264 SAJEMS NS 11 (2008) No 3 The SASOL/eNGeN (uhAmbO) merGer-fOrecLOSure ANd whiTe fueL demANd GrOwTh rATeS Nicola Theron Department of Economics, Stellenbosch University and Econex, Cape Town Abstract During October 2005, the Competition Tribunal heard evidence on the proposed merger between two large oil companies, Sasol Oil (Pty) Ltd and Engen Ltd. During the hearing it emerged that major aspects that would determine the outcome of the matter were: •  Potential foreclosure; •  Demand growth rates of white fuel; and •  Logistics. The aim of this paper is to examine how the Tribunal dealt with the issue of potential foreclosure, by examining the expected growth rates of white fuels. The Tribunal had to consider the extent to which foreclosure in the oil industry would be profitable. This depended partly on expected growth rates in the demand for petrol and diesel. It will be argued that although there was conflicting evidence on this point, a proper analysis of economic variables such as expected economic growth rates, petrol demand elasticities and income elasticities, provided sound reasons for the Tribunal to prohibit the merger. JEL G34, L41 1 Introduction During the latter half of 2005, the SA Tribunal  considered  the  proposed  merger  in  the  oil  industry between Sasol and Engen (the “Uhambo  merger”).  The  merger  hearing  lasted  several  weeks, as there were several interveners (mostly  the other major oil companies) who asked the  Tribunal to prohibit the proposed merger. The  final  decision  of  the  Tribunal,  prohibiting  the  merger  was  released  on  the  23rd  of  February  2006.  During  the  hearing  it  emerged  that  the  outcome  of  this  case  would  depend  primarily  on three issues:  •  Foreclosure; •  Demand growth rates of white fuel; and •  Logistics. During the merger hearing, the Tribunal heard  from  seventeen  witnesses,  of  whom  six  were  economists or economic experts (including the  economist  of  the  Competition  Commission).  Furthermore,  the  Tribunal  considered  a  total  of fourteen witness statements, of which three  were written by economic experts. The role of economists in these proceedings  is  not  always  well  understood  and  is  often  criticised.  Economists’  contribution  may  be  in the form of very complicated econometric  m o d e l s   w h i c h   t h e n   b e c o m e s   a   p o i n t   o f  contention  as  it  is  true  that  any  economic  model  is  only  as  good  as  its  assumptions.  The  Uhambo  case  has  been  no  different  and  the  divergence  of  economic  models  and  econometric  evidence  caused  the  Tribunal  (2006:  237)  to  remark  in  their  final  decision  that: “‘Garbage in; garbage out’ is the caution  customarily  urged  upon  those  who  rely  on  econometric and other statistical techniques  and  while  not  all  of  the  data  used  in  the  parties’ model is to be so characterised, some  of  it  does  appear  sufficiently  contrived  to  warrant that description”. SAJEMS NS 11 (2008) No 3 265 It  became  clear  during  the  hearing  that  the  outcome of the case depended on the profitability  of foreclosure. There was some consensus on the  basics of the oil industry structure and market  definition.  The  Tribunal  took  the  view  that  since their analysis showed that the merger will  lead  to  a  substantial  lessening  of  competition  in  the  markets  for  petrol  and  diesel,  it  was  not necessary to further examine all the other  petroleum product markets (e.g. bitumen, heavy  fuel oils, LPG, jet fuel, etc.). There was therefore  not the usual dispute about the relevant product  market  that  often  emphasises  the  role  of  the  economist in demarcating the relevant market  for competition purposes.  Similarly,  the  definition  of  the  relevant  geographic  markets  was  relatively  straight- forward. The history of the Main Supply Agree- ment  (MSA)  implied  a  separate  geographic  market  known  as  the  “inland  market  for  refined  fuels”.  The  Tribunal  found  that  the  geographic  upstream  market  was  inland  (the  so-called  market  for  “bulk  supply”),  and  that  the  geographic  downstream  (retail)  market  was national.  Given the fact that there was no real dispute  on market definition, it was common cause that  Uhambo  would  have  controlled  more  than  80  per cent of the output of refined fuel products  in  the  inland  geographical  market  and  that  its  retail  network  would  have  accounted  for  approximately 40 per cent of inland fuel sales. The aim of this paper is to show that on a very  basic (and crucial) point of the Uhambo analysis,  namely the demand growth rates of white fuels,  economic  analysis  could  have  provided  very  clear answers. In what follows, the evidence put  forward by the economists for both the merging  parties  and  the  interveners  will  be  examined  to  see  which  followed  established  economic  principles and theory.  