Scholarly article on topic 'Regulation and Efficiency & Productivity Considerations in Water & Wastewater Industry: Case of Iran'

Regulation and Efficiency & Productivity Considerations in Water & Wastewater Industry: Case of Iran Academic research paper on "Economics and business"

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Abstract of research paper on Economics and business, author of scientific article — Alireza Ebrahimi Nourali, Mohammad Davoodabadi, Hamed Pashazadeh

Abstract Since the beginning of 1980s, many countries decided to reform and regulate some public utilities such as water, electricity, gas etc. Since, the public utilities specially water are managed as regional or local entities, the benchmarking approaches are therefore applied to compare the performance of local firms active in an industry on the basis of their relative efficiency along with ways that are used to determine the yardstick model for evaluating the performance of such enterprises. Thus, this study aims at measuring the efficiency of water & wastewater companies (WWCs) as incentive regulation tools for stimulating efficiency of production and supply through cost reduction and improving the quality of services provide by water distributors. In this study, the performances of 34 WWCs were assessed using non-parametric methods as “Data Envelopment Analysis” (DEA) in 2011. Furthermore, we reviewed the DEA-based Malmquist approach for total factor productivity (TFP) and technology change in WWCs over the period of 2008 to 2011. An input variable includes operating costs, number of employees (staff) and number of water connections and output variables are the volumes of water billed and the number of customers. The results of analysis indicate that the average efficiency of WWCs under constant return to scale (CRS) is equal to 77% (technical efficiency) and under variable return to scale (VRS) is equal to 88% (scale efficiency). In other words, given the existing resources and facilities, the potential to improve water production equals to 23% and 12% respectively. Whereas in terms of constant return to scale (CRS), the cost saving potential amounts to 1874 billion Rials or 16% of the operating costs (price=2011). Also, the Malmquist index for total factor productivity (TFP) and technology change are calculated as 0.951 and 0.940 respectively, indicating a decrease of productivity in the Iranian water & wastewater industry during 2008 to 2011.

Academic research paper on topic "Regulation and Efficiency & Productivity Considerations in Water & Wastewater Industry: Case of Iran"

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Social and Behavioral Sciences

Procedia - Social and Behavioral Sciences 109 (2014) 281 - 289

2nd World Conference On Business, Economics And Management - WCBEM 2013

Regulation and Efficiency & Productivity Considerations in Water & Wastewater Industry: Case of Iran

Alireza Ebrahimi Nourali a *, Mohammad Davoodabadi b, Hamed Pashazadeh c

a Department of Regulation, Planning & Economics Affairs, Ministry of Energy, Iran b Department of Financial Monitoring, National Water & Wastewater Eng. Co, Iran _c Faculty of Economics, Allameh Tabatabaee University, Iran_

Abstract

Since the beginning of 1980s, many countries decided to reform and regulate some public utilities such as water, electricity, gas etc. Since, the public utilities specially water are managed as regional or local entities, the benchmarking approaches are therefore applied to compare the performance of local firms active in an industry on the basis of their relative efficiency along with ways that are used to determine the yardstick model for evaluating the performance of such enterprises. Thus, this study aims at measuring the efficiency of water & wastewater companies (WWCs) as incentive regulation tools for stimulating efficiency of production and supply through cost reduction and improving the quality of services provide by water distributors. In this study, the performances of 34 WWCs were assessed using non-parametric methods as "Data Envelopment Analysis" (DEA) in 2011. Furthermore, we reviewed the DEA-based Malmquist approach for total factor productivity (TFP) and technology change in WWCs over the period of 2008 to 2011. An input variable includes operating costs, number of employees (staff) and number of water connections and output variables are the volumes of water billed and the number of customers. The results of analysis indicate that the average efficiency of WWCs under constant return to scale (CRS) is equal to 77% (technical efficiency) and under variable return to scale (VRS) is equal to 88% (scale efficiency). In other words, given the existing resources and facilities, the potential to improve water production equals to 23% and 12% respectively. Whereas in terms of constant return to scale (CRS), the cost saving potential amounts to 1874 billion Rials or 16% of the operating costs (price=2011). Also, the Malmquist index for total factor productivity (TFP) and technology change are calculated as 0.951 and 0.940 respectively, indicating a decrease of productivity in the Iranian water & wastewater industry during 2008 to 2011.

