Street Level Bureaucrats as the Ultimate Policy Makers
Journal of Political Sciences & Public Affairs

Journal of Political Sciences & Public Affairs
Open Access

ISSN: 2332-0761

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Research Article - (2017) Volume 5, Issue 4

Street Level Bureaucrats as the Ultimate Policy Makers

Zedekia S*
Department of Political Science and Public Administration, University of Nairobi, Kenya
*Corresponding Author: Zedekia S, Department of Political Science and Public Administration, University of Nairobi, Kenya, Tel: 0723987358 Email:


This article examines road safety policy implementation process in the Kenyan capital Nairobi. For conceptualization purposes, it has employed the street level bureaucratic theory which is an offshoot of the principal agency theory. The data collection and analysis was guided by cross-sectional study design composed of structured observation, questionnaire survey, and a qualitative approach. It argues that front-line workers in the public service rarely have enough resources to respond to citizens’ demands and execute their legislative mandate. They are therefore forced to ration their services. This is done by developing the broad classification of clients based on their social status. On basis of these categorizations, decisions are made therefore on who to prioritize for service provision. In theory, this triaging practice is meant to increase efficiency and adaptability in street level bureaucracies. However, street level bureaucrats have their own interest that is different from those of policy makers, triaging therefore sometimes undermines policy implementation.

Keywords: Social profiling; Road safety; Public policy implementation; Target discretion; Street level bureaucratic discretion


The role of frontline workers in the policy process has been a subject of emotive debates for several years. From one point of view, the role of front line workers is purely technical. It involves execution of government directions as contained in the statutes. Police success from this point of view is the extent by which the implementing official maintains fidelity to the policy maker’s ambitions. To ensure that the aforesaid fidelity is maintained the managerial school has established a variety of ways in which of controlling administrative discretion [1].

This notwithstanding, scholars on from the bottom up perspective have argued that the work of front line workers is intricately political. This stems from the fact that public policy making process is a market place of ideas. More often than not the politicians fail to agree on the policy objectives and how those objectives can be reached. This problem is often transferred to the implementers who must deal with the challenge of having to implement policies with no clearly defined problem, ambiguous objectives and sometimes conflicting goals. This problem is further compounded by the fact that “in most welfare departments, regulations are encyclopaedic yet at the same time, they are constantly changed” [2]. To this end, the frontline workers must interpret the statutes; try to decipher policy goals and objectives and well as put measures in place to ensure the arrival of those policy goals.

Bureaucratic control is further undermined by the fact that the work was undertaken by frontlines workers in the public service also involves serving people. These people normally come from diverse background and experience diverse problems which cannot all be legislated [3] have noted that one of the ways in which street level bureaucrats cope with the gap between the legislative mandate, citizen’s demands and resource availability are rationings of services. This is done by dividing their clients into big categories from which decision on who to prioritize for service provision are made. A similar sentiment has been shared by Siegel, who observed that the likelihood of being stopped by the police at a traffic check point and whether he or she is going to be processed for prosecution is dependent on the driver’s characteristics.

In addition to the foregoing, policy makers and bureaucratic executives have weak control over street level bureaucrats. This stems from the fact that street level bureaucracies employ a very large number of professional frontlines workers making it extremely expensive to supervise them. Beyond the foregoing doing the work of police officers, teachers, and social workers requires professional training. Sometimes bureaucratic executives may not have similar skills as the tasks that s/he is supervising their execution, hence the need for professional independence [4]. Additionally, in many of these areas such as the police, counselling, and teaching, there are laws that protect the professional independence of workers [5]. Other challenges associated with controlling administrative discretion accountability are discussed within the rubrics of principle agency theory hereunder explained

The Principle Agency Theory

The principal agent theory has its origins in economics where it was used as an analytical tool to study relationships between shareholders and business executive. Over the years, however, the theory gained wide usage in a variety of other disciplines including political science where it is used to study the relationships between the policy makers and policy implementers. The principal-agent relationship entails “a contract under which one or more persons (the principal(s)) engage another person (the agent) to perform some service on their behalf that involves delegating some decision making authority to the agent” PAT is underpinned by three major assumptions hereunder explained.