2 Foreclosure and the relevance of white fuels demand growth rates The  Uhambo  merger  presented  horizontal  as  well as vertical concerns. Given the high market  shares  in  both  the  upstream  and  downstream  markets that Uhambo would have obtained, the  impact on both the upstream and downstream  markets would ordinarily have to be examined.  However, given the Tribunal’s definition of the  relevant upstream market as the inland market,  there  was  no  horizontal  overlap  between  the  Sasol  and  Engen  refineries  (Engen  owns  the  Enref  refinery  which  is  situated  at  the  coast).  At the retail level, the horizontal effect would  have  been  a  total  national  market  share  of  34  per cent of petrol sales and 36 per cent of diesel  sales, for Uhambo. Although this was considered  to be an important issue, the focus throughout  the hearing was on the vertical issues raised by  the proposed merger. The primary economic concern with vertical  mergers  is  the  ability  of  the  merged  firm  to  “foreclose” its rivals and to “raise rivals’ costs”.  In  the  case  of  Uhambo,  the  majority  of  the  economists  focused  on  the  issue  of  potential  foreclosure.  The  vertical  issue  was  clear  –  the  merger would combine the substantial upstream  refining  capacity  of  Sasol  with  the  significant  wholesale  and  retail  capacity  of  Engen.  This  immediately  raised  the  prospect  of  input  foreclosure, and the Tribunal also chose this as  their main focus.  However, in order to prove that foreclosure  would  be  profitable,  the  economists  built  some  sophisticated  models  to  present  various  foreclosure  scenarios.  The  economist  for  the  merging parties demonstrated in his model that  foreclosure  would  not  be  profitable.  This  was  disputed  by  the  economists  for  the  other  oil  companies who presented models that showed  that foreclosure would be profitable.  This  highlights  the  usual  debate  around  economic analysis; there will always be economic  experts  who  will  argue  for  and  against  certain  mergers or prohibited practices. But the outcome  of the models depends on the assumptions. The  foreclosure  models  all  had  to  make  certain  assumptions  regarding  the  aspects  of  demand  growth  for  retail  petrol  and  diesel  and  the  current state of the logistics infrastructure.  When  analysing  potential  foreclosure  as  a  result  of  a  vertical  merger,  economists  have  to  consider  the  ability  of  the  merged  entity  to  foreclose, the costs of doing so and the factors  that will affect the returns of such a strategy. The  266 SAJEMS NS 11 (2008) No 3 foreclosure  analysis  in  the  Uhambo  case  was  specifically sensitive to different demand growth  scenarios. It is shown in Figure 1 on page 269 that  in 2003 there was already a shortfall in the inland  market between demand and supply of petrol,  diesel and jetfuel. Given the logistics constraints  in terms of pipeline capacity in transporting fuel  from the coast to the inland area, the other oil  companies would have remained dependent on  Uhambo for sufficient supplies to service their  retail networks in the inland area (up to at least  2010 which was the expected completion date of  a new pipeline). This would have given Uhambo  the ability to foreclose. Uhambo would also have  had an incentive to foreclose as it would have  been  more  profitable  to  supply  its  own  retail  network, in the face of high demand growth for  inland fuel. With low white fuels demand growth  rates (such as those estimated by the parties to  the merger), the possibility of foreclosure would  seem rather remote. Conversely, high demand  growth  rates  especially  in  the  inland  market  would  permit  Uhambo  to  rapidly  gain  market  share and thus significantly strengthen the ability  of  Uhambo  to  self-deal  and  to  foreclose  its  rivals to its upstream production. High demand  growth for white fuels would therefore increase  the  returns  from  a  foreclosure  strategy  and  would make such a strategy more feasible.  The  logistics  constraints  faced  by  the  other  oil companies (the pipeline constraints as well  as  road  and  rail  constraints),  also  contributed  to  the  likelihood  of  foreclosure.  The  Tribunal  noted  that  whereas  under  the  previous  Main  Supply  Agreement  (MSA),  the  oil  companies  had  to  purchase  all  their  inland  requirements  from Sasol, the fact that they were free to ship  product from the coast post-MSA did not mean  that  it  was  truly  a  national  market.  “However  what was previously constrained by agreement,  is  now  constrained  by  logistical  capacity  –  the  inland  marketers  may  import  product  from  the coast, but because of inadequate logistical  capacity they are only able to supply a portion  of  their  needs  (2006:  66).”  Although  logistic  capacity was the other vital leg of the foreclosure  models,  ultimately  the  state  of  logistics  were  to  be  determined  from  the  evidence  of  the  factual witnesses of the various oil companies.  