Are small firms willing to pay for improved power supply? Evidence from a contingent valuation study in India

Energy Policy 109 (2017) 659–665

Contents lists available at ScienceDirect

Energy Policy journal homepage: www.elsevier.com/locate/enpol

Are small firms willing to pay for improved power supply? Evidence from a contingent valuation study in India

MARK

Ranjan Ghosha,⁎, Yugank Goyalb, Jens Rommelc, Julian Sagebield a

Indian Institute of Management Ahmedabad, Vastrapur, Ahmedabad, Gujarat 380015, India OP Jindal Global University, Sonipat Narela Road, Sonipat 131001, Haryana, India c Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany d Institute for Ecological Economy Research, Potsdamer Str. 105, 10785 Berlin, Germany b

A R T I C L E I N F O

A B S T R A C T

Keywords: Small-scale industry Power outages Reliability WTP India

This paper provides new estimates on Indian small-scale manufacturing firms’ willingness-to-pay (WTP) for reliable power supply. Almost half of Indian manufacturing lies in the small-scale sector, and its productivity is severely affected by power outages. However, there is a surprising paucity of research on small firms’ WTP for avoiding outages. We conduct a double-bounded dichotomous choice contingent valuation experiment with a random sample of 260 small-scale firms in the region around Hyderabad. We find that on average, firms are willing to pay approximately 20% more for uninterrupted power supply. The WTP estimates and the explanatory factors for the firms’ decisions were tested for robustness using both probit and bivariate probit models. In addition, a two-step Heckman correction was used to control for selection bias induced by protest responses. Our results are vital to understand behavior of small firms, which are crucial to India's economic growth. Further, the government's continued emphasis on power sector reforms makes the paper even more important as it provides realistic estimates for designing tariffs while keeping in mind the preferences of the small-scale industry.

1. Introduction Despite high electricity rates for the industrial sector, unscheduled and scheduled power outages frequently occur in India. The lack of power supply for manufacturers causes a significant decline in output (Hansen, 2008; Hanisch et al., 2010; Allcott et al., 2016; Fisher-Vanden et al., 2015). Indeed, economic costs of power outages in the context of a developing country are substantial, with variable impact depending on industry type and country. For instance, in Sri Lanka, the costs of outages in the industrial sector can mount to 0.9% of the GDP (Wijayatunga and Jayalath, 2004). In Pakistan, overall outages reduced the GDP by 1.8% (Pasha et al., 1989). In India, Bose et al. (2006) studied the state of Karnataka and pegged the loss value in high tension (HT) industries to range from 0.09% to 0.17% of state GDP. Indian industrial productivity is particularly undermined as a result. A recent estimate suggests that the reported level of electricity shortages in India lead to a reduction in plant revenues and producer surplus by 5–10% (Allcott et al., 2016). India's small-scale industrial sector, also known as the micro, small,

⁎

and medium scale enterprises (MSMEs), bears a heavy burden of these power outages given their liquidity constraints in setting up a captive power plant (Ghosh and Kathuria, 2014). As the backbone of Indian economic growth, MSMEs employ 40% of the Indian workforce, contributing to 45% of Indian manufacturing output and 40% of India's exports (Goyal, 2013). Yet, they contribute to a mere 17% of the GDP due to poor productivity (ibid). Part of the productivity losses can be attributed to interruptions in electricity supply.1 Most of the MSMEs operate in sectors in which production is highly sensitive to electricity supply, such as food and beverages, fabricated metal products, apparel and textiles, or pharmaceuticals. Since MSMEs suffer a disproportionately higher cost of power interruptions compared to larger firms, their productivity is highly elastic to power supply. An estimation of these costs, specifically to MSMEs, therefore merits serious research. Recent studies that engage with the issue (Allcott et al., 2016; Ghosh and Kathuria, 2014; Kim and Cho, 2017) do not single out effects of power outages on MSMEs. This dilutes the severity of its impact. Furthermore, there is little research that attempts to estimate cost of power outages by observing willingness-to-pay (WTP). In this paper, we

Corresponding author. E-mail addresses: [email protected] (R. Ghosh), [email protected] (Y. Goyal), [email protected] (J. Rommel), [email protected] (J. Sagebiel). Gujarat is an interesting example. Between 2007 and 2013, the number of SME clusters in this western state of India increased from 115 to 369 (highest in India) (Goyal, 2013). This has been, inter alia, a response to increasing access and supply of electricity in the state during this period. Today, Gujarat has one of the highest installed capacities of electricity and also the highest number of SMEs in any Indian state. 1

http://dx.doi.org/10.1016/j.enpol.2017.07.046 Received 2 February 2017; Received in revised form 14 July 2017; Accepted 19 July 2017 0301-4215/ © 2017 Elsevier Ltd. All rights reserved.

