CORRUPTION READICATION SURVEY. October Survey Report

Transcription

1 CORRUPTION READICATION SURVEY October 2012 Survey Report SF Smart Frontiers Prepared and submitted by Smart Frontiers P.O. Box , Nairobi, Kenya

2 Acknowledgements This study was undertaken by Smart Frontiers. We would like to thank everyone who contributed the various aspects towards the realization of this survey. We wish to convey thanks to the staff of KFS for their enthusiastic help in conducting this assessment: Abraham Wafula and Eric Wainaina greatly appreciative to the persons of the invaluable advice in relation to the consultative discussion that provided critical support and direction during the course of data collection. Special thanks are also extended to the data collection and data entry teams for their due dedication and commitment throughout the data collection and data entry process. Finally, and most important, appreciation is extended to the KFS stakeholder group who willingly participated in the survey and for providing the valuable information that is the outcome of this report. Smart Frontiers however solely remain responsible for any of the errors that may remain. Consultancy team June 2012 Smart Frontiers ii

3 Abbreviations and Acronyms DK GoK ENCOM KFC KFC KACA NR PC RBM SD SPSS Don t Know Government of Kenya Enforcement and Compliance Division Kenya Forest College Kenya Forest Service Kenya Anti-Corruption Authority No Response Performance Contracts Result Based Management Standard Deviation Statistical Products and Services Solutions Smart Frontiers iii

4 Table of Contents List of Figures... vi List of Tables... vii Executive Summary... 1 Section 1: Introduction Background This survey Geographical Scope Secondary data review Customer survey Employee survey Student survey Sample distribution Data collection Analysis approach 12 Section 2: The Forest Sector in Kenya The Kenya Forest Service Situation analysis of the forest sector Economic importance and role towards Vision Challenges 16 Section 3: Results and Discussion Levels and dynamics of corruption Corruption Indices Department comparison Factors influencing current opinions about corruption Forms of corruption malpractices experienced Corruption victimization Prevalence of bribery Frequency of bribery payments Amounts of bribes paid Purpose of bribe payments Corruption pressure Bribe seeking modalities Pressure exerted for bribery 28 Smart Frontiers iv

5 3.3.3 Perceived levels of staff involvement in corruption Values and attitudes towards corruption Perceptions about the trend of corruption 31 Conclusion Smart Frontiers v

6 List of Figures Fig 3.1: Corruption indices 14 Fig 3.2: Factors influencing current opinions about corruption 17 Fig 3.3: Patterns of corruption malpractices 18 Fig 3.4: Prevalence of bribery based on percentage that had direct experience 19 Fig 3.5: Prevalence of bribery based on number of bribes paid 20 Fig 3.6: Amounts of bribes paid 21 Fig 3.7: Reasons for making bribery payments 22 Fig 3.8: Bribe seeking modalities 23 Fig 3.9: Views on bribery execution scheme by frequency of payment 23 Fig 3.10: Level of pressure exerted for bribery 24 Fig 3.11: Reasons for making bribery payments 25 Fig 3.12: Percentage distribution of recourse measures in the face of administrative delay of service delivery 26 Fig 3.13: Views about the level of corruption over the past 12 months 26 Fig 3.14: Views about the level of corruption over the coming 12 months 27 Smart Frontiers vi

7 List of Tables Table 1.1: Overall sample distribution by stakeholder category 6 Table 1.2: Sample characteristics for the employee segment 7 Table 1.3: Sample characteristics for the customer segment 8 Table 1.4: Sample distribution for customer survey by stakeholder segment 9 Table 2.1: Level of forest coverage between 1990 and Table 3.1: Mean perceptions of corruption in KFS department 15 Table 3.2: Mean perceptions of corruption in the KFC departments 16 Smart Frontiers vii

8 Executive Summary This survey was conducted during the month of June by Smart Frontiers in the framework of Performance Contracts to assess the level of corruption levels in KFS and its constituent college, Kenya Forest College (KFC). In this way, the survey sought to evaluate the impact of KFS corruption prevention programmes, and whether over time the intended goals are being met. This primary research survey involved three levels of assessment, namely: customer survey, employee survey and student survey. The findings presented in this report highlight the key issues emerging, and ultimately, the actions required to mitigate against the corruption risks Key highlights of the survey findings are as follows: Corruption indices In this assessment, four types of corruption indices were computed: Corruption practice index, working out to 1.72 points, was based on self reporting of the participants in acts of corruption, in which they have offered KFS/KFC personnel money/gifts/favors. Corruption pressure index, working out to 0.75, reflects the degree to which participants subject to direct or indirect pressure to participate in corrupt practices. Corruption magnitude index, working out to 1.21, reflects perceived the spread of corruption among the staff in KFS/KFC. Corruption expectation survey, working out to 0.95, reflects the expectations of the customers about the capacity of KFS/KFC to address corruption in the coming year. Department comparison Perceptions of corruption in KFS departments, based on the overall mean scores, the results suggest fairly low negative outlook on the twelve departments, with mean scores varying from 2.26 to Comparatively, the data indicate that the perceptions of corruption are comparatively in the Human Resources & Administration (2.68) and Supply Chain Management (2.63) departments, and considered to be lowest in the Natural Forest Conservation & Management (2.28) and Corporate Communication (2.26) departments. Looking at the perceptions of corruption in KFC departments, the results suggest relatively strong negative outlook across all the seven departments, with scores varying from 4.04 to Notably, higher corruption ratings are apparent in respect to the Natural Forest Conservation and Smart Frontiers 1

