livingstone senyonga (phd candidate) olvar bergland school ... · olvar bergland (advisor) school...
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Livingstone Senyonga(PhD Candidate)
Olvar Bergland(Advisor)
School of Economics and Business
Research areaRegulation of (electricity) utilities: Empirical and theoretical aspects ofincentive regulation
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 1 / 34
Quality of service regulation, firm size, technicalefficiency, and productivity growth in electricity
distribution utilities
The 33RD USAEE/IAEE NORTH AMERICAN CONFERENCEPITTSBURGH — PENSYLAVANIA, USA
28TH October 2015
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 2 / 34
Outline
1 Motivation and background
2 Research question and objectives
3 Data, variables, and methods
4 Results
5 Conclusion
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 3 / 34
Post reform grid structure
Figure 1: Distribution networkL.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 4 / 34
Issues with electricity distribution
1 Electricity distribution utilities are natural monopolies
2 Pursue profit maximization
I Over pricing electricity
I Under-investment into the network (Poor quality services4! )
I Lack of competition leads X-inefficiency (Yarrow et.al.,1988)
3 Market failure(s) which requires regulationI Cost efficiency
I Innovations and technical change
I Optimal scale of operation
I Quality services
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Aim of the study
The study aims to find out how supplementary regulation for QoSrelates with the intended outcomes of incentive regulation.
I Cost efficiency
I Sustained productivity improvement
F Technical improvement and innovations
F Scale improvement
I apply the framework to study the Norwegian cost of energy notsupplied regulation(CENS) in a panel data framework.
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 6 / 34
Research question
1 How does QoS regulation relate with cost efficiency andproductivity growth?
2 What relationship exists between firm characteristics and level ofQoS?
Objectives1 To estimate the difference in technical efficiency and
productivity resulting from introducing QoS dimension into theanalysis.
2 To determine how firm size relates with efficiency andproductivity.
3 To determine how firm characteristics relates with level QoS.
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Quality of service (QoS) in utilities
QoS in distribution utilities is a three dimensional concept (Ajodhia,V.S. & Hakvoort, R. 2005)
1 Voltage or power quality
I Frequency stability (50Hz )
I Technical network losses2 Commercial quality
I How a distribution utility relates with customers
I Condition for new connections and reconnection
I Metering equipments, reading, and billing
I Complaint handling
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Elements of QoS contd
3 Network reliability or continuity of service
I Network’s ability to meet customer’s demand
I Focuses on continuity of supply
I Considers network’s capacity to meet peak demand
I Mainly measured by interruptionF Frequency
F Duration
F Energy based measures use energy not supplied ENS
1 It is an indicator of adequacy of investment
2 Not important under rate of return regulation but Key issue underincentive regulation
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 9 / 34
Historical perspective of regulation
1990I Energy sector reforms
I Creation of the regulatory institution - NVE1991 - 1996
I Cost-of-service regulation
I Guaranteed rate-of-return on investment, changes every year
I Yearly allowed revenues
I No cost efficiency requirements4!Likely outcomes
I No incentive to reduce Costs
I Averch-Johnson (1962) effect and consumer bears all the risk4!No need to regulate QoS
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 10 / 34
History contd:
1997 - 2001I Incentive regulation
I Revenue cap changed annually
I Cost base is actual cost with 2-years lag
I Target incentives based on individual DSOs inefficiencies capedbetween 0 –3% p.a
I Fixed R-O-R at 8.3 % for the whole period
I Requirement to report interruption using FASITLikely outcomes
I Incentives to reduce costs and increase profitsI Reduce investment to increase profits4!I A risk that QoS deteriorates (Ter-Martirosyan, 2003)