© 2014 The Authors. Published by Elsevier Ltd.

Selection and peer review under responsibility of Organizing Committee of BEM 2013.

Keywords: Incentive Regulation, Benchmarking, Data envelopment analysis (DEA); Malmquist Index;

1. Introduction

In most countries, infrastructural industries or public utilities such as water, electricity, gas, telecommunications and transportation, which are mostly run as monopolies, have always been owned and managed by the public sector, However the late 1980s, the industry started the privatization trend both in terms of management (private sector participation) and in terms of ownership (privatization). In addition to this, the services in some parts of or the entire industry have come out of monopoly. However, evidence shows that privatization can't improve the cost and the

* Alireza Ebrahimi Nourali. Tel.: +98(21) 81606789 or 00989127081606 E-mail address: ebrahimi@moe.gov.ir

1877-0428 © 2014 The Authors. Published by Elsevier Ltd.

Selection and peer review under responsibility of Organizing Committee of BEM 2013. doi: 10.1016/j.sbspro.2013.12.458

quality of services provided. Consistent with the provisions of a proper regulation, it is essential to provide sufficient incentives for operators in utilities to reduce costs and to increase efficiency. The regulation systems cover service cost, price cap, yardstick competition (YC) and franchise revenues. Some systems such as price cap and yardstick competition need benchmarking techniques as tools for regulation.

The process of benchmarking has long been used by private enterprises, but has only recently been applied in the public sector, particularly in the infrastructural services. As mentioned by Shleifer (1985), regulators need a simple benchmark, other than the firm's present or past performance, against which they can evaluate the firm's potentials and assure cost control, prevent waste, and promote cost reducing innovation. The basis of a benchmarking technique is the establishment of an efficiency frontier in which each firm occupies a relative position. Comparability of outputs and inputs across each firm is a key element, but the most crucial aspect is the selection of the measures of efficiency. The key point is that regulators must make choices reflecting overriding objectives of water users, firm managers, and policymakers. Selecting a measure of efficiency is also complicated by the fact that, although both financial and non-financial measures are used, there is a lack of an accepted framework for integrating the two.

In this article we have applied a particular benchmarking technique known as "Data Envelope Analysis" (DEA) to compare the performance of local firms operating in the water and wastewater industry of Iran. The performance and technical efficiency rankings extracted from the analysis can be used in the processes or systems of regularization including the price cap.

2. Need for Regulations in public utilities

According to economic theory, monopoly naturally provides a lower efficiency than the competitiveness, because firms holding monopoly have the market power to charge higher prices than the cost of the products and obtain a higher rate than normal profits. This leads to economic inefficiencies through higher prices, lower quality products, and non-optimal allocation of resources loss of welfare due to inefficiencies in the community.

Establishment of a regulation system is essential to ensure efficiency and to create incentives for monopoly firms in adjusting their pricing and production strategies to a fully competitive approach with the aim of protecting the consumers against monopoly power and maximizing the social welfare provided. Water and wastewater services in Iran were relegated to water and wastewater companies since 1990. According to legislation, these companies should be managed as private entities but the competition in this field is slow. It is necessary that this issue be investigated in Iran to actualize the potential for competition in the industry. In this context with proper regulation, regulatory reforms and deregulation of the economy, indicators of efficiency and public satisfaction should be defined to evaluate the provided services.

3. Regulation systems

In general, there are two types of approach to deal with the problem posed by the existence of natural monopolies. First, the regulatory agency approached. Second, the operation of a public enterprise that is self-regulated. The first approach is characterized by the presence of a regulatory agency, which has a duty to design regulatory schemes for natural monopoly operation, and the enforcement for compliance of such regulatory regime. Under the second general approach, there is a state owned company that operates as a public enterprise, which ideally, should allow the provision of services at least cost without the exertion of market power. In this second approach, because of political pressures, and financial constraints, self -regulated public companies are expected to provide water and sanitation services at very low tariff levels, without possibilities of raising enough revenues to fund their operational costs. They therefore face serious restrictions to develop investment intended to either improve supply coverage or the quality of the provision. For this reason, and considering the aim of this report, we

focused on the first approach to deal with natural monopoly problems; i.e. the existence of a regulatory agency. In this approach, there are different specific forms of regulations. Here we have considered cost-of-service (cost +), price-cap, yardstick competition, and franchise. A hybrid of these alternative systems can also be considered as alternative regulatory regimes^:

• Cost of service (Cost +)

• Price-Cap

• Yardstick Competition

• Franchise regulation

4. Benchmarking for regulatory purposes

One of the prime functions of quantitative benchmarking is to assist regulators to define the appropriate policy instruments for the water distribution sector, as well as for individual companies. Efficiency analysis and benchmarking was first applied to the price reviews of the UK water industry. The Water Service Regulation Authority (OFWAT) conducts these price reviews every 5 years (1994, 1999, 2004, and 2009). The approach is used by OFWAT in the 1994 review was described by Thanassoulis (2000a, b). OFWAT applied Data Envelopment Analysis (DEA) on a company-function level in order to facilitate discrimination in the model (i.e. fewer output variables; OFWAT recognized the potential problems resulting from limiting observations, but chose not to use panel data). The efficiency results were then compared to regression results, and entered into the price determination, with the exact usage being confidential. It is important to note that price caps are not automatically determined by OFWAT (stern, 2005), where the calculated efficiency scores are in practice not plugged one-to-one into the price cap formulas. The rationale is in general the reliability of data and performance measurement techniques as well as controlling the differences in operating characteristics and quality.

An outline of the use of DEA in the regulation of UK water companies by OFWAT through price cap is shown asPCj = RPIt + K]t, where PC]t is change to the average annual charges company j is permitted to make in year t, RPIt is the change in the Retail Prices Index from year t-1 to year is a company - specific factor,

determined by the regulator for year t.

5. Efficiency measurement concepts and Benchmarking Approaches

The primary purpose of this section is to outline a number of commonly used efficiency measures and to discuss how they may be calculated relative to an efficient technology, which is generally represented by some form of frontier function. Frontiers have been estimated using many different methods over the past 40 years (represented in figure1). The two principal methods are:

• Data envelopment analysis (DEA) and

• Stochastic frontiers analysis (SFA),

Which involve mathematical programming and econometric methods, respectively. This paper is concerned with the use of DEA methods. The discussion in this section provides a very brief introduction to modern efficiency measurement. A more detailed provided by Fare, Grosskopf and Lovell (1985, 1994) and Lovell (1993). Modern efficiency measurement begins with Farrell (1957) who drew upon the work of Debreu (1951) and Koopmans (1951) to define a simple measure of firm efficiency, which could account for multiple inputs. He proposed that the efficiency of a firm consists of two components: technical efficiency, which reflects the ability of a firm to obtain maximal output from a given set of inputs, and allocative efficiency, which reflects the ability of a firm to use the inputs in optimal proportions, given their respective prices. These two measures are then combined to provide a measure of

t - For more details about regulatory regimes, see Chavez CA, Quiroga MA. (2002).

total economic efficiency. The following discussion begins with Farrell's original ideas which were illustrated in input/input space and hence had an input-reducing focus. These are usually termed input-orientated measures. Farrell illustrated his ideas using a simple example involving firms which use two inputs (x1 and x2) to produce a

single output (y), under the assumption of constant returns to ...... Knowledge of the unit isoquant of the fully

efficient firm, represented by SS' in Figure 2, permits the measurement of technical efficiency. If a given firm uses quantities of inputs, defined by the point P, to produce a unit of output, the technical inefficiency of that firm could be represented by the distance QP, which is the amount by which all inputs could be proportionally reduced without a reduction in output. This is usually expressed in percentage terms by the ratio QP/0P, which represents the percentage by which all inputs could be reduced. The technical efficiency (TE) of a firm is most commonly measured by the ratio: TE = OQ / OPO

Which is equal to one minus QP/OP. It will take a value between zero and one, and hence provides an indicator of the degree of technical inefficiency of the firm. A value of one indicates that the firm is fully technically efficient. For example, the point Q is technically efficient because it lies on the efficient isoquant.

