Eviction and Poverty in American Cities — NBER Working Paper 26139 (August 2019, Revised August 2022)

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NBER Working Paper 26139, Eviction and Poverty in American Cities, by Robert Collinson, John Eric Humphries, Nicholas S. Mader, Davin Reed, Daniel I. Tannenbaum and Winnie L. van Dijk, dated August 2019 and revised August 2022. The abstract states that more than two million U.S. households have an eviction case filed against them each year and that the authors study the consequences of eviction using newly linked administrative data from two large cities. It states that they use an instrumental variables approach based on cases randomly assigned to judges of varying leniency. It states that they find an eviction order increases homelessness and reduces earnings, durable consumption and access to credit.

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Document type: Does Eviction Cause Poverty Quasi Experimental Evidence From Cook County Il
Case: Does Eviction Cause Poverty Quasi Experimental Evidence From Cook County Il

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NBER WORKING PAPER SERIES

EVICTION AND POVERTY IN AMERICAN CITIES

Robert Collinson
John Eric Humphries
Nicholas S. Mader
Davin Reed
Daniel I. Tannenbaum
Winnie L. van Dijk

Working Paper 26139
https://https://www.nber.org/papers/w26139

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
August 2019, Revised August 2022

Part of this research was conducted as a part of the U.S. Census Bureau's Evidence Building
Project Series. Any views expressed are those of the authors and not those of the U.S. Census
Bureau. The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have
reviewed this information product for unauthorized disclosure of confidential information and
have approved the disclosure avoidance practices applied to this release. This research was
performed at a Federal Statistical Research Data Center under FSRDC Project Number 2476
(CBDRB-FY22-072). This research uses data from the Census Bureau's Longitudinal Employer
Household Dynamics Program, which was partially supported by the following National Science
Foundation Grants SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging
Grant AG018854; and grants from the Alfred P. Sloan Foundation. This publication was
produced from raw data purchased from or provided by the New York State Department of
Health (NYSDOH). However, the conclusions derived, and views expressed herein are those of
the author(s) and do not reflect the conclusions or views of NYSDOH. NYSDOH, its employees,
officers, and agents make no representation, warranty or guarantee as to the accuracy,
completeness, currency, or suitability of the information provided here. The New York portion of
this research was conducted at the Center for Innovation Through Data Intelligence (CIDI). The
views expressed here are not those of CIDI, the Office of Court Administration, the New York
City Human Resource Administration, the Federal Reserve Bank of Philadelphia, or the Federal
Reserve System. The authors gratefully acknowledge financial support from the National Science
Foundation (SES-1757112, SES-1757186, SES-1757187), the Laura and John Arnold
Foundation, the Spencer Foundation, the Kreisman Initiative on Housing Law and Policy, the
Horowitz Foundation for Social Policy, the Robert Wood Johnson Foundation, the Becker
Friedman Institute, and the Tobin Center for Economic Policy. We would like to thank
Lawrence Wood and others at the Legal Assistance Foundation in Chicago, Melissa C.
Chiu and Kathryn Mcnamara at the U.S. Census Bureau, Lydia Stazen Michael at All
Chicago, Carmelo Barbaro, Ruth Coffman, and Emily Metz at UChicago Urban Labs, Eileen
Johns and Maryanne Schretzman at CIDI, Joe Altonji, Raj Chetty, Eric Chyn, Kevin
Corinth, Ingrid Gould Ellen, Angela Denis Pagliero, Michael Dinerstein, Rashmi Dyal-
Chand, Bill Evans, Alex Frankel, Peter Ganong, Pieter Gautier, Matt Gentzkow,
Michael Greenstone, Daniel Grossman, Jim Heckman, Tatiana Homonoff, Ali Hortaçsu,
Peter Hull, Louis Kaplow, Ezra Karger, Paymon Khorrami, Thibaut Lamadon, Kevin
Lang, Jeff Lin, Maarten Lindeboom, Hamish Low, Jens Ludwig, Sarah Miller, Magne
Mogstad, Derek Neal, Matt Notowidigdo, Dan O'Flaherty, Ed Olsen, Katherine O'Regan,
Francisca Richter, Azeem Shaikh, Beth Shinn, Jeff Smith, Jim Sullivan, Nicole Summers,
Chris Taber, Alex Torgovitsky, Bas van der Klaauw, Laura Wherry, and many seminar
participants for helpful discussion. Isabel Almazan, Iliana Cabral, Ella Deeken, Deniz Dutz,
Katherine Kwok, and Naomi Shimberg provided excellent research assistance. Any errors
are our own. This article subsumes and replaces our previous work: Collinson and Reed
(2019) and Humphries et al. (2019). The views expressed herein are those of the authors and do
not necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that
accompanies official NBER publications.

© 2019 by Robert Collinson, John Eric Humphries, Nicholas S. Mader, Davin Reed, Daniel
I. Tannenbaum, and Winnie L. van Dijk. All rights reserved. Short sections of text, not to
exceed two paragraphs, may be quoted without explicit permission provided that full credit,
including © notice, is given to the source.
Eviction and Poverty in American Cities
Robert Collinson, John Eric Humphries, Nicholas S. Mader, Davin Reed, Daniel I. Tannenbaum,
and Winnie L. van Dijk
NBER Working Paper No. 26139
August 2019, Revised August 2022
JEL No. H00,I30,J01,R38

ABSTRACT

More than two million U.S. households have an eviction case filed against them each year.
Policymakers at the federal, state, and local levels are increasingly pursuing policies to reduce the
number of evictions, citing harm to tenants and high public expenditures related to homelessness.
We study the consequences of eviction for tenants using newly linked administrative data from
two large cities. We document that prior to housing court, tenants experience declines in earnings
and employment and increases in financial distress and hospital visits. These pre-trends are more
pronounced for tenants who are evicted, which poses a challenge for disentangling correlation
and causation. To address this problem, we use an instrumental variables approach based on cases
randomly assigned to judges of varying leniency. We find that an eviction order increases
homelessness, and reduces earnings, durable consumption, and access to credit. Effects on
housing and labor market outcomes are driven by impacts for female and Black tenants.

Robert Collinson Davin Reed
University of Notre Dame Federal Reserve Bank of Philadelphia
Economics Department 10 N Independence Mall W
Wilson Sheehan Lab for Philadelphia, PA 19106
Economic Opportunities davin.reed@phil.frb.org
3060 Jenkins Nanovic Hall
Notre Dame, INDI 46556 Daniel I. Tannenbaum
United States Department of Economics
rcollin8@nd.edu University of Nebraska – Lincoln
HLH 525 B
John Eric Humphries P.O. Box 880489
Department of Economics Lincoln, NE 68588
Yale University dtannenbaum@unl.edu
37 Hillhouse Ave
New Haven, CT 06511 Winnie L. van Dijk
and NBER Department of Economics
johneric.humphries@yale.edu Harvard University
Littauer Center
Nicholas S. Mader 1805 Cambridge Street
Chapin Hall at the University of Chicago Cambridge, MA 02138
313 E 60th St and NBER
Chicago, IL 60637 winnie_vandijk@fas.harvard.edu
nmader@chapinhall.org

An appendix is available at http://www.nber.org/data-appendix/w26139
1 Introduction
More than two million eviction court cases are filed in the United States each year. These
cases predominantly involve low-income and minority households. About half of proceedings
end in a court order for eviction: a judgment requiring the tenant to vacate the property.1
According to data collected by the OECD, the U.S. is an outlier in the number of eviction cases
per renter household, with a rate 1.5 times higher than the next-highest country (Canada)
and at least 3.8 times higher than the remaining ten countries for which data are available
(OECD, 2020). In recent years, policymakers at the federal, state, and local levels have
introduced assistance programs and legislative changes aimed at reducing the number of
evictions, frequently citing harms to tenants and the high costs of homelessness-related public
services.2 Measuring the consequences of an eviction for tenants is crucial for evaluating
these reforms and, more broadly, for understanding the role of housing instability as a driver
of poverty and inequalities in income, socioeconomic mobility, and health that have been
documented in recent literature (Piketty and Saez, 2003; Chetty et al., 2014; Case and Deaton,
2015).
Despite the large number of tenants who interact with housing courts in the U.S. each year
and the growing interest from policymakers, the consequences of eviction for households are
not well documented or understood. While researchers have argued that eviction is a cause of
poverty, homelessness, poor health, and other forms of physical and material hardship (e.g.,
Desmond, 2012, 2016), quantitative empirical research in this area has been hampered by
two main challenges. First, it is difficult to link data on households facing eviction to data on
their subsequent outcomes. Second, it is not obvious how to separate the impact of eviction
from the impact of correlated sources of distress such as job loss or declining health. This
paper overcomes both of these barriers to provide new evidence on the effect of eviction on
earnings, employment, residential mobility, interactions with homelessness services, financial
distress, and health. We link newly constructed data sets based on housing court records
from two large urban areas—New York City, NY, and Cook County, IL (which includes the
City of Chicago)—to a broad range of administrative data sets. These linked data allow us to
document and characterize tenants’ outcome trajectories several years before and after their
eviction case. To identify the causal impact of the eviction order, we use an instrumental
variables (IV) research design that relies on the random assignment of cases to judges who
1
Based on the most complete data set of eviction court cases available, the Princeton Eviction Lab
estimates that more than two million cases were filed each year since 2002, and about one million cases ended
in an eviction order annually (Desmond et al., 2018a). Since this data set does not have national coverage,
these numbers are conservative. An alternative data point can be obtained from the 2017 American Housing
Survey, in which about 800,000 renter households reported being threatened with an eviction notice in the
past three months, which extrapolates to 3.2 million over the year (U.S. Census Bureau, 2017).
2
Appendix A provides an overview of recently passed or proposed reforms related to eviction, including
expansions of financial assistance, eviction diversion programs, increases in legal protections for tenants, and
programs that provide legal aid in housing court.

1
systematically vary in their tendency to evict.3
We first show that tenants in our linked housing court sample differ substantially from
randomly-chosen tenants who live in the same neighborhoods. Compared to these neighbors,
tenants we observe in housing court have lower earnings, lower employment, less access
to credit, and more debt in collections. In addition, both evicted and non-evicted tenants
experience striking drops in earnings, employment, and credit scores and rising hospital visits,
unpaid bills, and payday loan inquiries in the two years before the case. These “Ashenfelter
dips” are more pronounced for evicted tenants and suggest the presence of unobserved factors
that are correlated with both the eviction decision and post-court outcomes, and are likely to
introduce bias in estimates based on cross-sectional or difference-in-differences comparisons.
For this reason, our main estimates are based on a quasi-experimental IV research design
using the random assignment of judges.
Using the IV approach, we find that eviction causes spikes in homelessness and increases
in residential mobility. In the first year after case filing, an eviction order increases the
probability of observing the tenant at a new address by 8 percentage points (28% of the
non-evicted mean) and increases the probability of staying in emergency shelters by 3.4
percentage points (more than 300% of the non-evicted mean). The effects on residential
mobility and homelessness persist through the second year after filing. These increases in
housing instability do not result in large changes in neighborhood quality: after the court
case, evicted tenants live in neighborhoods with similar poverty rates as tenants who are not
evicted.
During this period of increased housing instability and homelessness, eviction negatively
impacts earnings. Our IV estimates imply that eviction lowers earnings in the year after
filing by $323 per quarter (8% of the non-evicted mean), which is similar to evicted tenants’
average drop in quarterly earnings in the year leading up to case filing ($337). The impact
on earnings is larger in the second year after the case, with eviction causing a $613 (14%)
reduction in quarterly earnings. The effects on employment are more modest, with eviction
causing a 1.5 percentage point reduction in the fraction of quarters employed in the year
after the case, and a 1.8pp reduction two years after the case, neither of which is statistically
significant. The labor market effects of eviction are largely concentrated in the two years after
filing. We find particularly sharp negative impacts for female and Black tenants, who drive
the effects on labor market outcomes, residential mobility, and interactions with homelessness
services. This pattern is consistent with ethnographic research that suggests eviction may
have a larger impact on women (Desmond, 2012; Desmond et al., 2013; Desmond, 2016) and
with research that finds that Black households experience discrimination while searching for
housing (Bayer et al., 2017; Christiansen and Timmins, 2019).
3
Many papers have used the random assignment of judges to study the impact of court orders in other
settings, including incarceration (Kling, 2006; Aizer and Doyle, 2015; Mueller-Smith, 2015; Bhuller et al.,
2018, 2020; Norris et al., 2021), bankruptcy protection (Dobbie and Song, 2015), disability claims (Maestas
et al., 2013; Dahl et al., 2014; French and Song, 2014), and foster care placement (Doyle, 2007; Bald et al.,
2019).

2
Eviction also worsens financial health and credit access beyond the initial period of
increased housing instability and homelessness. Using data from linked credit reports, we
find that eviction causes reductions in a composite index of financial health of roughly 0.1
s.d. in the first and second years after the case filing, by 0.21 s.d. 3-4 years after filing, and
by 0.26 s.d. 5-6 years after filing. The declines are driven by increases in debt and lower
credit scores.4 We find evidence that eviction reduces the likelihood of having an automobile
loan or lease, which may be viewed as a proxy for durable goods consumption (Dobkin et al.,
2018; Agarwal et al., 2020). The impacts on credit scores of 16.5 points in the second year
after the case are similar in magnitude to the effect of removing a bankruptcy flag (Gross
et al., 2020; Dobbie et al., 2020).
Finally, we find that eviction increases the number of hospital visits in the year following
court filing by 0.19 visits (29%) and increases visits for mental health-related conditions
during the same period by 0.05 visits (133%). The timing of these effects coincides with the
disruptions to tenants’ housing circumstances in the year after filing.
Our analysis has important policy implications. First, we find that eviction causes
significant disruptions that are reflected in increases in residential mobility, homelessness, and
hospital use; reductions in earnings; and sustained damage to credit records. These costs are
key inputs to the evaluation of a range of policies, such as emergency rental assistance, legal
aid to tenants facing eviction, and, most directly, making eviction proceedings more lenient
toward tenants.5 Given the large social costs of homelessness (Evans et al., 2019), our finding
that a court-ordered eviction increases the likelihood of emergency shelter use suggests a role
for policy in the eviction court setting to reduce homelessness. Second, we show that eviction
is frequently preceded by adverse events, which may reflect the inadequacy of existing social
insurance policies or self-insurance in preventing evictions. Third, we find that the effects of
eviction are driven by traditionally vulnerable groups: Black and female tenants. Since these
groups also tend to be over-represented in eviction proceedings, policies aimed at averting
eviction may especially benefit them.
This paper is related to a sizeable literature in sociology that studies eviction of low-
income renters (Desmond, 2012; Desmond et al., 2015; Desmond and Gershenson, 2016;
Desmond, 2016; Desmond and Gershenson, 2017). Our work builds on and extends this
literature in several ways. First, we show that the research designs used in previous work on
evictions may be vulnerable to selection bias. Second, to address this selection bias, we use a
quasi-experimental research design to estimate the causal effects of eviction by leveraging
the random assignment of judges to eviction cases. Third, we create a novel data set of
4
Several studies have used credit bureau data to measure financial strain, including studies of the
consequences of health shocks (Mazumder and Miller, 2016; Dobkin et al., 2018) and bankruptcy (Dobbie
et al., 2017). Our data additionally include information on payday loans in Cook County, which are common
among low-income households (Bhutta et al., 2015; Skiba and Tobacman, 2019).
5
Such policy reforms may also impact landlords, which could have consequences for rental housing supply,
rent prices, and screening practices. A full welfare analysis of policy counterfactuals is beyond the scope of
this paper.

