An Analysis of Electricity Outages in Delhi: 2024-25

by Upasa Borah and Renuka Sane.

Introduction

In a previous article, A Review of Outage Reporting by Indian DISCOMs, we examined the state of outage data reporting across India. We studied which distribution companies (DISCOMs) report such data and the variations in the way they do so. A natural next step is to thus look more closely at the available data to understand the kinds of analyses they enable.

This article focuses on the three privately owned DISCOMs operating in Delhi. Delhi's DISCOMs rank below the top 20 in the Ministry of Power's annual ranking of DISCOMs, all three graded B minus in the 13th Ranking exercise in 2025. They are similarly situated in terms of their billing and collection efficiency, power procurement portfolios and costs. There are, however, notable differences in the availability, structure and clarity of their reported outage data.

It is important to note that not all outages at a feeder level translate into outages for consumers due to the presence of redundancy in power systems. Most modern systems can re-route electricity through alternate feeders in case of faults. Understanding whether and how redundancy is accounted for is thus crucial to interpreting outage data. For instance, one of Delhi's DISCOMs, BSES Rajdhani Power Ltd., reports outages at the feeder level, but there is no information on which feeders have redundancy systems or how many outages were rerouted and thus did not cause interruptions for end consumers. On the other hand, Tata Power Delhi Distribution Ltd. reports outage data by zones and the number of consumers affected, allowing us to infer the extent of consumer impact. BSES Yamuna Power Ltd., however, reports outages by division and subdivisions and does not note the feeders or consumers impacted.

Given these data limitations, our analysis does not directly compare performance between DISCOMs. Instead, we study the available data to demonstrate the kinds of insights that can be drawn about the frequency, duration and spatial patterns of outages in Delhi. Specifically, we ask:

1. What is the pattern of outages on the following parameters:
1. Duration and frequency,
2. Intensity,
3. Geography,
4. Reasons for outages
2. What is the relationship between outages and electricity demand?

Methodology

There are four distribution companies operating in Delhi: i) BSES Rajdhani (BRPL) covering the southern and western areas, ii) BSES Yamuna (BYPL) covering the southeast and northeastern regions, iii) Tata Power (TPDDL) in the north and northwest areas, and iv) New Delhi Municipal Corporation (NDMC), which supplies to government buildings in central Delhi. Excluding NDMC, the first three DISCOMs are privately owned and supply to 93% of consumers in Delhi; BRPL supplies to 31 lakh consumers covering an area of approximately 700 sq km, TPDDL supplies to 20 lakh consumers in 510 sq km, and BYPL supplies to 19 lakh consumers in an area of around 200 sq km (Chitnis et al., 2025). In 2024-25, Delhi's electricity requirement stood at 38,287 MU, with peak demand hitting 8,685 MW.

We collected outage data from each DISCOM's website (see Data appendix). Lack of data for NDMC limited our analysis to the remaining three DISCOMs. The reported data includes date and time of outages, durations, areas affected, reasons for outages and measures taken to rectify the issue. However, there are inconsistencies in the data reported by the three. Table 1 summarises the variations in the availability of outage data for the three DISCOMs under study.

Table 1: Availability of data on power outages

DISCOM	Days of data availability	Spatial unit of reporting data	Number of spatial units
TPDDL	April, May, July and August 2024	Zones	12 zones
BRPL	April 2024 to March 2025	Grid and feeder	428 grids, 2,951 feeders
BYPL	April 2024 to March 2025	Division and sub-division	28 divisions and 108 subdivisions

TPDDL data is available only for April, May, July and August 2024. It reports data on zone-wise outages and the number of consumers impacted. BRPL, on the other hand, provides data on grid and feeder levels, without noting how many consumers were affected. Since outages at the feeder level may not always indicate consumer-level interruptions, understanding redundancy systems is important, but data on these was not available. There is also no data on how many consumers are serviced by a grid or feeder. Finally, BYPL reports outage data at the division and sub-division level without specifying feeder details or the number of consumers affected.

Aside from these differences, we also noticed inconsistencies in the way data is recorded, in terms of structure, format and number formatting. We extracted outage data from PDFs, conducted thorough cleaning and reorganisation. Although the datasets included reported outage durations, we recalculated the duration of each outage for all three DISCOMs based on recorded start and end dates and times. In terms of reasons for outages, TPDDL lists six broad reasons, which we retained. In contrast, BRPL and BYPL record a wider and more open-ended set of reasons, which we analysed and classified into six broad categories using text search.

TPDDL: consumer-facing outages

Between April and August 2024 (excluding June), the parts of Delhi serviced by TPDDL recorded an average of around 87 outages per day. Across all zones and feeders, these outages cumulatively amounted to roughly 159 hours of interruptions per day, and affected around 46,000 consumers. Figure 1 shows the daily frequency and total cumulative hours of outages across all TPDDL zones. On most days, outages occurred in 11 of the 12 reported zones.

Figure 1: Aggregate frequency and duration of outages for TPDDL

Over the four months for which data is available, we analysed outage days and duration for each TDPPL zone, and then averaged the results across zones. The median and mean values are presented in Table 2.

Table 2: Average days of outages, intensity and number of consumers impacted in the four reported months

Total number of zones	Number of consumers facing outages (lakhs)		Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean	Median	Mean
12	4.17	4.75	121	116	8.39	13.59

* cumulative value across all feeders

On average, a TPDDL zone experienced outages on 116 days, affecting around 4.7 lakh consumers. It is important to note that these are aggregate zone-level values, i.e. they do not represent outages faced by an average consumer but rather the cumulative outages across all feeders within a zone, covering multiple subdivisions and localities. For instance, Narela, Badli, and Bawala zones have the highest number of outage days, with Narela having the highest intensity (40 hours cumulatively per outage day) across the various areas in the zone, affecting 9.15 lakh consumers. The total duration exceeds 24 hours because a single zone has several feeders whose outages are aggregated when they occur simultaneously.

Around 16% of all outages reported by TPDDL are due to planned events. Figure 2 shows the share of outages by reason. 71% of outages, accounting for 60% of total outage hours, are due to external factors where the specific cause is not reported. A more detailed classification of these categories would help identify the underlying causes of outages more accurately. It also remains unclear what is included under "EODB compliance" outages, which account for 12% of all outage hours, and "Industrial weekly off" that accounts for 3% of outage hours.