To begin with, both the Principals and agents are rational actors seeking to maximize gain from the contractual relationship between them. The ability of the Principal to maximize from the contract is however limited by two problematic issues “supervisors cannot know their subordinates true preferences and abilities (adverse selection) nor can they observe all subordinates actions (moral hazard)”.

According to PAT, the relationship between the principal and the agent is characterized by goal conflicts. “The agent is trusted to make decisions that are in the best interest of the principal. However, the agent’s preferences derive from self-interest and self-preservation and do not {concern themselves} with the interest of the principal.” According to Brehm and Gates, the goal might arise from differences in their preference for the budget magnitude, policy goals, and leisure maximization. According to Asingo and Mitullah [6] conflict of interest is one of the main problems ailing the road safety policy implementation process. PSVs owned by police are never subjected to the same level of road safety law enforcement as other vehicles thus they violate traffic laws with impunity.

The theory is also based on the assumption that agents are typically endowed with more information than their principals, and hence information asymmetry. This stems from the fact that agents have specialized training and are directly involved in the bureaucratic processes. Knowledge disadvantage among the Principals limits their ability to design contracts that guarantee their profitability. In other words, the agents can hide some information about work such that they are paid more than what their efforts are really worth.

While PAT provides valuable insights on the public policy implementation process it is limited by the fact it lays the responsibility of public policy implementation on the principals who must carefully select the right employees, design favourable contracts and monitor the implementation process to guarantee implementation success. The agent’s behaviour is thus assumed to be dependent on his contractual relationship with the principal. In real life, however, a number of factors influence the bureaucrat’s job performance including their national pride, peer pressure and working conditions. In view of these limitations the theory does not fully explain the public policy implementation phenomena and hence the use of street level bureaucratic theory.

The Road Safety Policy Process in Kenya

Kenya is one of the countries in the world with a high level of traffic injury rates (RTI). According to WHO [7], RTI rate in the country is 34.4 per 100,000 populations. According to NTSA year, over 3000 people die on the Kenyan roads. RTI is the third leading cause of death in Kenya [8]. RTI has a variety of economic and health challenges. To begin with, it creates of stress in the Kenyan health sector that is already underfunded. According to Odero et al. [9], 45% to 60% all admissions in surgical wards are victims of RTIs. They also account for 75% of the patients in the spinal injury hospital. This matter is compounded by the fact that most the traffic injury patients take more than one month in hospitals. In addition to the foregoing, treatment of RTI, for the most part, requires use of X-rays and theatre [9].

From the economic perspective, the annual cost of road carnage in Kenya is KES: 14 billion. This is approximately five percent of the GDP [10]. These losses are associated with losses in income when one is disabled, the damages of goods that occur during accidents and direct in indirect cost incurred by families when they hospitalize their loved one who is a road carnage survivor [9].

Additionally, most of the victims and survivors of road traffic crashes are persons within their most productive ages. A survey conducted in the Rift Valley Provincial Hospital indicated that 76% of those admitted with injuries resulting from road accidents were between the ages 19 to 49. Relatedly, 76% of road accidents victims admitted in Kenyatta National Hospital were between the ages 15 to 44 years. In Moi Teaching and Referral Hospital the study observed average age of the admitted for road traffic casualties was 30.3 [9].

To deal with this problem a number of policy efforts have been made, to begin with, 2012 the national transport and road safety authority was established to coordinate road safety policy implementation processes in the country as well as to advise the government of policies that need to be put in place to ensure road safety policy implementation success. Additionally, several amendments have been made by the legislation so as to act as deterrence measure for road safety law violations. Notably, in 1993 the Kenya law reforms commission (KLRC) reviewed all the traffic laws in the country in order to contextualize them to the changing needs of road users. Similarly, in 2003 the minister for transport amended the traffic act through a gazette notice no 161 of 2003.