3
eviction court records linked to administrative data, which helps mitigate the concerns that
may arise when using survey data, including selective non-response and misreporting (Meyer
et al., 2015). The linked data additionally lets us characterize tenants’ housing, labor market,
health, and credit circumstances in the lead-up to and aftermath of filing. Finally, we provide
a unified analysis across two large U.S. urban areas not previously studied using more than a
decade of administrative data, lending support to the external validity of our findings.
We examine the impact of eviction on earnings, homelessness, and financial health,
outcomes that have not been studied in prior work. We find that eviction causes increases
in homelessness and reduces earnings in the two years after the case filing, and leads to
longer-run deterioration in financial health. Prior studies have examined the impact of
eviction on loss of employment (Desmond and Gershenson, 2016), mental health (Desmond
and Kimbro, 2015), and moves to high-poverty neighborhoods (Desmond and Shollenberger,
2015). Relative to these studies, we find more modest impacts of eviction on employment
and no impact on the poverty rate of neighborhoods to which evicted tenants move, using
our quasi-experimental research design. Taken together, our results imply that eviction
exacerbates the rising economic distress experienced by tenants in the lead-up to a court
filing, creating disruption for tenants and spillover costs to society.
While there is relatively little work on eviction in economics, related work examines the
impact of homeowners’ foreclosure on health outcomes (Currie and Tekin, 2015), subsequent
homeownership, housing and neighborhood conditions (Molloy and Shan, 2013), and credit
scores (Brevoort and Cooper, 2013). A related study by Diamond et al. (2020) examines
the impact of foreclosure on residential mobility, homeownership, divorce, measures of
neighborhood quality, and credit reports using a randomized judge design. As part of their
analysis, Diamond et al. (2020) consider the impact of a landlord’s foreclosure on tenants. We
view our work as complementary, since eviction and foreclosure are different court processes
and affect different populations.6 We consider several additional dimensions that eviction is
likely to impact, including employment, earnings, homelessness, and hospital use.
Lastly, we contribute to recent work studying the incidence and drivers of eviction filings.
Gallagher et al. (2019) find that expansions of ACA Marketplace subsidies substantially
reduced eviction filing rates, and Zewde et al. (2019) find that Medicaid expansions were
associated with reductions in county-level filing rates and eviction rates. These results are
consistent with our findings that adverse health, labor market, and credit outcomes precede
and may contribute to appearing in housing court and being evicted. Desmond et al. (2013)
point to children as a risk factor for eviction, consistent with our finding that women are over-
represented in housing court relative to the general low-income renter population. Desmond
and Gershenson (2017) find that family size, job loss, neighborhood crime, eviction rates, and
network disadvantage are additional risk factors. Kroeger and La Mattina (2020) find that
6
One distinction is that a landlord’s foreclosure need not lead to the eviction of their tenants. Under the
Protecting Tenants at Foreclosure Act of 2009, the new owner of a foreclosed property is required to continue
the lease agreed upon by the previous landlord.

4
criminal nuisance ordinances substantially increase eviction filing rates and eviction rates.
Finally, Fetzer et al. (2020) study the effect of cuts to rental subsidies in the U.K. and find
that these substantially increased rental arrears and evictions.
The remainder of this paper is organized as follows. Section 2 provides institutional details
relevant for understanding the eviction process in Cook County and New York. Section 3
describes the data collection and record linkage processes. Section 4 describes our samples,
provides new descriptive evidence on the evolution of outcomes among evicted and non-evicted
tenants around a court filing, and explores selection into eviction. Section 5 formalizes our
empirical framework and tests the key underlying assumptions. Section 6 presents the main
results of our analysis. Section 7 concludes.

2 Institutional context
This section describes the legal process of eviction and other relevant institutional details. In
Cook County and New York, as in most jurisdictions, the housing court process begins with
a notice served to the tenant by the landlord, followed by one or more court hearings, and
finally a judge’s decision on whether to issue an eviction order that requires the tenant to
vacate the property.
A landlord must serve the tenant a written notice to begin the eviction court process. The
notice typically includes the reason for terminating the lease and the number of days until
termination. A landlord may seek an eviction for any alleged violation of the lease terms,
and non-payment of rent is the most commonly-stated reason.7 In both Cook County and
New York, the landlord has no discretion over the district that will handle their case, since
the district is determined by the address of the property under dispute. As we discuss below,
cases in both jurisdictions are randomly assigned to courtrooms, with judges assigned to
courtrooms on a fixed rotational basis.
Nearly all eviction cases are handled in a resolution process overseen by a judge.8 When
the landlord and tenant meet in a courtroom, the hearing is typically brief: court observation
studies have found that the average eviction hearing lasts only a few minutes (Doran et al.,
2003). Tenants are usually unrepresented, while landlords are usually represented by an
attorney.9
7
In the 2013 American Housing Survey, 75 percent of households who reported being threatened with an
eviction reported that the reason for the threat was failure to pay rent. In Cook County and New York, over
three quarters of cases involve disputes over non-payment of rent, and studies of housing court in other cities,
e.g., Milwaukee (Desmond et al., 2013), have also found that non-payment of rent is the most commonly
stated reason for eviction.
8
In principle, either party may request a jury trial but, in our court records, such requests are made in
only 3 percent of Cook County cases and less than 1 percent of New York cases.
9
In our data, approximately 3 percent of tenants in Cook County and 1 percent of tenants in New York
were represented by an attorney, whereas 75 percent of landlords in Cook County and 95 to 99 percent of
landlords in New York were represented by an attorney.

5
In order to proceed with an eviction, the landlord needs a court order that authorizes
the enforcement agent, such as a Sheriff or Marshal, to execute the eviction order. In both
jurisdictions, we define an eviction as a case ending with an eviction order. This definition
is based on whether the last recorded outcome in the case history provides legal authority
for the landlord to take possession of the property via an enforcement agent.10 Appendix
C.3 explains in more detail how we construct eviction orders from the housing court data.
In cases where the landlord is seeking rental arrears, the judge may include an order to pay
rental arrears along with the eviction order, called a money judgment.
The alternative to an eviction order is often a formal agreement between the landlord
and tenant that is approved by the judge. Such agreements typically include a payment
plan, and they may also set terms for continued occupancy of the unit.11 The landlord may
return to court to pursue an eviction order if the tenant doesn’t satisfy the terms of an
initial agreement. Cases can also be discontinued, which happens if the landlord decides not
to pursue the case further. Only five percent of non-evictions are dismissals that bar the
landlord from bringing another eviction case with the same allegations against the tenant.
An eviction order may or may not be followed by the execution of the order by an
enforcement officer such as a Sheriff or Marshal. We refer to the execution of an eviction
order as an enforcement, and it typically involves changing the locks and the removal of the
tenant’s possessions. Whether an eviction order is enforced depends on several factors. For
example, the landlord may choose not to file the order with the enforcement agent because
they must pay an additional fee. The landlord and tenant may also come to an informal
agreement. Finally, the tenant may choose to vacate the unit before a Sheriff or Marshal is
scheduled to enforce the eviction order, in which case the landlord may cancel the enforcement
of the order.
There are several reasons an eviction order may affect tenants’ future outcomes. First,
an eviction order legally obligates a tenant to move, either following or in anticipation of
the enforcement of the order, and thus to incur the costs associated with searching for new
housing, relocating, and reorienting the household’s work and schooling arrangements. Second,
eviction orders and filings are public records in most jurisdictions, and an order can also
be recorded as a civil judgment on the tenant’s credit report. Eviction filings and eviction
orders are commonly used in background screenings by landlords, employers, and creditors,
and therefore an eviction can make it harder for tenants to secure future rental contracts,
employment, or loans. Finally, in cases where the landlord seeks a money judgment, an
eviction order will typically include a money judgement, which can be used by the landlord to
obtain an order for garnishment of wages, tax refunds, or other assets. Garnishment requires
a separate court process and is rare in practice. See Appendix B for additional institutional
10
This definition of an eviction is used by Desmond et al. (2018b), who compile the most complete national
database of eviction filings and orders to date based on court records.
11
For example, Summers (2020) studies housing court cases in New York and finds that agreements are
almost always payment plans, with only one percent of these cases involving a move-out agreement. In Section
4.2, we study the probability that evicted and non-evicted tenants move out using our linked data set.

6
details.

Cook County. Roughly 33,000 eviction cases are filed in Cook County each year. These
are handled by the Forcible Entry and Detainer Section of the Circuit Court of Cook County.
Roughly 80 percent of Cook County cases are joint action cases, which are cases where the
landlord is seeking payment of rental arrears in addition to possession of the property. The
remaining 20 percent of cases are single action cases, where the landlord is only seeking
possession of the property. The court divides the county into six districts, each with its
own courthouse, eviction courtrooms, and eviction case judges. Landlords must file eviction
cases in the district in which the property is located. The City of Chicago is located entirely
within Cook County, IL, and eviction cases filed in the city represent about 75 percent of the
county’s case volume.
Eviction cases are randomly assigned to courtrooms within a district by a computer
algorithm. Judge assignments to courtrooms are set in advance, and therefore random
assignment to a courtroom is effectively random assignment to a judge.
Approximately 65 percent of eviction cases in Cook County end with an eviction order. We
estimate the share of non-evicted cases with a formal agreement to be upwards of 39 percent.12
Around 45 percent of cases without an eviction order in Cook County are discontinued, and
roughly 5 percent are dismissed. The Cook County Sheriff’s Office executes about 26 percent
of cases ending with an eviction order.13

New York City. Each year, around 240,000 cases are filed in housing court in New York.
The Civil Court of New York City, part of the state Unified Court System, oversees the New
York City Housing Court. Housing Court hears cases involving landlord-tenant disputes
or housing code violations. Cases are handled by seven courthouses: one for each county
(borough) in New York City (Bronx, Kings, New York, Queens, and Richmond) and two
smaller, specialized courts in Harlem and Red Hook. The courthouse is determined by
location of the filing address. The vast majority of eviction cases heard in housing court are
non-payment filings (86 percent), with the remaining being other lease violation disputes
known as “holdover” cases (14 percent).
Cases are randomly assigned to courtrooms by the Housing Court Information Sys-
tem (HCIS) computers within the courthouse of the assigned case. Judges rotate through
courtrooms for year-long terms on a predetermined rotation system. Cases are assigned to
courtrooms rather than judges, and therefore if the judge rotates out of a courtroom during
12
The electronic court record, from which we collect our court data for Cook County, does not record
whether there was a formal agreement. We hand-collected and coded court microfilm records for a random
sample of court cases ending in dismissal. In Appendix C.4 we provide details on how we process the microfilm
information to arrive at our estimates for outcomes in non-evicted cases.
13
Enforcement rates reported here are not based on our main court record data in Cook County. The data
set used to calculate these rates for Cook County is obtained from the Sheriff’s Office and only covers the
years 2011 to 2016.

7
an active case, the case will remain in the assigned courtroom. Some types of cases, such as
those involving the public housing authority, are not randomly assigned to courtrooms, and
we exclude these from the analysis. For details, see Appendix C.2.
In New York, about 35 percent of nonpayment cases end with an eviction order. Among
those ending without an eviction order, approximately 64 percent end with a settlement
agreement, 29 percent are discontinued, and 5 percent are dismissed. The enforcement of
an eviction order is conducted by a City Marshal. In our data, 31 percent of cases ending
with an eviction order in New York result in an enforcement of the order conducted by a
City Marshal.

3 Data and linkage
Our empirical analysis uses court records from Cook County, IL, and New York, NY, linked
to administrative data sets measuring earnings and employment, residential address histories,
interactions with the homelessness services system, and credit bureau records. We additionally
link the New York court data to records of hospital visits. This section summarizes our data
sources, sample construction, data linkage, and main outcomes. We provide additional details
in Appendix C.

3.1 Court data
Our linked data sets are based on the near-universes of court records for Cook County for the
years 2000–2016 and for New York for 2007–2016. Each court record includes the residential
address of the disputed housing unit and the names of one or more tenants. The unit of
analysis is the case-individual, so that each tenant who appears as a defendant in the case
will have a separate record.14 Other key elements we observe in the court records are case
type, filing date, courtroom and date assignment, name of the landlord, attorneys’ names,
the amount claimed by the landlord (ad damnum amount), and whether an eviction order
was granted.15 We also observe other judge decisions throughout the case, such as whether
to grant a continuance in the case or a stay of the eviction order. We define an eviction as a
case ending with an eviction order.16
While the data are similar across our two settings, there are a couple of differences to
note. In Cook County, the data include the value of any money judgment awarded and the
14
Individuals living in the unit who are not named in the case filing, which may include children, other
family members, or cohabiting partners, are not included in the sample.
15
In Cook County, the case types are single action and joint action, and in New York, the case types are
holdover and nonpayment.
16
For a subset of years, we also link court records to data held by the Sheriff’s office (Cook County) or
Marshal’s office (New York) so that we know whether an eviction order is enforced. The New York court data
also contain information on enforced orders, which we validate with records of enforcement by City Marshals
from the Department of Investigations.

