Abstract
The main objective of this study was to examine the clearance rates of violent and non-violent offenses in the United States for the years 2011 to 2018. This study focused specifically on the differences in clearance rates of incidents involving crimes against persons, crimes against property, and crimes against society. The analyses are based on the FBI’s NIBRS data that have been reported by local, state, and federal law enforcement agencies for 8 years combined. The analyses focused on the characteristics of the distribution of clearance rates by the types of incidents. The clearance rates were calculated based on the number of incidents that were cleared by arrest or exceptional means. The results show that the average clearance rate for incidents involving crimes against persons is 48.6%, for incidents involving property crimes is 18%, and for incidents involving crimes against society is 78%. The trend analyses show that the clearance rates are gradually decreasing for all three types of offense categories.
Keywords
Introduction
Incident clearance rates are affected by a variety of factors. Some of them have a negative impact and some factors positively affect clearance rates. The most obvious factors that affect the clearance rates include lack of solvability factors in more serious crimes, lack of police resources, police response time, the number of offenders and victims involved, victim-offender relationship, witness-offender relationship, time of incident, department size, the volume of crimes, etc. (Alderden & Lavery, 2007; Avdija, 2019a; Blanes i Vidal & Kirchmaier, 2017; Greenwood & Petersilia, 1975; Jarvis et al., 2017; Mouzos & Muller, 2001; Wellford & Cronin, 2000). Some of these factors are affected by the nature of the offense itself and by the offender, while other factors, on the other hand, are within the control of the law enforcement agencies. Additionally, not all types of offenses are affected equally by the same factors. Some researchers even argue that clearance rates are affected by selective law enforcement practices (Alderden & Lavery, 2007; Litwin, 2004; Roberts & Lyons, 2011). Regardless, there is no one factor that can be judged as the sole cause of a low clearance rate or high clearance rate. The literature review section in this article focuses on examining some of the weaknesses of the national datasets that are used to evaluate the clearance rates and some of the frequently blamed factors responsible for low clearance rates of crime in the United States.
The Flaws in the Measurements of the Crime Rates
There are many unavoidable issues associated with the existing national database programs. The crime data that are reported to the FBI’s Uniform Crime Report, for example, are considered flawed and incomplete by many researchers (Blackman & Gardiner, 1984; Targonski, 2011; Uniform Crime Reporting Statistics, 2020). Originally, the UCR was designed to measure the prevalence and the incidence of crime in the U.S. One of the major problems with UCR, however, is the lack of uniform definition of crime across jurisdictions. Even before UCR was developed, there were a few cities that published crime data at the local level (Barnett-Ryan, 2007; Mosher et al., 2011). Nonetheless, as is the case with UCR, they did not have a standardized procedure for recording crimes. This makes us conclude that there are several issues with the UCR program itself. The name “Uniform Crime Reports” suggests that crimes should be reported uniformly, but, in reality, this is not the case. Many police departments use different methods of recording crimes; thus, making the UCR less reliable and less accurate as a crime database. There are many inconsistencies in crime data reported to the FBI’s UCR, and for that matter, in other data collection institutions and agencies as well. The inconsistencies have to do mainly with the way things are measured, defined, coded and recorded (Barkan & Bryjak, 2011; Maltz, 2006; Walsh & Jorgensen, 2012). Not all types of crimes or victimizations are classified in the same way when recorded by law enforcement agencies. For instance, the police respond to a shots-fired call, and when they arrive at the crime scene, they find out that a person got shot and then he fell off the balcony. There are two ways to record this death: (a) killed by a firearm, (b) the person in question is dead because he fell off of the balcony. The question is: which one is the accurate description of his death? If he did not get shot, he would not have fallen off the balcony (Mosher et al., 2011). On the other hand, even after he got shot, he would have survived if he had not fallen off of the building—it is a form of tautology and cases like these affect the accuracy of crime data reported to the police.
