The aim of animal protection legislation is to reduce the incidence of cruelty and improve the conditions that animals are raised in. To achieve this, it must deter potential offenders from committing violations. The most dominant and well-tested theory of offenders' behaviours in this regard is deterrence theory. With that said, there are a wide variety of other relevant theories, including strain, differential association, social bonding, and social learning (Pratt et al. 2006). Within deterrence theory, there are two methods of reducing crime, known as specific and general deterrence (Hodges 2015). Specific deterrence occurs when a punishment removes individuals who are more prone to committing offences from professions involving animals, either through incarceration or by removing the licences required to be employed in that industry. General deterrence aims to discourage other people from committing similar offences through a variety of methods.
Economists have primarily focused on general deterrence as this has the potential to have more overarching effects on crime rates. As such, they have modelled an individual's decision whether or not to commit a crime based on rational decision theory. This assumes that individuals will decide whether or not to commit a crime based on what maximises their utility (Becker 1974). Thus, crime occurs when the expected value of committing a crime exceeds the expected costs. Importantly, it is not the objective cost and benefits of a crime but rather an actor’s subjective perception of these that affects their decision-making (Paternoster and Simpson 2017)
This calculus applies to both individuals and firms as they will seek to maximise their utility and profit in a capitalist system. Under this model, we can only expect firms to pay the additional cost of complying with regulatory policy when they expect that non-compliance is likely to be detected and penalised (Hodges 2015). In fact, some have argued that this traditional model better fits corporate crime as offenders are less committed to offending than traditional criminals. These crimes are typically described as “calculated and deliberative and directed to economic gain”, making them a much better fit for rational decision theory (Paternoster and Simpson 2017).
Within these models, there are three main elements that act as general deterrents to individual crime: the likelihood that you will be caught for a crime, the severity of the criminal sanction, and how quickly the sanction is applied after the crime (Johnson 2019). An increase in any of these factors results in an increase in the expected cost of the crime and thus should lead to a reduction in the number of offences. The factor that is likely to have the greatest effect depends on the risk preference of the offender, with risk preference resulting in an equal percentage increase in the likelihood of being caught having a greater effect than the severity of punishment in risk preferring individuals, and the inverse for risk-averse ones (Becker 1974).
Although we weigh empirical evidence above these theoretical models, their findings can be used to develop hypotheses that we can then test empirically.
What motivates non-compliance with animal welfare regulations
One factor that could dramatically affect the ability of law enforcement to increase compliance with animal welfare regulations is the motivations of offenders. If crimes are more motivated by calculated economic gain, as Paternoster and Simpson argue in the corporate case (Paternoster and Simpson 2017), increasing the cost of non-compliance should have a larger effect. However, if most breaches of the animal welfare act occur due to negligence, ignorance, or are maliciously motivated, then other methods of enforcement should also be considered.
Existing evidence on motivations for animal cruelty is mixed. For animal cruelty perpetrated on an individual level, Kellert and Felthous (1985) identified some motivators for cruelty towards animals in convicted criminals. These included punitive training methods, punishment for undesirable behaviour, to satisfy a prejudice against a species, to instil violent tendencies in the animal, and displacement of hostility towards a human (Kellert and Felthous 1985). A similar study from Hensley et al (2005) found similar motivations in criminals, with the most common motivations for animal cruelty being anger, dislike, control or fear of the animal, or for fun (Hensley and Tallichet 2005). It is worth noting that these studies surveyed convicted criminals, who are likely to display more violent tendencies than the general population.
Research on the motivations for cruelty among farmworkers is rarer. The existing research associates reduced concern for welfare with a poor work environment (Inger Anneberg and Sandøe 2019), and cruelty to farm animals with the abuse of workers (Lovell 2016). Given this, more overarching risk factors for such cruelty could be controlled at the farm level by improving working conditions.
Overall, the motivations for individual acts of cruelty seem to be more emotionally or socially based rather than for economic gain. This, combined with the greater difficulty of detection, makes it unlikely that traditional deterrence theory would apply. In this case, greater screening of staff for risk factors, social programmes, improved working conditions, and training are more likely to have an effect. However, it is not clear how significant these would be.