8
name of the judge associated with each action in the court record, but we do not observe
either in New York.

3.1.1 Sample restrictions
We impose several restrictions on our court samples. In both locations, we drop eviction
cases associated with businesses, cases associated with condominiums, cases with a missing
defendant name or address, cases involving more than $100,000 in claimed damages, and
cases filed during a week in which only a single judge (courtroom in New York) was hearing
cases. These sample restrictions are necessary to focus our analysis on residential eviction
cases involving renters where we can link to outcomes and construct the instrument. We also
restrict the sample to cases in which the judge (courtroom in New York) presided over a
minimum number of cases during the year: 100 in Cook County and 500 in New York. This
restriction removes judges/courtrooms that hear substantially fewer cases than is typical in
the setting, which removes noise in the instrument.17
In New York, some cases are not randomly assigned to courtrooms: cases involving public
housing units, cases involving co-ops or condominiums, cases assigned based on zip code
through several policy initiatives, cases for family members of active military personnel, and
cases involving the District Attorney’s office or the New York City Police Department. We
can identify these cases directly in the New York courts data and drop them from our sample.
The court sample includes around 414,000 cases for Cook County and 580,000 cases for
New York before linking to outcomes data. Appendix Tables C.1 and C.2 describe how
sample counts change with these restrictions in Cook County and New York, respectively.

3.2 Outcomes data
We link the court records to multiple administrative data sets. Below, we describe these data
sets and define the outcomes we study in our analysis. We separately analyze linked records
for Cook County and New York because of data security restrictions.18 Additional details on
data linkage and sample construction are provided in Appendix C.

Earnings and employment. In both settings, we measure earnings and employment
using quarterly records derived from state Unemployment Insurance (UI) data systems. Our
main earnings outcome is quarterly wage earnings, and our main employment outcome is an
indicator for positive earnings. We restrict the analysis to tenants who are 18 to 55 years
old at the time of case filing to exclude individuals aging into retirement. Earnings and all
other dollar amounts are expressed in 2016 USD using the CPI-U for the two metropolitan
17
In Appendix G.1, we show that our first stage is robust to different choices of sample restrictions.
18
Due to restrictions in the data sharing agreement with the New York courts system, we were unable to
bring the New York courts data into the Census Bureau RDC for analysis.

9
areas we study. Employment and earnings records only cover formal employment and exclude
individuals not covered by UI benefits, such as the self-employed.
The UI records for New York are from the New York State Department of Labor and
do not include states other than New York. They cover the years 2004 to 2016. The UI
records for Cook County are from the Longitudinal Employer-Household Dynamics (LEHD)
Employer History File, a restricted Census Bureau data set (see Abowd et al., 2004; Vilhuber,
2018, for more details on the LEHD). We measure employment using the LEHD file that
contains a flag for any positive earnings in any of the fifty states or the District of Columbia.
We observe quarterly earnings for Illinois, the District of Columbia, and eleven other states
for which we were granted access to earnings data.19 The years available vary by state, but
all states have data from 1995 to 2014.20

Residential mobility. In Cook County, our primary data source for measuring residential
address changes is the Census Bureau’s Master Address File Auxiliary Reference File (MA-
FARF), which provides addresses of residence and associated Census geographic identifiers
by year.21 We use the MAFARF to build an indicator for the tenant being observed at the
filing address in each time period. While the data are rarely missing, some individuals do
not have an address listed in certain years. Appendix Figure E.1 plots the proportion of
evicted and non-evicted tenants with address information each year, relative to the filing
year. Evicted tenants are somewhat less likely to have a reported address, and this difference
grows moderately after the eviction case is filed. When studying mobility outcomes below, we
additionally report and discuss robustness results based on how these missing observations
are handled.
In New York, we combine two sources of address histories: consumer reference data from
Infutor Data Solutions and administrative benefits records.22 Similar to the Cook County
19
The eleven LEHD “Option A” states are: Arizona, Arkansas, Delaware, Indiana, Iowa, Kansas, Maine,
Maryland, Nevada, Oklahoma, and Tennessee.
20
For Cook County, the quarterly earnings variable is set to zero when the national indicator for positive
earnings is zero. It is set to missing and excluded from the analysis when the national employment indicator
is one but earnings are missing. In Appendix I we provide additional evidence on how eviction affects
migration out of state and migration out of the 13 states for which we observe LEHD earnings. For New
York, out-of-state earnings are not observed and therefore if a person moves or works out of state and has no
in-state earnings they would be recorded as having zero earnings in the data.
21
The MAFARF provides a link between unique individuals from various administrative records (identified
by Protected Identification Keys, or PIKs) and unique addresses (identified by Master Address File Identifiers,
or MAFIDs). Its source data include “the Census Numident, the 2010 Census Unedited File, the IRS 1040
and 1099 files, the Medicare Enrollment Database (MEDB), Indian Health Service database (IHS), Selective
Service System (SSS), and Public and Indian Housing (PIC) and Tenant Rental Assistance Certification
System (TRACS) data from the Department of Housing and Urban Development, and National Change of
Address data from the US Postal Service” (Finlay, 2016). The unique addresses are in the Census Bureau’s
Master Address File (MAF), which is an “accurate, up-to-date inventory of all known living quarters in the
United States, Puerto Rico, and associated island areas” and is used to support Census surveys such as the
Decennial Census and American Community Survey (U.S. Census Bureau, 2020).
22
Infutor compiles data from several sources including public and private telephone billing data, deed and

10
data, we define a tenant as not at their eviction address if we observe them at a different
address than the one listed on the court filing according to either the benefits data or the
Infutor data in the relevant outcome window. A concern with the New York sources of address
data is that the availability of address information could be affected by an eviction. However,
Appendix Table C.3 shows that eviction is only weakly correlated with the probability of
having an address from either the Infutor data or the benefits data. Appendix Table I.3
shows that estimates of the impact of eviction on residential mobility in New York are not
particularly sensitive to using either data source on its own in cases when both are available.
Measuring address-level moves at an annual frequency in the U.S. is challenging, and par-
ticularly so for our population of unstably-housed tenants, yet we believe these administrative
data sets provide the best measures available.
Using the address data described above, we additionally link to neighborhood poverty
rates. In Cook County, we use census tract-level neighborhood poverty rates constructed
from restricted-access American Community Surveys (ACS) from 2005 to 2018, based on
five-year moving averages. In New York, we use the publicly available census tract five-year
estimates from the ACS 2006-2010.

Homelessness. We measure interactions with homelessness services in both settings using
local Homeless Management Information System (HMIS) data.23 The Cook County HMIS
database is managed by All Chicago and is similar to the data set used in Evans et al. (2016).
The HMIS records are linked to Census identifiers and are studied within the Census RDC.
They capture the years 2014 to 2018 and include individual-level data on stays in emergency
shelters as well as other interactions with homelessness prevention services. Similarly, the
HMIS data in New York capture individual-level applications to and stays in the city’s vast
shelter system, as well as diversions through homeless prevention programs. These data come
from the New York City Department of Homeless Services and cover the years 2003 to 2017.
We use these data to construct two outcomes: an indicator for the individual staying in an
emergency shelter, and an indicator for the individual interacting with any homelessness
services. In Cook County, homelessness services include emergency shelter use, permanent
supportive housing, coordinated assessment of need, rapid rehousing, transitional housing,
and street outreach. In New York, this indicator additionally includes applications to shelter,

property information, customer information from utility companies, subscription services, and other sources.
The data have been used to track housing instability among low-income tenants but may miss households
with more limited paper trails (Phillips, 2020). The benefits records contain address histories for households
as long as they continue to receive or apply for assistance from any of the covered programs from the New
York City Human Resources Administration: Medicaid, the Supplemental Nutrition Assistance Program
(SNAP), Temporary Assistance for Needy Families (TANF), and other city-specific cash subsidies.
23
Maintaining an HMIS database is a data collection requirement imposed by the U.S. Department of
Housing and Urban Development for participation in the Continuum of Care and Emergency Solutions Grant
programs.

11
which cover instances where families are diverted or deemed ineligible.24

Financial health. We measure financial health with credit records from Experian, one of
the three major credit bureaus in the United States.25 For Cook County, the linked credit
report data are biennial snapshots from March 2005 to March 2017 and an additional snapshot
for September 2010. For New York, the linked credit report data are quarterly snapshots from
June 2014 to September 2019. For both locations, we measure overall financial health using
VantageScore 3.0, which is on a scale of 300–850; scores under 600 are considered subprime.
We measure unpaid bills as the total balance currently either 30 days or more delinquent or
in collections, where the latter are balances that the lender turns over to a collections agency
following a period of delinquency, typically at least 30 days. We construct an indicator for
any positive balance on an auto loan or lease, which has been used as a proxy for durable
goods consumption (Dobkin et al., 2018; Agarwal et al., 2020). We measure whether the
tenant has no open source of revolving credit, such as a credit card, which serves as a proxy
for having limited access to credit.
As a summary measure, we create an index of financial health based on the credit bureau
variables described above and following the approach of Dobbie et al. (2017). Each component
of the index is standardized based on the non-evicted mean and standard deviation in the
filing year. We then sum the standardized components, with the indicator for no revolving
credit and the amount of unpaid bills entering the index negatively, so that all components
can be viewed as contributing to financial health. We then re-standardize the index based
on the mean and standard deviation of the index for the non-evicted group in the filing
year. Lastly, we observe payday loan account inquiries and borrowing for individuals in Cook
County, which includes both online and storefront loans. The majority of these loans are
originated online. We describe the payday loans data in detail and present the analysis in
Appendix C.7.

Health. For New York, we also measure health outcomes using data from the New York
State Department of Health’s Statewide Planning and Research Cooperative System. This
data set includes all inpatient and outpatient (including Emergency Department) hospital
visits in New York State from 2004 to 2016.26 For each hospital visit, the data include the
date of intake and a primary diagnosis code (ICD-9 code). We focus on the total number
of (non-pregnancy-related) hospital visits, including inpatient or outpatient visits, the total
24
New York City has a right to shelter, and therefore all single adults applying to shelter are eligible for
shelter accommodations. However, families, unlike individuals, can be ineligible for shelter. Families are also
occasionally diverted from shelter, meaning they are directed to benefits or relocation assistance or otherwise
helped to find other housing options.
25
Avery et al. (2003) provide a detailed description of these data. We follow the literature in the selection
of credit bureau outcomes (Dobbie et al., 2017; Dobkin et al., 2018; Miller and Soo, 2020a).
26
An advantage of the data is that we can observe any hospital visits in New York State regardless of payer.
A limitation is that we do not observe primary care visits or prescription fulfillment.

12
number of emergency department visits, and the total number of hospital visits for mental
health conditions.27

3.3 Data linkage
We link court records to other administrative data sets using tenant names and addresses.
To link Cook County court records to Census Bureau-held data sets, the Census Bureau used
names and addresses to link individuals to their unique Protected Identification Key (PIK).28
The PIK rate for the Cook County sample is 52 percent. PIKs are then used to link to other
restricted data sets held in the Census Bureau Research Data Centers (RDCs).
To link New York court records to outcomes, we first use names and addresses to link
individuals to historical benefits data from the New York City Human Resource Administration
for the years 2004 to 2016. The data include individuals receiving Medicaid, the Supplemental
Nutrition Assistance Program (SNAP), Temporary Assistance for Needy Families (TANF),
or other city-specific cash subsidies. Appendix D.2 describes this process in detail. The data
have personal identifiers, including Social Security Number (SSN) and date of birth, that we
use to link individuals to the outcomes data. The data also include demographic information
such as age, gender, race, and ethnicity. The benefits data capture roughly 2 million unique
New Yorkers each year. Because receiving benefits may be endogenous to the eviction court
outcome, we restrict the sample to court records that match the benefits data prior to an
eviction filing. Roughly 40 percent of the court records have a match in the benefits data.
Individuals in the linked data have somewhat lower incomes and are more likely to be older,
female, and have children when compared to the overall population in housing court (NYC
Office of Civil Justice, 2016).
Lastly, we link court records to measures of financial health from Experian credit reports.
This linkage yields match rates of 61.3 percent in Cook County and 68 percent in New York,
which are comparable to match rates in previous studies that link data to records from the
major credit bureaus.29 The linked credit sample consists only of individuals who have a
credit record. In low-income neighborhoods, more than 70 percent of all adults have credit
records (Brevoort et al., 2015).
Appendix D compares the court record populations in Cook County and New York to the
27
We follow Currie and Tekin (2015) and use the Clinical Classification Software (CCS) to group ICD-9
diagnosis codes into broader categories. We define mental health visits as CCS codes 650–661, 663, and 670.
Appendix Table C.4 provides the category labels associated with these codes.
28
PIKs are assigned through the Person Identification Validation System (PVS), which uses probabilistic
matching to link individuals to a reference file constructed from the Social Security Administration Numerical
Identification File and other federal administrative data (Wagner and Layne, 2014).
29
Dobbie et al. (2017), perhaps the most closely related example, links bankruptcy filings to the same
identifiers we use and has a match rate of 68.9 percent. Dobkin et al. (2018), using additional identifiers
unavailable to us here (SSNs), are able to match 72 percent of their Medicaid sample to credit reports. The
linked data used to study the Oregon Health Experiment have a match rate of 68.5 percent (Finkelstein et al.,
2012).

13
sub-populations successfully linked to outcomes and also examines court record characteristics
predictive of a match. More disadvantaged tenants (those without legal representation or
those evicted) are somewhat less likely to be linked to Census records in Cook County and
slightly more likely to be linked to benefits data in New York. The pattern is similar for links
to Experian data in Cook County yet the opposite for links to Experian data in New York.
These patterns will not affect the internal validity of our results since, conditional on linking
to outcomes, the baseline characteristics of the case and tenant are not predictive of judge
stringency, as we show in Section 5.
Appendix D also studies the relationship between judge stringency and the probability
of being linked to outcome data. We find that in three out of the four analysis samples,
judge stringency is uncorrelated with the probability that a case is linked to outcomes. The
exception is the Cook County linked Census sample, which has an economically small but
statistically significant relationship between stringency and the probability of being assigned
a PIK (Appendix Table D.1). Moving from the 10th percentile of stringency to the 90th
percentile of stringency—a 7 percentage point difference—is associated with only a -0.38
percentage point reduction in the likelihood of having a PIK (−.054 × .07, using the estimate
from column 2 in Appendix Table D.1). This correlation likely arises due to the Census linkage
process, which may incorporate post-filing information that is impacted by eviction.30 We
emphasize that the correlation between stringency and the probability of having a Census PIK
does not threaten the internal validity of our estimates because conditional on having a PIK,
judge stringency is unrelated to individual and case characteristics, which we discuss below
and show in Table 3. We also show in Appendix Table G.4 that stringency is uncorrelated
with lagged values of all our outcomes that are linked using Census PIKs. Lastly, there is no
relationship between stringency and being linked to the New York benefits sample, which
yields a similar pattern of results to the Cook County linked Census sample, suggesting
differences in PIK rates are not driving the effects we document in Section 6.

