183
March 2024

The local effect of sanctuary policies on crime:
Evidence from New YorkYuki Otsu(Center for Spatial Information Science, The University of Tokyo)

The local effect of sanctuary policies on crime:
Evidence from New YorkYuki Otsu*†
Center for Spatial Information Science, The University of Tokyo
March 27, 2024

Abstract

Studies on sanctuary policies and their effects have been a important topic in
the political debate about immigration. This study examines the impact of thesepolicies on smaller geographic units, particularly on New York City. Using policeprecinct-level data from 2000 to 2010 and a difference-in-differences approach, thestudy compares the crime rates in precincts with varying concentrations of immigrants before and after the policy’s introduction. The results show that sanctuary
policies have uneven effects within the city, mainly contributing to lower robbery
rates in precincts with high immigrant concentrations. Additionally, an increase
in reported sex crimes is observed, likely reflecting changes in reporting behavior.
This suggests that sanctuary policies support improved relations between police
and immigrants at a more granular level than previously understood.

JEL Classification Code: J15, K37, R59
Keywords: sanctuary cities, undocumented immigrants, spatial heterogeneity* Center for Spatial Information Science, The University of Tokyo.

5-1-5 Kashiwanoha, Kashiwa,
Chiba 277-8568 Japan. Email: y.otsu@csis.u-tokyo.ac.jp† I appreciate P. Wesley Routon and Jonathan Kurzfeld for their comments and suggestions. I also
appreciate participants of WEAI 2022. This research is supported by JSPS KAKENHI Grant Number
21K20146.

1

1

Introduction

In countries hosting immigrants, immigrant-related crime represents a significant political concern. Debates about immigration commonly focus on its impact on nationaland community safety, often with a concern that immigration policies might worsen
crime rates. Contrary to these concerns, most studies in this field indicate that immigration has either a neutral or negative effect on crime rates. (Ousey and Kubrin,2018).
Sanctuary policies, which provide a sanctuary to undocumented immigrants, arean example of proimmigration policy. It is not ex-ante clear whether these policies
affect crime rates. Several studies are examining sanctuary policies and crime (Lyons
et al., 2013; Wong, 2017; Martı́nez-Schuldt and Martı́nez, 2017; O’Brien et al., 2019;
Hausman, 2020; Otsu, 2021; Manning and Burkhardt, 2022). These studies found either
no effect or a negative effect on certain types of crime, such as homicide and robbery,with none reporting positive impact on crime rates. Moreover, these papers suggestthat the reduction in crime is due to the improved relationship between the police andimmigrants.
However, this literature has focused on relatively large geographic units. Specifically, analyses of cities and counties are popular in the literature. Given that sanctuary
policies target undocumented immigrants, it is reasonable to assume that their effects
are localized and heterogeneous across neighborhoods within a city. Studies must also
focus on smaller geographic units to examine the causes of heterogeneity.
This study examines whether the effects of sanctuary policies vary within cities. As
sanctuary policies affect immigrants, their effects are likely to be more pronounced inareas with higher concentrations of immigrants. Thus, the main research question is
to identify which parts of the city are most affected by the policy.
Accordingly, the sanctuary policy adopted in New York City (NYC) in 2003, which
prohibits city officials from questioning or sharing the information about the immigration status. This study focuses on the police-precinct-level data and uses a differencein-differences approach to compare crime rates in precincts with high concentrations2

of immigrants and those with high concentrations of nonimmigrants before and after
the introduction of the policy.
The results show that New York’s sanctuary policy helps reduce the rate of robberies in precincts with high immigrant concentration. Moreover, the sanctuary policy
increases the number of reported sex crimes, possibly due to changes in reporting behavior. These results confirm that immigrant-police relations have improved, withfindings similar to those of the city- and county-level analyses at the intra-city level.
This study contributes to the literature on immigration policies, with some advantages over other studies. First, this study examines the impact of sanctuary policies
within a city. Although studies on sanctuary policies have emphasized the importance of the community–police relationship, most have evaluated the effect on a city
or a county as a whole. Therefore, variation within a city is unclear. This study findsthat the effect is found to be heterogeneous in a city, depending on the fraction of immigrants. Second, this study examines the effects of sanctuary policies that restrictinquiry and sharing of information. Although sanctuary policies are common, eachcity adopts a different type of policy. For example, Kittrie (2006) categorized sanctuary policies into don’t ask, don’t tell, and don’t enforce. However, most studies haveignored the variation in the policies and none has focused on a particular policy at the
local jurisdiction level. This study focuses on New York and, according to classification criteria by Kittrie (2006), the sanctuary policy in New York is a combination of
the “don’t ask” and “don’t tell” types. This study identifies the impact of a particularsanctuary policy adopted in New York.