Figure 2: Reasons for outages for TPDDL

BRPL: Feeder-level outages

On an average day, around 48 feeders under BRPL experience outages, amounting to a cumulative total of 50 outages and 119 total hours of interruptions across all feeders. Figure 3 shows the daily frequency and duration of these outages. The highest number of outages occurred on 7 January 2025, when 104 feeders were affected, resulting in a combined total of 386 cumulative outage-hours.

Figure 3: Aggregate frequency and duration of outages for BRPL

Of the 428 BRPL grids, an average grid had around 8 feeders under outages, with a mean of 28 days of outages in a year. Cumulatively, this results in approximately 1.8 hours of interruption per outage day across its multiple feeders. Table 3 presents the median and mean values of feeders under outage, days of outages and intensity of outages across the grids. The median values are lower than the means, indicating that while most grids experience relatively fewer and shorter outages, a few grids have significantly higher levels of outages. For instance, in 2024-25, the most outages occurred in Jaffarpur grid (187 days of outages with a cumulative intensity of 9.9 hours per outage day), followed by Nilothi grid (247 days, 4.6 hours), Mitraon grid (182 days, 6 hours), Hastal grid (236 days, 4.4 hours) and C-Dot grid (185 days, 5.39 hours).

Table 3:Average days of outages, intensity and number of feeders impacted 2024-25

Total number of grids	Number of feeders under outages		Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean	Median	Mean
428	2	8	2	28	0.75	1.80

* cumulative value across all feeders

Figure 4 shows the share of outages by reason. Planned events account for 54% of all outages and 82% of total outage hours. Fault-related outages follow, making up 31% of outages and 9% of total outage hours. Most outages of BRPL are thus planned rather than caused by unforeseen circumstances.

Figure 4: Reasons for outages for BRPL

BYPL: Area-wise outages

On an average day, BYPL areas recorded 16 outages, with a cumulative duration of 12.5 hours across all affected feeders. Figure 5 shows the daily frequency and duration of these outages. The highest number of outages occurred on 28 June 2024, when 98 outages were recorded, lasting a combined total of about 100 hours.

Figure 5: Aggregate frequency and duration of outages for BYPL

For an average subdivision serviced by BYPL, outages occurred on about 22 days in a year, with a cumulative average of 58 minutes per outage day. The median values are lower at just three days of outages (Table 4), indicating that most subdivisions experienced fewer days of outages, while a few faced disproportionately higher outages. Sonia Vihar recorded the most outages (201 days with a cumulative intensity of 1.86 hours per outage day), followed by Nand Nagri (196 days, 1.84 hours) and Karawal Nagar (179 days, 1.62 hours).

Table 4: Days of outages, intensity per outage day during the year 2024-25

Total number of subdivisions	Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean
108	3	22	0.92	0.96

* cumulative value across all feeders

Figure 6 shows the share of outages by reason. BYPL has zero outages explicitly listed as "planned". 51% of outages accounting for 47% of outage duration were due to faults, followed by maintenance outages and outages due to infrastructure damage.

Figure 6: Reasons for outages for BYPL

Electricity demand and outages

The lack of consistent and comparable data makes it difficult to analyse the yearly correlation between Delhi's electricity demand and outages. However, looking at BRPL and BYPL's outage data reveals contrasting results. BRPL's daily outage hours show no correlation with Delhi's electricity demand (Figure 7), while BYPL outages are positively correlated, significant at the 1% level (Figure 8).

Figure 7: BRPL outages and Delhi's total electricity demand

Figure 8: BYPL outages and Delhi's total electricity demand

Moreover, when we look at the time when most outages occur, we find similar divergence. Most of the outages of TPDDL and BRPL were recorded to have occurred between 6am to 12pm, which is different from Delhi's peak demand hours which are generally from 2 pm to 5 pm, and 11 pm to 1 am. BYPL's outages, on the other hand, seem to mostly occur around 12pm to 6pm. A detailed share of total outages by time of day is given in Table 5.

Table 5: Proportion of total outages and duration by time of day

Time of day	Share of TPDDL's total outages (%)		Share of BRPL's total outages (%)		Share of BYPL's outages (%)
	By frequency	By duration	By frequency	By duration	By frequency	By duration
12am - 6am	8.4	6.4	7.2	2.3	21.1	22.3
6am - 12pm	38.7	51.5	53.1	73.2	22.0	21.9
12pm - 6pm	37.3	28.6	31.3	21.8	32.5	31.3
6pm - 12am	15.5	13.4	8.4	2.6	24.4	24.6

Conclusion

Our analysis finds that the lack of a common standard and clarity in reporting makes it difficult to draw definitive conclusions about the frequency, duration, and causes of outages in Delhi. There seems to be a substantial number and hours of outages, but in the case of BRPL and BYPL, we do not know how many of those lead to consumer-facing outages, and thereby cannot assess the reliability of supply.

Several other issues also stand out. For example, TPDDL's outage reasons are not clearly defined: what exactly counts as EODB and Industrial weekly off outages? Meanwhile, most of BRPL's outages are marked as "planned". It is unclear if they translate to interruptions for consumers, but it is worth asking why such a large share is planned. On the other hand, BYPL does not report a single planned outage, which seems equally puzzling.

There are also differences in the spatial units used for reporting. That TPDDL reports 12 zones, BRPL 428 grids and BYPL 108 subdivisions implies that TPDDL's higher outages could be due to its larger geographical units. Even between BSES's two DISCOMs, outage data are reported differently, with no information on how many consumers are connected to a feeder or fall under a subdivision, making it difficult to assess the real impact of outages.

While much attention is paid to the financial performance of DISCOMs, it is also important to study the reliability of the electricity they supply. Internationally, countries like the United States and the United Kingdom publish country-wide, disaggregated outage data that enable detailed analyses of reliability, causes and impacts. For instance, studies using US Department of Energy data examine reliability and causes across states (Ankit et al., 2022) and counties (Richards et al., 2024), while data from the UK's National Fault Interruption Reporting Scheme has been used to analyse trends in outages and weather data (Shouto et al., 2024). These highlight the potential of regular, consistent and transparent reporting, which is missing in India.