The next distinct cause for broader breaches of animal welfare legislation specifically is neglect. Neglect does not occur from a motivation to cause harm to another being, or from some economic calculus of the cost and benefits of providing care as with other instances of non-compliance. Instead, neglect is most likely to occur when farmers are experiencing financial or social problems. In general, livestock farmers have a low risk of animal neglect problems. However, for those experiencing severe financial difficulties, divorce, or a psychiatric problem there is an increased risk of neglect of farm animals (Andrade and Anneberg 2014; Devitt et al. 2014; Kelly et al. 2011).
These problems can be caught with frequent inspections but penalties are less likely to change behaviour if the farmer is already distressed and not acting in their own best interests. This is particularly the case for financial difficulties and penalties where non-compliance may be caused by a lack of resources and thus fines will exacerbate the situation. In these instances, Overstreet and Anneberg suggest inspectors could instead provide additional support or guidance (K. O. A. Anneberg 2020). Although for instances of serious or chronic neglect, the previous use of lifetime bans from the industry (Stocks 2014; Duff 2010) seems appropriate as in these instances only specific deterrence is required.
Farmers themselves report that one cause for breaches in cross-compliance inspections comes from farmers who lack an awareness of all existing legislation and management problems in daily practice. Many farmers viewed the inspections as unfair for this reason, as inspectors would focus on a ‘small’ infraction without accounting for the larger broader picture of husbandry on their farm, particularly when this resulted in a reduction in subsidies (Inger Anneberg, Vaarst, and Sørensen 2012). Although, given these are the self-reports, these claims may be subject to the social desirability bias.
The final motivation for non-compliance with animal welfare regulation is management decisions to break with legislation motivated by economic factors. These have not been covered in the existing literature as these motivations are not observed in a more general population of criminals, while farms are unlikely to report being motivated by such self-interest.
Empirical evidence on preventing animal cruelty
In recent decades, numerous jurisdictions have increased sentencing for animal abuse (Morton, Hebart, and Whittaker 2018; UK Government 2021; Department of Agriculture and Rural Development 2016). These higher penalties are often driven by rhetoric that focuses on the need to "get tough" on people who abuse animals (Morton, Hebart, and Whittaker 2018). This rhetoric arises from the fact that, when increasing penalties for animal cruelty, legislators are usually driven by public perception (Whitehead 2017). Indeed, public perception was explicitly stated as the key motivation driving recent reforms in Northern Ireland (Department of Agriculture and Rural Development 2016).
However, this raises a crucial problem. Responding to community sentiment might please the public, but also means that the penalties are not actually designed to improve the lives of animals. As Morton et al (2018) explain: "The desired outcome should not be to increase the duration or dollar value of a sentence; it should be to reduce animal cruelty through the most efficient type of penalty."
Which combination of surveillance and/or penalties leads to the lowest rates of animal cruelty? Unfortunately, this question is impossible to answer with confidence. There is very little empirical evidence on the actual prevalence of animal abuse (Hughes and Lawson 2011). The best available data comes from on-farm inspections (e.g., Lomellini-Dereclenne et al. 2017; Väärikkälä, Hänninen, and Nevas 2019) or monitoring at slaughterhouses (e.g., Mullan, Stuijfzand, and Butterworth 2021). However, even this data does not allow the comparison of alternative surveillance or penalty regimes. As such, while many stakeholders assume that higher sentences translate to a lower prevalence of animal cruelty, there is no evidence to support this assumption. Higher sentences may also cause harm and perpetuate discrimination (see below, "Enforcement and discrimination against humans").
There have been calls for more frequent random inspections (e.g., Bennett et al. 2004). In a case study of French farms, Lomellini-Dereclenne et al. (2017) found that more frequent inspections seemed to improve compliance rates by a small amount. However, only 1% of farms are inspected each year in France (approximately the same as the UK). Only 23% of these farms improved compliance rates when re-inspected, suggesting the French inspection regime had little specific deterrence effect. Similarly, a study in the UK found that farms poor for broiler welfare tended to remain poor, despite those farms being targeted for follow-ups (Mullan, Stuijfzand, and Butterworth 2021). Additionally, a study in the US found that of companies that were given a warning for violating the Animal Welfare Act, 86.5% continued to violate that Act (Winders 2018). In that latter study, it was speculated that warnings were not a credible threat, since any subsequent penalties were very minor. Based on this evidence, regulations based primarily on inspections and warnings appear to have a limited effect on specific deterrence for farms that were inspected. However, this provides no evidence on the general deterrence effect of these inspections. Therefore, we cannot rule out the possibility that increasing the rate of inspection improves overall compliance across all farms in a country.