2

Background2.1

Sanctuary policies do not increase crime rates

There are varied definitions of sanctuary policies. For example, Executive Order 13768
defines it as locales that refuse to comply with federal statute 8 USC 1373 enhancing
information related to individuals’ immigration statuses with ICE or CBP. The Depart3

ment of Justice defines it as “jurisdiction that may have state laws, local ordinances,or departmental policies limiting the role of local law enforcement agencies and officers in the enforcement of immigration laws.”(Office of the Inspector General Audit
Division, 2007)
While definitions and specific policies vary across jurisdictions, sanctuary policies
are adopted to promote cooperation among residents, which is essential for the efficient operation of law enforcement agencies and the reduction of crime. For example,
witnesses have more information about suspects than police immediately after an incident. Without their cooperation, the agencies would have to spend more resources
to identify and arrest suspects, leading to lower arrest rates.1
Sanctuary policies make undocumented immigrants feel safer in the jurisdiction,
which results in better cooperation with city officials, including police officers. With
their cooperation, police operations become more efficient in apprehending criminals.
Some criminals give up on crime when they expect a higher arrest rate, thus leading to
reduced crime rate. Moreover, sanctuary policies increase the chance of employment
for undocumented immigrants. Generous immigration policies, which improve accessto the labor market and facilitate the application of welfare programs (Alsan and Yang,
2022; East et al., 2018), increase the opportunity cost of crime rises, thereby reducing
the crime rate.
However, sanctuary policies are also believed to increase crime rates, mainly because these policies alter the opportunity cost of crime for undocumented immigrants.
As generous immigration policies reduce the risk of deportation, undocumented immigrants may have more incentives to commit more crimes.
Moreover, sanctuary policies affect crime rates through sorting across areas. Specifically, sanctuary jurisdictions might attract undocumented immigrants and change thepopulation’s composition. When crime rates differ across population groups, crime
rates change due to such shifts. For example, the crime rate among undocumented
immigrants in Texas is lower than that of natives or legal immigrants (Light et al.,1 Cooperation may worsen the observed crime rate as sanctuary policies encourage immigrants toreport incidents when they become victims (Amuedo-Dorantes and Deza, 2022).

4

2020). Therefore, attracting undocumented immigrants could reduce the crime rates
in sanctuary jurisdictions.
While the impact of sanctuary policies on crime rates is theoretically unclear, existing empirical studies have found that sanctuary policies do not lead to increase incrime rates, while some actually lead to a decrease (Lyons et al., 2013; Wong, 2017;
Martı́nez-Schuldt and Martı́nez, 2017; O’Brien et al., 2019; Hausman, 2020; Otsu, 2021;
Manning and Burkhardt, 2022). These studies attribute the negative effects to improved police–immigrant relationship. As Otsu (2021) found no evidence of changes
in the composition of sanctuary cities following the implementation of the policy, sorting is unlikely to occur at the city level.

2.2

The sanctuary policy in New YorkEd Koch, the Mayor of NYC, adopted a sanctuary policy in 1989. He issued an Executive Order 124,2 that restricts city officials and employees from sharing information
about noncitizens with federal authorities. This policy aims to provide public services
such as education and police protection to all city residents, regardless of citizenshipand immigration status. By restricting transmission of information, the mayor aimed
to ensure that all residents, including undocumented immigrants, could use the public services without fearing immigration authorities. Specifically, Executive Order 124
mandates that no information about noncitizens will be disclosed by city officers unless (1) it is required by law, (2) authorized in writing by the noncitizen, or (3) the
noncitizen is suspected of engaging in criminal activity.
However, in 1999, the U.S. Court of Appeals for the second circuit declared the executive order unconstitutional. Subsequently, a new executive order (Executive Order34) was issued on May 13, 2003. The mayor amended it and issued Executive Order
413 in September 2003. The new Executive Order prohibits city officers from inquiring
about immigration status unless for a specific purpose. The order also prohibits the2 https://www.nyc.gov/html/records/pdf/executive_orders/1989EO124.PDF
3 https://www1.nyc.gov/assets/immigrants/downloads/pdf/eo-41.pdf

5

officers from sharing information about immigration status. The policy is considereda sanctuary policy (National Immigration Law Center, 2008): specifically, it is a combination of the “don’t ask” and “don’t tell” types (Kittrie, 2006; Otsu, 2021). The policy
has a few exceptions, such as the terrorist activity investigation. However, for mostundocumented immigrants, it lowers the potential risk of deportation.