Economists  could  not  testify  as  to  the  true  state of the logistics capacity and had to accept  the  evidence  provided  to  them  by  the  oil  companies. This  paper  will  focus  therefore  on  the  issue  of  petrol  and  diesel  demand  growth  and  the  way  it  was  dealt  with  in  the  Uhambo  merger,  which  finally  determined  the  outcome  of  the  foreclosure  analysis.  Foreclosure,  although  ultimately  the  decisive  factor  in  the  Uhambo  decision, will not be further dealt with here. It  should be briefly stated though, that the Tribunal  finally  decided  that  the  “credible  threat”  of  foreclosure was enough to prohibit the merger.  In their own words: “Neither the Tribunal, nor  the Commission, nor the merging parties, nor the  intervenors, can decide with absolute certainty  – beyond all reasonable doubt – whether or not  foreclosure will be profitable. We can however  say with  confidence  that it is a credible threat  (2006: 87).” This finding of a credible threat of  foreclosure  ultimately  caused  the  Tribunal  to  prohibit the proposed merger.  3 Demand growth rates for petrol and diesel- economic theory The  demand  growth  for  petroleum  products  is  the  focus  of  the  current  paper.  There  were  widely  divergent  views  on  the  demand  growth  rates  forecasted  for  petrol  and  diesel.  Yet,  the  forecasting  of  demand  growth  rates  is  not  a  Herculean  task  for  economists  who  should  consider  this  as  part  of  their  basic  toolkit.  Economists know that the demand for any retail  product  is  generally  a  function  of  the  price  of  the  product,  income  of  consumers,  the  size  of  the  population  and  the  price  of  substitutes  or  complementary  products.  In  some  instances,  product  specific  factors  and  consumer  tastes  are also included in the demand function, but  researchers often find it difficult to incorporate  qualitative  variables  (e.g.  tastes)  into  demand  equations. When specifying demand equations,  such  as  those  for  petrol  and  diesel  demand,  various  macroeconomic  variables  can  be  considered and tested for significance with the  dependent  variable  (demand).  The  important  point to make is that the choice of variables does  not depend primarily on the “goodness of fit”  SAJEMS NS 11 (2008) No 3 267 or the correlation values, but has to be based on  economic theory. As  the  demand  growth  rates  for  petrol  and  diesel were one of the main issues considered  by  the  Competition  Tribunal,  it  is  important  to ask what value can be added by economists  forecasting such growth rates. Before considering  the variety of growth rates that were presented  to  the  Tribunal  by  the  various  parties,  it  is  important  to  say  one  or  two  things  about  the  estimation of petrol and diesel demand.  The  economist  who  has  to  estimate  such  growth  rates  will  as  a  first  step,  based  on  economic theory, ask what might determine the  demand  for  a  specific  product.  This  question  must be answered with reference to the general  determinants of demand, as set out above. The  next step would be to look at the literature to  ascertain what other researchers have found to  be relevant determinants.  The  demand  for  petrol  is  a  well-researched  topic in economic theory. The literature shows  that  both  price  and  income  have  an  effect  on  the amount of petrol purchased by consumers.  But  the  consensus  in  the  literature  is  that  the  income  elasticity  is  generally  higher  than  the  price elasticity. In other words, petrol and diesel  sales  (in  volume  terms)  are  more  sensitive  to  changes in consumer income (or GDP) than to  changes in the real price of petrol or diesel. Drollas  (1984)  did  one  of  the  early  reviews  of  price  and  income  elasticity  in  1984.  He  surveyed  mostly  US  studies  and  found  that  the consensus view was that the long run price  elasticity of demand was around -0.8 while the  long  run  income  elasticity  was  slightly  below  unity.  Some  studies  (Blum  et al.,  1988)  found  larger ranges for some European countries, with  an  income  elasticity  varying  between  0.86  and  1.90. Sterner (1990) used pooled data for OECD  countries and found long run income elasticities  of between 0.6 and 1.6. With another technique  (time series data), he found the income elasticity  to vary between 1.1 and 1.3.  Later  work  by  Goodwin  (1992)  generally  found that elasticity estimates had to be revised  upwards from estimates calculated between the  1980s  and  1990s.  The  extent  to  which  strong  economic performance will affect fuel demand  is  quantified  by  the  income  elasticity  of  the  demand  for  fuel.  More  simply,  the  income  elasticity  measures  the  response  of  motorists  to an increase in income. This is assumed to be  a  positive  relationship.  Graham  and  Glaister  (2002: 1) in a comprehensive survey found that  the  long-run  income  elasticity  of  fuel  demand  falls in the range of 1.1 to 1.3, and between 0.35  and 0.55 in the short-run.  Later work by Dahl and Roman (2004) looked  at a total of 190 new studies since 1991 that dealt  with  energy  demand.  