Cost of unserved power in Karnataka, India

ARTICLE IN PRESS

Energy Policy 34 (2006) 1434–1447 www.elsevier.com/locate/enpol

Cost of unserved power in Karnataka, India Ranjan Kumar Bosea,, Megha Shuklaa, Leena Srivastavaa, Gil Yaronb a

The Energy and Resources Institute, Habitat Place, Lodhi Road, New Delhi 110003, India b GY Associates Limited, 32 Amenbury Lane, Harpenden, Herts AL52DF, UK Available online 8 November 2005

Abstract This paper proposes an empirical analysis concerning the cost of unserved energy (CUE) or value of lost load in agriculture and industrial sectors and provides insights that can provide useful inputs in designing effective policies for the power sector. About 500 manufacturing units and 900 farmers were surveyed in the south Indian state of Karnataka using a two-stage random sampling to provide interval estimates of CUE for the industrial and agricultural consumers. The results from the survey help in providing guidance on consumer perceptions and their willingness to pay different or higher tariffs. The estimated economic loss due to power outage in the agriculture sector varies from 1.9% to 3.6% of total State Domestic Product (SDP), i.e., Rs 950 billion at 1999/2000 prices, while in industry, the economic loss varies between 0.04% and 0.17% of total SDP depending upon the size of industry during the study period in 1999. r 2005 Elsevier Ltd. All rights reserved. Keywords: Electricity shortages; Opportunity cost; Willingness to pay

1. Introduction Reliable supply of electricity plays a key role in the economic and social development of India. Electricity is an important input in many industrial processes in the manufacturing industries, for computerization and modernization of the service sector, for irrigation and hence increasing the productivity of the agriculture sector, and in the household sector. India’s power sector has been characterized by large and frequent interruptions to electricity supply, arising from shortages of both capacity and energy. These shortages have been driven by both supply and demand factors. A key factor on the supply side has been the shortage of investment funds and the inability of the central and state governments to mobilize adequate resources for capacity expansion and modernization from public budgets (Planning Commission, 2001). Shortages have also been driven by the relatively poor performance of much of the existing capacity. On the demand side, the situation has been exacerbated by the inadequate tariffs

Corresponding author. Tel.: +91 11 24682100; fax: +91 11 24682144.

E-mail address: [email protected] (R.K. Bose). 0301-4215/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2005.09.017

and the failure to control non-technical losses, particularly theft. The demand for electricity for the country during the year 2003–04 was 559 billion kWh (kiloWatt hour) while the peak load demand was 84.6 GW (gigaWatt) (The Energy Resources Institute (TERI), 2004). Of the energy consumed by various classes of consumers in India, the industrial sector accounted for the largest share of
34% of the total energy sold by public utilities while agricultural consumers remained the second largest with a share of
25%. Since the availability of power was short of this demand, the country experienced a shortage of 7.1% in energy and 11.2% in peaking power. Recognizing the ever increasing gap between demand and supply, and its own inability to continue providing the budgetary support required to reduce this gap, the Government of India, in the year 1991, decided to appropriately restructure the Indian power sector in a phased manner (Baijal, 1999). While the central government started by promoting private sector participation in electricity generation, it was the state of Orissa that was the first to adopt comprehensive reforms including a vertical unbundling of the integrated electricity utility and private participation in

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electricity distribution. Many state governments in the country have since formulated reform programmes to address both the supply and demand factors. Most of these reform programmes are aimed at attracting private sector finance, particularly for generation and distribution, and changing incentive systems. Regarding the latter, a key part of the reforms is to ensure that tariff levels will be more reflective of the actual cost to serve and are set to ensure financial viability of power sector entities. Even in the best of systems, it is extremely difficult to estimate the cost to serve electricity to different categories of consumers due to the challenge of dealing with common cost allocations. In India, the additional challenge is of dealing with the political pressures that tend to provide a price protection to agricultural and domestic consumers— especially when a service is being provided through a public utility. Often such price protection is sought with little or no consideration to the quantity and quality of service provided. Due to such pressures, the electricity tariff, charged for Bhagya Jyothi1 category for domestic lighting in rural areas of Karnataka, is based on a fixed charge and energy charges. The tariff for the agricultural sector is a flat tariff based on the capacity (hp rating) of the agricultural pumpset in use.Over and above that, an energy charge per kWh is applicable but this tariff works out to be very low compared to actual cost of power supply. On the other hand, for the industrial sector the tariff break up in LowTension (LT) category has a fixed charge/hp motor (which differs based on the capacity of pump-sets in use) demandbased tariff/kW and energy charges. While for HighTension (HT) category industrial sector, the tariff is calculated based on applicable demand charges per kilo volt ampere (kVA) per month and energy charges per kWh (this varies for units consumed upto 100,000 and above from Rs 3.50 to Rs 4.00/kWh) (D’Sa and Narasimha Murthy, 2002). In such circumstances, it is important to have good estimates of the economic value of unserved energy or lost load. The cost of unserved energy (CUE) or the value of lost load will be of crucial importance in arguing for a more rational tariff for different classes of consumers and for determining the financial viability of new generation capacity. The CUE estimates will become more important as electricity supply businesses move towards the use of interruptible supply tariffs, and for the setting of time-ofuse tariffs. Of course a pre-condition to this option would be complete metering of all consumers. Given this background, TERI in association with London Economics (LE) and with financial support from 1 Bhagya Jyothi is a state government scheme where the beneficiaries are people living below the poverty line. Under this scheme, Rs 30 per month is supposed to be collected from beneficiaries who have one bulb. Those who have Bhagya Jyothi connections but possess more than one bulb are given meters by the Electricity Supply Companies and bills are paid according to the meter reading. However, the success rate for bill collection in rural areas is still very low in Karnataka.