3

Data

This study examines the effect within a city and confirms the variation of the effect.
Therefore, this study focuses on New York because of its large variations in areas
within the city and the availability of detailed crime data before and after the policy.
New York became a sanctuary city in 2003; however, the local crime data are available only from 2000. Therefore, this study uses annual crime data in New York from2000 to 2010 at the police-precinct level. This study also connects the aggregated demographic information from the 2000 census (Manson et al., 2023). The immigration
status is based on the foreign-born status, and this study defines immigrants as thoseborn in a foreign country. For the robustness checks, the main regression is performedby the citizenship status in Section 6. The sample includes all police precincts in NYC
except the Central Park Police Precinct. Because the Central Park Police Precinct covers only the Central Park area, the demographic information cannot be well defined.
The study included 75 police precincts.
Table 1 shows the number of major crimes from 2000 to 2010. On average, 1,825
crimes occur in each precinct every year. Larceny accounts for one-third of all crimecounts. Burglary and robbery follow, but rape and murder are rare among the offense
categories. To determine geographic variation, Figure 1 plots major offenses per capita
in 2000 on a map of New York. The crime rate is the highest in the Midtown South
precinct and the lowest in the 123rd precinct on Staten Island. To illustrate geographic
variation, Figure 1 plots the total number of major crimes per capita in 2000 on a mapof New York. The crime rate is highest in the Midtown South precinct and lowest in

6the 123rd Precinct on Staten Island.
Table 2 summarizes the demographic summary. The population varies from 16,179
to 242,951, with a mean of 106,778. The fraction of immigrants in each precinct also
varies by precinct, and it spans from 11% to about 70%. On average, immigrants account for about 33% of the population in each police precinct. Figure 2 shows thefraction of immigrants on the map of NYC. The figure shows that police precincts in
Queens have a higher percentage of immigrants and that in Staten Island have a lower
percentage.

4

Approach

A difference-in-difference (DID) approach is used to confirm the heterogeneity of theeffect of the sanctuary policy. For each police precinct, an indicator, HighForeignBorni ,is defined. This is set to one if the fraction of the foreign-born population in Precinct i
at 2000 exceeds the mean of the entire NYC. The log of crime rates in precinct i at year
t is regressed using the following equation:

log yit = αs Sanctuaryt + HighForeignBorni × Sanctuaryt + ηi + ηt + θi × t + eit . (1)Sanctuaryt is a binary indicator of the sanctuary status of precinct i in year t. As
the sanctuary policy applies to all police precincts, Sanctuaryit = Sanctuaryt for all i.
The terms ηi and ηt are precinct- and year-fixed effects. The term θi captures a linear
time trend for precinct i. As the sanctuary status depends on the year alone, one of
the year-fixed effects is omitted for the regression. Note that αs captures the meandifference of the outcome variable before and after the policy adoption. The meandifference includes both the effects of the sanctuary policy on the entire city and partof time trends.
Additionally, to find the accurate difference among precincts, the quintiles of thefraction of foreigners in each precinct (Qk ) are constructed. Similar to (1), the following

7

model is estimated:
5

log yit = αs Sanctuaryt + ∑ αk × Sanctuaryt × Qk + ηi + ηt + θi × t + eit .

(2)

k =2

As Sanctuaryt interacts with the quintile dummy Qk , the coefficient αk captures thedifferential effect of the sanctuary policy on immigrant-concentrated areas relative tothe area with the least foreign-born populations (Q1 ).4

5

Results

5.1

Major offenses

Tables 3 and 4 present the results of the regression analysis for major offenses. The estimation results in Table 3 show the sanctuary policy decreases robbery. The precincts
with a higher fraction of immigrants experience about a 7% lower robbery rate. However, no significant change is found for other crimes. Although most of the point
estimators are negative, the regressions on larceny and rape have positive coefficients.
Moreover, Table 4 divides the effects by quintile classes and summarizes the effects relative to quintile 1. Some coefficients of the interaction terms show a negative
effect. Additionally, robbery, assault, and total crime rates are lower in immigrantconcentrated areas. For example, compared with the first quintile, the fifth quintile
has 14.8, 12.3, 7.6% lower rates of assault, robbery, and total major offenses, respectively.
On the contrary, the rape rate is higher for precincts with a higher fraction of immigrants, and, for example, the fifth quintile has a 34.7% higher rate of rape.
As all crimes are reported crime counts, the higher rate of rape might reflect thechange in reporting behavior. (Amuedo-Dorantes and Arenas-Arroyo, 2021; Jácome,2022)
4 Figure 3 shows the quintile classifications on the map, and Figure 4 shows the crime trends by

quintiles. All types of crime have a downward trend over the period.