As we discussed in our previous article, several independent studies in India have tried to estimate outage data, largely through household surveys (Agrawal et al., 2020; Bigerna et al., 2024; Khanna & Rowe, 2024). However, DISCOMs are better positioned to provide granular, feeder-level data in an accessible and comparable form, but as of the writing of this article, they are not mandated to make this information public. There is also no command standard of reporting, which make it impossible to make meaningful assessments. While DISCOMs are investing in redundancy systems and infrastructure, they must also clarify which recorded outages translate into consumer-facing interruptions. Doing so would, in fact, allow for a more accurate evaluation of the measures undertaken to improve reliability.

Aklin et al. (2016) had conducted a household survey in six Indian states and found that not only are outages very frequent, but that increasing the reliability of supply has effects comparable to electrifying an unelectrified household. Improving reliability of supply, however, first requires an understanding of where, when and why outages occur, which in turn requires better data. We recommend adopting a common standard of reporting outage data that includes daily, consumer-facing feeder-level outages, with information on the outage start and end times, durations, reasons, the number of consumers and the localities impacted. A first-level reason can broadly indicate whether an outage is planned or unplanned, and then provide a detailed description of the underlying cause. The data should be updated regularly and historical archives should be publicly available. This would enable more accurate and regular analyses of outage patterns, across DISCOMs and states.

References

Factors affecting household satisfaction with electricity supply in rural India by Aklin, M., Cheng, C. Y., Urpelainen, J., Ganesan, K., & Jain, A., 2016, Nature Energy, 1(11), 1-6.

Stalemate - How Consumers are Losing in the Fight Between the Regulator and Discoms in Delhi by Chitnis, A., Dmonty, A. N., & Singh, D., 2025, CSEP.

Data appendix

The data on outages was extarcted from:

• BRPL, accessed on 2 June, 2025
• BYPL accessed 7 June, 2025
• TPDDL accessed on 7 July, 2025

Delhi's daily electricity demamd was accessed from Grid-India on 7 July, 2025

The cleaned datasets and code used in this analysis are available on our GitHub repository.

The authors are researchers at TrustBridge Rule of Law Foundation. They thank an anonymous referee for useful comments.

A Review of Outage Reporting by Indian DISCOMs

by Upasa Borah and Renuka Sane.

In 2023, 99.5% of India's population had access to electricity. This statistic, however, should be measured along with the data on consistency and quality of electricity supply. Frequent power outages and low and fluctuating voltage can adversely affect appliances, reduce productivity, increase the cost of production and reduce standards of living (Jha et al., 2021). It adds a financial burden on both households and firms, who are forced to invest in costly backup options like inverters and diesel generators (Pargal and Banerjee, 2014). As India expands its electricity access, it is useful to measure how it is faring on the quality of its electricity supply. The aggregate data does not appear promising. According to the 2019 Global Competitiveness Index by the World Economic Forum, India ranked 108 out of 141 countries in electricity supply quality.

A response to the question of quality should first begin with assessing its measurement. In this article, we examine the availability of outage data in India. Outages refer to any interruptions in the supply of electricity to end consumers, and are classified into three types, depending on the location of the interruption in either generation, transmission or the distribution segments of the electricity system. We focus on outages happening in the distribution system, as it captures the final impact on consumers and takes into account upstream interruptions. These fall under the purview of distribution companies (DISCOMs), so we study all the DISCOMs in the country, and ask:

1. How many DISCOMs report data on outages?
2. Is the format of available data consistent across DISCOMs on
1. methodology,
2. period of data availability, and
3. the spatial unit of reporting?
3. Is there consistency in the reporting of outage data across states?
4. Is there a relationship between DISCOM characteristics like fiscal health and ranking, ownership and location and the availability of outage data?

Measuring outages

The quality and reliability of electricity supply are estimated by relying on a measure of either frequency or duration, or a combination of both, of interruptions faced by consumers. Feeders, which could be underground or overhead wires connecting substations to service areas, are the backbone of the distribution network, and distribution outages are typically measured using data from these feeders.

Two of the most widely used reliability indices are the System Average Interruption Frequency Index (SAIFI), and the System Average Interruption Duration Index (SAIDI). The former measures how often an average customer experiences an interruption, while the latter denotes the total minutes (or hours) of interruption an average customer faces. For example, as an illustration, if a distribution network serves 1,000 customers and experiences 200 supply interruptions in a given year, SAIFI would be 200/1000, or 0.2, interruptions per customer. If the total duration of interruptions in the same network were 3,000 minutes, then SAIDI would be 3000/1000, i.e. 3 minutes of interruptions per customer.

Supply interruptions or outages can be planned or unplanned, where planned outages are those that have been scheduled in advance, like maintenance work, which the DISCOM is supposed to disclose to customers in advance. Unforeseen outages due to disruptions, faults in the distribution system, extreme weather events, etc., are unplanned outages. From the perspective of the consumer, however, both planned and unplanned outages disrupt daily consumption and production activities and thereby have costs associated with them. Moreover, many households report not receiving prior information on planned outages (Agrawal et al., 2020), and it is often unclear which specific events are categorised as planned.

Methods

As per the Electricity Act 2003 and the National Electricity Policy 2005, the Central Electricity Authority (CEA) is tasked with collecting and publishing reliability indices for DISCOMs. However, this is not a statutory mandate, and compliance remains voluntary (Sekhar et al., 2016). The State Electricity Regulation Commissions have Standards of Performance regulations that outline metrics for reliable supply and guidelines such as time taken to restore supply, penalties, etc. (Athawale, 2021) Further, the Electricity (Rights of Consumers) Rules, 2020 mandate that DISCOMs should supply power 24x7 as the norm, with the State Commissions specifying the acceptable levels of SAIDI and SAIFI values for unavoidable interruptions. It also states that DISCOMs should have a mechanism to monitor and restore outages and disclose feeder-wise outage data and efforts made to minimise outages. The Service Rating of DISCOMs by the Ministry of Power & Rural Electrification Corporation Limited factors outages in its rating of DISCOMs; however, this data on actual outages is not publicly available.

We compiled a list of all the DISCOMs in the country using the annual ranking of DISCOMs by the Ministry of Power. While the CEA publishes annual reliability indices, not all DISCOMs are included in their lists. Moreover, such annual data masks the granular, day-to-day variations needed to meaningfully study the reliability of electricity supply.

We reviewed each DISCOMs official website to assess their current reporting practices. We restricted our search to official websites, and on encountering broken or unsafe links, we considered the data to be unavailable.