Energy Policy 109 (2017) 659–665

R. Ghosh et al.

days together with a local consultant who possessed vast experience with industrial surveys. The enumerators were graduates in social sciences. After the training, enumerators were sent to industrial estates where they approached firms based on the pre-determined random walk. We asked enumerators to approach suitable management staff members that (a) have a clearly defined management role in the firm and are able to make firm relevant decisions and (b) have the necessary key information on electricity usage of the firm. Wherever such suitable management staff was unavailable, was not willing to respond, or was not involved in electricity-related decisions, the enumerators were instructed not to conduct the survey at the firm. Such firms were marked on a separate list of ‘no response’ data. For the purposes of monitoring and verification, we asked enumerators to collect the individual respondents’ business cards. Sometimes, one of the authors would accompany the enumerators randomly. The survey questions were developed by a team of Indian and German researchers from Humboldt-Universität zu Berlin and The Energy Resource Institute (TERI) in Delhi. The survey was conducted during SeptemberDecember 2010. We ran two pretests on the survey and adjusted the questionnaire before the final round. The 14-page questionnaire contained four modules: (a) general questions on the company, e.g., the number of employees, annual turnover, and productions cycles; (b) energy topics, such as energy carriers, changes in energy use, the use of captive generation, and the frequency of power outages; (c) the contingent valuation study; and (d) attitudes and knowledge. There is a long debate among economists on the best practice of contingent valuation method (CVM) studies (Welsh and Poe, 1998; Hanemann, 1994; Carson et al., 2003; Johnston et al., 2017). The contingent valuation part of our survey generally followed an up-todate methodology. A text describing a specific scenario was read aloud to respondents. We chose this procedure to ensure creating homogeneous conditions across respondents and enumerators. Respondents were offered a hypothetical improvement in electricity services with a reduction of all scheduled and unscheduled power outages to zero. At the end of the text, a budget reminder was included. Then, respondents were asked if they were generally willing to pay for the described improvement (cf. question 1.1 in Appendix C). If they replied ‘no,’ they were asked why they were unwilling to pay in order to distinguish protest zeros from true zeros (Yu and Abler, 2010).3 If respondents replied ‘yes,’ the enumerators continued with the double-bounded dichotomous choice questions as described above. (We provide the exact wording and questions in Appendix C.) The bid vector must be carefully chosen in a dichotomous choice CVM study (Johnston et al., 2017). Different approaches exist for designing bid vectors. In a recent study, Chung and Chiou (2017) test the validity of the CVM method using a triple-bound dichotomous choice model with multiple follow-up questions. In general, plausibility and statistical efficiency guide the choice. Plausibility means that bids should be realistic, credible, and accepted by respondents (Arrow et al., 1993; Bateman et al., 2002; Johnston et al., 2017). If, for example, bid values are unrealistically high, the probability of protest increases. Efficiency refers to the statistical properties of the bid vector. Especially in small samples, standard errors should be as small as possible. Bids should center around the median WTP (Alberini, 1995a, b; Cooper, 1993). From a statistical perspective, an equal distribution of yes and no responses is desirable. Thus, we used data from a previous study (Hanisch et al., 2010)

centralize the object of our analysis as the MSME, singling out the impact on small firms in India. In doing so, we produce most recent and robust results for estimating the cost of outages. In the literature, different methods have been proposed to estimate the cost of power outages (Baarsma and Hop, 2009). Most studies have based their estimates on observed losses in output, the cost of coping strategies, or stated preferences methods. The stated preferences method is particularly useful since it includes the full range of costs. As revealed costs for outages are not easy to discover, several studies have previously used this approach at the household level (Carlsson and Martinsson, 2007, 2008; Carlsson et al., 2011; Blass et al., 2010). While a number of studies have investigated the costs of outages in the industrialized economies (Morrison and Nalder, 2009; Baarsma and Hop, 2009; Goett et al., 2000), evidence on developing economies is sparse. Previous studies on India have focused on coping strategies and output analysis (Gulyani, 1999; Sari, 2003; Allcott et al., 2016). These studies typically use data that do not include the full range of costs (Allcott et al., 2016; Fisher-Vanden et al., 2015). We address these shortcomings and use a stated preferences approach, namely the contingent valuation method, to estimate the full range of outage costs. Contributing to a rather thin literature in this area, we make three new interventions in this study: first, by investigating heterogeneity in WTP for reduced power outages, we are able to distinguish WTP values for different types of MSME firms. This allows policy-makers and regulators to assess which firms should be prioritized and to what extent tariffs should discriminate between firms. Second, instead of asking ‘how much’ of a tariff would a firm be willing to pay for uninterrupted power supply, we ask ‘how much extra’ it would be willing to pay. This encourages the respondents to focus on the marginal costs (and benefits), making it a more reliable and accurate indicator of their preferences and costs. This difference leads to more realistic estimates as compared to the previous work (Bose et al., 2005). Third, we use probit, bivariate probit, and also Heckman models to ensure robust results, making the analysis richer and more rigorous. Our findings show that firms are willing to pay 20% in addition to the prevailing tariff for a reduction of scheduled and unscheduled power outages to zero. The current estimates are significantly lower than those of Bose et al. (2006) who pegged this value at 37%. The higher conservativeness and, arguably, greater reliability of our results owes itself to the design of the present questionnaire. This will be further discussed in Section 3.

