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Do Judges’ Characteristics Matter?Ethnicity, Gender, and Partisanship
in Texas State Trial Courts
Claire S.H. Lim∗
Cornell UniversityBernardo Silveira†
Washington UniversityJames M. Snyder, Jr.‡
Harvard University§
May 17, 2016
Abstract
We explore how government officials’ behavior varies with their ethnicity, gender,
and political orientation. Specifically, we analyze criminal sentencing decisions in Texas
state district courts using data on approximately half a million criminal cases from 2004
to 2013. We exploit randomized case assignments within counties and obtain precisely
estimated effects of judges’ ethnicity, gender, and political orientation that are near zero,
conditional on geographic factors. However, we find substantial cross-judge heterogene-
ity in sentencing. Exploiting a unique overlapping structure of Texas state district courts,
we find no evidence that this heterogeneity is driven by judges pandering to voters.
Keywords: Court, Criminal Sentencing, Ethnicity, Gender, Party, Political Orientation
JEL Classification: H1, H7, K4
∗Department of Economics, 404 Uris Hall, Ithaca, NY 14853 (e-mail: [email protected])†Olin School of Business, Campus Box 1133, One Brookings Drive, St. Louis, MO 63130-4899 (e-mail:
[email protected])‡Department of Government, 1737 Cambridge St, Cambridge, MA 02138 (e-mail: jsny-
[email protected])§This paper was previously circulated under the title “Preference Heterogeneity of the Judiciary and the
Composition of Political Jurisdictions”. We thank Andrew Daughety, Nate Hilger, Christine Jolls, JenniferReinganum, Maya Sen, Morgan Hazelton, and seminar and conference participants at Northwestern, Princeton,U.Chicago, Emory, Washington U., ALEA, CELS, and NBER for their comments and suggestions.
1
1 Introduction
The influence of decision makers’ demographic and political backgrounds on their behavior
has long been an important issue in the social sciences. Government officials from his-
torically under-represented demographic groups—e.g., women and minority ethnic or racial
groups—often have different policy preferences, and policy outcomes differ when they are in
power (Chattopadhyay and Duflo (2004), Pande (2003)). They may also differ significantly
in terms of performance (Gagliarducci and Paserman (2012)). Legislators from different po-
litical parties differ significantly in their roll call voting decisions (Lee et al. (2004)). In social
settings, the demographic and political backgrounds of decision-makers often influence the
type and degree of discrimination observed.1
In this paper, we explore how the behavior of government officials varies depending on
their ethnicity, gender, and political orientation in a setting where decisions are primarily
bureaucratic: U.S. state trial courts. Specifically, we analyze criminal sentencing decisions
in Texas state district courts using data on approximately half a million criminal cases from
2004 to 2013.
Analyzing sentencing decisions in state trial courts to study this question is useful for two
reasons. First, these decisions are primarily of a bureaucratic nature and, unlike policy deci-
sions in many other settings such as government budgeting or appellate court reviews, they
are not strongly ideological. This can be useful in assessing policies designed to achieve
gender, ethnic, and political balance in government bureaucracies, such as those in Korea
and Brazil. In 1996, South Korea mandated that at least 30% of new hires in all government
departments except the police and military be women.2 In 2014, Brazil enacted 20% quotas
1See, for example, Antonovics and Knight (2009) and Anwar et al. (2012) regarding the influence of deci-sion makers’ race on discrimination in law enforcement and criminal trials, respectively, and Bar and Zussman(2012) on the relationship between decision-makers’ political affiliation and grading in universities.
2See the following New York Times article, “Korean Women Flock to Government”, for a richer description:http://www.nytimes.com/2010/03/02/world/asia/02iht-women.html.
2
for mixed race and blacks in federal governmental posts.3 Although such policies may en-
hance the visibility of women and minorities in the public domain, it is unclear whether the
greater participation of these groups in bureaucratic functions leads to different policy out-
comes. There also exist policies that mandate balance in political affiliations in bureaucratic
functions. For example, several states have a “bipartisan requirement” for public utility com-
missions, which mandates that no more than two thirds of the commission members can be
from the same party.4 The degree to which party matters may depend heavily on the setting
(for example, it matters less for mayors (Ferreira and Gyourko (2009)). Therefore, we need
to investigate the degree to which party and other factors matter in diverse settings.
Second, trial court judges make individual rather than collective decisions. In addition,
more than half of the counties are served by multiple judges, and cases are randomly assigned
across judges. These features allow us to isolate the relationship between individual judges’
backgrounds and sentencing outcomes from various potential confounding factors. We can
also analyze the degree of various types of discrimination by interacting judges’ backgrounds
and defendants’ characteristics.
The Texas state district court system has several other features that are useful for our
analysis. It has a large number of judges (457 judges as of 2013) who perform comparable
tasks, as well as a large number of political jurisdictions (254 counties and 457 judicial dis-
tricts).5 Furthermore, judges in Texas are selected through partisan elections.6 Unlike many
3See http://www.planalto.gov.br/ccivil 03/ Ato2011-2014/2014/Lei/L12990.htm for details (in Por-tuguese).
4See the following webpage for more information on the bipartisan requirement for public utility com-mission in each state: http://powersuite.aee.net/portal/. In the case of public utility commissioners, Lim andYurukoglu (2015) show that party affiliation is strongly related to key decisions such as the adjudication ofreturn on equity to electric utilities.
5The large number of jurisdictions is chiefly due to the size of the state. However, large states do notnecessarily have a large number of jurisdictions. For example, California only has 58 judicial districts.
6In the U.S., all federal court judges are appointed by the president and life-tenured. At the state court level,there exists a variety of selection systems. In twenty-one states, trial court judges are appointed, mostly bythe governor. In twenty-two states, trial court judges are selected through non-partisan elections, an electoralprocess where candidates compete without party affiliation on the ballot. In twelve states, judges are selectedthrough partisan elections, an electoral process that is identical to that of major public offices such as the U.S.Congress. In partisan elections, each party selects its candidates through party primaries. Then, nominees from
3
states where judges are appointed by the governor or elected through nonpartisan elections
in which party affiliation is not disclosed on the ballot, voters in Texas directly elect judges
through party primaries and general elections with party affiliation on the ballot. Thus, Texas
is one of the states in which judges are most likely to be selected based on their political back-
grounds. If judges’ partisan affiliations influence their sentencing decisions, then we should
be able to observe it in Texas. Likewise, finding that judges’ partisan orientations appear
to have little influence on their decisions in Texas suggests that such influence may also be
small in states that use nonpartisan elections or gubernatorial appointment to select judges.
We find that the demographic and political backgrounds of judges have little effect on
sentence length. First, the mean difference in sentencing harshness across judges of differ-
ent races is less than one percent of the approximate range of judges’ discretion in crimi-
nal sentencing, conditional on geographic factors7 such as voter preferences.8 Second, the
matches between judges’ and defendants’ ethnicity, race, and gender also have negligible
effects. Specifically, sentencing harshness increases by less than one percent of the approx-
imate range of discretion when judges and defendants are of different ethnicities, races, or
genders. The party affiliation of judges also has a negligible influence on sentencing. The
difference between Democratic and Republican judges in sentencing harshness is less than
one percent of discretion, conditional on geographic factors. Most of these estimates are
precisely estimated, and their 95-percent confidence intervals include only negligible effects
of ethnicity, race, gender, and party affiliation.9
each party compete in general elections. For details, see Lim et al. (2015), Lim and Snyder (2015) as well asthe American Judicature Society website on judicial selection systems: http://www.judicialselection.us/.
7By “conditional on geographic factors,” we mean conditional on county-year fixed effects. County-yearfixed effects capture anything that can influence sentence lengths at the county-level, including factors thatvary over time. Examples are voters’ preferences, district attorneys’ electoral cycles and variation in the poolof criminal cases.
8The precise range of judges’ discretion we use as a measurement of sentencing harshness is the differencebetween the 90th and 10th percentiles of sentenced incarceration time within a group of cases that have identicalprimary offenses and are sentenced in the same year. Our measure is described in detail in Section 3.
9Even without conditioning on geographic factors, the relationships between sentencing harshness andjudges’ background characteristics are mostly small and statistically insignificant, which we report in addi-tional analyses described below.
4
Despite these null effects for key observable characteristics, we find substantial hetero-
geneity in sentencing harshness across judges. To explore its sources, we conduct three
additional analyses. First, we decompose variation in sentencing harshness into county-
specific and judge-specific factors. Second, we investigate how the variation in sentencing
harshness across judges is related to political and socio-economic characteristics of counties
and judicial districts they serve. Third, we investigate the relationship between judges’ sen-
tencing harshness and their race, gender, party affiliation and career history, unconditional
on geographic factors.
For these analyses, we exploit the unique overlapping structure of Texas state district
courts, where judicial districts composed of different sets of counties can partially overlap
with one another within the same county. This structure allows us to separately analyze the
influence of political and socio-economic characteristics measured at two levels: the local-
ities (counties) where cases are prosecuted and the political jurisdictions (judicial districts)
of the judges deciding the case. We then compare the magnitudes of the effects of these two
sets of variables to assess the role played by each one of them in shaping judges’ sentenc-
ing behavior.10 We also use this structure to compare within-judge cross-county variation in
sentencing decisions with aggregate cross-county variation. The difference between the two
measures of variation can be attributed to the strength of judges’ own preferences or to their
pursuit of consistency in sentencing (or both).
Our findings differ from a vast array of empirical papers by legal scholars, political sci-
entists and economists, which have found that individual characteristics of judges do affect
10This analysis is partly motivated by the literature on federalism. In discussions on federalism, it is oftenassumed that a political jurisdiction is the unit of policy decisions. However, in practice, political jurisdictionsare often only a unit for selecting public officials. Many public officials have discretion to use different policiesfor sub-units of their political jurisdictions. For example, state public utility regulators are selected at thestate level. But, when it comes to rate reviews for electric utilities, they have discretion to treat individualelectric utilities differently. That is, they can take different positions for different utilities (markets) in thesame state. Likewise, policing budgets are determined at the city level, but the deployment of police acrossdifferent neighborhoods may be decided based on the distribution of crime rates. The environment we study(state courts) is a limiting case where public officials can vary decisions easily for every issue within the samejurisdiction.
5
their decisions.11 One possible explanation for our different findings is that most previous
analyses employed data from the U.S. Supreme Court or the federal appeals courts, while
we examine state district court cases. Consistent with this explanation, Epstein et al. (2013)
find that the party of the appointing President has a substantial effect on the decisions of U.S.
Supreme Court justices and federal appeals judges, but its effect on federal district judges’
decisions is considerably more modest.12 A possible interpretation for these findings is that
the majority of cases decided at district courts are uncontroversial and leave little discretion
to the judges. However, in spite of our null effects findings concerning judges’ race and po-
litical alignment, we document substantial cross-judge heterogeneity in sentencing behavior.
This result suggests that, even in the relatively bureaucratic context of trial courts, judges
may vary considerably in their legal thinking and have a non-trivial degree of discretion.13
Our findings also differ from another set of studies that document significant influence of
decision-makers’ race in contexts other than judicial behavior (e.g., Antonovics and Knight
11George (2001) offers a very informative summary of the early literature. Several papers find evidencethat the political alignment of appellate court judges and Supreme Court justices (usually measured by theparty of the appointing President) is an important determinant of their decisions. See Segal and Cover (1989)for an example. The evidence of early studies on race effects is less clear but recent papers indicate that thejudges’ race plays a relevant role in very specific, racially related cases such as those involving voting rightsand discrimination. See Cox and Miles (2008) for an analysis of voting rights appellate court cases and Chewand Kelley (2008) for a study of workplace racial harassment cases. Both papers find that African-Americanjudges are more likely than their non-African-American peers to make decisions favoring the plaintiff.
12The contrast between our results and those of previous papers on the determinants of judicial behavioris also analogous to that between studies examining upper and lower-level officials outside of the judicialsystem. For example, there is a strong consensus that the behavior of U.S. Congressmen is highly partisan(e.g., Poole and Rosenthal (1984), Snyder and Groseclose (2000), Lee et al. (2004)). On the other hand,Ferreira and Gyourko (2009) document null effects of mayor’s party affiliation on the size of city government,the allocation of local public spending, and crime rates. The latter authors largely attribute their finding of nulleffects to Tiebout competition between localities. Though the mechanism behind null effects of party in ourstudy is not precisely Tiebout competition, an analogous incentive may influence judges’ decisions. Unlikein appellate courts, at the district court level there exist a large number of judges handling highly comparablecases. An implicit comparison among judges may prevent their racial and political identities from salientlyaffecting their decisions.
13A number of papers also find non-trivial cross-judge heterogeneity in criminal sentencing. See, for exam-ple, Yang (2014) for a study of federal district courts and Lim (2013), Lim et al. (2015), Silveira (2012) andMueller-Smith (2014) for analyses of state trial courts. Fischman and Schanzenbach (2012) also find evidenceconsistent with judicial discretion in sentencing in federal district courts; interestingly, they find that judicialdiscretion does not exacerbate racial disparities in sentencing, but may in fact reduce such disparities.
6
(2009) and Anwar et al. (2012)).14 The differences suggest that the influence of demographic
factors such as race or ethnicity may critically depend on decision-makers’ expertise. Anwar
et al. (2012) find that the racial composition of the jury pool substantially influences the
racial disparity in conviction rates. Specifically, having even one black person in the jury
pool almost completely eliminates the difference in conviction rates between black and white
defendants. Unlike jurors, judges are professionally trained and have acquired significant
experience in law. Finding little racial influence in judges’ decisions implies that expertise
may significantly reduce racial bias.