Findings: Availability of data

As per the Ministry of Power, there are a total of 72 DISCOMs in the country, all of which are included in our dataset. Among them, 36 (50%) have some form of outage data available on their websites, although irregular. The remaining 36 DICSOMs have no mention of outage or interruption data anywhere on their websites. Among the DISCOMs for which data is available, a closer look reveals the inconsistency and sporadic nature of the reported data. Broadly, there are three types of inconsistencies: i) the type of data reported and the methodology used, ii) the time period for which data is available, and iii) the spatial unit of measurement.

Reporting of data

The first inconsistency lies in the way outage data is reported. 17 out of the 36 DISCOMs use the SAIFI and SAIDI indices. The rest report interruptions by date and time, without noting how many customers were affected. Among the ones that report SAIFI and SAIDI, there is an inconsistency in the way the reliability indices are calculated. For instance, the Delhi Standard of Supply Code states that planned outages and outages less than five minutes shall not be included in calculating the reliability indices. On the other hand, the Haryana Standard of Supply Code includes planned outages in the calculation of the indices, while excluding outages of less than three minutes. DISCOMs like Karnataka's Chamundeshwari Electricity Supply Corporation (CESC) reference a "Reliability Index" without specifying which one. Other Karnataka DISCOMs provide feeder or area-wise frequency and duration of interruptions without calculating the SAIFI and SAIDI indices. Yet others, like Adani Electricity Mumbai Limited (AEML), report only the number of complaints registered and the duration taken to resolve them. Additionally, six of the 36 DISCOMs only reported scheduled or planned outages, and there was no data on unplanned outages.

Time period for which data is available

The second inconsistency concerns the time for which the data is reported. Only eight out of the 36 DISCOMs had data going back at least five years. For the rest, data availability was patchy and lacked any clear patterns. Some have data only for the past year, while others have data for sporadic years like 2022, or 2019 to 2024 and so on. Five DISCOMs had outage data only for the current date (as of visiting the website), and past archives were not available. In another instance, like that of West Bengal State Electricity Distribution Company (WBSEDCL), viewing outage data was allowed only for 60 days prior to the current date. Table 1 summarises the time period covered by the DISCOMs. There are also variations in the frequency of reporting outage data; some publish daily figures, others have data weekly, monthly or quarterly. 15 DISCOMs reported monthly SAIDI and SAIFI data, while two reported them daily.

Table 1: Coverage period of outage data

Time period covered	No. of DISCOMs
Last five years	8
Sporadic years	23
Current day	5

Spatial unit of measurement

The final inconsistency relates to the spatial unit of reporting, summarised in Table 2. 16 DISCOMs report interruptions both by feeders and areas, while four reported only feeder-wise data. Among these, some report outages in 33kV and 11kV feeders separately, while others club them together. The distinction is important because 33kV feeders carry electricity from high-voltage substations to 33/11kV substations where voltage is stepped down, and 11kV feeders then deliver power to local service areas through distribution transformers that further reduce voltage for end-users. 16 DISCOMs report outages in terms of geographic area, like zones, divisions or areas affected. However, it is unclear if these area lists are comprehensive; for instance, Assam Power Distribution Company (APDCL) reported district-wise data, but did not include all districts.

Table 2: Spatial units used in reporting outages

Unit of reporting	No. of DISCOMs
Area and feeder	16
Only feeder	4
Circles, divisions, towns, cities	13
Zones	2
Areas affected	1

Findings: Does DISCOM ranking, ownership, or state matter?

Next, we examined whether a DISCOM's characteristics, like the state where it is located, its ownership and ranking are correlated with the availability of outage data.

There were no visible patterns of data availability observed across states. In states with multiple DISCOMs like Uttar Pradesh, Gujarat and Maharashtra, most did not report outage data. In contrast, all four DISCOMs of Odisha and all three of Andhra Pradesh had outage data available on their websites. The three inconsistencies discussed earlier were also evident within states. For instance, among the four DISCOMs in Delhi, only three had data available, and among them, there were variations in the spatial units used (area vs feeder) and the time period for which data were reported.

We used the 13th DISCOM ranking by the Ministry of Power to see if better-performing DISCOMs tended to have better data availability. However, there was no clear correlation; both high-ranking and low-ranking DICSOMs seemed equally likely or unlikely to make outage data available. There was also no correlation between ownership and data availability.

Finally, we compared the DISCOMs that publish outage data on their websites to those for whom CEA has compiled annual reliability indices. Of the 49 DISCOMs included in CEA's 2021-22 list, only 26 had data available on their websites. Alternatively, among the 23 DISCOMs not included in the CEA list, 10 had outage data available on their websites. It is worth noting that for eight of these 10 DISCOMs, the data was available for sporadic years, which may explain their exclusion from the CEA's lists. Nonetheless, these findings point to the disconnect between the CEA and DISCOMs reporting practices.

Accuracy of reported data

The availability of data does not guarantee its accuracy. Several studies have raised concerns about the unreliability of outage data reporting, particularly in developing countries (Min et al., 2017). For instance, in January 2017, the National Load Dispatch Centre reported only a 0.9% shortfall in power supply in Uttar Pradesh, while Prayas Energy Group recorded a daily average of nine hours of power outages in rural areas and two hours in urban areas. Other studies point to similar discrepancies: scheduled power cuts in India often last longer than officially noted (Baskaran et al., 2015), there are logical inaccuracies in reported data (Mandal et al., 2019), and household survey data do not align with government-reported outage statistics (Agrawal et al., 2020).

These findings suggest that simple reporting of outage data is not sufficient. There is an urgent need for independent and transparent monitoring systems that complement official reporting and allow for verification of accuracy. Independent studies have attempted to fill this gap, using surveys (Agrawal et al., 2020; Bigerna et al., 2024; Khanna & Rowe, 2024), satellite night-light data (Min et al., 2017; Dugoua et al., 2022) or initiatives like the Supply Monitoring Initiative (ESMI) by Prayas Energy Group, but these efforts are usually restricted to specific regions and limited time periods. The lack of a single agency reporting outage data, combined with the inconsistencies in reporting practices by DISCOMs further complicates the process of data verification.

Conclusion

In July 2024, the National Feeder Monitoring System was inaugurated, which has data on around 2.5 lakh 11kV feeders across the country. Its dashboard provides data on hours of supply in rural and urban areas by state and DISCOM. However, to the best of our knowledge, it does not offer access to historical, granular data on daily hours of supply by feeders, state or DISCOM. The CEA reports annual reliability indices, but it should cover all DISCOMs in its list and augment it by including more granular data. A logical next step, however, is to ensure that the available data is accurate, which requires independent monitoring systems. Prayas Energy Group's ESMI has minute-wise data on supply from November 2014 to December 2018, recording not just outages but voltage fluctuations. Such efforts should be scaled up and maintained on an ongoing basis.