The main aim of the minister in bringing forth this new law was to restore sanity in the Kenyan roads. Some of its objectives were to ensure that only qualified people are employed as operators of public service vehicles; the prevalence of accidents would also be reduced by outlaws some of the RTI risk factors such as drunk driving and speeding. To check on fatalities in case of an accident all vehicles were to be fitted with seatbelts. The intense implementation of this policy legislation brought down the level of traffic accidents by 73% [10]. Soon after, however, the minister thing came back to normal after the minister who brought the amendments and oversaw the strict implementation process was transferred. With an increase in the number of traffic injuries in 2011 new amendments to the Traffic Act were made again in 2012 and later in 2014 [6].

With all these efforts, it is expected that the number of traffic injuries would have gone down drastically. However, that this has not been the case. Implementation studies are mainly divided into three categories, namely; 1) Top dawn theories 2) bottom up theories and synthesizers. On the one hand, top down theories associated policy failure with mistakes of the policy makers and bureaucratic executives. On the other hand, bottom up theories argue that policy outcomes are a product of the interaction between the policy target population and front lines workers in the implementing agency. The third category of studies seeks to draw lessons from implementation failure from the two schools of thought. This study uses the street level bureaucratic theory which is an offshoot of the principal agency theory for purposes of theorization. Hereunder discussed is the principal agency theory.


The data used in this study was collected in the Kenyan capital city Nairobi. The study employed both primary and secondary data. On the one hand, the primary data was collected through structured observation, questionnaire survey, and qualitative approach. On the other hand, the secondary data was extracted from police records and statistical abstracts. The respondents for questionnaire survey and structured observation were selected through clustered sampling strategy. Each traffic division was treated as a cluster from which traffic enforcement officer was randomly for purposes of questionnaire administration. Similarly, from each traffic division, all the bus stages where public service vehicles could be found and social gathering places where private cars could be found were listed and study locations selected. The triangulation of the method and sources employed in the study was intended to ensure increase validity of the study findings. With regard to data process, data collected through closedended questionnaires, statistical abstracts and structured observation were entered into SPSS data editor for the processing. The SPSS filed developed both the descriptive and analytical statistics used in this study were generated. The description statics were mainly used to explore the frequency of traffic checks among various types of vehicles as well as to document the prevalence of accidents among them. To test the study hypothesis Spearman’s rank of correlations were corrected. The study is based on the assumption that social profiling of road users undermines the policy implementation process. Consequently, the null hypothesis was accepted where a positive relationship was noted between the independent and dependent variable. It was however rejected where was no significant relationship or where there was a negative relationship.


Following a brief discussion on the road safety policy process in Kenya, theoretical overview and the study design this section presents the study findings. The section is organized according to the study questions. These include; 1) which types of vehicles are targeted for road safety policy implementation 2) which types of vehicles have the highest levels of policy non- compliance and 3) what is the relationship between those types of vehicles being targeted for road safety implementation and those that frequently violate road traffic laws.

Types of vehicles targeted for road traffic policy implementation

According to Schneider and Ingram [3], one of the coping mechanisms employed by street level bureaucrats to bridge the gap between legislative demands and resource availability is rationing of their services. This is done by dividing the clients into broad categories on which the decisions on whom among the eligible clients will be rated as more deserving and who will not. Those who are said to be more deserving are thus prioritized for service provision. These sentiments have been echoed by Kleinig [5] who observed that the police do not have the resources to arrest all the offenders all the time. They must, therefore, selected which cases to attend to and which ones not to. It has also been noted that when the police go out for patrol they are instructed to “strictly watch the conduct of all persons of known bad character” Reiss [11]. It is in light of the above observation that the study sought to find out if the traffic enforcement officers equally target all types of vehicles for road traffic enforcement (Figure 1).


Figure 1: Frequency of traffic checks disaggregated by vehicle type.