Some evidence on the deterrence effect of inspections comes from a review of the comparative compliance rates of certified and uncertified farms in the UK (Clark et al. 2016). Certified farms are subject to annual or biannual inspections by the certification scheme (in addition to any inspections by the government). Farms may lose their certification if they repeatedly fail inspection. This enforcement regime does appear to be associated with greater legislative compliance (Clark et al. 2016). Affiliation with one certification scheme lowered the percentage of farms that were non-compliant from 23.1% for uncertified farms to 11.6% for certified farms. Membership was also associated with fewer instances of directly observable ‘unnecessarily’ suffering, with results of 4.6% vs 3.1% respectively. These figures drop further for each additional certification scheme, with 8.3% and 2.5% for two and three schemes showing a dose dependent relationship. However, it is unclear whether or to what extent the association is causal. This relationship may be explained by market forces or farmer ethos and management rather than the increased inspections.
It has been suggested that higher penalties may indeed prevent animal cruelty in the context of farming. Since farming is a commercial operation, it is plausible that economic motivations may play a key role in determining whether a farmer chooses to abuse animals. If this is the case, then harsher sentencing may indeed reduce the prevalence of animal cruelty on farms (Wolf, Bagaric, and Kotzmann 2021). However, this argument is weakened by the fact that economic motivations are only a small part of the picture (see, "What motivates non-compliance with animal welfare regulations").
Meanwhile, there have been calls for the education of farmers, building dialogues, and raising awareness of common problems (I. Anneberg, Vaarst, and Sandœ 2013; Lomellini-Dereclenne et al. 2017). A case study in Finland found that compliance with a particular problem (calf housing conditions) was unsatisfactory, despite a national education programme on this problem and clear wording in the legislation (Väärikkälä, Hänninen, and Nevas 2019). It is unclear whether this represents evidence against educational programs in general, or if this particular programme was poorly designed or implemented.
There have also been calls for other measures to prevent animal cruelty, including the rehabilitation of offenders, finding ways to increase the certainty that offenders will be caught, and more speculatively that digital health sensors to be worn by animals (Morton, Hebart, and Whittaker 2018; Manning, Power, and Cosby 2021). Calls for restorative justice have also been made in the literature on consistent anti-oppression (Kirts 2020). While restorative justice may be a beneficial policy for preventing re-offending, it may be less applicable to preventing the incidence of animal cruelty on farms that are not inspected.
Given the paucity of evidence on the most effective ways to enforce animal welfare laws, we can be guided by general research on crime and deterrence.
Empirical evidence of Criminal Deterrence
Although there is widespread agreement amongst economists, sociologists, psychologists, and criminologists that the criminal justice system has a general deterrent effect there is some disagreement as to whether marginal changes in policy have measurable deterrent effects.
This is particularly true for the severity of punishments where the results of a larger percentage of studies conflict with deterrence theory (Dölling et al. 2009). In addition these estimates tend to be weaker with the effects of severity, even when statistically significant, are too weak to be of substantive significance (Pratt et al. 2006). This is especially true for increases to already long sentences where there is little evidence of a marginal deterrence effect (Nagin 2013). The current position of the literature is best summarised by Tonry (2008) who concludes that no major economic literature on the effects of sanctioning changes has withstood scrutiny by social scientists or by other economists (Tonry 2008).