Having accurate and accessible data on hours of supply and areas of outage is crucial not only for consumers to understand and plan their production and consumption but also for a thorough review of DISCOMs' performance. While much of the discussion on DISCOMs centres around their financial health, it is also important to assess their ability to supply reliable power to their customers. Standard of performance indicators should include data on feeder-wise outages, distribution transformer failure rates and SAIDI, SAIFI (Pargal & Banerjee, 2014; Mandal et al., 2019). Reliability indices are valuable for providing consistent, comparable measures of service quality over time, but daily reporting of feeder-wise data that includes time, duration, cause of outage and measures taken to resolve the issue, also has its benefits in allowing for spatial, minute-by-minute analysis to pinpoint weak links in the network. Whichever approach is used, however, it should be standardised across DISCOMs and reported collectively, with a common format agreed upon by all stakeholders. If the sector moves towards reliability indices, their calculation methods should be consistent and published more frequently to ensure meaningful assessments, comparisons and verifications.

References

State of Electricity Access in India: Insights from the India Residential Energy Survey (IRES) by Agrawal, S., Mani, S., Jain, A., & Ganesan, K., October 2020, CEEW Report.

India's electric grid reliability and its importance in the clean energy transition by Athawale, R., May 2021, Regulatory Assistance Project.

Election cycles and electricity provision: Evidence from a quasi-experiment with Indian special elections by Baskaran, T., Min, B., & Uppal, Y, June 2015, Journal of Public Economics.

India's Statistical System: Past, Present, Future by Bhattacharya, P., June 2023, Carnegie Working Paper.

An empirical investigation of the Indian households' willingness to pay to avoid power outages by Bigerna, S., Choudhary, P., Jain, N. K., Micheli, S., & Polinori, P., November 2024, Energy Policy.

Assessing reliability of electricity grid services from space: The case of Uttar Pradesh, India by Dugoua, E., Kennedy, R., Shiran, M., & Urpelainen, J., June 2022, Energy for Sustainable Development.

Blackouts: The Role of India's Wholesale Electricity Market by Jha, A., Preonas, L., & Burlig, F., December 2021, NBER Working Paper.

The long-run value of electricity reliability in India by Khanna, S., & Rowe, K., April 2024, Resource and Energy Economics.

Five Stitches in Time: Regulatory and policy actions to ensure effective electricity service by Mandal, M., Nhalur, S., Pandey, A., & Josey, A., May 2019, Prayas (Energy Group).

Whose Power Gets Cut? Using High-Frequency Satellite Images to Measure Power Supply Irregularity by Min, B., O'Keeffe, Z., & Zhang, F., June 2017, World Bank Research Working Paper 8131.

More Power to India: The Challenge of Electricity Distribution by Pargal, S., & Banerjee, S. G., 2014, World Bank Directions in Development.

Evaluation and Improvement of Reliability Indices of Electrical Power Distribution System by Sekhar, P. C., Deshpande, R. A., & Sankar, V., 2016, IEEE.

The authors are researchers at TrustBridge Rule of Law Foundation. They thank an anonymous referee for useful comments.

Survey-based measurement of Indian courts

by Pavithra Manivannan, Susan Thomas, and Bhargavi Zaveri-Shah.

Public institutions do not face a market test. Achieving state capacity is about establishing checks and balances. The traditional idea is to instrument the operations, and construct an operational MIS, which is released into the public domain. Through this, deficiencies of the working of the organisation are visible to researchers and the public. The other pathway is to ask the persons who interact with the state institution about what they feel, to elicit their perceptions. This is an important pathway to obtain evidence and thus create feedback loops. For instance, citizen surveys are commonly used to assess the quality and impact of public services such as health and education (UNDP 2021, Clifton et al, 2020, OECD-ADB 2019).

In the legal system, perception surveys of court users can generate useful knowledge about how well courts function in their delivery of justice (National Center for State Courts, 2005). Ongoing surveys of user experience of courts can help measure the performance of a component of the entire legal system, and in assessing the impact of interventions made for reforming the legal system.

Surveys of court users and the public on their perception of the judiciary have been prevalent in developed countries from the 1990s, and are gaining currency in India (eg., Dougherty et al, 2006; Rottman and Tyler, 2014; Staats et al, 2005; Daksh 2016). Such surveys seek to capture the perceptions of court users on qualitative metrics (Manivannan et al, 2022). Such metrics can be used to evaluate the functioning of a single court, or compare alternative courts.

On one hand, perceptions are not reality. On the other hand, the views of end-users of the justice system are particularly important because, ultimately, the justice system exists to serve end-users whose interests and preferences may differ from those of judges and lawyers. We can readily discern certain difficulties in survey-based measurement of perceptions:

1. There are many different users of a court, who differ in their extent of knowledge. Litigants who see a court case as a disruption of their daily lives, may see things differently when compared with lawyers, for whom courts are part of their professional lives.
2. A person who loses a case is likely to be unhappy with his experience of the court and vice versa.
3. Different individuals might be working on non-comparable cases, and their subjective experience of the court is then not comparable.
4. It is not clear what is an objective benchmark of sound performance. A perfect court may be prohibitively expensive. Users of courts may have normalised a variety of difficulties; their `satisfaction' may only flow from learned helplessness.
5. It is important to narrowly measure a court or a group of courts, and make claims about the narrow unit of observation, as opposed to bigger claims about the Indian legal system.

In 2023, we conducted two pilot surveys to evaluate their utility as feedback loops for courts.

One survey was administered to understand the functioning of five alternative forums that can be approached to adjudicate matters of debt disputes: the Bombay benches of the National Company Law Tribunal (NCLT), the Debt Recovery Tribunal (DRT), the Bombay High Court (Bom HC), the Metropolitan Magistrate (MM) courts (which adjudicates criminal proceedings for cheque bouncing cases), and the Alternative Dispute Resolution (ADR) process.