Trade-off between cost saving and provision of quality
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 11 / 34
History contd:
2002 - 2006
I Incentive regulation continued
I Targeted incentive based on DEA benchmarking
I Annual changes in R-O-R from 8.5% in 2002 -5.2% in 2006
Likely outcomes
I Cost efficiency is key
F Reduce costs to increase profits
F Reduce investment, reduce depreciation to increase profits
I Balance between cost efficiency and provision of quality
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2007 - 2011: Yardstick competition
I Incentive: Revenue cap based on DEA benchmarking and QoS
I Cost base (2 years lag) updated yearly
TOTEX = CAPEX + OPEX + LOSSES
I Revenue cap = 0.4*Base cost + 0.6*Norm cost + Quality costs
I Norm cost = Technical efficiency × Base costs
Likely outcomes
I QoS is internalized in cost efficiency
I RCt = (1−λ )Ct−2 +λ ∗T E(ec , eq)∗Ct−2 + E(IC) − IC
I DSOs invest in quality enhancing mechanisms to reduce qualitypenalties
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CENS regulation
Using FASIT, Energy not supplied is computed
All interruptions ≥ 3 minutes are considered
An estimate of expected interruption is made
Direct compensation for customers with outages ≥ 12 hours
Used to adjust the revenue cap 2002-2006
Likely outcomes
Non notified outages linked to natural phenomenon like badweatherInvestment planning and scheduling of maintenance to minimizedisconnection
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Cost of energy not supplied is given by IC = ∑Ii=1 ∑
Jj=1 ENSi, j •Ci, j
Customer specific interruptions costs are estimated from surveys
2000-2001 (e’s/KWh)
Customer category Notified outages Non-notified outages
Residential and Agriculture 0.4 0.5
Industry and commercial 4.4 6.3
2002-2006 (e’s/KWh)
Industry 5.8 8.3
Trade and Services 8.5 12.4
Wood processing 1.4 1.6
Households 0.9 1.0
Public facilities 1.3 1.6
Agriculture 1.9 1.3
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Energy not supplied in Norway
CENS regulation
0
10
20
30
40
50E
nerg
y no
t sup
plie
d (G
Wh)
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Year
Notified ENS Non notified ENSTotal ENS
Figure 2: System interruptions and Energy not supplied 1996-2014
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Outline
1 Motivation and background
2 Research question and objectives
3 Data, variables, and methods
4 Results
5 Conclusion
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Data
Firm level historical data used by NVE for regulation
119 DSOs
Annual from 2004 to 2012
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Table 1: Variables and model specification
Variable Cost only Cost qualityOutputsEnergy delivered X XNumber of customers X XLengths of network X X
InputsTotal expenditure (TOTEX) X XCost of energy not supplied (CENS) X
Environmental variablesGeographical variable 1 & 2 X XPortion of underground cable X XNumber of substations X XDummy: 1 if year ≥ 2007 X X
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Methods
1 Transformation functionF(X t ,Y t , t) = 1
2 Translog Input distance function
−lnTOT EX ti = T L
(Y t
i,m, lnCENS∗ti , t,θ)+g
(Zt
i,q
)−ui,t + vi,t
1 lnTOT EX ti −→ Input variable- TOTEX
2 lnCENS∗ti −→ Quality input variable - Interruption costs3 Y t
i,m −→ Output variable (En, CU, NL)
4 Zti,q −→ Environmental variable
5 θ −→ Vector of parameters6 t −→ Time trend7 ui,t −→ Time varying inefficiency8 vi,t −→ Noise
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Why distance function?
Multiple outputs
I Electricity services have joint service
I Economies of scope cannot be overlooked
Input price data NOT available
Cost minimization is unrealistic
I Political influence
I Regulation
I Optimal quality may not coincide with minimum costs
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Why input orientation?
Technical efficiency improves by reducing input usage -COSTS- atfixed outputs.
It is realistic that inputs are ENDOGENOUS while outputs areEXOGENOUS.
DSO’s have influence over input NOT outputs
Regulations aims at influencing DSO’s:
I Expenditure behavior
I Decisions on input-mix
IDF performs well with potentially endogenous inputs relative toIRF and PF
I Capital is potentially endogenous.
I Absence of natural instruments -input prices.
