Heterogenous systematic risk in electricity distribution - The case of Sweden

1. Heterogenous systematic risk in electricity distribution - The case of Sweden Jon Thor Sturluson

2. Motivation • Electricity distribution and transmission is a natural monopoly • Regulation of prices requires: • Estimate of cost of operation • Historical • Efficient benchmark • Estimate of reasonable cost of capital • Required return to capital • Appropriate cost of debt

3. Weighted Average Cost of Capital

4. Capital Asset Pricing Model (CAPM)

5. Weighted Average Cost of Capital-with CAPM

6. Scematic respresentation of WACC

7. Motivation • Estimates of capital costs are often highly aggregate • Two initial hypotheses • Does size of operations affect cost of capital? • Does geographic location affect cost of capital?

8. Beta andfirmsize • Negative relationship between size and returns is often suggested • Possiblereasons • Wrongriskmeasure • Wrongreturnmeasure • Transactioncosts • Vulnerablefirmstendtobe small • Cost of andaccesstodebtfinancing/ creditrisk • Economies of scaleandscope • Concentrationandlack of competition • Addingfirmsizemayrenderbetaaninsignificantpredictor of returns • Measurement of firmsize (morelater)

9. Beta and location • Operating a network in a rural area may be more risky • Higher counterparty risk due to fewer customers • Less diversified economy base • Regional specific business cycles not diversified • Economies of scale and scope • Higher operating leverage?

10. Available data • Data collected and published by the Energy Markets Inspectorate (EMI) • Complete financial statements and extensive technical data • Panel structure • 1998 to 2008 • 176 / 166 cross sections • Extensive adjustments due to mergers and changes in reporting

11. Two dimension of size • ENERGY correlates with equity and conventional notions of size • NETSIZE depends on geography and agglomeration • Together these two variables capture variation in SPARSENESS of networks

12. Two step method • Estimate beta for each firm in turn • Estimate a model with beta as a dependent variable • Three possible sources of heterogeneity considered • Spanor size of the network in km of wires (NETSIZE) • Size Volume of energy distributed per year (mWh), alternatively total revenu from operations • Sparseness or scope in relation to size • Other variables consdiered but do not improve fit or change the outcome

13. 1st stepfixed-effects panel Fixed-effects or betas for each firm estimated in a SUR regression Wide range of betas (not display) - Some are significant others are not

14. 2nd stepEstimation of size effects on beta

15. Resultsonbeta • Spareness effect is significant and nontrivial • Parameter estimates are robust to changes in specification • The hypothesis that a network with little distribution over a large network is more risky than on average is supported

16. Results for cost of debt • No significant relationship between cost of debt and size • Average interest rate premium over 10 year government bonds estimated at 1.19%

17. Applicability • Accounting beta need to be translated to market beta • Decomposition method (Mendelker and Rhee,1984) • Scale w.r.t. existing benchmarks • Example • Two equally large groups of firms, classified by sparseness • Beta scaled to an industry benchmark (0.40)* • Sparse firms: b=0.274  rwacc = 4.6% • Dense firms: b=0.533  rwacc =5.3% • Other assumptions: • Risk free rate = 4% • Market risk premium = 5% • Interest rate premium = 1.19% • Marginal tax rate = 38%