Against the Clock: Crime Victim Compensation Law and Temporality Across the 50 United States

Abstract

Violence can result in insurmountable losses for victim-survivors. Restitutive and compensatory measures have been historically and contemporarily implemented to address these harms. Crime victim compensation (CVC) is a means by which the state reimburses victim-survivors of violent harm without relying on offender restitution. Overall, the current study argues that CVC laws and regulations may marginalize victim-survivors by regulating the body through crime-reporting and application time limits, as well as through wait times for compensation receipt. With this argument, the current study aims to use a content analysis and quantitative analysis to examine the intricacies of crime-reporting and application time limits detailed within statutes and administrative regulations across the 50 United States, as well as benefit receipt wait times.

Keywords

crime victim compensation law policy time limits wait times

Violence is a common aspect of the social conditions that shape the lives of persons living in the United States (U.S.). According to analyses of National Crime Victimization Survey data, violent crime occurred against people in the United States at a rate on the order of 2%. That is, out of every 100 people aged 12 years and older in the U.S., at least two experience violent crimes, such as robbery and aggravated assaults by strangers (Thompson & Tapp, 2023). For example, stranger violence occurs against at least one in every 100 persons in the U.S. (Thompson & Tapp, 2023). In the realm of family and domestic violence, between 40% and 50% of the U.S. population has experienced intimate partner violence (IPV) across the lifespan (Leemis et al., 2022). Furthermore, it is estimated that, for every 100 seniors in the U.S., 3 will experience physical harm, verbal harm, or neglect (Pillemer & Finkelhor, 1988).

Victim-survivors of violent harm often experience considerable material and non-material losses due to their victimization status, which extrapolate to society-wide burden. Collectively, these costs are staggering as are the impacts on individual survivors. For example, the across-lifespan cost of intimate partner violence (IPV) has been estimated to be on the order of almost four trillion U.S. dollars (see Peterson et al., 2018). Child maltreatment is estimated to amount to a lifetime economic burden of 500 billion U.S. dollars (Fang et al., 2012). Research based on data from the 2010s shows that the cost of rape echoes well beyond three trillion U.S. dollars (Peterson et al., 2017).

To address the material costs to victim-survivors of violent harm, crime victim compensation (CVC) was developed as an answer to the gaps of restitution (Cares et al., 2015). CVC provides reimbursement for expenses such as crime-related relocation, counseling, and physical health medical costs (Newmark et al., 2003). However, an application can be denied for many reasons, like committing a crime due to victimization status (i.e., “contributory misconduct,” for example, if an offender forces a victim to commit a crime; Office for Victims of Crime, 2022a, 2022b, 2022c; Poor, In press), noncooperation with officials, “failure” to report, and others (Office for Victims of Crime, 2022a, 2022b, 2022c). Victim-survivors of violent harm may also have their claims denied for (a) not reporting within a specific window of time and (b) not applying for CVC within a specific window of time. Utilizing a content analysis methodological technique, the current study uses the idea of temporal domination to understand how the nuances of CVC application time limits—and their tug-of-war with wait times for compensation receipt—penalize violent harm victim-survivors for their collective victimization status.

Crime Victim Compensation

CVC has a connection with public policy, social control, and the body. First, CVC uses physical and psychological injury and disability, indicators of body and mind, to, in part, ground claims for compensation. Second, wait times in public program benefit and relief receipt can be used to marginalize and dominate less-status groups for their position in society. Third, violent harm victims may have the unique dual burden of being simultaneously marginalized/dominated through strict and seemingly abrupt CVC reporting and application deadlines and long waits for receipt of their compensation. In these ways, bureaucracy and public policy exact social control over the body by using time limits and wait times as dualized tools of marginalization and domination (e.g., Bhattacharjee, 2002; Carson & Carter, 2023; Josephson, 2002; Montanez et al., 2022; Reid, 2013; Seefeldt, 2017).

To understand CVC, there must first be an understanding of restitution. Crime victim restitution includes payments for harm damages paid from a violent offender through a state agency to a harm victim. Thus, CVC includes payments for harm damages from a state agency to a harm victim (see Cares et al., 2015). The difference between CVC and restitution is that restitution is derived from the offender, while CVC is derived from the State. CVC policies have various characteristics that differ across states including funding sources, maximum compensation awards, (Newmark et al., 2003; Evans, 2014), compensable costs (Newmark et al., 2003), reporting time limits, application time limits (Evans, 2014), and others. Organizational research shows that some harm victims are less easily granted compensation due to the incompatibility of the nature of such abuse and the eligibility criteria of CVC (e.g., domestic violence; Danis, 2003). In sum, the parameters and intricacies of CVC are complex.

Social control—“the imposition of specific behavioral requirements that must be met by recipients of public benefits in order to maintain their eligibility for those benefits” (Josephson, 2002, p. 6)—is a means through which the State regulates the lives of marginalized people. For example, child support enforcement mechanisms and work requirements for public benefits receipt can be seen as requirements that domestic violence victims must meet in exchange for help and support through the State (Josephson, 2002). Added to this list, at least for immigrant and refugee survivors, are character dispositions like timeliness, helpfulness, cooperation, work requirements, and others (see Montanez et al., 2022). Similarly, to intrusions that invite external actors into women’s homes (e.g., police and immigration raids; see Bhattacharjee, 2002), social control via policy and programming may invite the State into victims’ lives.

Women’s lives have many facets including the home, the workplace, the public sphere, and others. One aspect of women’s lives that has historically and contemporarily been subject to scrutiny and social control is the body. For example, abortion restriction legislation may limit women’s bodily autonomy through manipulating public health and attitudinal discourse (Carson & Carter, 2023). Anti-abortion legislation in the context of COVID-19 framed abortion and abortion-adjacent phenomena/issues in certain ways like downplaying its importance and time-sensitiveness, through conflating pregnancy with the social construction of motherhood, and weaponizing public health language as a vehicle to deem abortion a health risk (e.g., framing abortion in the context of unneeded “person-to-person contact”) as to portray abortion restriction as a public health necessity; Carson & Carter, 2023).

