MACRO-ECONOMIC FACTORS INFLUENCING THE FINANCING OF BUILD-

OPERATE-TRANSFER PROJECTS IN DEVELOPING COUNTRIES: A CASEOF RAILWAYS PROJECT IN KENYA

Charles M. RamboStephen O. Lucas

UNIVERSITY OF NAIROBI

OVERVIEW

Lake Victoria

Mombasa

Kisumu

http://www.greywall.demon.co.uk/rail/Kenya/nrm.html

The railway line started at the port of Mombasa in 1896 and reached Kisumu on the eastern shore of Lake Victoria in 1901.

Kenya’s economy has relied on railway transport for more than a century.

The railway provides freight and passenger services.

The railway network is about 2,778 kilometers long.

The Kenya-Uganda railway line was constructed by British Colonial Government.

OVERVIEW

• At its peak in 1983, the railway system moved some 4.3 million tons of freight.

• This declined to 1.9 million tons by end of 2005.

Declining volume of business significantly reduced net returns and threatened system’s very survival.

As the system became more inefficient, cargo transporters and passengers turned to road transport services.

• Kenya Railways Corporation (KRC) was established in 1978, to manage and coordinate an integrated system of rail and inland waterways transport services.

OVERVIEW

GOVERNMENT OF KENYA

GOVERNMENT OF UGANDA

The two Governments entered into a concessional agreement with Rift Valley Railways (RVR) under a build-operate-transfer (BOT) financing framework in November 2006.

Purpose =>inject new capital and technical skills; as well as improve management and efficiency in service delivery.

In response to declining performance, the Government of Kenya (GoK) and Government of Uganda (GoU) jointly concessioned railway transport services in 2003.

The decision arose from the recognition of historical links and mutual dependency of the two railway systems.

OVERVIEW

http://www.eastafricashuttles.com/train.htm

The concession agreement obligated RVR to improve the management, operation and financial performance.

Rehabilitate and maintain rail networks to enhance safety of trains.

The concession period was 25 years for freight services and 5 years for passenger services.

RVR further agreed to upgrade and modernize the locomotive fleet; rehabilitate the rolling stock, renovate facilities as well as install new information technology systems.

On their part, the GoK and GoU remained owners of the railway infrastructure and facilities

OVERVIEW

• RVR further committed to pay an annual concession fee of 11.1% of gross freight revenues to the two governments.

• Regarding passenger services in Kenya, RVR agreed to pay the GoK a fixed annual fee of US $1 million.

• The concession agreement obligated RVR to pay a one-off entry fee of US $3 million to the GoK and US $2 million to the GoU for the use of conceded assets.

• A third requirement was to invest at least US $40 million in the development of infrastructure during the first five years.

OVERVIEW• Anecdotal media literature and government reports reveal that ten years

into the concession period, RVR failed to meet performance targets. Fig 1: Passenger and Freight Volume Moved by RVR (2007-2011)

Source: Kenya National Bureau of Statistics, 2014

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1,000

1,500

2,000

2,500

2007/08 2008/09 2009/10 2010/11 2011/12

Pass

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r an

d fr

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t vol

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Passengers ('000) Freight Tons ('000)

Available data on annual freight and passenger volumes also suggest that RVR was way below performance targets.

Besides, RVR failed to fulfill concessional obligations, due to underperformance.

RVR’s underperformance caused a drop in revenue, leading to backlogs of unpaid concession fees and under-investment in the development of infrastructure.

OVERVIEW

• Underperformance was linked to lack of financial capacity and technical expertise.

• Despite this, no academic process had ever examined factors influencing the project’s financing and subsequent underperformance.

• This study examined the influence of various factors, including macro-economic, concessional, financial, legal and environmental.

• However, this article focuses on the influence of macro-economic , particularly in the Kenyan context.

METHODOLOGY• Design => causal-comparative, with both quantitative and

qualitative approaches in data sourcing, processing and analysis.

• Targeted stakeholders => KRC, RVR, Ministry of Finance (MOF) and Ministry of Transport (MOT).

• Target participants = > senior operational, managerial, technical, monitoring and evaluation, as well as advisory staff.

• Sampling process identified 402 eligible participants, who were all included in the sample.

• Self-administered semi-structured questionnaires were used.

• Primary data were sourced in May 2015. Of the 402 targeted participants, 348 (86.6%)completed and returned questionnaires.

METHODOLOGY• The analysis involved listing coding, digitalizing and

cleaning data for logical inconsistencies and misplaced codes.

• Methods => descriptive, Chi square tests, one-way analysis of variance (ANOVA); Relative Importance Index (RII); as well as Kendall’s Coefficient of Concordance.

• All quantitative analyses were performed using the Statistical Package for Social Sciences (SPSS) and Microsoft Excel.

• Qualitative analysis involved organizing data under thematic areas, followed by description and thematic analysis to identify emerging themes and patterns.