Among other problems with UCR is the area of coverage (Lynch & Addington, 2007). Since reporting crime to the FBI’s UCR is voluntary, not all police departments care to report their crime statistics. And some of them only report their data a few months during the year but not all crimes for the full year (Lynch & Jarvis, 2008). Another problem with UCR includes coding errors. A crime is recorded to the police as an aggravated assault, for instance, but later on, the victim dies as a result of it. This type of crime may show on UCR as an “aggravated assault” and not as a homicide. On the other hand, if that same aggravated assault is re-coded to include the homicide, than it would be recorded as two crimes, an assault and a homicide, which would inevitably lead to the inflation of crime. Thus, there is a lot of confusion about how to code crimes. One rubbery, for example, may involve multiple victims, and the police will record it as one crime. Victimization programs such as NCVS, on the other hand, will record as many incidents as the number of victims who happen to be in that particular location (Mosher et al., 2011). In other words, even though the information is there, UCR only records part of it. The statistics about crime presented by UCR can be misleading, to say the least. They are numbers; but for us to make sense of them, it requires a meaningful interpretation of them. Unfortunately, there is not a perfect approach in dealing with the problems associated with the classification and coding crimes. We may have one incident with multiple crimes or one crime with multiple victims on one hand, and we may have one victim, multiple crimes on the other. Thus, classifying these types of incidents becomes challenging. For example, a person commits a robbery, knocks down the store clerk, and when he gets outside, he hijacks a car to getaway. At first glance, it appears that three crimes occurred: (a) a robbery, (b) an aggravated assault, and (c) a car theft (Mosher et al., 2011). Under the hierarchical rule, the UCR records the most serious crime (Blumstein & Farrington, 1991). In the above scenario, they record it as a robbery. If they record all three types of crimes, then it would be an inflation of the number of crimes that occur. Needless to say, in this example scenario, there is one crime but multiple victims. Again, there is not a proper way to cover everything the right way that makes everyone happy. No one likes the inflation of crime because it has consequences. The inflation of crime rates will inevitably have a negative impact on citizens’ feeling of safety (Baker et al., 1983). Not reporting all types of crimes, on the other hand, would be unfair to the victims of those crimes.
Alternative Measures of Crime Rates
An alternative form of data collection method that is very useful for uncovering some of the crimes that go unreported is the National Crime Victimization Survey (NCVS). This program is used to gather information to reduce the unknown (e.g., the residual) number of crimes that are committed and never investigated or reported to the police (Bureau of Justice Statistics, 2018). However, one of the many weaknesses of NCVS is that it does not cover victimless crimes. This, nevertheless, is covered in part by other forms of data collections such as the UCR and the self-reported surveys. The data collection procedure that NCVS uses is very rigorous (Land, 2007). In fact, it is considered a more reliable and valid way of collecting data, for what it is designed to measures, than any other forms of data collection (e.g., the UCR and Self-reports). The NCVS collects data using a standardized form; meaning they use a questionnaire that has been refined over the years and that is administered to households in the national sample (Mosher et al., 2011). Unlike the other two forms mentioned above (UCR for instance, has problems with classification and coding, and self-reported survey has many problems with sampling, representativeness, and generalizability), NCVS uses the same questionnaire for all rotating samples. Although some of the problems with the self-reported survey are the same as those of NCVS, at least, NCVS collects data uniformly. However, one major problem with NCVS and that is worthy of mentioning here is that the number of victimization incidents reported to the police is disproportionately greater than the actual number of crimes reported to the police. That is, one crime can have multiple victims. When surveyed, each victim is considered an independent incident. For example, the police arrest a thief who has burglarized three storage units next to each other in one event. To UCR, this crime is reported as one burglary. The NCVS, on the other hand, will report three separate and independent incidents, if they happened to be in the same sample (Mosher et al., 2011). In short, victimization survey helps uncover some of the crimes that are unreported to the police, but at the same time, it helps inflate the true figure of crime (Farrell et al., 2005). Nonetheless, the NCVS is a measurement program that is intended to fill the gaps or reach out to where UCR cannot reach (Bureau of Justice Statistics, 2014). The two main crime data collection programs in the U.S. can be simply categorized as follow:
UCR/NIBRS = measures the amount of law enforcement