9 Management and Basic Science departments (both depicting 4.04), and lowest in respect to Outreach and Liaison (3.85). Corruption Victimization On account of those who provided opinions, it is noted that just above one-tenth (13%) of the three customer segments had direct exposure to a bribery experience with a KFS/KFC official at least once. When considering these figures by customer segment, it is evident that the victimization experience of the employees (16%) was fairly higher compared to those of either external customers or students (depicting 10% each). Another important indicator of prevalence of bribery is the frequency of payments, measured in terms of the number of times bribes were paid. On this, overall, just about onetenth (11%) gave bribes on only one occasion, while the largest proportion (65%) did so more than thrice. Looking at the differences between customer segments, the marked contrasts suggest a tilted distribution in the extent different groups carry the burden of bribery. As noted, the degree of repeat victimization is substantially higher among employees, reflected in 88% that reported making payments more than thrice, as compared with corresponding figures of 26% and 0% for customers and students. Looking at the purpose of paying bribes, the results show that the large majority of bribes are paid to get favour or better treatment (38%), with the customers segment dominating the motivation for making payments, reflected in corresponding scores of 50% and 34% for employees. The other important reason for paying bribes is to speed up the procedure (36%). Bribe seeking modalities A complexity of transaction modalities can be seen. In about 15% of bribery incidents, the customer initiates the payment, with slightly higher proportion of the customer segment affected by this mode of payment, depicting 18% (customers) against 13% (employees). In slightly lower than one-quarter of the incidences (23%), the staff explicitly makes request for payment, again with a higher proportion of the customer segment being the category most affected by this mode of payment, depicting 25% (customers) against 29% (employees). In the final mode of payment, the modality for paying is known beforehand (15%). In this mode, the employees segment are the ones affected more, depicting 7% (customers) against 20% (employees). Corruption pressure Participants views were overall evenly split between those with positive and negative views, with significant variations among the latter. A proportion of 60% that stated there was no pressure at all, and conversely 40% indicated existence of pressure, albeit at varying levels. Specifically, whereas the Smart Frontiers 2

10 positive view reaches this margin, just below one-fifth (19%) indicated that the pressure was little. On the other hand, the combined proportions for fair amount and a lot of pressure indicate that about one-fifth (21%) are subjected to substantive pressure. Values and attitudes towards corruption The results indicate that, in view of delays experienced while waiting for a service from a Department of the KFS/KFC, for most participants, notable malpractices, in this case; offer a bribe or a gift and use influential staff to help are fairly less tolerated, reflected by a fairly low proportions of 7% and 12% respectively that would consider these as recourse. On the other hand, half (51%) of the participants would be inclined to seek redress from the KFS/KFC management. Trend of Corruption Perceptions regarding the state of corruption over the past one year period suggest a broad view that corruption is on the decline, with 13% of the participants agreeing upon the same, while about two out of five (39%), however, thought it is on the increase. It is nonetheless noted that even though as noted, fairly high proportion (33%) did not have any opinion on the subject. On future trend, the outlook is modestly positive, noted by 33% proportion that held a view of no corruption at all, and a further 24% that held the view of low corruption. On contrary, 11% of the total sample held the view of moderate, while 11% held the view of very high. Smart Frontiers 3

11 Section 1: Introduction 1.1 Background The Government of Kenya (GoK) has embraced the Result Based Management (RBM) as a tool to improving public service delivery. The essence of RBM is to ensure efficiency and effectiveness in the delivery of public services, and that citizens can hold public servants accountable for the levels of service they receive from public institutions. This is in line with Kenya s public service accountability initiative known as Performance Contracts (PC), which was introduced in 2003, and which is expected to support the goals and objectives outlined in Kenya s Vision 2030 strategy. 1 The PC initiative forms the basis of improvement in the public service delivery, which seeks to progressively work towards increasing quality of output within public institutions. Under these broader public sector reforms, the Kenya Forest Service (KFS) aims to ensure that services are enhanced to the satisfaction of its customers and excellence is attained in its operations. In complement to this Reform Agenda, one pillar in this move is to monitor the level of satisfaction of both the internal and external stakeholders. 1.2 Corruption eradication indicator The inclusion of citizens service delivery charters and independent customer satisfaction surveys has been particularly important in the evolution of a citizen-focused culture in the public service these are vital instruments for enhancing and measuring the propriety of service delivery and also in reducing incidences of corruption. In the preamble of the guide for corruption survey offered by Kenya Anti-Corruption Commission, 2 corruption eradication indicators have been included in the performance contract; accordingly, all public 1 Muthaura, F., (2003), Head of Public Service 2 Renamed Kenya Anti-Corruption Authority Smart Frontiers 4