4 Trends and evidence of selection
This section provides new descriptive facts about the demographics, earnings, employment,
housing, health, and financial circumstances of tenants in housing court, based on the linked
panel data described in the previous section. We show that, while evictions primarily occur
in neighborhoods with high poverty rates, tenants in our linked housing court sample are also
negatively selected on pre-court earnings and employment relative to randomly-chosen renters
who live in the same neighborhoods. Within housing court, we also find notable differences
between evicted and non-evicted tenants. These differences show up in both levels and trends
leading up to the moment the case is filed for nearly all outcomes considered, suggesting
30
We are unable to impose restrictions on how the Census Bureau assigns PIKs, such as requiring the
linkage to use pre-filing information only, as we do in constructing the credit bureau samples and the New
York benefits sample.

14
the presence of unobserved factors that are correlated with both the eviction decision and
post-court outcomes. This motivates our IV research design described in Section 5.

4.1 Tenants in housing court
Figure 1 maps the location of evictions in 2010 by census tract for Cook County and New York,
together with tract-level poverty rates. While evictions occur throughout both areas, Figure
1 shows that they are concentrated in neighborhoods with higher poverty rates: 58 percent
of evictions in New York and 46 percent of evictions in Cook County occur in high-poverty
neighborhoods, which are defined as census tracts with more than 20 percent of residents
living below the poverty line. This spatial concentration is consistent with Desmond (2012),
Desmond and Kimbro (2015), and Desmond and Gershenson (2017), who find that eviction is
common in poor communities in Milwaukee. Appendix Figure B.1 shows how eviction filing
rates (the number of evictions filed relative to the number of occupied rental units) vary
across neighborhoods. Some neighborhoods have annual eviction filing rates as high as 1 in
10 renter households in Cook County and as high as 1 in 5 renter households in New York.

Figure 1: Evictions and Neighborhood Poverty

Eviction Orders
1 Dot = 20

Eviction Orders

Tract Poverty Rate
0%-10%
10% - 20%
20% - 30%
30+%

Esri, HERE, Garmin, (c) OpenStreetMap contributors, and the GIS Esri, HERE, Garmin, (c) OpenStreetMap contributors, and the GIS
user community user community

Notes: This figure depicts the approximate locations of court ordered evictions in Cook County (left) and New York
(right) in 2010 (each dot represents 20 eviction orders in the census tract), along with the poverty rate of the census
tract (based on 2006–2010 American Community Survey 5-year averages).

15
While evictions primarily occur in neighborhoods with high poverty rates, tenants in our
linked housing court sample are also negatively selected on pre-court earnings and employment
relative to randomly-chosen tenants who live in the same neighborhoods. Table 1 shows
descriptive statistics for three groups: evicted tenants, non-evicted tenants with a case filed
in housing court, and ACS respondents who are renters, weighted so the distribution of
their neighborhoods matches the distribution of neighborhoods for tenants in our linked
sample. Relative to renters from the same neighborhoods, tenants in the linked sample have
lower levels of earnings and employment than renters from the same neighborhoods. Within
housing court, differences persist, with evicted tenants showing lower levels of earnings and
employment than non-evicted tenants. For example, in Cook County, average quarterly
earnings in the eight quarters before case filing are $4,876 for non-evicted tenants and $3,907
for evicted tenants, and in New York these numbers are $3,628 and $3,080, respectively.31
Table 1 further shows that, relative to renters from the same neighborhoods, both evicted
and non-evicted tenants are more likely to be female (62 vs. 54 percent in Cook County
and 71 vs. 54 percent in New York) and more likely to be Black (68 vs. 47 percent in Cook
County and 58 vs. 35 percent in New York). Hispanic tenants are under-represented in
our linked housing court sample in Cook County (12 vs. 20 percent), but over-represented
in NYC (46 vs. 39 percent). By contrast, the demographic characteristics of evicted and
non-evicted tenants within housing court are similar.
The bottom panel of Table 1 displays case characteristics. The average ad damnum
amount—the judgment amount the landlord is seeking from the court—for evicted tenants is
around $2,000 in Cook County and $4,600 in New York, both of which are a few hundred
dollars more than for non-evicted tenants. In Cook County, evicted tenants are less likely
than non-evicted tenants to have no prior case (63 percent vs. 67 percent) and somewhat
more likely to be unrepresented (97 percent vs. 94 percent), while in New York, evicted and
non-evicted tenants are somewhat more similar in these respects (53 vs. 54 percent have
no prior case and nearly all tenants in NYC are unrepresented at the time of the initial
hearing).32

31
The lower earnings levels in New York relative to Cook County reflect that the New York sample is
restricted to those with some pre-filing benefits receipt.
32
In New York, we observe if a tenant is self-represented at the time of the first appearance in court and
these summary statistics may understate the level of representation if some tenants pursue representation
after their initial hearing.

16
Table 1: Linked Sample Summary Statistics

Cook County New York
Renters Renters
Not from same Not from same
Evicted Evicted
Evicted neighbor- Evicted neighbor-
hoods hoods
(1) (2) (3) (4) (5) (6)
Individual characteristics:
Age 37.51 37.44 33.98 38.55 40.49 34.98
(10.35) (10.22) (10.26) (9.50) (9.22) (10.51)
Female 0.62 0.62 0.54 0.71 0.74 0.54
(0.49) (0.49) (0.50) (0.46) (0.44) (0.50)
Black 0.69 0.66 0.47 0.58 0.58 0.35
(0.46) (0.47) (0.46) (0.49) (0.49) (0.43)
Hispanic 0.12 0.11 0.20 0.46 0.47 0.39
(0.32) (0.31) (0.42) (0.50) (0.50) (0.47)
Quarterly earnings 3,907 4,876 6,237 3,080 3,628 7,059
(4,636) (5,561) (9,251) (4,066) (4,471) (14,987)
Employment 0.58 0.61 0.72 0.47 0.51 0.70
(0.46) (0.47) (0.43) (0.42) (0.44) (0.45)
Neighborhood poverty rate (5 yr avg) 0.21 0.20 0.20 0.29 0.29 0.24
(0.13) (0.13) (0.09) (0.12) (0.12) (0.10)
Neighborhood median rent (5 yr avg) 762 788 1,045 973 962 1,163
(195) (229) (159) (199) (214) (327)
Case characteristics:
Ad damnum (1000s) 2.01 1.74 4.60 4.16
(2.99) (3.09) (27.79) (31.45)
No prior case 0.63 0.67 0.54 0.53
(0.48) (0.47) (0.50) (0.50)
Tenant without attorney 0.97 0.94 1.00 0.99
(0.16) (0.23) (0.07) (0.10)
Observations 193,000 108,000 36,559 87,294 70,474 103,614

Notes: The statistics in columns (1), (2), (4), and (5) are for the samples matched to earnings and employment
records. Columns (1) and (4) include summary statistics for those in housing court who are evicted. Columns
(2) and (5) include summary statistics for those in housing court who are not evicted. For these samples,
quarterly earnings is the average quarterly earnings in quarters 1-8 before filing, and employment is the fraction
of quarters with positive earnings in quarters 1-8 before filing. Columns (3) and (6) include summary statistics
for renters aged 18-55 in the ACS PUMS 2006–2010, weighted to match the distribution of neighborhoods
(Public Use Microdata Areas) for tenants who have housing court cases filed against them. For the ACS
samples, quarterly earnings is the annual wage income divided by four, and employment is approximated by
the proportion of people with any wage income. Cook County observation counts are rounded in accordance
with Census Bureau disclosure requirements. Cook County results were approved for release by the U.S. Census
Bureau, authorization numbers CBDRB-FY22-072.

17
4.2 Trends around court filing
We next plot the trends in our main outcomes for evicted and non-evicted tenants relative
to the time the eviction case is filed. Figure 2 shows these trends for earnings, employment,
residential mobility, neighborhood poverty, emergency shelter use, and use of homelessness
services; Figure 3 shows these trends for financial outcomes; and Figure 4 shows these trends
for health outcomes.
The figures are based on the regression
F
X F
X
Yi,t = γt + α × Ei + βr + δr × Ei + i,t , (4.1)
r=S;r6=O r=S;r6=O

where r indexes time relative to the eviction filing, Ei is an indicator for the case ending in
an eviction order, βr are coefficients on indicators for time relative to the case filing, and δr
are coefficients on indicators for relative time interacted with the eviction outcome. The only
controls we include are calendar year dummies γt . The start and end periods are S and F ,
respectively, and O is the omitted period. We estimate equation 4.1 separately by location
and present equal-weighted averages. Location-specific trends are presented in Appendix E.
Figures 2-4 display regression estimates of βr and α + δr + βr , with the non-evicted group
mean in the omitted period added to both sets of coefficients so that the magnitudes are easy
to interpret. Since we add the mean in the omitted period, the levels in the figure are not
sensitive to the choice of omitted period.
The top two panels of Figure 2 depict trends in quarterly earnings and employment—the
result of estimating equation 4.1 between 16 quarters prior to filing and 24 quarters after
filing. Both evicted and non-evicted groups show signs of declining earnings in the year prior
to case filing. This decline is steeper for tenants who are evicted (-$340) than for those who
are not evicted (-$157). Similarly, the probability of being employed for both evicted and
non-evicted tenants declines in the year prior to filing, with the decline for evicted tenants
more severe (-1.3pp) than for non-evicted tenants (-0.5pp). Following eviction, employment
does not recover to its pre-filing peak over the next six years. There is a slight tapering of
employment for the entire sample period after filing, which is not due to aging into retirement
since our sample includes individuals between 18 and 55 years old at the time of the eviction
filing.
Turning next to residential mobility, Figure 2, panel C shows the probability that we
observe a tenant at an address different from the filing address. We study the same time
window as for employment and earnings, now at the annual frequency that is imposed by the
MAFARF. In the year of filing, 22 percent of tenants are observed at an address different
from that recorded in the case. The fact that this estimate is not zero reflects moves in the
year of filing as well as noise in the mobility data. The probability of observing a tenant at a
new address increases to about 37 percent for the evicted group in the first year after filing
and rises to 81 percent six years after filing. This probability rises faster for evicted than for

18
non-evicted tenants, yielding a gap of about 16 percentage points six years after filing.33 This
gap may be an underestimate if evicted individuals are less likely to have updated addresses,
which we find some evidence of in Appendix Figure E.1.34 While evictions are associated
with increased residential mobility, panel D shows that there is little change in the average
neighborhood poverty rate before or after the case is filed.
One of the most striking results is that the use of homelessness services spikes in the year
after filing, particularly for the evicted group (Figure 2, panel E).35 The relative magnitudes
of these increases are sizeable: for the evicted group, the probability of using homelessness
services increases from 1.4 percent in the filing year to 7.1 percent in the first year after filing,
an increase of approximately 400 percent. The non-evicted group also increases their use
of homelessness services over the same period but the increase is smaller, from 1.3 percent
to 1.9 percent. Panel F shows that this increase in use of homelessness services is primarily
due to increased use of emergency shelters: for evicted tenants, the probability of using an
emergency shelter jumps from 1 percent to 6 percent between the year of case filing and the
following year.
We next examine trends in financial health, presented in Figure 3. We study trends
between eight quarters prior and 20 quarters after the case filing because there are fewer years
available in the credit bureau sample in New York. Mirroring the trends in earnings, the
financial health index declines in the year prior to filing by roughly 0.067 s.d. for non-evicted
tenants and 0.085 s.d. for evicted tenants. Looking at the index’s components, credit scores
fall, unpaid bills rise, and access to credit decreases in the year prior to filing.36 These figures
reveal that tenants facing eviction are financially distressed prior to court: they have low
average credit scores and high levels of indebtedness in the years prior to housing court,
and the mean tenant would be considered a subprime borrower. Following the eviction case,
tenants have diminished financial health—including elevated indebtedness and diminished
credit access—for several years regardless of the outcome of the court case. In the four years
following the case, the financial health index does not return to its pre-filing peak for either
group. The gap in financial health between evicted and non-evicted tenants also widens in
the aftermath of eviction court, increasing from about -0.14 s.d. two years prior to the case
33
High mobility among non-evicted tenants is consistent with Brummet and Reed (2021). Using linked
Census Bureau microdata from the Census 2000 and American Community Surveys 2010–2014, they find
that 70 percent of high school-educated renters living in low-income central city neighborhoods in 2000 are in
a different neighborhood 10 to 14 years later.
34
Appendix Figure E.1 shows that in Cook County, evicted tenants are around one to two percentage
points less likely to be observed in the years prior to the case, with this gap growing to around 5 percentage
points by three years after the filing. A similar check is not possible in New York because the sources of
residential addresses only record address changes, and therefore we cannot distinguish between the tenant not
moving and the lack of an updated address.
35
For homelessness services, we study the period between one year prior and three years after filing, as the
data are only available from 2014 to 2018 for Cook County.
36
Appendix C.7 shows trends in payday loan inquiries for the Cook County sample, and shows rising
demand for payday loans in the two years prior to filing.

19
to about -0.18 s.d. two quarters after the case (before tapering slightly over the next two
years). While the gap in unpaid bills that arises immediately following the case closes by
quarter 4, the gap in access to credit widens in the aftermath of the court case. The difference
in the likelihood of having no source of revolving credit is about 3.7 percentage points four
quarters before the case, rises to about 5.4 percentage points by one quarter after the case,
and remains elevated through quarter 12.
Figure 4 shows trends in total hospital visits, total emergency room visits, and total
hospital visits related to mental health in the New York sample. Panel A shows that total
hospital visits increase in the two years leading up to the eviction filing and peak during
the quarter of filing, which coincides with the point where earnings are at their lowest. The
increase preceding housing court hints at the possibility that health shocks could be a source
of earnings losses that lead to non-payment of rent, although it is not clear in which direction
causality runs. Panel B shows that the vast majority of these hospital visits are trips to the
emergency room, while panel C shows that the total number of mental health-related hospital
visits also increases during the period leading up to housing court. The gap between evicted
and non-evicted tenants in hospital visits widens following eviction in all three panels.