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the World Bank and DFID undertook an empirical study titled ‘CUE’ (TERI, 2000). The objective of the study was to estimate the CUE in the agricultural and industrial sectors in two Indian states, namely Haryana and Karnataka, during the period from July 1998 to June 2000 and to determine the overall economic losses in these two states due to power outages in the two sectors based on the estimated CUE. This paper presents the study results for Karnataka state that provides guidance on consumer perceptions and their willingness to pay different or higher tariffs in the industrial and agriculture sector. These two sectors have been selected due to (i) their high shares in total energy consumption, and (ii) the cross subsidy provided by the industrial sector to the agricultural sector. Having said that it needs to be recognized that agricultural electricity consumption is largely unmetered leading to unaccounted consumption and Industry’s dependence on grid supply is declining with increased dependence on captive generation. The paper is divided into six sections. Section 2 provides a description of the economic profile of the state and a brief overview of the electricity sector. Section 3 elaborates the methodological framework that is used for estimating CUE and the economic loss due to power outage in the two sectors. Section 4 summarizes the CUE estimates and analyses the key factors influencing the study results. Section 5 highlights the policy implications that can provide inputs in designing effective policies for the power sector. Section 6 presents concluding remarks particularly on the three alternative methods used for deriving CUE estimates and the limitations of the methods. 2. The study area: Karnataka state 2.1. Socio-economic profile Karnataka is a state located in the southern part of India with an area of 192,000 km2 and is divided into 27 districts with over 66% of its population residing in rural areas. The 2001 Census results reveal that the state’s population is 52.9 million as against 45.0 million a decade ago with a density of 275 persons/km2. The decennial growth rate (between 1991 and 2001) is 17.25%. The urban and rural growth rate composition is 28.85% and 12.05%, respectively (GoK, 2005). The state registered a 7.8% growth rate of gross state domestic product (SDP) over the last year. The Planning Commission has targeted an ambitious growth rate of 10.1% till the end of 2006/07 for the state (GoK, 2005). An early, good monsoon gave an impetus to agricultural growth during the year 2004–05 which resulted in a considerable improvement in the production of all crops and trigged an upsurge in the growth rate of primary sector to 6.7%. The estimated growth rate for the secondary sector was 6.5% during 2004–05, which is mainly attributable to better performance in the manufacturing and information technology sector. The tertiary sector will

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Table 1 Progress in power sector in Karnataka Description

Units

2001/02

2002/03

2003/04

Total installed capacity (cumulative) Electricity generation Electricity imported (total from central and other states) Pump sets electrified (cumulative)

MW MkWh 106 kWh 105

4748 20054 7609 13

5147 19015 9043 14.02

5880 18675 10220 14.16

Source: KPTCL: Post 1 June 2002, the data given in Table 1 are not published by KPTCL and is compiled by each of the four power distribution companies. The data given in the table are collected from General Manager, Corporate Office, KPTCL.

register an anticipated growth of 12% at current prices. This has been mainly because of software exports and significant contributions from the communication and transport sectors. 2.2. Power sector profile The reforms in power sector in Karnataka started in early 1990s in order to improve the sector’s performance (see Rao, 2004). The electricity generation in the state has come down over the last 3 years, i.e., from 2001–02 to 2003–04 and the dependence on other states for meeting electricity requirements has increased as given in Table 1. The secondary data on the consumption side reveals that the share of domestic and agriculture sectors in total power consumption has increased considerably, while the share of industrial sector in the power supply from the grid has drastically declined over the years implying a substantial growth in the captive generation during this period. The major consumer category of electricity in Karnataka is agriculture, mainly for irrigation practices. The electricity consumption for irrigation pump-sets in Karnataka has almost doubled over a period of 12 years between 1992/ 93 and 2003/04. It has increased from 5340 million units in 1992/93 to 8929 million units in 2003/04 according to the Karnataka Power Transmission Corporation Limited (KPTCL). Of the total 22 TWh (teraWatt hour) of electricity sold to various consumers in Karnataka in 2003–04, share of agriculture consumption was 41%, industrial 24% (with LT power 6% and HT power 18%),2 domestic 21%, and commercial 7%. Traction 0.2%, public water works 2%, public lighting 2.8%, distribution licensee and KPC (Karnataka Power Corporation) installations and temporary supply 2% consume the balance 7% electricity. In its tariff filing for the financial year 2001 (FY01), KPTCL maintained that un-metered consumption by electric pump-sets has a great bearing on the estimation of T&D losses on account of inherent errors in estimation. 2 In Karnataka, industries with contracted demand below 63 kVA or any industry with contracted demand (or sanction load) given in hp (horse power) is classified as LT consumers, and those with contracted demand of 63 kVA and above are classified as HT consumers.

KERC envisaged a reduction of 12.5% from the level of 36.5% in 2 years covering 2000–2002, but the actual reduction was only 1.6% by the end of FY02. The Commission fixed 28% as the T&D loss target for FY03, which again remained unmet. 3. Methodological framework Considering the multiple ways unserved energy can affect productivity, it is hard to decide a method to estimate CUE. The CUE would vary from case to case depending on any or all of the following factors.

   

The customer who suffers because of power outage or fluctuations in voltage and frequency. Whether and to what extent the consumer is forewarned about such interruptions of supply. The time of the day and the season during which the supply fails or fluctuations. The coping strategies that the customer may have in place

Since the study only covers industrial and agricultural consumers and not other categories of consumers (like domestic, services and commercial), it cannot provide an estimate of the total weighted average CUE and will need to be supplemented by data procured from the remaining consumer classes. The importance and potential value of the study lies in the following three main areas.

  

It is the first extensive use of contingent valuation methodology (CVM) to estimate the willingness to pay for reliable electricity supply, in India or elsewhere.3 The use of three different methods to measure CUE provides a useful assessment of the reliability of the CVM in the power sector. Perhaps most important, the results of the study provide potentially valuable information for the on-going

3 CVM has primarily been used to estimate the economic costs that people assign to environmental damage but has also been used to estimate how much consumers value improved services—e.g., the value of clean water. Carson et al. (1995) provides a bibliography of over 2000 contingent valuation papers and studies from over 40 countries. For guidance on the use of CVM and other stated preference techniques see Pearce et al. (2002).

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process of reforming the power sector in many states and for the reform of tariffs. About 500 manufacturing industries and 900 farmers were randomly surveyed in Karnataka, spread over the entire state, using pre-tested schedules to collect primary data for the estimation of CUE in the two sectors, namely agriculture and industry. A two-stage stratified random sampling method was used for collecting field data from both industrial and agricultural consumers in the state. The method used for estimating CUE and sample survey design for data collection is described below. 3.1. Estimation of CUE This study provides an interval estimate of CUE using the following three methods covering the industrial and agricultural consumers in Karnataka state:

  

Production loss method, which provides the value of production loss for each unit of power outage. (direct assessment) Captive generation method, which provides the cost of alternative or back-up power generation. (indirect assessment) Willingness to pay method (WtP), which uses the contingent valuation technique for reliable and uninterrupted electricity supply.