12 institutions are expected to mainstream anti-corruption strategies in their management systems. Part of this entails conducting a baselines survey to determine levels of corruption. Thus, in pursuit of ensuring that services are enhanced to the satisfaction of its customers, KFS commissioned a survey with a view to obtain knowledge regarding the relevance and effectiveness of the support measures to reduce corruption. The outcomes are intended to assist the KFS to align management and directorial processes with employee and customer expectations in order to facilitate greater performance. 1.3 This survey The broader motivation underpinning this survey was to enable the assessment of corruption levels in KFS and its constituent college, Kenya Forest College (KFC). In this way, the survey sought to evaluate the impact of KFS corruption prevention programmes, and whether over time the intended goals are being met. The specific objectives of the survey comprised: 1. Establish the kind of corrupt practices that are taking place 2. The extent to which customers are subjected to direct or indirect pressures to participate in corruption practices in KFS/C 3. Assess the magnitude and spread of corruption in KFS/C 4. Assess customer s expectations about the capacity of KFS/C to curb corruption 5. Recommend mitigation against corruption risks Geographical Scope For addressing the objectives of the survey, it was recognized that in order to obtain breadth of perspectives, the data collected should represent a range of locations across the country. Working with three zones per conservancy (exception being Nairobi, Ewaso North and North Eastern), the survey covered a total coverage of 24 zones. While it would be of interest to include more zones, the cost of so doing and the logistical requirements involved would outweigh its potential value. These zones are nonetheless selected to reflect differences in terms of in terms of geographical location, socio-economic diversities and size of conservancies, and can thus reflect representativeness to that regard. Table xxx displays the sample distribution by the conservancies covered. Smart Frontiers 5

13 1.3.2 Research strategy Given the scope of objectives to be addressed, this survey involved four levels of assessment; namely: 1) Secondary data review 2) Customer survey 3) Employee survey 4) Student survey Secondary data review The preliminary tasks involved a review of existing data and literature. One important goal was to evaluate how KFS has performed in terms of service delivery over the past years. The previous survey approach and findings will be partly fed into the primary research in order to allow for tracking and comparison on a number of aspects. The sources to be reviewed will also include the following: 1) Previous corruption eradication survey reports 2) KFS Service Charter 3) Pertinent corruption survey documents 4) Other documents on the forestry sector Customer survey A fundamental consideration and one that informs the entire research strategy is the necessity to draw inferences at the individual, community and institutional levels. The quantitative survey was thus conducted using purposive sample of respondents drawn from KFS delineated range of stakeholders (customers). Accordingly, the following are considered the important stakeholders that were interviewed at various points during the data collection sessions: i) Civil Society Organizations ii) Forest dependent/adjacent communities iii) Community Based Organizations iv) Women Groups Smart Frontiers 6

14 v) Youth Groups vi) Private Sector (saw millers, timber loggers, charcoal dealers, commercial tree growers, ecotourism based enterprises) vii) Agro-based Industry (coffee, tea, wheat farmers) viii) Public Institutions (government and local authorities) ix) Research/Academic Institutions x) Suppliers Employee survey The study population of this survey consisted of all KFS employees, including the staff of Kenya Forest College (KFC). The sampling frame used was therefore constituted a-priori list of employees obtained from KFS to build an estimate of staff distribution. In distributing the sample, due consideration was given to employee diversity in terms of geographical location (zones). To achieve this, a three-stage cluster sampling design was employed; this entailed the following stages: i. First stage: census selection of all the 10 conservancies, plus Londiani KFC. ii. Second stage: Unequal selection probabilities of the Zones, with three Zones being selected in each Conservancy. Technically speaking, it would not be feasible to employee equal selection probabilities of the zones due to the small number of Zones in each conservancy. However, given the relatively higher heterogeneity levels, this was not likely to compromise representation. iii. Third stage Unequal selection probabilities of the Forest Stations. Again, unequal selection probabilities was undertaken due to the small number of Stations in each Zone, and to ensure that diversity in terms of location and size is reflected iv. Fourth stage: Census-based approach for selection of survey participants at station level. On this account, the survey provided opportunity to all employees in the respective stations to respond to the questionnaire. However, given that not all employees would be available or willing to participate in the survey, it was anticipated that at least 50% to 60% would be reached approximately 600 employees. This calculation was determined using hit rates of 60%. Smart Frontiers 7