8

5.2

Nonmajor offenses

Tables 5 is the same estimation for nonmajor offenses. The coefficients for the interaction terms in Table 5 have almost insignificant effects. Although in the literature theassociation between immigrants and drug crime is mixed (Adelman et al., 2021), thecoefficient on felony drug usage indicates that felony drug increases more in foreignborn concentrated areas. However, the felony drug rate increased by 75% after the
sanctuary policy was adopted. Thus, while there appears to be geographic heterogeneity in the effect, the citywide increase would be more significant for this type ofcrime.
The overall felony sex rate decreases after adopting the sanctuary policy, but increases on Q5. This could be attributed to increased reporting, as evidenced by theincrease in rape rate.
In summary, the sanctuary policy appears to have a heterogeneous effect on felony
drug and felony sex rates but does not have any heterogeneous effect on other minor
offenses.

6

Robustness checks and discussion

For robustness checks, Table 6 shows the results of the regressions weighted by the
population. The results are similar to that in Table 4; however, the magnitude of theeffect is pronounced for rape and robbery. For example, the fifth quintile has 41.1%
higher and 14.9% lower rates of rape and robbery, respectively.
To check the robustness of the results, the following regression is run based on the
proportion of noncitizen population.

log yit = αs Sanctuaryt + HighNonCitizeni × Sanctuaryt + ηi + ηt + θi × t + eit .

(3)

There is a high correlation between HighForeignBorn and HighNonCitizen, and in
most precincts, both are zero or one. Only five precincts (47, 69, 75, 105, 111) have9one for HighForeignBorn but zero for HighNonCitizen. Hence, the differences in the
results are mainly driven by the five precincts.
The results are summarized in Table 7 and 8. Unlike the main regression, varying proportions of noncitizens do not result in heterogeneous effects of the sanctuary
policies. In addition to the statistical interpretation, the coefficients indicate that theeconomic impacts are less significant than those in the case of foreign-born categorization. Table 8 divides the effects by quintile classes. While the results largely mirror
the case of foreign-born categorization in terms of statistical significance, the additional impacts of the sanctuary policy relative to Q1 become smaller in magnitude.
In summary, the results in Table 7 and 8 indicate that the foreign-born status is more
important than the actual citizenship.5
The main results confirm the heterogeneity of policy effects based on foreign-born
categories, with the negative effect is being specific to certain crimes such as robbery.
Moreover, the increase in rape rates is likely due to increased reporting. These results
lend support to improved police–community relations changing crime rates. Hence,
the analysis of smaller geographic units provides additional evidence. However, theanalysis of smaller units does not help to identify the source of the policy effect.
This study faces a limitation related to the parallel trend assumption, because thedataset begins three years before the policy was adopted. While there are no obvious
differences in the trends between groups for at least these three years, the data are
insufficient to draw conclusions about that assumption. Even if data from earlier years
were available, excluding the possibility that the trends were altered by the September
11, 2001, terrorist attacks would be a challenge. Hence, this study cannot exclude thepossibility that the estimators are affected by the time trends.
5 The weighted regression results are in Table 9 and the results are similar to Table 8 both qualitatively

and quantitatively.

10

7Conclusion

This study focuses on smaller geographic units to confirm the spatially heterogeneous
impact of sanctuary policies. Specifically, it uses the police-precinct-level data in NYC
from 2000 to 2010. Using a difference-in-differences approach, the crime rates in precincts
with high concentrations of foreign-born population are compared with those in low
concentration areas before and after the policy was introduced.
The results show that the sanctuary policy introduced in 2002 in New York reduced
the rates of robbery in precincts with high immigrant concentration. Given that thepolicy targets immigrants, the reduced rates of robbery supports the improved police–
immigrant relationship and confirms the macro-level findings in the literature within acity. Moreover, the increase in the rape rate indicates the change in reporting behavior
resulting from the sanctuary policy.