2. Survey design and data The experiments were conducted with MSMEs in and around Hyderabad, the joint capital of the Indian states of Andhra Pradesh and Telangana. The region has over 18,000 industrial units employing more than 220,000 people, which makes it a prominent location for small and medium scale manufacturers in south India. It houses several key industrial clusters.2 Most notably, metallurgy, paper and printing, plastic and rubber, engineering machinery, food processing, wood, chemical, and repair and services dominate. With a consistent growth in the number of MSMEs in the previous decade, the region offers a valuable setting to examine the small-scale firms’ WTP for a reliable power supply. Our sample consisted of 260 small-scale firms and was geographically stratified. In the first step, we pre-selected four different industrial areas in the Greater Hyderabad Municipal Area. The selection was based on expert interviews. In the second step, using detailed maps of the selected areas, we did a random walk to approach industrial units. In order to administer the survey, we trained enumerators for two

3 The literature discusses different ways to treat those observations (Meyerhoff and Liebe, 2006; Yu and Abler, 2010). Because the determinants of a general willingness-topay (e.g., missing trust) differ from those of the amount (e.g., size of the firm or dependency on reliable supply), it is not advisable to jointly estimate one coefficient per variable for both decisions. In addition, different protest motives may have different determinants or at least coefficients of different size. One way to deal with the problem is to estimate a multi-step hurdle model (Yu and Abler, 2010). However, a larger number of observations would be needed to accurately distinguish between the different protest motives and their determinants in our case. Therefore, we excluded “true zeros” from the analysis.

2 See, Brief Industrial Profile of Hyderabad and Brief Industrial Profile of Medak published by MSME Development Institute, Ministry of MSME, Government of India, available at http://dcmsme.gov.in/dips/hyd%20profile.pdf and http://dcmsme.gov.in/dips/medak. pdf respectively (last accessed on 21 January 2017).

660

Energy Policy 109 (2017) 659–665

R. Ghosh et al.

to determine bid levels that asked for WTP in an open-ended format. Specifically, we used rounded quintiles from this study as bid levels for the present study to ensure both plausibility and efficiency. Five bid levels with a per-unit increase at INR4 0.50, 0.80, 1.20, 1.80, and 2.50 were used in three different versions of the questionnaire, each of which contained one out of the three different starting-bid values. The first question started with either INR 0.80, 1.20, or 1.80. Depending upon the answer, the bid value of the followup question was shifted to the upper or lower neighbor. For example, if the service improvement was offered at INR 0.80 and the respondent rejected, then the follow-up bid would be INR 0.50. In case the respondent accepted, the follow-up bid would have been INR 1.20.

Table 1 Summary statistics. Source: Own calculations Variable

Description

Mean

Std. Dev.

Min.

Max.

N

TARIFF

Average tariff paid in INR 1 = firm has backup power Yearly turnover in million INR Average production hours per day Hours of unscheduled power cuts per day Hours of scheduled power cuts per day

4.654

0.983

3

8.5

260

0.538

0.499

0

1

260

51.442

104.586

0.05

540

257

11.258

4.942

6

24

252

3.052

2.882

0

10.667

260

1.171

1.364

0

10.667

260

BACKUP TURNOVER PRODHOURS UNSCHED

3. Results and discussion

SCHED

3.1. Descriptive evidence 3.2. Contingent valuation experiment

Our survey responses indicated that power outages occur on a daily basis for firms in and around Hyderabad. They are highly seasonal with summer time peaks in both scheduled (pre-announced) and unscheduled (unannounced) outages. During summer, there is low power generation from hydro plants while there is high demand for water pumping, cooling, and air conditioning (Appendix Table B.1). Firms in the region face a daily average of six hours of power outages. More than 90% of our respondents experienced unscheduled power outages during the summer. Scheduled outages, which last less than one hour per day, play a rather minor role in this context. Even during winter and monsoon months, unscheduled power outages exceed a daily average of two hours, significantly eroding firms’ productivity. We asked the respondents to self-assess the severity of a number of electricity-related problems on a scale from 1 to 5, where 1 denoted no problems at all and 5 denoted severe problems (Appendix Table B.2). We discovered that power outages are the most severe problem for firms with the unscheduled outages being ranked higher than the scheduled. Less severe problems included low responsiveness of utilities to complaints, voltage fluctuations, bribes to service staff for fixing broken equipment, and difficulties to access additional installed loads or a completely new connection. The losses due to an outage of one hour go up to INR 1,000,000 with the average loss being a little more than INR 10,000. Only 10% of the respondents suffered no losses from power outages. More than half of the respondents had facilities for back-up supply – entirely based on diesel engine electric generators. The median cost incurred (which is also the most frequent answer representing 45.9% with back-up) with generators is about INR 10 per kWh, which is more than twice what firms usually pay for power. The installed back-up capacity ranges from small generators with 5 kW to larger engines with 1000 kW, and the costs per kWh ranges from INR 5–50. However, more than 25% of the surveyed firms were paying INR 20 or more per kWh generated by their back-up facilities (Appendix Table B.3). This implies that firms already pay substantial amounts to manage power outages. Table 1 summarizes other important variables which we expect will impact the WTP for reduction in power outages.5 The average tariff a firm pays (TARIFF) is INR 4.6 with a standard deviation of almost 1. The highest tariff reported is INR 8.5. More than half of the firms have a power backup system (BACKUP). The yearly turnover (TURNOVER), an indicator for the size of the firm, varies greatly. The mean turnover is about INR 51,000 million with a standard deviation of INR 104,000 million. Similarly, production hours per day (PRODHOURS) vary. The mean hours of production are 11.3 with a standard deviation of 4.9. Some firms produce 24 h per day.