3.1.1. Direct assessment (Method 1) The direct assessment approach uses questionnaires to ascertain the cost of an interruption of supply or a reduction in its quality as perceived by industrial and agricultural consumers. The approach provides a useful first indication of the possible costs of power outages. The direct assessment or the production loss method is defined as the loss in production due to the non-availability of electricity supply from the grid. The production loss per unit of power outage by the ith consumer denoted by Li within a particular category (i.e., industry or agriculture) is estimated by using the following formula (1):

Li ¼

weighted average of Li would then provide the average cost of production loss per unit of electricity not available and is denoted by L. The estimated value of L expressed mathematically in Eqs. (2) and (3) provides the CUE within a consumer category. This method is referred to as the direct assessment method, or Method 1 P Li U i L ¼ iP , (2) Ui i

Li ¼

ðPi =Oi Þ , ðU i =Ai Þ

(3)

where L is the weighted cost of production loss per unit of power outage from the grid, expressed in Rs/kWh; P the annual production opportunity loss, expressed in Rs; A the annual hours of electricity available from grid; O the annual hours of electricity not available from grid; U the annual electricity consumption from the grid, expressed in kWh; and i the number of valid cases. Ideally, for estimating the CUE by the production loss method, it is important that net value lost by a consumer due to power outages is ascertained. However, in spite of best efforts, a large number of surveyed industries reported only the gross production loss figures without giving any account for (1) value of scrap and (2) production loss which are generally saved by adopting various coping methods to minimize the loss. As a result, in the industrial sector, the estimated cost of power outages is based on the reported value of gross production loss attributable to disrupted power supply. Such is not the case in agriculture sector where the farmers have provided notional values for the increased yield due to additional irrigation made possible by the extra hours electricity is available from the grid. In the agriculture sector, therefore, it is the net production loss attributable to power outages which is ascertained and used in estimating the CUE. More specifically, the estimation method used in agriculture is based on the incremental crop output not realized (opportunity lost) due to non-availability of power for irrigation. Thus, the approaches used to estimate the CUE in agriculture and industries are different.

ðvalue of production loss=hours of outageÞi . ðelectricity consumed from grid=hours of electricity available from gridÞi

However, by taking the simple average of Li’s across consumers within a particular category, one cannot capture correctly the production loss per unit outage for the category. This is due to the large variations in power consumption and size of the consumers. To correct this, it is necessary to assign a weight parameter to Li and compute the weighted average. In view of this, electricity consumption from the grid by the ith consumer was considered to be the weight corresponding to Li. The

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(1)

3.1.2. Indirect assessment (Method 2) The indirect assessment method for estimating the CUE uses data on the costs that customers incur to ensure a reliable power supply. These include the costs of investing in auto-generators or captive power units and diesel pumps. The indirect assessment method has been widely used and can provide useful information on the implicit value of a unit of electricity.

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The economic cost of captive power generation or diesel pumping for the ith consumer using jth back-up unit denoted by Cij is given by the following formula (4): C ij ¼

willingness to pay (WtP) a particular amount to fix the problems they face is a fair estimate of the CUE for an industrialist or a farmer.

ðannualized capital cost þ annual maintenance cost þ annual fuel costÞij . ðtotal units of electricity generated in a yearÞij

Further, the average economic cost of captive power generation or diesel pumping for the ith consumer denoted by Ci would be given by the weighted average of the economic cost of electricity generated by each captive unit or equivalent electricity generated by the diesel pump-set of the ith consumer. The weights assigned are—the amount of electricity generated by each captive unit or the equivalent of electrical energy produced to run each pump-set. Mathematically, the following Eq. (5) is used to estimate Ci: 0P 1 C ij U ij B j C Ci ¼ @ P (5) A. U ij j

Finally, the average economic cost of back-up power generation across all consumers within a particular category is weighted by the electricity generated by the captive units or equivalent electricity generated by diesel pumps. Such a weighted average denoted by C provides the CUE within a consumer category using an indirect assessment method, referred to as Method 2. Mathematically, this is expressed in Eqs. (6)–(8). P Ci U i i C¼ P , (6) U ij ij

C ij ¼

K ij Rj þ M ij þ F ij , U ij

(7)

Rj ¼

r , 1  ð1 þ rÞnj

(8)

where C is the annualized cost of back-up power or diesel pumping, expressed in Rs/kWh; K the capital cost of the back-up device at current prices, expressed in Rs; U the electrical units generated by the back-up units, expressed in kWh; R the capital recovery factor; M the annual operating and maintenance cost, expressed in Rs; F the annual fuel cost of back-up generation, expressed in Rs; r the annual rate of interest (assumed to be 10%); n the total life of the back-up device, expressed in years; j the number of back-up units; and I the number of valid cases. 3.1.3. Willingness to pay survey (Method 3) Consumers who believe that there is a problem with their current electricity supply may be willing to pay more for increased hours, stability, and regularity of supply. This

(4)

Eliciting this WtP is done using the CVM. This method of estimating the CUE differs from the two methods discussed earlier as these attempt to infer the cost of supply interruptions either from the value of production lost due to these interruptions (Method 1) or from the cost of alternative supply sources (Method 2) while the CVM approach allows us to estimate the CUE even when there are no data on the alternative cost of supply. This is an advantage as an industrialist’s recall of the value of production lost due to certain hours of planned and unplanned outages is likely to be an overestimate of the CUE to the extent that some mitigating action can be taken (by rescheduling production or re-using some spoiled products). The CVM approach allows us to capture just how serious supply interruptions are to this industrialist. However, any existing investment in back-up generation will tend to reduce the WtP and hence, in India, the results from this method for those with back up are inevitably conservative. The marginal value of improved supply produced by the WtP estimates applies only to those who are willing to pay for improvements. In this respect, the WtP approach is analogous to the cost of alternative supply, in which we record a CUE estimate only for those with back-up generators. In contrast, the value of lost production is recorded for all producers (whether or not there is any production loss). An average value can be derived by combining the WtP for improved supply (for those WtP40) with the current payment (for those WtP ¼ 0). This is an ad hoc measure but useful as a means of ascertaining how much more would be paid for improved supply if the current price is taken as the minimum acceptable figure. There is considerable academic debate over the appropriate way of asking for people’s WtP (the bid elicitation method) in contingent valuation studies. Dichotomous choice (closed ended) formats typically produce higher response rates and is incentive-compatible but is also vulnerable to ‘yea-saying’ and requires larger sample sizes. In contrast, open-ended formats are more demanding to answer and tend to produce underestimates of the true WtP but require smaller samples. An open-ended question also has to be used for a sub-sample of respondents to produce sensible ranges for dichotomous choice questions. Of the various open-ended payment vehicles, the straightforward ‘What is your maximum WtP?’ question did not appear well suited to this study. Pre-testing showed that even where people said they agreed in principle to

