Efficiency Measurement in the Electricity and. A. Introduction. Importance of the empirical understanding. and cost efficiency ) is relevant

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1 Effcency Measurement n the Electrcty and Gas Dstrbuton sectors Prof. Dr. Massmo Flppn FIMA, second nternatonal conference 28 G To present and dscuss the applcaton of mathematcal and statstcal methods n the measurement of the company's productve effcency Appled econometrcs Appled mcroeconomcs O Introducton Research queston Econometrc models Emprcal study A. Introducton In the last two decades d the electrcty t and gas dstrbuton b t sectors have experenced a wave of regulatory reforms Competton n producton and new regulaton nstruments n the dstrbuton (stll a natural monopoly). For the desgn of these reforms as well for busness decsons, the emprcal understandng on dfferent effcency concepts (scale effcency, scope effcency, and cost effcency ) s relevant Importance of the emprcal understandng Frst, the knowledge of the value of the economes of scale and the economes of scope provdes nformaton about the valdty of the natural monopoly argument the defnton of the optmal sze of servce areas. the potental synerges through horzontal ntegraton

2 Scale and scope effcency Emprcal relevance Economes of scale exsts f = f (Q) CE C = Q Q C AC p 1 Q Second, more and more n the applcaton of ncentve regulaton schemes, regulators make use of cost effcency ndcators. Economes of scope exsts f (Q 1,) + (,Q 2 ) > (Q 1,Q 2 ) Country Regulaton Method Explct use of benchmarkng Netherlands Yardstck Yes Unted Kngdom Prce-cap Yes Norway Revenue-cap Yes Cost fronter and cost neffcency Prce cap: P = P (1 +Δ CPI X + Z ) growth rate of the total factor productvty (TFP) t + 1 t + Z n the entre sector decomposng the TFP growth nto 3 1. techcal progress 2. Scale effcency annual target change n productve effcency 3. frm-specfc effcency obs fro Eff EFF = obs fro 1 Cost effcency measures the ablty of energy dstrbuton companes to mnmze costs for each ndvdual company Y 8

3 B. Research queston In the lterature we can dstngush two prncpal types of approaches to measure effcency Parametrc Fronter Analyss Non- Parametrc A man problem s the choce of the approach and wthn each Two approaches Both approaches econometrc c and lnear programmng have ther own advocates. At least n the scentfc communty nether one has emerged as domnant. The purpose of ths presentaton s not to stress the advantages and dsadvantages of these two dfferent approaches. method the choce among several legtmate models. 9 Emprcal evdence The emprcal evdence n the electrcty ectr c ty sector suggests that the results n terms of effcency are senstve to the approach used (parametrc and non parametrc methods). Jamasb and Pollt (23), Estache et al. (24), Fars and Flppn (24, 25) show that t there are: substantal varatons n estmated effcency scores and rank orders across dfferent approaches (parametrc and nonparametrc) and Unobserved heterogenety Part of ths dscrepancy s related to the unobserved heterogenety across frms (network characterstcs and envronmental factors). In the context of parametrc methods, panel data can be helpful to dstngush effcency dfferences from unobserved heterogenety..research AREA among dfferent econometrc models

4 Parametrc: Stochastc Fronter Methods SFA Stocastc fronter obs Observed cost, Y Cross secton models Panel data models fro Ineffcency term Stocastc term lnc = α + α Q ln Q + u + v u Fronter Cost Ineffcency term Stocastc t term 13 Y 14 Econometrc Modelng: two problems Stochastc Fronter Methods lnc = α + α Qln Q + u + v u SFA Cross secton models Panel data models Excluded varables bas Unobserved heterogenety ncluded n the effcency term FE model RE (GLS) True random True fxed ML model model effects effects Mundlak s formulaton of RE model Problem estmaton economes of scale Problem estmaton cost neffcency G 25 F F G 25 G 25 F F F K 26 F K 25 16