The relative importance of other factors on deterring crime has been investigated in a few reviews but tends to focus on the broad category of the independent variable; severity, probability or celerity. Within these reviews, authors tend to conclude that the probability of sanctions has the largest effect particularly for non-violent property crimes (“Deterrence in Criminal Justice: Evaluating Certainty vs. Severity of Punishment” 2016; Chalfin and McCrary 2017; Nagin 2013). Such crimes include theft, tax evasion, environmental offences or driving while drunk (Dölling et al. 2009). The general suggestion is that violent or more severe crimes are motivated by stronger emotions and are therefore less likely to be deterred by changing the rational benefits of crime. Thus depending on the type of offence, as discussed in on the motivations for cruelty or neglect, the literature would suggest a small to moderate effect of probability-based deterrence of crime rates.
The most extensive review of the specific factors that have the greatest deterrence effect in the existing literature is from Dölling et al (2009). They conducted a meta-analysis of 700 previous studies which estimated the deterrence effects of various parameters on crime (Dölling et al. 2009). Complete or partial agreement with deterrence theory was found in 70% of studies based on criminal statistics in whereas survey-based or death penalty studies were more likely to have weaker agreement or rejection of the hypothesis.
Dölling et al. (2009) corroborate the broad results of other reviews, finding that the likelihood of punishment seems to exercise a greater deterrent effect than the severity of the punishment, regardless of the type of data used (Dölling et al. 2009). Most notably, within the studies that used criminal statistics, the variables that had the most significant effects were the ratio of convictions to reported crimes, arrested persons per crime, and ratio of convicted persons to suspects. However for survey-based studies, which used self-reported delinquency as a dependent variable, the effects sizes were smaller. The most significant effects in these studies came from the ‘expected probability of an informal sanction by friends/family’, ‘expected probability of a sanction by the criminal justice system’, and ‘assessment of the risk of discovery by the police’. Both forms of evidence suggest that detection of non-compliance and then subsequent sanctions are the most important aspect of deterrence.
Empirical evidence of Corporate Deterrence
The best empirical evidence on corporate deterrence comes from the systematic literature review and meta-analysis performed by Schell-Busey et al. (2016). In that study, the authors reviewed the evidence for three single-strategy treatments for deterring corporate crime: law (e.g., characteristics of legislation), punitive sanctions (e.g., higher penalties), and regulatory policy (e.g., inspections). Only regulatory policy had sufficient evidence to conclude that there is a deterrent effect, and the deterrence was only detected at the company level (not the individual level). There was also evidence of a deterrent effect from combining multiple treatments, such as combining inspections with punitive sanctions. This evidence was also observed at both the company level and the individual level.
The studies used for that meta-analysis came from diverse areas that included environmental protection, immigration hiring, anti-competition, and consumer protection. Intuitively, these areas seem somewhat analogous to animal welfare, so we believe that it is reasonable to extrapolate the inferences from this study to animal welfare enforcement (at least in the absence of evidence specific to animal welfare). However, there are two key reasons to be cautious in extrapolating these inferences. The definition of corporate crime used in the meta-analysis included all levels of employees and a diverse range of behaviours, though only crimes committed to benefit the corporation were included. In other words, these crimes are calculated risks performed by corporations acting with profits and costs in mind (Paternoster and Simpson 2017). Merely benefiting an individual employee was not counted as corporate crime. Secondly, as was expressed in one of our expert interviews, animal crime is extremely niche, and comparing animal crime to other areas of crime is inherently problematic. As such, we believe that it is only reasonable to extrapolate these findings to animal welfare non-compliances that benefit agricultural companies. We will not extrapolate these findings to non-compliances that result from individual employee motivations (particularly social or emotional problems), which appear to be common (see below, "Why do people abuse or neglect animals?"). That said, the risk of individuals abusing animals for their own reasons may be increased by corporate crime, such as with worker abuse.
This evidence provides support for enforcing animal welfare law via regulatory policy, such as inspections, particularly when those inspections have an educational component. The evidence also suggests that regulatory policy may be more effective if combined with other treatments, such as punitive sanctions. As noted, these inferences are only justified for instances of animal welfare non-compliance that benefit a company and not necessarily non-compliances for individual reasons.