15 1.3.6 Student survey Similar to the approach used for employee survey, a census-based approach for selection of the student participant. On this account, the survey provided opportunity to all students in the respective stations to respond to the questionnaire Sample distribution As depicted in Table 1.1, the total sample achieved for this survey was 1,138. In terms of customer categories, the employee segment constituted the bulk of the sample (N=661), representing 58% of the total sample, against 427 and 50 respondents for the customer and student segments respectively, and corresponding proportions of 38% and 4%. Table 1.1: Overall sample distribution by stakeholder category Number Percent Total Employee Customer Student Sample distribution for the employee survey As shown in Table 1.2, the employee sample was spread across the ten KFS conservancies in addition to the KFC. The highest percentages were drawn from Mau (14%), Central Highlands (11%) and Coast (11%). Conversely, the lowest sample was drawn from North Eastern (2%) and Ewaso North (3%). In terms of gender, the sample depicts askew in favour of males, depicting 73% against 25%, female. Regarding years in service, it is seen that the bulk of the sample (27%) has worked for over 10 years with KFS, while 10% have worked for 1 year or less. In terms of age, the highest proportion (34%) was in the middle age 31 to 44 years, with a combined proportion of 48% being 45 years and above. Smart Frontiers 8

16 Table 1.2: Sample characteristics for the employee segment Conservancy Number Percent Central Highlands Coast Eastern Ewaso North 22 3 KFC 49 7 Mau Nairobi North Eastern 11 2 North Rift 47 7 Nyanza Western Gender Male Female NR 18 3 Years in service 1 yr to 3 yrs to 5 yrs to10 yrs yrs NR Age to Above NR 22 3 Smart Frontiers 9

17 Sample distribution for the customer survey Similarly, as shown in Table 1.3, the customer sample was spread across the ten KFS conservancies in addition to the KFC. The highest percentages were drawn from Nairobi (23%), and the lowest drawn from North Eastern (3%) and Ewaso North (4%). In terms of category, 44% represented community segment (comprising forest adjacent communities, youth and women s groups). Agro and forest based businesses constituted 37% of the sample, while the institutional segment (comprising public institutions and NGOs) and suppliers constituted 10 and 8% respectively. Regarding number of years of interaction with KFS, half the sample (50%) have interacted with KFS, slightly less than one-fifth (18%) have done so for 2 to 3 years, while one-tenth has done so for 1 year or less. Another one-tenth (9%) did not comment on this. Table 1.3: Sample characteristics for the customer segment Conservancy Number Percent Central Highlands 30 7 Coast Eastern 38 9 Ewaso North 15 4 Mau Nairobi North Eastern 14 3 North Rift 33 8 Nyanza 33 8 Western Central Highlands 30 7 Coast Period of interaction with KFS 1 year or less to 3 yrs to 6 yrs to 10 yrs yrs 22 5 Smart Frontiers 10

18 NR 40 9 Customer category Community segment Agro and forest based businesses Institutional segment (Public institutions and NGOs) 36 8 Suppliers Sample distribution for the student survey The sample for the student survey is displayed in Table 1.4. For this segment, the sample was spread to have all departments represented, although as noted, relatively high proportion did not indicate their department. For the one that indicated, it is seen that 34% were drawn from Forest Management, while Basic Science and Siviculture had proportions of 18% and 10% respectively. In terms of period of interaction with KFC, the bulk (58%) had done so for 1 year or less Across gender, it is seen that the highest proportion were male (68%) against 28% proportion for females. On the other hand, by age, it is again seen that relatively high proportion disinclined to do so, although it is expected that the bulk would be between 18 to 24 years of age. Table 1.4: Sample distribution for customer survey by stakeholder segment Number Percent Forest Management Basic Science 9 18 Silviculture 5 10 NR Period of interaction with KFC 1 yr yrs yrs 3 6 Gender Male Female NR 2 4 Age Over NR Smart Frontiers 11

19 1.3.8 Data collection Data collection involved the use of face to face interviews with the customers, and self-completion approach by the employees. The latter was preferred because it enhanced anonymity. On this account, personal details (name, employee number etc.) were not be required, but their personal details in terms of age, department, gender and number of years in service were essential Analysis approach The analysis was been undertaken using SPSS 12.0 to enable manipulation of the data using frequencies and cross tabulations. For a number of questions, the survey adopted the Likert scale type questions, intended to measure each item by possible two opposing positive and negative scales, and a middle or neutral scale. However, few open-ended questions were also included to explore participants suggestions. To facilitate more useful interpretation of the survey results, the data has been disaggregated by the three customer sub-groups. It will, however, be seen that the coverage for students data appear is too thin. Additionally, on a number of aspects of the questionnaire, there appears to have been a reluctance to give answers to the bribery situations, probably due sensitive situations towards corruption. Whereas these limitations may have constrained analysis (technically speaking), it is not likely to be a source for less credible information due to relatively high levels of homogeneity within the sample. Qualitative analysis procedures were used whereby responses were categorized and assigned with codes which served to summarize, synthesize, and sort responses made by the participants. Keeping in view the broad objectives of the survey, the data has been triangulated among four main categories of analyses, namely: 1. Corrupt practices The focus of analysis is on the corruption victimization. The aim is to obtain an understanding of the level, nature or forms of corrupt practices the participants encountered or experienced among the staff and /or management in the 12 months prior to the survey. Smart Frontiers 12

