ELECTR-5744; No <strong>of</strong> Pages 26Figure 4: Development Finance for <strong>the</strong> <strong>Energy</strong> Sector by Purpose; Commitments, 1980-2008, Using 3-Year Moving Averages [constant2000 USD]T here has been an almostcontinuous increase inelectricity-related ODF in non-OECD countries during recentyears. However, ODF to LDCs hasvaried widely over <strong>the</strong> years; it isnone<strong>the</strong>less also on an increasingtrend (Table 3).D. Relationship betweenGFCF, FDI, and ODA/FA number <strong>of</strong> scholars havelooked into <strong>the</strong> relationshipbetween ODA/F, FDI, and GFCF.Obviously, <strong>the</strong> effect <strong>of</strong> FDI oncapital formation largely dependson whe<strong>the</strong>r FDI is directed togreenfield projects as opposed tocross-border mergers andacquisitions. Several authors haveinvestigated <strong>the</strong> relation betweenFDIs and Official DevelopmentAssistance. UNCTAD notes that,especially in least developedcountries, FDIs tend to concentrateon <strong>the</strong> primary sector (mining)until a sufficient level <strong>of</strong> capabilityand infrastructure are built. In thiscontext LDCs could leverage ODAfor improving <strong>the</strong> conditions in<strong>the</strong>ir respective economies toattract more FDI and enhance <strong>the</strong>irimpact (UNCTAD, 2010, p. 62).W hile empiricallyexamining FDI and GFCFfor <strong>the</strong> economies in transition,Krkoska (2001) found that a 1percent increase in FDI flowstranslated into 0.7 percentincrease <strong>of</strong> GFCF in <strong>the</strong> recipientcountry. FDI represented anTable 3: Overview <strong>of</strong> ODF for <strong>Energy</strong> Generation and Supply (USD billion) [constant 2000 USD].2000 2001 2002 2003 2004 2005 2006 2007 2008LDC 0.74 1.11 0.87 0.88 0.81 1.34 0.72 1.84 1.56O<strong>the</strong>r non-OECD 5.63 7.60 7.33 7.51 8.43 8.58 10.02 12.15 9.07Global 6.44 7.80 7.48 7.56 8.69 8.72 10.73 12.40 9.678 1040-6190/$–see front matter # 2011 Elsevier Inc. All rights reserved., doi:/10.1016/j.tej.2011.07.006 The Electricity JournalPlease cite this article in press as: Bazilian M, have surnames. <strong>Informing</strong> <strong>the</strong> <strong>Financing</strong> <strong>of</strong> <strong>Universal</strong> <strong>Energy</strong> <strong>Access</strong>: <strong>An</strong> <strong>Assessment</strong> <strong>of</strong> Current Financial FlowsElectr. J. (2011), doi:10.1016/j.tej.2011.07.006
ELECTR-5744; No <strong>of</strong> Pages 26average 15 percent <strong>of</strong> <strong>the</strong> GrossFixed Capital Formation(UNCTAD, 2010), but its sharecan be higher for somedeveloping countries (<strong>the</strong> averagefor African countries is above 20percent). The relationshipbetween ODA/F and gross fixedcapital formation is much moreambiguous. First, ODA/F canfinance a variety <strong>of</strong> activities thatdo not necessarily translate intoany increase <strong>of</strong> fixed assets.Secondly, ODA/F statisticsmeasure <strong>the</strong> declarations <strong>of</strong> <strong>the</strong>donor countries ra<strong>the</strong>r than actualflows (both for commitments andfor disbursements). During <strong>the</strong>last couple <strong>of</strong> decades, FDIincreased while ODA decreasedin 13 LDCs (UNCTAD, 2011).W hile still below <strong>the</strong> level <strong>of</strong>ODA flows, FDI inflowsfor energy appear to haverepresented <strong>the</strong> major externalprivate capital flows for LDCs in<strong>the</strong> past decade (UNCTAD, 2011).Our analysis suggests that morethan one-third <strong>of</strong> <strong>the</strong> energyrelatedinvestment in LDCs stemsfrom foreign sources, mainly fromODF. Although that share is high,<strong>the</strong> flow in absolute termsremains low (about 1/10th withrespect to o<strong>the</strong>r non-OECDcountries, or 4 percent <strong>of</strong><strong>the</strong> estimated world total)compared to o<strong>the</strong>r groups <strong>of</strong>countries.V. <strong>Energy</strong> PovertyOur interest in exploring macr<strong>of</strong>inancial flows is to set a contextfor finance for energy access.