4.3 Considerations for empirical design
The analysis up to this point has revealed patterns that are consistent with changes to
pre-filing earnings, health, and financial circumstances being correlated with both the case
filing and with receiving an eviction order. Evicted tenants have lower earnings, worse credit,
and higher rates of hospitalization than non-evicted tenants several years before filing, and
they experience sharper drops in earnings, and steeper jumps in unpaid bills and hospital
visits in the immediate run-up to filing. This raises concerns about selection on correlated
unobservables at both the filing and the eviction stage.
The presence of such correlated unobservables can bias frequently-used methods for
identifying the effects of eviction, such as cross-sectional comparisons corrected only for
observable characteristics, and difference-in-differences methods. We explore the potential
bias of such methods using our data. We first examine what a simple demographic- and
location-adjusted cross-sectional comparison of evicted tenants to renters outside of court
would yield for the impacts of eviction on earnings. The result appears as the left-most bar in
Appendix Figure F.1 and implies that eviction reduces average quarterly earnings by roughly
$1,600 in Cook County and $1,100 in New York. Moving to a within-court comparison of
evicted and not evicted tenants (middle bar), the estimates shrink by approximately one-third
to $1,000 in Cook County and $600 in New York. This suggests that comparisons of tenants
outside of court to those inside will likely overstate the effect of eviction because they will
incorrectly attribute selection into court to the eviction itself, echoing similar patterns found
in a different court setting by Aizer and Doyle (2015).
While the estimates shown in the second bar remove bias due to selection into court, they
do not address bias stemming from the selection on levels or trends within court. Difference-

20
in-differences (DiD) is a natural choice of method for addressing selection on levels. The
third bar of Appendix Figure F.1 shows estimates from a DiD specification.37 Adjusting for
differences in levels between evicted and not-evicted in the lead-up to case filing shrinks the
estimates further. However, DiD estimates are likely to still be biased due to the differential
pre-trends among evicted and non-evicted tenants within court that we see in Figures 2, 3,
and 4.
Whether DiD estimates are biased upward, biased downward, or unbiased will depend on
properties of the data generating process. As a result, we would need to make assumptions
about these properties to know whether the estimates in the third bar are biased, and in which
direction. Rather than making such assumptions, we instead rely on our quasi-experimental
instrumental variables research design, which we describe in the next section. This design
addresses the sources of selection that we document above and allows us to identify a local
average treatment effect of eviction.

37
The DiD estimates reported in Appendix Figure F.1 are from a panel DiD specification with a symmetric
base period and outcome window, which is described in more detail in Appendix J. Heckman and Robb (1985)
show that under an (arguably strong) stationarity assumption, this symmetric DiD estimator is unbiased.

21
Figure 2: Labor Market and Housing Outcomes Relative to Time of Eviction
Filing

A. Earnings B. Employment

C. Residential Mobility D. Neighborhood Poverty

E. Homelessness Services F. Emergency Shelter Use

Notes: This figure shows trends in labor market and housing outcomes relative to eviction filing, combined across
Cook County and New York. For each location, we estimate equation 4.1 and plot the equal-weighted average for the
evicted and non-evicted groups in each time period. The only controls are calendar year dummies. For both sets of
coefficients, we add in the non-evicted group mean in the omitted period so that the magnitudes are easy to interpret.
The employment and earnings outcomes are measured at a quarterly frequency, while the housing outcomes are
measured at an annual frequency. Appendix E shows these trends by location. Cook County results were approved
for release by the U.S. Census Bureau, authorization number CBDRB-FY22-072.

22
Figure 3: Financial Health Outcomes Relative to Time of Eviction Filing

A. Financial Health Index

B. Credit Score C. No Open Revolving Account

D. Total Balance: Collections and Delinquencies E. Any Auto Loan or Lease

Notes: This figure plots trends in credit report outcomes relative to eviction filing, combined across Cook County
and New York. For each location, we estimate equation 4.1 and plot the equal-weighted average for the evicted and
non-evicted groups in each time period. The only controls are calendar year dummies. For both sets of coefficients, we
add in the non-evicted group mean in the omitted period so that the magnitudes are easy to interpret. All outcomes
are measured at a quarterly frequency. Appendix E shows results by location.

23
Figure 4: Health Outcomes Relative to Time of Eviction Filing (New York)

A. Hospital Visits B. Emergency Room Visits

C. Mental Health Visits

Notes: This figure shows trends in health outcomes relative to eviction filing in New York. We observe health
outcomes in the New York sample only. We estimate equation 4.1 and plot results for the evicted and non-evicted
groups in each time period. The only controls are calendar year dummies. For both sets of coefficients, we add in
the non-evicted group mean in the omitted period so that the magnitudes are easy to interpret. All outcomes are
measured at a quarterly frequency.

24
5 Empirical framework
This section describes our instrumental variables approach based on judges’ tendency to evict
in cases randomly assigned to them. We discuss how the assumptions that underlie this
identification strategy are supported by the institutional environment and provide tests of
these assumptions. We also describe how we combine estimates across locations.

5.1 Instrumental variables
The evidence in Section 4 suggests that whether a tenant is evicted may depend on unobserved
characteristics as well as unobserved shocks that affect both eviction and subsequent outcomes.
If a suitable instrument is available, it can be used to solve this endogeneity problem and
estimate causal effects of eviction. A common approach in court settings is to exploit the
random assignment of cases to judges and use Zj(i) as an instrumental variable, where Zj(i)
is the leave-one-out estimate of stringency for judge j assigned to individual i’s case. This
approach estimates the following two-stage least squares model:

Ei = γZj(i) + Xi0 α + i (5.1)
Yi = βEi + Xi0 δ + νi , (5.2)

where the least squares regression is run separately for each outcome and time period. Here
Ei is an indicator for whether case-individual i has an eviction, Yi is the observed outcome,
and Xi is a set of controls for individual and case characteristics. Controls include court-by-
year-quarter fixed effects, ad damnum amount, gender, race indicators, census tract poverty
rates, census tract rent, a cubic in age at filing date, and indicators for missing controls.38 Our
main OLS and IV specifications include additional controls for lagged values of the dependent
variable, which are described in the table notes. If the IV assumptions are satisfied, this
analysis will recover a positive weighted average effect of eviction among compliers, where
compliers are defined as tenants who would have received a different eviction outcome had
their case been assigned to a different judge (Imbens and Angrist, 1994).

5.1.1 The judge stringency instrument
We measure judge stringency using the yearly leave-one-out mean eviction rate for the initial
judge (Cook County) or courtroom (New York) assignment. Using the sample described in
Section 3, we calculate the stringency of the judge to which tenant i’s case is assigned, Zj(i) ,
as the leave-one-out mean eviction rate (omitting i) for judge j(i) in the same year. In a
38
The age, gender, and race controls are constructed using the Census Bureau Numident file and supple-
mented with the 2010 Decennial Census in Cook County, and using the administrative benefits data in New
York. In the credit bureau samples, we omit race controls because of data use restrictions. Similarly, we do
not observe gender in the New York credit sample, so we omit the gender control in the New York financial
outcomes analysis.

25
typical year, there are 21 judges in Cook County and 29 courtrooms in New York hearing
cases. Over our sample period, we observe 127 unique judges in Cook County. We construct
the instrument from an average of 1,600 cases per judge (per year) in Cook County and 3,400
per courtroom (per year) in New York.
Figure 5 shows the distribution of judge stringency, residualized by court-year-quarter,
across cases in Cook County and New York. The variation in judge stringency is substantial
and similar across locations: a 7 percentage point difference between the 10th percentile and
90th percentile of judge stringency in Cook County and a 6 percentage point difference in
New York.
Figure 5: Judge Stringency

(a) Cook County (b) New York

50000
0.75 70000

60000
40000
0.40
0.70 50000

Pr(Eviction Order) Pr(Eviction Order)
30000
40000
Density Case Count 0.35

20000 0.65 30000

20000
10000 0.30

0.60 10000

0 0

−0.09 −0.06 −0.03 0.00 0.03 0.06 0.09 −0.09 −0.06 −0.03 0.00 0.03 0.06 0.09

Stringency Stringency

Notes: For each location, this figure shows a histogram of judge stringency, residualized by court-year-quarter, with
the number of cases indicated along the left vertical axis. Each panel also depicts fitted values from a local linear
first-stage regression of eviction on judge stringency and court-year-quarter fixed effects (solid line, plotted along the
right vertical axis). Dotted lines show 95 percent confidence intervals.

5.1.2 Validating the IV design
This section discusses conditions for judge stringency to be a valid instrument and for the
IV estimand to be interpretable as a positive weighted average of local treatment effects
on compliers: relevance, exogeneity, exclusion, and monotonicity. We discuss each of these
assumptions below and support them with arguments based on institutional details and
empirical evidence.

Relevance. Columns 1 and 3 in Table 2 report first-stage estimates from equation 5.1 for
Cook County and New York, respectively. Judge stringency has a large and statistically

26
Table 2: First Stage

Cook County New York
(1) (2) (3) (4)

Judge stringency 0.741*** 0.740*** 0.831*** 0.825***
(0.025) (0.024) (0.057) (0.057)
Controls No Yes No Yes
Observations 268,000 268,000 150,662 150,662

Notes: ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. This table reports results from the first stage regression of eviction on
judge stringency, for Cook County and New York using the linked labor market sample. Columns (1) and (3) include
our judge stringency measure with court-year-quarter fixed effects, but without individual controls. Columns (2) and
(4) add controls. The additional controls include ad damnum amount, gender, race indicators, census tract poverty
rate, census tract rent, a cubic in age at filing date, and indicators for missing controls. Appendix Table G.1 provides
additional evidence on the robustness of the first-stage regression. Cook County observation counts are rounded in
accordance with Census Bureau disclosure requirements. Cook County results were approved for release by the U.S.
Census Bureau, authorization number CBDRB-FY22-072.

significant impact on evictions, with a partial F-statistic for judge stringency of 934 in Cook
County and 288 in New York, relieving concerns about weak instruments. Columns (2) and
(4) show that the first stage coefficients change very little when we include controls, consistent
with judge stringency being uncorrelated with individual and case characteristics. Appendix
Table G.2 additionally reports F-statistics for the Black and female subgroups that we also
study in Section 6.
Appendix G.1 provides additional robustness checks on the first stage. We show that the
first stage is robust to: (i) controlling for other dimensions of judge behavior, (ii) using an
alternate approach to measuring the first judge or courtroom in the court records, and (iii)
using different sample selection criteria.

Exogeneity. Table 3 shows the result of a standard balance test of random assignment.
As we would expect, columns (1) and (3) show that case and tenant characteristics predict
receiving an eviction order in both locations. Importantly, columns (2) and (4) show that
these characteristics are not predictive of the stringency of the judge randomly assigned to the
case. Only one of the seventeen coefficients in columns (2) and (4) is statistically significant
and is quantitatively small (a -0.017 s.d. decrease in stringency for joint action cases). In
addition, we fail to reject the null hypothesis that the coefficients are jointly equal to zero
in both locations, consistent with random assignment. Appendix G.2 provides additional
evidence that judge stringency is uncorrelated with lagged values of our key outcomes.

Exclusion. Our estimation strategy relies on the assumption that judge stringency affects
tenant outcomes only through the eviction order. As discussed in Section 2, judges determine
whether to issue an eviction order but may also influence other aspects of the case such as

27
the judgment amount (in cases in which the landlord is seeking rental arrears or damages)
or whether or not a stay of enforcement is granted (which allows extra time for the tenant
to move before an enforcement). The multidimensionality of judge discretion could make it
challenging to isolate the impact of the eviction order (Mueller-Smith, 2015; Bhuller et al.,
2020).
Exclusion will be violated if judge stringency is correlated with other dimensions of judge
discretion that affect tenant outcomes. To assess the plausibility of the exclusion restriction,
we first examine whether eviction order stringency is correlated with other dimensions of
judge stringency. Appendix Tables G.6 and G.8 report pairwise correlations between eviction
order stringency (the instrument) and stringency constructed along alternative dimensions
of the case.39 In each instance, the correlations are weak. Next, in Appendix Table G.7 we
re-estimate our first stage with and without these alternative stringency measures and find
that including these measures has minimal impact on the first stage coefficient, providing
additional support for the plausibility of the exclusion restriction. Additionally, in Cook
County—where we can observe judgment amount—we re-estimate the main IV regressions
for housing, labor, and financial outcomes in the first year, with an additional control for
judgment amount stringency, and find that the main conclusions are unchanged.
Finally, the practical aspects of case proceedings provide additional reassurance that judge
discretion in judgment amounts is unlikely to be a threat to our research design. For instance,
we find the judgment amount for a case is closely linked to the amount the landlord initially
requests in the filing. In Cook County, the correlation between the judgment amount and the
ad damnum amount (the amount the landlord requests) is 0.81 . This lends support for the
idea that judges’ differences along this dimension are likely to be small and unlikely to be
driving our results. Taken together, the robustness checks in Appendix G.3 suggest that the
multidimensionality of judge discretion is unlikely to be a threat to the exclusion restriction
in our settings.

Monotonicity. For the IV estimates to be interpreted as a positively weighted average
of local average treatment effects (LATEs), we need monotonicity to be satisfied (Imbens
and Angrist, 1994). In our setting, monotonicity requires that evicted tenants would also
have been evicted by a more stringent judge, while non-evicted tenants would not have been
evicted by a less stringent judge. This condition can fail in randomized judge designs if
judges are relatively harsh for some types of cases or individuals and relatively lenient for
others, or if judges differ in both diagnostic skills and preferences regarding the outcome of
the case, as discussed by Chan et al. (2022). We perform two tests of this assumption. First,
under monotonicity, the first-stage estimates should be non-negative for any subsample of
tenants. Appendix Tables G.11 and G.12 show non-negative first-stage estimates for various
subsamples in Cook County and New York. As a second test, we calculate judge stringency
on one sub-population (for example, women) and then use that stringency measure in the first
39
As discussed in Appendix G, these dimensions differ across locations due to differences in data availability.