The results of the double-bounded dichotomous choice contingent valuation questions are presented here.6 As the inclusion of the second bid question can lead to bias in the estimated WTP (Bateman et al., 2001), the results of a probit model using only the first bid are also shown. We can estimate WTP only for the sub-sample of respondents for whom WTP question data are available. If these observations are not missing randomly, a selection bias would be introduced. In order to account for this bias, we estimated a two-step Heckman probit model. The equation of interest is the first probit equation of the dichotomous choice contingent valuation question. The selection equation is the generally willing question we have put in front of the WTP questions (cf. Appendix C). The null hypothesis of the likelihood ratio test of independent equations had to be rejected (X2 = 5:35; p value = 0.0207), indicating that a selection bias has been introduced by the generally willing question. However, while comparing the results of the Heckman probit model and the simple probit model, only minor differences in estimated parameters are found. Therefore, we will continue our analysis with the simple probit and bivariate probit models. The results of the Heckman Probit model are presented in Appendix Table B.4. Besides the bid variables, the variables summarized in Table 1 were included in the models. The contingent valuation results from the probit (only first bid question) and the bivariate probit (both bid questions) are presented in Table 2. Both the probit and the bivariate probit models are highly significant (X2 = 35.16 in the probit and ×2 = 43.55 in the bivariate probit). Yet, in the probit model, none of the estimated parameters except BID1 and BACKUP are significant. In the bivariate probit model all estimated parameters except SCHED are significant at least at the 10% level. The reason for these differences may be related to the efficiency gains obtained by including the followup question (Hanemann et al., 1991). In both models, the bid variables are significant and negative, which means the higher the bid amount, the lower the probability to answer with a ‘yes.’ WTP increases when the firm (a) faces higher average tariffs, (b) uses back-up power during power cuts, (c) has higher turnover, (d) logs fewer production hours, and (e) sustains more unscheduled power cuts. Finally, in the bivariate Probit model, ρ is significant which means that the error terms of the two equations are correlated, justifying the use of a bivariate Probit model instead of two separate probit models. Table 3 gives the mean WTP values in INR per kWh, 95% confidence intervals, and p-values for the probit and bivariate probit models. The WTP depends on the values of the firm-specific exogenous variables so that the mean WTP represents the sample average (Table 1). The mean WTP in the probit model is slightly smaller than in the bivariate probit model (INR 1.09 and INR 1.22), but the confidence intervals overlap,

4 INR is Indian National Rupee. During the time of our survey, 1 USD was equivalent to INR 44.34. By January 2017, it has increased to INR 68.11. 5 Note that for the questions on PRODHOURS and TURNOVER we did not get responses from all firms. These firms are excluded from the regression models that use these variables.

6

661

For an explanation of the econometric model, please see Appendix A.

Energy Policy 109 (2017) 659–665

R. Ghosh et al.

electricity they consume. Even if we assume that their LT-cohort can be compared to our MSME sector, our estimate for WTP is 20% higher than the prevailing tariff, almost half of their estimate for the LT-cohort. Our conservative estimate indicates more accuracy. In fact, in their sample, almost half the firms were not willing to pay more for improved electricity supply, making a 37% increase in WTP rather skewed. Furthermore, we did not ask the respondents about their WTP for improved power supply but about their ‘desired increase’ in WTP, nudging them to focus on the problem directly. Our results can be very helpful in informing policy-makers about the power consumption preferences of small-scale firms. In December 2016, the Government of India (GoI) unveiled a Draft Electricity Plan in an effort to outline the government's efforts and plans for the power sector.7 It acknowledges the high share of MSMEs in total industrial energy consumption, which is roughly 25%, and proposes an energy mapping of the sector. Even the Ministry of Micro, Small, & Medium Enterprises continues to implement an Infrastructure Development Program scheme, which lays special importance to power distribution to MSME clusters. Our paper provides solid estimates which would otherwise be very difficult to obtain through secondary mapping exercises. The insights on behavior as per industrial classification also show that impacts and preferences are differential across firm types, and hence, policy should be more targeted in nature. Industrial tariffs in India have increased from an average of INR 4.16/kWh in 2007 to INR 7.64/kWh in 2015. This increase is in part because industrial electricity tariff cross-subsidizes electricity in the agricultural sector (Bhattacharyya and Ganguly, 2017). As a response, the National Tariff Policy 2006 and the Electricity Amendment Bill of 2014 aim to gradually phase out the subsidy. Findings from our study suggest that more than the price, it is the supply that matters. A policy of removing cross-subsidization must be driven by the impetus to ensure better power supply through increased financial surplus rather than to focus only on tariff reduction for industrial consumers. Perhaps addressing the issues of power theft, which is responsible for a loss of 20% of electricity generated in India (Gaur and Gupta, 2016), could be more useful in this context. As our results on the WTP indicate (and given the difficulty of bureaucracy in addressing problems of outages), an alternative solution could be the interruptible electricity supply contracts (Baldick et al., 2006). These contracts offer rebates to firms for accepting outages during scarcity. This triggers a trade in outages and develops a mechanism to allocate them to the least affected. Allcott et al. (2016) conducted simulations on offerings of interruptible contracts in India and found that their nationwide implementation could substantially reduce impact of shortages. The interruptible contracts may be designed between industrial, agricultural, and household users to reap further gains (Müller et al., 2016; Kimmich and Sagebiel, 2016). Since agriculture could be seasonal in nature, the rebates for them could be higher, and their power could then be channelized to MSMEs who would bear a higher cost of power outages. Indeed, this would be especially useful for small-scale firms that generally face allocative efficiency challenges owing to diseconomies of scale. Another option could be to explore the possibility of creating group captive power plants – G-CPPs, shared in an MSME cluster (Goyal and Ghosh, 2015). Here, the MSME cluster can create a Special Purpose Vehicle, invest to establish a small power plant to satisfy the cluster's captive needs, and sell the surplus power to the government. From a policy perspective, our study centralizes the problem of energy shortages for MSMEs in India. However, given the diverse experience of stakeholders in power supply dynamics, care must be taken to generalize the results. That said, the 20% higher price that MSMEs are willing to pay for better power services suggests that the government can potentially improve power supply even if part of the cost is shared by the industry.