<strong>Energy</strong> poverty, <strong>the</strong> lack <strong>of</strong> accessto modern, reliable and affordableenergy services, affects billions <strong>of</strong>people. More than a fifth <strong>of</strong> <strong>the</strong>world’s population does notbenefit from access to electricity. Itis well recognized that energy is anecessary ingredient for humandevelopment and <strong>the</strong>achievement <strong>of</strong> <strong>the</strong> MillenniumDevelopment Goals (Modi et al.,2005). Projections indicate that<strong>the</strong>se issues will persist or worsenin <strong>the</strong> foreseeable future withoutdedicated action (IEA, UNDP andUNIDO, 2010).In 2010 <strong>the</strong> UN SecretaryGeneral’s Advisory Group on<strong>Energy</strong> and Climate Change(AGECC) suggested two bold, yetachievable global objectives, one<strong>of</strong> which urges <strong>the</strong> internationalcommunity to work towardachieving universal energyaccess by 2030 (AGECC, 2010).While, at <strong>the</strong> regional, national,and local levels, significantefforts are underway toaddress <strong>the</strong> lack <strong>of</strong> energyaccess, <strong>the</strong> issue <strong>of</strong> ‘‘unlocking’’<strong>the</strong> requisite financing isparamount.A. <strong>Energy</strong> inequalityPoor data make it extremelydifficult to determine <strong>the</strong> fraction<strong>of</strong> energy-related investment thatgoes to expanding access ra<strong>the</strong>rthan to, say, increasinggeneration. One approach tobetter understanding <strong>the</strong>relationship between overallinvestment in expansion <strong>of</strong> accessis to develop Lorenz curves andGini coefficients, which arewidely used in economics toestimate income inequality(Gastwirth and Glauberman,1976). These metrics can help toestimate distributions <strong>of</strong> energyconsumption. The Lorenz curve isa ranked distribution <strong>of</strong> <strong>the</strong>cumulative percentage <strong>of</strong> <strong>the</strong>population <strong>of</strong> recipients plottedagainst <strong>the</strong> cumulativepercentage <strong>of</strong> <strong>the</strong> resourcedistributed. The Gini coefficient isa numeric measure <strong>of</strong> inequalitythat reveals <strong>the</strong> differencebetween a uniform distributionand <strong>the</strong> actual distribution <strong>of</strong> aresource. 32Figure 5 shows <strong>the</strong> Lorentzcurves and energy Ginicoefficients (inset) for fivecountries computed by Jacobsonet al. (2005). The curve for Kenya,where energy access is mostinequitable (and where gridbasedenergy access is lowest atabout 20 percent <strong>of</strong> <strong>the</strong>population) highlights <strong>the</strong>differences in services andconsumption in nations withlarge fractions <strong>of</strong> <strong>the</strong> populationusing biomass as <strong>the</strong>ir primarymeans <strong>of</strong> energy. We canpostulate that, ceteris paribus,Aug./Sep. 2011, Vol. 24, Issue 7 1040-6190/$–see front matter # 2011 Elsevier Inc. All rights reserved., doi:/10.1016/j.tej.2011.07.006 9Please cite this article in press as: Bazilian M, have surnames. <strong>Informing</strong> <strong>the</strong> <strong>Financing</strong> <strong>of</strong> <strong>Universal</strong> <strong>Energy</strong> <strong>Access</strong>: <strong>An</strong> <strong>Assessment</strong> <strong>of</strong> Current Financial FlowsElectr. J. (2011), doi:10.1016/j.tej.2011.07.006