Previous papers using data from federal district courts have documented minimal effects
of judges’ backgrounds on their sentencing behavior.15 We regard our study as comple-
mentary to these. Texas District Court judges are elected in partisan elections, as opposed
to their counterparts from federal courts, who are appointed for life by the President. The
former group of judges thus faces much stronger incentives than the latter to respond to the
preferences of voters and interest groups from their districts. Whether these different incen-
tives magnify the effects of race and party affiliation on the sentencing behavior of elected
judges is an empirical question that our paper begins to address. It is also important to note
that the vast majority of criminal cases in the United States are under state jurisdiction.16 The
pre-eminent role of state trial courts in the U.S. criminal justice system thus makes studying
the behavior of state judges important for its own sake.
14The results in these studies all show that preference-based discrimination affects decision making. Thereare also studies on racial bias that show very different results. For example, Knowles et al. (2001) show thatlaw enforcement officer behavior in motor vehicle searches is consistent with statistical discrimination, but notwith preference-based discrimination. Sanga (2014) analyses discrimination in officer stop rates and finds acomplex pattern that is more consistent with information than with preference-based discrimination.
The results in these studies all show that preference-based discrimination affects decision making. Thereare also studies on racial bias that show very different results. For example, Knowles et al. (2001) show thatlaw enforcement officer behavior in motor vehicle searches is consistent with statistical discrimination, but notwith preference-based discrimination.
15Ashenfelter et al. (1995) analyze civil rights and prisoner cases. Schanzenbach (2005) and Yang (2015)examine criminal cases.
16In 2012 a total of 553,843 inmates were admitted to state jails or prisons to serve a sentence of at least oneyear. The corresponding number for federal jails and prisons, which handle inmates convicted in the federaljustice system and in the District of Columbia, was 55,938. See Carson and Golinelli (2013) for details.
7
Our paper is also related to Abrams et al. (2012), who examine heterogeneity in sen-
tencing patterns across state trial courts judges. They focus on variation across judges in
their different treatment of African-American and white defendants, and find evidence that
judges vary in their disparity across defendants’ races in the incarceration of convicted de-
fendants. Interestingly, they find no significant differences in the distributions of sentence
lengths across judges, whereas we are able to document such differences. A potential ex-
planation for the different findings is that they employ data from a single county (Cook
County) comprising cases decided by 70 judges. Our data set consists of cases decided in
254 counties by judges from 457 different courts, which allows us to observe considerably
more variation in the decision patterns across judges. Consistent with our results, Abrams
et al. (2012) also find that the heterogeneity in judges’ propensities to incarcerate African-
American defendants cannot be explained by judges’ own race.
There are also a few recent studies, such as Rehavi and Starr (2014) and Starr (2015),
which analyze prosecutors’ role in the disparity between demographic groups in outcomes
of the criminal justice system. One implication of these studies is that the primary offense
or the presumptive jail time of a case are highly endogenous variables, which may hinder
the validity of the analyses that focus only on judges’ sentencing behavior controlling for
such case characteristics. Our study controls for relatively coarse crime categories rather
than offense severity or presumptive jail time. Thus, our results are less likely to suffer from
the bias caused by the prosecutor’s choice of offense in the charging decision. The fact that
our analysis still yields only a very small bias of judges helps us to put the relative role of
judges and prosecutors’ discretion in perspective. Rehavi and Starr (2014) and Starr (2015)
also show that the relative weights of prosecutors’ and judges’ discretion are different in
racial and gender disparity of punishments. While the racial disparity is primarily driven by
prosecutors’ decision on the charged offense, each step of the criminal procedure plays a sig-
nificant role in generating the gender disparity. Our study makes the racial/ethnic and gender
8
bias in sentencing directly comparable, which enhances our understanding of different types
of disparities.
The rest of the paper is organized as follows. In Section 2, we introduce the institutional
background of Texas state district courts. In Section 3, we describe the data. In Section 4,
we present and discuss our analyses. In Section 5, we conclude.
2 Institutional Background
Texas state district courts are trial courts of general jurisdiction.17 District court judges han-
dle felony crime cases, as well as civil cases in which the disputed amount exceeds 200
dollars. Judges tend to have significantly more discretion in criminal than in civil cases. Un-
like in civil cases, in which outcomes are mostly decided by the jury, criminal sentencing is
primarily under the discretion of judges once defendants are convicted by the jury.18 Hence,
we focus on criminal sentencing.
The Texas Penal Code separates felonies into classes, according to their severity. Within
each class, judges have considerable discretion for setting sentences, following a conviction.
The felony classes, organized from least to most severe, and their respective incarceration
sentence ranges are as follows: state jail felonies (e.g., credit card fraud) may result in 180
days to two years; third-degree felonies (e.g., drive-by shooting with no injury) may result in
two to ten years; second-degree felonies (e.g., aggravated assault) may result in two to twenty
years; first-degree felonies (e.g., aggravated sexual assault) may result in five to 99 years or
17In most U.S. states the court system is organized in three tiers: supreme, appellate, and district (cir-cuit, trial) courts. The structure of Texas state court is analogous to this standard structure, except thatthe highest court is divided between the supreme court and the court of criminal appeals. For details, seehttp://www.courts.state.tx.us/
18The division of discretion between judges and the jury in Texas is slightly different from other states. Texasis one of the five states (together with Arkansas, Missouri, Oklahoma and Virginia) that allow jury sentencing.In Texas, defendants can choose to be sentenced by the jury, and judges cannot override the jury’s decision.In principle, jury sentencing poses a challenge to the econometric specification of sentencing decisions. Weabstract from this issue in our analysis because, in practice, jury sentencing is only employed in a negligibleproportion of cases.
9
life; and capital felonies (e.g., murder) may result in the death penalty or life. Aggravating
factors, such as previous convictions, might increase the class of an offense for sentencing
purposes. For example, an individual who has been previously convicted of two state jail
felonies must receive a punishment equivalent to that of a third degree felony upon a new
state jail felony conviction.
The Texas state district court system is composed of 457 judgeships. The term of dis-
trict court judges is four years. They are selected through partisan elections, an electoral
process identical to that of the governor and state legislators. State parties hold primaries to
select their candidates for judicial elections. Then, nominees from each party compete in the
general election.
Each judge constitutes one judicial district. Thus, there exist 457 judicial districts. Each
judicial district is composed of one or more counties, and does not divide a county. Since
there are 254 counties in the state, multiple judicial districts overlap over the same county.
Figure 1 shows the structure of Texas state district courts.19
Table 1 shows six different patterns of overlap between judicial districts. In pattern A
multiple judges serve a single county exclusively. This pattern appears in urban counties
with large populations such as Harris County, which has the City of Houston, and Dallas
County. In pattern B a single judge serves a single county. Pattern C is the case in which
multiple judges serve an identical set of multiple counties. In pattern D one judge serves
many counties, which typically have small populations. Patterns A, B, C, and D are common
geographical structures of state court districts that are not unique to Texas.
In Patterns E and F, judges who serve different sets of counties overlap partially with
one another. An example of Pattern E is Nueces and adjacent counties. There are eight
judges who serve Nueces County (district 28, 94, 117, 148, 21, 319, 347, and 105). One of
these judges (district 105) also serves Kenedy and Kleburg Counties. An example of pattern
19The map in Figure 1 is available at http://www.txcourts.gov/judicial-directory/court-jurisdiction-maps.aspx.
10
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11
Table 1: Jurisdictional Overlap Patterns
Number Number of Number ofJurisdictional Overlap Patternsof Areasa Counties Courts
Single County & Multiple CourtsANo Courts Serve Another County
28 28 273
Single County & Single CourtBCourt does not serve another county
15 15 15
Multiple Counties & Multiple CourtsCIdentical Jurisdictions
6 23 13
D Multiple Counties & Single Court 26 76 26Multiple Counties & Multiple CourtsEOne separate Jurisdiction
13 39 54
Multiple Counties & Multiple CourtsFMany Separate Jurisdictions
11 73 76
Total 99 254 457Source: “Complexities in the Geographical Jurisdictions of District Courts,” available athttp://www.courts.state.tx.us/courts/pdf/JurisdictionalOverlapDistrictCourts.pdf.a Areas are the smallest units that form a partition of the entire state.
F is El Paso and adjacent counties. There are fourteen judges who serve El Paso County
(district 34, 40, 41, 65, 120, 168, 171, 205, 210, 243, 237, 346, 383, and 384). One of these
judges (district 205) also serves two other counties (Culberson and Hudspeth). Another judge
(district 394) does not serve El Paso, but serves counties that are linked to El Paso through
district 205. The judge in district 394 serves five counties (Brewster, Culberson, Hudspeth,
Jeff Davis, and Presidio). As described in Section 1, these unique overlapping patterns help
us assess the importance of localities (counties) versus political jurisdictions in shaping the
sentencing behavior of the judges.
3 Data
We obtained criminal sentencing data from the Texas Department of Criminal Justice. The
data set includes all felony crime cases that resulted in the conviction and incarceration of
12
defendants from years 2004 to 2013, approximately 440,000 cases.20 The data set contains
key information regarding each case, including the name, gender, race, ethnicity, and birth
date of the defendant, all of the convicted offenses in the case and their severity, the location
(county) and the date of crime, sentence length, information related to probation and parole,
and the judicial district where the defendant was convicted and sentenced. By linking the
judicial district of conviction in the sentencing data with court administrative data on the
match between judicial districts and judges, we identify the judge that handled each case.
We supplement the sentencing data with four auxiliary data sets. First, since our raw sen-
tencing data does not contain the defendants’ criminal history, we obtained criminal records
of defendants from the Texas Department of Public Safety. We computed the number of
prior felony convictions and violent felony convictions for each defendant that appears in
our sentencing data.
Second, we obtained judges’ party affiliation, their tenure (number of years in office) and
their electoral proximity (the number of days remaining until their next election) using data
on elections of judges in Texas.21 We also obtained judges’ career history from the American
Bench, a directory of all U.S. judges. We use total legal experience prior to being a judge,
experience in private law practice and in prosecution—all of which are measured in number
of years.
Third, we obtained information on the judges’ race and ethnicity from several sources.
A comprehensive list of Hispanic judges is in the National Directory of Latino Elected Of-
ficials, organized by the National Association of Latino Elected and Appointed Officials
20In principle, it would be more desirable to employ data sets that include probations and acquittals. How-ever, to our knowledge, data that include both probations and acquittals are rare at the state-level. Further,those states that provide such data along with judge identifiers for individual felony cases are not large or di-verse enough to be suitable for the purpose of this study. For example, Silveira (2012) analyzes data fromNorth Carolina that includes probations and acquittals. That state has only 50 judicial districts handling felonytrials, and none of these districts had Hispanic judges as of 2008. Likewise, Lim (2013) and Gordon and Huber(2007) study sentencing data from Kansas that include probations along with judge identifiers for individualcases. Kansas has only 31 judicial districts, and Hispanic and black judges together constitute less than 4% ofthe state trial court judgeships as of 2009.
21This data set on elections of judges is analyzed in Lim and Snyder (2015).
13
(NALEO). We obtained an incomplete list of African-American judges from several edi-
tions of the National Roster of Black Elected Officials. We complemented the latter using
the Encyclopedia of African-American History, the Directory of African American Judges,
online research in Judgepedia, Wikipedia, judges’ personal websites and newspaper reports
covering judicial elections. We determined the judges’ gender based on their first names.
Fourth, we include political and demographic characteristics of counties and judicial dis-
tricts. For political characteristics, we use the average Democratic vote share of all the
non-judicial elections held in the state, also acquired from the election data. We call this
the Democratic Vote Share (DVS) and use it to measure the ideology of each judges’ elec-
torate. We also use the turnout rate in the most recent presidential election. For demographic
characteristics, we include per capita income and the shares of Blacks and Hispanics in the
total population. We also include the total number of crimes reported to the police. These
variables are obtained from the U.S. census data and are computed both at the county-level
and judicial district-level.
Summary statistics of these variables as well as the normalized measure of sentencing
harshness described below are presented in Table 2. Panel C of the table makes it clear that
very few judges in our sample are African-American. Thus, our results concerning African-
American judges—especially those in Section 4.1—should be interpreted with caution. On
the other hand, 15 percent of the judges in our sample are Hispanic and 20 percent are
female, allowing us to assess differences in sentencing patterns across ethnicities and gender
with considerable precision.
Measuring Sentencing Harshness Each judge handles multiple cases at any one point in
time. The sets of cases vary across judges even when cases are randomly assigned. Thus,
using the length of sentenced jail time as a measure of sentencing harshness may lead us
to confound variation in judges’ sentencing harshness and variation in the set of cases as-
14
Table 2: Summary Statistics
Variable Mean S.D. Min Max # ObsPanel A: Defendant Characteristics
Age 31.9 10.7 17 87.1 437509Female 0.2 0.4 0 1 437836Race
White 0.3 0.5 0 1 437836Black 0.3 0.5 0 1 437836Hispanic 0.3 0.5 0 1 437836
Previous Felony Convictions 1.1 1.6 0 20 437375Previous Violent Crime Convictions 0.1 0.3 0 5 435564
Panel B: Sentencing OutcomesSentenced Jail Time (days) 1754 3650 30 73059 437497By crime category
Aggravated Assault 2460 3443 60 36525 19009Burglary 1526 2078 60 36525 45084Drug possession 913 1390 30 36525 105130Drug trafficking 2305 2973 90 36525 33935Fraud, Forgery and Embezzlement 578 951 45 36525 24174Larceny 553 1010 30 36525 42442Motor Vehicle Theft 420 487 30 9131 10146Homicide 19148 14205 121 73059 4531Other Violent 1933 3031 90 36525 27017Other Offenses 1420 2138 60 36525 81259Robbery 3571 4245 60 36525 22925Sexual Assault 6784 8351 121 36525 12654Weapon offenses 1487 1591 180 36159 9191
Normalized Harshness 0.31 0.33 0 1 436677Panel C: Judge Characteristics
Tenure (years) 10 7 0 31 721Republican 0.60 0.49 0 1 721Female 0.26 0.44 0 1 721Career History
Total Legal Experience 18.51 8.06 5 42 350Private Law Practice 11.17 9.91 0 41 300Prosecution 4.38 6.00 0 25 297
RaceBlack 0.02 0.14 0 1 721Hispanic 0.15 0.36 0 1 721
Panel D: Political and Demographic Characteristics of CountiesShare of Race
White 0.6 0.21 0.03 0.93 2121Black 0.07 0.07 0 0.34 2121Hispanic 0.32 0.23 0.02 0.97 2121
Democratic Vote Share (DVS) 0.31 0.14 0.02 0.86 2121
Note: In Panels A and B, the unit of observation is individual criminal case. In Panel C,it is judge by year for tenure, and judge for other variables. In Panel D, it is county byyear. Summary statistics for district-level characteristics and other county-level charac-teristics are omitted.