From the figure above it is noticeable that commercial vehicles including commuter minibuses commonly known as matatus, lorries, and taxes are most targeted for purposes road traffic policy implementation is the comparison to none commercial vehicles such as private cars and school buses. “My vehicle is not branded PSV because when you do that the police will always stop you on the way”, reported one of the Taxi drivers who participated in the key informant interviews. These sentiments have been echoed by Chitere and Kibua, [10] who noted that the PSVs are being unfairly targeted for traffic law violation as a result of the police greed for money. On the other hand, when the police were asked why they target certain vehicles for traffic law enforcement in comparison to others they noted that their decisions on which type of vehicle to target for law enforcement are determined the frequency of violations among those types of vehicles. In view of these, the police were asked to state which kinds of vehicles are more prone to traffic law violations in comparison to others and the responses to these questions are hereunder.

Types of road users with high levels of road safety policy noncompliance

Beyond using indicators of vulnerability make decisions on prioritization of clients for government services outlined by Schneider and Ingram [3], street level bureaucrats also use indicators of success to determine who among the qualified clients will be served first. The use of parameters of success in client selection process in the street level bureaucracies is called “creaming” [12]. The application of this principle in the law enforcement context involves targeting vehicles with the highest possibilities of traffic law violations at the traffic check points. For instance [13] studied the effects of driver’s race and gender on police officers decided to search the vehicle and take legal action. The study employed logistic regression analysis of 10,210 traffic stops on a university campus. In order to reduce bias, it controlled confounders such as the driver’s demeanour, dress code, and traffic offense. The study reported that black male drivers were more likely to be arrested in comparison to their white male counterparts. In view the above discussion, the study sought to find out the police discretions over which type of vehicle to stop at the traffic checks is by anyway to the vehicles probability to be involved in the traffic law violation (Figure 2).


Figure 2: The vehicle type responsible for the highest number of traffic violations.

According to Gaines and Kappeler [14], one of the key determinants on whether the police will conduct an arrest of not is the seriousness of the offense. To this end, it is expected that those types of vehicles who frequently violate traffic laws and thereby have the highest level of propensity to cause accidents are more likely to be stopped in compared to those which rarely violate traffic rules. To this end, efforts were made during the study to find out which types vehicles mostly cause road traffic accidents. The table below provides data on the traffic accidents in the last six years (Table 1).

Year 2010 2011 2012 2013 2014 2015 Total
Cars and utilities 570 408 406 417 280 299 2,380
Lorries 80 102 104 93 77 72 528
Trailers 30 39 38 30 41 53 231
Tankers 27 28 29 44 30 39 197
Buses 472 265 265 268 273 241 1,784
College/school 17 29 27 18 37 61 189
Taxis 99 92 94 89 49 110 533
Matatus 556 661 664 658 427 384 3,350
Total 1851 1624 1627 1617 1214 1259 9192

Source: Police Records, 2016.

Table 1: Prevalence of traffic accidents by type of vehicle involved.

From the table above it is noticeable, the vehicles with the highest number accidents belonged to different categories including those being used for commercial purposes, private cars as well as government vehicles. It is noticeable however that the number of accidents attributable to some of the categories of vehicles highly targeted for law enforcement such as Lorries is very low. It is therefore worth investigating whether the police decisions to target certain vehicles for traffic law enforcement is related in any way to their level of involvement in traffic accidents.

The relationship between “creaming” and traffic law violations

According to Kleinig [5] police discretion is permissible by law and police practice in light of the fact that in certain circumstances use of professional judgment can lead to better results than if the laws, rules, regulations, and procedures were written in a non-flexible manner which cannot be adapted to the prevailing policing context. These sentiments have been echoed by Lipsky [2]. He reckons that ideally, street level discretion is comparable to triaging by the emergency medical personnel in a war situation. In light of the limited medical supplies, the doctors must choose who among the wounded soldiers will be treated and who will not. In doing this selection the patients are divided into different categories such as mortality wounded and may not be saved even with medically attention, critically wounded but can be saved with urgent medical attention and slightly wounded and therefore can service without immediate medical attention. The doctors, therefore, choose to concentrate on the second category of patients so as to maximize on the limited medical supplies by saving the largest number of lives. In the same breath in the criminal justice systems, the police do not have enough resources to deal with all the law offenders. To this end, they are expected to conduct themselves in a manner that helps them to maximize on implementation resources. One way of doing this is by targeting those road users who frequently commit traffic law offenses as well as drivers who frequently cause accidents. However, from the data presented in section 3.1 and 3.2 above road traffic enforcement practice seems to be motivated by other factors apart from efficiency. To ascertain that the observed relationship did not occur by chance Spearman’s tests of correlations were conducted between the indicators of road safety policy outcomes and those of street level bureaucratic discretion. The table below provides data on provides data on the relationship between a frequency of violations and frequency of traffic checks (Table 2).