To help improve data quality, the survey was conducted on practitioners who had multiple instances of interacting with the five courts. By selecting practitioners that have had repeated instances of approaching these forums to resolve disputes, the survey results are less vulnerable to the 'loser' effect. To obtain comparability, we presented a hypothetical, canonical problem of debt dispute resolution to each survey respondent. We then asked them to rank the five forums on five dimensions of court performance, namely, efficiency, effectiveness, predictability, independence, cost and convenience, and calculated the average rank for each forum on each of these dimensions.

The second survey was conducted with litigants at the DRT, with the objective of understanding the functioning of this court. For this, we deployed a team of four, who visited the premises of the Bombay bench of the DRT. The team administered a survey questionnaire on individuals, in order to evaluate the performance of the DRT on the above mentioned five dimensions. The participants were asked to rate their experience at the DRT on a five-point scale.

Method

Survey design

We used a combination of qualitative (in-depth expert interviews and open-ended comments) and quantitative surveys (multiple choice and scaled questions). Qualitative surveys with experts provide more contextual insights, enable comprehensive analysis. They helped validate our founding conjecture, the idea that there was a class of disputes which could go to multiple different forums. However, these surveys were time-intensive and it was difficult to obtain the interest and involvement of experts.

Survey mode

We administered the survey in both online and offline formats. Surveying litigants on court premises was challenging in two ways. First, litigants do not always accompany their lawyers to courts, especially in disputes of larger sizes involving firms. Second, one forum may deal with multiple type of disputes (civil v. criminal; mergers v. insolvency). This poses difficulty in identifying a litigant with a desired case-type.

The questionnaire used for the surveys and the responses collected can be found here.

Results: The perceptions of practitioners

The practitioner survey involved eliciting their choice of forum for the following hypothetical, canonical problem:

Q is a large public listed company. It has availed of a working capital loan of Rs. 7 crores from N, a small sized NBFC, repayable within three years with simple interest @16% p.a. Q and N are 100% domestically owned. As collateral for the loan, Q has granted N a floating charge over some of its movable assets, for example, its machinery or its inventory. One year into the loan, Q defaults on its loan to N. The outstanding amount exceeds Rs.1 crore. Post-dated cheques issued by Q towards interest payment bounce due to insufficient funds. The collateral is not sufficient to cover the outstanding amount. You are advising N.

The survey respondents were asked to make two assumptions, namely, that the limitation period is the same across all the courts; and that all courts have jurisdiction.

We collected responses from 18 respondents, of which 16 were lawyers and two were key managerial personnel at an asset reconstruction company and a debt restructuring advisory firm. Six of our respondents had between 20 to 30 years of experience in this area, eight of them had experience of less than 20 years, and two of them had more than 30 years experience in this field. They had significant experience with many of the venues of interest: 14 had experience with the NCLT and the Bom HC, 11 with the DRT and ADR process, and 5 with the MM Courts.

We aggregated the ranks assigned by the respondents to each of these forums on the parameters of independence, efficiency, effectiveness, predictability and access, and averaged them to arrive at an overall rank for each forum. The specific statements on which the respondents ranked the forums and their ranks are presented in Table 1. The forums are arranged in increasing order of the average rankings on each parameter. The NCLT was ranked the highest on the parameter of Efficiency, followed by ADR, the Bom HC, the DRT and the Metropolitan Magistrate. On the other hand, the Bom HC was ranked as the most preferred forum of choice on the parameter of independence.

Table 1: Preference ordering of five debt enforcement forums

Metric	Survey Statement	Ranking
		1	2	3	4	5
Efficiency	Most likely to dispose of my matter in a timely manner	NCLT	ADR	Bom HC	DRT	MM Courts
Effectiveness	Easiest to recover the amount awarded in the judgement decree.	NCLT	Bom HC	DRT, ADR	MM Courts
Predictability	(i) Expected sequence of stages in my matter was clear.	NCLT	ADR	Bom HC	DRT	MM Courts
	(ii) Hearings are most likely to be held as scheduled.	ADR	NCLT	Bom HC	MM Courts	DRT
Independence	Decisions are most likely made based on the merits of the case.	Bom HC	ADR	NCLT	MM Courts	DRT
Access	(i) Can afford to take my case to this forum.	MM Courts	DRT	NCLT	Bom HC	ADR
	(ii) Ease of navigation; staff helpfulness; website; ease of filing process	ADR	Bom HC	NCLT	DRT	MM Courts

Table 1 contains new insights on a specific court on each attribute. For example, while the Bom HC and the ADR process are perceived to be most unbiased, they are perceived as more expensive to access. ADR is perceived to be most predictable, but less effective on actually getting the relief. The NCLT, on the other hand, is perceived to be more efficient and effective, when compared to the other forums, but less likely to also be unbiased. The DRT and the Metropolitan Magistrate courts are perceived unfavourably on all aspects, except affordability.

Results: The perceptions of litigants

The in-person survey conducted at the DRT observed 55 persons, who were presently a party to a dispute at the DRT. Among these, 24 were debtors, 19 were creditors, and 12 belonged to the residual category, such as court/privately appointed receivers and auction awardees. Of these, 30.6% were at early stages (admission), 28.6% were at advanced stages (such as post-admission or pending last hearing), and 22.4% were awaiting a final hearing or pronouncement of judgement.

Litigants at the DRT had more positive perceptions than practitioners. Litigants ranked the DRT the highest on predictability of the hearing: most litigants agreed that when a hearing for their case is scheduled at the DRT, it will be held on the scheduled date. About 67-69% of litigants perceived the DRT to be an affordable and unbiased forum to resolve their dispute. More creditors ranked it higher (85-89%) on these two metrics than debtors (58-62%). However, 52% of litigants did not think that the DRT resolves cases in a timely manner.

Discussion

Good performance by the judicial branch in a country is essential. As with all aspects of public policy, this requires the loop of evidence, identification of difficulties, creative policy proposals, policy reforms, and measurement of the gains. In the legal system, generally, evidence and measurement involves quantitative measures. In this article, we have shown a case study where survey-based evidence was useful. This constitutes a useful additional pathway to measurement of the legal system.

Litigants are the ultimate end-users of courts, so their views matter greatly, but their information set may be limited. Legal practitioners have better information through repeated interactions and potentially observation of multiple venues, but their views may not capture the views of the litigants themselves. In the future, it would be useful to go further, by way of surveying the general public, measuring the view of persons who have not experienced litigation at a given location.

References

Shaun Bowler, Joseph L. Staats, and Jonathan T. Hiskey (2005). Measuring Judicial Performance in Latin America, Latin American Politics and Society.