NCVS = measures the amount of victimization
The total amount of crime = remains unknown
Factors that Affect the Clearance Rates
Solvability Factors
The solvability factors are not equally available for all types of crimes. “Solvability factors are bits of pieces of information that help solve cases” (Avdija, 2019b, p. 104). Some of them may be just leads that the investigators use to act upon, or to determine if a case has a chance of being solved or not (Olphin & Coupe, 2019). In fact, it is safe to say that all crime cases rely on some form of solvability factors. The more solvability factors cases have, the higher the chances of clearing those cases (Hirschy, 2003; Keppel & Weis, 1992). From a broader perspective, those factors include the physical evidence related to the typical crime; factors related to the offenders, the victims, and the police resources. In specific terms, solvability factors include the presence of witness to the crime scene, the number of witnesses, the number of victims, knowledge about the suspect or the identity of the suspect, the number of suspects, traceable or identifiable evidence or property (if dealing with property crimes), existence of a specific suspect’s M.O., circumstances surrounding the crime scene, victim’s willingness to cooperate with the police, witnesses’ willingness to cooperate, victim-offender relationship, witness-offender relationship, etc. (Avdija, 2019a; FBI, 1990; Grau & Jacobson, 1981; Knutsson, 1983; Riggs et al., 2019; Wellford & Cronin, 2000). The initial stage of every investigation starts with reviewing the solvability factors. Typically, it involves identifying the victim, the potential offenders, crime scene/jurisdiction, and the evidence that links these three.
According to Wellford and Cronin (2000), there are at least 51 important solvability factors that can be taken into account when it comes to whether a case can be solved or not. Of those 51, more than 30 of them are factors that the police have control over. Some of those include police response time (Brookman et al., 2018; Greenwood & Petersilia, 1975; Weis & Keppel, 1994), the number of detectives assigned to a case or responding to the crime scene (Avdija, 2019b), communication between agencies (e.g., information sharing), etc. Although for a number of these factors the police have control over, unfortunately, there are other factors that the offenders have control over. Such factors include the time of crime occurrence, the type of and the nature of the crime, the location of the crime and how far it is from the police presence, etc. Research shows that crimes that occur between 6:00 pm and 6:00 am are less likely to be solved as opposed to crimes that occur between 6:00 am and 6:00 pm during the day (Alderden & Lavery, 2007; Mouzos & Muller, 2001). In addition, cases involving multiple victims are more likely to be solved compared to cases involving only one victim (Mouzos & Muller, 2001). Such cases will force the police to prioritize their commitment to those cases with more victims than cases with fewer victims.
Police Effectiveness
In addition to the influence of solvability factors on clearance rates, police investigative effectiveness, when judged based on the clearance success rates, depends largely on resources available, the manpower, the workload, the innovative efforts (utilization of technology), diversity of the police force, collaboration between agencies, and the agency size (Avdija, 2019b; Carter & Carter, 2015; Skogan, 1976; Worrall, 2016). Avdija’s (2019b) study, for example, suggests that an increase in the number of investigators and first responders responding to the crime scene helps increase the arrest rates, which ultimately leads to an increase in the clearance rates. Cordner’s (1989) study suggests that a reduced workload is associated with an increase in the clearance rate and vice versa. The negative effect of an increased police workload is manifested in the form of selective caseload processing, in which the police are forced to focus on only the most serious crimes while leaving aside minor offenses (Pare et al., 2007). Furthermore, Garicano and Heaton’s (2006) study suggests that the use of computerized technology, when the department chose to use it, helps increase the clearance rates. Unfortunately, this cannot be compared or verified because, at the national level, the NIBRS does not report the clearance rates for individual departments. The data that are reported by the NIBRS are aggregated and cannot be used to evaluate the clearance success rates of individual departments. Perhaps the main justification for not reporting such data is to prevent comparisons between departments so that evaluative judgments can be eliminated (Cordner, 1989; Pare et al., 2007). Regardless of the clearance rates, it should be noted that the police effectiveness should not be judged purely from the number of crimes they solve. Police have multiple functions, in addition to solving crimes. In fact, preventing crime is more important than just focusing the entire energy on solving past crimes. After all, the absence of crime resulting from effective crime prevention efforts is better than police effectiveness in solving crimes, the very crimes that could be prevented in the first place.
