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The Determinants of State Hate Crime Legislation

The Determinants of State Hate Crime Legislation

Abstract

Although hate- and bias -motivated crimes have occurred for centuries in the United States, widespread recognition and codification into law is still in development. There is immense variation in hate crime legislation across all 50 states and the District of Columbia, including, but not limited to, the categories of people protected against hate crimes, provisions enhancing penalties for hate crime perpetrators, and legislation establishing data collection methods to track and report hate crimes. Though studies recognize the inconsistencies between state laws, there is minimal research explaining the factors that influence the existence of such laws. This study seeks to analyze whether a state’s racial, political, socioeconomic, educational, and law enforcement-related attributes impact that state’s likelihood to enact hate crime legislation. The results suggest wealthy states with Democratic-controlled state legislatures and high rates of international immigrants are more likely to possess hate crime legislation. Ultimately, the political will of a state plays a primary role in its ability to recognize hate crimes and respond to them effectively. 

Introduction

In the month following November 9th, 2016, the day the United States elected a new president, the Southern Poverty Law Center reported 1,094 bias-related incidents occurring nationwide, a significant spike compared to the same time last year. 315 of these incidents were anti-immigrant, 221 were anti-black, 109 were anti-LGBT, and 45 were anti-woman.[1]Although hate-motivated behavior is not new, its widespread recognition and codification into law is still in development. As of 2014, 22 states lacked legislation classifying ‘gender’ as a protected category and crimes based on gender bias as hate crimes. 18 states lacked legislation protecting people on the basis of sexual orientation.[2]Without legislation that properly identifies crimes based on their motives, states are failing to recognize trends in crimes against different groups of people. 

For this thesis, I explored what factors impact the existence and passage of hate crime legislation. More specifically, I examined a variety of political, racial, and socioeconomic variables to study whether states with certain attributes are more likely to possess or pass into law hate crime legislation. I found that states with Democratically-controlled state legislatures, higher per capita income, and larger rates of international immigrants will have more expansive hate crime legislation. Ultimately, political control has the most substantive impact. I also found that states that go from a Republican-controlled or split legislature, to a Democratically-controlled legislature, will lead to an addition of 1.35 hate crime categories in legislation (the categories are defined later in the paper). 

Since there have been only 38 changes recorded with regards to a state adding or removing hate crime-related legislation over the course of 11 years, my thesis seeks to deepen knowledge not only on the attributes of states that enact or repeal legislation, but also on the attributes of states that possess many hate crime-related laws. Some preliminary questions I asked included: is a state with a Democratic-controlled state legislature more likely to possess hate crime legislation, or enact hate crime legislation? Do states with higher levels of educational attainment have higher levels of hate crime legislation? Does a state’s proportion of females or proportion of racial minorities have any impact?

Key Concepts:

Hate Crime

In 1990, the United States Federal Government passed the Hate Crime Statistics Act, mandating the collection and report of “hate crime” data. At the time, “hate crimes” were defined as “crimes committed against a victim because of their race, religion, disability, sexual orientation, or ethnicity.” The purpose of this act was to collect data “about crimes that manifest evidence of prejudice” based on a certain identity of a person.[3] The federal definition of a “hate crime” has transformed over time; in 2009, President Obama signed the Matthew Shepard and James Byrd Jr. Hate Crimes Prevention Act, expanding the federal definition of hate crimes.[4] Today, the Federal Bureau of Investigation defines a hate crime as a “criminal offense against a person or property motivated in whole or in part by an offender’s bias against a race, religion, disability, sexual orientation, ethnicity, gender, or gender identity.” 

However, the federal definition of a hate crime differs greatly from state definitions. As recorded in Abt Associates Inc’s “Study of Literature and Legislation on Hate Crime in America” conducted for the Department of Justice, there is a large amount of state variability in “the specification of ‘protected groups,’ or identifiable sets of people whose traits are legally defined as targets of hate crime motivation (e.g. race, religion, sexual orientation).”[5] States also vary in their definition of what range of crimes are covered under the category of hate crimes. Thus, my research acknowledges the differences in state definitions of hate crimes by examining the variability in state hate crime provisions. 

Significance of the Question: Improving Accuracy of Hate Crime Statistics 

There is great variability in what is considered a “hate crime” in the United States. A crime committed against someone because of their gender could be recorded as a hate crime in California, but not in Alabama, as California recognizes gender as a protected group whereas Alabama does not. Once a crime is classified as a hate crime, some states provide compensation to the victim e.g. legal services, financial support etc. and enhanced penalties for the perpetrator. The crime also provides the city, state and federal government with a greater understanding of crimes taking place against specific segments of the population.[6] The unfortunate reality is that a victim’s case would be handled in completely different ways based on the location of the incident. This issue of varying state crime legislation is important to recognize because the trends in certain categories–e.g. income levels, educational attainment, international immigration rates–can potentially demonstrate how some states have more resources to address hate crimes compared to others. 

The FBI’s annual Hate Crime Statistics Report reveals how many local, state and federal agencies are participating in collecting, analyzing and reporting incidents of hate crime.[7] These reporting methods are often only implemented if states pass legislation mandating data collection methods for compiling hate crimes, and police training requirements to ensure law enforcement personnel are equipped to assess and accurately determine the nature of the crime.[8] Although Georgia has one law enforcement agency for approximately every 24,390 state residents and California has one law enforcement agency for approximately every 82,644 state residents[9], California has 208 agencies submitting hate crime incident reports whereas Georgia only has four. This discrepancy is a direct effect of the existence–or lack thereof–of state legislation on data collection and police trainings.[10] The deficit of reports then fails to give local, state and federal officials an accurate idea of the types of crimes being committed, and against which specific groups. The FBI recognizes the huge disparity in the number of agencies participating from state to state, and even discourages users from utilizing agency-oriented hate crime statistics “to evaluate locales or the effectiveness of their law enforcement agencies.”[11]

Additionally, hate crime legislation is extremely important to me given that I am a Muslim American. Per the FBI’s 2015 Hate Crime Statistics Report, there was a 67 percent surge in hate crimes reported against Muslims from 2014 to 2015.[12] To understand the current condition of our country and what protections and policies need to be implemented to protect vulnerable groups like American Muslims, legislation needs to be present to ensure such crimes are being recorded and reported. 

