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GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 328 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1
The Government of Kenya entered in a concessional agreement with Rift Valley Railways (RVR) in 2006, under the build-operate-transfer financing arrangement, to boost economic growth. However, 10 years later, RVR’s performance failed to meet performance targets, due to financing and technical capacity constraints, as per anecdotal reports. This article examined the influence selected macro-economic factors on the project’s financing. We sourced primary data from 348 staff of key stakeholders. We applied Relative Importance Index to rank the factors based on their importance; besides, we applied Kendall’s Coefficient of Concordance (W) to determine the degree of agreement among participants. Findings show that inflation rates ranked highest, scoring an index of 0.8; followed by interest rates (0.7), debt ratio (0.6) and taxation burden (0.6). The study obtained a strong level of concordance in perceptions regarding influence of macro-economic factors on the project’s financing, which was also statistically significant at
0.01 error margin (W = 0.833, χ2 = 41.8223, df = 3 & ρ-value = 0.000). Besides financial and technical capacity, stakeholders should consider macro-economic environments, when evaluating RVR’s performance. The study suggests the need for appropriate adjustments of the monetary, fiscal, taxation and domestic borrowing policies, among other interventions.
JEL: O16 KEYWORDS: Macro-economic, Financing, Build-Operates-Transfer, Railways Project
INTRODUCTIONRailways transport is an important factor in Kenya’s economy, having provided freight and passenger services for more than a century (Ministry of Transport, Kenya, 2014). At its peak in 1983, the railways system moved some 4.3 million tons of freight, before a precipitous decline to 1.9 million tons by the end of 2005 (Mwiti, 2013). The period saw a significant reduction in net returns and financial stability, which threatened system’s very survival (IEA-Kenya, 2014). The resulting inefficiency pushed away cargo transporters and passengers to use road transport services, albeit at a higher cost. In response to declining performance, the Government of Kenya and Government of Uganda entered into a concessional agreement with Rift Valley Railways (RVR) under a build-operate-transfer (BOT) financing framework in 2006. The purpose of the concession was to inject new capital and technical skills, as well as improve management of the railways systems; thereby, enhance efficiency in the delivery of services (Ministry of Transport, Kenya, 2014). Consequently, RVR committed to provide freight services for twenty-five (25) years and passenger services for five (5) years.
Under the agreement, RVR bore the obligation of rehabilitating and maintaining rail networks, as well as improve the management, operation and financial performance (IEA-Kenya, 2014). The concession agreement obligated RVR to pay the two governments for use of conceded assets a one-off entry fee of US $3 million to the Government of Kenya and US $2 million to the Government of Uganda. In addition, RVR committed to pay an annual concession fee of 11.1% of gross freight revenues to the two governments.
GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 329 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Regarding passenger business in Kenya, the concessionaire agreed to pay GoK a flat annual fee of US $1 million. A third requirement was to invest up at least US $40 million in the infrastructure development and rolling stock over the first five years. However, nearly ten years after concession’s onset, RVR was unable to meet performance and investment targets as well as concessional obligations. Available data show that both freight and passenger volumes dropped by 30.7% between 2007/08 and 2011/12 financial years (Kenya National Bureau of Statistics, 2014). A recent performance update report confirms that RVR handles an average of 1.5 million tons of goods annually, down from 2.4 million tons in 2007/08 financial year. Besides, passenger traffic fell by 30% from about 600,000 in 2007/08 to about 400,000 in 2011/2012, leading to a drop in revenue, backlogs of unpaid concession fees and under-investment in infrastructure (KRC, 2012; Mwiti, 2013).
Anecdotal reports show that stakeholders linked RVR’s underperformance to lack of financial capacity and technical expertise on the part of the lead investor (Mwiti, 2013), which may not be the only factors at play.
Notably though, no academic process had ever examined and provided a comprehensive picture of factors influencing the project’s financing and underperformance. The study examined various factors influencing the project’s financing; however, this article focuses on macro-economic factors. Public-Private Partnership (PPP) initiatives describe a range of possible relationships between public and private sector operators, to develop infrastructural facilities and deliver essential services, such as energy, communication, transport, as well as water and sanitation, among others (Asian Development Bank, 2010). In many developing countries, where governments face the twin challenges of limited financial resources and lack of technical capacity, PPP initiatives provide opportunities to improve the supply of such essential services (United Nations, 2011). A review of literature reveals that PPP options range along a spectrum - at one end are those in which the government retains full responsibility for operations, maintenance, capital investment, financing, and commercial risk; while at the other, are those in which the private sector takes on much of this responsibility (World Bank, 1997). PPP options fall under five broad categories, viz. service contracts, management contracts, leases, concessions and divestitures.
In concessions, governments define and grant specific rights to a private operator (concessionaire) to build and operates a facility for a fixed period (United Nations, 2011). Concessions can assume two models, viz.
Build-Operates-Transfer (BOT) of Build-Operates-Own (BOO) (Walker, 1993). Although the public authority owns facilities, the private operator has wide-ranging powers over the operation and finances of the system. Concessions thrive by contracts, which set out performance targets, including service coverage, quality, standards, arrangements for capital investment, mechanisms for adjusting tariffs, as well as arbitration over disputes (World Bank, 1997). Furthermore, the concessionaire assumes full responsibility for all capital investments required to build, upgrade, or expand facilities, and for financing those investments out of own resources.