Their  results  generally  correspond with the earlier findings of Graham  and Glaister  (2002),  who  found that  the  short  run  price  elasticity  of  the  demand  for  petrol  typically falls between -0.2 and -0.3, and the long  run price elasticity between -0.6 and -0.8. Dahl  and  Roman  found  that  the  mean  value  of  the  long run income elasticity for diesel was above 1  (1.13), and for petrol, the value was 0.69. These  values  are  once  again  in  line  with  the  earlier  research summarised above. D a t a   o n   c o m p a r a b l e   m i d d l e - i n c o m e  countries are not as widely available as data for  industrialised countries. Ramanathan examined  fuel demand for India in 1999. The author found  a short run income elasticity of 1.18 and a long  run  income  elasticity  of  2.68.  These  estimates  are  very  high,  compared  to  the  international  benchmarks  from  industrialised  countries.  Ramanathan believes that the low level of fuel  consumption in India and the gradual increase  in economic growth can explain the differences  between his results and those obtained elsewhere  (Graham & Glaister, 2002: 17).  The  following  table  summarises  the  results  of  similar  studies  done  for  the  South  African  economy. 268 SAJEMS NS 11 (2008) No 3 Table 1 Data on South African white fuels price elasticities Source Country Short-term price elasticity Long-term price elasticity S.A. Cloete & vd M. Smit (1988) South Africa –0.25 –0.37 S.D. Ngumedi (1994) South Africa –0.1 to –0.2 Bureau for Economic Policy Analysis (1989) South Africa –0.31 Bureau for Economic Research (BER) (2003) South Africa Petrol –0.21 Diesel –0.18 Petrol –0.51 Diesel –0.06 Source: BER, 2003 It follows from the above that based on economic  theory and international models used to estimate  petrol demand, the two variables that have to be  included in any petrol or diesel demand function  are  the  income  and  price  variables.  Based  on  the  difference  between  the  long-run  price  and income elasticities, it is also clear that the  income variable has a larger effect than the price  variable.  The  income  variable  is  therefore  the  most  important  explanatory  variable  in  petrol  and diesel demand functions. Before looking at  the specific choice of variables, it is worth stating  the economic assumptions very simply. If there  is an increase in income, people earn more and  they  have  more  disposable  income,  which  will  increase  their  demand  for  petrol,  as  they  buy  more  or  bigger  cars,  or  go  on  more  holidays  or  generally  use  their  cars  more.  The  data  on  long-term income elasticity suggest that this is a  positive relationship with values that vary quite  widely. And, it is quite likely that the percentage  increase in fuel demand will be higher than the  percentage  increase  in  income,  especially  for  developing countries. The relationship between the price of petrol  (or diesel) and the demand for petrol (or diesel)  is a negative relationship (indicated by the minus  sign  on  the  price  elasticities).  The  economic  reason  is  simple:  as  the  price  of  a  product  increases, the demand decreases.  Having  found  then,  based  on  available  economic  literature  that  there  are  strong  positive  relationships  between  income  and  petrol demand and income and diesel demand,  and  a  smaller  negative  relationship  between  the  price  and  demand,  the  economist  can  use  a  time  series  for  these  independent  variables  to estimate the petrol or diesel demand growth  rates.  This  is  fairly  straightforward,  as  long  as  good  estimates  of  the  forecasted  variables  can  be  obtained.  The  petrol  and  diesel  prices  are  notoriously  difficult  to  forecast,  but  there  are  estimates  available.  But  given  the  strong  correlation between income and the demand for  petrol,  good  results could have  been  obtained  by  only  using  the  correlation  between  income  and petrol demand. In fact, this approach was  used  by  the  economists  for  Shell,  as  will  be  explained below. 4 Demand growth rates – estimates of the economists Petrol demand and supply balance The following graphs show the inland demand  and  supply  situation  for  2003  and  2010,  as  forecasted by BP (one of the interveners) at the  time of the hearing. SAJEMS NS 11 (2008) No 3 269 Figure 1 Inland demand and supply (2003 and 2010) Source: BP, 2005 The  important  point  to  note  is  that  in  2003  there was already a shortfall in inland supply for  Mogas (petrol), Gasoil (diesel) and Jet (Jetfuel).  In order to meet the inland demand, the balance  was  transported  from  the  coastal  areas  by  the  oil companies, either via rail, road or pipeline.  The second graph shows the dramatic increase  in  the  deficit  for  all  products  in  the  face  of  rapid  demand  growth.  Given  infrastructure  constraints and the time it would take to build  a new pipeline from Durban to the inland area,  the graphs illustrate that there would have been  an incentive for the merging parties to foreclose  on the other oil companies in the inland market.  