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paying more for an improved service, they found it difficult to name a sum straight away. The choice thus left open was those of a bidding game and a payment card. Both these methods fall somewhere between strict referendum and open-ended formats. The range of prices for the bidding game (for each consumer group) was created by asking open-ended questions using in-depth interviews in the pre-testing phase of the questionnaire in the field. It is generally agreed that the starting point (the first price asked) for the bidding may influence the final price bid. One approach to this potential ‘starting point bias’ is to start the bidding game at low prices. However, our experience in India and in other developing countries suggests that this causes more problems than it solves. Starting with a very low price tends to signal that state subsidies may be available, making respondents less likely to give a feasible bid. Besides, starting with a low bid level runs counter to the well-established bargaining principle of beginning with a high price. Consequently, the bidding game always started with the highest price being offered first. To balance the values thus arrived at, it was necessary to derive mean bid values which are taken as the mid-point between the highest price accepted and the lowest price rejected, i.e., if a respondent says ‘Yes’ to Rs 4/ kWh but ‘No’ to Rs 4.5/kWh, the bid is taken as Rs 4.25/ kWh. Effectively, using these three methods of estimating the CUE produces three different values. The results of all the three methods are compared and then combined to arrive at the best-possible estimate of the CUE. While interpreting the results, the following approximate relationships between the approaches must be kept in mind:

and LT industries separately, from the survey data of 1999–2000 for Karnataka as given in Eq. (9)



where E is the economic loss due to power outages in agriculture sector, expressed in Rs; CUE the CUE value estimated using two methods mentioned above, expressed in Rs/kWh; A the additional power demand estimated from survey of 1999–2000 in agriculture sector, expressed in kWh; and k the method used to estimate CUE values. These estimates are further used to determine the percentage share of losses to total SDP at 1999/2000 prices and as percentage share of loss to sectoral SDP for the state.





The direct cost method (or method 1) may overestimate the CUE, since it may be possible to avoid or reduce some of the costs by rescheduling production, etc. The indirect cost estimate (or Method 2) is likely to underestimate the CUE, since it will simply have shown that industrial and agricultural enterprises are willing to spend, or are already spending, at least this amount (though they may be willing to pay substantially more). The WtP estimate (or Method 3) will also underestimate the CUE to the extent that investments to avoid interruptions to supply have already been made.

E i ¼ CUEj Ai ,

(9)

where E is the economic loss from HT and LT sectors, expressed in Rs; CUE the CUE values from three methods, expressed in Rs/kWh; A the additional power demand estimated from the survey from each sector, expressed in kWh; i the sector, LT and HT; and j the method used for estimation of CUE. The CUE estimate from the production loss method could lead to an overestimation as often industries revealed gross value of production loss instead of net values. This has been corrected by multiplying the estimated values of CUE from this method by the ratio of value added to value of output in industries. These estimates are further used to determine the percentage share of losses to total SDP at 1999–2000 prices and as percentage share of loss to sectoral SDP for the state. 3.2.2. Agriculture The economic losses from the power outages in agriculture sector of Karnataka have been determined using the estimated value of CUE in agriculture derived from the net production loss method and cost of alternative generation method using electric pump-sets. The other methods of alternative power generation using diesel pump-sets and willingness-to-pay methods have not been used for this purpose.5 These CUE estimates are multiplied by the additional power demand estimated from the survey data of 1999–2000 from this sector as given in Eq. (10). E k ¼ CUEk A,

(10)

3.3. Sampling design 3.2. Economic loss due to power outage 3.2.1. Industry The levels of economic losses for Karnataka due to power shortages in industries are estimated using the value of CUE/kWh estimated by the three methods multiplied with the additional power demand4 as estimated for HT 4

Estimates of additional power demand for a sector were arrived based on the feedback of consumers selected in the survey undertaken in the year 1999–2000.

A two-stage stratified random sampling method was used for collecting field data from both industrial and 5 The cost of alternative generation method using diesel pump-sets is not considered as the depth of water table is very low in the state and almost all farmers rely only on electric pump-sets. As a result, the cost of using diesel pump-sets does not provide a statistically significant result for estimating CUE due to the limited number of cases. In case of the WtP method, an insignificant percentage of farmers agreed to pay more than the current tariff per unit. Therefore, a statistically significant estimate of CUE using the WtP method was also not possible.

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agricultural consumers in 1999. In this method, the sampling is carried out in stages. At the first stage, the first-stage sampling units are sampled by some suitable random method. At the second stage, a sample of secondstage units is selected from each of the selected first-stage units, again by some suitable random method to get a sample of the ultimate sampling units. Multistage sampling introduces flexibility into the sampling procedure and enables a great saving in operational costs, particularly if the survey covers a large area including underdeveloped pockets. The sampling units considered in the two stages for both industrial and agricultural consumers are elaborated in the following two sections. 3.3.1. Industry A total of about 501 manufacturing industries, with different levels of contractual load granted by the state electricity board (SEB) in Karnataka were randomly selected for a questionnaire survey. Industries were classified into LT and HT categories. The LT industries were further stratified into four groups according to the sanctioned load: up to 20, 21–40, 41–80 and 480 HP. Similarly, the HT industries were stratified into four groups according to the contract demand: up to 200, 200–400, 400–600 kVA. While selecting the industrial units for survey under each category, the complete range of industries6 with varying characteristics was considered to capture a representative sample. There were 2794 HT and about 299,000 LT industries in Karnataka as on 1 April 1998 (KEB, 1997–98). The operations and maintenance functions of the SEB are divided under a number of zones; each zone is further divided into circles; each circle has a number of divisions, and each division has many subdivisions. Nine out of ten circles (except Gulbarga circle) were covered in Karnataka. The selection of these circles was governed by such parameters as the distribution of various types of industries and the nature of the industry. In each circle, the industrial sample is representative of the typical industry profile of that circle. In the first stage, data were collected from the SEB for different regions on the number of industrial consumers, their electricity consumption and contractual load. In the second stage, the number of LT and HT consumers was determined on the basis of actual distribution of consumers in these categories in different circles. Given the time and resources, a total of 294 LT and 207 HT industries were randomly selected for the field survey. While selecting industrial sample, it was ensured that the number of industries of various types is adequate (at least 30 observations are required for each type of industry to ensure validation of the statistical estimate of the study 6 The major industries in the state are electronics, computer engineering, telecommunication, aeronautics, machine tools, watch-making, electrical engineering, aluminium, steel, cement, sugar, silk, textiles, mining, food processing, paper, drugs and pharmaceuticals, garments, and edible oils.

parameters) to represent the true population characteristics. The selection of type and number of units selected for the survey was based on secondary data on volume of sales, production cost, sanctioned load, electricity purchased, electricity generated by captive units, etc. and also in consultation with SEB officials. The number and type of LT and HT industries surveyed in the state is given in the final report (TERI, 2000). 3.3.2. Agriculture A total of 70 villages in the first stage, and of 908 rural households in the second stage (from a selected 30 out of 70 villages) were considered for a questionnaire-based survey in each state. In the first stage, the state was divided into distinct agroecological zones. The total number of villages to be surveyed in a particular zone was decided according to the ratio of the zone’s area to the total area of the state. Each zone comprises several districts. In each district, the number of villages to be surveyed was based upon the proportion of net irrigated area to the total cultivable area in the district. After fixing the number of villages to be surveyed in each district, the villages were randomly selected for a questionnaire survey using a village schedule. By using a pre-tested village schedule, the following information was collected from the 70 villages, besides data on a number of other variables: population, number of households and farmers in different land holding categories; average ground water depths during different seasons; major cropping seasons and the crops grown in the village; and the number of diesel and electric pump-set owners in these villages. In the second stage, 30 out of the 70 villages were selected for a detailed stratified random survey (the strata being various groundwater depths) using an agricultural schedule with particular focus on use of irrigation pumpsets. The different classes of depths were 0–50, 51–100, 101–150, 151–200, 201–250, and 4250 ft. After sorting the villages into the appropriate depth categories, households were selected at random to represent the maximum variation in cropping pattern and type of pump-sets used (electricity or diesel) in the village. More than 900 households, from the 30 selected villages, were randomly surveyed. The number of households surveyed in different villages was drawn according to the true population distribution of households in a village with different landholding size (large, medium, semi-medium, small and marginal farmers) and of farmers with and without diesel pump-sets. 3.4. Data collection and analysis The data collection exercise was spread over 6 months from December 1998 to May 1999. The data from the questionnaires were then collated as Excel spreadsheets and the software Statistical Package for Social Scientists (SPSS) was used for data analysis.