15
signed.22 To minimize the influence of cross-judge heterogeneity in the sets of cases, we
construct a measure of normalized harshness of sentencing, Harshness. To construct this
measure we normalize with respect to the 10th and 90th percentiles of the sentences (in-
volving incarceration) among those cases resolved in the same year with the same primary
offense. We classify primary offenses using the National Crime Information Center (NCIC)
Offense Codes (nciccd) included in the data. NCIC codes provide more detailed information
about the nature of offenses compared with other commonly used classification codes such
as the Uniform Crime Reporting rule by the Federal Bureau of Investigation or the classifica-
tion by the National Judicial Reporting Program. In our data, primary offenses are classified
into 39 categories using NCIC codes.23 The measure of sentencing harshness we employ is
defined as
Harshness =
0 if Sentence < P10,
Sentence−P10P90−P10
if P10 < Sentence < P90,
1 if Sentence > P90,
(1)
where P10 and P90 are 10th and 90th percentiles in the group of cases that have the NCIC
code of the same primary offense and sentence year.24 We use 10th and 90th percentiles
rather than the minimum and the maximum to avoid the influence of outliers on our measure.
Averaging across the cases in the sample, a variation of one percentage point in Harshness
represents roughly one month in the assigned sentence.25 To put this length in perspective,
22Including fixed effects for crime categories in regression analysis does not resolve this issue because themean jail time is not the only outcome that varies across crime categories. The range of jail time specified bythe penal code also varies considerably across crime categories.
23NCIC Offense Codes are available online at http://wi-recordcheck.org/help/ncicoffensecodes.htm.24In cases in which the defendant received the death penalty or a life sentence was the maximum sentence,
we top-code the sentence as 200 years (death penalty) and 100 years (life sentence). We conducted numerousrobustness checks with this top-coding. Changes in it do not affect our results in a meaningful way becausehomicides resulting in either the death penalty or a life sentence constitute a very small proportion of the casesthat judges handle.
25Given the primary offense’s NCIC code and the sentence year of a case, an x percentage point variation inthe range of Harshness is equivalent to a change of x
100 (P90−P10) in the sentence measured in days, assumingthat the sentence is between P10 and P90. On average in our sample, a one percentage point variation in therange of Harshness represents a change of 33.36 days in the assigned sentence. Considering separately specificcrime categories, the same variation in Harshness represents changes of 75.36, 140.39, 9.02 and 28.19 days for
16
notice that the average (median) sentence in our sample is 4.8 (1.99) years. Considering only
violent offense convictions, the average sentence goes up to 11.21 (4.99) years.
Similarly to what happens in the trial courts of most U.S. states, the vast majority of
cases in Texas district courts are resolved by plea bargain.26 Thus our measure of sentenc-
ing harshness largely captures the outcome of a bargaining process involving the judge, the
district attorney’s office, the defendant, and the defense attorney, among others. However,
insofar as settlement negotiations take place in the shadow of a trial, plea-bargained sen-
tences still reflect the harshness of the judge responsible for the case. Moreover, our baseline
analyses include county-year fixed effects, which filter out any influence of district attorneys
or their reelection incentives. Previous research also indicates that the expected harshness
of the judge at trial does indeed affect sentencing in settled cases.27 Our findings are fully
consistent with these existing results. In Section 4.3, we provide evidence that the assigned
sentences in our data (i.e., to a very large extent plea-bargained sentences) are strongly influ-
enced by the judges deciding the case. Moreover, we show evidence that the effect attributed
to any given judge tends to be relatively constant across the counties over which such a judge
has jurisdiction. That our estimated judge-specific effects do not seem to vary with the coun-
ties where the cases are prosecuted provides further support for the interpretation that they
indeed reflect the sentencing behavior of the judges, rather than the influence of prosecutors
or other agents involved in the plea negotiations. It is worth noting that, with very few ex-
ceptions, the empirical research on the sentencing behavior of trial judges in criminal cases
has extensively employed data on plea-bargained sentences.28
violent crimes, sexual assaults, property crimes and drug-related offenses, respectively.2695.70% of all criminal convictions statewide in 2013 were resolved by a guilty plea or a plea of nolo
contendere. In 2012 this share was 96.85%. Shares for other years were similarly high. We obtained thesestatistics from the Court Activity Reporting and Directory System, on the website of the Texas Office of CourtAdministration.
27See for example LaCasse and Payne (1999) and Boylan (2012).28Recent examples include Huber and Gordon (2004), Gordon and Huber (2007) and Abrams et al. (2012).
Silveira (2012) proposes a structural approach for explicitly dealing with the plea bargaining process in theempirical analysis of criminal cases. However, such a framework is out of the scope of this paper since itrequires information on case disposition that we do not have in our data.
17
Randomized Case Assignment An important advantage of using trial court cases to study
the influence of racial and political bias in decision-making is that cases are randomly as-
signed across judges. In Texas district courts, cases are randomized at the county level,
taking into account overall caseloads and vacancies in the schedule of judges.29
To assess the degree to which counties followed the principle of randomization in case
assignment, we conduct Pearson’s χ2-test for the independence between each of several key
variables and judge assignment. The variables are: the crime category of the primary of-
fense, the crime severity of the primary offense, a dummy indicating that the primary of-
fense was violent, the race of the defendant, the gender of the defendant, and a dummy
variable indicating that the defendant was under age 30. We use thirteen crime categories:
aggravated assault, burglary, drug possession, drug trafficking, fraud, forgery and embezzle-
ment, larceny, motor vehicle theft, homicide, robbery, sexual assault, weapon offenses, other
violent offenses and other non-violent offenses.
The χ2-tests indicate that in most county-years case assignment appears to be as good as
random. Also, for some variables, such as race and gender of the defendant, the balance
across judges appears to be relatively good in the vast majority of cases. For race, the p-
value of the χ2-statistic is less than .05 about 7.6% of the time and the p-value is less than
.10 about 13.7% of the time. For gender, the p-value of the χ2-statistic is less than .05 about
9.2% of the time and less than .10 about 15.5% of the time. Thus, for these variables the null
hypothesis of random assignment is rejected only a bit more often than we would expect by
chance.
For other variables the deviations from random assignment are more frequent. Consider,
for example, the dummy variable indicating a violent offense. For this variable the p-value
29As described in Section 2, judges in a given county may have different sets of counties to serve. As a result,judges in the same county may have very different caseloads at any given point of time. For example, supposethat Judges A and B serve County X and only Judge B also serves Counties Y and Z. To make caseloadsbalanced across judges, County X should assign considerably fewer cases to Judge B than to Judge A. Thus,there can be considerable variation in the number of cases assigned to each judge from a given county. However,even in such cases, the principle in case assignment is randomization.
18
of the χ2-statistic is less than .05 about 19.7% of the time, so the null hypothesis of random
assignment is rejected almost 4 times as often as we would expect by chance.30
In some cases this is due to specialization. For example, in El Paso and Jefferson Counties
some judges specialize in covering particular types of crime categories. In other cases, it is
likely due to the fact that certain crimes, such as murder, are relatively rare. For example,
Victoria county is served by two courts (Districts 24 and 377). In 2007, there were just four
homicide cases in the county in our dataset, and all were assigned to District 377. In 2009
there were four such cases and all were assigned to District 24. In 2010 there were four cases,
two assigned to each District. Summing across all years the division of homicide cases was
quite even—13 to District 24 and 12 to District 377—and a χ2-test would clearly not reject
the null hypothesis of random assignment. But in some years, such as 2007 and 2009, the
distribution of cases was quite skewed and statistical tests for balance could lead to rejection.
In most county-years we find that even if the χ2-tests reject the null hypothesis of inde-
pendence (at, say, the .01 level) for one or more variables, after dropping one judge—or, in
some cases two or three judges—the χ2-tests fail to reject the null on all of the variables stud-
ied. For some county-years—e.g., Harris county in every year except 2005—this is not the
case. We therefore constructed a “cleaned” sample of county-years by dropping all county-
years for which (i) the p-value of the χ2-statistic is below .01 for any of the seven variables
checked, or (ii) two or more of the p-values for the seven variables are below .10.
In the sample remaining after dropping these cases, the distribution of p-values from the
χ2-tests look quite good. Table 3 shows the fraction of p-values that fall below various
thresholds for the subsample. For the .05 and .10 thresholds, the fraction of cases with p-
values falling below the threshold is much lower than what we would expect by chance. This
is of course not too surprising given our criterion for dropping cases. But it is even true for
the .15 threshold. And, except for the Category variable, the fraction of cases with p-values
30The p-value of the χ2-statistic is less than .10 about 26.9% of the time, so even using this threshold P90null hypothesis of random assignment is rejected more than twice as often as we would expect by chance.
19
falling below the .20 threshold is also about what we would expect by chance.
Table 3: Random Assignment p-values
Fraction of Cases with P <Variable.05 .10 .15 .20
Male 0.028 0.043 0.056 0.179Race 0.012 0.037 0.040 0.179Young 0.018 0.062 0.065 0.185Category 0.009 0.065 0.080 0.292Severity 0.015 0.074 0.092 0.225Violent 0.012 0.055 0.074 0.203
Note: P-values are from χ2-tests of independence forthe given variable, where the cases are county-years.In all cases the number of observations is 324.
In the remainder of the paper, we present results obtained using the “cleaned” sample.
The results do not change substantially if, instead, we use the complete, uncleaned sample in
the analysis. Nor do they change if we use an even more restrictive subsample of cases that
is constructed similarly to the “cleaned” sample, except that we completely drop all counties
that satisfy criteria (i) or (ii) described above for some county-year. In the interest of space,
we do not report these two sets of results. They are available from the authors upon request.
We also ran multinomial logit regressions for each county-year to generate tests of joint
significance for the vector of variables. For many of the courts with a large numbers of
judges (7 or more) the likelihood function has non-concave and/or flat regions, and all of
the standard optimization routines failed to converge properly (also, even when convergence
was achieved the standard errors were often suspect, due to the number of binary independent
variables). For smaller courts this was rarely a problem. For the cases where convergence
was achieved, the test of joint significance from the multinomial logit and the χ2-tests done
one variable at a time produced the same result—in terms of rejection at the .05 level—
about 78% of the time. Also, where the results differed, there were cases in which the
20
test of joint significance rejected the null hypothesis but none of the individual χ2-tests did
(about 9%), as well as cases in which at least one of the individual χ2-tests rejected the null
hypothesis but the test of joint significance did not (about 14%). That is, neither the χ2-
square tests one variable at a time, nor the joint test based on the multinomial, is uniformly
more “conservative” in terms of rejecting the hypothesis that cases are randomly assigned.
4 Analysis
We first conduct three baseline analyses: (1) the influence of judges’ race, ethnicity and
gender on their sentencing harshness, (2) the influence of judges’ political backgrounds on
their sentencing harshness, and (3) the extent of judges’ preference heterogeneity.
4.1 The Influence of Judges’ Race, Ethnicity and Gender
We analyze the influence of judges’ race, ethnicity and gender on sentencing harshness with
three specifications. In the first specification, we estimate the influence of these characteris-
tics without interacting them with characteristics of the defendants:
Harshnessijt = β0 +β1Black Judgei +β2HispanicJudgei +β3FemaleJudgei
+γxi jt +δwit + εi jt , (2)
where Harshnessijt is the normalized sentencing harshness, as defined on page 16, of judge
i in case j in year t, Black Judgei, HispanicJudgei and FemaleJudgei are dummy variables
indicating that judge i is Black, Hispanic and female, respectively, xi jt is a vector of case
characteristics, and wit is a vector of other characteristics of judge i and his/her county in
year t. In the second specification, we also estimate the influence of the match between
21
judges’ and defendants’ race, ethnicity and gender:
Harshnessijt = β0 +β1Different Raceij +β2Different Genderij +β3Black Judgei
+β4HispanicJudgei +β5FemaleJudgei + γxi jt +δwit + εi jt , (3)
where Different Raceij (Different Genderij) is a dummy variable that takes value one if judge
i and the defendant in case j are of different race or ethnicity (gender), and zero otherwise.