6. Non - Parametric Method: Data Envelop Analysis (DEA)

Data envelopment analysis (DEA) was pioneered by Charnes et al (1978) and based on the work by Farrell (1957). DEA is a linear programming technique, which estimates organizational efficiency by measuring the ratio of total inputs employed to total output produced for each organization. This ratio is then compared to others in the sample group to derive an estimate of relative efficiency. DEA identifies the most efficient providers of a good or service by their ability to produce a given level of output using the least number of inputs. Other organizations in the sample group receive an efficiency score determined by the variance in their ratio of inputs employed to outputs produced relative to the most efficient producer in the sample group. DEA is therefore a measure of relative efficiency against the sample group's benchmark best practice. The advantage is that it can be used without input or output prices, which is useful in the case of the water industry where these are often distorted by a lack of competitive forces or political decisions. Instead, simply volumes of output (including quality indicators) and inputs can be used. DEA analysis undertaken to date has tended to rely on a small number of variables (e.g. volume of water delivered, the number of properties connection; operating expenditure, capital) (see Lambert, Dichev and Raffiee 1993. Sawkins and Accam 1994; Thanassouloulis 2000, 2002; Coelli and Walding 1994. Garcia-Sanchez 2006), although there are also a number of instances where a greater number of variables have been utilized (see Anwandter and Ozuna 2002; Byrnes, Grosskopf and Hayes 1986; Woodbury and Dollery 2004).

Charnes proposed a flexible approach, Cooper & Rhodes CCR (1978) and it is relative efficiency measurement. The model can be briefly described as follows.

There are m inputs (indexed by the subscript i), s outputs (indexed by subscript r) and n decision making unitsDMUs indexed by subscript j); additionally one assumes that xtj > 0 and yrj > 0 denote positive inputs and outputs respectively. CCR consider the following optimization problem:

The problem needs to be solved for each DMU and represents the objective of maximizing a virtual output relative to a virtual input subject to the constraint that no DMU can operate beyond the efficiency frontier (constraint 2) and the constraint relating to non-negative weights (constraint 3). In this paper, we will focus on the behavior of technical efficiency as obtained from a DEA model that allows variable returns to scale. Inputs and outputs weights are endogenously determined as the solution to the problem for each DMU. In fact, Banker, Charnes & Cooper-BCC (1984) and Banker (1984) extended the CCR model from the constant returns to scale to the variable returns to scale case. That extension accounts to including an extra convexity restriction in the CCR model. An important result that emerges from those papers refers to the possibility of factoring total efficiency (as obtained from de CCR model) as

the product of technical efficiency (as obtained from the BCC model) and scale efficiency. Therefore, the BCC model is more relevant for analyzing sectors where variable returns to scale is an important feature.

The previously mentioned model admits a linear programming representation that has to be solved for each DMU. The resulting scores are relative efficiency measures where a score of 1 indicates an efficient unit whereas scores that are less than 1 indicate inefficient units. Inefficient units are identified by comparison with reference units (peers). Therefore, it is desirable that the involved units must be comparable. It is also important to emphasize that the model imposes few restrictions on the production set and an associated convenience is that one does not need to assume a direct transformation of the postulated inputs into the chosen outputs.

7. Malmquist Productivity Index

So far, the focus has been on evaluating firm performance at appointed in time. To evaluate the efficiency change overtime, the Malmquist productivity index is issued in the following analysis:

Suppose each DMUj(J = 1,2,..., n) produces a vector of outputs Kj = (K^, — ' ^Ij) by using a vector of inputs Xlj = (Xy,...,Xlmj)at each time period t, t=1, 2, T. When multiple inputs are used to produce multiple outputs, Shephard's (1953) distance functions provide a functional characterization of the Structure of production technology. The output distance function is defined on the output set, P (x), as:

do(x,y) = min{8-.y/S e P(x)} (1)

The Malmquist productivity index is defined as:

0 < (2)

M0 Measures the productivity change of DMU0 between period t and t+1. A value greater than one indicates positive productivity growth from period t to period t+1. A value less than one indicate negative productivity growth from period t to period t+1(Fareetal.1994a).

The Malmquist productivity index can be decomposed in to two components: efficiency change (catch-up effect) and frontier shift (technological change).

Efficiency change: EC=

4+1(4+'.>'ir'y

Technology Change: TC= (4)

M0 = ECxTC (5)

According to Fare et al. (1994b), EC can be further decomposed into scale efficiency change and pure technology change. Ray and Desli (1997) pointed out the internal potential inconsistency problem of the further decomposition — both CRS and VRS models are used in the same decomposition. Consequently, the current paper uses the accepted decomposition shown in (6).