28
stage for the complementing sub-population (for example, men), as in Bhuller et al. (2020)
and Norris et al. (2021). Appendix Table G.13 presents this test and shows non-negative and
similar-sized first-stage estimates across specifications. Hence, neither of these tests provide
evidence against the monotonicity assumption.

5.2 Combining estimates across locations
Due to restrictions on data sharing, we are unable to pool individual observations from Cook
County and New York. We therefore estimate each specification separately by location and
then report average point estimates in the tables in Section 6, along with each location-specific
estimate. The combined point estimates weight results from the two locations equally, and
we calculate the standard errors for the combined estimates as
q
c combined = ω 2 × SE c2 2 c2
SE N Y C + (1 − ω) × SE CC ,

where ω = 0.5. Under the assumptions outlined in Section 5.1, the combined estimates can
be interpreted as the average effect of eviction for compliers in Cook County and New York.

29
Table 3: Testing Balance

Cook County New York
Evicted Stringency Evicted Stringency
(1) (2) (3) (4)

Age at case -0.03329*** -0.00012 -0.00403*** -0.00001
(0.00376) (0.00020) (0.00016) (0.00001)
Female 0.00882 0.00041 -0.04413*** -0.00009
(0.00644) (0.00036) (0.00310) (0.00011)
Black 0.06297*** 0.00012 0.00923*** 0.00010
(0.00628) (0.00028) (0.00323) (0.00018)
White 0.00358 0.00011 -0.01494** -0.00032
(0.00582) (0.00030) (0.00616) (0.00027)
Hispanic 0.05957*** 0.00045 -0.00743** 0.00001
(0.00603) (0.00030) (0.00368) (0.00017)
Neighborhood poverty rate (5 yr avg) 0.5540*** 0.00208 -0.02487* -0.00025
(0.04813) (0.00221) (0.01453) (0.00066)
Ad damnum (in 1000s) 0.00731*** 0.00001 0.00001*** -0.00000
(0.00055) (0.00002) (0.00000) (0.00000)
No prior case -0.04037*** -0.00013 -0.01228*** -0.00014
(0.00221) (0.00013) (0.00413) (0.00014)
Joint action 0.01183** -0.00061**
(0.00525) (0.00025)
Observations 301,000 268,000 150,662 150,662
Joint F-Statistic 102.3 1.497 224.8 1.007
P-Value 0.000 0.104 0.000 0.443

Notes: Notes: ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. For each location, the left column presents results from a
regression of eviction on case and defendant characteristics, and the right column shows results from a regression of
judge stringency on case and defendant characteristics. Neighborhood poverty rate is the 5-year average poverty rate
in the defendant’s census tract. Ad damnum is the amount the landlord listed as owed by the defendant at the time
of filing. Joint action is an indicator for the case type in which the landlord is seeking both an eviction order and a
money judgment rather than only an eviction order, and is specific to Cook County. No prior case is an indicator for
the defendant having no prior eviction case in our sample. All regressions also include indicators for each right-hand
side variable having a missing value, which are not reported in the table. All regressions include court-year-quarter
fixed effects. Standard errors are shown in parentheses and are clustered at the judge(courtroom)-year level. Cook
County observation counts are rounded in accordance with U.S. Census Bureau disclosure requirements. Cook County
results were approved for release by the U.S. Census Bureau, authorization number CBDRB-FY22-072.

30
6 Results
This section presents OLS and IV estimates of the effects of eviction on tenants’ residential
mobility, use of homelessness services, labor market outcomes, financial strain, and hospital
use. The estimates show that eviction increases residential mobility and causes spikes in
emergency shelter use and hospital visits (particularly for mental health-related conditions)
in the year after filing. Housing instability persists in the second year after filing, with
eviction triggering increased use of homelessness services. These findings suggest a period of
instability of at least two years. During this period, evicted tenants also experience reductions
in earnings. In the longer-run, we find that eviction worsens financial health through increased
indebtedness and reductions in credit scores.

6.1 Eviction order enforcements, residential mobility, and neigh-
borhood poverty
We first study how eviction impacts a tenant’s housing situation, focusing on enforced eviction
orders, residential moves, and neighborhood poverty. We report estimates for the full sample,
separately by location, and separately for female and Black tenants. We focus on female
and Black tenants in our subgroup analysis because these groups are over-represented in
housing court in Cook County and New York and because prior research suggests they may
face greater adverse consequences of eviction. Qualitative research (Desmond et al., 2013;
Desmond, 2016) points to two potential reasons for more severe impacts of eviction on women,
both revolving around children in the household. First, as a result of both greater childcare
responsibilities and larger household size, women may face more difficulties securing and
maintaining new accommodation (Sugrue, 2005; Desmond, 2012; U.S. Department of Housing
and Urban Development, 2019). Second, landlords may be reluctant to rent to households
with children because children may cause nuisances to neighbors or attract inspections by
Child Protective Services or the city’s health department for lead hazards (Roberts, 2001).
Black households may experience more adverse impacts of eviction because of discrimination
while searching for new housing (Bayer et al., 2017; Christiansen and Timmins, 2019), which
would exacerbate the disruptive effects of eviction (Desmond and Gershenson, 2016).

Order enforcement. To better characterize the treatment, we first consider the extent to
which eviction orders are enforced by a Sheriff or Marshal. This experience may cause tenants
to move out more quickly or unexpectedly, leaving them unable to secure new housing before
they are locked out. So, in addition to potentially increasing the likelihood of moving, an
eviction order may change the circumstances under which households move. Table 4 shows
that receiving an eviction order substantially increases the probability of experiencing an
enforcement within one year, with an IV estimate of 43.5 percentage points and an OLS
estimate of 30.1 percentage points. Moves occurring after enforced orders may be more
likely to occur under greater stress and exigency, and may potentially result in moves to

31
lower-quality neighborhoods or homelessness. We investigate the effects on neighborhood
quality and homelessness below.

Residential mobility. As we showed in Section 4, tenants in housing court have high move
rates regardless of the case outcome, with evicted tenants being more mobile both before
and after the case. The IV models allow us to estimate how much additional residential
mobility is caused by an eviction. Table 4 shows that, for compliers, receiving an eviction
order increases the probability of appearing at a new address by 8.2 percentage points one
year after filing (an increase of 28 percent relative to a mobility rate of 29.2 percent for the
non-evicted group). The OLS estimate is similar though slightly smaller (7.3 percentage
points). In Appendix I, we explore alternative approaches to defining moves and find that
eviction increases residential mobility under a variety of alternative definitions.40 The impacts
of eviction on residential mobility are similar across locations and subgroups. Columns (4)-(6)
show that these effects persist into the second year, and the IV estimate implies that eviction
increases the probability of not being at the eviction address by 11.1 percentage points (23
percent).41 In both time periods, the effects on residential mobility are larger for women.42

Neighborhood quality. In the bottom panel of Table 4, we consider the effect of an
eviction order on neighborhood quality, as measured by the census tract poverty rate. We
find little evidence that eviction causes tenants to move to neighborhoods with higher poverty
rates, either in the combined estimates, the location-specific estimates, or the demographic-
specific estimates. These estimates are fairly precise, and we can rule out an impact on
the neighborhood poverty rate of more than 2.2 percentage points for the combined sample
with 95% confidence. Neither our IV nor our OLS specifications point to an increase in the
neighborhood poverty rate. Individuals at risk of eviction live in high-poverty neighborhoods
prior to filing, which may help explain why eviction does not cause them to move to even higher-
poverty neighborhoods on average. Our findings contrast with Desmond and Shollenberger
(2015), who find that among recent movers, those who experience a forced move relocate to
neighborhoods with 5 percentage points higher poverty rates.43 Given that tenants who are
40
This estimate of 8.2 percentage points may in fact be an underestimate. As we show in Appendix Table
I.1, evicted tenants are more likely to have a missing address. Appendix Table I.1 provides an alternative
specification that defines the outcome as being observed at a new address or not observed at all, which more
than doubles the IV estimate.
41
In Appendix Table H.10, we report estimates from an OLS regression of appearing at a new address on
judge stringency. These reduced-form estimates have a causal interpretation even if the exclusion restriction
or monotonicity assumption fail to hold. The reduced-form estimates are very similar to the IV estimates,
due to the strong relationship between judge stringency and eviction orders documented in Table 2.
42
Appendix Table H.9 shows that only 8.8 percent of tenants who avoid an eviction receive a new eviction
order within one year at the same address, and 13.9 percent receive a new order within two years. This
suggests that residential mobility among non-evicted tenants is not driven by follow-up eviction cases at the
same address.
43
An important distinction is that our study population is tenants facing eviction, and we compare evicted
to non-evicted tenants. Desmond and Shollenberger (2015) compare forced movers to other recent movers, a
comparison group that may include upwardly mobile tenants.

32
evicted move to observably similar neighborhoods, the effects we consider below on other
socioeconomic outcomes likely do not arise due to changes in neighborhood environment, as
in studies of housing mobility programs (Chetty et al., 2016) or public housing demolitions
(Chyn, 2018).

6.2 Homelessness
Homelessness carries substantial private and social costs (Evans et al., 2019). While eviction
has the potential to be a direct cause of homelessness, there is currently no causal evidence
on this relationship. The event studies in Section 4 show a striking increase in homelessness
after filing for evicted tenants, which suggests a causal link. We investigate this link directly
using our IV research design. In addition to their policy relevance, the effects on homelessness
are informative as a measure of material hardship and a possible mechanism for the labor
market impacts studied in Section 6.3.
Table 5 shows that an eviction order increases the probability of using emergency shelter
in the year after filing by 3.4 percentage points in the IV specification and 3.1 percentage
points in the OLS specification, which are both large relative to the non-evicted mean of
0.9 percent. We don’t find evidence of increased use of emergency shelters after the first
year, as seen in column (6) and in the longer-run results presented in Appendix Table H.5.
Similarly, the OLS estimates are approximately half as large after the first year. These results
suggest that evicted tenants experience difficulty finding alternative housing in the immediate
aftermath of the court case and are consistent with economic models of homelessness that
emphasize the transitory dynamics of homelessness (O’Flaherty, 2004).
We find similar impacts in the first year when looking at use of any homelessness service
for both IV and OLS, though the IV estimate is not statistically significant. While the effects
on shelter use are concentrated in the year after filing, the effect on using any homelessness
service remains elevated beyond the first year. The IV estimates indicate that evicted tenants
are 3.6 percentage points more likely to use homelessness services than tenants who avoid
eviction in the second year after filing (an increase of 200 percent relative to the non-evicted
group mean of 1.2 percentage points). As with residential mobility, longer-term interactions
with homelessness services are driven by effects for female and Black tenants, with an IV
estimate for female tenants of 6.8 percentage points (467 percent) and an IV estimate for
Black tenants of 5.7 percentage points (307 percent).
The results above indicate that eviction causes a large increase in homelessness both in
the first year after a case (through increases in emergency shelter use) and beyond (through
elevated use of homelessness services). We view these results as complementary to work on
short-term emergency financial assistance and homelessness (Evans et al., 2016), which finds
that temporary assistance to at-risk tenants can lead to persistent reductions in homelessness.
These results also connect to research emphasizing the socioeconomic consequences of changes
to proceedings in eviction court (Greiner et al., 2012). While homelessness remains rare,
even for tenants in eviction court, our estimates nevertheless imply substantial additional

33
homelessness caused by evictions. In a given year, across our two locations, we estimate that
evictions produce more than 3,600 adults staying in emergency shelter in the year after filing
and 2,500 adults using homelessness services the following year.

6.3 Earnings and employment
We now shift attention to estimates of the causal effects of an eviction order on earnings and
employment. Table 6 reports estimates for quarters 1-4 and 5-8 after case filing. The first row
reports the combined estimates for earnings. The IV estimate shows that eviction decreases
average quarterly earnings in quarters 1-4 by $323 (7 percent of the non-evicted mean of
$4,300). This effect is similar in magnitude to the earnings drop among evicted tenants in
the year prior to filing. The effects of eviction on earnings are larger in the second year after
filing, reducing average quarterly earnings by $613 (14 percent of the non-evicted mean). The
point estimates are larger for female and Black tenants, although formal tests of equality fail
to reject a null hypothesis of equality (see Appendix Table H.1). The estimated effects are
also comparable across the two locations.44 Comparing the IV and OLS estimates, the OLS
estimates are systematically smaller, suggesting that impacts may be larger for compliers. As
discussed in Section 3, in New York, earnings are not observed when an individual moves
out of state, and in Cook County, earnings are not observed outside of the select 13 states
for which we have access to LEHD wage income records. Appendix I provides evidence that
differential migration is likely not driving our results.45
Turning to employment, the IV estimate shows that for marginal tenants, eviction causes
a 1.5 percentage point reduction in employment 1-4 quarters after filing, though this estimate
is not statistically significant. The OLS estimate is statistically significant and similar in
magnitude, suggesting that evicted tenants have employment rates that are 1.3 percentage
points lower than non-evicted tenants. The IV point estimates in quarters 5-8 are similar and
remain statistically insignificant. In contrast, the subgroup estimates suggest that eviction
decreases Black employment by 8.9 percentage points (15 percent of the non-evicted mean),
which is statistically significant but somewhat imprecisely estimated. Nevertheless, we can
reject a test of equality of effects for Black and non-Black tenants at conventional levels (see
Appendix Table H.1).
44
Since our analysis period coincides with the Great Recession, in Appendix H.1 we study Great Recession
years and non-Great Recession years separately and find that the estimates are similar across time periods,
although they are somewhat imprecise.
45
In Appendix I, we show that eviction has a negative and statistically significant impact on moving out of
state. We show that selection into moving out of state is unlikely to be driving our earnings estimates for
two reasons. First, the estimates are quantitatively small and therefore a selection pattern would have to be
implausibly large to drive the earnings estimates, which we show with a simple simulation exercise. Second,
the negative impact of eviction on earnings is larger in quarters 5-8 compared to quarters 1-4, while the the
out-of-state moves estimates have the opposite pattern—larger in quarters 1-4 and small and insignificant in
quarters 5-8—suggesting that if anything, selection is likely attenuating our earnings estimates in the short
run.