Table 4 presents results of estimating equations (2) and (3). All specifications include
county-year fixed effects. The key parameters are precisely estimated and have magnitudes
close to zero. The results using the full sample indicate that neither Hispanic nor female
judges assign sentences in a systematically different manner than their non-Hispanic and
male peers, respectively. African-American judges tend to assign shorter sentences, although
the magnitude of the effect is small—roughly one percent of the range of Harshness, or just
over one month on average (see the discussion of the interpretation of Harshness for the
full sample and specific crime categories in Section 3). When specific crime categories are
considered in isolation, the results change slightly. For example, the estimates suggest that
female judges assign longer sentences in violent offense cases (2.33 percent in the range of
Harshness), African-American judges assign longer sentences in sexual assault cases (5.25
percent in the range of Harshness) and Hispanic judges assign shorter sentences in drug-
related cases (1.67 percent in the range of Harshness). The interaction between judges’ and
defendants’ race is positive and significant at ten percent in the whole sample and at one per-
cent in drug crimes, while the interaction between judges’ and defendants’ gender is negative
and significant at one percent for violent offenses only. Again, the magnitudes are small. For
example, using the full sample, we find that cases in which the judge and the defendant are
of different races are associated with an increase of 0.61 percent in the range of Harshness—
corresponding, on average, to to an increase of twenty days. As a basis of comparison, the
22
Table 4: The Influence of Judges’ Race/Ethnicity on Sentencing Harshness - Baseline
Dependent variable: Harshness(1) (2) (3) (4) (5) (6)Full Full Violent Sexual Property DrugVariables
Sample Sample Offenses Assaults Crimes Offenses
Different Race 0.0061* -0.0072 -0.0138 0.0031 0.0179***(0.0035) (0.0075) (0.0133) (0.0115) (0.0057)
Different Gender -0.0017 -0.0156* 0.0018 0.0015 0.0019(0.0026) (0.0085) (0.0182) (0.0047) (0.0038)
Black Judge -0.0125*** -0.0125*** 0.0043 0.0525*** -0.0063 -0.0014(0.0038) (0.0043) (0.0103) (0.0152) (0.0085) (0.0079)
Hispanic Judge -0.0047 -0.0034 -0.0049 -0.0013 0.0197 -0.0167**(0.0118) (0.0115) (0.0110) (0.0122) (0.0148) (0.0069)
Female Judge 0.0034 0.0045 0.0233* 0.0151 -0.0048 0.0017(0.0052) (0.0057) (0.0114) (0.0220) (0.0063) (0.0055)
Years in Office -0.0002 -0.0002 -0.0004 -0.0009 0.0003 -0.0001(0.0003) (0.0003) (0.0004) (0.0007) (0.0005) (0.0003)
Black Defendant -0.0072** -0.0126** -0.0010 -0.0149 -0.0329** -0.0044(0.0033) (0.0053) (0.0083) (0.0167) (0.0129) (0.0098)
Hispanic Defendant 0.0010 -0.0035 -0.0171*** -0.0369** -0.0295** 0.0404***(0.0025) (0.0042) (0.0067) (0.0151) (0.0130) (0.0075)
Female Defendant -0.0609*** -0.0600*** -0.0729*** -0.1234*** -0.0360*** -0.0626***(0.0023) (0.0024) (0.0087) (0.0248) (0.0059) (0.0042)
Age at Offense 0.0014** 0.0014** 0.0072*** 0.0303*** 0.0060*** 0.0026*(0.0005) (0.0005) (0.0013) (0.0025) (0.0012) (0.0014)
Age squared -0.0000 -0.0000 -0.0001*** -0.0003*** -0.0001*** -0.0001***(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Observations 228,557 228,557 44,566 7,322 38,770 70,691R-squared 0.160 0.160 0.153 0.307 0.177 0.273
Note 1: Results from OLS regressions. Standard errors, clustered by county, in parentheses: ∗∗∗ significant at1%, ∗∗ significant at 5% and ∗ significant at 10%.Note 2: All specifications include county-year fixed effects, offense categories (12 dummy variables for 13 cat-egories), and the criminal history of defendants as control variables. For criminal history, we use five dummyvariables for the number of previous convictions in felony: one, two, three, four, and five or more. The basegroup is one with no previous convictions. We use three dummy variables for the number of previous violentfelony convictions: one, two, and three or more.
23
average sentence in the sample is roughly five years. For drug crimes, this effect increases
to 1.79 percent in the range of Harshness (an increase of fifty days, while the average sen-
tence for this offense category is roughly four years). Several defendant characteristics are
statistically significant, but they may well reflect unobserved case characteristics. For exam-
ple, female defendants consistently receive more lenient sentences across crime categories,
which may reflect the possibility that offenses by females are less heinous.
Despite the common belief that racial identity affects decision making, the negligible es-
timated effects of judges’ race and ethnicity have intuitive explanations. Unlike jurors that
are randomly drawn from the population, judges are professionally trained and selected un-
der considerable scrutiny. Judicial candidates with minority backgrounds may face stronger
scrutiny and may be selected only when it is unlikely that they will fit racial or ethnic stereo-
types.31 Moreover, while serving on the bench, local bar associations conduct and publish
ratings of judges. A judge whose behavior clearly fits racial or ethnic stereotypes might
easily attract the attention from these associations, causing a controversy that could be detri-
mental to her career. An analogous reasoning could explain why male and female judges do
not differ substantially in their sentencing behavior.
In the third specification, we incorporate full interactions between judges’ and defendants’
31For example, in the case of the U.S. Supreme Court, the only black justice, Clarence Thomas, is on theconservative side of the ideological spectrum.
24
race:
Harshnessijt = β0 +β1Black Judgei ∗Black Defj +β2Black Judgei ∗HispanicDefj
+β3Black Judgei ∗WhiteDefj +β4HispanicJudgei ∗Black Defj
+β5HispanicJudgei ∗HispanicDefj +β6HispanicJudgei ∗WhiteDefj
+β7FemaleJudgei ∗MaleDefj +β8FemaleJudgei ∗FemaleDefj
+β9Black Defj +β10HispanicDefj +β11FemaleDefj
+γxi jt +δwit + εi jt , (4)
where Black Defj, HispanicDefj, WhiteDefj , MaleDefj and FemaleDefj are dummy vari-
ables indicating that the defendant in case j is Black, Hispanic, White, male and female,
respectively, xi jt is a vector of case characteristics, and wit is a vector of other characteristics
of judge i and his/her county in year t. Table 5 shows the results.
The coefficients for the full interactions between judges’ and defendants’ race, ethnicity
and gender are less precisely estimated than those in Table 4 because the number of observa-
tions in each group becomes smaller. However, the results in Table 5 are still consistent with
those in Table 4. African-American judges show some favoritism (relative to non-Hispanic
white judges) for minority defendants. Specifically, using the full sample, cases in which
both the judge ad the defendant are African-American are associated to a decrease of 2.68
percent in the range of Harshness (an effect of roughly three months, while the average sen-
tence is about five years), and those with African-American judges and Hispanic defendants
are associated with a decrease of 1.22 percent in the range of Harshness (forty one days). For
violent crimes in which the judge is African-American and the the defendant is non-Hispanic
white, Harshness increases by 2.89 percentage points (218 days, while the average sentence
for this category is roughly 11 years). For drug-related offenses, Harshness decreases by
2.87 percentage points (81 days, while the average sentence is approximately four years)
25
Table 5: The Influence of Judges’ Race on Sentencing Harshness - with Full Race Interaction
Dependent variable: Harshness(1) (2) (3) (4) (5)Full Violent Sexual Property DrugVariables
Sample Offenses Assaults Crimes Offenses
BlackJudge*BlackDef -0.0268*** 0.0007 0.0237 0.0044 -0.0287***(0.0103) (0.0266) (0.0736) (0.0086) (0.0070)
BlackJudge*HispDef -0.0122*** -0.0040 0.0725*** -0.0281** 0.0030(0.0041) (0.0065) (0.0166) (0.0131) (0.0085)
BlackJudge*WhiteDef 0.0081 0.0289*** 0.0424 0.0084 0.0417*(0.0078) (0.0111) (0.0347) (0.0059) (0.0222)
HispJudge*BlackDef 0.0095 -0.0049 -0.0091 0.0402* 0.0029(0.0160) (0.0164) (0.0185) (0.0219) (0.0105)
HispJudge*HispDef -0.0108 0.0043 0.0172 0.0101 -0.0366***(0.0096) (0.0123) (0.0162) (0.0151) (0.0079)
HispJudge*WhiteDef -0.0060 -0.0184 -0.0064 0.0159 -0.0148(0.0105) (0.0112) (0.0270) (0.0199) (0.0096)
FemJudge*MaleDef 0.0029 0.0076 0.0158 -0.0029 0.0036(0.0052) (0.0063) (0.0119) (0.0052) (0.0053)
FemJudge*FemDef 0.0064 0.0389** 0.0122 -0.0063 0.0003(0.0072) (0.0193) (0.0383) (0.0099) (0.0080)
Black Defendant -0.0076** -0.0078 -0.0264** -0.0319*** 0.0118*(0.0031) (0.0050) (0.0131) (0.0063) (0.0065)
Hispanic Defendant 0.0025 -0.0244*** -0.0512*** -0.0247*** 0.0580***(0.0025) (0.0047) (0.0121) (0.0051) (0.0060)
Female Defendant -0.0617*** -0.0885*** -0.1212*** -0.0344*** -0.0609***(0.0028) (0.0072) (0.0352) (0.0045) (0.0041)
Age at Offense 0.0014** 0.0072*** 0.0304*** 0.0060*** 0.0026*(0.0005) (0.0013) (0.0025) (0.0012) (0.0014)
Age squared -0.0000 -0.0001*** -0.0003*** -0.0001*** -0.0001***(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Years in Office -0.0002 -0.0004 -0.0009 0.0003 -0.0001(0.0003) (0.0004) (0.0007) (0.0005) (0.0003)
Observations 228,557 44,566 7,322 38,770 70,691R-squared 0.160 0.153 0.308 0.177 0.273
Note 1: Results from OLS regressions. Standard errors, clustered by county, in parentheses: ∗∗∗
significant at 1%, ∗∗ significant at 5% and ∗ significant at 10%.Note 2: All specifications include county-year fixed effects, offense categories (12 dummy vari-ables for 13 categories), and the criminal history of defendants as control variables.
26
when both the judge and the defendant are African-American and by 3.66 percentage points
(roughly 100 days) when both are Hispanic.32 The results in Table 5 also indicate that female
judges assign longer sentences to female defendants in violent offense cases (Column (2)).
Similarly to Table 4, the magnitudes of all these effects are relatively small.
The results in Tables 4 and 5 refer to specifications that include county-year fixed-effects.
We use these fixed-effects as controls in our main specification because the race, ethnicity
and gender of the judges in our sample correlate with county-level variables that may affect
the length of the sentences assigned.33 But, as discussed in Section 4.3.3, our results do not
change substantially if, instead, we consider specifications without county-year fixed effects.
Indeed, female judges remain largely indistinguishable from male ones. African-American
and Hispanic judges seem to assign shorter sentences than their non-Hispanic white peers,
but the magnitude of the effects is small.
A potential concern with our measures of sentencing harshness is that our data contains
only incarceration sentences. Cases resulting in other outcomes such as probation or com-
munity service are not observed, which causes a sample selection problem. One way to
address this issue is to treat the selection process as one of truncation—i.e., model incarcer-
ation sentences as positive realizations of a latent harshness variable that assumes negative
values when a case results in a non-incarceration sentence. Under this assumption, the selec-
tion problem can be addressed by estimating a truncated regression model. A challenge with
32Drug-related offenses (those involving drug possession in particular) are often classified as relatively mild.One could interpret our findings as suggesting that racial and ethnical biases in sentencing tend to occur in theless serious cases, maybe because judges deciding these cases are normally under low scrutiny. To address thispossibility, we estimated equation (4) using only non-drug-related offenses classified as “state jail felonies”, arelatively mild severity level to which most drug procession cases in our data belong. The results indicate noracial or ethnical bias by the judges. Estimating the same specification for drug-related state jail felonies, wefound results similar to those in column (5) of Table 5. These findings, which are available from the authorsupon request, provide further support for the hypothesis that drug-related cases are the only ones in whichsentencing is (slightly) biased in favor of defendants of the same race or ethnicity as the judge.
33The correlation coefficients between Democratic Vote Share (DVS) and Black Judgei, HispanicJudgei andFemaleJudgei are 0.19, 0.41 and 0.20, respectively. Those between (log) per-capita income and Black Judgei,HispanicJudgei and FemaleJudgei are 0.16, -0.37 and 0.01, respectively. As shown in Section 4.3.2, both DV Sand per-capita income are correlated with Harshness.
27
this approach in our setting is incorporating county-year fixed effects. Using our full sample,
there are too many county-year parameters to be estimated, which hinders convergence of
the estimator. We therefore restricted our attention to cases resolved in four large counties—
Harris, Dallas, Tarrant and Bexar. We estimated equations (2), (3), (4) and (5) (the last of
which is to be discussed in section 4.2) by OLS and truncated regression model, controlling
for county and year-specific fixed effects. The results, which are available from the authors
upon request, are almost identical for the OLS and the truncated model. This suggests that
the OLS results presented throughout the paper are not heavily affected by sample selection.
In the Appendix – Section A, we present sensitivity analyses of the results in this section
with alternative measures of sentencing harshness. The results are overall robust to variation
in the measures.
4.2 The Influence of Judges’ Political Background
We now turn to the analysis of how judges’ political background is reflected in their sentenc-
ing harshness. We first estimate partisan bias without interacting it with defendant charac-
teristics:
Harshnessijt = β0 +β1Republicani + γxi jt +δwit + εi jt , (5)
where Republicani is a dummy variable indicating that judge i is Republican, xi jt is a vector
of case characteristics, and wit is a vector of other characteristics of judge i and his/her county
in year t.