Social Structure Affects the Clearance Rates
According to Black (1976), there is s disparity in the way the law is enforced. This is manifested in the form of layered social structure, in that cases involving victims who come from higher social status are more likely to get solved compared to cases involving victims who come from lower social status, specifically the poor and the minorities. Roberts and Lyons’s (2011) study, for example, suggests that homicide cases involving Hispanic victims had a lower clearance rate compared to non-Hispanic White victims. This would be a form of selective law enforcement practice where the police will not assign equal priority status or delay/postpone the investigative efforts for cases involving victims of crimes against persons and crimes against property who come from the low socio-economic status and minority groups. Alderden and Lavery’s (2007) study also shows that the clearance rates were lower for both Hispanic victims and Black victims compared to White victims. Litwin (2004) found similar results in his study. Cases involving White victims had a higher clearance rate compared to cases involving Latino victims. In addition, Litwin found that the clearance rates were higher for communities with higher homeowner rates compared to communities with lower homeowner rates (Borg & Parker, 2001; Litwin, 2004). This goes to show that there is some evidence indicating the effects of social structure on the crime clearance rates are plausible. The clearance rate is also affected selectively by gender and age, in addition to race and socio-economic status (Riedel, 2008). Litwin and Xu (2007), for example, found that the clearance rates were higher for victims of younger age compared to older victims, and higher for male victims compared to the female victim (see Riedel & Rinehart, 1996 also). These studies overall suggest that if the victims are female minorities who come from low socio-economic status, and older in age, the likelihood of solving those cases is significantly lower than, for example, cases involving white male victims who come from a higher socio-economic status. Although these studies show evidence of selective law enforcement, they do not show the actual cause of it. In most cases, the police cannot be accused of selective law enforcement because it is lack of cooperation with the police that creates this disparity in the clearance rates.
Method
The Current Study
The main goal of this study is to measure the amount of law enforcement in the United States through the clearance rates. The focus of this study is to analyze the clearance rates for 24 types of crimes incidents committed in the United States from 2011 to 2018. Under this main goal, there are three distinctive objectives. The first objective is to analyze the total number of cleared incidents for 8 years combined. The second objective is to examine the differences in the clearance rates for crimes against persons, crimes against property, and crimes against society, by breaking it down into individual crime categories. And the third objective is to examine the clearance rates trends for the above three crime categories. Under this third objective, the focus will be on examining whether the clearance rates are increasing or decreasing for each offense category expanding over an 8-year period of time.
The Data Source
This study uses national crime data that were published by the FBI’s National Incident-Based Reporting System (NIBRS) from 2011 to 2018. This study uses the latest data that were published by the NIBRS on December 9, 2019, for the year 2018. The analysis includes clearance rates for 24 types of crime-incidents that were reported to the NIBRS in this 8-year period of time. It is worthy to note that this study analyzes the clearance rates of incidents rather than individual crime reports; meaning one incident could involve multiple crimes, and one incident could also involve multiple types of offenses, which are categorized in three distinctive groups. The first group includes incidents related to crimes against persons, the second group includes incidents related to crimes against property, and the third group includes incidents related to crimes against society. The clearance rates are calculated based on the number of incidents that were cleared by arrest or exceptional means. When an offense is cleared by arrest, it means that all offenses within that incident are marked cleared and each incident contains at least one arrestee. On the other hand, incidents cleared by exceptional means is interpreted as cases cleared because there was not enough evidence to prosecute the offenders resulting from lack of probable cause, or deceased offender, offender was already arrested in another jurisdiction, or offender was a juvenile and the case is handled without placing him or her in custody (National Incident-Based Reporting System, 2018). Another piece of important information that should be noted here is the fact that not all police agencies report crime data to the FBI, and there are police agencies that submit partial reports (i.e., only more serious offenses), which does not include all types of offenses.