This  is  due  to  the  increased  profitability  of  a  foreclosure strategy in the face of high demand  growth  and  demand  exceeding  supply  in  the  inland  area.  The  merged  entity  would  find  it  270 SAJEMS NS 11 (2008) No 3 more profitable to supply its own downstream  retail  network  due  to  the  limited  supply,  and  this would increase the costs of its rivals as it will  have to import product at a higher cost from its  own refineries at the coast.  Against  this  background,  one  can  then  consider  the  various  estimates  of  demand  growth presented by the merging parties as well  as the interveners.  Growth forecasts and their foundations Using  a  petrol  and  diesel  demand  model  developed by the Bureau for Economic Research  (BER)  one  of  the  interveners  (Masana)  presented  the  following  petrol  and  diesel  demand growth forecasts.  Table 2 Petrol and diesel growth rates for the national market and for the inland market PETROL NATIONAL MARKET Year Demand1 Growth (%) Capacity Surplus Surplus (%) 2004 11918 13202 1284 10.8% 2005 12550 5.3% 13202 652 5.2% 2006 12964 3.3% 13202 238 1.8% 2007 13340 2.9% 13202 –138 –1.0% 2008 13753 3.1% 13202 –551 –4.0% 2009 14276 3.8% 13202 –1074 –7.5% DIESEL NATIONAL MARKET Year Demand Growth (%) Capacity Surplus Surplus (%) 2004 8621 10580 1959 22.7% 2005 9131 5.9% 10580 1449 15.9% 2006 9467 3.7% 10580 1113 11.8% 2007 9799 3.5% 10580 781 8.0% 2008 10328 5.4% 10580 252 2.4% 2009 10927 5.8% 10580 –347 –3.2% PETROL INLAND MARKET Year Demand Growth (%) Capacity Surplus Surplus (%) 2004 5779 5769 –10 –0.2% 2005 6085 5.3% 5769 –316 –5.2% 2006 6286 3.3% 5769 –517 –8.2% 2007 6468 2.9% 5769 –699 –10.8% 2008 6669 3.1% 5769 –900 –13.5% 2009 6922 3.8% 5769 –1153 –16.7% 1  Millions of litres SAJEMS NS 11 (2008) No 3 271 DIESEL INLAND MARKET Year Demand Growth (%) Capacity Surplus Surplus (%) 2004 3640 3545 –95 –2.6% 2005 3855 5.9% 3545 –310 –8.0% 2006 3997 3.7% 3545 –452 –11.3% 2007 4137 3.5% 3545 –592 –14.3% 2008 4361 5.4% 3545 –816 –18.7% 2009 4614 5.8% 3545 –1069 –23.2% Source: BER/ Econex, 2005. The  table  above  shows  how  the  demand  (in  millions of litre) would increase given an initial  level of demand in both the national and inland  markets. According to this forecast, the average  growth rate for petrol over the five years would  be  3.7  per  cent  and  for  diesel  over  the  same  period, 4.9 per cent.  These growth rates were significantly higher  than  those  presented  by  the  merging  parties.  The following graph compares the shortfall of  petrol  in  the  inland  market  in  2009  based  on  four  different  scenarios.  Scenario  1  is  that  of  the  BER  as  described  above,  scenario  2  is  a  “low” scenario of constant 2 per cent growth in  petrol demand, scenario 3 assumes a constant  growth  rate  of  4  per  cent,  and  the  last  bar  in  the graph below is based on the forecast of the  merging parties. Figure 2 Petrol shortfall (2009): Inland market Source: Econex, 2005 Figure  2  illustrates  again  the  sensitivity  of  the  foreclosure  scenario  to  the  different  forecasts  of  the  growth  in  petrol  demand.  The  merging  parties argued that the shortfall will be minimal  even up to 2009 and that any further shortfall  will be overcome by the upgrading of the existing  pipeline  that  runs  from  Durban  to  the  inland  area (the “DJP pipeline”).  The  Tribunal  summarised  the  growth  rate  of  petrol demand forecasts of the parties as follows: 272 SAJEMS NS 11 (2008) No 3 Table 3 Various estimates of petrol demand growth rates Source Petrol demand growth rate RBB (for BP) 3% Econometrix 2.2% Total (2005-2014) 2.2% RBB (for Shell) 2% ECONEX (for Masana) 3.6% Sasol (2006 budget) 1.4% Uhambo business plan 1% Source: Tribunal, 2006, p. 91 The  Tribunal  (2006:  92)  noted  in  its  decision  regarding  the  growth  estimates  of  the  various  economists  that:  “We  have  been  presented  by  bald  estimates  by  the  participants  in  these  hearings  –  many  of  whom  appeared  to  rely  on  independent  experts  –  but  surprisingly  few  have  attempted  to  explain  the  underlying  basis  for  their  estimates”.  This  is  a  serious  criticism  against  the  economic  evidence  that  was presented during the hearing. The Tribunal  had to come to their own conclusions regarding  the determinants of petrol demand (2006: 92):  “In  our  view,  common  sense  would  suggest  a  high  degree  of  correlation  between  income  growth  and  rates  of  fuel  consumption.  It  may  also reasonably be hypothesised that changes in  the distribution of income would correlate with  shifts in demand for fuel products.”  We have argued above, based on the economic  evidence  on  the  income  elasticity  and  price  elasticity of demand for petrol, that the positive  income  effect  is  more  important  than  the  negative price effect. The Tribunal also referred  to the positive correlation between income and  fuel  demand.  When  developing  a  model  to  explain the demand for petrol, one has to choose  the  appropriate  income  variable.  