Table 2 Estimation results: probit and bivariate probit.

Response_BID1 Constant BID1 TARIFF BACKUP TURNOVER PRODHOURS UNSCHED SCHED ρ N Log lik.(Null) Log lik. Chi-squared

(1) Probit

(2) Bivariate Probit

0.049 (0.561) −0.010*** (0.002) 0.110 (0.101) 0.601*** (0.229) −0.000 (0.001) −0.002 (0.020) 0.049 (0.037) 0.106 (0.095)

0.163 (0.364) −0.008*** (0.002) 0.145** (0.065) 0.438*** (0.140) 0.001* (0.001) −0.025* (0.013) 0.050** (0.023) 0.014 (0.056) 0.483*** (0.163) 199

199 136.362 −118.779 35.165

-228.518 43.559

Standard errors in parentheses; own calculations. * p < 0.10. ** p < 0.05. *** p < 0.01.

Table 3 Willingness-to-pay.

Probit Bivariate Probit

Mean WTP

Lower Level

Higher Level

p-value

1.09 1.22

0.80 1.07

1.28 1.39

0.0002 0.0000

which indicates that the difference in these values is statistically not significant. The mean WTP is thus within a range of INR 0.80 INR and INR 1.39. These WTP values make up approximately 20% of the average tariff that firms paid during the survey period. However, the results also indicate that the WTP is lower than what most firms pay when using back-up power such as diesel generators. We also assess if WTP varies with the industry type and nature of production. In Table B.5, we present WTP results of representative firms from the prominent industry classifications in our sample of firms. We control for firm size and other power supply covariates and observe that the WTP is highest for the pharmaceutical representative firm (INR 1.73) followed by the food and chemical firms (1.58 and 1.35 INR, respectively). They are comparatively lower for paper, electronics, and machinery and tool firms. This is consistent with the results from Ghosh and Kathuria (2014) who report that for firms in the same region, the likelihood of opting for a captive power plant (CPP) is highest for food, chemical, and pharmaceutical firms.

4. Conclusions and policy implications Our study augments a rather thin literature on estimating how much Indian small-scale firms (MSMEs) are willing to pay for reliable power supply. This is done using a randomized survey of 260 MSMEs in and around the Hyderabad region. Using the contingent valuation method, we show that MSMEs in the region are willing to pay 20% more than the prevailing tariffs. The results are instructive and depart from the previous work (Bose et al., 2006) in a number of ways, not only in studying the impact on MSMEs alone but also in producing a more reliable estimate. Bose et al. (2006) had surveyed 501 firms across the state of Karnataka (more than 300,000 units) while our examination was performed for 260 firms in the Hyderabad region (18,000 units), making it more focused (and indeed more recent). In terms of the method, we enrich the analysis further with a bivariate probit model and a Heckman model as a robustness check. Bose et al. categorized firms into HT (high tension) and LT (low tension) based on how much

7 See, Draft Electricity Plan, at http://www.cea.nic.in/reports/committee/nep/nep_ dec.pdf (last accessed on 13 April 2017).

662

Energy Policy 109 (2017) 659–665

R. Ghosh et al.

Acknowledgments

Kai Rommel. We also thank two anonymous referees whose comments helped improve the paper. This work was conducted as part of the research project ‘Sustainable Hyderabad’ and financed by the German Federal Ministry of Education and Research (Grant Number: 01LG0506A).

We thank Philip N Kumar, Vamsi Krishna, and their team for the support in conducting the field work. We are grateful to Veena Aggarwal, Deepika Garg, Markus Hanisch, Kaushik Deb, Krithika Ramakrishnan, and Appendix A. Econometric model

A firm i's profit is defined as the difference of firm-specific revenues Ri and costs Ci. We assume the firm to maximize profits by adjusting the produced quantity Xi . The maximum attainable profit is a function of exogenously given output and input prices Pi , firm-specific characteristics Si and electricity quality qi . The profit maximization problem can be written as:

max πǐ = Ri (Xi ) − Ci (Xi ) = πǐ *(Pi , Si, qi )

(1)

Where π̌i and πǐ * denote profit and the maximum attainable profit for given prices, firm-specific characteristics, and electricity quality. We assume pi , Xi,Si as fixed and that improved electricity quality has a non-negative impact on profits (i.e. the first derivative of the profit function is equal to or larger than zero).

