separate lists for each eligible charge which meant that

the same individual could in theory appear on multiple lists. For example, if a person had

a petty theft conviction and a drug possession conviction, each of these charges would be

listed on the respective “crime lists.”

The OPD started with the largest crime list, consisting of individuals with felony drug

convictions (designated as “health and safety” or HS crimes). Nearly 85% of individuals

for whom petitions were filed had a crime on this HS list. These lists were alphabetical by

last name of the eligible individual and OPD personnel worked through these lists in various

chunks, initially starting alphabetically with A, although sometimes switching to the other

end of the alphabet to reduce workload with filing clerks who split petitions between A-L

last names and M-Z last names. This process was effectively quasi-random, with the crime

list and the first letter of last name dictating when an individual’s proactive petition would

be filed.

Figure B.1 depicts the impact of the first letter of last name on the order of petition

filing. This figure presents the timing of petitions filed for individuals on the HS crimes

list, as described above. The figure presents cumulative density functions (CDFs) for the

proportion of petitions filed by date for each first letter of last name. One can see a very

clear pattern whereby a vast majority of petitions for those with, say, “A” last names, were

filed within a few months of each other during a “surge” period.39

The list was obtained from a court record system known as CJIS, which only digitized records going

back to 1990. As a result, any eligible charge from before 1990 was not on the list.

Appendix ?? presents these CDFs for each crime list, where analogous “surges” can be seen for each

letter of last name. Occasionally, there were deviations from the alphabetical ordering for idiosyncratic

reasons, which include the someone mistyping the court case number or name and the mistyped case was

also eligible; codefendants on an eligible case are also eligible and would be filed together; referrals of eligible

clients to the public defender’s office from attorneys; referrals of eligible individuals from local organization

“Justice Fairs.” Deviations from alphabetical ordering within a crime list also occurred if an individual

appeared on more than one crime list, as the OPD filed a petition for all of each individual’s eligible charges

at the same time.

Figure B.1: Alphabetical Ordering for HS (Drug) Crime Petitions

Notes: The timing of proactive felony reductions in San Joaquin County was determined in an alphabetical

manner. Figure shows the CDF of felony reductions for HS crimes, by the indicated first letter of last name.

Appendix ?? contains the CDFs for other letters.

After preparing a petition for each eligible individual, the OPD sent the petition to the

DAO for review. Throughout this process, there was a general understanding between the

two agencies that the vast majority of petitions would be approved. The approved petition

would then be sent to a judge to officially secure the Proposition 47 reduction. This was a

time- and labor-intensive process.

Not all petitions filed by the SJOPD were done through proactive reductions. Some

individuals directly called the OPD to inquire about their eligibility to receive a reduction

under Proposition 47 and to ask for a petition to be filed on their behalf.40 Collectively, these

individuals were prioritized by the SJOPD and had their petitions filed soon thereafter.

We leverage the alphabetical nature of the proactive petition filing to identify likely selfpetitioners, as we describe in Section 2.2.5. In Section 4, we will turn to the sample of

self-petitioners to assess the importance of selection bias.

The Public Defender’s office made an effort (with the aid resources from the District

Attorney’s office) to notify at least a subset of individuals about these reductions. The notifications took place in randomized waves, with 4086 individuals with reductions being chosen

to be notified in the first wave. These notifications took place between June 2019 and March

2020. Contact information was collected by the Public Defender’s office from Transunion’s

Another small group of individuals were referred to the SJOPD based on their participation in local

“record change and justice fairs,” during which community members receive free legal consultations to see

if they are eligible for reduction under Proposition 47 (N=96).

TLO product which provides most recent addresses, e-mails, and phone numbers. Of the

4610 individuals randomly chosen to be contacted in this first-wave, contact information

could be located for 3982 (86.3%). Between June 2019 and March 2020, SJOPD personnel

with carefully written scripts attempted to call these 3982 individuals in a random order;

in January 2020 letter were mailed to individual homes (with self-addressed postcards included to return upon receipt); and in January 2020 e-mails were sent as well. Text messages

were sent between December 17, 2019 and May 15, 2020.41 Text messages were staggered

randomly so as to not overwhelm the SJOPD call center.42

Through this effort, SJOPD was able to confirm successful contact, either by phone,

return of postcard, or email, of 1,175 individuals (29.5% of those with contact information,

and 25.5% of the full first-wave notification group). The true contact rate is likely higher

since not everyone who received a letter called the SJOPD or mailed back the included

pre-addressed postcard. SJOPD reported that 411 individuals were surveyed by phone who

had received proactive reductions and asked them if they were previously aware of having

received a reduction. Only 6.1 percent of the group responded that they were aware.

B.4

Pennsylvania Clean Slate Law

In 2018, Pennsylvania enacted the Clean Slate Law (Act 56 of 2018), which implemented

automated sealing of all non-conviction records with no waiting period, as well as certain

low-level conviction records after ten years. Those who still owed court fines and fees were

not (initially) eligible.43 Eligible records were sealed between June 2019 and June 2020

and the law has since resulted in nearly 40 million criminal records sealed for over 1.2

million individuals. Under this law, these records are automatically shielded from the vast

majority of employers, landlords, schools, and the general public, but are still accessible to

law enforcement and judicial officers.

Match Algorithm

This appendix outlines our approach to matching the names and birth dates f

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