Like the levying of anti-abortion laws against women’s bodies, CVC can potentially be a vehicle through which social control is unintentionally and more supply exacted against violent harm survivors’ bodies. For example, physical and emotional injuries, emblematic of harm against the body and mind, are part of eligibility criteria for CVC (Newmark et al., 2003). Furthermore, beyond the baseline maximum compensation amounts allowed by statutes and regulations, states may offer an inflation of compensation amounts based on, for example, “catastrophic” or “permanent and total” disability (Montanez et al., 2022). Thus, through mechanisms like injury and disability, the body becomes subject to scrutiny by the State through CVC.

Sociology of Time, Social Control, and Temporal Domination

Sociology views time as a phenomenon that extends beyond being natural to also being socially organized (Zerubavel, 2020). For example, dates and times are nested into categories (e.g., duration, tempo; Zerubavel, 1976). Contouring the categorization of time are cultural and political dimensions. In terms of culture, time may be seen as scarce or finite. While time is constant, how humans react to it varies. This can be seen in “saving time” or “wasting time” (Zerubavel, 1976, 2020). Venturing beyond the cultural and into the political, time is also an indicator and tool of power (Zerubavel, 2020). Being on the burdened end of these power imbalances is known as sociotemporal marginalization (Reid, 2013).

Temporal domination, a form of sociotemporal marginalization, involves the power to impose wait times upon an individual for their needs (Reid, 2013). Reid (2013) analyzed field notes and qualitative interview transcripts on the experiences of displaced hurricane survivors. Results indicated that there was a disconnect between survivors’ backgrounds and the framing of assistance programming, and many participants experienced extended wait times. In these ways, wait times partially constructed the parameters of deservingness. Similarly, a study of women seeking public assistance found grueling wait times are collectively a method of social control against non-wealthy women (Seefeldt, 2017). At the level of state policy, Montanez et al. (2022) argue that “timeliness” is one of many personal characteristics, among “helpfulness,” “cooperation,” and “good character,” that immigrant and refugee survivors of IPV may have to exchange in order to receive assistance and protection. In these ways, waiting a long time for “something” is an indicator of social position and a tool to reinforce and remind people of said social position.

CVC and Time

To receive CVC, time is an important factor. For example, not applying or reporting within a certain window of time can result in denial of a CVC application. In 2021, while Vermont had zero CVC application denials based on not filing an application within the state’s time limit, this number was 22 and 28 for Alabama and Illinois, respectively. Moreover, the time for processing of applications varies widely by state. While Vermont took on average 51 days to process CVC claims from application receipt to final decision, Alabama and Illinois took an average of 105 and 270 days respectively to process claims in 2021 (see Office for Victims of Crime, 2022a, 2022b, 2022c). Thus, for victim-survivors of violent harm, there exists a seemingly paradoxical experience of being rushed (for application submittal) and subsequently being patient (for relief receipt). In these ways, a study scrutinizing timeliness requirements of CVC is necessary.

Argument Summary and the Current Study

The current study has its origins in another study that aimed to predict poverty with anti-violence policy, including CVC policies ([Removed for Review]). One observation the author made during the previous project was that CVC statues had various “time limits.” Thus, the current study focuses on another form of temporal domination; instead of dominating by elongating time for benefit/relief receipt (Reid, 2013; Seefeldt, 2017), CVC may penalize victim status through simultaneously elongating compensation receipt and cutting the application time short (for a depiction, see Figure 1). By laying out time limits for reporting harms and filing for relief, CVC law defines the parameters of social control against survivors’ bodies by scrutinizing timeliness, and therefore, injury and disability. Accordingly, the current study looks at CVC statutes and administrative regulations across the 50 United States, specifically looking at the dynamics of time limits for violent harm reporting and CVC application filing. Through this effort, it is hoped that policy revision recommendations can be made to better serve victims of violent harm.

Methods

Data

Data for the current research on time limits and wait times for CVC claims come from (a) statute codes across the 50 United States, (b) administrative regulations across the 50 United States, (c) CVC board website documents and webpages (i.e., that detail eligibility criteria for CVC), and (d) administrative data on CVC reported by state CVC boards to the federal government. To obtain statute information, the statute codes of each state were assessed through each state’s respective legislative website. If the information was not found in a statute, the state’s respective administrative regulations (mostly an administrative code) were consulted to identify necessary information.

Variables

This study examines average CVC wait times for 2021 and 2022, application time limits, and reporting time limits.

Average Wait Times

Average wait time data are available via the U.S. Office of Justice Programs website, in the form of Victim Compensation Formula Grant Program Performance Reports (https://ovc.ojp.gov/states). The average number of days between application filling and final decision was extracted from these documents for each state. This was done for Fiscal Years 2020 and 2021 for all 50 states. For Year 2020, Lower Wait Time states included states with average wait time values of less than or equal to 66 days; Higher Wait Time states included states with average wait time values of greater than 66 days. For Year 2021, Lower Wait Time states included states with average wait time values of less than or equal to 66.5 days; Higher Wait Time states included states with average wait time values of greater than 66.5 days. The decision to dichotomize the variables was to account for a skewed distribution while ensuring that outliers/extreme values could still be included in the dataset.

Legal and Administrative Time Limits

To extract time limits from legal and administrative codes, a multistep approach was employed. First, the statute codes were consulted to see if all or some characteristics were able to be identified. If a statue code did not provide the necessary information, the administrative code was searched. If the administrative code did not provide the necessary information, the CVC board website was searched. Figure 2 provides a conceptual roadmap for the process of obtaining timeline information.