11

References
Adelman, R. M., Yang, Y., Reid, L. W., Bachmeier, J. D., and Maciag, M. (2021). Using
estimates of undocumented immigrants to study the immigration-crime relationship. Journal of Crime and Justice, 44(4):375–400.
Alsan, M. and Yang, C. (2022). Fear and the Safety Net: Evidence from Secure Communities. The Review of Economics and Statistics, pages 1–45.
Amuedo-Dorantes, C. and Arenas-Arroyo, E. (2021). Police trust and domestic violence among immigrants: evidence from VAWA self-petitions. Journal of Economic
Geography, 22(2):395–422.
Amuedo-Dorantes, C. and Deza, M. (2022). Can Sanctuary Policies Reduce Domestic
Violence? American Law and Economics Review, 24(1):116–170.
East, C., Luck, P., Mansour, H., and Velasquez, A. (2018). The labor market effects ofimmigration enforcement. IZA Discussion Paper.
Hausman, D. K. (2020). Sanctuary policies reduce deportations without increasing
crime. Proceedings of the National Academy of Sciences, 117(44):27262–27267.
Jácome, E. (2022). The effect of immigration enforcement on crime reporting: Evidence
from dallas. Journal of Urban Economics, 128:103395.
Kittrie, O. F. (2006). Federalism, Deportation, and Crime Victims Afraid to Call the
Police. Iowa Law Review, 1(91):1449–1508.
Light, M. T., He, J., and Robey, J. P. (2020). Comparing crime rates between undocumented immigrants, legal immigrants, and native-born us citizens in texas. Proceedings of the National Academy of Sciences, 117(51):32340–32347.
Lyons, C. J., Vélez, M. B., and Santoro, W. A. (2013). Neighborhood Immigration,
Violence, and City-Level Immigrant Political Opportunities. American Sociological
Review, 78(4):604–632.
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Manning, D. T. and Burkhardt, J. (2022). The local effects of federal law enforcement
policies: Evidence from sanctuary jurisdictions and crime. Contemporary Economic
Policy, 40(3):423–438.
Manson, S., Schroeder, J., Riper, D. V., Knowles, K., Kugler, T., Roberts, F., and Ruggles,
S. (2023). IPUMS National Historical Geographic Information System: Version 18.0
[dataset]. Minneapolis, MN: IPUMS. 2023. http://doi.org/10.18128/D050.V18.0.
Martı́nez-Schuldt, R. D. and Martı́nez, D. E. (2017). Sanctuary Policies and City-Level
Incidents of Violence, 1990 to 2010. Justice Quarterly, pages 1–27.
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Across the U.S. Limiting Enforcement of Immigration Laws by State and Local Authorities. Technical report, National Immigration Law Center.
O’Brien, B. G., Collingwood, L., and El-Khatib, S. O. (2019). The Politics of Refuge:
Sanctuary Cities, Crime, and Undocumented Immigration. Urban Affairs Review,
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Ousey, G. C. and Kubrin, C. E. (2018). Immigration and Crime: Assessing a Contentious Issue. Annual Review of Criminology, 1(1):63–84.
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for American Progress and National Immigration Law Center Paper.

13

Table 1: Summary statistics (2000–2010: Crime counts by precinct)
N = 825
Burglary
Assault
Larceny
Auto theft
MurderRape
Robbery
Total (Major crime)

Mean
346.9
255.5
603.7
262.5
7.6
22.7
325.8
1,824.6

S.D. Min
183.3
51
162.2
25
541.5
77
202.3
15
6.8
0
16.0
0
169.4
22
847.9 330

Max
1,086
1,061
4,870
1,223
40
106
1,169
6,857

Table 2: Demographics by Precinct (2000)
N = 75
Mean
S.D.
Min
Max
Population
106,778 50,274 16,179 242,951% Hispanic
27.3
20.6
4.6
75.9
% White (NonHispanic)
34.3
28.0
0.7
88.2
% Black (NonHispanic)
25.9
26.3
0.4
90.4
% Foreign-born
32.9
13.7
11.4
67.8
% Noncitizen
18.6
8.6
3.3
46.4
% Noncitizen (Born in Central&South America)
7.8
6.6
0.4
30.0

Table 3: Precinct-level analysis in New York: 2000–2010 (Binary)
VARIABLES
Sanctuary
Sanctuary × HighForeignBorn

Observations
Number of PCT
Year&PCT FE
Trend
Weighted(1)
Burglary(2)
Assault(3)
Larceny(4)
Auto Theft(5)
Murder(6)
Rape-0.263***(0.0161)-0.0122(0.0451)-0.364***(0.0181)-0.0323(0.0378)0.512***(0.0183)
0.0448(0.0385)-1.533***(0.0175)-0.0136(0.0364)-0.165***(0.0594)-0.0451(0.144)0.197*** -0.193*** -0.349***(0.0358) (0.0152) (0.0108)
0.0925 -0.0723** -0.0253(0.0957) (0.0333) (0.0246)

825
75
Y
Y
N

825
75
Y
Y
N

825
75
Y
Y
N

825
75
Y
Y
N

781
75
Y
Y
N

824
75
Y
Y
N

(7)
Robbery

825
75
Y
Y
N

(8)
Total

825
75
Y
Y
N

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Note: The outcome variable is the log of annual crime rates from 2001 to 2005. No control is used. The
data on crime are from the NYC Police Department. Standard errors are clustered at the precinct level.