Although  there is a positive correlation between the gross  domestic  product  (GDP)  and  petrol  demand,  most  studies  on  petrol  demand  find  that  real  disposable  income  of  households  explains  petrol  demand  better  than  GDP.  This  income  variable makes sense intuitively, as petrol sales  to households constitute a large proportion of  total petrol sales. Real disposable income is the  key driver of demand for most retail products,  including petrol sales to households.  The  evidence  from  the  literature  as  well  as  the  SA  data  is  that  the  income  variable  is  the  most important determinant of fuel demand; the  GDP growth rate in the case of diesel and real  disposable income in the case of petrol. There is  a negative relationship between the real prices  and volumes sold, but this relationship is weaker  than the income relationship. In  order  to  use  this  information  to  forecast  growth  rates  for  petrol  and  diesel,  one  needs  to  develop  an  econometric  model  that  can  incorporate  these  relationships.  But  before  moving  to  an  overview  of  the  formal  models  presented to the Tribunal by the various parties,  it is worth noting that at the time of the hearing,  South Africa experienced the longest upswing in  economic growth since 1970. This would a priori  lead one to assume that this increase in general  economic activity would also have implied higher  demand for fuel. At the time of the hearing, the  GDP  forecast  of  the  National  Treasury  for  growth was exceeding 4 per cent between 2005  and 2007. The Bureau for Economic Research at  that stage had an average GDP growth forecast  of 3.7 per cent between 2005 and 2009.  Economic models used The  literature  and  SA  data  confirm  the  assumption of the Tribunal that there is a high  correlation between income growth and growth  in  the  demand  for  petrol.  Given  this  high  SAJEMS NS 11 (2008) No 3 273 correlation,  one  can  assume  that  much  of  the  demand for petrol and diesel can be explained  by using only an income variable.  a) Shell This  was  the  approach  taken  by  one  of  the  interveners,  Shell.  They  used  the  following  formulas to forecast the demand for petrol and  diesel.  Although  both  equations  are  affected  by the fact that there is only one variable, and  that GDP (rather than disposable income) was  used in the case of petrol, Shell presented these  as a general indication of what drives petrol and  diesel demand.  •  Demand for petrol = (GDP – 1.5 per cent) •  Demand for diesel = (GDP + 1.4 per cent) Applying  these  formulas,  the  economists  for  Shell  (RBB  Economics)  made  the  following  forecasts  for  growth  in  petrol  and  diesel  demand. Table 4 Shell demand forecasts for the Uhambo hearing 2005 2006 2007 2008 2009 2010 GDP growth 3.5% 3.7% 4.4% 3.3% 2.9% 3.0% Petrol demand growth 2.0% 2.2% 2.9% 1.8% 1.4% 1.5% Diesel demand growth 4.9% 5.1% 5.8% 4.7% 4.3% 4.4% Source: Uhambo, economic report filed by RBB for Shell, 2005. The question is whether such a ‘rule of thumb’  gives an adequate forecast of the real demand.  The  following  two  graphs  show  what  happens  if we use the formula of Shell and apply that to  historic demand data for petrol and diesel. Figure 3 Shell petrol demand growth forecasts vs real petrol demand growth. Source: own calculations 274 SAJEMS NS 11 (2008) No 3 Figure 4 Shell diesel demand growth forecasts vs real diesel demand growth. Source: own calculations It is clear from the graphs above that the Shell  formula does not capture all of the fluctuations  in  the  volumes.  A  simple  examination  of  the  graphs shows that the diesel graph seems to have  a better fit than the petrol graph. The next step  would be to add more explanatory variables to  increase the predictive power of the models. b) Masana (BER) Masana based their demand forecasts on a petrol  demand model that was developed by the BER.  The  BER  estimated  the  demand  for  petrol  and  diesel in 2005, based on quarterly data between  1984  and  2004.  They  used  seasonally  adjusted  volume  sales  of  petrol  and  diesel  as  dependent  variables.  A  regression  analysis  yielded  a  model  where the volume sales of petrol are explained by  real disposable income of households and the real  (inflation-adjusted)  price  of  petrol.  This  model  had an R-squared value of 0.71. The results of this  model suggest that the demand for petrol (volume  sales) is relatively inelastic with regard to changes  in  the  real  retail  price  of  petrol.  The  short-run  price elasticity was –0.19 and the long-run elasticity  –0.62. The short-term income elasticity of petrol  in  SA  was  found  to  be  around  0.10,  while  the  long-run income elasticity was around 1.0.  For diesel, the BER regression results showed  that seasonally-adjusted volume sales of diesel  are  explained  by  the  gross  domestic  product  and the real (inflation-adjusted) price of diesel.  However,  the  demand  for  diesel  was  found  to  be  less  sensitive  to  price  changes  than  the  demand for petrol. (This might explain why the  Shell model yielded a better outcome for diesel  demand than for petrol demand (where the price  variable was omitted). The diesel demand model  had  an  R-squared  value  of  0.63.  