The results are presented in Table 6. The influence of party affiliation is precisely es-
timated, and suggests that, if anything, Republican judges tend to assign shorter sentences
than Democrats and independents in sexual assault and property crime cases—2.23 and 2.56
28
Table 6: The Influence of Judges’ Party Affiliation on Sentencing Harshness - Baseline
Dependent variable: Harshness(1) (2) (3) (4) (5)Full Violent Sexual Property DrugVariables
Sample Offenses Assaults Crimes Offenses
Republican -0.0088 -0.0133 -0.0223* -0.0256** 0.0058(0.0092) (0.0172) (0.0123) (0.0108) (0.0063)
Black Defendant -0.0078** -0.0061 -0.0247* -0.0302*** 0.0104(0.0032) (0.0051) (0.0128) (0.0057) (0.0069)
Hispanic Defendant 0.0005 -0.0213*** -0.0462*** -0.0282*** 0.0523***(0.0026) (0.0044) (0.0121) (0.0065) (0.0062)
Female Defendant -0.0612*** -0.0821*** -0.1272*** -0.0360*** -0.0615***(0.0024) (0.0066) (0.0302) (0.0043) (0.0037)
Age at Offense 0.0014** 0.0074*** 0.0304*** 0.0061*** 0.0026*(0.0005) (0.0013) (0.0026) (0.0012) (0.0014)
Age squared -0.0000 -0.0001*** -0.0003*** -0.0001*** -0.0001***(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Years in Office -0.0001 -0.0005 -0.0011 0.0003 0.0001(0.0003) (0.0004) (0.0008) (0.0005) (0.0003)
Observations 219,489 42,933 7,047 37,370 67,542R-squared 0.161 0.153 0.305 0.177 0.275
Note 1: Results from OLS regressions. Standard errors, clustered by county, in parentheses: ∗∗∗
significant at 1%, ∗∗ significant at 5% and ∗ significant at 10%.Note 2: All specifications include county-year fixed effects, offense categories (12 dummy vari-ables for 13 categories), and the criminal history of defendants as control variables.
percent less in the range of Harshness, respectively.34 For other crime categories, the effect
of party affiliation is statistically insignificant. As shown in Section 4.3.3, the result holds
in specifications that exclude county-year fixed-effects. In the Appendix – Section B, we
present sensitivity analyses of these results using alternative specifications and measures,
which show strong robustness.
Overall, our findings are consistent with an early study by Ashenfelter et al. (1995), which
presents evidence that the party affiliation of a judge’s nominating president does not signif-
34The effect for sexual assault cases is equivalent to a reduction of 313.07 days in the sentence assigned. Toput this reduction into perspective, notice that the average sentence for sexual assault cases in the sample isroughly 20 years. The effect on property crimes corresponds to a reduction of 23.09 days, while the averagesentence for this offense category is around one year and a half.
29
icantly affect trial outcomes. Unlike many appellate court decisions, sentencing decisions
by trial court judges are essentially bureaucratic tasks rather than policy-making. Thus, the
absence of partisan bias is plausible.
It is possible that, even though trial judges would like to act in a more explicitly partisan
manner, the influence of party affiliation on their behavior is limited by the possibility of
having controversial decisions overturned by the appellate courts, which would rationalize
the null results reported above. If this hypothesis is true, it is possible that decisions taken
by trial judges depend on how liberal or conservative the appellate courts are. We tested
this hypothesis using variation in the partisan composition of the Texas courts of appeals.
The state is geographically divided into fourteen courts of appeals.35 Using electoral data,
Texas Courts Online, the Appellate Advocate, and the American Bench, we reconstructed
the partisanship of each court of appeals for each year of our sample. The courts of appeals
are predominately Republican, reflecting the political orientation of the state. However, two
of the courts had a majority of Democrats in the period covered by our data, and one court
had a Democratic majority during a part of this period.36 Seven of the fourteen courts ex-
hibited some changes in partisanship during the data period.37 We added the interaction of
the district judge’s party with the proportion of Republican judges at the court of appeals to
equation (5). We found no systematic evidence of the influence of appellate court partisan-
ship on sentencing.
35Except in a few cases, counties are not split across these Courts. Harris County (Houston) is served by the1st and 14th courts of appeals. The number of justices varies across courts, from three to 13.
36The average proportion of Republican judges in the courts of appeals over the 2004-2013 period is 76.38%.Both the 8th and the 13th courts of appeals start 2004 as entirely Democrat. The former court remains entirelyDemocrat over the period covered by our data, while, in the latter court, the proportion of Republican judgesincreases to one-third by 2013. The 6th court of appeals has a proportion of one-fourth Republican judges in2004, which increases to two-thirds by 2013.
37The overall standard deviation of the shares of Republican judges across courts of appeals and over timein our data is 0.32. If we subtract from these shares the means at the court of appeals level and leave onlythe variation over time, the standard deviation is still 0.08. This within-court variation allows us to control forcounty fixed effects (without the interaction with year fixed effects) in our regressions.
30
4.3 The Extent of Judge Heterogeneity
The analyses above show that key observable characteristics of judges do not have much
explanatory power for sentencing harshness, conditional on geographic factors. This finding
leads to an important question: do judges vary at all in sentencing harshness in Texas? Sev-
eral studies found substantial cross-judge variation in decisions on criminal cases in other
settings and using other measures. For example, Abrams et al. (2012) find significant cross-
judge heterogeneity in the racial gap in incarceration rates in Cook County, Illinois. Lim
(2013) also finds that sentencing harshness varies substantially across the political orientation
of electorates when judges are selected and retained through partisan elections in Kansas.38
To investigate this issue, we first document the extent of cross-judge heterogeneity in
sentencing harshness. Then, we conduct additional analyses to explore its causes. First, we
examine the extent to which cross-judge variation in sentencing is explained by the fact that
they serve in different areas.39 Second, we analyze the extent to which electorates’ racial
composition and political orientation are related to judges’ sentencing harshness. Third, we
examine the relationship between various characteristics of judges and sentencing harshness,
unconditional on geographic factors. To quantify the extent of cross-judge heterogeneity in
38She argues that reelection incentives play an important role in sentencing variation across districts. Shealso conducts various simulations to show that the relationship between the political orientation of electoratesand sentencing harshness critically depends on judges’ payoff from the office. If judges’ payoff from the officeis not significantly higher than their potential payoff from outside options (e.g., law practice), then reelectionincentives may be weak, which in turn reduces differences in sentencing harshness across areas.
39Our analysis in this section is, to some extent, related to an empirical literature that investigates the impactof teachers on the performance of students. For a survey of that literature, see McCaffrey et al. (2004). Similarto a sentencing decision—which depends on the judge, the county of prosecution and the characteristics of thecase—the performance of a student depends on the teacher, the classroom and the said student’s characteristics.Papers in the teachers’ impact literature often employ empirical Bayes “shrinkage” methods to distinguishteacher-specific effects from classroom-specific ones. Recent examples include Kane and Staiger (2008) andChetty et al. (2005). In principle, we could adapt these methods to separately estimate judge and county-specific effects in our setting. However, to the extent of our knowledge, doing so would require us to assumethat these two effects are independently distributed. Such an assumption is likely to be unreasonable in thecontext of our analysis, since Texas is a large and heterogeneous state, and judges are locally elected. Indeed,in the current section, we present evidence that observable characteristics of judges’ political jurisdictions arerelated to sentencing harshness. Therefore, we decided against using shrinkage methods in our study.
31
sentencing harshness, we estimate a regression model of the following form:
Harshnessijt = α+βxi jt + γwi +δzt + εi jt , (6)
where xi jt is a vector of case characteristics, wi is a judge fixed effect, zt is a year fixed
effect, and εi jt captures idiosyncratic, unobservable characteristics of the case. Figure 2
01
23
4D
ensi
ty
-.2 0 .2 .4 .6Judge Fixed Effects
Mean 0.00SD 0.11Min -0.25p25 -0.07Median 0.01p75 0.07Max 0.70Count 400.00
F=55.959p-value=0.00
Figure 2: Judge Fixed Effects
shows a histogram of judge fixed effects from the above model (6), their summary statistics,
and the F-test result of a hypothesis that all the judge fixed effects are zero.40 The standard
deviation of judge fixed effects is .11 points, that is, 11% of the approximate range of judges’
discretion—which, on average, is equivalent to a change of roughly one year in the sentence
assigned and is comparable to the effect of increasing the number of previous violent crime
convictions of the defendant from none to two.41 The interquartile range, 0.14 points, is
close to the effect of changing criminal history from none to three violent crime convictions.
Using the conventional F-test, we reject the hypothesis that judge fixed effects do not affect
sentencing.
40To avoid estimates of judge heterogeneity being driven by judges who handle a small number of cases, werestrict this analysis to judges who handled at least 50 cases.
41In our data, having one, two, and three previous convictions of violent crimes increases sentencing harsh-ness by 0.06, 0.10, and 0.19 points, respectively, compared with a defendant who has no history of violentcrime convictions.
32
4.3.1 Variation Across Judges vs. Variation Across Counties
How much of the variation in sentencing behavior across judges is driven by the fact that
different judges serve in different areas, and how much is due to judge-specific factors? We
can address this question by comparing different judges in the same county. For judges who
serve multiple counties, we can also assess the degree to which judges are consistent in their
sentencing behavior across counties. While judges might seek consistency, they might also
vary their sentencing behavior across counties, perhaps to cater to local tastes, or to avoid
“sticking out” relative to other judges serving in a given county. To address these issues, we
estimate a regression model of the following form:
Harshnessijt = α+βxi jt + γwic +δzt + εi jt , (7)
where xi jt is a vector of case characteristics (the criminal history of defendants and crime
category), wic is a vector of judge-county dummies, zt is a vector of year indicators, and εi jt
captures idiosyncratic, unobservable characteristics of the case.
The vector γ captures judge-county fixed effects. Our estimates for these fixed effects
present three revealing patterns. First, the within-county variation across judges is much
larger than the within-judge variation across counties. Specifically, let γic denote the esti-
mated fixed-effect for judge i in county c. Averaging across counties, the standard deviation
of the γic across judges within counties is .252. Averaging across judges, the standard devia-
tion of the γic across counties within judges is just .152.
Second, as a corollary, the variation across judges accounts for much more of the overall
variation in the γic’s than the variation across counties. For each judge i, let γi be the average
of the γic across the counties i serves, and for each county c, let γc be the average of the γic
across the judges who serve in c. Regressing γic on γi yields an R2 of .97, while regressing
33
Table 7: Decomposition of Judge-County Fixed Effects
Dependent variable: γicγc,−i -.004
(.030)γi,−c .961
(.026)constant -.000
(.006)R2 .923Observations 159
γic on γc yields an R2 of only .16.42
Third, there is little evidence that judges shift their sentencing behavior toward other
judges in counties they serve. For each judge i and each county c, let γc,−i be the average of
the γic across all judges who serve in c other than judge i. Also, let γi,−c be the average of the
γic across all counties served by judges i other than c. Regressing γic on both γc,−i and γi,−c
yields the results in Table 7. The coefficient on γc,−i is nearly zero and statistically insignif-
icant, while the coefficient on γi,−c is large and highly significant. That is, the idiosyncratic
features of a given judge’s sentencing behavior are essentially unrelated to the behavior of
other judges in the counties served by the judge. On the other hand, the idiosyncratic fea-
tures of a given judge’s sentencing behavior are quite similar across the counties served by
the judge.
4.3.2 The Influence of Localities and Political Jurisdictions
We now investigate the influence of localities (counties) and political jurisdictions (judicial
districts) to understand sources of cross-judge heterogeneity in sentencing harshness. In the
analyses presented in Table 8, we regress our normalized measure of sentencing harshness,
Harshness, on demographic characteristics, political orientation, and their interaction with
42Regressing γic on both γi and γc also yields an R2 of .97.
34
defendant and judge characteristics.43 For demographic characteristics, we use the share
of black population, the share of Hispanics, (log) per-capita income, and (log) crime rate.
For political orientation, we use Democratic Vote Share. We measure these variables at two
levels—the county where the case was prosecuted and the district of the judge deciding the
case.
Columns (1)-(3) report the results obtained using our full sample and measuring demo-
graphic and political orientation variables at the county level. In Column (1), we interact the
defendant’s race with the racial composition of the county where the case was prosecuted.
In Column (2), we interact the defendant’s race with the political orientation of the county.
In Column (3), we add the judge’s party affiliation interacted with the political orientation
of the county. The results suggest that the share of African-Americans and Hispanics in the
county population is negatively correlated with the harshness of the assigned sentences. The
effect of the African-American population is non-trivial. An increase of ten p.p. in the share
African-Americans is associated to a decrease of approximately two percent in Harshness
(−0.20∗0.10 =−0.02), or, on average, 66.72 days. The same increase in the share of His-
panics is associated with a decrease of only one percent in the measure of harshness. The
effects of racial and ethnic composition of the county do not depend on the defendants’ race
and ethnicity.
All of the three columns consistently show a moderate but statistically significant influ-
ence of the political orientation and income level of the communities.44 Counties that are
liberal (with a large value of DVS) or have high income tend to have more lenient judges. A
one standard deviation (14 percentage point) increase in Democratic Vote Share decreases
Harshness by approximately four percent (0.003 ∗ 14 ≈ 0.042) of the range of sentencing
harshness, which, averaging across the cases in the sample, is equivalent to a decrease of
43We also control for crime categories and include year fixed effects in all specifications.44Crime rate is also significantly correlated with Harshness. This could be interpreted as the result of reverse
causality (i.e., the influence of sentencing on crime rates rather than vice versa).
35
Tabl
e8:
The
Influ
ence
ofL
ocal
ities
and
Polit
ical
Juri
sdic
tions
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Bla
ckD
efen
dant
-0.0
211*
0.01
590.
0109
0.00
69-0
.007
1-0
.005
00.
0065
-0.0
068
-0.0
014
(0.0
113)
(0.0
168)
(0.0
163)
(0.0
134)
(0.0
201)
(0.0
192)
(0.0
140)
(0.0
214)
(0.0
206)
Shar
eB
lack
-0.2
253*
*-0
.192
5*-0
.216
3**
0.02
260.
0156
-0.0
679
0.06
960.
0573
-0.0
223
(0.1
041)
(0.0
979)
(0.1
076)
(0.1
106)
(0.1
135)
(0.1
099)
(0.1
209)
(0.1
162)
(0.1
147)
Bla
ckD
efen
dant
*Sh
are
Bla
ck0.
0837
-0.0
778
-0.0
940
(0.0
722)
(0.1
570)
(0.1
707)
Bla
ckD
efen
dant
*D
VS
-0.0
007
-0.0
006
0.00
020.
0001
0.00
01-0
.000
1(0
.000
5)(0
.000
4)(0
.000
7)(0
.000
6)(0
.000
7)(0
.000
7)H
ispa
nic
Def
enda
nt0.
0212
0.01
790.
0061
0.01
49-0
.000
7-0
.013
70.
