Productive Efficiency and Ownership When Market Restructuring Affects Production Technologies

Transcription

1 Productive Efficiency and Ownership When Market Restructuring Affects Production Technologies Astrid Cullmann, Maria Nieswand, Julia Rechlitz Graduate Center DIW Berlin AFiD-Nutzerkonferenz / 37

2 Outline 1. Introduction and Literature 2. Data 3. Model 4. Empirical Results 5. Conclusions 2 / 37

3 Outline I. Introduction and Literature 3 / 37

4 German Electricity Distribution Sector Figure: http: //zone.ni.com/reference/en-xx/help/ B-01/lveptconcepts/ep_grids/ DSOs operate networks which distribute electricity on a local level Tasks of DSOs: Operate the grid Provide a connection to each consumer Strength the network in a reasonable manner 883 firms in 2012 in Germany Natural monopoly 4 / 37

5 Surrounding conditions of DSOs Sector has undergone reasonable changes during the last 20 years: Liberalization Unbundling "Incentive" Regulation introduced in 2009 German Energiewende 5 / 37

6 Ownership Structure of the Distribution Sector Privatization paradigma in the 1990 s Recently, de-privatization (re-municipalization) Numerous concession contracts have been expired Increase of public influence Favourable conditions for remunicipalization Since 2005, about 200 networks have been remunicipalized 6 / 37

7 Remunicipalization There is an ongoing political debate about remunicipalization. Examples for referendums: Hamburg: successful remunicipalization in 2014 Berlin: rejection of the referendum in 2013 Critical view of German Monopolies Commission and German Cartel Office Fear a lack of efficiency of publicly owned firms Higher costs and prices for consumers 7 / 37

8 Literature: Ownership and Firms Performance Agency theory: principal-agent dilemma Property rights: public ownership attenuates property rights, (Alchian and Demsetz, 1973; Demsetz, 1967) Public choice: politicians impose their objectives on public firms, (Shleifer and Vishny, 1994; Villalonga, 2000; Boardman and Vining, 1989) Regulated Firms: Superiority of private versus public firms depends on contract (Laffont and Tirole, 1991 and 1993) 8 / 37

9 Empirical Evidence Conclusions from international empirical studies No differences between publicly and privately owned firms Atkinson and Halvorsen (1986) Private firms outperform publicly owned ones Bagdadiogul et al. (1996) Kumbhakar and Hjalmarsson (1998) Public firms reach a higher efficiency level Kwoka (2005) Empirical literature not conclusive. 9 / 37

10 Research Question Is there an efficiency gab between public and privately owned firms in electricity distribution? 10 / 37

11 Outline II. Data 11 / 37

12 Data Set AFiD Data Set German Federal Statistical Office (FDZ), official micro data All German utilities ( > 10 employees ) Panel covers the years 2005 to 2012 Ene t Data Set Information of the distribution networks Grid-specific network charges Characteristics of municipalities Panel covers the years 2003 to / 37

13 Definitions of Variables Variable Name Type Defintion y C number of consumers output variable in Thous. y E electricity distributed output variable in Mwh x N network length input variable in km x L amount of worked hours input variable in hours z D consumer density operation environment per km2 z O share of overhead lines operation environment km per km own ownership structure Dummy 13 / 37

14 Sample Size Number of Obs Number BNetzA / 37

15 Outline III. Model and Estimation 15 / 37

16 Introduction to Technical Efficiency 16 / 37

17 Model Multi-Output Production Technology via Input Distance Function IDF are extensively used for modeling inefficiency in electricity distribution (Kumbhakar & Sun, 2012) Inputs are endogenous and outputs are exogenous Firms minimize costs, input ratios are exogenous, (Das & Kumbhakar, 2012) IDF representation of the transformation function (Kumbhakar, 2013) X 1 1 = f ( X, Y ) ln x L,it = θ + β N ln ( x j,it) + k {C,E} γ k ln(q k,it ) + l {D,O} δ l ln(z l,it ) + v it 17 / 37

18 Flexible Stochastic Input Distance Frontier Model (Sun et al., 2015) Technology parameters are unknown smooth functions of firm and/or time effects (non neutrally shift) ln x L,it = θ(i, t) + β j(t) ln ( x j,it) j {N} + γ k (t) ln(q k,it ) k {C,E} + δ l (t) ln(z l,it ) + v it l {D,O} 18 / 37

19 Interpretation of Inefficiency Frontier concept: Difference between the minimal input and the actual observed inputs of the firms θ(i, t) = α(t) + m it with α(t) = max θ(i, t) i ln x L,it = α(t) + β N (t) ln ( x j,it ) + γ k (t) ln(q k,it ) k {C,E} + δ l (t) ln(z l,it ) + v it u it + µ i ηi l {D,O} m it ε it 19 / 37