14

Table 4: Precinct-level analysis in New York: 2000–2010
VARIABLES
Sanctuary
Sanctuary × Q2
Sanctuary × Q3
Sanctuary × Q4
Sanctuary × Q5

Observations
Number of PCT
Year&PCT FE
Trend
Weighted(1)
Burglary(2)
Assault(3)
Larceny(4)
Auto Theft(5)
Murder

(6)
Rape

(7)
Robbery-0.275***(0.0183)-0.0352(0.0617)-0.123(0.0750)-0.0837(0.0653)-0.0168(0.0751)-0.381***(0.0210)-0.0952*(0.0571)-0.113*(0.0610)-0.0464(0.0499)-0.148**(0.0595)

0.500***(0.0201)-0.0417(0.0585)-0.0840(0.0505)
0.0898(0.0599)-0.0820(0.0538)-1.535***(0.0197)
0.0442(0.0397)-0.0588(0.0626)-0.00766(0.0500)-0.0314(0.0542)-0.162**(0.0655)
0.131(0.254)-0.0553(0.210)-0.0598(0.253)-0.0603(0.218)0.232***(0.0445)0.285*(0.144)0.109(0.135)0.191(0.142)
0.347**(0.172)-0.206*** -0.359***(0.0178) (0.0114)-0.0629-0.0220(0.0451) (0.0389)-0.116** -0.114***(0.0533) (0.0341)-0.106**-0.0322(0.0478) (0.0283)-0.123** -0.0761**(0.0541) (0.0371)

825
75
Y
Y
N

825
75
Y
Y
N

825
75
Y
Y
N

825
75
Y
Y
N

781
75
Y
Y
N

824
75
Y
Y
N

825
75
Y
Y
N

(8)
Total

825
75
Y
Y
N

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Note: The outcome variable is the log of annual crime rates from 2001 to 2005. No control is used. The
data on crime are from the NYC Police Department. Standard errors are clustered at the precinct level.

Table 5: Precinct-level analysis in New York: 2000–2010 (Nonmajor offenses)
VARIABLES
Sanctuary
Sanctuary × Q2
Sanctuary × Q3
Sanctuary × Q4
Sanctuary × Q5

Observations
Number of PCT
Year&PCT FE
Trend

(1)
Arson

(2)
(3)
Mischief Fel. Drugs(4)
Fel. Weapons(5)Fel. Possess.

(6)
Fel. Sex(7)(8)
Forgery Other Fel.

-0.755***(0.0461)
0.0168(0.132)-0.112(0.163)-0.0519(0.135)-0.0857(0.179)-0.596***(0.0467)-0.107(0.139)-0.220(0.166)-0.130(0.126)-0.117(0.113)0.754***(0.0400)
0.0372(0.116)
0.0487(0.133)0.331*(0.179)0.284**(0.125)1.390***(0.0532)-0.0141(0.185)-0.250(0.173)-0.171(0.202)0.0364(0.210)-0.319**(0.131)0.250(0.248)0.0312(0.200)0.193(0.214)-0.0898(0.238)-0.230***(0.0499)
0.234(0.169)0.0937(0.139)0.144(0.162)0.304*(0.168)2.011***(0.0422)-0.0629(0.135)-0.260(0.165)-0.126(0.125)-0.0646(0.143)-0.424***(0.0254)0.0157(0.0903)-0.195**(0.0862)0.0159(0.0778)-0.00134(0.0663)

822
75
Y
Y

825
75
Y
Y

825
75
Y
Y

824
75
Y
Y

821
75
Y
Y

823
75
Y
Y

825
75
Y
Y

825
75
Y
Y

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Note: The outcome variable is the log of annual crime rates from 2001 to 2010. No control is used. The
data on crime are from the NYC Police Department. Standard errors are clustered at the precinct level.