5 Unobserved heterogenety,panel data and cost neffcency obs Observed cost, Y Stochastc cost fronter lnc t = α + α Q lnq t + α + u t + v t u t fro Ineffcency term Fronter Cost Stocastc term Heterogenety term I S Y 17 C) Emprcal Analyss Ths analyss explores the presence of economes of scale and scope as well cost neffcency n the electrcty, gas and water utltes. These ssues s have a crucal mportance n the actual polcy debates about unbundlng the ntegrated utltes nto separate enttes Usng regulaton nstruments combned wth benchmarkng studes. Prevous studes of mult-utltes Mayo (1984) Chappell and Wlder (1986) Sng (1987) Fraquell et al. (24) Pacenza and Vannon (24) Fars et al. (27b) Data Cross-secton Cross-secton Cross-secton Pooled (1994- Pooled ( , Panel data (1979, US) (1981, US) (1981, US) 96, Italy) Italy) ( , Swtzerland) Model OLS OLS SUR NLSUR NLSUR GLS, RCM Output Electrcty and gas dstrbuton Electrcty and gas dstrbuton b t Electrcty and gas dstrbuton Electrcty, gas and water dstrbuton b t Electrcty, gas and water dstrbuton b t Electrcty, gas and water Factor prces Labor, fuel - Labor, captal, fuel Labor, other nputs Labor, other nputs Labor, captal, fuel Other characterstcs - - Customer densty - - Customer densty Economes of Exst only for Exst over Output Exst, but Exst wth all the Exst over scope small companes (+.77%), for large companes dseconomes (up to -11.7%) most of the output ranges, +12% for small, -1% for largest combnatons of both scope economes and dseconomes, no economes of sgnfcant only for companes producng less than the medan output models except wth the translog cost functon. For the medan output between most of the output ranges, except for largest companes scope for the 16 and 64% companes mean output (- 7.2%) Economes of scale Product-specfc economes of scale for gas Global and product- specfc Product-specfc economes of scale for Exst, but sgnfcant only for companes All the models show economes of scale except Global economes of scale exst over all outputs, for electrcty only for small companes economes of scale exst electrcty, dseconomes for gas producng less than the medan output the translog model over vrtually all outputs

6 Model Specfcaton Typcal problems and typcal trade-off Choce and defnton of the varables problems wth N C = C q q q r w w w w D t (1) () (2) (3) () () (1) (2) (3) (,,,,,,,, ) where C represents total costs; q (1), q (2), q (3) are respectvely the dstrbuted electrcty, gas and water durng the year, and w (), w (1), Choce of the functonal form quadratc/translog Choce of the econometrc specfcaton Pseudo panel data w (2), w (3) are respectvely the nput factor prces for captal and labor servces and the purchased electrcty and gas; r s the customer densty Functonal form ( k) m ( m) r k wt 1 mm ( m) α q t + α r t + β + α ( q t ) Ct ln( ) = ln + ln + ln + ln () () w m k w 2 m t ( ) mn ( m) ( n) rr rm ( m) α ln q ln ln ln ln t q t α rt α q t r mm ( n) n m t 2 ( ) ( ) ( ) 1 k k l kk w t kl wt wt ln ln ln () () () β β 2 k ( w k k l) l t wt w t t + + t t 2 + δ D + α, t Data: 237 observatons from 34 companes from 1997 to 25 Varable Unt Mnmum Medan Mean Maxmum C Total cost Mo. CHF (1) q Electrcty dstrbuton GWh '23.59 (2) q Gas dstrbuton GWh '294.2 (3) q Water dstrbuton Mo. m r Customer densty Customers/ km '554.9 () w Captal prce CHF/ km 11'853 31'167 38' '796 (1) w Labor prce CHF/ employee 77'789 16'466 17' '816 (2) w Electrcty prce CHF/ MWh (3) w Gas prce CHF/ MWh

7 Economes of scale Condtons for Natural Monopoly (convexty and ray economes of scale) Model I Model II Model III Model IV Output Quartle GLS (Schmdt-Sckles) ML (Ptt-Lee) ML (Battese-Coell) True RE (Greene) 1 st d 2 nd rd Overall, the above results ndcate the exstence of weak cost-complementarty and strong ray economes of scale. In lne wth Gordon et al. (23) we consder ths as a suggestve evdence of subaddtvty (natural monopoly) for all practcal purposes. Cost-neffcency Model I GLS (Schmdt-Sckles) Model II ML (Ptt-Lee) Model III ML (Battese-Coell) Model IV True RE (Greene) Mean Std. Devaton Mnmum st Quartle Medan rd Quartle fcency Relatve Ineff Dstrbuton of neffcency scores for ndvdual frms Model I (GLS) Model IV (True RE) Maxmum Company Number (sorted n GLS effcency estmate)