0107
-0.0
037
-0.0
171
(0.0
155)
(0.0
184)
(0.0
148)
(0.0
133)
(0.0
173)
(0.0
162)
(0.0
137)
(0.0
181)
(0.0
162)
Shar
eH
ispa
nic
-0.0
966*
-0.1
106*
*-0
.099
7*0.
0078
0.00
34-0
.016
70.
0117
0.00
94-0
.008
7(0
.055
7)(0
.053
3)(0
.051
9)(0
.043
7)(0
.043
3)(0
.042
1)(0
.043
8)(0
.042
2)(0
.042
3)H
ispa
nic
Def
enda
nt*S
hare
His
pani
c-0
.043
5-0
.018
8-0
.010
8(0
.043
0)(0
.031
5)(0
.031
5)H
ispa
nic
Def
enda
nt*
DV
S-0
.000
3-0
.000
10.
0003
0.00
060.
0003
0.00
07(0
.000
5)(0
.000
4)(0
.000
5)(0
.000
5)(0
.000
5)(0
.000
5)R
epub
lican
Judg
e-0
.071
1**
-0.0
445*
-0.0
455
(0.0
353)
(0.0
267)
(0.0
285)
Rep
ublic
anJu
dge
*D
VS
0.00
20**
0.00
20**
0.00
19**
(0.0
009)
(0.0
008)
(0.0
009)
Fem
ale
Def
enda
nt-0
.060
7***
-0.0
606*
**-0
.059
7***
-0.0
627*
**-0
.062
7***
-0.0
635*
**-0
.063
5***
-0.0
635*
**-0
.064
2***
(0.0
022)
(0.0
021)
(0.0
023)
(0.0
043)
(0.0
043)
(0.0
043)
(0.0
043)
(0.0
043)
(0.0
043)
Age
AtO
ffen
se0.
0016
***
0.00
16**
*0.
0017
***
0.00
070.
0008
0.00
090.
0007
0.00
080.
0009
(0.0
005)
(0.0
005)
(0.0
006)
(0.0
010)
(0.0
010)
(0.0
010)
(0.0
010)
(0.0
010)
(0.0
010)
Age
Squa
red
-0.0
000
-0.0
000
-0.0
000
0.00
000.
0000
0.00
000.
0000
0.00
000.
0000
(0.0
000)
(0.0
000)
(0.0
000)
(0.0
000)
(0.0
000)
(0.0
000)
(0.0
000)
(0.0
000)
(0.0
000)
Dem
ocra
ticVo
teSh
are
(DV
S)-0
.003
2***
-0.0
029*
**-0
.003
9***
-0.0
012*
-0.0
015*
-0.0
019*
*-0
.001
2-0
.001
5*-0
.002
0**
(0.0
007)
(0.0
007)
(0.0
007)
(0.0
007)
(0.0
008)
(0.0
008)
(0.0
007)
(0.0
008)
(0.0
008)
(log
)Per
-cap
itaIn
com
e-0
.161
2***
-0.1
514*
**-0
.145
0***
-0.0
072
-0.0
031
-0.0
089
0.00
120.
0056
-0.0
028
(0.0
348)
(0.0
362)
(0.0
353)
(0.0
331)
(0.0
336)
(0.0
337)
(0.0
393)
(0.0
393)
(0.0
414)
(log
)Cri
me
Rat
e-0
.012
3**
-0.0
131*
*-0
.013
6**
-0.0
033
-0.0
037
-0.0
035
-0.0
096
-0.0
100
-0.0
094
(0.0
056)
(0.0
056)
(0.0
056)
(0.0
049)
(0.0
051)
(0.0
045)
(0.0
068)
(0.0
069)
(0.0
067)
Geo
grap
hic
Uni
tC
ount
yC
ount
yC
ount
yC
ount
yC
ount
yC
ount
yD
istr
ict
Dis
tric
tD
istr
ict
Obs
erva
tions
248,
151
248,
151
219,
350
61,4
6661
,466
58,9
6861
,589
61,5
8959
,091
R-s
quar
ed0.
117
0.11
70.
116
0.09
60.
096
0.09
80.
096
0.09
60.
098
36
140.11 days. Interestingly, column (3) shows a statistically significant coefficient estimate
of Republican∗DVS. The sign of the coefficient is positive, indicating that Republican judges
tend to become harsher on the defendant as the county gets more liberal, but the magnitude
of the effect is small. As for income, a one standard deviation (.20) increase in (log) per-
capita income decreases Harshness by approximately three percent (−0.15∗0.20 =−0.03)
of its range.45
We are interested in assessing the relative importance of the county of prosecution versus
the judicial district in explaining variations in sentencing harshness. Unfortunately, in our
full sample, the correlation between variables measured at the county and at the district
levels is very high.46 We therefore consider a subsample of cases from judicial areas with
a multi-county, multi-district overlapping pattern.47 Focusing on these areas alleviates to
some extent the correlation between county-level and district-level variables.48 Although, in
practice, it is still not possible to consider specifications simultaneously including county-
level and district-level characteristics, we are able to separately analyze the influence of these
two sets of variables on sentencing harshness and compare the magnitude of the estimated
effects.49
Columns (4)-(6) of Table 8 present regression results using only cases from multi-county,
multi-district areas and county-level variables. Columns (7)-(9) present the results of similar
regressions using district-level variables. We find no evidence of a strong relationship be-
45Theoretically, it is not obvious in what direction income level should be correlated with sentencing harsh-ness. On one hand, low income communities may be more conscious of social problems associated with crimes(gang activities, drug problems, etc.), which would in turn generate strong social pressure to reduce crime. Onthe other hand, affluent communities may be more sensitive to property crimes than poor communities be-cause the economic loss would be larger in the former. The overall relationship between the income level ofcommunities and sentencing harshness will be the combination of these two forces.
46Using variables measured at the district level in specifications analogous to the ones in columns (1)-(3) ofTable 8 generates nearly identical results. The correlation between variables measured at the county and districtlevels using the full sample ranges between 0.98 and 0.99 for each one of the variables considered.
47Specifically, we use areas with overlapping patterns C, E and F in Table 1.48The correlation coefficients are as follows: 0.97 for the Democratic vote share, 0.93 for the black popula-
tion share, 0.97 for the Hispanic share, 0.89 for per capita income and 0.73 for the crime rate.49The results of regressions simultaneously including county-level and district-level variables, which are
available from the authors upon request, show clear signs of multicollinearity.
37
tween racial composition and sentencing harshness neither at the county level nor at the dis-
trict level. Concerning political orientation, the absolute value of the coefficients is smaller
than in the full sample. However, they are statistically significant, and the magnitude is sim-
ilar for the county and the district levels. The coefficient estimates of Republican ∗DVS in
columns (6) and (9) are statistically significant and very close to the estimates obtained with
the full sample. Neither the local racial and ethnic composition nor crime or per-capita in-
come are significant in any of Columns (4)-(9), unlike in the full sample. But coefficient esti-
mates are of similar magnitude for the county and district levels. Overall, we do not find any
systematic evidence that relationships between characteristics of communities and sentenc-
ing harshness are driven by district-level versus county-level variations. Rather, county-level
variables seem to be related to sentencing in a similar way to district-level variables.
4.3.3 The Influence of Judges’ Backgrounds without County-year Fixed Effects
In this subsection, we present additional analyses of the relationship between judges’ sen-
tencing harshness and their backgrounds. The results from the preceding analyses can be
summarized as follows: (1) judges’ demographic characteristics have almost no explanatory
power conditional on geographic factors (county-year fixed effects); (2) nevertheless, there is
substantial cross-judge heterogeneity in sentencing harshness; and (3) observable character-
istics of localities and political jurisdictions only have moderate relationships to sentencing
harshness. These observations lead us to the following question: to what extent do judges’
demographic backgrounds explain their sentencing harshness if we do not condition on geo-
graphic factors? Do other elements of the judges’ backgrounds, such as career history, have
any explanatory power? We address these questions below.
In Table 9, we regress judge fixed effects, obtained in Section 4.3, on judges’ demographic
characteristics and their career history. For their career history, we use three variables: total
legal experience, experience in private law practice, and experience in prosecution—all of
38
Table 9: Regression of Judge Fixed Effects on Demographic and Career Backgrounds
Dependent Variable: Judge Fixed EffectVariables (1) (2) (3) (4) (5) (6)
Total Legal Experience 0.0004 -0.0016 -0.0052(0.0008) (0.0012) (0.0049)
Total Legal Experience2 0.0001(0.0001)
Private Law Practice 0.0017** 0.0029*** 0.0084***(0.0007) (0.0011) (0.0029)
Private Law Practice2 -0.0002**(0.0001)
Prosecution -0.0009 0.0010 -0.0018(0.0012) (0.0014) (0.0039)
Prosecution2 0.0002(0.0002)
Black Judge -0.0517 -0.0389 -0.0478 -0.0393 -0.0281 -0.0533(0.0459) (0.0510) (0.0519) (0.0517) (0.0514) (0.0455)
Hispanic Judge -0.0416* -0.0302 -0.0307 -0.0216 -0.0244 -0.0428**(0.0213) (0.0232) (0.0237) (0.0243) (0.0241) (0.0210)
Female Judge -0.0247 -0.0213 -0.0291 -0.0219 -0.0163 -0.0258(0.0171) (0.0187) (0.0184) (0.0192) (0.0191) (0.0168)
Republican Judge 0.0008 0.0020 0.0010 0.0036 0.0050 0.0001(0.0141) (0.0156) (0.0159) (0.0162) (0.0161) (0.0139)
Observations 304 256 256 243 243 307R-squared 0.043 0.060 0.036 0.065 0.095 0.042
Note: Results from OLS regressions. Standard errors, clustered by county, in parentheses: ∗∗∗ significantat 1%, ∗∗ significant at 5% and ∗ significant at 10%. In all regressions, unit of observation is individualjudge.
which are measured in number of years.
The results indicate that Hispanic judges are slightly more lenient than non-Hispanic ones,
although the effect is only statistically significant in two out of six specifications. The point
estimates suggest that female and African-American judges are associated with lenient sen-
tencing, but the effects are always small in magnitude and statistically insignificant. Inter-
estingly, party affiliation has almost no relationship with sentencing harshness, even uncon-
ditional on geographic factors.
The results also indicate that career history has little relationship with sentencing harsh-
39
ness. Only the experience in private law practice has a statistically significant relationship
with sentencing harshness in a subset of regressions, and the magnitude of the coefficient is
small. In Columns (2) and (4) and (5), one standard deviation (10 years) increase in private
law practice is associated with an increase in sentencing harshness by 1.7 (0.0017*10=0.017)
to 8.4 percent of the range.
In Table 10, we present case-level regressions of sentencing on judges’ backgrounds. In
Column (3), we measure the lengths of experience in private law practice and prosecution as
their ratio to the total legal experience. The key difference between this set of regressions and
its counterpart in Section 4.1 is that we do not control for county-year fixed effects. The key
results are consistent with Table 9. African-American and Hispanic judges are moderately
associated with lenient sentencing, though the relationship is statistically insignificant in
many of the specifications. Career history and party affiliation have little explanatory power.
5 Conclusion
This paper studies the influence of judges’ race, ethnicity, and party affiliation on criminal
sentencing decisions. Our key results show precisely estimated null effects, conditional on
geographic factors. Even without conditioning on geographic factors, we find no systematic
evidence that sentencing decisions are strongly influenced by judges’ race, ethnicity, or party
affiliation.
The difference between our results and previous studies on the influence of race and party
affiliation in other settings suggests that the influence may critically depend on the nature
of decision-making. Quick decisions (e.g., by sports referees), decisions by non-experts
(e.g., jurors in criminal trials), or decisions by policy-makers (e.g., U.S. Congressmen) may
be significantly influenced by race or political orientation. In contrast, decisions by those
who perform relatively bureaucratic functions that require significant expertise and are also
40
Tabl
e10
:Cas
e-le
velR
egre
ssio
nsof
Har
shne
sson
Judg
es’B
ackg
roun
ds
Vio
lent
Sexu
alD
rug
Prop
erty
Full
Sam
ple
Cri
me
Ass
ault
Off
ense
Cri
me
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Judg
e’s
age
0.00
050.
0018
0.00
160.
0017
0.00
31*
0.00
190.
0014
0.00
13(0
.000
6)(0
.001
7)(0
.001
6)(0
.001
7)(0
.001
6)(0
.002
0)(0
.001
8)(0
.001
8)To
talL
egal
Exp
erie
nce
-0.0
010
-0.0
024
-0.0
013
-0.0
067
-0.0
059
-0.0
037
-0.0
015
-0.0
045
(0.0
007)
(0.0
019)
(0.0
017)
(0.0
043)
(0.0
039)
(0.0
049)
(0.0
050)
(0.0
044)
Tota
lLeg
alE
xper
ienc
e20.
0001
0.00
000.
0001
0.00
000.
0001
(0.0
001)
(0.0
001)
(0.0
001)
(0.0
001)
(0.0
001)
Priv
ate
Law
Prac
tice
-0.0
006
0.00
130.
0052
0.00
53*
0.00
56*
0.00
300.
0026
(0.0
009)
(0.0
012)
(0.0
032)
(0.0
028)
(0.0
032)
(0.0
034)
(0.0
036)
Priv
ate
Law
Prac
tice2
-0.0
001
-0.0
001
-0.0
002*
*-0
.000
1-0
.000
0(0
.000
1)(0
.000
1)(0
.000
1)(0
.000
1)(0
.000
1)Pr
ivat
eL
awPr
actic
e(R
atio
)0.
0397
(0.0
260)
Pros
ecut
ion
0.00
020.
0019
0.00
100.
0003
-0.0
004
0.00
17-0
.003
0(0
.000
8)(0
.001
6)(0
.004
5)(0
.004
1)(0
.004
2)(0
.004
5)(0
.004
8)Pr
osec
utio
n20.
0001
0.00
020.
0001
0.00
000.