Background

Literature Review

Understanding the critical role of legislation in accurately identifying hate crime trends, one must further examine the discrepancies in state legislation, along with the trends that have arisen within new types of legislation. According to Abt Associates Inc’s “Study of Literature and Legislation on Hate Crime in America,” there are five primary ways in which hate crime legislation exists differently within each of the 50 states. They are “(1) the specification of “protected groups,” or identifiable sets of people whose traits are legally defined as targets of hate crime motivation (e.g., race, religion, sexual orientation); (2) whether and how they address criminal penalties and civil remedies; (3) the range of crimes covered; (4) whether the statutes contain hate crime reporting requirements; and (5) whether they require training of law enforcement personnel to support improved prevention, response, and recording of hate crimes.”[13]Given the vast amount of variation, little research has been done to assess how these differences emerged and what factors affect the existence or nonexistence of specific statutes within each state.

Another key finding made by Abt Associates Inc was the three areas in which hate crime legislation all states were developing. There is an increasing number of state statutes expanding the number of ‘protected groups’, specifically groups defined by disability, gender, and sexual orientation. There is also more legislation focused on providing penalty enhancements for hate-motivated crimes, and on ensuring data collection is being enforced across agencies. With these three trends in mind, my research determining what factors impact the passage of such legislation becomes extremely relevant, as it can indicate how states may be similarly transforming with their new laws. 

Figure 1 is a 2014 map by the Movement Advancement Project clearly highlighting the disparity in state hate crime legislation regarding gender identity and sexual orientation. Indicators impacting these differences in law need to be studied.[14]

Figure 1

Figure 1

Picture2.png

Lastly, Figure 2 is a map I created utilizing data from the Anti-Defamation League’s 2014 report on the number of hate crime-related provisions (‘categories’ as referred to in the map key) covered under each state’s laws. This data further exemplifies the current research done in tracking the variability of state legislation. 

Figure 2

Figure 2

Theory and Intuition

Based on the research conducted so far on state hate crime legislation, there is little evidence on which factors within a state actively affect the existence and creation of hate crime legislation. In the assessment of my dependent variable provided by the Anti-Defamation League, there are six areas I believe will display trends in legislation.

Racial Composition: States that have more racially diverse populations are likely to have larger minority groups. In 2014, the FBI reported that of the 5,462 single-bias incidents recorded, 47 percent were racially motivated.[15]As the racial diversity within a state increases over time and racial minorities make up a larger part of the population, there is a greater likelihood that the hate crime legislation will be more expansive. The reasoning being, if a state’s general population has more interaction with a minority racial group, then that state is likely to be more conscious of the needs and issues facing such a group. Hate crime-related legislation would assist in substantively tracking attacks against the group, potentially leading to that group receiving more support and resources. 

Additionally, Blacks are the largest racial minority within the U.S. and have had the longest history as victims of bias- and hate -related incidents. To study Blacks and African Americans as a separate factor could potentially shed light on how hate crime legislation in a specific state expands.

Another major aspect influencing racial composition would be migration levels. With more international immigrants coming into a specific state, the population will become more diversified, making it more conducive for legislation to be passed. As of 2010, Mexico, India, the Philippines, China, and Vietnam were the top five countries from which immigrants were coming to the U.S.[16]Such immigrants would be both in the racial and ethnic minority in the U.S. State populations that interact with such groups will have a greater awareness of their issues and hate crime legislation can be a direct result of a state seeking to track and respond to xenophobia- and racist-motivated crimes directed at such groups. 

Gender Composition: Increased diversity within a state’s gender composition can encourage legislation designed to protect citizens of various gender identities and support victims of gender-based hate crimes. A 2012 report indicated that 26 percent of hate crimes were perceived by the victim to be motivated by a bias towards their gender.[17]With more women in a state, the likelihood of witnessing a crime against someone because of their gender can lead to more legislation protecting groups based on gender; the same can be said for men. With more women and individuals who do not conform to the gender binary, legislation will be created to protect these groups. 

Average Income: The Anti-Defamation League measures provisions, including “civil services,” meaning victim compensation. States with family income levels that drop may be more likely to pass legislation to financially and legally support victims of hate crimes who do not have the resources to undergo a legal proceeding. However, as income levels rise, state hate crime legislation may remain stagnant, as there may not be any need to modify or add legislation. In principle, a hate crime occurs because of an individual being associated with a group, and in all state and federal provisions enacted so far, no law recognizes biases towards one’s socioeconomic class as the motivating factor for committing a hate crime. 

Education Levels: Highly educated populations could have been better exposed to the realities facing minority groups and had more interactions with people of diverse backgrounds. In areas like rural America, where residents are less likely to interact with someone of a different religion or race, institutions of higher education can help facilitate the interaction among individuals living in rural America and a variety of racial, religious, and ethnic groups. As education levels rise within a state, their population may be more receptive to the needs and concerns of a minority group and will have legislation passed to better protect them.

Crime and Law Enforcement Levels: If general crime levels increase, states will want to ensure that such levels are being controlled. Many crimes motivated by hate may not be legally defined as “hate crimes” in certain states. However, if crime levels rise, states may be more likely to pass legislation to increase penalties for perpetrators of hate crimes and enhance police trainings. Data collection methods may also be implemented to better track trends in types of crimes.

Additionally, a state with a larger number of law enforcement employees in proportion to their population may have more encounters with a variety of hate crimes including hate- and bias-motivated crimes. As a state’s number of law enforcement employees increases, there is a greater likelihood that experiences with such crimes would lead to hate crime legislation being enacted to control the levels of those crimes and track the rate of said crimes. 

Political Parties within the State Legislature, Election Years and Political Affiliation: Democrats tend to better represent minority groups and their views. As indicated in a Gaullup research poll, Democrats are the most racially diverse party, with four in 10 Democrats identifying as someone other than non-Hispanic white.[18]It is likely that as a state legislature becomes more Democratic or becomes Democrat-controlled, the views of minority groups and those most commonly victimized because of their race or ethnicity are better represented and hate crime legislation provisions are expanded. 