20 2. Magnitude of corruption The focus of analysis is on the perceived spread of corruption in KFS; in terms of the level of spread of corruption among the employees. 3. Corruption pressure The focus of analysis is the degree to which the customers and employees are subjected to direct or indirect pressure to participate in corruption with the KFS departments. 4. Expectations about future corruption The focus of analysis is on future outlook of corruption. The analysis reflects on the expectation of customers and employees about the capacity of KFS to address corruption in the coming year. Smart Frontiers 13

21 Section 2: The Forest Sector in Kenya 2.1 The Kenya Forest Service The oversight institution responsible for management of the forest sector in Kenya is the KFS. The KFS was established by an act of Parliament as a body corporate under the Forest Act, 2005, to provide for the establishment, development and sustainable management, including conservation and rational utilization of forest resources for the socio-economic development of the country. Under the existing legislation, the broader mandate of KFS extends to all forests. Mandate To provide for the establishment, development and sustainable management, including conservation and rational utilization of forest resources for environmental protection and socio-economic development of the country Core functions On this account, the specific functions of KFS include the following: 1. To sustainably manage natural forests for social, economic and environmental benefits 2. Increase productivity of industrial forest plantations and enhance efficiency in wood utilization 3. Promote farm forestry and commercial tree farming 4. Promote efficient utilization and marketing of forest products 5. To promote sustainable management of forests in the drylands 6. To protect forestry resources and KFS property 7. To develop and maintain essential infrastructure for effective forest management and protection Vision To be the leading organization of excellence in sustainable forest management and conservation Smart Frontiers 14

22 Mission To enhance conservation and sustainable management of forests and allied resources for environmental stability and socio-economic development Stakeholders In undertaking it functions, KFS works with a broad range of stakeholders (customers). The key customers include but not limited to: 1. Communities and land owners e.g. community forest associations, organized community groups, farmers, pastoralists and commercial tree growers. 2. Private Sector e.g. licensees, concessionaires, saw miller, ecotourism based enterprises, Small and Medium Enterprises and Suppliers 3. Non Government Organizations (local and international) 4. Development partners (governmental and non-governmental) 5. Government agencies: these include local authorities and parastatals 6. The General public 7. Service providers 2.2 Situation analysis of the forest sector Forests are among the Kenya s important natural resources as they supply goods and services for socio economic and cultural development. The gazetted forestland is estimated to be 1.7 million hectares. This is just about 2.5 % per cent of the country s land area. Recent estimates, based on remote sensing, indicate that Kenya has a critical 1.7 per cent of closed canopy forest cover (UNEP, 2001). The gazetted forests constitute 0.12 million hectares of plantation forest, 1.21 million hectares of indigenous forest, and 0.5 million hectares of protective bush and grassland. A total of 36.7 million hectares of other forest associations exist under other legal frame works that include National Parks, Ranches, Trustlands and forests private ownership. As Water Catchment, forests in Kenya play a vital role as home to the nation s Water towers, i.e. the five main water catchments (Mt Kenya, Cherangani, Aberdare s, Mau and Mt Elgon) constituting the bulk of Kenya s high forests Economic importance and role towards Vision 2030 Kenya s Vision 2030 notes that, globally, deforestation and forest degradation accounts for 20% of green house gases (GHG) emissions and that forest conservation can provide 20% of the solution to Smart Frontiers 15

23 global warming. It therefore calls for the urgent need to conserve, protect and rejuvenate Kenya s forests. The Vision 2030 recognizes the importance of forest industries to the national economy - accounting for more than 2% of the GDP. Forestry business in many African countries is mainly transacted in the informal sector. This is a sector that operates at the interface of the monetized and traditional economies. The types of activities that characterize the sector include subsistence collection of forest products, processing and trade in firewood, charcoal, forest foods and handicrafts. It is assumed that in some countries, business conducted in the informal forestry sector may contribute more to rural livelihoods than that in the formal forestry sector. They provide employment opportunities in various industries, thereby contributing to improved income and livelihoods of many Kenyans. These include: sawmills, pulp and paper industry, woodfuel industry, basketry, charcoal production, marketing and transportation industry, wood curving industry, and non-timber forest products industries. The Forest Act 2005 and the KFS strategic plan (2006 to 2011) stipulate that Kenya s forests will be broadly managed for the following purposes: 1) Biodiversity, soil and water conservation and provision of other environmental services; 2) Wood production (timber, pulp, woodfuel and poles) and employment current and potential; 3) Conservation of wildlife habitats; and, 4) Production of non-wood forest products and ecotourism development. More specifically, the Act clearly states that plantation forests will be managed primarily for the production of wood and other forest products and services for commercial purposes. The ban on forest timber harvesting which has now been reviewed was for a long time a major constraint to forest management and in particular, it affects the level of royalty collection, the quality of timber products (including exposing plantations to risk of fires and disease/insect infestation), insufficient or lack of raw materials supply, and increased cost of forest commodities Challenges One of the main challenges currently facing the sector is deforestation and degradation. Loss of forest resources, general land degradation and desertification are serious environmental as well as socio- Smart Frontiers 16