The  long-run  demand elasticity for diesel was found to be –0.1.  The long-run income elasticity was calculated as  1.36 (BER, 2005: 6). While  the  BER  found  that  South  Africa’s  long-run  income  elasticity  for  both  petrol  and  diesel  seem  to  be  in  line  with  international  research,  it  might  be  the  case  that  income  elasticities  are  higher  in  developing  countries.  There may be several explanations for this. As  more  people  become  economically  active  and  the  economy  keeps  growing,  the  number  of  cars purchased may increase and this will lead  to an overall increase in the demand for petrol.  Over time, the income elasticity may decrease  as  the  country  develops,  while  still  remaining  positive. SAJEMS NS 11 (2008) No 3 275 c) BP The  other  interveners  also  presented  growth  forecasts. BP based their forecasts on analysis  by  Econometrix.  The  report  by  Econometrix  also  found  demand  and  income  elasticities  in  the same range as that of the BER. Table 5 Demand elasticities – 1999-2004 Price elasticity Income elasticity Petrol –0.238162 0.381192 Diesel –0.138065 1.474488 Source: Table 2.1 – Econometrix report, par. 2.2.2 Econometrix  used  real  GDP  as  the  income  variable for both diesel and petrol. It was argued  above  that  for  petrol  demand,  real  disposable  income of households would be a better income  variable. Based on this model, BP predicted a  petrol  demand  growth  rate  of  3  per  cent  over  the relevant period. d) Sasol and Engen The  economists  for  the  merging  parties  did  not  present  a  formal  economic  model  to  base  their  fuel  demand  growth  forecasts  on.  They  accepted the growth forecasts that came from  the business plans of the merging parties, and  therefore  predicted  that  the  current  (at  the  time of the merger) best estimates was a petrol  demand  growth  rate  over  the  relevant  period  of 1.4 per cent, a diesel demand growth rate of   3.4 per cent and a kerosene demand growth rate of   2.9 per cent. In the original Uhambo business  plan presented to the Competition Commission,  the petrol demand was predicted to grow by only  1 per cent (see Table 5 above). Especially  in  the  case  of  petrol,  such  low  demand growth rates would have minimised the  threat of foreclosure. The higher diesel growth  rate was assumed to be driven by factors such  as  the  governments’  taxi  recapitalisation  plan  and consumers’ increased preference for diesel  vehicles.  The  following  petrol  demand  equation  was  presented by the merging parties (it appeared  in the affidavit of Millard & Kanfer (Business  Enterprises at the University of Pretoria), p. 8). PETROSSA =   0  +  1 CPI -1  +  2 REALPP +  3 JAN +  4 FEB +  5 APRIL +  6 MAY    +  7 DEC +  In the equation above, the dependent variable  is  the  nominal  Rand  value  of  petrol  sales  (PETROSSA).  This  is  then  specified  as  a  function of the consumer price index (lagged),  the  real  petrol  price  and  various  dummy  variables to model the seasonal effects of petrol  demand. There is no income variable. This  model  clearly  disregards  the  body  of  economic  theory  set  out  above  as  well  as  the  evidence  from  the  SA  data  described  above.  What  the  merging  parties’  model  stated  in  essence  is  that  there  is  a  positive  relationship  between  the  demand  for  petrol  and  the  CPI,  and  between  the  demand  for  petrol  and  the  real price of petrol. Economic theory predicts  that  inflation  (i.e.  price  increases)  erodes  the  purchasing power of households, in other words,  the higher inflation or prices become, the less  consumers  can  afford  in  volume  terms  (i.e.  with a given budget, they can afford to buy less  petrol  in  volume  terms).  Increasing  inflation  will  therefore  reduce  consumers’  demand  for  petrol, i.e. if anything, there should be a strong  negative  relationship  between  inflation  and  petrol sales.  It  is  therefore  impossible  to  find  a  positive  relationship between CPI and real petrol sales.  The  equation  above  is  flawed  as  the  positive  correlation  was  found  between  the  nominal  value  of  petrol  sales  and  the  level  of  the  CPI.  A  positive  correlation  is  meaningless  as  the  correlation between most nominal variables in  276 SAJEMS NS 11 (2008) No 3 the  economy  would  be  positive,  if  inflation  is  not accounted for. The correct way to deal with this issue would  be to examine the relationship between the real  petrol demand and the real petrol price (as was  done in the model of the BER described above).  The real petrol price was the second explanatory  variable in the equation of the merging parties,  and was also found to be positively correlated  to  the  nominal  petrol  sales.  