Time Limits for Reporting Crimes

In CVC, a crime must generally be reported to officials and/or authority figures before an application can be submitted. The current study featured the screening and full appraisal of statute language to search for time limits on reporting harm to officials and/or authority figures. First, the statutes were consulted. If all necessary information was found, further searching was not conducted. If the statute did not provide necessary information on reporting time limits, the state’s administrative code was consulted. CVC board websites were explored to identify necessary information if administrative codes did not provide necessary information. For the analyses, Smaller Reporting Windows equate to reporting time limits less than or equal to 72 hours; Larger Reporting Windows equate to reporting lime limits greater than 72 hours. The decision to dichotomize the variables was to account for a skewed distribution while ensuring that outliers/extreme values could still be included in the dataset.

Time Limits for Applying for CVC

After a crime is reported, an application for CVC can be filed. To find these time limits, the same process outlined in the “Time Limits for Reporting Crimes” section of the current research was used. For the analyses, Smaller Application Windows equate to application time limits of less than or equal to 2 years; Larger Application Windows equate to application time limits greater than or equal to 2 years. The decision to dichotomize the variables was to account for a skewed distribution while ensuring that outliers/extreme values could still be included in the dataset.

Other Institutional and Structural Variables

Three institutional/structural variables were used as well: percentage of the state population in rural areas, crime victim applicant diversity index, and crime victim applicant sex ratio.

Percent Rural

To understand rurality in the context of CVC, the percentage of the states’ populations considered rural was extracted from data from the 2020 Census. For example, in Alabama, the 2020 total population was 5,024,279 people. The rural population was 2,123,399 people. Dividing the rural population by the total population, and subsequently multiplying the quotient by 100 provides a percentage of 42.3.

Crime Victim Applicant Diversity Index

Crime victim race and ethnicity data are available via the U.S. Office of Justice Programs website, in the form of Victim Compensation Formula Grant Program Performance Reports. The average number of days between application filling and final decision was extracted from these documents for each state. This was done for Fiscal Years 2020 and 2021 for all 50 states. The data provided several categories: “American Indian or Alaska Native,” “Asian,” “Black or African American,” “Hispanic or Latino,” “Native Hawaiian or Other Pacific Islander,” “White Non-Latino or Caucasian,” “Some Other Race,” “Multiple Races,” “Not Reported,” and “Not Tracked.” A diversity index was constructed to ensure that all categories were included, except for “Not Reported” and “Not Tracked.” The diversity index involved (a) summing all categories to obtain a total, (b) obtaining the proportion of major white/non-white categories, (c) squaring such proportions, (d) summing the squared values, and (e) subtracting the sum from the value of one.

Crime Victim Applicant Sex Ratio

Crime victim sex data are available via the U.S. Office of Justice Programs website, in the form of Victim Compensation Formula Grant Program Performance Reports. The average number of days between application filling and final decision was extracted from these documents for each state. This was done for Fiscal Years 2020 and 2021 for all 50 states. The data provided four categories: “Male,” “Female,” “Not Reported,” and “Not Tracked.” Using the substantive categories—“Male” and “Female”—a sex ratio was created by dividing the number of male applicants by female applicants.

Analysis

Statistical Levels

Univariate Statistics

To understand the general distribution of temporality variables across states, as well as to become more familiar with the data, descriptive statistics (i.e., n, %, mean, median, minimum, and maximum) were reported for appropriate variables.

Bivariate Statistics

To understand the connections between/among CVC reporting time limits, CVC application time limits,¹ and CVC wait times, four chi-square tests were used, with reporting of both the chi-square value, as well as the Continuity-Corrected value in accordance with sample size considerations (see Cochran, 1952). To report alternative means of interpretation for readers, respective Phi coefficients, and corresponding Binomial Effect Size Display (BESD) values are reported for each cross-tabulation. BESD was developed as an intuitive way to report effect size for Phi coefficients (i.e., Pearson Product-Momen Correlation Coefficient for binary variables; Rosenthal & Rubin, 1982).

Multivariate Statistics

To be able to control for relevant variables, as well as explore if alternative variables sharpen understanding of wait times, a series of binary logistic regression models were conducted.

Analytic Considerations

For the current study, the threshold for statistical significance is p ≤ .10. This approach is used for several reasons:

To partially account for the relatively small sample size and risk of Type II error in macro-level research (see, Donley & Wright, 2008; Hawkins et al., 2013; see also, Witmer, 2023).

To follow recent guidance to not simply discard findings that are above p < .05 (see, e.g., Wasserstein et al., 2019).

In addition to the continuously reported p-value of each, the effect size is also considered and reported via the BESD to follow statistical guidance that more than one statistical measure should be used to come to a conclusion on findings (see Li et al., 2021). Moreover, to not privilege significant findings over non-significant findings, the researchers actively work to try to explain non-significant findings as well.

Results

Between application and reporting timelines, the overwhelming majority of information was able to be found by just identifying the statutes (82%; n = 41). Six percent (n = 3) of states had information that had to be found by searching a CVC board website. Eight percent (n = 4) of states had information that had to be found by looking at both statute and CVC board website information. One state had information that had to be found by looking just at administrative regulations while one state had information that had to be found by looking at both administrative regulations and CVC board websites.

Table 1 presents descriptive statistics for CVC dynamics across the 50 states. The details of these numbers are provided in four sections: reporting time limits, application time limits, exemptions, and CVC wait times.

Table 1.

Descriptive Statistics on State-level CVC Laws and Receipt Wait Times, United Sates, 2023.