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Estimation strategy
Two frontier models with same output and Z-variablesI Cost-only: One input- Totex = OPEX + CAPEX + LossesI Cost-quality: Two inputs- Totex + CENS
Restriction tests were done
One-step MLE - Random effects frontier estimator (Battese &Coelli, 1995)
Truncated normal distribution
ui,t ≈ N+(µi,t , σ2
u)
Observed heterogeneity is considered
µi,t = ξ′Z j,i,t
Technical efficiency via Battese & Coelli(1988)
T Ei,t = E [expe(−ui,t |vi,t − ui,t)]
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Outline
1 Motivation and background
2 Research question and objectives
3 Data, variables, and methods
4 Results
5 Conclusion
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 24 / 34
IDF estimates at sample average DSO
All output variables have the expected -ve sign and are significantInput are significant with expected +ve signsTechnical change has significant output-input effects
Estimate Cost-only Cost-qualityConstant returns-to-scale No (1.043) yes (1.020)Rate of technical change by 2008 3.0% 2.5%
Growth in technical change 0.8% p.a 0.5% p.a
Technical efficiency 0.87 0.89
Shadow price of quality (ENS) 8.4 e’s /kWh
Predicted Variations due to inefficiency 65.1% 66 %
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Results: Continued
Effect of non-discretionary variables on inefficiency
Variable Cost-only Cost-quality QoS ChangePortion of underground cable -ve -ve Increases
Number of substations +ve +ve Increases
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Variable Cost-only Cost-quality Variable Cost-only Cost-qualityCoef/se T-stat Coef/se T-stat Coef/se T-stat Coef/se T-stat
intercept -0.157*** -9.68 -0.153*** -9.61 12 t2 -0.008*** -4.34 -0.005*** -2.64
(0.016) (0.016) (0.002) (0.002)lnEn -0.109** -2.34 -0.137*** -2.88 t(lnEn) 0.001 0.11 -0.005 -0.56
(0.046) (0.048) (0.008) (0.009)lnCu -0.359*** -7.14 -0.355*** -7.07 t(lnCu) 0.014 1.50 0.028*** 2.89
(0.050) (0.050) (0.009) (0.010)lnNl -0.492*** -21.71 -0.489*** -20.38 t(lnNl) -0.014*** -2.57 -0.025*** -4.10
(0.023) (0.024) (0.005) (0.006)lnTotex 1.000 0.916 t(lnCens) -0.012 -1.57lnCens 0.084*** 2.83 (0.008)
(0.029) Environmental Variables12 (lnEn)2 -0.072 -0.91 0.113 1.32 Geog 1 -0.081*** -4.09 -0.079*** -3.70
(0.080) (0.086) (0.020) (0.021)12 (lnCu)2 -0.297* -1.89 -0.175 -1.15 Geog 2 -0.158*** -4.00 -0.150*** -3.56
(0.157) (0.152) (0.040) (0.042)12 (lnNl)2 0.050 0.86 -0.115* -1.90 Ucabel -0.582*** -2.73 -0.695*** -2.76
(0.059) (0.061) (0.213) (0.252)12 (lnCens)2 -0.032 -0.45 lnN Subt 0.099*** 2.64 0.166*** 3.96
(0.070) (0.037) (0.042)(lnEn)(lnCu) 0.199* 1.86 -0.007 -0.06 RC 0.059 1.06 0.039 0.61
(0.107) (0.109) (0.056) (0.064)(lnEn)(lnNl) -0.179*** -3.11 -0.077** -1.33 Parameters
(0.058) (0.058) Sigma(u2) 0.227*** 11.45 0.231*** 11.03(lnEn)(lnCens) -0.150** -2.22 (0.020) (0.021)
(0.068) Sigma(v2) 0.122*** 21.33 0.120*** 23.36(lnCu)(lnNl) 0.118 1.56 0.151** 2.08 (0.006) (0.005)
(0.076) (0.073) lambda(λ ) 1.867*** 87.82 1.929*** 87.54(lnCu)(lnCens) 0.256*** 3.40 (0.021) (0.022)
(0.075) Gamma(γ) = (δ2u )
(δ2u +δ2u )0.651 0.659
(lnNl)(lnCens) -0.177*** -3.68 Log likelihood 428.85 473.68(0.048)
t -0.030*** -8.92 -0.025*** -6.48 Number of DSOs 119 119(0.003) (0.004) T 9 9
In parentheses are standard errors, and ∗∗∗, ∗∗, and ∗indicate significant at 1%, 5% and 10% critical level respectively.L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 27 / 34
Firm level empirical estimates
Figure 3: Kernel density of empirical estimates
L.Senyonga (HH-NMBU) The 33RD USAEE/IAEE Conference 28TH October 2015 28 / 34
Small-scale output is associated with higher RTS
Figure 4: Returns-to-scale by firm size and QoS
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Technical efficiency is positive related with RTS
With QoS consideration TE is negatively related to CENS
Figure 5: Technical efficiency and firm size (RTS)
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With QoS dimension into consideration,Small scale firms aremore efficiency and provide higher QoS
Figure 6: Technical efficiency and QoS
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Outline
1 Motivation and background
2 Research question and objectives
3 Data, variables, and methods
4 Results
5 Conclusion
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Conclusions
Substantial evidence of economies of joint production
Unexploited economies of scale mainly in small-scale firms
Ignoring QoS dimension in benchmarking is likely to cause:-I Underestimation of technical efficiencyI Overstating technical changeI Overestimating returns-to-scale and productivity growth
Small-scale firms are relatively more efficient and provide higherQoS.
Results are in support of proximity to customers hypothesis(Kwoka,2005)
Under incentive regulation, QoS should be part of everyeconomic, efficiency, and productivity analysis.
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