ARTICLE IN PRESS R.K. Bose et al. / Energy Policy 34 (2006) 1434–1447 Table 2 CUE for industry at 1999 prices (Rs/kWh) Method

LT

HT

All together

1. Production loss 2. Captive generation 3. Willingness to pay Estimated tariff

15.46 (35.54)a 4.05 (2.18) 4.73 [57]b 3.46

22.40 (105.71) 2.61 (0.98) 4.90 [43] 4.33

22.10 (103.67) 2.63 (1.02) 4.89 [51] 4.31

a

Total sample 501

Figures within parentheses indicate standard deviation. Figures within square brackets indicate percentage of only those industries that were willing to pay more than what they were paying for the grid supply.

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Accept Scenario 473

Willing to pay more per kWh

Improved Supply Scenario

No 232 (49%)

Yes 241 (51%)

b

Reject Scenario 28

4. Results Fig. 1. Responses by the industrial consumers on their WtP.

4.1. Industry The three analytical methods have given different estimates of CUE in manufacturing industries (Table 2). These estimates are based on primary data collected from 501 manufacturing industries (294 LT and 207 HT). The estimates of CUE are Rs 22.10/kWh (production loss method), Rs 2.63/kWh (captive generation method), and Rs 4.89/kWh (WtP method). As expected, the WtP estimates of the marginal CUE are higher than those based on the cost of captive (alternative) generation and lower than those based on the value of the production loss for industrial consumers. The WtP estimates are higher than the captive figures partly because some industries are willing to pay more than the prevailing tariffs for supply improvements even though the losses incurred by them due to power shortages/poor power quality are not so high as to justify captive power generation.7 The key factors that appear to be driving the results from each method are discussed below. (a) Compared to the other two methods discussed above, the production loss method is relatively weak. Using this method, with its reliance on recall, it is very difficult to obtain accurate estimates of the net production loss due to power outages (or the lost value added). As mentioned earlier, this method is likely to over estimate the values of the CUE. (b) The average cost of captive generation depends on two factors: (a) total cost of installing and running a generator, say in a year, and (b) units of electricity generated annually. Clearly, the higher the utilization factor of a generator, the lower is the cost of generating each kWh. The average capacity utilization factor of all captive units in all types of industries taken together is around 29.7% as collected from the SEB. The average cost of captive power generation is lowest (Rs 2.61/ 7 Industrial users whose utilization factor is relatively low have much higher than average captive generation costs. Hence this group does not rely extensively on captive generation but typically is willing to pay more than the average captive generation cost for reliable supply.

kWh) in the HT industries compared to LT (Rs 4.05/ kWh) as estimated from the survey data. The likely reasons for the difference in estimated values are: (a) capacity utilization of a generator is very high (93.2%) in the HT industries where over 92% industries have captive back-up facilities compared to only around 11.2% in LT industries with 56% having back-up facilities, and (b) two industries account for half of the captive generation in the sample and their cost of captive generation is very low compared to the other sample. (c) For WtP, values are only calculated for those who accept the scenario of paying for improved supply (94% of the sample). Within this group, only a fraction was willing to pay more than what they were paying in 1999. Fig. 1 illustrates these numbers with average CUE value of Rs 4.89/ kWh (Table 2).8 (d) A larger percentage of LT consumers (57%) is likely to pay more for improved power supply than HT consumers (43%). Almost half of the sample industries were not willing to pay more than the prevailing cost of electricity for improved power supply. The key reasons given for this were: (a) the current cost is unaffordably high; (b) belief that the industrial tariff is already above the cost of supply; (c) belief that supply is cheaper in other states; and (d) reliance on self-generation.

The factors that determine whether an industrial consumer is willing to pay more for improved supply and, if so, how much were also analysed. The first aspect of the WtP bid (whether to pay for improved supply) is investigated by Probit regression, while the second aspect (the level of the WtP bid) is investigated using ordinary least-squares regression techniques. 8

Using a kWh weighted average, this is just 13.4% higher than the existing tariff but 37% higher than the existing tariff for LT consumers and 13% for HT consumers—see Table 2.

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Table 3 Economic loss due to power outages in the industrial sector at 1999/2000 prices Particulars

HT

LT

All

6

339 108 Additional power demand (10 kWh) CUE (Rs/kWh) 1. Production loss (revised) 4.90 3.40 4.85 2. Captive generation 2.61 4.05 2.63 3. Willingness to pay 4.90 4.73 4.89 Total SDP (Rs billion) 950 Manufacturing sector SDP (Rs billion) 157 Economic loss due to power outages (Rs billion) 1. Production loss 1.7 0.4 2. Captive generation 0.9 0.4 3. Willingness to pay 1.7 0.5

The Probit regression results, presented in Annex 1, identified the following variables that have a significant effect on the decision to bid for improved electricity supply.

     

The share of electricity in total cost is relatively small. Consumers have at least some captive generation facility. Consumers do not rely largely on self-generation for their electricity supply. Consumers experience high levels of unscheduled outages. Identified scheduled outages or demand cuts are a serious problem. Consumers do not believe tariffs are already too high.

However, the following variables do not have a significant effect on the decision to bid for improved electricity supply.

  

The duration of most types of supply interruptions (excluding unscheduled outages and possibly demand cuts in summer). The size of the contracted demand or scheduled load in HT or LT industry. Problems of voltage fluctuation.

Table 3 provides the estimated values of CUE and the additional power demand derived from the survey carried out in 1999–2000 in the industrial sector. The estimates of economic losses due to power outages as a percentage of total SDP and sectoral SDP (for manufacturing sector) have also been estimated. These losses vary from Rs 0.9 billion to Rs 1.7 billion for HT industries and Rs 0.4 billion to Rs 0.5 billion for LT industries (Table 3). The percentages of economic losses to total SDP for HT industry varies from 0.09% to 0.17%, while for LT industry it varies from 0.04% to 0.05%. On the other hand, the percentage of economic losses to sectoral SDP varies from 0.56% to 1.06% for HT industries and 0.23% to 0.28% for LT industries.