24 economic problems globally and in Kenya. 3 Three key drivers have been identified as having immensely contributed to this problem in Kenya. These include: Clearing for agriculture, unsustainable utilization and poor governance and institutional failures. Thus, the past two decade has seen decline in the coverage of the sub-total forest land by about 12,050 ha annually. Table 2.1: Level of forest coverage between 1990 and 2010 Category of forest resource Annual Change Area ( 000 Ha) (using FAO definitions) ( 000 Ha) Indigenous closed Canopy Forest 1,240 1,190 1,165 1, Indigenous Mangroves Open woodlands 2,150 2,100 2,075 2, Public Plantation Forests Private Plantation forests Sub - total Forest land (total of above categories) 3,708 3,582 2,357 3, Bush-land 24,800 24,635 24,570 24, Farms with Trees 9,420 10,020 10,320 10, Total Area of Kenya 58,037 58,037 58,037 58, Mr Aeneas Chuma, Resident Representative, UNDP, Kenya Smart Frontiers 17

25 Section 3: Results and Discussion 3.1 Levels and dynamics of corruption Corruption Indices In this assessment, four types of indices were computed. The results, presented in Figure 3.1 works out as follows: Corruption practice index, working out to 1.72 points, was based on self reporting of the participants in acts of corruption, in which they have offered KFS/KFC personnel money/gifts/favors. Corruption pressure index, working out to 0.75, reflects the degree to which participants subject to direct or indirect pressure to participate in corrupt practices. Corruption magnitude index, working out to 1.21, reflects perceived the spread of corruption among the staff in KFS/KFC. Finally, Corruption expectation survey, working out to 0.95, reflects the expectations of the customers about the capacity of KFS/KFC to address corruption in the coming year. Fig 3.1: Corruption indices Corruption practice Corruption pressure Corruption magnitude Corruption expectation Smart Frontiers 18

26 3.1.2 Department comparison Next, department comparison of perceived prevalence of corruption is examined. In this respect, the mean perceptions of corruption examined correspond to 12 departments of KFS and 7 KFC departments. In respect to the former, the assessment targeted KFS employees and external customers; whereas, in respect to the latter, the assessment targeted KFC students. Tables 3.1 and 3.2 report the mean scores based on the question: Please rate the prevalence of corruption using the scale of 1 to 5, where least prevalent is and 5 represents very prevalent. First, looking at the perceptions of corruption in KFS departments, based on the overall mean scores, the results suggest fairly low negative outlook on the twelve departments, with mean scores varying from 2.26 to Comparatively, the data indicate that the perceptions of corruption are comparatively in the Human Resources & Administration (2.68) and Supply Chain Management (2.63) departments, and considered to be lowest in the Natural Forest Conservation & Management (2.28) and Corporate Communication (2.26) departments. Looking at dispersion, the standard deviation (SD) range of 1.17 to 1.25 can be observed across the 12 departments, on the whole suggesting a fairly lower degree of agreement among participants on these ratings. Looking at the departments with lower SD values (in this case this being less than 1.20) suggest nuanced differences in corruption ratings between employee and customer segments in five departments, namely: ENCOM, Finance, Forest Extensions Service Division, Natural Forest Conservation and Management and Corporate Communication Department. As noted, the results show higher corruption ratings among the customers segment. Smart Frontiers 19

27 Table 3.1: Mean perceptions of corruption in KFS department Overall mean Std Dev Employee Customer Average Human Resources & Administration Supply Chain Management Enforcement and Compliance Division ENCOM Plantation and Enterprise Division Finance Internal Audit Forest Extensions Service Division Corporate Service Kenya Forestry College Legal Department Natural Forest Conservation and Management Corporate Communication Department Subsequently, looking at the perceptions of corruption in KFC departments, the results suggest relatively strong negative outlook across all the seven departments, with scores varying from 4.04 to Looking at dispersion, the results suggest a higher degree of agreement among the participants on these ratings across all the seven departments (SD range from 1.48 to 1.60). Notably, higher corruption ratings are apparent in respect to the Natural Forest Conservation and Management and Basic Science departments (both depicting 4.04), and lowest in respect to Outreach and Liaison (3.85). Table 3.2: Mean perceptions of corruption in the KFC departments Mean Std. Deviation Average Natural Forest Conservation and Management Basic Science Forest Engineering Office of the Principal Forest Extension Silviculture Outreach and Liaison Smart Frontiers 20

