livingstone senyonga (phd candidate) olvar bergland school ... · olvar bergland (advisor) school...

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


Outline

1 Motivation and background

2 Research question and objectives

3 Data, variables, and methods

4 Results

5 Conclusion


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


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.


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.


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


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


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


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


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


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


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


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


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


Outline




4 Results

5 Conclusion


Data

Firm level historical data used by NVE for regulation

119 DSOs

Annual from 2004 to 2012


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


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


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


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.


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)]


Outline




4 Results

5 Conclusion


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 %


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


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


Small-scale output is associated with higher RTS

Figure 4: Returns-to-scale by firm size and QoS


Technical efficiency is positive related with RTS

With QoS consideration TE is negatively related to CENS

Figure 5: Technical efficiency and firm size (RTS)


With QoS dimension into consideration,Small scale firms aremore efficiency and provide higher QoS

Figure 6: Technical efficiency and QoS


Outline




4 Results

5 Conclusion


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.


Thank you very much for listening!

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