∂πǐ * ≥0 ∂qi

(2)

̌ i for an improvement in electricity quality from level q to q for all q < q . Eq. (2) implies that there exists a non-negative willingness-to-pay WTP 0 1 1 0 Willingness-to-pay for improvements in electricity quality is defined as the maximum amount of money a firm would be willing to give up to become indifferent between the two provision levels q0 and q1: ̌ i = πǐ *(p , Si, q ) − πǐ *(p , Si, q ) WTP i 0 i 1

(3)

̌ i and πǐ * by the amount a of electricity in kwh which the firm In our survey, we are interested in the WTP per kwh. Thus we have to divide WTP uses . This can be expressed as WTPi =

̌ i WTP a

and πi* =

πǐ * . a

With this simple transformation the relevant formula in willingness to pay per unit of kwh is

WTPi = πi*(pi , Si, q0) − πi*(pi , Si, q1)

(4)

Respondents were presented with a status quo scenario with the current level of power quality q0 and an improved scenario with electricity quality q1 in which there would be no power cuts. We then asked the respondents if they would be willing to pay a pre-given amount T (the bid) for the improved scenario. The respondents can either accept or reject the offer. In a follow-up question, respondents get a higher (lower) price if they accepted (rejected) the first offer. This double-bounded dichotomous contingent valuation method has been developed by Hanemann et al. (1991) who show that efficiency is increased compared to a single-equation technique (Hanemann, 1984) In this format, willingness-to-pay, as defined in Eq. (3), cannot be estimated directly, but we can observe whether the respondents’ willingness to pay is larger or smaller than T . Formally, improved scenario with q1 is chosen if

πi*(P , Si, q1) − πi*(Pi , Si, q0) + T = ∆π (Pi , Si, q0 , q1, T )> 0

(5)

For simplicity, we assume the profit difference ∆π to be linear in its arguments and leave out Pi as we have no information collected on prices.

∆π (Si, q0 , q1, T ) = α + βSi + γT + ϵ

(6)

where α , β, and γ are parameters and ϵ represents the random and unobserved elements of the profit function. γ requires further explanation. Any amount T that the firm is willing to give up for improved electricity supply will come at an opportunity cost since this money cannot be used otherwise. Thus, γ is the marginal profit lost due to this forgone opportunity to invest. Analogous to utility theory, γ can be interpreted as the marginal profit from one unit of additional money. In other words, if the firm has one more unit of money, profits will increase by γ . Using this interpretation, we can calculate willingness-to-pay as

WTPi = −

α + βSi γ

(7)

To estimate the parameters of ∆π , we must make assumptions regarding the stochastic part of the profit function. The probability to respond a no, i.e., not to pay for the improvement in electricity quality is

Prob(q0) = Prob(α + βSi + γT + ϵ< 0) = Prob(ϵ > α + βSi + γT

(8)

And the probability to respond with a yes is

Prob(q1) = 1 − Prob(q0)

(9)

Assuming normal distribution for ϵ , the cumulative distribution function is

Prob(q0) = Φ[α + βSi + γT ]

(10)

This equation represents the standard probit model and can be estimated using the maximum likelihood method. Since we have asked a follow-up question, we have two equations to be estimated, and it is highly likely that the error terms ϵ are correlated for each respondent. More precisely, the unobserved part regarding the first question is correlated with the unobserved part of the second question which can be incorporated with a bivariate probit model. The corresponding log-likelihood function can be maximized using the Newton-Raphson method. A more detailed explanation of the model can be found in standard textbooks on contingent valuation (e.g., Bateman et al., 2002; Freeman et al., 2014; Haab and MacConnell, 2002). 663

Energy Policy 109 (2017) 659–665

R. Ghosh et al.

Appendix B. See Tables B.1–B.5 Table B.1 Power outages (hours/day). Variable

N

Mean

SD

Min

Max

Freq.

Scheduled Summer Scheduled Winter Scheduled Monsoon Unscheduled Summer Unscheduled Winter Unscheduled Monsoon

260 260 260 260 260 260

2.12 0.61 0.73 3.86 2.55 2.74

1.97 1.32 1.35 3.28 3.5 3.47

0 0 0 0 0 0

12 12 12 12 16 12

29.62% 51.92% 46.92% 8.85% 23.85% 16.92%

Table B.2 Self-assessment of supply issues. Variable

N

Mean

SD

Voltage fluctuations Scheduled power cuts Unscheduled power cuts No response to complaints Bribes required for repairs Getting a new connection Getting additional load

244 247 250 244 244 240 238

1.92 2.44 3.87 1.97 1.91 1.54 1.82

1.18 1.52 1.12 1.08 1.00 0.84 0.98

Table B.3 Summary statistics. Variable

N

Mean

SD

Min

Max

Captive Binary Paid Tariff Stated Loss of a one hour power cut in INR Capacity in kW Costs INR/kWh

260 260 260

0.54 4.65 10724.78

0.5 0.98 63261.85

0 3 0

1 8.5 1000000

133 122

245.65 17.06

254.83 13.72

5 5

1000 50

Table B.4 Results from Probit (1) and Probit with Heckman correction (2).