	Mean	SD	Median	Min	Max	n	%
States that define a timeline for reporting	—	—	—	—	—	41/50	82.0
States with reporting limit > 120 hours	—	—	—	—	—	3/41	7.3
States with reporting limit of 120 hours	—	—	—	—	—	10/41	24.4
States with reporting limit of 96 hours	—	—	—	—	—	1/41	2.4
States with reporting limit of 72 hours	—	—	—	—	—	21/41	51.2
States with reporting limit of 48 hours	—	—	—	—	—	5/41	12.2
States with reporting limit of 30 hours	—	—	—	—	—	1/41	2.4
States that define a timeline for applying	—	—	—	—	—	48/50	96.0
Application time limit across states^a	2.2	1.4	2.0	1	7	—	—
2020 CVC receipt wait time^b	77.9	52.5	66.0	7	198	—	—
2021 CVC receipt wait time^b	77.8	56.3	66.5	7	270	—	—
Percent rural^c	27.6	14.8	27.3	5.8	64.9	—	—
2020 crime victim diversity^c	0.4	0.4	0.1	0.2	0.5	—	—
2021 crime victim diversity^c	0.4	0.4	0.1	0.2	0.5	—	—
2020 crime victim sex ratio^c	0.7	2.3	0.5	0.2	2.3	—	—
2021 crime victim sex ratio^c	0.8	2.3	0.6	0.2	2.3	—	—

Note. CVC = crime victim compensation; Min = minimum; Max = maximum; SD = standard deviation.

n = 48; statistics measured in years.

n = 50; statistics measured in days.

n = 50.

Law Results

Reporting Time Limits

Forty-one states defined reporting time limits. That is, they explicitly stated how much time a person had to file a report with law enforcement to be eligible for CVC. The slimmest timeframe in which a person could report was 48 hours (e.g., Maryland, South Carolina). The majority of states with a defined reporting time limit provided survivors 72 hours to report their victimization (e.g., Montana, Nebraska). The next most common reporting time limit was 120 hours (e.g., Alaska), followed by 48 hours, followed by greater-than-120 hour limits. In an example of a 72-hour reporting limit, Indiana’s Code of Laws states as follows:

Except for an alleged victim of a child sex crime, the division may not award compensation under this chapter unless the violent crime was reported to a law enforcement officer not more than seventy-two (72) hours after the occurrence of the crime. (Indiana Code § 5-2-6.1-17)

Application Time Limits

Forty-eight states defined application/claim time limits. That is, they explicitly stated how much time after a crime occurred that a person had to file a CVC claim. Across these 48 states, the mean and median application timelines were about 2 years. The slimmest timelines were 1 year in length. The largest timeline was seven years (i.e., California). For example, the Alaska statute defines the application parameters of CVC by stating the following:

(a) An order for the payment of compensation may not be made. . .unless

(1) the application has been made within 2 years after the date of the personal injury or death. (Alaska Stat. § 18.67.130[a][1]).

Exemptions

The legal language setting forth the parameters of time limit exemptions was diverse, specifically in terms of the synonyms used to describe the nature of exemptions. States used the terms good cause (e.g., Alabama), reasonably (e.g., Connecticut), interest of justice (i.e., North Dakota), without undue delay (i.e., Hawaii), timely (under the circumstances) (Illinois), and justified (e.g., South Carolina). Other terms—such as excuse (e.g., New Mexico), failure (e.g., Kansas, Oregon), and waiver (Delaware), exemption (e.g., Arkansas)—were used to define the particular mechanisms of exemption.

At the intersection of exemptions’ natures and mechanisms are good cause exemptions, reasonableness, and good cause delay justifications. Alaska, Georgia, and Florida describe these interconnections. In Alaska: “. . .or, if the incident or offense could not reasonably have been reported within that period, within five days of the time when a report could reasonably have been made” (A.C.A. § 16-90-712[a][1]). In Georgia: “. . . and provided, further, that upon good cause shown, the board may extend the time for filing a claim” (G.A. Code § 17-15-5). In Florida: “. . . For good cause the department may extend the time for filing a claim” (Fla. Stat. § 960.07[2]). Indeed, the parameters and language are variable.

CVC Federal Data Results

2020 and 2021 CVC Wait Times

The mean CVC receipt wait time in 2020 was about 78 days while the median CVC receipt wait time in 2020 was 66 days. Nevada had the shortest wait time of 7 days. By contrast, Illinois had a wait time of almost 200 days. For 2021, the mean CVC receipt wait time was 78 days and the median CVC receipt wait time in 2021 was about 67 days. Nevada again had the shortest wait time of 7 days and Illinois had the longest wait time of almost 270 days.

Cross-Tabulation Analysis

To run cross-tabulation analysis, all study variables were dichotomized at their medians, so that “lower” categories of the variables included values spanning from the minimum value to the median value; “higher” categories of the variables included all values above the median value (up to, and including, the maximum value). Table 2 shows a cross-tabulation of 2023 state reporting window lengths and 2020 wait time lengths. Generally, higher wait times tended to be found in states with the smaller reporting windows, a finding significant at the .10 level.

Table 2.

Cross-tabulation of 2023 State Reporting Windows Lengths and 2020 Wait Time Lengths (N = 41).

	Reporting window lengths
Wait time lengths	States with smaller reporting windows (n = 27) %	States with larger reporting windows (n = 14) %
States with lower wait times	44.4	55.6
States with higher wait times	71.4	29.6

Note. Percentages may not amount to 100% due to rounding. Analysis excludes California, Hawaii, Louisiana, Ohio, Pennsylvania, Texas, Utah, and Vermont. Chi-Square = 2.700, p = .100, Phi = −.277; Continuity Correction = 1.172, p = .189; Smaller Reporting Windows: ≤72 hours; Larger Reporting Windows: >72 hour; Lower Wait Times: ≤66 days; Higher Wait Times: >66 days.