15

Table 6: Precinct-level analysis in New York: 2000–2010 (Weighted)
VARIABLES
Sanctuary
Sanctuary × Q2
Sanctuary × Q3
Sanctuary × Q4
Sanctuary × Q5

Observations
Number of PCT
Year&PCT FE
Trend
Weighted(1)
Burglary(2)
Assault(3)(4)
Larceny Auto Theft(5)
Murder(6)
Rape-0.266***(0.0192)-0.0332(0.0563)-0.102(0.0640)-0.0898(0.0606)-0.0290(0.0907)-0.374***(0.0212)-0.108**(0.0487)-0.127**(0.0570)-0.0560(0.0480)-0.141**(0.0589)0.515***(0.0212)-0.0448(0.0555)-0.0602(0.0558)
0.109*(0.0596)-0.0610(0.0585)-1.545***(0.0172)
0.0399(0.0326)-0.0447(0.0590)-0.0223(0.0497)-0.0536(0.0429)-0.185*** 0.228*** -0.214*** -0.354***(0.0641) (0.0410) (0.0225) (0.0121)
0.155
0.270**-0.0898-0.0331(0.245)(0.127) (0.0684) (0.0343)-0.0206
0.148-0.167** -0.112***(0.234)(0.126) (0.0649) (0.0331)-0.116
0.292** -0.158*** -0.0402(0.281)(0.127) (0.0581) (0.0291)-0.0389 0.411*** -0.149** -0.0777**(0.238)(0.143) (0.0646) (0.0388)

825
75
Y
Y
Y

825
75
Y
Y
Y

825
75
Y
Y
Y

825
75
Y
Y
Y

781
75
Y
Y
Y

824
75
Y
Y
Y

(7)
Robbery

825
75
Y
Y
Y

(8)
Total

825
75
Y
Y
Y

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Note: The outcome variable is the log of annual crime rates from 2001 to 2005. No control is used.
The data on crime are from the NYC Police Department. Regression is weighted by population andstandard errors are clustered at the precinct level.

Table 7: Precinct-level analysis in New York: 2000–2010 (Noncitizen-binary)
VARIABLES
Sanctuary
Sanctuary × HighNonCitizen

Observations
Number of PCT
Year&PCT FE
Trend
Weighted(1)
Burglary(2)
Assault(3)(4)
Larceny Auto Theft(5)
Murder(6)
Rape-0.260***(0.0158)
0.0117(0.0460)-0.365***(0.0177)-0.0488(0.0378)0.507***(0.0180)0.0100(0.0396)-1.529***(0.0175)
0.0238(0.0367)-0.157**(0.0598)
0.0567(0.146)0.201*** -0.189*** -0.347***(0.0359) (0.0151) (0.0107)
0.147-0.0425-0.0107(0.0973) (0.0341) (0.0249)

825
75
Y
Y
N

825
75
Y
Y
N

825
75
Y
Y
N

825
75
Y
Y
N

781
75
Y
Y
N

824
75
Y
Y
N

(7)
Robbery

825
75
Y
Y
N

(8)
Total

825
75
Y
Y
N

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Note: The outcome variable is the log of annual crime rates from 2001 to 2005. No control is used. The
data on crime are from the NYC Police Department. Standard errors are clustered at the precinct level.
Threshold is sCitizen > 83.62

16

Table 8: Precinct-level analysis in New York: 2000–2010 (Noncitizen)
VARIABLES
Sanctuary
Sanctuary × Q2
Sanctuary × Q3
Sanctuary × Q4
Sanctuary × Q5

Observations
Number of PCT
Year&PCT FE
Trend
Weighted(1)
Burglary(2)
Assault(3)
Larceny(4)
Auto Theft(5)
Murder

(6)
Rape

(7)
Robbery-0.252***(0.0166)
0.101(0.0641)-0.0155(0.0606)
0.0329(0.0526)
0.0477(0.0589)-0.377***(0.0217)0.00705(0.0541)-0.141**(0.0610)-0.0680(0.0557)-0.119**(0.0594)

0.507***(0.0200)
0.0440(0.0588)-0.0427(0.0607)0.0452(0.0566)-0.0243(0.0641)-1.536***(0.0176)-0.0324(0.0529)-0.0479(0.0518)
0.0250(0.0509)-0.0309(0.0456)-0.150**(0.0635)
0.212(0.237)-0.106(0.224)0.235(0.244)-0.0495(0.199)0.234***(0.0461)
0.157(0.149)0.240*(0.141)0.244(0.184)0.316**(0.122)-0.204*** -0.351***(0.0180) (0.0116)-0.04090.0309(0.0470) (0.0400)-0.122** -0.0748**(0.0560) (0.0337)-0.0770 -0.00648(0.0531) (0.0351)-0.117**-0.0441(0.0494) (0.0336)

825
75
Y
Y
N

825
75
Y
Y
N

825
75
Y
Y
N

825
75
Y
Y
N

781
75
Y
Y
N

824
75
Y
Y
N

825
75
Y
Y
N

(8)
Total

825
75
Y
Y
N

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Note: The outcome variable is the log of annual crime rates from 2001 to 2005. No control is used. The
data on crime are from the NYC Police Department. Standard errors are clustered at the precinct level.