8. Inputs and Outputs Variables

Table 2 presents the name of input and output variables. And Table 3 presents means and standard deviations for input and output variables of the 35 water & wastewater companies.

9. Results

Technical and scale efficiency scores for water & wastewater companies can be found in table 4. Also the potential cost saving and productivity changes based on Malmquist Index can be found in tables 4 and 5 respectively.

10. Conclusion

The results of analysis indicate that average efficiency of WWCs under constant return to scale (CRS) is equal to 77% (technical efficiency) and under variable return to scale (VRS) to 88% (scale efficiency). In other words, there is a possibility for improvement of water production equal to 23% and 12% respectively with the existing resources and possibilities. Also, in terms of constant return to scale (CRS), by improving technical efficiency, cost savings potential account to 1874 billion Rials, which is over 16% of the total operating cost of 35 major water & wastewater companies in 2010/2011. Also, the Malmquist index for total factor productivity (TFP) and technology change were calculated as 0.951 and 0.940 respectively, indicating a decrease of productivity in Iran's water & wastewater industry during 2008 to 2011.

The results prove that DEA analysis is a powerful tool for water industry regulators who seek to defend the public interest against the potential abuse of monopoly power and to encourage water providers to improve efficiency.

11. Tables

Table 1: DEA Model Specification

Variables Model 1 CCR; Model 2 BCC

Operating costs

Inputs Number of Employees

Number of water connections

Outputs Volume of water billed

Number of customers

Table 2: Means and Standard Deviations for Efficient and Inefficient water & wastewater companies

Sample Summary Statistics

Variable Mean Standard Deviation Minimum Maximum

Outputs :

Water Billed (m3) 117,746,791 196,727,822 22,464,299 1,201,337,762

Number of customers 528,413 733,183 104,977 4,397,155

Inputs :

Operating costs (Million Rials) 342,748 651,902 60,210 4,002,776

Number of Employees 616 817 133 4,937

Number of water connections 410,051 474,822 104,196 2,874,757

Table 3: Technical & Scale Efficiency Scores

Firm Efficiency Scale Firm Efficiency Scale

CRS VRS SC type CRS VRS SC type

1 0.917 1 0.917 drs 19 0.674 0.706 0.955 drs

2 1 1 1 - 20 0.856 0.858 0.998 irs

3 0.699 0.745 0.938 irs 21 0.504 1 0.504 irs

4 1 1 1 - 22 0.954 0.958 0.995 irs

5 0.482 0.99 0.487 irs 23 0.723 0.76 0.952 irs

6 0.667 0.912 0.731 irs 24 0.892 0.897 0.994 drs

7 0.971 1 0.971 drs 25 0.886 0.933 0.949 irs

8 0.568 0.844 0.673 irs 26 0.78 1 0.78 irs

9 0.693 0.697 0.994 drs 27 0.797 0.957 0.833 irs

10 0.778 0.817 0.952 drs 28 0.834 0.842 0.99 irs

11 0.612 0.857 0.715 irs 29 0.56 0.618 0.906 irs

12 0.632 0.783 0.807 irs 30 0.635 1 0.635 irs

13 0.593 0.733 0.809 irs 31 0.74 0.759 0.976 drs

14 0.488 0.495 0.986 irs 32 0.608 0.747 0.814 irs

15 1 1 1 - 33 0.934 0.952 0.981 irs

16 0.947 0.959 0.987 irs 34 0.902 0.905 0.997 irs

17 0.815 1 0.815 irs 35 1 1 1 -

18 0.915 0.922 0.993 irs Mean 0.773 0.876 0.887

CRS = Constant returns to scale, VRS = variable returns to scale, irs= increasing returns to scale, drs= decreasing returns to scale, -

SC = scale efficiency = constant returns to scale

Table 4: Potential Cost Saving DMUs Score Inefficiency Operation Potential Cost Score Scale