0004
(0.0
002)
(0.0
002)
(0.0
002)
(0.0
002)
(0.0
003)
Pros
ecut
ion
(Rat
io)
0.02
09(0
.032
4)B
lack
Judg
e-0
.014
9*-0
.086
8**
-0.0
827*
*-0
.081
1*-0
.025
80.
0381
-0.1
184*
**-0
.080
9**
(0.0
079)
(0.0
402)
(0.0
397)
(0.0
417)
(0.0
472)
(0.0
344)
(0.0
404)
(0.0
326)
His
pani
cJu
dge
-0.0
348*
-0.0
239
-0.0
241
-0.0
263
-0.0
605
-0.0
567*
-0.0
025
0.00
42(0
.020
1)(0
.038
5)(0
.037
8)(0
.038
6)(0
.036
8)(0
.032
1)(0
.037
8)(0
.034
4)Fe
mal
eJu
dge
0.00
71-0
.010
7-0
.006
6-0
.006
6-0
.003
9-0
.019
4-0
.018
70.
0075
(0.0
126)
(0.0
182)
(0.0
173)
(0.0
173)
(0.0
158)
(0.0
202)
(0.0
217)
(0.0
186)
Rep
ublic
anJu
dge
-0.0
305*
**0.
0274
0.02
790.
0289
0.02
850.
0111
0.02
950.
0002
(0.0
101)
(0.0
253)
(0.0
256)
(0.0
254)
(0.0
197)
(0.0
211)
(0.0
265)
(0.0
317)
Obs
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41
compared to a large number of peers may not be influenced much by factors other than their
professional knowledge and skills.
Despite the null effect of judges’ racial, ethnic, and political backgrounds, we find sub-
stantial variation in judges’ sentencing harshness. Our analyses also show a remarkable
degree of consistency in individual judges’ sentencing behavior across counties. Further re-
search that examines the influence of other factors (e.g, competitiveness of the election of
judges or campaign contributions by trial lawyers) will help to enhance our understanding of
how to improve fairness in the application of law.
42
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Appendix
A Sensitivity Analysis – Race, Ethnicity, and Gender
In this section, we analyze the sensitivity of our results in Section 4.1 to alternative measures
of sentencing harshness. We consider five variants of our baseline measure defined in equa-
tion (1) on page 16. Our first alternative measure (Measure A) uses the minimum and the
maximum sentence lengths in each group of cases, instead of the 10th and 90th percentiles,
as anchoring values in the normalization of sentencing harshness. That is,
Measure A =Sentence−minimummaximum−minimum
. (8)
Our second alternative measure (Measure B) uses the 10th and 90th percentiles for normal-
ization as in our baseline measure. However, we do not use any observations whose jail time
is below the 10th percentile or above the 90th percentile, while in the baseline measure we
coded them as 0 and 1, respectively. That is,
Measure B =
missing if Sentence < P10,
Sentence−P10P90−P10
if P10 < Sentence < P90,
missing if Sentence > P90.
(9)
Our third alternative measure (Measure C) is identical to the baseline measure except that we
do not use bottom-coding or top-coding. That is, instead of coding sentence lengths below
the 10th percentile as 0 and above the 90th percentile as 1, we leave them as values below 0
and above 1, respectively. That is,
Measure C =Sentence−P10
P90−P10. (10)
49
Our fourth alternative measure (Measure D) is based on a different categorization of cases.
In addition to crime categories and sentencing year used for sentence normalization in our
baseline measure, Measure D considers the defendants’ criminal histories. For two cases to
belong to the same group, they should have identical number of defendants’ previous con-
victions in felony and violent felonies. In sum, Measure D normalizes sentencing harshness
relative to the group of cases that were sentenced with the same crime categories and defen-
dant criminal history and in the same year. Our fifth alternative measure (Measure E) is the
logarithm of the assigned sentence, without any kind of normalization.50
Tables A1 and A2 show the sensitivity analyses of the key results in Tables 4 and 5, re-
spectively. The results are remarkably robust to changes in the measure of harshness. Judges
are slightly more lenient towards defendants of the same race or ethnicity. The results con-
cerning gender are more mixed, with Columns (2) and (4) (measures A and C, respectively)
indicating that judges are more lenient towards defendants of the same gender and Column
(6) (measure E) suggesting the opposite effect. The analysis with the full set of interactions
suggests that the race and ethnicity result are driven by African-American and Hispanic
judges. The specification using the logarithm of the sentence as the dependent variable and
the full set of interactions as regressors (Table A2, column (6)), in particular, indicates that
African-American judges might assign shorter sentences to defendants of the same race.
As the evidence in Tables 4 and 5 suggest that the effects of race and ethnicity on sentenc-
ing are more substantial for drug-related offenses, we re-estimate the logarithm specifications
from Tables A1 and A2 restricting the sample to this offense category. Table A3 shows the
results of this subsample analysis. The most parsimonious specification (Column (2)) in-
dicates that, in drug-related cases, judges assign sentences that are 6.20 percent longer to
defendants of different race or ethnicity. Similarly, the specification with the complete set of
50As a sixth alternative measure, we also considered log(Max{Min{Sentence,P90},P10}). That is, we cen-sored the assigned sentences at the 10th and 90th percentiles in each group of cases, and took their logarithm.The results employing this sixth alternative measure, which are available upon request, are very similar to theones obtained using the logarithm of the uncensored sentences.
50
Table A1: Sensitivity Analysis using Alternative Measures - Baseline Specification
Dependent Variable (Measure of Sentencing Harshness)(1) (2) (3) (4) (5) (6)
Variables Baseline Measure A Measure B Measure C Measure D Measure E
Different Race 0.0061* 0.0006 0.0043* 0.0142* 0.0063** 0.0203*(0.0035) (0.0007) (0.0026) (0.0076) (0.0029) (0.0115)
Different Gender -0.0017 -0.0014* 0.0026 -0.0165** -0.0023 0.0176**(0.0026) (0.0008) (0.0017) (0.0069) (0.0026) (0.0080)
Black Judge -0.0125*** 0.0008 -0.0118*** -0.0135 -0.0144*** -0.0261**(0.0043) (0.0008) (0.0030) (0.0098) (0.0033) (0.0101)
Hispanic Judge -0.0034 -0.0008 -0.0034 -0.0074 -0.0073 -0.0067(0.0115) (0.0017) (0.0095) (0.0190) (0.0101) (0.0268)
Female Judge 0.0045 0.0019 0.0011 0.0216* 0.0068 0.0041(0.0057) (0.0012) (0.0044) (0.0114) (0.0055) (0.0158)
Years in Office -0.0002 -0.0001 -0.0001 -0.0003 -0.0003 -0.0007(0.0003) (0.0001) (0.0003) (0.0005) (0.0003) (0.0009)
Black Defendant -0.0126** -0.0003 -0.0118*** -0.0093 -0.0093** -0.0511***(0.0053) (0.0010) (0.0038) (0.0110) (0.0047) (0.0165)
Hispanic Defendant -0.0035 -0.0017*** -0.0014 -0.0142* -0.0009 0.0065(0.0042) (0.0006) (0.0034) (0.0083) (0.0037) (0.0111)
Female Defendant -0.0600*** -0.0106*** -0.0388*** -0.1182*** -0.0571*** -0.2192***(0.0024) (0.0009) (0.0018) (0.0098) (0.0024) (0.0163)
Age at Offense 0.0014** 0.0015*** -0.0011*** 0.0117*** 0.0021*** 0.0056***(0.0005) (0.0002) (0.0004) (0.0017) (0.0006) (0.0016)
Age squared -0.0000 -0.0000*** 0.0000** -0.0001*** -0.0000 -0.0000(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Observations 228,557 228,549 200,490 228,549 208,675 229,035R-squared 0.160 0.197 0.133 0.080 0.154 0.398
Note 1: Results from OLS regressions. Standard errors, clustered by county, in parentheses: ∗∗∗ significant at1%, ∗∗ significant at 5% and ∗ significant at 10%.Note 2: All specifications include county-year fixed effects, offense categories (12 dummy variables for 13categories), and the criminal history of defendants as control variables.
51
Table A2: Sensitivity Analysis using Alternative Measures - with Full Race Interactions
Dependent Variable (Measure of Sentencing Harshness)Variables (1) (2) (3) (4) (5) (6)
Baseline Measure A Measure B Measure C Measure D Measure E
BlackJudge*BlackDef -0.0268*** -0.0013 -0.0225*** -0.0440 -0.0234*** -0.1031***(0.0103) (0.0029) (0.0051) (0.0269) (0.0085) (0.0342)
BlackJudge*HispDef -0.0122*** 0.0006 -0.0102*** -0.0099 -0.0145*** 0.0005(0.0041) (0.0019) (0.0024) (0.0216) (0.0027) (0.0196)
BlackJudge*WhiteDef 0.0081 0.0031** 0.0025 0.0143 -0.0007 0.0289(0.0079) (0.0017) (0.0084) (0.0154) (0.0076) (0.0240)
HispJudge*BlackDef 0.0095 -0.0008 0.0119 0.0031 0.0024 0.0218(0.0160) (0.0025) (0.0117) (0.0254) (0.0152) (0.0400)
HispJudge*HispDef -0.0108 -0.0010 -0.0099 -0.0196 -0.0143* -0.0256(0.0096) (0.0017) (0.0084) (0.0186) (0.0084) (0.0246)
HispJudge*WhiteDef -0.0060 -0.0005 -0.0089 0.0017 -0.0057 -0.0100(0.0105) (0.0017) (0.0086) (0.0222) (0.0097) (0.0251)
FemJudge*MaleDef 0.0029 0.0005 0.0037 0.0051 0.0045 0.0215*(0.0052) (0.0008) (0.0041) (0.0080) (0.0048) (0.0123)
FemJudge*FemDef 0.0064 0.0033* -0.0014 0.0382* 0.0092 -0.0130(0.0072) (0.0018) (0.0053) (0.0170) (0.0072) (0.0222)
Black Defendant -0.0076** 0.0003 -0.0090*** 0.0050 -0.0039 -0.0323***(0.0031) (0.0006) (0.0025) (0.0065) (0.0030) (0.0086)
Hispanic Defendant 0.0025 -0.0012** 0.0027 -0.0005 0.0055** 0.0247***(0.0025) (0.0006) (0.0021) (0.0056) (0.0023) (0.0081)
Female Defendant -0.0617*** -0.0120*** -0.0362*** -0.1347*** -0.0595*** -0.2017***(0.0028) (0.0007) (0.0023) (0.0096) (0.0029) (0.0128)
Age at Offense 0.0014** 0.0015*** -0.0011*** 0.0117*** 0.0020*** 0.0056***(0.0005) (0.0002) (0.0004) (0.0017) (0.0006) (0.0016)
Age squared -0.0000 -0.0000*** 0.0000*** -0.0001*** -0.0000 -0.0000(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Years in Office -0.0002 -0.0001* -0.0001 -0.0003 -0.0003 -0.0006(0.0003) (0.0001) (0.0003) (0.0005) (0.0003) (0.0009)
Observations 228,557 228,549 200,490 228,549 208,675 229,035R-squared 0.160 0.197 0.133 0.080 0.154 0.398
Note 1: Results from OLS regressions. Standard errors, clustered by county, in parentheses: ∗∗∗ significant at 1%,∗∗ significant at 5% and ∗ significant at 10%.Note 2: All specifications include county-year fixed effects, offense categories (12 dummy variables for 13 cate-gories), and the criminal history of defendants as control variables.
52
interactions (Column (4)) suggests that African-Americans and Hispanics convicted of drug
offenses receive sentences that are roughly nine percent shorter if the judge responsible for
the case is of the same race or ethnicity.
Table A4 reports the results of separately considering drug trafficking and possession
cases. The table shows both the estimates using the baseline harshness measure and the ones
employing the logarithm of the assigned sentence as the dependent variable. As before, we
find that judges assign shorter sentences to defendants of their own race or ethnicity. But, in
most specifications, only the effects for drug offenses are statistically significant and similar
in magnitude to the estimates from Table A3.51 The only exceptions are the specifications
including the full set of race and ethnicity interactions, which indicate that African-American
judges assign substantially shorter sentences to African-American defendants convicted of
drug trafficking (columns (5) and (6)). As discussed in Section 3, the small number of
African-American judges in our sample leads us to take the findings concerning such judges
with a large grain of salt.
Another potential mechanism for the effects of race and ethnicity on the sentencing of
drug-related crimes is the conviction offense. The distinction between possession and traf-
ficking often depends on contestable facts, and it is possible that minority defendants are less
likely to be convicted of traffic when the judge responsible for the case is of the same race
or ethnicity. We investigate this possibility by restricting the sample to drug-related offenses
and considering a linear probability model in which the dependent variable is a dummy in-
dicating a drug trafficking conviction. Table A5 presents the results. As shown in the table,
we find no evidence that the judges’ race and ethnicity matter to explain the likelihood that
minority defendants are convicted of trafficking.
To summarize the discussion in this section, some of our estimates of race and ethnicity
effects on the sentencing of drug-related offenses are large and economically meaningful.
51Notice that the lack of statistical significance of the effects for drug trafficking could be due to the samplesize. There are roughly three times as many drug possession cases as trafficking cases in the sample.