However,  when  the year on year changes in the real values are  used,  one  finds  a  negative  relationship  (with  a  correlation  coefficient  of  -0.73),  as  illustrated  below (Figure 5). Figure 5 Negative relationship between petrol sales and real (CPI deflated) price of petrol Source: Econex, 2005 The  other  obvious  problem  with  the  petrol  demand  equation  of  the  merging  parties  is  the  omission  of  an  income  variable.  It  was  explained  above  that  the  income  effect  is  stronger  than  the  price  effect  in  determining  petrol  demand.  If  they  did  not  find  a  strong  positive  correlation  between  GDP  and  petrol  demand,  another  income  variable  could  have  been considered such as real disposable income  or even a proxy for income such as sales of new  vehicles or the stock of new and used vehicles  on  the  road,  etc.  But  there  is  no  justification  for omitting the income variable, as this is the  most important determinant of petrol demand.  Omitting  the  income  variable  also  ignores  completely the positive growth outlook shared  by most economists at the time of the proposed  merger. An examination of the growth in the sale of  passenger  cars  over  the  five  years  before  the  merger  hearing  would  already  have  indicated  that the demand for petrol must be increasing  over  the  next  few  years,  as  illustrated  below  (Figure 6). SAJEMS NS 11 (2008) No 3 277 Figure 6 Sales of passenger cars Source: NAAMSA, 2006. Clearly, the predictions of such a misspecified  model,  which  has  no  foundation  in  economic  theory, must be disregarded. The Tribunal was  correct not to accept the very low petrol demand  growth  rates  (1.4  per  cent)  that  resulted  from  this model. This caused them to accept that there  was a credible threat of foreclosure in the face  of higher fuel demand growth rates. 5 Conclusions This paper has argued that although the results  of economic models are highly sensitive to the  assumptions of the models, this does not mean  that such models should not be used. In the case  of the Uhambo merger, the most important issue  considered  by  the  Tribunal  was  the  possibility  or profitability of foreclosure. This in turn was  determined mainly by two issues, the growth in  fuel demand and the state of logistics.  While  the  second  is  primarily  a  factual  issue,  the  first  –  white  fuels  demand  growth  –  is  something  which  can  be  estimated  by  economists.  Based  on  economic  theory  and  standard economic analysis, the Tribunal found  that  the  fuel  demand  growth  rates  assumed  by  Uhambo  in  their  business  model  are  “low  outliers”. This article has shown that assuming  a  1  per  cent  growth  rate  for  petrol  demand  when the economy was growing at much higher  rates and sales of passenger cars were reaching  record  levels,  was  indeed  conservative,  if  not  totally unrealistic. Changing this assumption in  the foreclosure models immediately raised the  profitability of foreclosure, and this ultimately led  to the Tribunal’s conclusion that foreclosure was   indeed a “credible threat” and that the proposed  merger should therefore be prohibited. 6 References 1  BLUM, U.; FOOS, G. & GUADRY, M. (1988)  “Aggregate time series gasoline demand models:  Review of the literature and new evidence for West  Germany”, Transportation Research A, 22A: 75-88.  2  BUREAU FOR ECONOMIC RESEARCH  (2003) “The feasibility of a fuel tax levy in the  Western Cape”, Document for public comment,  based on a research report for the Western Cape  Provincial Treasury. 3  BUREAU FOR ECONOMIC RESEARCH  (2005) “Petrol and diesel sales forecasts for South  Africa: 2005 to 2009”, Report prepared for BP SA.  August 2005. 4  BUSINESS ENTERPRISES, UP (2005)  “Statistical evaluation of SASOL prediction  models: Petrol and diesel”, Non-confidential version.  278 SAJEMS NS 11 (2008) No 3 5  COMPETITION TRIBUNAL (2006) “Decision  in the large merger between Sasol Ltd, Engen Ltd,  Petronas International Corporation And Sasol  Oil (Pty) Ltd and Engen Ltd”, Case No: 101/LM/ Dec04. 6  DAHL, C. & ROMAN, C. (2004) “Energy demand  elasticities – Fact or fiction: A survey update”,  Paper presented at the Energy, Environment and  Economics in a new Era conference, Washington  DC, July 8-10, 2004. 7  DROLLAS, L. (1984) “The demand for gasoline:  Further evidence”, Energy Economics, 6: 71-82. 8  ECONEX (2005) “Analysis of the competition  aspects of the proposed Uhambo merger – a  Masana perspective”, Non-confidential version.  9  ECONOMETRIX (2005) “Estimating long term  fuel demand scenarios”, Non-confidential version.  10  GRAHAM, D.J. & GLAISTER, S. (2002)  “The demand for automobile fuel: A survey of  elasticities”, Journal of Transport Economics, 36:  1-26.  11  GOODWIN, P. (1992) “A review of new demand  elasticities with special reference to short and long  rune effects of price changes”, Journal of Transport Economics and Policy, 26: 155-163. 12  RAMANATHAN, R. (1999) “Short and long  run elasticities of gasoline demand in India: an  empirical analysis using cointegration techniques”,  Energy Economics, 21: 321-330.  13  STERNER, T. (1990) The Pricing of and Demand for Gasoline, Swedish Transport Research Board,  Stockholm.