The Analysis Method
This study is primarily descriptive in nature and it is useful for the practitioners in the field as the targeted audience. The data are analyzed primarily using the measures of central tendency by looking at the characteristics of the distribution of clearance rates by the types of incidents for the years 2011 to 2018. The objectives of this study were met by determining the central location of the distribution of clearance rates. The analyses included a three-step procedure. In the first step, the percentage of the total number of cleared incidents was calculated out of the total number of incidents for each year. Then, a trend line-graph was developed based on the percentages of cleared incidents for all 8 years. This included a trend analysis of all types of incidents combined. Since not all crime categories have the same clearance rates, thus, step two included the analysis of cleared incidents by the major offense category and the year. In other words, the total number of cleared incidents presented in Table 1 was further explored by the type of category (i.e., incidents involving crimes against persons, incidents involving crimes against property, and incidents involving crimes against the society). Then, a trend line-graph was developed for each of the three major offense categories to determine if the clearance rates have increased or decreased over time. Furthermore, when looking at the different types of crimes within each of the three major types of offense categories, it is obvious that not all types of crimes within each crime category (e.g., within crimes against persons category, for example) have the same clearance rates. Thus, the third step included the calculation of the percentages of cleared incidents within each of the 24 types of crimes, as shown in Tables 5–7. The most important information that resulted from these analyses include the average number of cleared incidents for all crimes combined (see Table 1), the average number of cleared incidents by crime category (e.g., for crimes against persons, crimes against property, and crimes against the society) presented in Tables 2–4, and the mean score of averages by the type of individual offense for all 24 types of offenses (see Tables 5–7).
Average Clearance Rates by Year.
Note. SD = standard deviation.
Average Clearance Rates by Crime Category and Year.
Note. SD = standard deviation.
Average Clearance Rates by Crime Category and Year.
Note. SD = standard deviation.
Average Clearance Rates by Crime Category and Year.
Note. SD = standard deviation.
Average Clearance Rate for Each Year by the Offense Type (Results in Percent).
Note. SD = standard deviation. The data in this table represent the clearance rates for incidents. One incident may include multiple crimes, and one crime may include multiple victims.
Average Clearance Rate for Each Year by the Offense Type (Results in Percent).
Note. SD = standard deviation. The data in this table represent the clearance rates for incidents. One incident may include multiple crimes, and one crime may include multiple victims.
Average Clearance Rate for Each Year by the Offense Type (Results in Percent).
Note. SD = standard deviation. The data in this table represent the clearance rates for incidents. One incident may include multiple crimes, and one crime may include multiple victims.
Findings
The descriptive statistics for the percentages of the total number of cleared incidents that were calculated out of the total number of incidents for each year are presented in Table 1. The average number of incidents for years 2011 to 2018 was 5,162,629. On the other hand, the mean score of percentages for 8 years was 36.5% (SD = 2.20). This shows that, on average, only 36.5% of the cases are cleared by an arrest or exceptional means. This includes incidents that fall in all three types of offense categories (i.e., crimes against persons, property, and society). The trend analysis line graph presented in Figure 1 shows that the clearance rate overall has been fairly stable these past 8 years. The biggest decrease in clearance rates was recorded in 2012 with only 31.9% of cases cleared, and the highest was recorded in 2017 with 38.8% of the total number of cases cleared. Although this gives us the total percentage of cleared cases, this piece of information, by itself, is not enough to draw any type of conclusions because the incident clearance rates vary by the offense category, the crime type, and the volume of incidents.
Average clearance rates by year for all types of crimes combined.
The descriptive statistics for the average clearance rates and the total volume of incidents by the offense category are presented in Tables 2–4. Table 2 shows the descriptive statistics for crimes against persons, Table 3 shows descriptive statistics for crimes against property, and Table 4 shows descriptive statistics for crimes against society. The data in these three tables explain one step beyond just the average percentage of cleared cases when all of them are combined into one total. The data in Table 2 show that the average clearance rate for crimes against persons is 48.6% (SD = 1.18). The trend analysis line graph presented in Figure 2 shows that, overall, the clearance rates for crimes against persons are declining. In 2011, the clearance rate was 49.4%, whereas, in 2018, the clearance rate dropped down to 46.1%. In 8 years, the clearance rates declined gradually but steady reaching a 3.3% total decline in 2018. Taking into consideration the gradual decline of clearance rates these past 8 years, it is thus projected that the clearance rates will further continue to decline if the current clearance rate trend continues.