34
Appendix Table H.6 shows that the longer-run impacts of eviction on earnings and
employment (quarters 9-16 and 17-24 after filing) are for the most part smaller in magnitude,
though estimated with somewhat less precision. We can rule out effects larger than a $837
reduction in quarterly earnings in quarters 9-16 after filing with 95 percent confidence.
Our results show that eviction causes reductions in earnings in the first two years after the
case, consistent with the disruptive effects of eviction on housing stability described previously.
Perhaps the closest prior research on earnings is based on the Milwaukee Area Renters Study
and matched comparisons of renters who report experiencing a forced move in the past two
years to those who do not. Desmond and Gershenson (2016) report that a forced move
increases job loss by 11 to 22 percentage points, depending on the specification and estimation
method. Our analysis differs on a number of dimensions. First, our treatment is an eviction
order rather than the broader category of forced moves, which includes court-ordered evictions
but also informal evictions, landlord foreclosures, and housing condemnations. Second, we
study tenants in eviction court rather than tenants considered at risk of eviction. Relative to
Desmond and Gershenson (2016), we find more modest effects on the extensive margin of
employment. Nevertheless, we find economically meaningful effects on the intensive margin
of earnings, which to our knowledge has not been studied. These impacts on earnings and
employment are concentrated in the first two years after filing when housing disruptions are
also the most pronounced.

6.4 Financial health
We next examine the effects of eviction on financial health and present these results in Table
7. The first row reports estimates of the impact of eviction on our index of overall financial
health, and the remaining panels report impacts on each outcome that is used to construct
the financial health index.46
Eviction worsens tenants’ financial health, reducing the financial health index by 0.11
s.d. in quarters 1-4 after filing for IV, which is marginally significant, and 0.10 s.d. for OLS.
During this period, we find that eviction reduces the probability of having any auto loan
or lease, which may be viewed as a proxy for durable goods consumption (Dobkin et al.,
2018; Agarwal et al., 2020), by 6.1 percentage points (36 percent relative to the non-evicted
group mean), which is driven entirely by Cook County. The other point estimates during the
first year imply reductions in credit access and increasing debt, but none of the estimates
are individually significant. In quarters 5-8, the point estimate for effects on the financial
health index are even more negative but also less precise and not significant. Eviction reduces
credit scores by 16.5 points in this period.47 By reducing credit scores, eviction could lead to
46
We do not report results by race or gender as race is not included in the data provided by the credit
bureau for either location, and gender is not included in the credit bureau data for New York.
47
We explore effects on payday loan inquiries and borrowing for Cook County only in Appendix C.7. The
IV estimates for the impacts on payday loan inquiries and borrowing are imprecise and do not permit strong
takeaways.

35
increased borrowing costs for tenants and, to the extent landlords use credit scores to screen
tenants, hamper tenants’ ability to secure new housing.
The negative impacts of eviction on financial health are more pronounced in the longer
run. In Appendix Table H.7, we report estimates for effects in quarters 9-16 and 17-24 after
filing. Eviction reduces the composite index by 0.21 and 0.26 s.d. in years 3-4 and 5-6,
respectively, both of which are statistically significant at the 5% level. In quarters 9-16 after
filing, eviction lowers credit scores (IV estimate of -16.8) and increases balances in delinquent
accounts (IV estimate of $847).48 In quarters 17-24, eviction increases the probability of
having no open source of revolving credit (IV estimate of 9.3pp, p < 0.10) and decreases
the likelihood of having an auto loan or lease (IV estimate of 8.3pp, p < 0.10). For both
balances in delinquent accounts and credit scores, the IV estimate is larger than the OLS
estimate, suggesting that compliers are more likely to be on the margin of having access to
conventional credit sources.
Taken together, these results suggest that eviction causes further deterioration in tenants’
financial circumstances and reduces subsequent access to credit. We find reductions in the
financial health index that are comparable to the effect of having a Chapter 13 bankruptcy
filing dismissed (Dobbie et al., 2017). Our estimated impacts on credit scores are similar in
magnitude to the effect that moving to a low-poverty neighborhood has on children’s future
credit scores (Miller and Soo, 2020a), or the effect of removing a bankruptcy flag from a
credit report (Gross et al., 2020; Dobbie et al., 2020). In contrast to the impacts on housing,
homelessness, and labor market outcomes documented above, the impacts on financial health
are larger in the longer run.

6.5 Hospital visits
We next investigate the effects of eviction on hospital use in New York, where we have access
to hospital data. Table 8 reports estimates for three measures of hospital use: the total
number of non-pregnancy-related hospital visits, the total number of emergency room visits,
and the total number of hospital visits for mental health conditions. The table includes
results for the first and second year following a case.
In the first year after the case, eviction increases total hospital visits by 0.19 visits in
the first year following the case (29 percent relative to the non-evicted mean). Estimates for
the total number of emergency room visits are similar in magnitude, although they are not
statistically significant. Eviction also increases the number of visits to a hospital for mental
health conditions by about 0.05 visits in the first year, a more than 100 percent increase over
the non-evicted mean.49 In the second year after the case, the IV estimates are insignificant
48
An eviction may affect debt directly if the defendant does not pay the money judgment associated with
the eviction case, but in practice this rarely occurs. In this situation, the plaintiff would use the court process
to collect the judgment amount, including obtaining a citation to discover assets and a wage garnishment
order, and then send any unpaid debt to a collections agency.
49
The most common category of mental health conditions among the evicted is anxiety-related diagnoses.

36
and less precise. We explore longer-run effects on hospital use in Appendix Table H.8, where
results remain statistically insignificant and imprecise. Compared to the IV estimates, OLS
estimates tend to be somewhat smaller in the first year and somewhat larger in later years.
Overall, the effects of eviction on hospital use appear concentrated in the period shortly
after the case filing. The finding that eviction causes increases in hospital visits is consistent
with evidence from Currie and Tekin (2015), who find that foreclosures increase trips to the
hospital. These impacts may reflect a deterioration in tenants’ health, but they may also
reflect the use of hospitals as an alternative temporary source of shelter.50

6.6 Comparisons across locations
In this section, we compare estimates for Cook County and New York, document where
estimates are similar, and explore potential sources of differences when they diverge. Figure
6 plots estimates for Cook County on the vertical axis and estimates for New York on the
horizontal axis. We standardize all estimates by multiplying the regression coefficient by the
standard deviation of the eviction indicator and dividing by the standard deviation of the
outcome. Across outcomes, the estimates are very similar across locations, with many of
the OLS and IV estimates falling close to the 45 degree line, and very similar estimates for
employment and earnings across locations. The impacts on financial health outcomes tend to
be somewhat larger in Cook County, with statistically significant differences in having an
auto loan or lease one year after the case. This may partially be driven by higher rates of car
ownership in Cook County.51 The impacts on residential mobility are somewhat larger for
New York, which is consistent with New York’s lower vacancy rate, and may be driven by
fewer non-evicted tenants choosing to leave in the year or two after the case in New York.
Impacts on homelessness outcomes are also somewhat larger for New York, which is again
consistent with a tighter housing market and may also stem from New York’s more extensive
homeless shelter network and right to shelter law.

6.7 What about non-complier cases?
The IV estimates above can be interpreted as the causal effects of eviction for compliers. One
might additionally be interested in whether these estimates are representative of effects for
the full population of evicted tenants. One possible approach to drawing inference about
these effects is difference-in-differences (DiD). However, as discussed in Section 4, there
are differential pre-trends between evicted and non-evicted tenants for several outcomes in
our settings, raising concern about the parallel trends assumption.52 Heckman and Robb
(1985) and Ashenfelter and Card (1985) show that if shocks to outcomes follow a transitory
50
See Elejalde-Ruiz (2018) for anecdotal evidence of this. Moore and Rosenheck (2016) also discuss the
need of shelter as a potential reason for emergency department visits.
51
Appendix Table H.2 tests for equality of the IV estimates between the two locations.
52
For example, see the figures in Section 4.3 and Appendix Figure E.2.

37
Figure 6: Comparing Estimates Across Locations

Notes: This figure plots standardized estimates for Cook County (y-axis) against standardized estimates for New
York (x-axis). All coefficients have been standardized by multiplying by the ratio of the standard deviation of the
fraction evicted to the standard deviation of the outcome. Results are for one and two years after the case filing.
Cook County results were approved for release by the U.S. Census Bureau, authorization number CBDRB-FY22-072.
Circles report IV estimates, while triangles report OLS estimates.

and covariance stationary process, the DiD estimator will be unbiased when the pre- and
post-period are chosen symmetrically around the treatment period, even when the parallel
trends assumption does not hold.53 In Appendix J, we further develop the symmetric DiD
approach and compare symmetric DiD estimates to IV estimates which, under the appropriate
assumptions, allow us to compare the ATT to the IV estimates.
Appendix Tables J.1-J.4 compare the IV estimates to symmetric DiD estimates. The
symmetric DiD estimates for housing outcomes are quite similar to the IV estimates, while the
effects for residential mobility are somewhat smaller. For labor market and financial health
outcomes, DiD estimates have the same sign but also tend to be smaller in magnitude. For
health-related outcomes, the DiD and IV estimates both point to sizeable increases in hospital
use in the year after filing, but DiD estimates remain positive and statistically significant in
the second year. Overall, the DiD estimates consistently show results that are broadly similar
to the IV estimates but smaller in magnitude, suggesting that the effects for the average
evicted tenant may be smaller than those for the marginal tenant.

53
See also, Chabé-Ferret (2015), which further evaluates the bias from DiD and matching estimators for
evaluating job training programs. The paper considers several combinations of assumptions on the earnings
and selection process and argues that symmetric DiD typically outperforms matching.

38
Table 4: Impact on Housing Situation

1 Year After Filing 2 Years After Filing
E[Y |E = 0] OLS IV E[Y |E = 0] OLS IV
(1) (2) (3) (4) (5) (6)

Enforcement: 0.002 0.301*** 0.435*** 0.002 0.313*** 0.422***
(0.031) (0.005) (0.039) (0.032) (0.005) (0.037)
[329,279] [307,837]
By Location
Cook County 0.004 0.270*** 0.451*** 0.004 0.275*** 0.442***
(0.059) (0.004) (0.050) (0.062) (0.004) (0.049)
New York 0.000 0.333*** 0.419*** 0.000 0.351*** 0.401***
(0.017) (0.009) (0.060) (0.018) (0.010) (0.057)
By Group
Female 0.002 0.290*** 0.425*** 0.002 0.302*** 0.418***
(0.030) (0.005) (0.046) (0.032) (0.005) (0.045)
Black 0.002 0.307*** 0.464*** 0.002 0.319*** 0.436***
(0.032) (0.005) (0.046) (0.035) (0.006) (0.044)

Not at eviction address: 0.293 0.073*** 0.082** 0.478 0.129*** 0.111**
(0.318) (0.003) (0.036) (0.348) (0.003) (0.053)
[218,228] [183,227]
By Location
Cook County 0.363 0.031*** 0.093 0.568 0.070*** 0.074
(0.481) (0.003) (0.057) (0.495) (0.004) (0.064)
New York 0.222 0.116*** 0.071 0.389 0.188*** 0.149*
(0.415) (0.006) (0.045) (0.487) (0.004) (0.084)
By Group
Female 0.280 0.081*** 0.093** 0.461 0.139*** 0.136**
(0.312) (0.004) (0.046) (0.343) (0.003) (0.060)
Black 0.272 0.079*** 0.066 0.454 0.138*** 0.098
(0.310) (0.004) (0.056) (0.345) (0.004) (0.080)

Neighborhood poverty rate: 0.247 -0.000 -0.002 0.246 -0.001 -0.008
(0.088) (0.000) (0.010) (0.090) (0.001) (0.014)
[173,909] [127,891]
By Location
Cook County 0.195 0.001 -0.014 0.196 0.002** 0.011
(0.130) (0.001) (0.018) (0.133) (0.001) (0.021)
New York 0.298 -0.001*** 0.009 0.295 -0.004*** -0.027
(0.120) (0.000) (0.007) (0.123) (0.001) (0.020)
By Group
Female 0.258 -0.000 -0.004 0.256 -0.001 -0.009
(0.090) (0.001) (0.012) (0.091) (0.001) (0.020)
Black 0.267 -0.001 -0.005 0.266 -0.003*** -0.023
(0.088) (0.001) (0.013) (0.090) (0.001) (0.022)

Notes: ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. This table reports equally-weighted averages of Cook County and New
York non-evicted sample means (E[Y |E = 0]), as well as equally-weighted averages of location-specific OLS (OLS)
and two-stage least squares (IV) estimates of the impact of eviction on outcomes related to the tenant’s housing
situation. Outcomes are listed on the left of each row. Results are shown for one year (columns (1)-(3)) and for two
years (columns (4)-(6)) after eviction case is filed. Each panel shows results for a given outcome. Below the combined
estimates in each panel, we report estimates separately for each location and for the female and Black subsamples.
“Enforcement” is defined as an enforcement officer executing the eviction order associated with the case, and is defined
cumulatively. “Not at eviction address” is an indicator for being observed living at a different address than the filing
address. “Neighborhood poverty rate” is the tract-level poverty rate. The main set of controls included in all model
specifications are: ad damnum amount, gender, race, census tract poverty rates, census tract rent, a cubic in age at
filing date, dummies for missing controls, and court-by-year fixed effects. In Cook County regressions only, we also
include an indicator for case type. For “Not at eviction address” and “Neighborhood poverty rate” we also control for
whether tenants were not at eviction address 1 year and 2 years prior and tenants’ neighborhood poverty rate 1 year
and 2 years prior to eviction case filing, respectively. Standard errors for regression model coefficients are included
in parentheses and are clustered at the judge(courtroom)-year level. Observation counts for the full sample are listed
under the standard errors, in brackets. Observation counts for all outcomes and subgroups can be found in Appendix
Table H.15. The reduced form results for all outcomes can be found in Appendix Table H.10. Cook County results
were approved for release by the U.S. Census Bureau, authorization number CBDRB-FY22-072.
39
Table 5: Impact on Use of Homelessness Shelter and Services