4.2. Agriculture Before considering the estimates of CUE for pumping in the agriculture sector, it is important to determine the cost of electricity, or the imputed tariff rate for agricultural consumers to run irrigation pump-sets. The majority of the farmers who own electric pump-sets pay for electricity using a flat rate depending upon the installed capacity of the pump-set. For instance, the cost was Rs 300/brake horse power (BHP) per year to a farmer irrespective of the depth of the water table during the survey period. To convert these rates to costs expressed in Rs/kWh, one needs to know the average installed capacity of electric pump-sets and their utilization pattern. The average installed capacity of an electric pump-set is 5.2 kW as was arrived at based on the sample data from the survey. Similarly, on an average, a farmer getting about 1320 h of electricity in a year during the three cropping seasons (rabi, kharif, and summer) was also arrived at from the collected survey data. Given the average capacity and utilization pattern of the electric pump-sets, the imputed electricity tariff for all sample farmers together is calculated to be Rs 0.27/kWh (Table 4). The three methods have given different estimates of CUE across different category of farmers, based on size of their land holding in agriculture (Table 4).9 These estimates are based on primary data collected from 910 farmers in the state. The results derived from each method are discussed below. (a) The value of production loss estimated in agriculture is the net value lost. This is based on the farmers’ perceptions of reduction in profits due to nonavailability of electricity. The estimated value is Rs 3.21/kWh which is significantly higher than the imputed electricity tariff in Karnataka where cash crops are grown which are high value-added components. (b) Of the 908 farmers surveyed, we found farmers in Karnataka predominantly depend on electric pumpsets, 758 had electric pump-sets and 59 had diesel pump-sets (16 had both electric and diesel pump-sets) primarily due to the non-suitability of diesel pumps for the deeper water table in the state. (c) The costs of running electric pump-sets in Karnataka decrease from marginal to large farmers (Table 4). (d) Despite the capital cost of a diesel pump-set of 1 kW capacity being about half of that of an electric set, the cost of running a diesel pump-set (captive method) works out on an average to be higher than the cost of running an electric pump-set. The average CUE values estimated are Rs 9.56/kWh and Rs 1.99/kWh, respectively. 9 WtP results in agriculture sector are statistically insignificant and are therefore not reported.

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Table 4 CUE for irrigation at 1999 prices (Rs/kWh) Method

Marginal (o1 ha)

Small (1–2 ha)

Semi-medium (2–4 ha)

Medium (4–10 ha)

Large (410 ha)

All

Net production loss

3.89 (50.67)a 15.82 (7.31) [1.3]b 2.14 (6.07) [8.2] 0.29

4.29 (9.42) 11.59 (4.70) [3.5] 2.31 (4.36) [9.1] 0.30

3.10 (31.05) 7.37 (2.82) [4.2] 1.90 (3.07) [11.3] 0.25

2.47 (5.74) 14.10 (1.19) [1.3] 1.93 (4.54) [11.5] 0.25

2.03 (4.12) na

3.21 (25.79) 9.56 (4.83) [3.0] 1.99 (4.11) [10.4] 0.27

(a) Cost of running diesel pumpset

(b) Cost of running electric pumpset

Imputed electricity tariff a

1.61 (1.72) [13.8] 0.26

Figures within parentheses indicate standard deviation. Figures in parentheses indicate the average capacity utilization factor (in %) of pump-sets.

b

Accept Scenario 863

Total Sample 908

Improved Supply Scenario

Willing to pay more per hp

Yes 500 (58%)

No 363 (42%)

Willing to pay more per kWh

Yes 23 (5%)

No 477 (95%)

Reject Scenario 45

Fig. 2. Responses by the agricultural consumers on their WtP.

(e) Clearly, the higher the utilization factor of a pump-set, the lower the average cost of running it. For instance, in Karnataka, the average cost of running a diesel pump is estimated to be Rs 15.8/kWh for marginal farmers with a very low utilization factor of 1.3%. But, with a higher utilization factor of about 4.2% in the semi-medium category, the average cost works out to be Rs 7.37/kWh. (f) Around 58% of the farmers in principle willing to pay more on a BHP basis (Rs 300/BHP/year in Karnataka). However, considering the farmers’ requirements for extra hours of electricity supply, we found that only around 3% (only 23 farmers) of the farmers surveyed were willing to pay more per kWh than what they paid for improved electricity supply. This is likely to reflect the political situation at the time that made it difficult for farmers to imagine paying more in kWh terms. Fig. 2 illustrates these numbers. The sample size is too small to produce a meaningful estimate of CUE and

therefore regression analysis could not be carried out to analyse the factors influencing willing to pay bid. (g) Only a very small percentage of respondents agreed to pay more than what they currently pay, which is very different from the results in industrial consumers. As a result, the value of CUE in Karnataka is best estimated using the value of net production lost method. The following broad conclusions could therefore be drawn:





Compared to large farmers, marginal and small farmers typically pay more per unit for the electricity they get from the grid. There is a need for appropriate policy to address this tariff distortion. The majority of farmers are willing to spend more on electricity on a BHP basis if they receive an improved supply of electricity. Farmers were willing to spend up to

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Table 5 Economic losses due to power outage in the agriculture sector at 1999–2000 prices Additional power demand from TERI survey (106 kWh) CUE estimates (Rs/kWh) 1. Production loss 2. Cost of running an electric pumpset

3.64 1.99

Total SDP (Rs billion) Agriculture sectoral SDP (Rs billion)

950 256

Economic loss due to power outages (Rs billion) 1. Production loss 2. Captive generation (electric pumpsets)

34 18





9288

50% more for improved supply compared to what they pay now on a BHP basis. The extra hours of electricity demanded (unmet demand) for irrigation exceeds 50%. As a result, for most of the farmers who had indicated a willing to pay more on a BHP basis, the bid is lower than the current tariff they are paying when translated into a per kWh basis. The imputed tariff estimated from the survey data, which provides an understanding of what farmers currently pay on a kWh basis, indicates their ability to pay rather than their willingness to pay.