Average clearance rates by year for crimes against persons.
Unlike the clearance rates of crimes against persons, the clearance rate for incidents involving property crimes is alarmingly low, as shown in Table 3, with an average of only 18% (SD = 0.55). The data in Table 3 show that the highest clearance rate was in 2014 with 18.9% and the lowest was in 2011 and 2018 with 17.3%. The trend analysis line graph presented in Figure 3 does not show a clear path for predicting the direction in which the clearance rates are going. However, in the first 4 years (i.e., from 2011 to 2014), the clearance rates increased, followed by a four-year continuous decrease from 2014 to 2018. If this four-year trend continues, then inferentially it is expected that the clearance rates will start to increase starting with the 2019 data.
Average clearance rates by year for crimes against property.
The descriptive statistics presented in Table 4 show that, of all three types of the offense categories, the clearance rates for crimes against the society are the highest, with an average of 78% (SD = 0.85). More importantly, this clearance rate has been fairly high for most of the years. Nonetheless, the line-graph in Figure 4 shows that the clearance rates for the incidents related to crimes against society have started to decrease. In 2011, the clearance rate was the highest with 79.1% of incidents cleared. In the next 7 years, the clearance rates have started to gradually decline. An increase was recorded in 2017 (with 78.3%) but it did not remain stable and it did not reach the highest point that was recorded in 2011. The trend analysis line graph shows that, overall, the clearance rates for incidents involving crimes against society are trending a decline.
Average clearance rates by year for crimes against the society.
The average clearance rate for crimes against persons is 48.6%. However, this does not mean that types of crimes against persons have the same clearance rates. Thus, it is important to examine if there is a difference in clearance rates within the categories of crimes against persons. Table 5 shows the clearance rate percentages for six types of crimes against persons. Of those six types of crimes, kidnapping/abduction incidents had the highest clearance rates with 53.3%, followed by incidents involving homicide cases with 52.3%. Sex offenses had the lowest clearance rates with only 32.8% of cleared cases.
Table 6 shows the percentages of cleared incidents for property crimes. The total average clearance rate for crimes against property is only 18%. This percentage includes the clearance rates for all twelve types of property crimes combined (as listed in Table 6). It would be misleading, however, to use this percentage without pointing out that not all property crimes have the same clearance rates. For example, the data in Table 6 shows that burglary incidents have the lowest clearance rates with only 11.4%, whereas bribery incidents have the highest clearance rates with an average of 65.2%, which is one of the lowest occurring crimes in comparison to other types of property crimes. The top four most frequently occurring crimes against property are larceny/theft offenses (with an average clearance rate of 20%), vandalism or destruction of property (with an average clearance rate of 15.5%), burglary incidents (with an average 11.4% clearance rates), and fraud-related incidents with an average 16.3% clearance rates. Robbery incidents have a dual-category. Robberies are considered primarily as crimes against property, but these types of crimes are also crimes against persons; depending on the nature of the circumstances surrounding each case and the situation in which it occurs. The data in Table 6 show that robbery incidents have an average clearance rate of 25.3%. It is worthy to note that incidents involving extortion/blackmail crimes experienced a significant decrease in the clearance rates. The data in Table 6 show that in 2011, the clearance rate for extortion/blackmail incidents was 29.3%, but with each year, there was a significant decrease, reaching the lowest clearance rate in 2018 with only 9.2% total. Although the average clearance rate remains high for extortion/blackmail incidents (18.9%), based on the existing trend, it is projected that the clearance rates will continue to decrease.