Table 3 shows a cross-tabulation of 2023 state reporting window lengths and 2021 wait time lengths. Wait time lengths seemed roughly equally distributed across states with smaller and larger reporting window lengths. This finding was not significant at the .10 level.

Table 3.

Cross-tabulation of 2023 State Reporting Windows Lengths and 2021 Wait Time Lengths (N = 41).

Wait time lengths	Reporting window lengths
Wait time lengths	States with smaller reporting windows ( n = 27) %	States with larger reporting windows ( n = 14) %
States with lower wait times	48.1	51.5
States with higher wait times	50.0	50.0

Note. Percentages may not amount to 100% due to rounding. Analysis excludes California, Hawaii, Louisiana, Ohio, Pennsylvania, Texas, Utah, and Vermont. Chi-Square = .03, p = .910, Phi = -.018; Continuity correction = .000, p = 1.00; Smaller reporting windows: ≤ 72 hours; Larger reporting windows: >72 hour; Lower wait times: ≤66.5 days; Higher wait times: >66.5 days.

Tables 4 shows a cross-tabulation of 2023 state application window lengths and 2020 wait time lengths. States with smaller application windows seemed more likely to have higher wait times than states with larger application windows. This finding was not significant at the .10 level.

Table 4.

Cross-tabulation of 2023 State Application Windows Lengths and 2020 Wait Time Lengths (N = 48).

Wait time lengths	Application window lengths
Wait time lengths	States with smaller application windows (n = 33) %	States with larger application windows (n = 15) %
States with lower wait times	48.5	60.0
States with higher wait times	59.3	40.0

Note. Percentages may not amount to 100% due to rounding. Analysis excludes Vermont and Virginia. Chi-Square = .548, p = .459, Phi = −.107; Continuity Correction = .184, p = .669; Smaller application windows: ≤2.0 years; Larger application windows: >2.0 years. Lower wait times: ≤66 days; Higher wait times: >66 days.

Table 5 shows a cross-tabulation of 2023 state application window lengths and 2021 wait time lengths. It seemed that states with smaller application windows were more likely to have higher wait times than states with larger application windows. This finding was not significant at the .10 level.

Table 5.

Cross-tabulation of 2023 State Application Windows Lengths and 2021 Wait Time Lengths (N = 48).

Wait time lengths	Application window lengths
Wait time lengths	States with smaller application windows (n = 33) %	States with larger application windows (n = 15) %
States with lower wait times	48.5	53.3
States with higher wait times	59.3	46.7

Note. Percentages may not amount to 100% due to rounding. Analysis excludes Vermont and Virginia. Chi-Square = .097, p = .775, Phi = −.045; Continuity correction = .000, p = 1.00; Smaller application windows: ≤2.0 years; Larger application windows: >2.0 years; Lower wait times: ≤66.5 days; Higher wait times: >66.5 days.

Logistic Regression Analyses

Model 1 of Table 6 shows a logistic regression predicting 2020 wait times with 2023 reporting windows and level of rurality. While the model itself was significant, neither variable breached the .10 significance threshold. Model 2 presents a logistic regression predicting 2020 wait times with 2023 reporting windows and crime victim diversity. The model was not significant. Model 3 presents a logistic regression predicting 2020 wait times with 2023 reporting windows and sex ratio. While the model itself was significant, neither variable breached the .10 significance threshold. Model 4 presents a logistic regression predicting 2020 wait times with rurality, crime victim diversity, and crime victim sex ratio. The model was not significant.

Table 6.

Binary Logistic Regression of 2023 State Reporting Windows Lengths and 2020 Wait Time Lengths (N = 41).

Variables	Model 1	Model 2	Model 3	Model 4
Variables	B (SE)	B (SE)	B (SE)	B (SE)
2023 reporting time^a	−1.21 (0.74)	−1.17 (0.72)	−0.91 (0.73)	—
2020 rurality	0.04 (0.02)	—	—	0.04 (0.03)
2020 crime victim diversity	—	−2.80 (4.27)	—	1.46 (4.85)
2020 crime victim sex ratio	—	—	1.16 (0.85)	1.40 (0.83)
Constant	−0.887 (0.80)	1.44 (1.90)	−0.67 (0.74)	12.98 (2.70)
	AIC = 57.134	AIC = 59.419	AIC = 57.666	AIC = 58.621
	χ² = 5.485, p = .064	χ² = 3.199, p = .202	χ² = 4.952, p = .084	χ² = 5.997, p = .112

Note. Percentages may not amount to 100% due to rounding. Analysis excludes California, Hawaii, Louisiana, Missouri, Ohio, Pennsylvania, Texas, Utah, and Vermont. Smaller Reporting Windows: ≤ 72 hours; Larger reporting windows: > 72 hour; Lower wait times: ≤66 days; Higher wait times: >66 days. AIC = Akaike information criterion.

Reference category = smaller reporting time limit.

Model 5 of Table 7 reports a logistic regression predicting 2021 wait time lengths with 2023 reporting times and rurality. The model was not significant. Model 6 of Table 7 reports a logistic regression predicting 2021 wait time lengths with 2023 reporting times and 2021 crime victim diversity. The model was not significant. Model 7 of Table 7 reports a logistic regression model predicting 2021 wait time lengths with 2021 sex ratio. The model was not significant. Model 8 of Table 7 reports a logistic regression predicting 2021 wait time lengths with rurality, 2021 victim diversity, and 2021 sex ratio. The model was not significant.

Table 7.

Binary Logistic Regression of 2023 State Reporting Windows Lengths and 2021 Wait Time Lengths (N = 41).