The other aspect of politics is the role of election years. In the year following the election of a state governor, a variety of legislation–including those addressing hate crimes–will likely be enacted. Governors who win an election are likely to do so based on campaign promises they make to the general population. Given that the clear majority of states have minimal hate crime legislation, it is feasible to believe that governors will make promises in this area and enact legislation in the year after they are elected. 

Lastly, there is value in studying the political ideologies of the residents of each state. Historically, in states with populations that hold more conservative views, fewer rights are granted to minority groups. Per a Pew Research Center study, of 44 percent of whites surveyed, the largest proportion identified as holding some form of conservative views. Federal statistics tell us that anti-white incidents only made up 10.5 percent of all hate crimes reported in 2015, whereas 48.4 percent of hate crimes were perpetrated by whites.[19]In the context of hate crime legislation, it is likely that states with a higher proportion of residents holding conservative views will have fairly minimal hate crime legislation because whites, who are relatively likely to identify as conservative, are not as heavily victimized as other racial groups. 

Hypotheses

Based on what I have provided as the factors impacting the existence and enactment of state hate crime legislation, I will be making the following hypotheses.

Racial Composition

·     Hypothesis 1: An increase in the proportion of non-whites in a state will lead to more expansive hate crime legislation.

·     Hypothesis 2: An increase in the proportion of international immigrants for any given year will lead to more expansive hate crime legislation.

·     Hypothesis 3: An increase in the proportion of Blacks and African Americans in a state will lead to more expansive hate crime legislation.

Gender Composition

·     Hypothesis 4: An increase in the proportion of women in a state will lead to more expansive hate crime legislation.

Average Income

·     Hypothesis 5: An increase in the income per capita in a state will not lead to more expansive hate crime legislation.

Education Levels

·     Hypothesis 6: An increase in the proportion of the population that possesses a bachelor’s degree or higher will lead to more expansive hate crime legislation.

Crime and Law Enforcement Levels

·     Hypothesis 7: An increase in a state’s violent crime rate per state will lead to more expansive hate crime legislation.

·     Hypothesis 8: An increase in the number of law enforcement employees per state will lead to more expansive hate crime legislation. 

Political Parties within the State Legislature and Election Years

·     Hypothesis 9: A state legislature that is or becomes Democratic-controlled (as opposed to split or Republican-controlled) will lead to more expansive hate crime legislation.

·     Hypothesis 10: In the year following the election year of the state governor, there will be an expansion of hate crime legislation. 

·     Hypothesis 11: An increase in the proportion of state residents who identify as “conservative” will lead to a decrease in hate crime legislation.

Data Description

Independent Variables:

There are precisely 11 independent variables present within this thesis that are being observed to assess if they impact the expansion of hate crime legislation. These variables are being measured across 50 states and the District of Columbia over the course of 11 years. I have provided details on each of these variables below.

·      Racial Composition – Proportion of Non-Whites: U.S. Census data on the “Annual Estimates of the Resident Population by Sex, Race, and Hispanic Origin for the Unites States, States, and Counties” from 2000-2015 was utilized to build this variable. After having acquired data on all 50 states and D.C., I summed the columns on race that were non- “white” and divided them over the total population. These columns included “Black or African American,” “American Indian or Alaska Native,” “Asian,” “Native Hawaiian or Other Pacific Islander,” and “two or more races.” It is important to note that the U.S. Census defines “Hispanic origin” as the following: “[it] can be viewed as the heritage, nationality, lineage, or country of birth of the person or the person’s parents or ancestors before arriving in the United States. People who identify as Hispanic, Latino, or Spanish may be any race.”[20]Due to such a definition, I was unable to determine the proportion of Hispanics in a population as they could be classified under any race. 

·      Racial Composition – Proportion of African Americans and Blacks: In creating an independent variable through the U.S. Census that specifically measures the proportion of African Americans and Blacks, I can study if the existence of one specific racial group impacts the passage and existence of legislation.

·      Racial Composition – Proportion of International Immigration: The U.S. Census data tables detailing “population, population change, and estimated components of population change” were utilized to build this variable. I acquired the number of international immigrants into each state and divided this by the total state population to acquire the proportion of the population that had internationally immigrated that year. 

·      Gender Composition – Proportion of Women: U.S. Census data on the “Annual Estimates of the Resident Population by Sex, Race, and Hispanic Origin for the Unites States, States, and Counties” from 2000-2015 was utilized to acquire this variable. It tells us the proportion of the population that identify as female over the total population. 

·      Average Income – Per Capita Income (PCI): The PCI for each state was acquired through data provided by the Bureau of Economic Analysis. The PCI is calculated as the total personal income divided by the total midyear population of that state. To adjust for population sizes within each state, I took the logarithm of this variable to ensure a more balanced distribution across all 50 states and D.C. All dollar estimates are in September 2016 dollars and are not adjusted for inflation. 

·      Education Level – Bachelor’s or More: To measure educational attainment, I looked at three sources of data: The National Center for Education Statistics (NCES), the U.S. Census’ American Community Survey (ACS) and the U.S. Census’ Annual Capital Expenditures Survey (ACES). Unfortunately, the data from NCES covered the years 2000, 2005-2008 and 2011-2014 with several years missing in between. The ACES provided data from only 2000 to 2006. The most comprehensive data was acquired through the ACS and spanned from 2006-2014. In the ACS data, estimates are made in March of every year to determine what percent of the population that is 25 years or older holds a bachelor’s degree or more. 