Table 9: Precinct-level analysis in New York: 2000–2010 (Weighted: Noncitizen)
VARIABLES
Sanctuary
Sanctuary × Q2
Sanctuary × Q3
Sanctuary × Q4
Sanctuary × Q5

Observations
Number of PCT
Year&PCT FE
Trend
Weighted(1)
Burglary(2)
Assault(3)
Larceny(4)
Auto Theft(5)
Murder

-0.249***(0.0184)0.0509(0.0574)-0.0273(0.0564)
0.0210(0.0541)0.000308(0.0828)-0.370*** 0.523***(0.0210) (0.0206)-0.02880.0306(0.0505) (0.0556)-0.127** 0.00539(0.0572) (0.0714)-0.08180.0605(0.0532) (0.0598)-0.119** -0.00758(0.0558) (0.0637)-1.544***(0.0159)-0.0367(0.0515)-0.0478(0.0559)
0.0246(0.0469)-0.0457(0.0369)-0.177*** 0.223*** -0.210*** -0.346***(0.0634) (0.0425) (0.0227)(0.0122)
0.154
0.122
-0.05970.00750(0.237)(0.127) (0.0692)(0.0353)-0.1640.237* -0.181*** -0.0825***(0.219)(0.135) (0.0677)(0.0297)
0.162
0.275*-0.122*-0.0157(0.277)(0.163) (0.0639)(0.0367)-0.0192 0.374*** -0.136**-0.0500(0.217)(0.105) (0.0629)(0.0357)

825
75
Y
Y
Y

825
75
Y
Y
Y

825
75
Y
Y
Y

825
75
Y
Y
Y

781
75
Y
Y
Y

(6)
Rape

824
75
Y
Y
Y

(7)
Robbery

825
75
Y
Y
Y

(8)
Total

825
75
Y
Y
Y

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Note: The outcome variable is the log of annual crime rates from 2001 to 2005. No control is used.
The data on crime are from the NYC Police Department. Regression is weighted by population andstandard errors are clustered at the precinct level.

17Figure 1: Total crime per 100,000 population in 2000

18

(a) Fraction of foreign-born population(b) Fraction of noncitizen population

Figure 2: Demographic composition
19

(a) Quintiles by % foreign-born population(b) Quintiles by % noncitizen population

Figure 3: Quintiles by demographic composition
20

Burglary

Assault

Murder

1.4

Rape

1.4
1

1.2
1.2
1.1

1.2

1

1

.9

1

.8

.8

.7
.9

.8

.6

.6

.8
.4

.5

.6
2000 2002 2004 2006 2008 2010
Year

2000 2002 2004 2006 2008 2010Year

Larceny

Car theft

1.3

1.4

1.2

1.2

1.1

1

1

.8

.9

.6

.8

2000 2002 2004 2006 2008 2010
Year

Robbery
1.4

Total (7 categories)
1.2

1.2
1
1
.8

.8

.4
2000 2002 2004 2006 2008 2010
Year

2000 2002 2004 2006 2008 2010
Year

.6
2000 2002 2004 2006 2008 2010
Year

Q1

.6
2000 2002 2004 2006 2008 2010
Year

Q2

Q3

Q4

2000 2002 2004 2006 2008 2010
Year

Q5

(a) Major crimes
Arson
2

1.5

Felony (Mischief)

Felony (Pos. stolen prop.)

1.2

1.2

1

1

.8

.8

1

.6

.6

.8

1

2000 2002 2004 2006 2008 2010
Year

2000 2002 2004 2006 2008 2010
Year

Felony (Drug)
1.6
1.4

1.2

.4

.4

.5

.6
2000 2002 2004 2006 2008 2010
Year

Felony (Weapons)
2.5

2.5

2

2
1.5

1
.9
.8

1

.8
1
.6

.7

.5
2000 2002 2004 2006 2008 2010
Year

Other Felonies
1.1

1.5

1

Q1

2000 2002 2004 2006 2008 2010
Year

Felony (Forgery etc.)3

1.22000 2002 2004 2006 2008 2010
Year

Felony (Sex crimes)
1.4

2000 2002 2004 2006 2008 2010
Year

Q2

Q3

Q4

2000 2002 2004 2006 2008 2010
Year

Q5

(b) Nonmajor crimes

Figure 4: Crime trends by fraction of foreign-born individuals

21