53
Table A3: Sensitivity Analysis using Alternative Measures - Drug Cases
Dependent Variable (Measure of Sentencing Harshness)Variables (1) (2) (3) (4)
Baseline Measure E Baseline Measure E
Different Race 0.0179*** 0.0620***(0.0057) (0.0170)
Different Gender 0.0019 0.0130(0.0038) (0.0113)
Black Judge -0.0014 -0.0060(0.0079) (0.0225)
Hispanic Judge -0.0167** -0.0409(0.0069) (0.0279)
Female Judge 0.0017 0.0062(0.0055) (0.0180)
BlackJudge*BlackDef -0.0287*** -0.1108***(0.0070) (0.0205)
BlackJudge*HispDef 0.0030 0.0300(0.0085) (0.0256)
BlackJudge*WhiteDef 0.0417* 0.1247*(0.0222) (0.0700)
HispJudge*BlackDef 0.0029 0.0149(0.0105) (0.0423)
HispJudge*HispDef -0.0366*** -0.1051***(0.0079) (0.0288)
HispJudge*WhiteDef -0.0148 -0.0309(0.0096) (0.0309)
FemJudge*MaleDef 0.0036 0.0191(0.0053) (0.0181)
FemJudge*FemDef 0.0003 -0.0050(0.0080) (0.0248)
Black Defendant -0.0044 -0.0153 0.0118* 0.0417**(0.0098) (0.0286) (0.0065) (0.0190)
Hispanic Defendant 0.0404*** 0.1168*** 0.0580*** 0.1758***(0.0075) (0.0206) (0.0060) (0.0177)
Female Defendant -0.0626*** -0.1932*** -0.0609*** -0.1808***(0.0042) (0.0148) (0.0041) (0.0144)
Age at Offense 0.0026* 0.0059 0.0026* 0.0058(0.0014) (0.0046) (0.0014) (0.0046)
Age squared -0.0001*** -0.0002*** -0.0001*** -0.0002***(0.0000) (0.0001) (0.0000) (0.0001)
Years in Office -0.0001 -0.0002 0.0003 -0.0001(0.0003) (0.0011) (0.0005) (0.0012)
Observations 70,691 70,691 70,691 70,691R-squared 0.273 0.272 0.273 0.272
Note 1: Results from OLS regressions. Standard errors, clustered by county, in paren-theses: ∗∗∗ significant at 1%, ∗∗ significant at 5% and ∗ significant at 10%.Note 2: All specifications include county-year fixed effects, offense categories (12dummy variables for 13 categories), and the defendant’s criminal history as controls.
54
Table A4: The Influence of Race/Ethnicity and Sentencing - Drug Trafficking and Possession
Offense typeTrafficking Trafficking Possession Possession Trafficking Trafficking Possession Possession E
Dependent VariableHarshness Measure E Harshness Measure E Harshness Measure E Harshness Measure E
Variables (1) (2) (3) (4) (5) (6) (7) (8)
Different Race -0.0025 0.0234 0.0176*** 0.0565***(0.0124) (0.0341) (0.0041) (0.0127)
Different Gender 0.0061 0.0079 0.0039 0.0237(0.0098) (0.0301) (0.0041) (0.0147)
Black Judge -0.0380** -0.1535*** 0.0000 0.0116(0.0157) (0.0508) (0.0057) (0.0190)
Hispanic Judge -0.0143 -0.0241 -0.0169** -0.0427(0.0183) (0.0626) (0.0069) (0.0297)
Female Judge -0.0078 -0.0064 -0.0012 -0.0078(0.0109) (0.0367) (0.0050) (0.0171)
BlackJudge*BlackDef -0.0530*** -0.2379*** -0.0146*** -0.0463**(0.0103) (0.0299) (0.0052) (0.0198)
BlackJudge*HispDef -0.0173 -0.0699 0.0016 0.0346*(0.0283) (0.0797) (0.0039) (0.0191)
BlackJudge*WhiteDef 0.0127 0.0246 0.0162 0.0524(0.0478) (0.1888) (0.0145) (0.0426)
HispJudge*BlackDef 0.0116 0.0515 -0.0098 -0.0233(0.0197) (0.0565) (0.0124) (0.0497)
HispJudge*HispDef -0.0176 -0.0589 -0.0351*** -0.0992***(0.0226) (0.0753) (0.0079) (0.0309)
HispJudge*WhiteDef -0.0541* -0.1199 -0.0017 0.0052(0.0303) (0.0923) (0.0077) (0.0295)
FemJudge*MaleDef -0.0013 0.0026 0.0027 0.0157(0.0079) (0.0228) (0.0058) (0.0209)
FemJudge*FemDef -0.0118 -0.0075 -0.0051 -0.0312(0.0198) (0.0645) (0.0071) (0.0242)
Black Defendant -0.0691*** -0.2195*** 0.0277*** 0.0814*** -0.0739*** -0.2029*** 0.0445*** 0.1357***(0.0211) (0.0645) (0.0063) (0.0189) (0.0149) (0.0468) (0.0049) (0.0142)
Hispanic Defendant 0.0546*** 0.1424*** 0.0350*** 0.1035*** 0.0505*** 0.1596*** 0.0528*** 0.1591***(0.0144) (0.0404) (0.0061) (0.0179) (0.0103) (0.0280) (0.0058) (0.0174)
Female Defendant -0.1068*** -0.3088*** -0.0506*** -0.1621*** -0.1010*** -0.3021*** -0.0466*** -0.1385***(0.0100) (0.0328) (0.0058) (0.0250) (0.0091) (0.0256) (0.0046) (0.0176)
Age at Offense 0.0110*** 0.0345*** 0.0004 -0.0017 0.0110*** 0.0344*** 0.0004 -0.0017(0.0021) (0.0064) (0.0012) (0.0040) (0.0021) (0.0064) (0.0012) (0.0040)
Age squared -0.0002*** -0.0005*** -0.0000 -0.0001 -0.0002*** -0.0005*** -0.0000 -0.0001(0.0000) (0.0001) (0.0000) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000)
Years in Office 0.0002 0.0010 -0.0003 -0.0011 0.0002 0.0010 -0.0003 -0.0011(0.0007) (0.0021) (0.0003) (0.0014) (0.0007) (0.0021) (0.0003) (0.0014)
Observations 17,667 17,666 53,024 53,024 17,667 17,666 53,024 53,024R-squared 0.319 0.338 0.150 0.168 0.320 0.339 0.150 0.168
Note 1: Results from OLS regressions. Standard errors, clustered by county, in parentheses: ∗∗∗ significantat 1%, ∗∗ significant at 5% and ∗ significant at 10%.Note 2: All specifications include county-year fixed effects and the defendant’s criminal history as controls.
55
But the results from most specifications suggest that African-American and Hispanic judges
assign only slightly shorter sentences to defendants of the same race and ethnicity. Thus,
judges’ race, ethnicity and gender seem to matter in some cases. But, with the possible
exception of drug-related offenses, the smallness of the estimates suggests that the overall
magnitude of the influence is rather small.
B Sensitivity Analysis – Party
In this section, we analyze the sensitivity of our results in Section 4.2. Table A6 presents
the results. In Column (2), we include other characteristics of judges, in addition to party
affiliation. Specifically, we include judges’ party affiliation interacted with the dummy vari-
able of having short tenure (less than four years), as well as their race and gender. The
rationale is that inexperienced judges may rely more heavily on their intuition rather than
formal knowledge of law, which may in turn make them more influenced by their political
orientation. The estimate shows that there is no such effect. In Column (3), we use only
the set of counties where the average Democratic Vote Share is between 45 and 55 percent,
i.e., counties that are ideologically balanced. The result shows almost no change from the
baseline specification using all counties.52 In Column (4), we include interactions between
judges’ party affiliation with defendants’ race and gender: This is to investigate whether
judges with more conservative ideology treat female defendants or defendants with minority
backgrounds differently. The result shows no such effect. In Columns (5)-(8), we use the
alternative measures of harshness defined in Section A of the Appendix. The key result is
again robust to using alternative measures.
52Given that the estimate of party influence in the whole set of counties is negligible, it is natural that weobtain negligible estimates in ideologically balanced counties. The main reason why existing studies oftenfocus on ideologically balanced electorates (e.g., Lee et al. (2004); Ferreira and Gyourko (2009)) is becauseusing the entire set of counties leads to confounding the influence of public officials’ party affiliation with thatof the electorate’s preferences. This in turn leads to an over-estimate of the party influence. That is, focusingon ideologically balanced electorates only reduces the estimate of party influence.
56
Table A5: Linear Probability Model - Drug Cases
Dependent Variable: Drug TraffickingVariables (1) (2)
Different Race -0.0052(0.0107)
Different Gender 0.0055(0.0044)
Black Judge -0.0146(0.0090)
Hispanic Judge 0.0002(0.0067)
Female Judge -0.0010(0.0061)
BlackJudge*BlackDef 0.0146(0.0122)
BlackJudge*HispDef -0.0289(0.0236)
BlackJudge*WhiteDef -0.0588***(0.0206)
HispJudge*BlackDef -0.0124(0.0121)
HispJudge*HispDef 0.0047(0.0130)
HispJudge*WhiteDef 0.0126(0.0130)
FemJudge*MaleDef 0.0046(0.0069)
FemJudge*FemDef -0.0069(0.0084)
Black Defendant 0.0814*** 0.0769***(0.0206) (0.0124)
Hispanic Defendant 0.0582*** 0.0544***(0.0118) (0.0083)
Female Defendant -0.0243*** -0.0187**(0.0080) (0.0073)
Age at Offense -0.0034*** -0.0034***(0.0013) (0.0013)
Age squared 0.0000* 0.0000*(0.0000) (0.0000)
Years in Office 0.0007* 0.0007*(0.0004) (0.0004)
Observations 70,691 70,691R-squared 0.101 0.101
Note 1: Results from OLS regressions. Standard errors, clusteredby county, in parentheses: ∗∗∗ significant at 1%, ∗∗ significant at 5%and ∗ significant at 10%.Note 2: All specifications include county-year fixed effects and thedefendant’s criminal history as controls.
57
Tabl
eA
6:T
heIn
fluen
ceof
Judg
es’P
arty
Affi
liatio
non
Sent
enci
ngH
arsh
ness
-Sen
sitiv
ityA
naly
sis
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Var
iabl
esB
asel
ine
Aug
men
ted
Bal
ance
ddi
stri
cts
Inte
ract
ion
Mea
sure
AM
easu
reB
Mea
sure
CM
easu
reD
Mea
sure
E
Rep
ublic
an-0
.008
8-0
.008
1-0
.013
3**
-0.0
112
-0.0
013
-0.0
047
-0.0
124
-0.0
099
-0.0
203
(0.0
092)
(0.0
105)
(0.0
064)
(0.0
099)
(0.0
018)
(0.0
084)
(0.0
197)
(0.0
098)
(0.0
217)
Rep
*Sho
rtTe
nure
-0.0
102
(0.0
070)
Bla
ckJu
dge
-0.0
133*
**(0
.004
7)H
ispa
nic
Judg
e-0
.006
7(0
.013
0)Fe
mal
eJu
dge
0.00
38(0
.005
1)R
ep*B
lack
Def
0.01
02(0
.008
0)R
ep*H
isp
Def
0.00
06(0
.004
6)R
ep*F
emD
ef-0
.004
3(0
.005
6)B
lack
Def
enda
nt-0
.007
8**
-0.0
078*
*-0
.003
1-0
.014
4**
0.00
02-0
.008
0***
0.00
12-0
.004
6-0
.034
8***
(0.0
032)
(0.0
032)
(0.0
052)
(0.0
065)
(0.0
006)
(0.0
025)
(0.0
068)
(0.0
031)
(0.0
087)
His
pani
cD
efen
dant
0.00
050.
0005
0.00
62-0
.000
2-0
.001
5***
0.00
18-0
.005
00.
0030
0.01
92**
(0.0
026)
(0.0
026)
(0.0
081)
(0.0
036)
(0.0
005)
(0.0
022)
(0.0
060)
(0.0
024)
(0.0
085)
Fem
ale
Def
enda
nt-0
.061
2***
-0.0
612*
**-0
.056
4***
-0.0
584*
**-0
.011
4***
-0.0
374*
**-0
.128
3***
-0.0
590*
**-0
.209
2***
(0.0
024)
(0.0
024)
(0.0
039)
(0.0
041)
(0.0
007)
(0.0
020)
(0.0
100)
(0.0
024)
(0.0
141)
Age
atO
ffen
se0.
0014
**0.
0014
**0.
0007
0.00
14**
0.00
15**
*-0
.001
1**
0.01
17**
*0.
0021
***
0.00
55**
(0.0
005)
(0.0
005)
(0.0
011)
(0.0
005)
(0.0
002)
(0.0
004)
(0.0
017)
(0.0
006)
(0.0
017)
Age
squa
red
-0.0
000
-0.0
000
-0.0
000
-0.0
000
-0.0
000*
**0.
0000
***
-0.0
001*
**-0
.000
0-0
.000
0(0
.000
0)(0
.000
0)(0
.000
0)(0
.000
0)(0
.000
0)(0
.000
0)(0
.000
0)(0
.000
0)(0
.000
0)Y
ears
inO
ffice
-0.0
001
-0.0
002
-0.0
010*
-0.0
001
-0.0
001*
-0.0
001
-0.0
003
-0.0
002
-0.0
006
(0.0
003)
(0.0
005)
(0.0
006)
(0.0
003)
(0.0
000)
(0.0
003)
(0.0
005)
(0.0
003)
(0.0
009)
Shor
tTen
ure
-0.0
053
(0.0
086)
Obs
erva
tions
219,
489
219,
489
43,1
5521
9,48
921
9,48
119
2,49
221
9,48
120
0,42
221
9,93
7R
-squ
ared
0.16
10.
161
0.11
10.
161
0.19
70.
134
0.08
40.
155
0.40
0
Not
e1:
Res
ults
from
OL
Sre
gres
sion
s.St
anda
rder
rors
,clu
ster
edby
coun
ty,i
npa
rent
hese
s:∗∗∗
sign
ifica
ntat
1%,∗∗
sign
ifica
ntat
5%an
d∗
sign
ifica
ntat
10%
.N
ote
2:A
llsp
ecifi
catio
nsin
clud
eco
unty
-yea
rfixe
def
fect
s,of
fens
eca
tego
ries
(12
dum
my
vari
able
sfo
r13
cate
gori
es),
and
the
crim
inal
hist
ory
ofde
fend
ants
asco
ntro
lvar
iabl
es.
58