·     Crime and Law Enforcement Levels – Violent Crime Rate: The Uniform Crime Reporting (UCR) Statistics database was utilized to build this variable. The violent crime rate measures the number of violent crimes that occur per every 100,000 of the population. The violent crime rate provided encompasses murder, legacy rape, revised rape, robbery and aggravated assault. To clarify, “legacy rape” is defined as “the carnal knowledge of a female forcibly and against her will. Rapes by force and attempts or assaults to rape, regardless of the age of the victim, are included.” “Revised rape” is defined as “penetration, no matter how slight, of the vagina or anus with any body part or object, or oral penetration by a sex organ of another person, without the consent of the victim. Attempts or assaults to commit rape are also included.”[21]

·     Crime and Law Enforcement Levels - Proportion of Law Enforcement Employees: The FBI’s UCR Tool provides information on the total number of law enforcement employees. I created a variable that would provide the number of law enforcement employees per every 10,000 of the population. 

·     Politics - Dominant Party Within State Legislature: The National Conference of State Legislatures provides a record of whether a state’s legislature is split, Democratic-controlled, or Republican-controlled. Nebraska is the one exception in that its state legislature is unicameral and nonpartisan, and thus is classified in the data as “N/A.”[22]In coding this into a numerical variable, I set “0” equal to split, “1” equal to a Democratic-controlled state legislature and “-1” equal to a Republican-controlled state legislature. 

·     Politics - Year After Election Year: Ballotpedia offers yearly information on the different state governor elections taking place. Utilizing this data, I created a variable that tracks the year following an election year in a state. To make this a numeric variable, I coded “1” as the year after a state governor election occurs with all other years being coded as “0.” 

·     Politics – Proportion of Conservatives: To obtain the proportion of conservatives per state, I utilized Gallup Analytics’ annual “Political Views & Party Affiliations” poll.[23]Annual data sheets were available with all 50 states and D.C. However, data exists only from 2008 and onwards. Due to my dependent variable lasting from 2003-2014, I chose to separate this variable and run regressions both with and without this variable present. 

Dependent Variables:

The dependent variable in this thesis is a result of a compilation of data provided by the Anti-Defamation League (ADL). Beginning from 2003-2014, the ADL has released several reports tracking the amount of hate crime legislation that covers 11 categories that they have established. My usage of this data relies upon the ADL’s judgement as to whether a state’s legislation adequately encompasses a specific category. 

To create a “sum” variable that encompasses all 11 categories, I transformed each category into a numerical format. A state that has legislation in a specific year that the ADL determines sufficiently encompasses a category will be classified as a “1.” A state lacking sufficient legislation in a specific category in a specific year is classified as a “0.” After coding for 0s and 1s across all 11 categories for all 50 states and D.C. over the course of 12 years, I summed the number of 0s and 1s in each year for each state to create a “sum” variable that ranges from 0 to 11.

The 11 categories are as follows:

1) Penalty Enhancement - A state has legislation that increases the penalty for a perpetrator convicted of a hate crime.

2) Protected Category: Race, Religion, Ethnicity - A state has legislation that classifies a hate- or bias-related incident as a hate crime if it occurs because of prejudice towards one’s race, religion or ethnicity. The ADL has chosen to group race, religion, and ethnicity into one category when tracking state legislation.

3) Protected Category: Sexual Orientation - A state has legislation that classifies a hate- or bias-related incident as a hate crime if it occurs because of prejudice towards one’s sexual orientation. 

4) Protected Category: Gender & Gender Identity - A state has legislation that classifies a hate- or bias-related incident as a hate crime if it occurs because of prejudice towards one’s gender. Although the ADL tracks gender and gender identity as two separate categories, I decided it would be more efficient in tracking gender-related legislation by grouping the two categories together. The overwhelming majority of states either had no legislation covering either gender or gender identity, legislation covering gender or legislation covering both gender and gender identity. Colorado is the only state to have legislation covering gender identity but not gender. 

5) Protected Category: Disability - A state has legislation that classifies a hate- or bias-related incident as a hate crime if it occurs because of prejudice towards one’s disability or disabilities. 

6) Protected Category: Political Affiliation - A state has legislation that classifies a hate- or bias-related incident as a hate crime if it occurs because of prejudice towards one’s political affiliation.

7) Protected Category: Age - A state has legislation that classifies a hate- or bias-related incident as a hate crime if it occurs because of prejudice towards one’s age. 

8) Civil Action - A state has legislation that provides financial assistance and reimbursements to hate crime victims for costs associated with the crime. Such legislation is also meant to encourage victim cooperation and participation in the criminal justice system.[24]

9) Data Collection - A state has legislation that develops institutions and systems through state and local agencies are given the appropriate resources and are required to track and report hate crimes.

10) Police Training - A state has legislation through which mandatory workshops and trainings are provided to law enforcement officials on how to accurately assess, handle and respond to a hate crime. 

11) Institutional Vandalism - A state has legislation that tracks any hate- or bias-related actions that target places of worship. 

Figure 3.1

Figure 3.1

Figure 3.2

Figure 3.2

To exemplify a comparison in how the independent variable has changed over time, Figure 3.1 is a 2003 bar plot displaying the 50 states and D.C. on the x-axis and the number of ADL categories (out of 11) on the y-axis. Figure 3.2 is a bar plot with 2014 data on the same ADL categories. Apart from California and Louisiana, both of which possess legislation covering all 11 categories, there is much work to be done across the United States in achieving uniform hate crime legislation.

The primary reason for summing all 11 categories into one variable is that only 38 changes, in which a state’s hate crime legislation encompassed additional categories or decreased the number of categories, were recorded over the course of 12 years. In other words, there were only 38 instances where, in any of the 11 categories, the value went from 0 to 1 or from 1 to 0. Given the low number of changes and the large number of observations, it was not statistically appropriate to measure the change as its own variable, as most of the observations would then not be considered. In utilizing a “sum” variable, all categories for each state for each year are recorded. 

The ADL released annual reports tracking what state legislation covered which categories in 2003, 2004, 2008, 2011, 2012, and 2014. Unfortunately, this has led to a few gaps in the data compiled. For example, Colorado enacted legislation that protected groups on the basis on sexual orientation, gender and disability between 2005 and 2008. Due to the missing years of data, it is unknown in exactly which year that data was enacted, but it is known that the legislation did not exist in 2004 and existed in 2008. For the years in which it is unknown whether legislation encompassed a certain category, I left those observations blank and in my “sum” variable, those states in those years are not considered. Fortunately, the overwhelming majority of legislation across all states and D.C. remains relatively similar over the course of the 12 years studied. In the years for which data is missing, the legislation usually remains the same before and after those gaps.