Table 5 indicates the CUE estimates that are used to arrive at the economic losses due to power outages in the agriculture sector of Karnataka at 1999–2000 prices. The value of CUE from production loss method is adjusted by weighing the CUE estimates for different categories of farmers with the true population distribution of farmers in the corresponding category and this value is estimated as Rs 3.64/unit for the additional power requirement. The estimates of economic losses due to power outages in agriculture sector vary from Rs 18 billion to Rs 34 billion as in 1999–2000. These values are translated into the estimates of losses in terms of percentage of total and sectoral SDP figures. For the agriculture sector, these estimates of economic losses vary from 1.9% to 3.6% with respect to total SDP, while it varies from 7.2% to 13.2% with respect to sectoral SDP for Karnataka. 5. Policy implications The research and analysis undertaken in this case study provides useful insights that can provide inputs in designing effective policies for the power sector. However, it needs to be recognized that there is no single approach that can be used to arrive at electricity tariffs that different segments of the market will accept and therefore generalizations for a customer group in a state should not be made for the country as a whole. (a) The WtP method provides useful results, particularly, in the industrial sector. While the policy planners fret

about the cross-subsidy that the industrial tariffs provide to agriculture, a significant proportion of industrial consumers are willing to pay even more (
13.5% higher than the tariff prevailing in 1999) for improved power supply! Having said that, they also believe that it is only the private utility that would give them value for money in terms of a reliable, goodquality supply of electricity. (b) The WtP method provided less useful results in the agriculture sector, as it was difficult to provide a credible scenario that involved paying for improved supply. Although the farmers are willing to pay more in hp terms but this combined with the demand for increased usage does not indicate a willingness to pay more in terms of rupees/kWh. The majority of farmers are willing to spend up to 50% more for improved supply if charged, as was prevailing in 1999 in the state, as per BHP terms. However, taken together with the increased hours of supply demanded, the willingness to pay in terms of rupees/kWh remains the same. Seen in the context of the lament that the low load factors and consumption in rural areas inflates cost of supply, the demand for more electricity would help reduce/unit cost. This also raises the question on whether it is supply constraints that are driving up the per unit cost of electricity supplied to rural areas or demand constraints? (c) As has been often said, charging farmers on the basis of capacity of pump-set does not provide the right incentives for rational consumption of power. Average revenue realization in Karnataka from the agricultural sector is a mere Rs 0.25 to Rs 0.30/kWh whereas the farmers are willing to pay at least Rs 1.99 for reliable good-quality supply based on the estimated cost of running an electric pump-set (see Table 4). In a reformed power system, consideration should be given to switching to metered consumption. This will reflect the correct and the actual price paid by the consumers’, induce rational consumption behaviour and also assist in improving the supply of power. Thus there is an immediate need to launch a massive awareness campaign and work towards changing the mindset of consumers with respect to electricity tariffs. (d) A number of factors have been identified (or reconfirmed) that must be addressed if consumers are to be persuaded to pay higher tariffs. These include factors related to the organization of the power sector (for example, the need to address the issue of ‘unofficial’ payments made to utility’s staff) and factors related to the quality of electricity supply. The key here is to give consumers good information so that they can plan accordingly. Thus the study found an urgent need to address unscheduled outages, followed by scheduled outages and voltage fluctuations. In other words, addressing issues relating to quality of service and supply are important precursors to reform.

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(e) The imputed tariff estimated from the survey data, which provides an understanding of what farmers currently pay on a kWh basis, indicates their ability to pay rather than their willingness to pay. Compared to large farmers, marginal and small farmers typically pay more per unit for the electricity they get from the grid. There is a need for appropriate policy to address this tariff distortion. These facts should be brought to the attention of both the politicians and farmers in the state, which could provide the platform for introducing a metered tariff system.

responsibility of maintaining the generator. Despite these limitations, the captive generation method does provide a lower bound on the tariff a consumer is willing to pay at the margin. The main drawbacks of the indirect cost estimate method are as follows:



 6. Concluding remarks The three alternative methods provide useful insights in addressing the question of the extent of tariff revision for industrial and agricultural consumers in Karnataka. First method, the production loss method gives the maximum level that a consumer can pay. This can be interpreted as the upper bound on electricity tariffs. However, this method suffers from certain limitations as this estimate is based on the recall ability of the respondent. Often the value of loss may be overstated as the entire loss in production as attributed to the power cut. Under normal circumstances, the estimated price or value of electricity may make production non-viable. In an ex-ante analysis, power shortage is valued at the loss in fixed costs whereas in an ex-post consideration all losses are assigned to the power cut, especially if it is an unscheduled one. Despite these limitations, the production loss method provides an estimate of the upper limit on the price for electricity. Observing the industrial growth trend in Karnataka, the state capital (Bangalore) has emerged as the software and info-tech city of India, and a large percentage of investment in software and information technology sectors is being directed to this state. As a result, the high CUE estimates by production loss method for industrial sector in Karnataka confirm that industry is very productive in the state and therefore has significantly higher value addition per unit of output. The second method, the captive generation method estimates indirectly the tariff a consumer will pay for electricity. The assumption is that if the consumer were willing to undertake self-generation, he or she would be willing to pay at least the cost of self-generation as the price for grid power. Again there are limitations in this approach. On the one hand, this approach may be an overestimate in that the consumer may be willing to pay the estimated price for a fraction of the units (those generated during the power cut) but not for all the electricity units required in the production process. On the other hand, the price may be underestimated in this approach as there are some costs that cannot be monetized, for example, the additional investment required, the inconvenience due to the hot air and noise from the generator, and the additional

1445

For customers who do not install back-up power, the only information provided is that the value they place on reliable power supplies, is less than the cost of the back-up supplies. Their WtP may still exceed existing tariff rates, but the methodology would not reveal this. For customers who do install back-up supplies, the methodology only tells us that their WtP is at least equal to the cost of back-up power, though it may well exceed that level. The methodology would give little, if any, information about the actual WtP and how high it is likely to go.

The cost of captive generation in the state was lower than the cost of grid supply. Despite a liberal captive policy10 and underutilized captive capacity in the state, there is a greater degree of reliance on grid supply when less expensive captive generation is possible. Also, it is interesting to note that the willingness to pay is higher than the cost of captive generation. These results indicate that the perception and mindset of industrial consumers about costs are very different than the actual costs of alternative sources. The third method, the willingness to pay method measures the price the consumer is willing to pay. There are limitations with this approach as well. The approach is highly dependent on questionnaire design and respondents understanding of the hypothetical scenarios. In the agriculture sector, this difficulty was faced as most of the farmers were unused to kWh billing. As a result, the questionnaire had to be designed for capacity (hp)-based tariffs. Responses received from the respondents showed that while a significant number of farmers are willing to pay higher on a per hp basis, the same did not hold true on per kWh basis. As the additional hours of power demand were factored into the analysis with the bid levels, only an insignificant number of farmers were found willing to pay higher on a kWh basis. The survey results have shown that of the three approaches used, the consumer is willing to pay somewhere between the upper bound (production loss method) and the lower bound (captive generation method). The economic losses due to power outages in 1999–2000, estimated for the industry varies from 0.04% to 0.17% as percentage of total SDP, while it varies from 0.23% to 1.06% as percentage of sectoral SDP depending upon the size of industry and method used for CUE estimation. 10 Karnataka allows wheeling and banking of power, third-party power sales, and sales to the state utility.