Response_Cuts_BID1 Cuts_Bid1 AVG_tariff Have_captive turnoversmall Prod_hours_day UNSCHED SCHED Constant WTP_Cuts_General AVG_tariff Have_captive turnoversmall Prod_hours_day UNSCHED SCHED Constant atanh(rho) Constant N pseudo R2 Log lik. Chi-squared

(1) Probit

(2) Heckman probit

−0.0103*** (0.0024) 0.1099 (0.1011) 0.6011*** (0.2286) −0.0003 (0.0010) −0.0019 (0.0205) 0.0490 (0.0368) 0.1060 (0.0952) 0.0486 (0.5612)

−0.0088*** (0.0020) 0.0498 (0.0917) 0.2582 (0.1891) −0.0006 (0.0009) 0.0043 (0.0162) −0.0354 (0.0318) 0.1146 (0.0808) 0.8398* (0.4803) 0.1676* (0.1014) 0.5448*** (0.2034) 0.0010 (0.0010) −0.0372** (0.0172) 0.2290*** (0.0428) −0.1970*** (0.0760) −0.1638 (0.5074)

199 0.129 −118.7795 35.1647

Standard errors in parentheses; own calculations. * p < 0.10. ** p < 0.05. *** p < 0.01.

664

−11.9453 (66.5740) 249 −219.6840 31.4814

Energy Policy 109 (2017) 659–665

R. Ghosh et al.

Table B.5 WTP heterogeneity by industry-type.

Industry classification (ASIa 2-digit code)

Firm 1 Food (10)

Firm 2 Paper (17)

Firm 3 Chemicals (20)

Firm 4 Pharma (21)

Firm 5 Electronics (26)

Firm 6 Machinery and Tools (28)

Tariff (INR) Backup (yes/no) Turnover (M-INR) Production hours Scheduled PC (hours) Unscheduled PC (hours) WTP (INR/KWH)

4 Yes 100 8 1 4 1.58

4.8 No 17 8 2 3 1. 02

6 Yes 200 24 0 0 1.35

4 Yes 100 7 5 5 1.73

4.75 No 12 10 1 4 0.99

5.75 No 100 10 1 4 1.30

a

Annual Survey of Industries, Government of India.

Appendix C. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.enpol.2017.07.046.

Ghosh, R., Kathuria, V., 2014. The transaction costs driving captive power generation: evidence from India. Energy Policy 75, 179–188. Goett, A.A., Hudson, K., Train, K.E., 2000. Customers' choice among retail energy suppliers: the willingness-to-pay for service attributes. Energy J. 21 (4), 1–28. Goyal, M., 2013. SMEs employ close to 40% of India's workforce, but contribute only 17% to GDP. Economic Times. 9 June. Goyal, Y., Ghosh, R., 2015. Group Captive power plants in small and medium-scale industrial clusters in India. In: Mohieldin, Mahmoud, Petkoski, Djordjija (Eds.), Financing Sustainable Development: Ideas for Action. World Bank Group, Washington DC. Gulyani, S., 1999. Innovating with infrastructure: how India's largest carmaker copes with poor electricity supply. World Dev. 27 (10), 1749–1768. Haab, T.C., MacConnell, K.E., 2002. Valuing Environmental and Natural Resources: the Econometrics of Non-market Valuation. Elgar, Cheltenham. Hanemann, M., Loomis, J., Kanninen, B., 1991. Statistical efficiency of double-bounded dichotomous choice contingent valuation. Am. J. Agric. Econ. 73 (4), 1255–1263. Hanemann, W.M., 1984. Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses. Am. J. Agric. Econ. 66, 332–341. Hanemann, W.M., 1994. Valuing the environment through contingent valuation. J. Econ. Perspect. 8, 19–43. Hanisch, M., Kimmich, C., Rommel, J., Sagebiel, J., 2010. Coping with power scarcity in an emerging megacity: a consumers' perspective from Hyderabad. Int. J. Glob. Energy 3–4, 189–204. Hansen, C. J., 2008. Bottom-up Electricity Reform Using Industrial Captive Generation: A Case Study of Gujarat, India. Oxford Institute for Energy Studies. Johnston, R.J., Boyle, K.J., Adamowicz, W., Bennett, J., Brouwer, R., Cameron, T.A., Hanemann, W.M., Hanley, N., Ryan, M., Scarpa, R., 2017. Contemporary guidance for stated preference studies. J. Assoc. Environ. Resour. Econ. 4 (2), 319–405. Kim, K., Cho, Y., 2017. Estimation of power outage costs in the industrial sector of South Korea. Energy Policy 101, 236–245. Kimmich, C., Sagebiel, J., 2016. Empowering irrigation: a game-theoretic approach to electricity utilization in Indian agriculture. Uti. Policy 43 (B), 174–185. Meyerhoff, J., Liebe, U., 2006. Protest beliefs in contingent valuation: explaining their motivation. Ecol. Econ. 57 (4), 583–594. Morrison, M., Nalder, C., 2009. Willingness to pay for improved quality of electricity supply across business type and location. Energy J. 30 (2), 117–133. Müller, M., Rommel, J., Kimmich, C., 2016. Farmers' Adoption of Irrigation Technologies: experimental evidence from a coordination game with Positive Network Externalities in India. Ger. Econ. Rev. ( In Press). Pasha, H.A., Ghaus, A., Malik, S., 1989. The economic cost of power outages in the industrial sector of Pakistan. Energy Econ. 11 (4), 301–318. Sari, E. N., 2003. Economic Impact of Poor Power Quality on Industry: Review of Studies (India),〈http://pdf.usaid.gov/pdf_docs/PNACX087.pdf〉. Welsh, M.P., Poe, G.L., 1998. Elicitation effects in contingent valuation: comparisons to a multiple bounded discrete choice approach. J. Environ. Econ. Manag. 36, 170–185. Wijayatunga, P.D., Jayalath, M., 2004. Assessment of economic impact of electricity supply interruptions in the Sri Lanka industrial sector. Energy Convers. Manag. 45 (2), 235–247. Yu, X., Abler, D., 2010. Incorporating zero and missing responses into CVM with openended bidding: willingness to pay for blue skies in Beijing. Environ. Dev. Econ. 15 (5), 535–556.