20 Outline IV. Results 20 / 37

21 Estimation Results Table: Estimated coefficients of the input distance function Year ˆβN (t) ˆγ C (t) ˆγ E (t) ˆδD (t) ˆδO (t) * * * * * * * * * * * * * * * * * * * * * * Note: * denotes the significance at the 10 percent level. 21 / 37

22 Development of Input Coefficient Figure: Input coefficient, ˆβ N (t), over time Network β^ (t) N year 22 / 37

23 Development of Output Coefficients Figure: Output coefficients, ˆγ C (t) and ˆγ E (t), over time Customers Electricity γ^c (t) γ^ E(t) year year 23 / 37

24 Efficiency Scores Table: Transient efficiency scores TE tran,it = exp( u it ) Year Type 25% Quart. Median Mean 75% Quart public private public private public private public private public private public private public private / 37

25 Distribution of Transient Efficiency Scores Wilcoxon rank sum test with continuity correction H0: Both groups have the same mean (p-value = ) 25 / 37

26 Outline V. Conclusions 26 / 37

27 Conclusions The common technology frontier shows a development over time Publicly owned firms do not perform less efficient compared to private ones Concerns of the German Monopolies Commission are not supported 27 / 37

28 Thank you for your attention 28 / 37

29 Backup - Further Empirical Evidence Focus on US electricity sector (Atkinson and Halvorsen, 1986; De Alessi, 1974; Peltzman, 1971; Rose and Joskow, 1990; Neuberg, 1977; Peters, 1993; Pollitt, 1995; Kwoka, 2005) Studies of the EU s power markets are scarce (Kumbhakar and Hjalmarsson, 1998; Arocena and Waddams Price, 2002) 29 / 37

30 Backup - Summary Statistics Table: Summary statistics of the variables Variable Name Type 25% Quart. Median Mean 75% Quart. Std. Dev. y C customers public 7,996 15,707 25,572 26,776 36,998 private 2,889 14,361 45,906 44,176 72,686 y E electricity public 96, , , ,441 1,158,509 private 40, ,329 1,033, ,961 2,158,137 x N network public private ,676 1,426 2,788 x L labor public 47,672 94, , , ,701 private 8,098 22,329 91, , ,029 z D density public 486 1,062 1,165 1, private ,085 1, z O overhead public private / 37

31 Backup - Sample Size Sum Number of Obs Public Private Number BNetzA / 37

32 Backup - Estimation Strategy Adapted (Sun et al., 2015) to an input distance function Estimation of slope coefficients via Robinson transformation and nonparametric regression Robinson transformation: ln(x L,it ) = ln(x L,it ) E(x L,it i, t) ln(b it ) = ln(b it ) E(B it i, t) Estimation of the slope coefficients: ln(x L,it ) = φ(t) ln(b it ) + v it ˆφ(t) and ˆv i,t 32 / 37

33 Backup - Estimation Strategy Estimation of ˆθ(i, t) via nonparametric regression ln(x L,it ) = ˆφ(t) ln(b it ) + v it Decomposition of ˆθ(i, t) res it = ln(x L,it ) ˆφ(t) ln(b it ) ˆΘ(i, t) = E(res it i, t) ˆα(t) = max{ˆθ(i, t)} i ˆm it = ˆθ(i, t) ˆα(t) 33 / 37

34 Backup - Estimation Strategy Recall the definition of the components of ˆɛ it Rewrite ˆɛ it ɛ it = v it u it Trans.Ineff. + µ i Firm Effect η i Pers.Ineff. ɛ it = E( u it ) + E(µ i ) + E( η i ) + E(v it ) + α 0 µ i [η i + E( η i )] ψ i + v it [u it + E( u it)] χ it 34 / 37

35 Backup - Estimation Strategy Decomposition of ˆɛ it ˆɛ it = α 0 + ψ i D i + χ it ˆα 0, ˆψ i, ˆχ it Estimation of persistent efficiency ˆψ i = τ 0 + η i µ i ˆµ i Estimation of transient efficiency ˆχ it = λ 0 + v it u it û it 35 / 37

36 Backup - Robustness Checks Table: Estimated coefficients of the input distance function using stochastic frontier model Model ˆβN (t) ˆγ C (t) ˆγ E (t) ˆδD (t) ˆδO (t) SFA without firm effect *** *** *** *** * SFA with firm effect *** Note:*** denotes a significance level smaller than 0.1 percent, * denotes the significance at the 5 percent level. 36 / 37

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