Table 7 shows the percentages of cleared incidents for crimes against society. The total average clearance rate for crimes against the society is 78%, which is four times higher than the clearance rates for crimes against property and 30% higher than the clearance rates of incidents involving crimes against persons. Of the six types of crimes against society, the data in Table 7 show that incidents involving animal cruelty have the lowest clearance rate with only 28.1%. Incidents involving drug/narcotic offenses have the highest clearance rate with an average of 79.9% of cleared cases, followed by incidents involving prostitution offenses, which is equally high with an average of 79.6% clearance rate. Furthermore, gambling offenses show an interesting trend change in the clearance rates. In 2011, the incidents involving gambling offenses recorded an all times high clearance rate with 68.8%. Since then, the clearance rates have continuously decreased reaching the lowest point in 2018 with only 41%, which is a linear decrease from 2011 to 2018 by 18.8% in total. A similar decrease experienced the clearance rates of incidents involving pornography/obscene cases as well.
Discussion
This study contributes significantly to the existing literature by examining the information pertaining to the clearance rates of most violent and non-violent offenses. Most prior studies offer bits and pieces of information on the clearance rates, focusing primarily on one or two specific types of crimes. On the contrary, the current study includes 24 types of crimes, and it expends over a period of 8 years. We believe that this study will be useful when practitioners and researchers need to know a more reliable clearance rate for a specific type of crime or multiple types of crimes, rather than relying on yearly clearance rates.
When interpreting the results of the trend analyses in this study, practitioners and researchers are advised to take into account the nature of the offense category first. Although the trend analyses for the total number of clearance rates for all offense categories combined show a diminutive increase, when breaking it down to three major offense categories (e.g., crimes against persons, property, and society), the trend analyses show a gradual decrease for each category. This effect is due to the differential volume of crimes for certain categories. In other words, certain types of crimes are very high in volume (e.g., larceny/theft offenses, vandalism, etc.), but they have a low clearance rate, versus, certain types of crimes have a very low volume (e.g., bribery, kidnapping, etc.), but have a high clearance rate. This effect, thus, should not be misinterpreted as an increase in the total clearance rates because the clearance rates are affected more by the volume of individual types of crimes rather than the total number of crimes combined. The readers, thus, should focus on Tables 5–7 for more detailed information about the clearance rates, and on Tables 2–4 for a more generalized (yet still compartmentalized in three major categories) clearance rates.
Overall, the findings of this study show that the type of offense (and the offense category) makes a difference as to whether a crime incident is solved or not. For a quick reference, within the three major offense categories, crimes against the society have the highest clearance rates with an average of 78% incidents solved, followed by crimes against persons with a 48.6% average clearance rate. Crimes against property have the lowest clearance rates with only 18% of incidents solved. Furthermore, within each offense category, the trend analyses show a gradual decrease in the clearance rates. One possible explanation as to why property crimes have the lowest clearance rate may be in that these types of crimes are not as serious as crimes against persons and society. When comparing crimes against property and crimes against society, it can be said that crimes against property are not as “visible” as crimes against society or crimes against persons. Due to the visible nature of these crimes, crimes against the society (e.g., prostitution offenses, drug/narcotic offenses, etc.) have a tendency to result in more arrests than property crimes. Moreover, with regards to crimes against persons, crimes that are committed with certain types of weapons leave better clues (e.g., more physical evidence) at the crime scene, and that helps increase the probability of making an arrest, which in turn contributes to an increase in the clearance rates (manifested in the form of a subsequent effect). Some researchers even argue that the clearance rates are affected by selective law enforcement practices (Alderden & Lavery, 2007; Litwin, 2004; Roberts & Lyons, 2011). Others believe that clearance rates are affected more by the lack of solvability factors (Hirschy, 2003; Keppel & Weis, 1992; Wellford & Cronin, 2000). On the other hand, there are researchers who directly or indirectly put the burden of clearance rates directly on the law enforcement agencies by criticizing the response time to crime scenes as a factor that affects the clearance rates (Blanes i Vidal & Kirchmaier, 2017; Brookman et al., 2018). Nonetheless, when questioning police effectiveness, it should be noted that its effectiveness is first affected by the intervening factors such as resources available, the manpower, the workload, the innovative efforts (utilization of technology), diversity of the police force, collaboration between agencies, and the agency size (Avdija, 2019b; Carter & Carter, 2015; Skogan, 1976; Worrall, 2016). In other words, certain types of factors that affect police effectiveness may be beyond their control, even if those factors have a direct effect on the clearance rates for violent and non-violent crimes.
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.