Empirical evidence of environmental law deterrence
Another analogous area to enforcement of animal welfare laws is environmental law enforcement. Environmental law covers areas such as pollution, fracking, flooding, and noise pollution (“Law and Your Environment - Home” n.d.). Typical sanctions and enforcement mechanisms progress in a similar manner to animal welfare violations, including monitoring potential violators and providing progressive enforcement actions from advice and guidance to prosecution (“Environment Agency Enforcement and Sanctions Policy” n.d.). One benefit empirical research into environmental law enforcement has over animal welfare is that the aggregate outcomes of improved enforcement are measured through improvements in environmental indicators.
Existing reviews have assessed both the broad approach taken to environmental enforcement, either cooperative or deterrence-based, and more granularly the relative impacts of different monitoring and enforcement tools.
Thus far there is wider agreement between researchers on the best broad approach to enforcement, even in different contexts. In a review of the US environmental enforcement system, Rechtschaffen (1998) discussed a call to reform the system towards a more cooperative approach under the assumption that all firms were inherently motivated to comply (Rechtschaffen 1997). However, Rechtschaffen concluded from the available literature that the credible threat of meaningful enforcement provided by deterrence-based enforcement was essential to promote widespread voluntary compliance. Notable benefits of deterrence-based enforcement over cooperative approaches include more consistent treatment and less agency capture (see below, "Effects on the future of the movement (regulatory capture)". However, Rechtschaffen suggests that overall, an improved solution would be to integrate some of the most constructive features of a cooperative model within a deterrence-based system. Gunningham (2017) corroborates this conclusion, noting that the best results come from strategies which employ a judicious blend of persuasion and coercion, with the mix being adjusted to the particular circumstances and motivations of the entity that regulators are dealing with (Paddock, Markell, and Bryner 2017).
Reviews on the relative effectiveness of different monitoring and enforcement tools are less conclusive. Cohen (2000) examined 11 studies evaluating the effect of methods used for environmental deterrence on various compliance and environmental outcomes (Cohen 2000). Industries included oil tankers, paper mills, and steel mills. Overall, these found that broad deterrence effects could be observed across industries for monitoring, inspections, and number of enforcement actions. However, there was no strong evidence for which of these was most significant. Gray (2011) found similar results in a U.S. regulatory settings (Gray and Shimshack 2011), concluding that monitoring and enforcement actions resulted in substantial specific and general deterrence, as well as significant reductions in emissions. However, when discussing the relative impacts of different regulatory tools Gray notes that ‘the literature suggests that the relative impacts of different monitoring and enforcement tools vary across pollution media, industrial context, and time period’.
The direct evidence on the most effective ways to enforce animal welfare laws is sparse as the area seems to have been neglected by both animal welfare specialists and those focused on regulatory compliance. Therefore, any recommendations for reducing non-compliance from the literature must be made from drawing inferences from other similar areas in criminal, corporate, or environmental law compliance. Within this, we see that non-compliance with animal protection regulations occurs for a wider variety of reasons on an individual and business level. The first is for individual instances of animal cruelty which occur more to due to negligence or strong emotions. For crimes motivated for similar reasons in the criminal deterrence literatures the effect sizes of all methods of deterrence tends to be small. This means some instances of individual cruelty are difficult to deter with marginal increases in enforcement. Instead, changes to broader social norms or improving working conditions are likely to have larger effects.
Fortunately, broader instances of non-compliance caused by neglect or management decisions are more significantly affected by the available enforcement mechanisms. There is general agreement from the criminal and corporate compliance research that increasing the likelihood of being caught has the largest effect on crime rates. This is particularly relevant in the corporate case where regulatory policy, such as inspections, were most effective in ensuring compliance. However, evidence from environmental law enforcement is more mixed with the most effective tool varying depending on the context. Generalising from this available literature to the animal setting, this relates most to the cause of non-compliance. Smaller farmers who are more likely to be in non-compliance due to ignorance or negligence may benefit more from a cooperative approach, while larger ‘rational’ firms may require a deterrence-based model to bring them into compliance. However, even then the most impactful enforcement mechanisms to improve on the margin is difficult to conclude from the existing evidence in environmental enforcement.
Although the evidence is generally weak for animal welfare enforcement, and to some extent for the enforcement of other crimes, the existing evidence suggests that monitoring and inspections are the most effective tool for increasing regulatory compliance. These are followed by the average number of enforcement actions taken against non-compliant firms which is itself affected by the rate of detection.
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