Control Variables & Fixed Effects:

The main control variable included in this study is population. It is necessary to control for population given the large population differences amongst all the states and D.C. To do this, I took the logarithm of each state’s population in each year. The data for this variable is coming from the U.S. Census.

Additionally, I will be running my analyses with fixed effects for U.S. regions. The U.S. Census Bureau has divided the 50 states and D.C. into four regions: The Northeast (1), the Midwest (2), the South (3) and the West (4).[25] The number corresponding to each region is how that region will be coded. The purpose for running fixed effects is to consider institutional and societal differences that have historically existed within different parts of the U.S. Although the South may contain a large proportion of non-white people, the long history of slavery in the South bore discriminatory policies that have systematically oppressed African Americans, among other populations. In this case, the proportion of non-white people will have little impact on whether a state expands its hate crime legislation because of pre-existing laws and institutions that were initially designed in favor of one racial group. 

Methodology

Empirical Design:

To measure the impact of state factors on the existence and passage of hate crime legislation, I will be conducting a primary analysis (Part I) along with some supplemental analyses (Part 2). The primary part of my analysis uses an Ordinary Least-Squares (OLS) regression to measure the impact of factors on the “sum” variable. I will run an OLS regression both with and without fixed effects for U.S. regions.  

As a supplement, I will be taking each of the 11 categories defined by the Anti-Defamation League and setting them as the dependent variable. I will then run OLS regressions with each of the 11 categories on the same independent variables. Through this analysis, I can study what specific factors impact legislation in a specific category e.g. data collection methods. 

This analysis can demonstrate how state trends impact specific provisions of hate crime legislation and thus offer more insight into how states can be become better suited to adopt more expansive hate crime legislation. I will be discussing some of the results below and providing tables for each of these regressions in the appendix.

 Part I. Ordinary Least-Squares with “sum” variable

            In measuring the impact of state factors on the number of categories covered by legislation per state per year, I am creating several models to study the various effects of independent variables. Model 1 and Model 1’ measure 9 independent variables with a control variable for the population. Model 1’ includes the fixed effects for U.S. region.

Model 1:

Sum of 11 hate crime categories = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 1’:

Sum of 11 hate crime categories = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

*

Model 2 incorporates the proportion of conservatives in each state. Due to the variable covering only from years 2008-2014, it is being placed in a separate model with the 9 independent variables and control variable above. Model 2’ adds fixed effects for U.S. regions.

Model 2:

Sum of 11 hate crime categories = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + β11(Proportion of Conservatives) + ε

Model 2’:

Sum of 11 hate crime categories = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + β11(Proportion of Conservatives) + Fixed Effects(Region) + ε

*

To study the effects of Blacks as a racial group, Model 3 switches the variable measuring the proportion of non-whites in a state with the variable that measures the proportion of Blacks and African Americans. Model 3’ adds fixed effects for regions.

Model 3:

Sum of 11 hate crime categories = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Blacks and African Americans) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 3’:

Sum of 11 hate crime categories = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Blacks and African Americans) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

*

Model 4 incorporates the variable measuring the proportion of conservatives into Model 3 that measures the impact of Blacks as a racial group. Model 4’ adds fixed effects for U.S. regions. 

Model 4:

Sum of 11 hate crime categories = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Blacks and African Americans) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + β11(Proportion of Conservatives) + ε

Model 4’:

Sum of 11 hate crime categories = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Blacks and African Americans) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + β11(Proportion of Conservatives) + Fixed Effects(Region) + ε

Part II: OLS Regressions on ADL Categories

For part II of my analysis, I am taking each of the 11 categories as defined by the ADL and setting them as the dependent variable to the same variables applied in Model 1 of Part 1. 

Model 5:

ADL Category – Penalty Enhancement = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 5’:

ADL Category – Penalty Enhancement = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 6:

ADL Category – Protected Group: Race, Religion, Ethnicity = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 6’:

ADL Category – Protected Group: Race, Religion, Ethnicity = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 7:

ADL Category – Protected Group: Sexual Orientation = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 7’:

ADL Category – Protected Group: Sexual Orientation = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 8:

ADL Category – Protected Group: Gender = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 8’:

ADL Category – Protected Group: Gender = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 9:

ADL Category – Protected Group: Disability = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 9’:

ADL Category – Protected Group: Disability = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 10:

ADL Category – Protected Group: Political Affiliation = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 10’:

ADL Category – Protected Group: Political Affiliation = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 11:

ADL Category – Protected Group: Age = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 11’:

ADL Category – Protected Group: Age = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 12:

ADL Category – Civil Action = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 12’:

ADL Category – Civil Action = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 13:

ADL Category – Data Collection Methods= α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 13’:

ADL Category – Data Collection Methods = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 14:

ADL Category – Police Training = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 14’:

ADL Category – Police Training = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Model 15:

ADL Category – Institutional Vandalism = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + ε

Model 15’:

ADL Category – Institutional Vandalism = α + β1(Logged Total Population) + β2(Logged Per Capita Income) + β3(Year After Governor Election) + β4(Proportion of Non-Whites) + β5(Bachelors or more) + β6(Proportion of Law Enforcement Employees) + β7(Proportion of Females) + β8(Proportion of International Immigrants) + β9(Dominant Party Within State Legislature) + β10(Violent Crime Rate) + Fixed Effects(Region) + ε

Results and Analysis

            In examining Part 1 of the analysis in which an OLS regression was run with the “sum” variable, the results suggest that an upward or downward trend in specific state factors significantly impacts the number of ADL categories a state will cover through its hate crime legislation. 

In Model 1 (shown in Figure 4), there are four factor variables, along with the control variable, for the population that have some degree of statistical significance in their impact on the total number of ADL categories covered, as shown in the table below. Both the control variable (the logged population) and the logged per capita income had a positive impact on the total number of statistically significant ADL categories, the logged population being at the 99 percent confidence level and the logged per capita income at the 90 percent confidence level. 