Variables	Model 5	Model 6	Model 7	Model 8
Variables	B (SE)	B (SE)	B (SE)	B (SE)
2023 reporting time^a	−0.06 (0.68)	−0.08 (0.66)	0.09 (0.68)	—
2020 rurality	0.03 (0.02)	—	—	0.04 (0.03)
2021 crime victim diversity	—	−0.85 (4.02)	—	1.99 (4.45)
2021 crime victim sex ratio	—	—	0.58 (0.64)	0.55 (0.63)
Constant	−0.87 (0.77)	0.44 (1.77)	−0.45 (0.69)	−2.28 (2.40)
	AIC = 60.662	AIC = 62.756	AIC = 61.946	AIC = 61.765
	χ² = 2.152, p = .341	χ² = .058, p = .971	χ² = .868, p = .648	χ² = 3.05, p = .384

Note. Percentages may not amount to 100% due to rounding. Analysis excludes California, Hawaii, Louisiana, Missouri, Ohio, Pennsylvania, Texas, Utah, and Vermont. Smaller Reporting Windows: ≤72 hours; Larger reporting windows: >72 hour; Lower wait times: ≤66 days; Higher wait times: >66 days. AIC = Akaike information criterion.

Reference category = smaller reporting time limit.

Model 9 of Table 8 presents a logistic regression model predicting 2020 application wait times with application time limit and rurality. The model was not significant. Model 10 shows a logistic regression model predicting 2020 application wait times with application time limit and crime victim diversity. The model was not significant. Model 11 shows a logistic regression predicting 2020 application wait times with application time limit and crime victim sex ratio. The model was significant at the .10 level, showing further that sex ratio was significantly associated with application wait times. Increases in by over 500%. Model 12 presents a logistic regression model predicting 2020 application wait times with rurality, crime victim diversity, and crime victim sex ratio. The model was significant at .05 level, with rurality and sex ratio as significant predictors. Every one-unit increase in the percentage of the state considered rural was associated with an increase in the odds of being a high application wait period state by 5%. Every one-unit increase in the sex ratio was associated with an increase in the odds of being a high application wait period state by over 400%.

Table 8.

Binary Logistic Regression of 2023 State Application Windows Lengths and 2020 Wait Time Lengths (N = 48).

Variables	Model 9	Model 10	Model 11	Model 12
Variables	B (SE)	B (SE)	B (SE)	B (SE)
2023 application time^a	−0.26 (67)	0.27 (1.78)	−0.66 (0.68)	—
2020 rurality	0.04 (0.02)	—	—	0.04 (0.03)^†
2020 crime victim diversity	—	−0.47 (0.64)	—	23.89 (4.51)
2020 crime victim sex ratio	—	—	1.71 (0.87)*	1.53 (0.82)^†
Constant	−1.03 (0.74)	0.27 (1.78)	−1.07 (0.66)	−3.65 (2.41)
	AIC = 68.877	AIC = 71.893	AIC = 66.831	AIC = 66.425
	χ² = 3.582, p = .167	χ² = .566, p = .754	χ² = 5.628, p = .060	χ² = 8.034, p = .045

Note. Percentages may not amount to 100% due to rounding. Analysis excludes Vermont and Virginia. Smaller Application Windows: ≤2.0 years; Larger Application Windows: >2.0 years; Lower Wait Times: ≤66.5 days; Higher Wait Times: >66.5 days. AIC = Akaike information criterion.

Reference category = Smaller Application Window.

†

p < .01. *p < .05.

Model 13 of Table 9 presents a logistic regression of 2021 wait time lengths with 2023 state application windows and rurality. The model was not significant. Model 14 of Table 9 presents a logistic regression of 2021 wait time lengths with 2023 state application windows and 2021 crime victim diversity. The model was not significant. Model 15 presents a logistic regression of 2021 wait time lengths and 2021 victim sex ratio. The model was not significant. Model 16 presents a logistic regression predicting 2021 wait time lengths with rurality, 2021 crime victim diversity, and 2021 crime victim sex ratio. The model was not significant.

Table 9.

Binary Logistic Regression of 2023 State Application Windows Lengths and 2021 Wait Time Lengths (N = 48).

Variables	Model 13	Model 14	Model 15	Model 16
Variables	B (SE)	B (SE)	B (SE)	B (SE)
2023 application time^a	0.04 (0.67)	0.57 (3.81)	−0.08 (0.64)	—
2020 rurality	0.04 (0.02)	—	—	0.04 (0.02)
2021 crime victim diversity	—	−.19 (.63)	–	2.91 (4.10)
2021 crime victim sex ratio	—	—	0.75 (0.62)	0.67 (0.61)
Constant	−1.02 (0.75)	−0.19 (1.68)	−0.56 (0.62)	2.83 (2.12)
	AIC = 69.5	AIC = 70.42	AIC = 70.865	AIC = 69.83
	χ² = 3.042, p = .219	χ² = .119, p = .942	χ² = 1.677, p = .432	χ² = 4.716, p = .194

Note. Percentages may not amount to 100% due to rounding. Analysis excludes Virginia and Vermont. Smaller application windows: ≤2.0 years; Larger application windows: >2.0 years; Lower wait times: ≤66.5 days; Higher wait times: >66.5 days. AIC = Akaike information criterion.

Reference category = Smaller Application Window.

Discussion

The current research theoretically drew upon the Sociology of Time to frame and conduct an analysis of CVC laws, as well as pandemic-era CVC wait times, across the 50 United States. This section first summarizes the main findings of the current study. This discussion then discusses major limitations. Finally, implications for policy revision are provided.

The results of the study found that not all states are clear in their legal operationalization of parameters defining CVC reporting time limits, as well as application time limits. When these time limits were discussed, most reporting time limits were equivalent to or below 72 hours; most application time limits were equivalent to about 2 years. The exceptions to these figures are notable outliers; for example, California allows CVC applicants to file a claim up to 7 years post-crime commission. Furthermore, some states have longer wait times than others, while also having similarly sized reporting and/or application time limits. Thus, some states provided more evidence of temporal domination than others, anecdotally (e.g., Illinois compared to Iowa).