28 3.1.3 Factors influencing current opinions about corruption Participants responses (Figure 3.2) to the question When rating the level of corruption in KFS/KFC, what did you base your assessment on? indicate that, prevailing views are for the most part influenced by direct experiences (9%) and indirect experiences based on discussions with colleagues/friends (7%), distantly followed by media (3%) and information from (KFS/KFC) (3%). The analysis of reasons by the three customer segments shows limited contrasts in terms of the factors related to personal experience and discussion with colleagues/friends, as can be seen from the general correspondence in the scores; the range of scores is minimal from 7% to 10% in respect to personal experience, and 6% to 9% in respect to discussion with colleagues/ friends. In terms of media, however, a striking contrast appears in respect to the student segments, which dominates with regard to opinions on this subject. Fig 3.2: Factors influencing current opinions about corruption Personal Experiences Discussions with colleagues/friends Information from the media Information from KFS/KFC Information from the clients Other Total Employee Customer Student Forms of corruption malpractices experienced Respondents were presented with a listing of various corrupt malpractices and asked to affirm the ones they had encountered or experienced among the staff and /or management in the 12 months prior to the Smart Frontiers 21

29 survey. Figure 3.3 reveals that corruption is reflected in a range of malpractices. According to the experience of participants who encountered these malpractices, the largest share of these occurred in form of abuse of office (58%) and extortion (44%). Other notable forms encountered were tribalism/nepotism (29%) and unprocedural tendering process (20%). Making a comparison across the three customer segments demonstrate that the features of corruption are all too familiar, although notable variations are observed in the extent these malpractices are or have been encountered. With exception of extortion, it is observed that all the categories of corruption malpractices examined elicited the highest scores among the employee population. On the other hand, it seems that when it comes to extortion, the possibility of falling victim exists at a much higher degree for the student population. Fig 3.3: Patterns of corruption malpractices Total Employee Customer Student % Abuse of office Extortion Tribalism/ Nepotism Unprocedural Tendering Processes Bribery demand Favouritism Misappropriation of resources 3.2 Corruption victimization This indicator was based on the actual experiences of corruption. The analysis presented here reflects self-reported instances when participants provided informal payments, the frequency of those payments, amounts and reasons for making the payments. Smart Frontiers 22

30 3.2.1 Prevalence of bribery In looking at prevalence, the survey addressed the victimization experience of the participants in relation to bribes paid. Prevalence in this case is calculated as the number of participants who reported that gave a bribe on at least one occasion. In Figure 3.4, prevalence of bribery is computed for overall and separately for each customer segment. The relatively higher level of DK/NR should also be noted; it is nonetheless unclear just why such a high portion would be unwilling to respond. On account of those who provided opinions, it is noted that just above one-tenth (13%) of the three customer segments had direct exposure to a bribery experience with a KFS/KFC official at least once. When considering these figures by customer segment, it is evident that the victimization experience of the employees (16%) was fairly higher compared to those of either external customers or students (depicting 10% each). Fig 3.4: Prevalence of bribery based on percentage that had direct experience "Have you ever given a bribe/unofficial payment or favor in order to get services due to you?" DK/NR, 24% 40 % - Paid a bribe Never paid, 62% Paid, 13% % Employee Customer Student Smart Frontiers 23

31 3.2.2 Frequency of bribery payments Another important indicator of prevalence of bribery is the frequency of payments, measured in terms of the number of times bribes were paid. As Figure 3.5 shows, overall, just about one-tenth (11%) gave bribes on only one occasion, while the largest proportion (65%) did so more than thrice. Looking at the differences between customer segments, the marked contrasts suggest a tilted distribution in the extent different groups carry the burden of bribery. As noted, the degree of repeat victimization is substantially higher among employees, reflected in 88% that reported making payments more than thrice, as compared with corresponding figures of 26% and 0% for customers and students. (Note: The statistical base of the distribution of these results, especially for the student sample is quite thins, and therefore likely to yielded limited amount of information on bribery payments) Fig 3.5: Prevalence of bribery based on number of bribes paid % % Once Twice Thrice More than thrice Employee (N=59) Customer (N=27) Student (N=5)* * Caution: Low base figures Amounts of bribes paid The values reflected in Figure 3.6 indicate the value per encounter. First, in looking at the amounts of bribes paid, the results of this survey showed a wide variation in respect to the amounts paid as bribe; this ranged from a low of Ksh. 100 to a high of Ksh. 10,000 (though this maximum is based on one incidence). The amounts most frequently cited were Ksh. 200, Ksh, 1,000 and Ksh. 2,000. Smart Frontiers 24

32 Looking at the distribution of the amounts paid the results of this survey show that 78% of all bribes were for amounts smaller than Ksh One-tenth (10%) of the bribes were in the range of Ksh. 501 to 1,000. Some 7% were in the range of Ksh. 1,001 to 2,000, while, on aggregate, 4% were above Ksh. 2,000. When focusing on the amounts paid by customer categories, it is evident that, for a given encounter, employees are is likely to make exceptionally higher payments. On the other hand, however, although the average amount paid for bribes by customers segment is relatively low, it should also be taken into account that those affected had to make such payments on a quite regular basis, as demonstrated in Figure 3.4. Fig 3.6: Amounts of bribes paid % % Employee (N=14) Ksh. 500 or less 36 Ksh. 501 to Ksh to Ksh to Above Ksh Customer (N=22) Student (N=5)* * Caution: Low base figures Purpose of bribe payments The reasons given for those who reported making bribery payments are next presented. It should be noted that, on the question asked, only the views of employees and customers were obtained, and it remains unclear why the student segment were unable or disinclined to offer any view at all on this issue. The results, displayed in Figure 3.7, show that the large majority of bribes are paid to get favour or better treatment (38%), with the customers segment dominating the motivation for making payments, Smart Frontiers 25