Type of vehicle Violations Checks Rank violations Rank checks
Buses 24 9,7 6 1
Matatus 29 29 7 2
Taxies 4.4 19.4 2 3
Lorries 16.2 16.1 4 4
Tankers 17 12.9 5 5
cars and utilities 16 7.7 3 6
college/school 3.2 3.2 1 7

Source: Police interviews,
Spearman's rank correlation rho,
Data: violations and checks,
S=96, p-value=0.0881,
Alternative hypothesis: true rho is not equal to 0,
Sample estimates: rho -0.7142857.

Table 2: Violations by type of vehicles.

As it can be noticed from the table above there is a negative relationship between the police ranking on the frequency of traffic violations and their ranking of traffic checks. This is evidenced by the fact that the correlation figure is rho 0.71 this means that in 71% of the times the data on road targeting and those of traffic violation were inconsistent. To this end, it is deductible that as far as the police are concerned, they do not target road users who frequently violate traffic rules. However, in light of the fact that data collected from police interviews are self-reported and therefore susceptible to recall bias a repeat test was conducted with data from observation (Table 3).

Type of vehicle Violations Checks Rank violations Rank checks
Matatu 27% 28.60% 1 1
Lorries 3% 23.00% 7 2
Taxis 6% 22.20% 5 3
Tankers 5% 20.00% 6 4
Buses 21% 16.70% 3 5
College 25% 14.30% 2 6
Cars and utilities 12% 10.00% 4 7
Government vehicles 2% 1% 8 8

Source: Observation Data
Spearman's rank correlation rho
Data: violation2 and check3
S=62, p-value=0.5364
Alternative hypothesis: true rho is not equal to 0
Sample estimates: rho 0.2619048.

Table 3: Spearman’s rank correlation between frequency of violations and traffic checks.

Just like in the case of police perceptions survey data, the data from observation indicated that there is a no significant relationship between the type of vehicle targeted for road traffic policy implementation and the frequency of road traffic violations associated with such category of vehicles.

In light of the fact that road safety policies are mainly intended to prevent traffic accidents, a repeat test was conducted to ascertain the relationship between the frequency of accidents and frequency of traffic checks (Table 4).

Road user domain Checks Accidents Rank: accidents Rank: checks
large companies checks 3 4 5 6
PSV small SACCOS 9 10 1 2
Tankers 7 7 2 3
Government vehicles 1 3 6 8
College/school vehicles 2 2 7 7
Lorries 10 1 8 1
Taxies 4 5 4 5
Private cars 5 6 3 4

Source: Police Interviews, 2016
Spearman's rank correlation rho
Data: accident and check4
S=58, p-value=0.4618
Alternative hypothesis: true rho is not equal to 0
Sample estimates: rho 0.3095238.

Table 4: Correlations between accidents and traffic checks.

As noticeable from the table above there is a positive relationship between the frequency of accidents and frequency of traffic checks. It is worth noting however that the correlation figure is rho 0.3095238. This means that in only 31% of the time were the paired data on the frequency of accidents consisted with the data of frequency of traffic checks. In 69% of the time, however, there was an inconsistency between the ranking for traffic stops and those of accidents. Consequently, it is deductible that the police reasons for targeting certain vehicles for traffic law enforcement are informed by other considerations apart from the need to prevent traffic accidents.