1 Year After Filing 2 Years After Filing
E[Y |E = 0] OLS IV E[Y |E = 0] OLS IV
(1) (2) (3) (4) (5) (6)

Emergency shelter: 0.009 0.031*** 0.034** 0.008 0.014*** -0.001
(0.068) (0.001) (0.017) (0.062) (0.001) (0.013)
[210,840] [198,898]
By Location
Cook County 0.007 0.010*** 0.023 0.006 0.006*** -0.019
(0.086) (0.002) (0.028) (0.077) (0.001) (0.019)
New York 0.011 0.052*** 0.046** 0.009 0.022*** 0.016
(0.105) (0.002) (0.019) (0.097) (0.001) (0.017)
By Group
Female 0.009 0.032*** 0.024 0.008 0.016*** 0.024
(0.066) (0.002) (0.018) (0.061) (0.001) (0.015)
Black 0.010 0.034*** 0.036 0.009 0.015*** 0.007
(0.072) (0.002) (0.024) (0.068) (0.001) (0.019)

Any homelessness services: 0.015 0.036*** 0.029 0.012 0.019*** 0.036**
(0.086) (0.002) (0.023) (0.076) (0.001) (0.015)
[210,840] [198,898]
By Location
Cook County 0.017 0.016*** 0.012 0.012 0.013*** 0.048**
(0.128) (0.002) (0.042) (0.110) (0.002) (0.023)
New York 0.013 0.056*** 0.046** 0.011 0.025*** 0.024
(0.114) (0.002) (0.018) (0.104) (0.001) (0.019)
By Group
Female 0.015 0.038*** 0.030 0.012 0.020*** 0.068***
(0.086) (0.002) (0.023) (0.077) (0.001) (0.020)
Black 0.017 0.040*** 0.049* 0.014 0.020*** 0.057***
(0.092) (0.002) (0.029) (0.083) (0.001) (0.022)

Notes: ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. This table reports equally-weighted averages of Cook County and New
York non-evicted sample means (E[Y |E = 0]), as well as equally-weighted averages of location-specific OLS (OLS)
and two-stage least squares (IV) estimates of the impact of eviction on outcomes related to the tenant’s homelessness
situation. Outcomes are listed on the left of each row. Results are shown for one year (columns (1)-(3)) and two
years (columns (4)-(6)) after eviction case is filed. Each panel shows results for a given outcome. Below the combined
estimates in each panel, we report estimates separately for each location and for the female and Black subsamples.
“Emergency shelter” is an indicator for if the individual was observed staying at an emergency homeless shelter. “Any
homelessness services” is an indicator for having any interaction with homelessness services. The main controls for all
model specifications are the same as those described in Table 4. In each regression, we also control for whether tenants
stayed at an emergency shelter and whether tenants had any interaction with emergency homelessness services 1 year
and 2 years prior to eviction case filing. Standard errors for regression model coefficients are included in parentheses
and are clustered at the judge(courtroom)-year level. Observation counts for the main combined specifications are
reported in brackets below the standard errors in columns (3) and (6). Observation counts for all regressions shown
above can be found in Appendix Table H.15. The reduced form results for regressions shown above can be found
in Appendix Table H.11. Cook County results were approved for release by the U.S. Census Bureau, authorization
number CBDRB-FY22-072.

40
Table 6: Impact on Earnings and Employment

1-4 Quarters After Filing 5-8 Quarters After Filing
E[Y |E = 0] OLS IV E[Y |E = 0] OLS IV
(1) (2) (3) (4) (5) (6)

Earnings: 4,300 -229*** -323* 4,254 -269*** -613**
(3,809) (9) (175) (3,885) (13) (248)
[374,400] [336,396]
By Location
Cook County 4,821 -286*** -445* 4,821 -320*** -627*
(5,810) (12) (249) (5,956) (17) (337)
New York 3,779 -172*** -201 3,687 -218*** -599*
(4,926) (14) (245) (4,991) (19) (363)
By Group
Female 4,136 -195*** -504*** 4,094 -238*** -767***
(3,545) (10) (185) (3,610) (14) (295)
Black 4,319 -199*** -377 4,252 -247*** -931***
(3,664) (12) (234) (3,718) (16) (307)

Employment: 0.565 -0.013*** -0.015 0.549 -0.019*** -0.018
(0.317) (0.001) (0.021) (0.322) (0.001) (0.027)
[376,400] [340,396]
By Location
Cook County 0.623 -0.012*** 0.003 0.613 -0.014*** -0.010
(0.432) (0.001) (0.027) (0.438) (0.002) (0.030)
New York 0.507 -0.014*** -0.032 0.485 -0.024*** -0.027
(0.465) (0.002) (0.032) (0.471) (0.002) (0.046)
By Group
Female 0.585 -0.013*** -0.036 0.568 -0.019*** -0.003
(0.315) (0.001) (0.025) (0.320) (0.002) (0.034)
Black 0.583 -0.011*** -0.059* 0.566 -0.018*** -0.089**
(0.316) (0.001) (0.031) (0.321) (0.002) (0.040)

Notes: ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. This table reports equally-weighted averages of Cook County and New
York non-evicted sample means (E[Y |E = 0]), as well as equally-weighted averages of location-specific OLS (OLS),
and two-stage least squares (IV) estimates of the impact of eviction on labor outcomes. Outcomes are listed on the
left of each row. Results are shown for 1-4 quarters (columns (1)-(3)) and 5-8 quarters (columns (4)-(6)) after the
eviction case is filed. Each panel shows results for a given outcome. Below the combined estimates in each panel
we report estimates separately for each location and for the female and Black subsamples. “Earnings” are average
quarterly wage income from our labor market data described in Section 3. “Employment” is the share of quarters
with positive wage income from our labor market data described in Section 3. Controls for all model specifications
are the same as those described in Table 4. In each regression, we also control for tenants’ earnings and employment
in each of the four quarters before filing, as well as averaged values over the eight quarters (2 years) prior to the
case filing. Standard errors for regression model coefficients are included in parentheses and are clustered at the
judge(courtroom)-year level. Observation counts for the main combined specifications are reported in brackets below
the standard errors in columns (3) and (6). Observation counts for all regressions shown above can be found in
Appendix Table H.15. The reduced form results for regressions shown above can be found in Appendix Table H.12.
Cook County results were approved for release by the U.S. Census Bureau, authorization number CBDRB-FY22-072.

41
Table 7: Impact on Financial Health

1-4 Quarters After Filing 5-8 Quarters After Filing
E[Y |E = 0] OLS IV E[Y |E = 0] OLS IV
(1) (2) (3) (4) (5) (6)

Financial health index: -0.054 -0.107*** -0.107* 0.008 -0.103*** -0.141
(0.737) (0.005) (0.060) (0.747) (0.005) (0.094)
[269,814] [271,230]
By Location
Cook County -0.075 -0.127*** -0.202** -0.027 -0.130*** -0.230
(0.990) (0.008) (0.102) (1.002) (0.009) (0.174)
New York -0.032 -0.087*** -0.012 0.043 -0.077*** -0.053
(1.091) (0.005) (0.063) (1.108) (0.006) (0.072)

Credit score: 547.59 -8.40*** -7.86 551.84 -7.99*** -16.53**
(67.11) (0.38) (5.18) (68.61) (0.41) (6.67)
By Location
Cook County 531.94 -9.19*** -8.69 536.62 -9.40*** -24.16**
(74.04) (0.55) (8.29) (74.56) (0.59) (11.15)
New York 563.24 -7.60*** -7.03 567.06 -6.58*** -8.90
(111.94) (0.53) (6.21) (115.19) (0.56) (7.33)

No open revolving account: 0.481 0.032*** -0.039 0.468 0.037*** 0.052
(0.334) (0.002) (0.025) (0.331) (0.003) (0.051)
By Location
Cook County 0.587 0.032*** -0.072* 0.589 0.034*** 0.080
(0.491) (0.003) (0.043) (0.491) (0.005) (0.099)
New York 0.375 0.032*** -0.006 0.347 0.041*** 0.024
(0.452) (0.002) (0.025) (0.445) (0.002) (0.027)

Total balance: collections and delinquencies: 2,550 153*** 310 2,378 44* 548
(4,099) (24) (393) (3,936) (25) (502)
By Location
Cook County 2,759 54 735 2,516 -34 739
(5,504) (36) (659) (5,291) (38) (930)
New York 2,342 253*** -115 2,240 122*** 357
(6,075) (32) (428) (5,829) (31) (377)

Any auto loan or lease: 0.170 -0.021*** -0.061** 0.176 -0.025*** 0.031
(0.264) (0.001) (0.030) (0.269) (0.002) (0.036)
By Location
Cook County 0.197 -0.040*** -0.130** 0.198 -0.047*** 0.037
(0.396) (0.002) (0.054) (0.397) (0.003) (0.066)
New York 0.142 -0.002 0.008 0.155 -0.004** 0.025
(0.349) (0.002) (0.027) (0.362) (0.002) (0.026)

Notes: ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. This table reports equally-weighted averages of Cook County and New
York non-evicted sample means (E[Y |E = 0]), as well as equally-weighted averages of lagged dependent variable OLS
(OLS) and two-stage least squares (IV) estimates of the impact of eviction on outcomes related to the tenant’s financial
health. Outcomes are listed on the left of each row. Results are shown for 1-4 quarters (columns (1)-(3)) and for 5-8
quarters (columns (4)-(6)) after the eviction case is filed. Each panel shows results for a given outcome. Below the
combined estimates in each panel, we report estimates separately for each location. “Financial Health Index” is the
average of any observed values of the index during the listed quarters. The index is an equally weighted index of the
attributes listed below, and described in Section 3. “Credit Score” is the average of observed Vantage Scores during
the listed quarters. “No open revolving account” is the average of indicators for having no open revolving account over
the listed quarters. “Total balance: collections and delinquencies” is the average of the balance in delinquent accounts
or in collections over the listed quarters. “Any auto loan” is an indicator for whether the individual is observed with
an auto loan or lease in any of the listed quarters. Controls for all model specifications are those described in Table 4,
except we do not control for race, which is not included in the data provided by the credit bureau. We additionally
control for lagged values of the relevant outcome for up to two years prior to filing. Standard errors for regression
model coefficients are included in parentheses and are clustered at the judge(courtroom)-year level. Observation
counts for the main combined specifications are reported in brackets below the standard errors in columns (3) and
(6), in the top panel. Observation counts for all regressions shown above can be found in Appendix Table H.15. The
reduced form results for regressions shown above can be found in Appendix Table H.13.

42
Table 8: Impact on Hospital Use

1-4 Quarters After Filing 5-8 Quarters After Filing
E[Y |E = 0] OLS IV E[Y |E = 0] OLS IV
(1) (2) (3) (4) (5) (6)

Number of hospital visits 0.739 0.055*** 0.188** 0.632 0.039*** -0.113
(1.321) (0.006) (0.094) (1.208) (0.006) (0.142)

Number of emergency visits 0.588 0.045*** 0.106 0.511 0.028*** -0.065
(1.091) (0.005) (0.089) (1.010) (0.005) (0.124)

Number of mental health visits 0.047 0.016*** 0.054* 0.045 0.012*** -0.035
(0.295) (0.001) (0.030) (0.346) (0.002) (0.055)
[179,024] [154,531]

Notes: ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. This table reports the impacts of eviction on hospital use in New York.
The table includes the non-evicted sample means (E[Y |E = 0]), OLS (OLS) estimates and two-stage least squares
(IV) estimates of the impact of eviction on hospital use. Outcomes are listed on the left of each row. Results are
shown for 1-4 quarters (columns (1)-(3)) and 5-8 quarters (columns (4)-(6)) after the eviction case is filed. “Number of
hospital visits” is the total number of non-pregnancy-related hospital visits. “Number of emergency visits” is the total
number of non-pregnancy-related emergency room visits, and “Number of mental health visits” is the total number
of non-pregnancy-related hospital visits for mental health conditions, where mental health conditions are defined in
C.4. Controls for all model specifications are the same as those described in Table 4 and lagged values of the number
of hospital visits, and visits by diagnosis type . Standard errors are included in parentheses and are clustered at the
courtroom-year level. Observation counts for all outcomes are listed at the bottom of the table, in brackets. The
reduced form results for regressions shown above can be found in Appendix Table H.14.

43
7 Conclusion
Evictions are a widespread phenomenon in the U.S. housing market, affecting more than 2
million households each year who overwhelmingly reside in poor or minority neighborhoods.
Growing concern over evictions has spurred governments to pursue policies to reduce their
incidence, such as legal aid to tenants facing eviction, emergency rental assistance, and just
cause eviction laws, citing substantial costs to tenants and local governments in the fallout
from eviction. Despite the large number of evictions and the growing policy interest, the
consequences of eviction are not well understood. We explore how eviction impacts tenants
in housing court using newly linked administrative data from two large urban areas and a
quasi-experimental research design that enables us to isolate the causal effect of eviction.
We document signs of increasing economic distress in the lead-up to case filing across
a broad range of measures: falling earnings, decreased attachment to the labor market,
rising unpaid bills, and increases in hospital visits. This suggests many eviction cases are
precipitated by adverse events. As we show, these patterns are likely to bias both comparisons
of evicted tenants to renters outside of court and comparisons of evicted to not-evicted tenants
within court, underscoring the value of our IV design that uses the random assignment of
judges to estimate the impact of an eviction order for complier cases.
Using our IV design, we find that eviction exacerbates the economic distress experienced
by tenants in the lead-up to a court filing. In the two years following a case, eviction increases
homelessness, residential mobility, and hospital visits. During this period of disruption,
eviction also reduces earnings, with particularly large effects for female and Black tenants. In
the longer run, eviction worsens financial health through reduced credit scores and increased
indebtedness.
This research speaks to an active policy debate on how, if at all, governments should
address evictions. While aspects of the ongoing debates around the desirability of various
eviction-related policies remain unsettled, we make significant progress on the key question
of whether and how eviction affects tenants’ outcomes. Our results suggest that averting
an eviction order may yield considerable benefits for tenants, particularly female and Black
tenants, two groups that are over-represented in housing court. Beyond the reductions in
earnings and worsened credit, the increases in hospital visits and use of homelessness services
suggest that eviction impacts physical, mental, and material hardship. The high cost to local
governments of providing healthcare and homelessness services (Evans et al., 2019) imply
that there are also considerable spillover costs for society from eviction. These costs are
important inputs to evaluating eviction-related policies. While our results suggest policies
to prevent evictions could be welfare-enhancing, many of these policies may have important
general equilibrium effects that are not well understood. We leave it to future work to fully
assess the costs and benefits of individual policies to reduce evictions.

44
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