33 reflected in corresponding scores of 50% and 34% for employees. The other important reason for paying bribes is to speed up the procedure (36%). Making a comparison by the employee and customer segments, it is striking that the employees segment are the most affected by these administrative delays, in this case leading by 45% against 11% rating apparent among the customers segment. The third important reason for paying bribes, as reported by participants arises from bribery demand. A comparison across the two customer segments shows that, bribery demands, in this respect, affect both the employees and customers segments in almost equal measures. The fourth motivation for bribe payments, as observed, stems from voluntary giving, in this case, this essentially being an expression of thank you (10%). Comparatively, both the employees and customers segments are subject to this motivation in almost equal measures. Fig 3.7: Reasons for making bribery payments To obtain service or favors Due to too much delay in service delivery from KFS Total Employee Customer It was demanded Voluntarily offered it as a token of appreciation Corruption pressure The indicator on corruption pressure was constructed on the basis of potential or opportunities for corruption. The analysis presented here reflects bribe seeking modalities, bribery pressure and perceived levels of staff involvement in corruption. Smart Frontiers 26

34 3.3.1 Bribe seeking modalities In examining bribe seeking modalities, participants opinions were sought using three response options: i) A staff indicated or asked for a payment; ii) The person offers payment on her/his own accord; iii) It is known beforehand who to pay and how much to pay. In this section, only the views of employees and customers were obtained, with the student segment likewise unable or disinclined to offer any view at all on this issue. As shown in Figure 3.8, once again, the DK/NR figures are quite high (overall 47%). On account of the responses obtained, a complexity of transaction modalities can be seen. In about 15% of bribery incidents, the customer initiates the payment, with slightly higher proportion of the customer segment affected by this mode of payment, depicting 18% (customers) against 13% (employees). In slightly lower than one-quarter of the incidences (23%), the staff explicitly makes request for payment, again with a higher proportion of the customer segment being the category most affected by this mode of payment, depicting 25% (customers) against 29% (employees). In the final mode of payment, the modality for paying is known beforehand (15%). In this mode, the employees segment are the ones affected more, depicting 7% (customers) against 20% (employees). Fig 3.8: Bribe seeking modalities The offer make payment on own accord Customer Employee Total A staff indicate/ask for a payment It is known beforehand who & how much to pay DK/NR Smart Frontiers 27

35 Modalities of bribe-paying also show distinct patterns when the frequency of payment is considered. Looking at Figure 3.9, it is apparent that, on the whole, the three modalities of payments become relatively established with repeat bribery transactions, notably more than three, and conversely least established during the first three transactions. It is worth noting that the payment modality where it is known beforehand who and how much to pay remains relatively weak during the first three transactions. Fig 3.9: Views on bribery execution scheme by frequency of payment 100 A staff indicate/ask for a payment The offerer make payment on own accord It is known beforehand who & how much to pay Initial bribery transaction Second bribery transaction Third bribery transaction More than three bribery transactions Pressure exerted for bribery In examining pressure exerted for bribery, participants opinions were sought using four response options: i) No Pressure at all; ii) A little pressure; iii) A fair amount of pressure; and iv) A lot of pressure. In response to the question asked, only the views of employees and customers of the three customer segments were obtained. As observed on Figure 3.10, participants views were overall evenly split between those with positive and negative views, with significant variations among the latter. A proportion of 60% that stated there was no pressure at all, and conversely 40% indicated existence of pressure, albeit at varying levels. Specifically, whereas the positive view reaches this margin, just below one-fifth (19%) indicated that the pressure was little. On the other hand, the combined proportions for fair amount and a lot of pressure indicate that about one-fifth (21%) are subjected to substantive pressure. Smart Frontiers 28

36 Fig 3.10: Level of pressure exerted for bribery Total Employee Customer No Pressure at all A little pressure A fair amount of pressure A lot of pressure Perceived levels of staff involvement in corruption In examining perceived levels of staff involvement in corruption, participants opinions were sought using four response options: i) Hardly any official is involved; ii) Only a few are involved in it; iii) Most officials are involved in it; and iv) Almost all officials are involved in it. Again, only the views of employees and customers were obtained, with the student segment similarly unable or disinclined to offer any view at all on this issue. Figure 3.11 presents the results. First, as can be noted, just below one-third (30%) of the participants provided a DK/NR response. Nonetheless, considered in terms of the degree of staff involvement, participants views are somewhat more positive, reflected in the overall proportion of 39% of the total sample that felt only a few staff are involved. Conversely, on aggregate, just below one-fifth (19%) felt that most officials or all officials were involved in corruption. Smart Frontiers 29