Beyond looking at the frequency of accident and traffic stops, efforts were also made to discuss the relationship between traffic accidents and the number of drivers arrested (Table 5).

Vehicle Type Accidents Arrest Rank accidents Rank checks
Lorries 528 223 5 1
Matatus 3350 1,354 1 2
Trailers 231 15 6 3
Tankers 197 5 7 4
Taxis 533 150 4 5
Cars and utilities 2380 970 2 6
Buses 1784 50 3 7
College/school 189 30 8 8

Source: Police Records, 2016
Spearman's rank correlation rho
Data: accident and check
S=68, p-value=0.6646
Alternative hypothesis: true rho is not equal to 0
Sample estimates: rho 0.1904762.

Table 5: Correlation between Target Discretion and Frequency of accidents.

As noticeable from the table above there is a positive relationship between the frequency of accidents and frequency of traffic checks. It is worth noting however that the correlation figure is rho 0.1904762. This means that in only 19% of the time were the paired data on the frequency of accidents consisted with the data of frequency of traffic checks. In 81% of the time, however, there was an inconsistency between the ranking for traffic stops and those of accidents. Consequently, it is deductible that the police reasons for targeting certain vehicles for traffic law enforcement are informed by other considerations apart from the need to prevent traffic accidents.

Conclusions and Recommendations

The study established that public service and other commercial vehicles are targeted more in comparison to those that are not engaged in profit making ventures such as private cars, college vehicles, and government vehicles.

The study observed the level of non-compliance to road safety policies is high among certain categories of both commercial and noncommercial vehicles. For instance, traffic violations among commuter minibuses were observed to be much higher than Lorries and taxes. Similarly, private car drivers were reported to have a high level of noncompliance to road safety policies.

The study established that the police reasons for targeting certain types of vehicles for road safety policy implementation are different from the desire to reduce the number of road traffic violations. This evidence by the fact that a Spearman’s test of correlation between police rank of vehicle types according to the frequency to traffic checks and the rank of the same vehicle types according to the frequency of traffic violations revealed no significant correlation. Similarly, the test of correlations between frequency of accidents and frequency of traffic checks revealed a non-significant correlation. A repeat test with data on the frequency of arrest also resulted in none significant correlation.


So as to ensure that the policy objective of both the Traffic Act and the none motorized policy are successfully implemented, the following measures need to be put in place.

At the practical level, the police need to embrace result based management system. To this end, every officer needs to be allocated performance objectives which are aligned to the organizational objectives. The work of the supervisors would be to monitor the staff achievement of these objectives as well as helping them to address challenges associated with the achievement of the same. Sanctions and rewards need to be associated with the performance management system.

Efforts should be made to increase accountability among the traffic enforcement officers. This is important in light of that fact that every year. The traffic police department leads to the list of the most corrupt institution in Kenya. It has also been noted that one of the reasons for safety public policy failure in Kenya is the conflict of interest. To this end, efforts should be made to increase the level of accountability among the police.

At the theoretical level more efforts should be made to investigate ways of controlling police discretion. The study observed that the traditional hierarchical methods of monitoring police performance are not working in light of the fact that they too many of them doing different kinds of jobs and the supervisors cannot be always where they are to see what they are doing.

At the policy formulation level, efforts should be made to reduce police discretion over whom to arrest and who not to. The CCTV cameras in Nairobi city should be configured to monitor and automatically report traffic violations such as overlapping, over speeding among others. Once such reports will have been generated the system should also automatically send a fee note to the vehicle owner.


This report would not have appeared the way it is if not for the unyielding support of my supervisors Professor Dorothy McCormick, Dr. Patrick Asingo and Dr. Justine Magutu. I would also like to take this opportunity to thank Dr. Oyoo Yaye of Pwani University for their editorial advice during the process of writing this report.


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Citation: Zedekia S (2017) Street Level Bureaucrats as the Ultimate Policy Makers. J Pol Sci Pub Aff 5: 306.

Copyright: © 2017 Zedekia S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.