8 Pearson correlaton matrx between neffcency estmates Conclusons and polcy mplcatons (I) The mult-utlty dstrbuton utltes can be characterzed as a natural monopoly. Model I Model II Model III Model IV There are economes of scope whch cannot be exploted f GLS (Schmdt-Sckles) ML (Ptt-Lee) ML (Battese-Coell) True RE (Greene) mult-utltes are unbundled horzontally. I 1.863**.715**.124* II 1 793**.793** 14**.14** III 1.128** There are sgnfcant unexploted economes of scale that should be consdered n any structural reform n the future. The analyss ndcates certan cost-neffcency n the sector, whch motvates an ncentve regulaton of the utltes Conclusons and research mplcatons (II) n the context of parametrc methods, panel data could be helpful to dstngush effcency dfferences from unobserved heterogenety. However, the results are not completely satsfactory. Further research on: choce of the functonal form, defnton of the varables and the econometrc specfcaton THANK YOU FOR YOUR INTEREST!

9 Regresson results Model I Model II Model III Model IV GLS (Schmdt-Sckles) ML (Ptt-Lee) ML (Battese-Coell) True RE (Greene) 1 α (Electrcty output).55 ** (.53).46 ** (.69).418 ** (.63).527 ** (.2) 2 α (Gas output).317 ** (.32).298 ** (.41).245 ** (.45).258 ** (.12) 3 α (Water output).92 ** (.39).178 ** (.53).212 ** (.47).146 ** (.15) r α (Customer densty).64 ** (.27).43 (.38).26 (.37).7 (.9) 1 β (Labor prce).242 ** (.57).229 ** (.54).236 ** (.58).21 ** (.27) β 2 (Electrcty prce).326 ** (.59).317 ** (.51).333 ** (.52).37 ** (.33) 3 β (Gas prce).234 ** (.43).243 ** (.39).223 ** (.38).215 ** (.24) 11 α.646 ** (.197).368 * (.221).218 (.193).231 ** (.86) 22 α 234**.234 (55) (.55) 154*.154 (8) (.8) (71) (.71) 93**.93 (23) (.23) 33 α.287 ** (.141).42 (.176).186 (.167).89 * (.52) α rr.19 (.61) -.63 (.95) ** (.89) ** (.26) 12 α ** (.86) * (.15) -.48 (.91) -.99 ** (.41) 13 α ** (.149) (.158) (.148) ** (.58) 23 α -.2 (.59).49 (.72).51 (.68).37 (.26) The remanng coeffcents are not lsted. Example cost neffcency (Fars and Flppn 24) Company Ineffcency Score OLS RE (GLS) RE (ML) FE A B C D E The companes are adopted based on the rankng obtaned from the RE (GLS) model: A: medan; D: 1st quartle; B: most effcent; E: 3rd quartle. C: least effcent; 34 Anatomy of econometrc modellng Economc problem to be analyzed: demand, cost and scale effcency Data collecton, constructon of a data set Economc theory, prevous studes Model specfcaton General, mathematcal, econometrc c Estmaton of econometrc model Econometrc Modelng: total cost functon = f (Q A, Q B ) Ln Q A = α + α A LnQ A + α B LnQ B + ε Ln Q A = LnQ A +.3 LnQ B + ε Predcton Hypothess testng Interpretaton of Coeffcents, computaton of cost elastctes,

10 Econometrc specfcatons Stochastc term Model I Model II Model II Model IV GLS (Schmdt-Sckles) ML (Ptt-Lee) ML (Battese-Coell) True RE (Greene) Frm-specfc effect α α ~ d (, σ α 2 ) α ~ N + (, σ α 2 ) α ~ N(, σ α 2 ) Tme-varyng neffcency u t u t = u exp{ η(t-t)} u ~ N + (, σ 2 u ) u t ~N + (, σ u 2 ) Random v t ~ d (, σ 2 v ) v t ~ N (, σ 2 v ) v t ~ N(, σ 2 v ) v t ~N(, σ 2 v ) nose v t Ineffcency E α ˆ ω1, ˆ ω2,... E ˆ α mn{ ˆ α} E u t ˆ ε t u t rˆ t estmate wth wth hωt = α + vt wth ε t = u t + v t r t = α +u t +v t