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While, these losses for agriculture sector show variation from 1.9% to 3.6% as percentage of total SDP and 7.2–13.2% as percentage of sectoral SDP at 1999/2000 prices. Acknowledgements This paper is prepared based on the TERI’s research project titled Cost of Unserved Energy prepared in association with London Economics with financial support provided by the World Bank and Department for International Development (TERI, 2000). Appendix A. Econometric analysis of the industry WtP results Variable definitions WtPunit ¼ WtP for improved supply? (0 or 1) LnWtPub ¼ log (WtP bid) PAYNOW ¼ total cost of electricity purchased/units consumed INVECSH ¼ 1/(share of total cost of electricity in total costs) PROFIT ¼ imputed profit of company TOTAL_EP ¼ total kWh of electricity purchased LT ¼ LT consumer (0 or 1) SMALIND ¼ small LT consumer (0 or 1) CDKVA ¼ contracted demand or sanctioned load in kVA SL_KW_1 ¼ sanctioned load in kW BUYLT2 ¼ is captive genset less than 2 years old? (0 or 1) CAPTIVE ¼ has some captive generation (0 or 1) SGENSH ¼ share of captive generation in total electricity use D_CUT_S ¼ hours of demand cuts in summer D_CUT_W ¼ hours of demand cuts in winter UN_CUT_S ¼ hours of unscheduled cuts in summer UN_CUT_W ¼ hours of unscheduled cuts in winter S_CUT_S ¼ hours of scheduled cuts in summer S_CUT_W ¼ hours of scheduled cuts in winter SCORE_1 ¼ average score (1–5) for voltage fluctuation

SCORE_2 ¼ average score (1–5) for frequency fluctuation SCORE_3 ¼ average score (1–5) for scheduled outages SCORE_4 ¼ average score (1-5) for unscheduled outages SCORE_5 ¼ average score (1–5) for demand cut/peak load cut SCORE_6 ¼ average score (1–5) for there is no response to complaints SCORE_7 ¼ average score (1–5) unofficial payments required for repairs SCORE_8 ¼ average score (1–5) for getting new connection very difficult SCORE_9 ¼ average score (1–5) for getting additional load vs. difficult SCORE_10 ¼ average score (1–5) for ‘other 1’ problems SCORE_11 ¼ average score (1–5) for ‘other 2’ problems PROD_AFF ¼ is your production affected by supply interruptions? (0 or 1) HRS_WORK ¼ how many hours is your standard work day? C1 ¼ Circle 1 C2 ¼ Circle 2 C3 ¼ Circle 3 C4 ¼ Circle 4 C5 ¼ Circle 5 C6 ¼ Circle 6 C7 ¼ Circle 7 C8 ¼ Circle 8

Probit results Binomial Probit Model: maximum likelihood estimates dependent variable ¼ WtPunit weighting variable ¼ ONE number of observations ¼ 377 iterations completed ¼ 8 log likelihood function ¼ 148.2550 restricted log likelihood ¼ 261.1560 chi-squared ¼ 225.8020 degrees of freedom ¼ 20 significance level ¼ .0000000

Index function for probability Variable

Coefficient

Standard error

B/St.Er.

P½jZj4z

Mean of X

Constant PAYNOW INVECSH LT BUYLT2 CAPTIVE SGENSH D_CUT_S

3.043336159 .8616521056 .1188204214E-02 .3033834137 .2296904072 .5399065583 1.913838264 .1460970319

.60270387 .99669530E-01 .84421041E-03 .23269900 .22687694 .21263097 .64011063 .85778340E-01

5.049 8.645 1.407 1.304 1.012 2.539 2.990 1.703

.0000 .0000 .1593 .1923 .3113 .0111 .0028 .0885

5.3146141 60.141173 .61007958 .20689655 .66578249 .65407415E-01 .45897056

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D_CUT_W UN_CUT_S UN_CUT_W SCORE_2 SCORE_3 SCORE_5 SCORE_10 PROD_AFF C3 C4 C7 C8 HRS_WORK

.1632002932 .1472259715 .5417576981 .5441072489E-01 .8147959045E-01 .1174814600 .9924941366E-01 .2116646659 .6594456183 .5175070673 .4713233540 1.230550757 .1299427103E-01

.22265941 .62170717E-01 .14408304 .57604922E-01 .47183357E-01 .66489556E-01 .47283933E-01 .18859457 .26909228 .52550949 .31280504 .42890946 .15766385E-01

.733 2.368 3.760 .945 1.727 1.767 2.099 1.122 2.451 .985 1.507 2.869 .824

1447

.4636 .0179 .0002 .3449 .0842 .0772 .0358 .2617 .0143 .3247 .1319 .0041 .4098

.73040849E-01 1.7703546 .52153952 1.0557676 2.6339748 .75339257 .99742467 .71087533 .11140584 .37135279E-01 .71618037E-01 .45092838E-01 13.148541

Frequencies of actual and predicted outcomes Actual

0 1 Total

Predicted

Total

0

1

144 30 174

39 164 203

References Baijal, P., 1999. Restructuring power sector in India: a base paper. Economic and Political Weekly, Mumbai, vol. XXXIV, No. 39, 25 September, pp. 2795–2803. Carson, R.T., Wright, J.L., Carson, N.J., Alberini, A., Flores, N.E., 1995. A Bibliography of Contingent Valuation Studies and Papers. NRDA, Inc., La Jolla, CA, January. D’Sa, Antonette, Narasimha Murthy, K.V., 2002. Karnataka’s power sector and suggested ways forward. International Energy Initiative, Bangalore, p. 40, available at www.iei-asia.org. GoK, 2005. Economic survey 2004–2005, March 2005. Planning and Statistics Department, Government of Karnataka, p. 181. KEB, 1997–98. Annual Administrative Report. Karnataka Electricity Board, Bangalore.

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Pearce, D.W., O¨zdemiroglu, E., et al., 2002. Economic valuation with stated preference techniques: summary guide. Department for Transport, Local Government and the Regions, London, March (http:// www.odpm.gov.uk/stellent/groups/odpm_researchandstats/documents/ page/odpm_research_037552.pdf) Planning Commission, 2001. Annual report (2000–01) on the working of State Electricity Boards and Electricity Departments. Power and Energy Division, Planning Commission, Government of India, June. Rao, S.L., 2004. Governing power. A new institution of governance: the experience with independent regulation of electricity. Published by The Energy and Resources Institute, New Delhi, p. 484. TERI, 2000. Cost of unserved energy. Report submitted to the World Bank and Department for International Development. The Energy and Resources Institute, New Delhi, p. 276, 98PG42, 28 June. TERI, 2004. TERI energy data directory & yearbook. The Energy and Resources Institute, New Delhi, ISBN 81-7993-057-2, p. 109.