Judith Cliftona, Marcos Fernandez-Gutierrez and Michael Howlett (2020). Assessing public services from the citizen perspective: What can we learn from surveys?, Journal of Economic Policy Reform.

Daksh (2016). Access to Justice Survey, A DAKSH report.

David B. Rottman and Tom R. Tyler (2014). Thinking about judges and judicial performance: Perspective of the Public and Court users, Onati Socio-legal Series.

Devendra Damle and Tushar Anand (2020). Problems with the e-Courts data, NIPFP Working Paper Series 314.

George W. Dougherty, Stephanie A. Lindquist and Mark D. Bradbury (2006). Evaluating Performance in State Judicial Institutions: Trust and Confidence in the Georgia Judiciary, State and Local Government Review.

Institute of Social Studies and Analysis (2021). Satisfaction with Public Services in Georgia, United Nations Development Programme.

National Center for State Courts (2005). CourTools: Trial Court Performance Measures.

Pavithra Manivannan, Susan Thomas and Bhargavi Zaveri-Shah (2022). Evaluating contract enforcement by courts in India: a litigant's lens, XKDR Working Paper No. 16.

Pavithra Manivannan, Susan Thomas and Bhargavi Zaveri-Shah (2023). Helping litigants make informed choices in resolving debt disputes, The Leap Blog.

OECD-ADB (2019). Government at a Glance Southeast Asia, Serving Citizens: Citizen satisfaction with public services and institutions, OECD Publishing, Paris.

Pavithra Manivannan and Susan Thomas are researchers at XKDR Forum, Mumbai. Bhargavi Zaveri-Shah is a doctoral candidate at the National University of Singapore. We thank Surya Prakash B.S., Renuka Sane, and Anjali Sharma for their suggestions on the design of the surveys. We acknowledge the very diligent assistance by Nell Crasto and Balveer Godara, students at Kirit P. Mehta School of Law, NMIMS Mumbai, on conducting the litigant survey. We are grateful to all the survey respondents for their generous participation, and thank Mahesh Krishnamurthy, K.P. Krishnan, Sachin Malhan, Harish Narsappa, Rashika Narain, Geetika Palta, Siddarth Raman, Ajay Shah, and Arun Thiruvengadam for their comments and suggestions on this work.

How are securities laws enforced in India: some facts from a new data-set of SEBI orders

by Devendra Damle and Bhargavi Zaveri Shah.

Introduction

The Securities and Exchange of Board of India (SEBI) is one of the most powerful regulators in India. As the regulator of one of the world's largest stock markets by market capitalization, SEBI has a variety of enforcement tools at its disposal. These include the imposition of monetary penalties, license cancellation and pursuing criminal proceedings against violators. The law empowers SEBI to issue directions to intermediaries, and more broadly, to persons associated with the securities market. Such directions may be of a prohibitory nature, such as restricting companies from raising capital in the public markets, disqualifying persons from acting on the board of publicly traded issuers and restricting access to the capital market altogether. They may also be of a remedial nature such as disgorging illegal gains made by violators or directing restitution to wronged investors. The grounds for issuing such directions are wide.

How has SEBI used these enforcement powers over time? Has it prioritized enforcement against some kinds of misconduct over others? If yes, have the priorities stayed static or changed over time? Do certain types of violations consistently entail certain types of sanctions? How efficient are the enforcement proceedings in terms of the time taken, and what is the success rate for enforcing such sanctions? Unlike some Indian financial sector regulators, SEBI follows a due process before issuing such orders, involving the issuance of a show cause notice and a hearing and publishes each enforcement order passed by its officials systematically on its website. This transparency in enforcement allows us to establish some basic facts on securities laws enforcement in India over a long observation period. In a new paper, we analyse over 8,000 enforcement orders passed by SEBI over a span of ten years to answer some of the questions we mentioned above. In this article, we summarize the key findings of our work.

Data description

In our study period beginning 1st January, 2011 and ending on 31st December, 2020, SEBI passed 9048 enforcement orders, of which we were able to sucessfully download and parse 8032 orders. We then analysed these orders, using text-mining software we designed ourselves, to arrive at some summary statistics on the frequency and type of enforcement undertaken by SEBI during the study period. To answer more detailed questions on the nature of enforcement, we manually analysed a stratified random sample of about 10% of these orders. The sample was drawn from the set of orders involving four regulations, which are most frequently enforced by SEBI (as per our data), namely, orders pertaining to fraudulent and unfair trade practices in the Indian securities market (FUTP), violations of the Insider Trading regulations, the Takeover Code and Broker regulations.

As mentioned above, the SEBI Act empowers SEBI to pass two types of orders, namely, orders imposing monetary penalties and orders issuing directions. Such orders can be issued against intermediaries, market participants, issuers of capital or persons generally associated with the securities market. Until 2019, monetary penalty orders could be passed only by adjudication officers and directions would be issued by whole time members of the SEBI board. With effect from 2019, the members of the SEBI board have also been empowered to pass orders imposing monetary penalties. In addition to these, the law also empowers SEBI to settle violations upon the payment of a settlement fee, without passing a guilty verdict against the violator. Basis this scheme of the SEBI Act, we categorize the enforcement orders in our data set into three categories shown in the Table. On an average, SEBI issues 250 enforcement orders with directions and double the number of orders imposing monetary penalties each year. The SEBI Act also empowers SEBI to initiate criminal prosecution against persons accused of having violated the SEBI Act or the regulations made by it, but we do not take account of this typology of enforcement proceedings in our study.

Table: Enforcement orders (2011-20)

Type of order	Type of sanction	Total^
Orders by Adjudicating officers	Monetary penalties	4911 (61)
Orders by Chairperson/member	Non-monetary sanctions	2484 (31)*
Settlement orders	Settlement fee	637 (8)
Total		8032 (100)
^Numbers in brackets are a percentage of the total. *We estimate that not more than 30 orders may involve a monetary penalty.

As is evident from the Table, securities law enforcement is largely undertaken in India through monetary penalties, but the proportion of enforcement undertaken through non-monetary sanctions is not trivial. Settlements account for less than 10% of the total enforcement orders in our data. The annual distribution of these types of orders is shown in the Figure. The Figure shows that from 2018 onwards, there has been a sharp increase in the intensity of enforcement, with the number of monetary penalty orders nearly doubling from the previous years. The proportion of settlements has also increased over time, particularly after 2016. While the growth in the size of the market, an increase in the intensity of regulation and enforcement capacity are intuitive explanations for this jump, more precise, causal explanations require further research.