Interpreting the Phi coefficient for the reporting time limit and 2020 wait time data (through the BESD) shows a small effect in general, but larger than any effect of the other three cross-tabulations. If a person selects one of the states with a smaller reporting window, that person could select a state with a higher waiting time and be correct 64% of the time. Interpreting the reporting time limit and 2021 wait time data (through the BESD) shows that, if a person selects one of the states with a larger reporting window, that person could select a state with a higher waiting time and be correct about 51% of the time. Interpreting the application time limit and 2020 wait time data (through the BESD) shows that, if a person selects one of the states with a larger reporting window, that person could also select a state with a higher waiting time and be correct about 55% of the time. Interpreting the application time limit and 2021 wait time data (through the BESD), if a person selects one of the states with a larger application window, that person could also select a state with a higher wait time and be correct about 52% of the time. The results potentially hint to the disruptive nature of the COVID-19 pandemic. Across states in 2020, about 70% of states with smaller reporting windows had higher wait times. In 2021, this rate dropped to 50%. In general, the findings that more closely approached significance occurred when the time limit variables were applied to the 2020 wait times.

Beyond bivariate analyses, logistic regression shows significant relationships between wait times for 2020 and rurality and crime victim sex ratio (particularly, in Models 11 and 12). That the relative numerical imbalance of males versus female victims may impact wait times because men are more likely to be victims of violent crimes (mostly by other men) and thus create backlog in the processing of applications. Relatedly, rurality may have a relatively marginal impact on compensation receipt by way of resource limitations in rural areas, and thus, backlog in processing applications.

Limitations

This analysis of CVC law and temporal domination is not without limitations and alternative potential explanations, especially in the context of non-significant results. First, this study was developed without the potentially critical input of officials from CVC boards. This step would account for the administrative aspects of CVC. For example, CVC is a mechanism in which the burden of funding a social program is placed on certain people in a society (see Levine & Russell, 2023). Particularly, this part of CVC functioning is based on offender fees (Evans, 2014; Levine & Russell, 2023). Popular press investigations show that the pot of money from which funds are drawn for compensating victims is often strained (Catalini & Lauer, 2023). This concern could be partially explanatory in this context because underfunding, short-staffing, and other complications (e.g., risk of dishonest application, not being able to provide proper documentation) could compound to affect timeliness of compensation.

Second, an important step forward would be to obtain interviews with survivors in states with the most disparate and difficult timelines and wait times. This step forward would account for the experiencing of CVC. For example, such laws should account for the variability in time needed per various victimization experiences, such as when survivors of partner violence return to abusive partners, then leave again (as well as when partner violence survivors are in elongated planning stages of leaving).

Third, as previously mentioned, there was a risk of not having enough statistical power to detect an effect; it is hoped that the expected count considerations and relaxing of the significance level could, together, partially account for this limitation. Moreover, while statistical power may be of concern, the current analysis uses the entire universe of available units (i.e., the 50 United States; see also, e.g., Donley & Wright, 2008).

Implications

One important implication links back to the social organization of time. Indeed, although time is finite and cannot be “controlled” per se, humans often attempt to shift the parameters of time in relation to their own lives (Zerubavel, 1976, 2020). For example, people may try to “beat the clock” or “kill time.” Time may also be “borrowed,” or also, on one’s “side.” In connection to the current study, it would be helpful for CVC law to formally “respect” survivors’ time through policy revision. While time limits and “rushing” are grounded in ensuring that CVC claims are processed with consideration to statutes of limitations and to ensure accurate reporting across time (e.g., to mitigate the impact of fading memories and statement recantation), standardization of timelines may be helpful. This could be completed through developing a model policy, off of which states can derive guidance for CVC policy change. For example, in the 1990s, a team of Florida practitioners and researchers collaborated on a model policy regarding how law enforcement agencies should handle domestic violence cases. This model policy was intended to serve as a guide for Florida law enforcement to implement evidence-based strategies of domestic violence response into standard operating procedure (Tatum & Clement, 2007). Similarly to how the State of Florida provided guidance for sub-state-level jurisdictions (e.g., counties, cities, and towns), federal symbolic policy that sets forth a model can provide guidance for sub-national level jurisdictions (i.e., states and territories). Even if it is a sizable task, a model policy based on updated empirical evidence on the experiences of crime victims could potentially serve as a source of insight across time, even if states revise their CVC legislation at different times. In any case, and in the above ways and beyond, honoring and respecting survivors’ time becomes paramount.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Julio Montanez

Notes

Author Biographies

Julio Montanez is a doctoral student in the Department of Sociology at the University of Central Florida. His research interests broadly span the intersections of intimate partner violence and social policy. His works can be found in Oxford Research Encyclopedia of Criminology and Criminal Justice and others.

Amy Donley is an associate professor in the Department of Sociology at the University of Central Florida. Dr. Donley’s research spans several areas and includes substance use disorders, poverty, housing insecurity and homelessness, and intimate partner violence, among others. Her works can be found in Journal of Black Studies, Journal of Social Distress and the Homeless, Deviant Behavior, and Journal of Interpersonal Violence.

References

Bhattacharjee

(2002). Private fists and public force: Race, gender, and surveillance. In Silliman

Bhattacharjee

(Eds.), Policing the national body: Sex, race, and criminalization (pp. 1–54). South End Press.