In regards to the proportion of non-whites, the proportion of international immigrants, and the dominant political party in the state legislature, each of these three factors significantly impacted the “sum” variable at the 99 percent confidence level. We learn that a 10-percentage point increase in the proportion of non-whites leads to a decrease of .34 ADL categories covered by state hate crime legislation. A 1-percentage point increase in the proportion of international immigrants leads to an increase of 3.96 ADL categories covered. It is important to note that most states had an annual proportional increase of international immigrants of around .001. Lastly, a democratic-dominated state legislature will yield an additional 1.34 ADL categories covered by legislation.

This table ultimately concludes that rich, highly populated, Democratic-controlled states with relatively higher rates of international immigration will have more expansive hate crime legislation compared to states with other attributes. The influence of conflict between a state’s proportion of non-whites and proportion of international immigrants on hate crime legislation is uncertain. 

Figure 4

Figure 4

 In looking at Model 1’, the fixed effects did not have a statistically significant impact on the total number of ADL categories covered. The only change in the significance of factors is that the proportion of non-whites seems to now only impact state hate crime legislation at the 90 percent confidence level. 

In Model 2 (shown in Figure 5) which includes the proportion of conservatives, the same four factor variables along with the factor variable continue to have an impact on the “sum” variable of some varying degree of statistical significance. 

Figure 5

Figure 5

Here, the logged population and the dominant party in the state legislature both have positive impacts of statistical significance at the 99 percent confidence level. The proportion of non-whites, the proportion of international immigrants and the proportion of individuals who are conservative are all impactful only at the 90 percent confidence level. In examining the substantive effects, we notice that for every 1-percent point increase in the proportion of Conservatives, there are .08 fewer ADL categories of legislation. The proportion of non-whites continues to have a negative impact on the “sum” variable of ADL categories, and in this case, we see a 1-percentage point increase in the proportion of non-whites yielding .02 fewer ADL categories of legislation. In running fixed effects on Model 2, there were no regions that had a statistically significant impact. 

In Model 3’ (shown in Figure 6) where the variable representing the proportion of non-whites is swapped with a variable representing specifically the proportion of Blacks and African Americans in a state, our results including fixed effects shows 6 factor variables having some statistically significant impact on the “sum” variable along with the control variable for the population. 

The control variable, which is the logged population, positively impacts the “sum” variable of ADL categories at the 99 percent confidence level. Although the proportion of Blacks, the proportion of females, and the dominant party in the state legislature all impact the “sum” variable at the 99 percent confidence level, the proportion of Blacks negatively impacts the “sum” variable while the latter two factors positively influence it. Lastly, at the 90 percent confidence level the violent crime rate (VcrimeR) and the fixed effects for the South, which is represented as Region 2, both positively impact the dependent variable. 

Figure 6

Figure 6

In regards to substantive effects, for every 1-percentage point increase in the proportion of Blacks and African Americans in a state’s population, there is a drop of .12 in ADL categories covered by legislation. Institutionally speaking, states that have held histories of slavery may naturally have laws in place that, regardless of how large a minority group exists in the population, legislation will not be favorable to them. As the proportion of international immigrants goes up by 1-percentage point, there is an increase of 2.94 ADL categories covered in legislation. A Democrat-controlled state legislature will have 1.18 more ADL categories covered. Unique substantive impacts include the violent crime rate and the proportion of females. As the violent crime rate increases by 100 for every 100,000 individuals of a population, there is a 0.19 increase in the number of ADL categories covered through legislation. As the proportion of females goes up by 1-percentage point, we see an increase of .973 ADL categories. 

Model 4 includes the swap of the racial variable to look at just the proportion of Blacks and African Americans and the variable measuring the proportion of conservatives, the statistical significance of the impact of the proportion of international immigrants, the violent crime rate and the logged per capita income drops. In Model 4’, fixed effects have no statistically significant impact.

To briefly mention the results of the second part of the analysis, I noticed that a Democrat-controlled state legislature was statistically significant in the increase from ‘0’ to ‘1’ (going from non-existent to existent) in each of the 11 categories, excluding institutional vandalism. A state’s PCI was also significant at some level for most of the 11 categories measured. We noticed in Part 1 that an increase in the proportion of non-whites continuously yielded a decrease in the total number of ADL categories covered. The same can be said about the impact of a rise in the proportion of non-whites on the existence of each individual category. A final point to mention is the number of law enforcement employees per every 10,000 of the population did have a statistically significant impact on the implementation of data collection methods and police trainings, the two ADL categories tied to the tracking of hate crimes. 

Hypothesis Testing: Results

In assessing the strength of our hypotheses in accordance with Model 1, the following can be noted. 

Racial Composition

Hypothesis 1: An increase in the proportion of non-whites in a state will lead to more expansive hate crime legislation.

o  The null hypothesis is rejected, but not in favor of the alternative hypothesis. The increase in the proportion of non-whites had a statistically significant negative impact on the number of ADL categories covered through legislation.

Hypothesis 2: An increase in the proportion of international immigrants for any given year will lead to more expansive hate crime legislation.

o  The alternative hypothesis can be accepted. An increase in the proportion of international immigrants had a statistically significant impact.

Hypothesis 3: An increase in the proportion of Blacks and African Americans in a state will lead to more expansive hate crime legislation.

o  Based on Model 3, the null hypothesis can be rejected but not in favor of the alternative hypothesis. The increase in the proportion of Blacks and African Americans had a statistically significant negative impact on the number of ADL categories covered through legislation.

Gender Composition

Hypothesis 4: An increase in the proportion of women in a state will lead to more expansive hate crime legislation.

o  Based on Model 1, the null hypothesis cannot be rejected.

o  Based on Model 3’, the alternative hypothesis can be accepted. An increase in the proportion of females had a statistically significant impact.

Average Income

Hypothesis 5: An increase in the income per capita in a state will not lead to more expansive hate crime legislation.

o  The alternative hypothesis can be accepted. An increase in the proportion of international immigrants had a statistically significant impact.