DMU_01 0.917 0.083 370115 30,720 1 0.917

DMU_02 1.000 0 211957 - 1 1.000

DMU_03 0.699 0.301 138159 41,586 0.745 0.938

DMU_04 1.000 0 557377 - 1 1.000

DMU_05 0.482 0.518 113517 58,802 0.99 0.487

DMU_06 0.667 0.333 169156 56,329 0.912 0.731

DMU_07 0.971 0.029 4002776 116,081 1 0.971

DMU_08 0.568 0.432 123686 53,432 0.844 0.673

DMU_09 0.693 0.307 276341 84,837 0.697 0.994

DMU_10 0.778 0.222 636030 141,199 0.817 0.952

DMU_11 0.612 0.388 168700 65,455 0.857 0.715

DMU_12 0.632 0.368 133659 49,186 0.783 0.807

DMU_13 0.593 0.407 385646 156,958 0.733 0.809

DMU_14 0.488 0.512 484550 248,089 0.495 0.986

DMU_15 1.000 0 88862 - 1 1.000

DMU_16 0.947 0.053 131192 6,953 0.959 0.987

DMU_17 0.815 0.185 78763 14,571 1 0.815

DMU_18 0.915 0.085 96217 8,178 0.922 0.993

DMU_19 0.674 0.326 289599 94,409 0.706 0.955

DMU_20 0.856 0.144 180326 25,967 0.858 0.998

DMU_21 0.504 0.496 96885 48,055 1 0.504

DMU_22 0.954 0.046 184771 8,499 0.958 0.995

DMU_23 0.723 0.277 176659 48,935 0.76 0.952

DMU_24 0.892 0.108 357514 38,611 0.897 0.994

DMU_25 0.886 0.114 241738 27,558 0.933 0.949

DMU_26 0.78 0.22 60210 13,246 1 0.780

DMU_27 0.797 0.203 310500 63,032 0.957 0.833

DMU_28 0.834 0.166 151406 25,133 0.842 0.990

DMU_29 0.56 0.44 279839 123,129 0.618 0.906

DMU_30 0.635 0.365 73492 26,824 1 0.635

DMU_31 0.74 0.26 221823 57,674 0.759 0.976

DMU_32 0.608 0.392 223533 87,625 0.747 0.814

DMU_33 0.934 0.066 229437 15,143 0.952 0.981

DMU_34 0.902 0.098 390614 38,280 0.905 0.997

DMUs Score Inefficiency Operation Potential Cost Score Scale

DMU 35 1.000 0 361140 - 1 1.000

0.7730286 1,874,498 0.8756 0.887

Table 5: Productivity changes based on Malmquist Index

Firm EFCH TCH PECH SECH TFPCH Firm EFCH TCH PECH SECH TFPCH

1 1.032 1.012 1.071 0.963 1.044 18 0.959 0.946 0.967 0.992 0.907

2 1 1.049 1 1 1.049 19 1.011 0.916 1.001 1.01 0.926

3 0.945 0.949 0.966 0.979 0.897 20 1.008 0.955 1.012 0.996 0.962

4 1 1.007 1 1 1.007 21 1.083 0.87 1 1.083 0.942

5 1.034 0.835 1.007 1.027 0.863 22 1.072 1.011 1.053 1.018 1.084

6 1.027 0.915 0.996 1.032 0.94 23 0.882 0.899 0.884 0.998 0.793

7 1 0.865 1 1 0.865 24 1 0.921 1 1 0.921

8 0.978 0.948 0.958 1.02 0.926 25 1.038 0.99 1.027 1.011 1.028

9 1.029 0.953 1.033 0.996 0.98 26 0.998 0.999 1 0.998 0.997

10 1.103 0.801 1.091 1.011 0.883 27 1.02 0.954 1.042 0.979 0.973

11 1.025 0.922 1.011 1.014 0.944 28 0.998 0.954 1.008 0.99 0.952

12 0.931 0.937 0.934 0.997 0.873 29 1.082 0.931 1.103 0.982 1.008

13 1.06 0.872 1.112 0.953 0.925 30 0.973 0.982 1 0.973 0.955

14 0.993 0.872 0.976 1.018 0.866 31 0.992 0.99 0.993 0.999 0.982

15 1 0.946 1 1 0.946 32 1.025 0.943 1.014 1.01 0.966

16 1.034 0.954 1.014 1.02 0.986 33 1.07 0.925 1.026 1.043 0.99

17 1.05 0.964 1 1.05 1.012 34 0.973 1.029 0.965 1.008 1.001

mean 1.012 0.94 1.007 1.005 0.951

EFCH = efficiency change, TCH = technical change, PECH = pure efficiency change, SECH = scale efficiency change, TFPCH = total factor productivity change

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