RESULTSI. Participants background profile

• Of the 348 participants, 134 (38.5%) were staff of KRC; 179 (51.4%) were staff of RVR; 12 (3.4%) were officers of MOF, while 23 (6.6%) served at MOT.

• 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).

• However, there was no significant variation in terms of participants’ distribution based on: -

▫ Gender (χ2 = 1.420, df = 3 and ρ-value = 0.701)

▫ Age (F = 1.627 & ρ = 0.183)

▫ Years of professional experience (F= 2.255 & ρ-value = 0.102)

RESULTS II. Macro-Economic Factors Influencing the Project’s II. Macro-Economic Factors Influencing the Project’s Financing Financing

MACRO-ECONOMIC FACTORS

KRC RVR MOF MOT TOTAL Freq Pct Freq Pct Freq Pct Freq Pct Freq Pct

Inflation Very strong 70 52.2 64 35.8 5 41.6 7 30.4 146 42.0 Strong 17 12.7 47 26.3 2 16.7 6 26.1 72 20.6 Average 16 11.9 27 15.1 3 25.0 2 8.7 48 13.8 Weak 9 6.8 15 8.3 0 0.0 2 8.7 26 7.5 Very weak 22 16.4 26 14.5 2 16.7 6 26.1 56 16.1 Total 134 100.0 179 100.0 12 100.0 23 100.0 348 100.0 Interest rates Very strong 45 33.5 57 31.8 4 33.3 7 30.4 113 32.5 Strong 40 29.9 49 27.4 2 16.7 5 21.8 96 27.6 Average 13 9.7 26 14.5 4 33.3 6 26.1 49 14.1 Weak 19 14.2 16 8.9 0 0.0 2 8.7 37 10.6 Very weak 17 12.7 31 17.4 2 16.7 3 13.0 53 15.2 Total 134 100.0 179 100.0 12 100.0 23 100.0 348 100.0

RESULTS• Inflation => no significant variation in perceptions regarding the

influence of inflation rate’s influence on financing of the concession project (χ2 = 8.024, df = 12 & ρ-value = 0.115).

• Interest rates => no significant variation in perceptions regarding the influence of interest rates on the project’s financing (χ2 = 3.120, df = 12 & ρ-value = 0.360).

http://www.capitalfm.co.ke/business/2013/11/rvr-invests-sh357mn-for-new-locomotives/

RESULTS

MACRO-ECONOMIC FACTORS

KRC RVR MOF MOT TOTAL Freq Pct Freq Pct Freq Pct Freq Pct Freq Pct

Debt ratio Very strong 39 29.1 32 17.9 3 25.0 8 34.8 82 23.6 Strong 53 39.6 73 40.7 6 50.0 7 30.4 139 39.9 Average 19 14.1 42 23.5 1 8.3 3 13.0 65 18.7 Weak 15 11.2 14 7.8 0 0.0 4 17.4 33 9.5 Very weak 8 6.0 18 10.1 2 16.7 1 4.4 29 8.3 Total 134 100.0 179 100.0 12 100.0 23 100.0 348 100.0 Taxation burden Very strong 64 47.8 88 49.2 7 58.3 7 30.4 166 47.8 Strong 37 27.6 60 33.5 4 33.4 10 43.5 111 31.9 Average 20 14.9 17 9.5 1 8.3 6 26.1 44 12.6 Weak 6 4.5 9 5.0 0 0.0 0 0.0 15 4.3 Very weak 7 5.2 5 2.8 0 0.0 0 0.0 12 3.4 Total 134 100.0 179 100.0 12 100.0 23 100.0 348 100.0

RESULTS• Debt ratio =>No significant variation in perceptions regarding

the influence of debt ratio on the project’s financing (χ2 = 7.592, df = 12 & ρ-value = 0.129).

• Taxation burden => No significant variation in perceptions regarding the influence of taxation burden on the project’s financing (χ2 = 13.499, df = 12 & ρ-value = 0.334).

http://www.railpictures.net/viewphoto.php?id=368110&nseq=8

RESULTSIII. Relative Importance of Macro-Economic Factors

Influencing the Project’s Financing

• Inflation rates scored a relative importance index of 0.774 (≈0.8)

• Inflation is usually a challenge when its rate rises above 2%.

• In Kenya, inflation rates averaged at 7.8% during the concession’s first decade (2007 to 2014)

• The highest inflation rate (14.3%) occurred in 2012, while the lowest (4.8%) was experienced in 2007.

MACRO-ECONOMIC FACTORS

RELATIVE IMPORTANCE RELATIVE STRENGTH β Relative Weights

Inflation rates 0.205 0.774 Strong Interest rates 0.200 0.701 Strong Debt ratio 0.136 0.582 Average Taxation burden 0.117 0.556 Average

RESULTS• High inflation rates increased the cost of essential supplies,

including fuel, electricity and water, among others.

• High inflation rates also affected the cost of hiring and maintaining technical labour.

• The concessionaire experienced increasing demands for higher wages from its workers to keep-up with escalating consumer prices.