Education Levels

Hypothesis 6:An increase in the proportion of the population that possesses a bachelor’s degree or higher will lead to more expansive hate crime legislation.

o  The null hypothesis cannot be rejected.

Crime and Law Enforcement Levels

Hypothesis 7: An increase in a state’s violent crime rate per state will lead to more expansive hate crime legislation.

o  Based on Model 1, the null hypothesis cannot be rejected.

o  Based on Model 3’, the alternative hypothesis can be accepted. An increase in the proportion of females had a statistically significant impact.

Hypothesis 8: An increase in the number of law enforcement employees per every 10,000 of the population per state will lead to more expansive hate crime legislation.

o  The null hypothesis cannot be rejected.

Political Parties within the State Legislature and Election Years

Hypothesis 9: A state legislature that is or becomes Democrat-controlled (as opposed to split or Republican-controlled) will lead to more expansive hate crime legislation.

o  The alternative hypothesis can be accepted. A Democrat-controlled state legislature had a statistically significant impact.

Hypothesis 10: In the year following the election year of the state governor, there will be an expansion of hate crime legislation. 

o  The null hypothesis cannot be rejected.

Hypothesis 11: An increase in the proportion of state residents who identify as “conservative” or “very conservative” will lead to a decrease in hate crime legislation.

o  Based on Model 3, the alternative hypothesis can be accepted. A state with a higher proportion of people who identify as “conservative” or “very conservative” had a statistically significant negative impact. 

Conclusion

The purpose of this thesis was to assess what state factors impact the existence of state hate crime legislation. Based on these results, it is evident that a variety of political, socioeconomic, racial and other factors are significant in impacting the types of provisions covered in a specific state’s hate crime legislation. Although factors like PCI and what party controls the state legislature are more likely to be associated with more expansive legislation, other variables, including the proportion of international immigrants and the proportion of Blacks and African Americans, indicate societal conditions also play a role in such legislation. With such information, we can better assess which states have conditions potentially conducive towards expansion of their hate crime provisions. 

In most of the regressions that were run, the dominant political party in the state legislature remained statistically significant to some degree in regards to how many ADL categories were covered. This indicates that the actual assessment of, tracking of, and response to hate crimes on a state and local level is specifically tied to political will. In terms of a state legislature’s party affiliation, a variety of evidence indicates that Republican-controlled state legislatures are more likely to put their state populations at risk by not implementing legislation that effectively tracks crimes against groups because of their religion, gender, sexual orientation, disability status etc. The issue is not whether hate crimes exist, but whether politicians are willing to step beyond partisan lines to enact legislation so their governments can become more receptive to the needs of and challenges faced by the most commonly victimized groups. 

Another major takeaway is, for the most part, the violent crime rate and the number of employed enforcement is not significant in expanding legislation in a state. The reality is that even if law enforcement agencies are witnessing more crimes, until there are institutional policies in play to inform how certain crimes should be interpreted as hate crimes, trends of bias- and hate-related events against specific groups will continue unnoticed. 

Due to the nature of this research, and the fact that few other projects that have assessed the impact of similar state factors, I hope this can serve as encouragement for groups like the Anti-Defamation League and the Southern Poverty Law Center to keep a more detailed record of legislation enacted annually from state-to-state so that more trends in how hate crime legislation is expanding can be tracked. This paper can serve as a reference to show how certain trends within states that have not previously been tracked are now providing a great deal of information on the type of legislation that state will pass.

Ultimately, I hope this research can be a driving factor for federal and state governments as well as other organizations to study the factors that impact hate crime legislation so that more uniform and expansive policies can be applied nation-wide to track crimes targeting populations because of their identity and provide support to said populations. 


Biography

Afraz Khan

Afraz Khan is a recent graduate from New York University where he attained his Bachelors in International Relations. During his college years, he was heavily involved in a variety ofcommunity outreach and engagement efforts through his work as the president of the Muslim Students Association and through his time working in Washington D.C. and Argentina. In this past year, he coordinated several programs, public forums and rallies uniting thousands of students and community members to tackle issues of police brutality, Islamophobia, and the deportation of undocumented students. He currently works at the Manhattan Borough President's office as a Community Board Coordinator, engaging with local residents on issues from affordable housing and tenant harassment to homelessness and food deserts. Through his own experience as a South Asian Muslim in America, Afraz hopes to continue advocating for equity for all minorities.


Bibliography 

Curry, T., Grattet, R. & Jenness, Valerie. “The Homogenization and Differentiation of Hate Crime Law in the Unites States, 1978 to 1995: Innovation and Diffusion in the Criminalization of Bigotry.” American Sociological Review1998. Web. http://www.jstor.org/stable/pdf/2657328.pdf

Foley, C. & Smith, A. “State Statutes Governing Hate Crimes.” Congressional Research Service28 September 2010. Web. https://fas.org/sgp/crs/misc/RL33099.pdf

Goldstein, K. “The Case for Hate Crime Statutes.” The 13thStory2003. Web. http://www.the13thstory.com/krg/words/hatecrimes.html

Green, D., McFalls, L. & Smith, J. “Hate Crime: An Emergent Research Agenda.” Annual Review of Sociology2001. Web. http://www.jstor.org/stable/pdf/2678630.pdf

Hatewatch Staff. “Hatewatch.” Southern Poverty Law Center 2016. Web. https://www.splcenter.org/hatewatch/2016/12/16/update-1094-bias-related-incidents-month-following-election

Jenness, V. “Managing Differences and Making Legislation: Social Movements and the Racialization, Sexualization, and Gendering of Federal Hate Crime Law in the U.S.” Social Problems1999. 

McPhabil, B.A. “Hating Hate: Policy Implications of Hate Crime Legislation.” The Social Service Review2000. Web. 

Perry, Barbara. In the Name of Hate: Understanding Hate Crimes.New York: Routledge Publications, 2001. 