The public authority is responsible for establishing performance standards and ensuring that the concessionaire meets them. At the end of the contract period, the public authority assumes ownership of project facilities and can opt to assume operating responsibility too, renew the operator’s contract, or award a new contract to a new concessionaire (Asian Development Bank, 2010). The concessionaire collects tariffs directly from service users. Payments can take place both ways: concessionaire paying the authority for concession rights or the authority paying the concessionaire, based on target achievements (Asian Development Bank, 2010). Typical concession periods range between 25 to 30 years, which provide sufficient time for the concessionaire to recover the capital invested and earn an appropriate return over the life of the concession.
DATA AND METHODOLOGYThe study adopted a causal-comparative design, which permitted the application of quantitative approaches in data collection, processing and analysis. The study targeted senior operational, managerial, technical, GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 330 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 monitoring and evaluation, as well as advisory staff, affiliated to all key stakeholders, including KRC, RVR, Ministry of Finance (MOF) and Ministry of Transport (MOT). The sampling process identified 402 eligible participants. We collected primary data in May 2015 after obtaining necessary approval from University of Nairobi, National Council of Science and Technology, as well as KRC. Of the 402 targeted participants, 348 (86.6%) successfully completed and returned the questionnaires. The analysis involved listing coding, digitalizing and cleaning data for logical inconsistencies and misplaced codes. The methods used included descriptive, Chi square tests, one-way analysis of variance (ANOVA) as well as Relative Importance Index (RII) analyses. Furthermore, we applied Kendall’s Coefficient of Concordance to determine the degree of agreement among the four categories of participants with respect to their ranking. We performed all quantitative analyses using the Statistical Package for Social Sciences (SPSS) and Microsoft Excel. In addition, qualitative analysis involved organizing data under thematic areas, followed by description and thematic analysis to identify emerging themes and patterns (Kometa, Oloimolaiye & Harris, 1994;
Frimpong, Olowoye & Crawford, 2003).
RESULTS AND DISCUSSIONS
The study sourced primary data from 348 participants, of whom 134 (38.5%) were staff of KRC; 179 (51.4%) were staff of RVR; 12 (3.4%) were officers of MOF and 23 (6.6%) served at MOT. By cadre, 109 (31.3%) were operational staff, while 39 (11.2%) were managerial staff. Besides, technical staff were 174 (50.0%), monitoring and evaluation staff were 12 (3.4%) while 14 (4.0%) participants served as policy advisory staff at the ministries. The analysis revealed up to 99% chance that the institutions varied significantly in terms of the cadre of staff who participated in the study (χ2 = 251.091, df = 12 and ρ-value = 0.000). The participants included 230 (66.1%) men and 118 (33.9%) women. However, the analysis revealed that the institutions did not vary significantly in terms participants’ distribution based on gender (χ2 = 1.420, df = 3 and ρ-value = 0.701).
The results further show that participants were aged between 22 and 54 years. The mean age for the entire group was 38.7 (≈39) years. Besides, participants from RVR reported the lowest mean age (38.1 years), while those from MOF reported the highest mean age (43.5 years). However, one-way analysis of variance (ANOVA) revealed that there was no significant variation among staff of various stakeholders regarding age (F(3, 344) = 1.627 & ρ = 0.183). The participants reported a mean of 16.41 (≈16 years), with the lowest being 1 year and the highest 35 years. Whereas staff of RVR reported the lowest duration of professional experience (15.8 years), the results suggest that the staff of the MOF were the most experience (22.2 years).
Based on this, the ANOVA results show lack of a significant variation among staff of various stakeholders (F(3, 344) = 2.255 & ρ-value = 0.102).
The results show that there was no significant variation between participants involved in this study in terms of gender, age and years of professional experience. Based on this, further analyses, including ranking macro-economic factors influencing the project’s financing as well as determination of the coefficient of concordance, assumed that participants were homogenous in terms of most background attributes. This assumption was important for offsetting the risk of invalidity.
Macro-Economic Factors Influencing Financing of the Concession
The results in Table 1 show that of the 348 participants, 146 (42.0%) believed that the influence of inflation rates on the project’s financing was ‘very strong’, while 72 (20.6%) felt that the influence of inflation rates was ‘strong’. Contrastingly, 56 (16.1%) participants described the influence inflation rates as ‘very weak’, while 26 (7.5%) indicated that the indicator’s influence was ‘weak’. However, the analysis revealed lack of a significant variation in perceptions regarding inflation rate’s influence on financing of the concession project (χ2 = 8.024, df = 12 & ρ-value = 0.115). The results in Table 1 further show that 113 (32.5%) participants believed that the influence of interest rates on financing of the concession project was ‘very GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 331 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 strong’, while 96 (27.6%) felt that the indicator had a ‘strong’ influence on the project’s financing.
However, 53 (15.2%) participants perceived that the influence of interest rates was ‘very weak’, while 37 (10.6%) believed that the indicator’s influence was ‘weak’. Based on this, the analysis revealed lack of a significant variation in perceptions regarding the influence of interest rates on the project’s financing (χ2 = 3.120, df = 12 & ρ-value = 0.360).