• An increase in the unit cost of labour inflated RVR’s wage bill, which ate into revenues and undermined RVR’s ability to meet its concessional obligations.

• Low revenues and failure to meet concessional obligations transmitted negative signals to potential financiers, which delayed commitment for new or further financing.

RESULTS

• Interest rates scored a relative importance index of 0.7. Commercial banks’ lending interest rates averaged at 15.6% between 2007 and 2014.

• Existing Central Bank of Kenya data show that lending interest rates increased from a low of 13.3% in 2007 to a high of 19.6% in 2012.

• Escalating interest rates ate into revenues, which prevented RVR partners from ploughing back sufficient resources, as per the financing share.

• Rising interest rates made local credit too expensive for partners; thus, discouraging or delaying further borrowing to meet investment targets.

MACRO-ECONOMIC FACTORS

RELATIVE IMPORTANCE RELATIVE STRENGTH β Relative Weights

Inflation rates 0.205 0.774 Strong Interest rates 0.200 0.701 Strong Debt ratio 0.136 0.582 Average Taxation burden 0.117 0.556 Average

RESULTS

• Debt ratio scored a relative importance index of 0.582 ≈ 0.6.

• CBK data show that Kenya’s debt to GDP ratio average at 46.2% between 2007 and 2014; increasing from a low of 42.8% in 2008 to a high of 49.5% in 2014.

• High debt ratio indirectly influenced the project’s financing by triggering domestic borrowing by the Government, which increased the risk of high inflation and interest rates.

• Rising debt ratio interrupted the financial market, making lending terms too expensive for RVR partners, causing a delay in the project’s financing.

MACRO-ECONOMIC FACTORS

RELATIVE IMPORTANCE RELATIVE STRENGTH β Relative Weights

Inflation rates 0.205 0.774 Strong Interest rates 0.200 0.701 Strong Debt ratio 0.136 0.582 Average Taxation burden 0.117 0.556 Average

RESULTS

• Taxation burden also scored a relative importance index of 0.556 ≈ 0.6.

• Taxation burden affected the project’s financing by increasing overhead costs, reducing revenues and preventing attainment of investment targets.

• Participants singled out fuel levy, which the government use to maintain roads and not to improve railway tracks.

• Fuel levy disadvantages RVR, while favoring road transporters.

• Import duty and excise tax on imported hardware also affected the project’s financing by increasing the burden, while reducing revenues.

MACRO-ECONOMIC FACTORS

RELATIVE IMPORTANCE RELATIVE STRENGTH β Relative Weights

Inflation rates 0.205 0.774 Strong Interest rates 0.200 0.701 Strong Debt ratio 0.136 0.582 Average Taxation burden 0.117 0.556 Average

RESULTS IV. Concordance of Perceptions regarding Influence of Macro-

Economic Factors on the Project’s Financing

• Inflation rates ranked first (2.68); followed by interest rates (2.64), debt ratio (2.53) and taxation burden (2.16).

• The analysis obtained a strong level of concordance in the ranking of macro-economic factors influencing the project’s financing.

• The level of concordance was statistically significant (W = 0.833, χ2 = 41.822, df = 3 & ρ-value = 0.000).

• The results imply up to 99% chance that participants were concordant and that the identified factors had a strong influence on the project’s financing.

FACTORS MEAN RANK TEST STATISTICS Inflation rates 2.68 N 348 Interest rates 2.64 Kendall's W 0.833 Debt ratio 2.53 Chi-Square 41.822 Taxation burden 2.16 df 3

Asymp. Sig. 0.000

SUMMARY & CONCLUSIONS• Purpose of the study: -

▫ Determine the relative importance of selected macro-economic factors with regards to financing of the concession project.

▫ Determine the level of concordance in participants’ perceptions regarding the influence of macro-economic factors on the project’s financing.

• The study reveals that inflation rates (0.8) and interest rates (0.7) were strong predictors of the project’s financing, while debt ratio (0.6) and taxation burden (0.6) were average predictors.

• The study obtained a strong level of concordance in the ranking of macro-economic factors vis-à-vis their influence on the project’s financing, which was also statistically significant at a high level of precision.

SUMMARY & CONCLUSIONS• Based on the findings, stakeholders should recognize the significant

influence of macro-economic factors on the project’s financing.

• The Government need to improve effort in regulating the macro-economic environment to create a supportive and level play ground.

• It is unfair for Government officers to continue judging RVR’s performance by dwelling on internal weaknesses, while overlooking the crucial role of the macro-economic environment.

• Formulating appropriate and/or adjusting existing monetary, fiscal, and taxation policies, is likely to create the support required by RVR, as well as other public-private enterprises.

• In addition, the Government should consider appropriate measures to cushion RVR against global market shocks by subsidizing essential supplies such as electricity, fuel, and water, to provide room for the concessionaire to meet revenue targets.

• Doing so will enable the concessionaire to come up with more competitive tariffs, which will make services more appealing to consumers.

END OF PRESENTATION

THANK YOU

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