Perry, Barbara. “Where do we go from Here? Researching Hate Crime.” Internet Journal of Criminology2003. Web. http://www.internetjournalofcriminology.com/where%20do%20we%20go%20from%20here.%20researching%20hate%20crime.pdf

Shively, Michael. “Study of Literature and Legislation on Hate Crime in America.” National Institute of Justice31 March 2005. Web. https://www.ncjrs.gov/pdffiles1/nij/grants/210300.pdf

“An Introduction to Hate Crime Laws.” Anti-Defamation League. Web. http://www.adl.org/assets/pdf/combating-hate/Introduction-to-Hate-Crime-Laws.pdf

 “Equality Maps – Hate Crime Laws.” Movement Advancement Project2014. Web. http://www.lgbtmap.org/equality-maps/hate_crime_laws

“Hate Crimes Act (Matthew Shepard Act.” NOLO2010. Web. https://www.nolo.com/legal-encyclopedia/content/hate-crime-act.html

“Hate Crime Statistics – 2015” F.B.I. 14 November 2016. Web. https://www.fbi.gov/news/stories/2015-hate-crime-statistics-released

“Office for Victims of Crime: Victims of Crime Act – Victim Compensation Grant Program”. Department of Justice2001. Web. https://ojp.gov/ovc/voca/pdftxt/voca_guidelines2001.pdf

“The Top Sending Countries of Immigrants in Australia, Canada, and the United States.” Migration Policy Institute2010. Web. http://www.migrationpolicy.org/programs/data-hub/top-sending-countries-immigrants-australia-canada-and-united-states

Data Sources 

Analytics Campus, Gallup Poll. Political Views & Party Affiliation: 2008-2014.Available at analyticscampus.gallop.com/Tables 

Anti-Defamation League. State Hate Crime Statutory Provisions: 2003, 2004, 2011, 2012, 2014.Available at adl.org/sites/default/files/documents/assets

Ballotpedia (2014). State Legislatures: 2003-2014.Available on ballotpedia.org/State_Politics

Bureau of Economic Analysis, U.S. Department of Commerce (2013). Income Per Capita Per State: 2000-2015.Available from bea.gov/iTable

National Conference of State Legislatures (2017). State Partisan Composition: 2003-2014.Available at ncsl.org/research

Uniform Crime Reporting, U.S. Federal Bureau of Investigation (2015). Law Enforcement Personnel: 2000-2015.Available from ucrdatatool.gov

Uniform Crime Reporting, U.S. Federal Bureau of Investigation (2015). Number of Law Enforcement Agencies: 2000-2015.Available from ucrdatatool.gov

Uniform Crime Reporting, U.S. Federal Bureau of Investigation (2015). Violent Crime Rates and property Crime Rates: 2000-2015.Available from ucrdatatool.gov

U.S. Census (2015). ACS Educational Attainment Rates (% of Population, 25 or older): 2006-2014. Available from census.gov

U.S. Census (2015). Annual Estimates of the Resident Population by Sex, Race, and Hispanic Origin for the United States, States, and Counties: April 1, 2010 to July 1, 2015.Available from census.gov

U.S. Census (2015). Annual Estimates of the Resident Population – International Immigrants for the United States and States: 2000-2016.Available from census.gov


[1]Southern Poverty Law Center “Hatewatch”  

[2]Anti-Defamation League “2014 State Hate Crime Statutes” 

[3]Federal Bureau of Investigation “2010 Hate Crime Statistics” 

[4]Department of Justice “Hate Crime Laws”

[5]Abt Associates Inc “Study of Literature and Legislation on Hate Crime in America” 

[6]Goldstein, K. “The Case for Hate Crime Statutes”

[7]Federal Bureau of Investigation “2015 Hate Crime Statistics Report”

[8]Foley, C. & Smith, A. “State Statutes Governing Hate Crimes.” 

[9]FBI “2014 Crime in the United States” Table 77 – Full-time Law Enforcement Employees

[10]FBI “2014 Crime in the United States” Table 12 – Agency Hate Crime Reporting by State

[11]FBI “Uniform Crime Reporting Statistics: Their Proper Use

[12]FBI “2015 Hate Crime Statistics”  

[13]Abt Associates Inc “Study of Literature and Legislation on Hate Crime in America” 

[14]Movement Advancement Project “2014 Equality Map”  

[15]FBI “2015 Hate Crime Statistics”  

[16]Migration Policy Institute “The Top Sending Countries of Immigrants in Australia, Canada and the United States 

[17]US Department of Justice “Hate Crime Victimization, 2004-2012 – Statistical Tables”  

[18]Gallup “Democrats Racially Diverse; Republicans Mostly White”  

[19]Pew Research Center “Survey of Ideological Views”  

[20]US Census “Definition: Hispanic Origin” 

[21]Uniform Crime Reporting “UCR Offense Statistics”  

[22]Official Nebraska Government Website “Nebraska Unicameral” 

[23]Gallup “Analytics Table – Proportion of Conservatives” 

[24]Department of Justice “Victim Compensation Grant Program” 

[25]US Census “Census Regions and Divisions of the United States” 


Appendix

Model 1

Model 1

Model 1’

Model 1’

Model 2

Model 2

Model 2’

Model 2’

Model 3

Model 3

Model 3’

Model 3’

Model 4

Model 4

Model 4’

Model 4’

Model 5

Model 5

Model 5’

Model 5’

Model 6

Model 6

Model 6’

Model 6’

Model 7

Model 7

Model 7’

Model 7’

Model 8

Model 8

Model 8’

Model 8’

Model 9

Model 9

Model 9’

Model 9’

Model 10

Model 10

Model 10’

Model 10’

Model 11

Model 11

Model 11’

Model 11’

Model 12

Model 12

Model 12’

Model 12’

Model 13

Model 13

Model 13’

Model 13’

Model 14

Model 14

Model 14’

Model 14’

Model 15

Model 15

Model 15’

Model 15’

Vol. 20 - Spring 2017

Potential Implementation Challenges Analysis of the Experimental Reform of Shanghai’s National Higher Education Entrance Examination System

Potential Implementation Challenges Analysis of the Experimental Reform of Shanghai’s National Higher Education Entrance Examination System