References Alberini, A., 1995a. Efficiency vs bias of willingness-to-pay estimates: bivariate and interval-data models. J. Environ. Econ. Manag. 29 (2), 169–180. Alberini, A., 1995b. Optimal designs for discrete choice contingent valuation surveys: single-bound, double-bound, and bivariate models. J. Environ. Econ. Manag. 28 (3), 287–306. Allcott, H., Collard-Wexler, A., O'Connell, S.D., 2016. How do electricity shortages affect industry? Evidence from India. Am. Econ. Rev. 106 (3), 587–624. Arrow, K., Solow, R., Portney, P. R., Leamer, E. E., Radner, R., Schuman, H. 1993. Report of the NOAA panel on contingent valuation. Federal Register, 58,10, pp. 4601–4614. Baarsma, B.E., Hop, J.P., 2009. Pricing power outages in the Netherlands. Energy 34 (9), 1378–1386. Baldick, R., Kolos, S., Tompaidis, S., 2006. Interruptible electricity contracts from an electricity retailer's point of view: valuation and optimal interruption. Oper. Res. 54 (4), 627–642. Bateman, I.J., Carson, R.T., Day, B., Hanemann, M., Hanley, N., Hett, T., Jones-Lee, M., Loomes, G., Mourato, S., Pearce, D., Özdemiroglu, E., et al., 2002. Economic Valuation with Stated Preference Techniques: a Manual. Edward Elgar, Chaltenham. Bateman, I.J., Langford, I.H., Jones, A.P., Kerr, G.N., 2001. Bound and path effects in double and triple bounded dichotomous choice contingent valuation. Resour. Energy Econ. 23 (3), 191–213. Bhattacharyya, R., Ganguly, A., 2017. Cross subsidy removal in electricity pricing in India. Energy Policy 100, 181–190. Blass, A.A., Lach, S., Manski, C.F., 2010. Using elicited choice probabilities to estimate random utility models: preferences for electricity reliability. Int. Econ. Rev. 51 (2), 421–440. Bose, R.K., Shukla, M., Srivastava, L., Yaron, G., 2006. Cost of unserved power in Karnataka, India. Energy Policy 34 (12), 1434–1447. Carlsson, F., Martinsson, P., 2007. Willingness to pay among Swedish households to avoid power outages: a random parameter Tobit model approach. Energy J. 28 (1), 75–89. Carlsson, F., Martinsson, P., 2008. Does it matter when a power outage occurs?—A choice experiment study on the willingness to pay to avoid power outages. Energy Econ. 30 (3), 1232–1245. Carlsson, F., Martinsson, P., Akay, A., 2011. The effect of power outages and cheap talk on willingness to pay to reduce outages. Energy Econ. 33 (5), 790–798. Carson, R.T., Mitchell, R.C., Hanemann, M., Kopp, R.J., Presser, S., Ruud, P.A., 2003. Contingent valuation and lost passive use: damages from the Exxon Valdez oil spill. Environ. Resour. Econ. 25, 257–286. Chung, Y.-S., Chiou, Y.-C., 2017. Willingness-to-pay for a bus fare reform: a contingent valuation approach with multiple bound dichotomous choices. Transp. Res. Part A: Policy Pract. 95, 289–304. Cooper, J.C., 1993. Optimal bid selection for dichotomous choice contingent valuation surveys. J. Environ. Econ. Manag. 24, 25–40. Fisher-Vanden, K., Mansur, E.T., Wang, Q.J., 2015. Electricity shortages and firm productivity: evidence from China's industrial firms. J. Dev. Econ. 114, 172–188. Freeman, A.M.I.F., Herriges, J.A., Kling, C.L., 2014. The Measurement of Environmental and Resource Values: Theory and Methods. Routledge. Gaur, V., Gupta, E., 2016. The determinants of electricity theft: an empirical analysis of Indian states. Energy Policy 93, 127–136.

665