Cares

A. C.

Haynes

S. H.

Ruback

R. B.

(2015). Reducing the harm of criminal victimization: The role of restitution. Violence & Victims, 30(3), 450–469. https://doi.org/10.1891/0886-6708.VV-D-13-00049

Carson

Carter

S. K.

(2023). Abortion as public health risk in Covid-19 anti-abortion legislation. Journal of Health Politics, Policy and Law. Advance online publication. https://doi.org/10.1215/03616878-10449950

Catalini

Lauer

(2023). Takeaways from AP’s report on financial hurdles in state crime victim compensation programs. Associated Press. https://news.yahoo.com/takeaways-aps-report-financial-hurdles-163646482.html?fr=sycsrp_catchall

Cochran

W. G.

(1952). The χ2 test of goodness of fit. Annals of Mathematical Statistics, 23(3), 315–345. https://www.jstor.org/stable/2236678

Danis

F. S.

(2003). Domestic violence and crime victim compensation: A research agenda. Violence Against Women, 9(3), 374–390.

Donley

A. M.

Wright

J. D.

(2008). For richer or for poorer: The impact of state-level legislation on marriage, divorce, and other outcomes. Sociological Spectrum, 28(2), 133–159. https://doi.org/10.1080/02732170701796239

Evans

(2014). Compensating victims of crime. Research & Evaluation Center, John Jay College of Criminal Justice, City University of New York.

Fang

Brown

D. S.

Florence

C. S.

Mercy

J. A.

(2012, February). The economic burden of child maltreatment in the United States and implications for prevention. Child Abuse & Neglect, 36(2), 156–165.

10.

Hawkins

A. J.

Amato

P. R.

Kinghorn

(2013). Are government-supported healthy marriage initiatives affecting family demographics? A state-level analysis. Family Relations, 62(3), 501–513.

11.

Josephson

(2002). The intersectionality of domestic violence and welfare in the lives of poor women. Journal of Poverty, 6(1), 1–20.

12.

Leemis

R. W.

Khatiwada

F. N.

Chen

M.S.

Kresnow

Smith

S. G.

Caslin

Basile

K. C.

(2022). The national intimate partner and sexual violence survey: 2016/2017: Report on intimate partner violence. National Center for Injury Prevention and Control, Centers for Disease Control and Prevention.

13.

Levine

J. R.

Russell

K. L.

(2023). Crime pays the victim: Criminal fines, the state, and victim compensation law 1964-1984. American Journal of Sociology, 128(4), 1158–1205.

14.

Walter

S. D.

Thabane

(2021). Shifting the focus away from binary thinking of statistical significance and towards education for key stakeholders: Revisiting the debate on whether it’s time to de-emphasize or get rid of statistical significance. Journal of Clinical Epidemiology, 137, 104–112.

15.

Montanez

Donley

Reckdenwald

(2022). Between systems and violence: State-level policy targeting intimate partner violence in immigrant and refugee lives. Routledge.

16.

Newmark

Bonderman

Smith

Liner

(2003). National evaluation of state Victims of Crime Act assistance and compensation programs: Trends and strategies for the future. Urban Institute Justice Policy Center. https://ojp.gov/pdffiles1/nij/grants/203198.pdf

17.

Office for Victims of Crime. (2022a). Alabama: Annual performance measures report, 2021. [PDF file].

18.

Office for Victims of Crime. (2022b). Illinois: Annual performance measures report, 2021. [PDF file].

19.

Office for Victims of Crime. (2022c). Vermont: Annual performance measures report, 2021. [PDF file].

20.

Peterson

DeGue

Florence

Lokey

C. N.

(2017). Lifetime economic burden of rape among U.S. adults. American Journal of Preventive Medicine, 52(6), 691–701.

21.

Peterson

Kearns

M. C.

McIntosh

W. L.

Estefa

L. F.

Nicolaidis

McCollister

K. E.

Gordon

Florence

(2018). Lifetime economic burden of intimate partner violence among U.S. adults. American Journal of Preventive Medicine, 55(4), 433–444.

22.

Pillemer

Finkelhor

(1988, February). The prevalence of elder abuse: A random sample survey. The Gerontologist, 28(1), 51–57.

23.

Poor

. (In press). Disentangling the civil-carceral state: An abolitionist framework for the non-criminal response to intimate partner violence. New York University Review of Law and Social Change. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4186152

24.

Reid

(2013). Social policy, “deservingness,” and sociotemporal marginalization: Katrina survivors and FEMA. Sociological Forum, 28(4), 742–763. https://doi.org/10.1111/socf.12051

25.

Rosenthal

Rubin

D. B.

(1982). A simple, general purpose display of magnitude of experimental effect. Journal of Educational Psychology, 74(2), 166.

26.

Seefeldt

K. S.

(2017). Waiting it out: Time, action, and the process of obtaining social benefits. Qualitative Sociology, 16(3), 300–316. https://doi.org/1010.1177/1473325015606188

27.

Tatum

K. M.

Clement

(2007). An exploratory analysis of florida law enforcement domestic violence policies. American Journal of Criminal Justice, 32, 45–56. https://doi.org/10.1007/s12103-007-9009-8

28.

Thompson

Tapp

S. N.

(2023). Criminal victimization, 2022. Bureau of Justice Statistics. Office of Justice Programs, U.S. Department of Justice.

29.

Wasserstein

R. L.

Schirm

A. L.

Lazar

N. A.

(2019). Moving to a world beyond “p< 0.05”. The American Statistician, 73(1), 1–19.

30.

Witmer

(2023) What should we do differently in STAT 101? Journal of Statistics and Data Science Education. https://doi.org/10.1080/26939169.2023.2205905

31.

Zerubavel

(1976). Timetables and scheduling: On the social organization of time. Sociological Inquiry, 46(2), 87–94.

32.

Zerubavel

(2020). The sociology of time. In Reinecke

J. R.

Suddaby

Langley

Tsoukas

(Eds.), Time, temporality, and history in the process of organization. (pp. 44–49) Oxford University Press.