Figure: Year-wise types of enforcement orders (2011-2020)

Findings

SEBI draws its substantive powers from a set of three laws, over and above the SEBI Act, namely, the Companies Act, 2013 (and its preceding legislation), the Securities Contracts (Regulation) Act,1956 (SCRA) and the Depositories Act, 1996. While the Companies Act largely deals with the incorporation of Indian companies and the governance of their affairs, it also governs primary issuances, the requirements to be met by public offer documents and some aspects of the governance of listed companies. These matters under the Companies Act are administered by SEBI. The SCRA governs the conceptual definition of securities and securities contracts, regulates some types of securities contracts and governs the licensing and affairs of stock exchanges. The Depositories Act, 1996 deals with the regulation of depositories and depository participants. Under each of these laws, and in particular under the SEBI Act, SEBI has issued regulations defining the registration and reporting requirements for intermediaries, the kinds of misconduct that will elicit penalties, and so on.

We find that orders against fraudulent and unfair trade practices (FUTP) are the single largest group (15%), followed by orders dealing with violations of the provisions of the Companies Act (11%), insider trading regulations (10%) and the takeover code (9%). The enforcement actions (i.e. the number of orders) under the remaining regulations are few, with some of them having witnessed enforcement not more than once during the study period. Some of these seemingly rarely-enforced regulations, such as the regulations governing alternative investment advisors, are relatively new, which may explain why they do not appear more often in our data. However, others, such as the regulations governing venture capital funds, stock exchanges and clearing corporations, are older, but we see fewer orders issued under these regulations as compared to other regulations. Whether this is because the regulations themselves are not violated as frequently by market participants, or because SEBI chooses not to enforce them, requires further study.

To answer more specific questions of these enforcement orders, we manually analysed a random sample of 818 orders (approximately 10% of the total sample) from amongst the orders against the following types of violations: (1) FUTP, (2) insider trading, (3) violations of the takeover code and (4) violations of brokers' regulations. Some findings from this micro-study are summarised below:

1. Duration of the enforcement proceedings: The formal enforcement process at SEBI begins with the appointment of an investigating authority who investigates the facts and reports her findings to the SEBI board. If the findings are adverse, a show cause notice is issued to the accused by the adjudication officer (where the proposed sanction is a monetary penalty) or a whole time member of the SEBI board (where the proposed intervention is a direction). We find that the median time for the issuance of a show cause notice is a little more than three years from the date on which the violation was committed. Further, the median time from the date of issuance of a show cause notice to the date of an order imposing monetary penalties is a year and a half. It is a little more than two years for orders issuing directions. A regulation-wise analysis of the duration suggests no relationship between the complexity of the violation involved and the duration of the enforcement proceeding.
2. Subject and outcome of enforcement: A bulk of the enforcement actions are in respect of unregulated entities, that is, entities that are not SEBI-licensed intermediaries. This phenomenon could be attributed to the type of violations that are most often enforced against, namely FUTP and insider trading. Both these practices would likely involve traders and market participants that are not SEBI-licensed intermediaries. Further, in nearly 80% of the cases, SEBI found the person(s) guilty of all the violations that they were charged with, with a marginally higher conviction rate for unregulated entities compared to regulated entities. The conviction rate for violations of the Takeover Code is also marginally higher, compared to violations under the three sets of regulations. It is hard to comment on the optimality of this high conviction rate as these enforcement proceedings are undertaken and decided by SEBI officers themselves. All orders of SEBI, except those rejecting an application for settlement, are appealable to the Securities Appellate Tribunal (SAT). The rate of appeals and the outcome of appeals before the SAT could be a rough proxy to evaluate the optimality of this conviction rate and would be a good direction for further research.
3. Proportionality of sanction: We find a lot of variation in the amount of penalty levied across cases. While the median (i.e. typical) size of the penalty is in the range of Rs 5,00,000, the average is in the range of Rs. 57,00,000. This difference indicates that while there are few cases where large penalties are issued, the size of these penalties is very large compared to the typically-imposed penalties. One explanation that could account for this variation is the amount involved in the violation. The SEBI Act requires an Adjudicating Officer to take into account, among other factors, the amount of disproportionate gain or unfair advantage made as a result of the default or the amount of loss caused to investors as a result of such default. However, we find that in a vast majority of the cases in our sample (90%), the size of the violation was not calculated.

   We similarly find a lot of variation in the orders that impose sanctions other than monetary penalties. Out of 118 such orders, 82 orders restricted the market access of the accused. The duration of such restrictions varied from 15 days to 4 years, and we could not discern any relationship between the duration of the restriction on the one hand and the violation or the purpose of the restriction on the other. Further, courts have repeatedly held that SEBI's direction making powers are remedial and preventive in nature, and not punitive. However, it is unclear at what point an order that operates to restrict market access starts to become punitive in nature, since none of the orders in our data clearly draw the line between remedial and punitive measures.

Conclusion

In India, the field of securities laws is often studied from the perspective of a specific case, individual legislative amendments or specific judgements of courts. While such analysis is useful, a slightly different, more quantitative approach is necessary to gain a systematic understanding of the manner in which the regulator uses the wide variety of enforcement tools available to it, the manner in which it seeks to enforce against different kinds of misconduct and the efficiency of its enforcement functions. The consistent publication of easily accessible enforcement orders by SEBI on its website makes it possible to undertake such systematic research on securities laws enforcement in India. This paper is one such effort to begin developing more systematic knowledge on enforcement of private law in India.

The data used for this analysis can be found here. The data-set can be cited as Zaveri Shah, Bhargavi; Damle, Devendra (2022), "Securities law enforcement in India", Mendeley Data, V1, doi: 10.17632/ppdk9pzfdp.1.

Devendra Damle is an independent researcher. Bhargavi Zaveri Shah is a doctoral candidate at the National University of Singapore.

Response to the Consultation Whitepaper on 'Strategy for National Open Digital Ecosystems (NODEs)'

by Rishab Bailey, Harleen Kaur, Faiza Rahman, and Renuka Sane.

The Ministry of Electronics and IT, Government of India (MeitY) had sought public comments on a Consultation Whitepaper (CW) titled a "Strategy for National Open Digital Ecosystems (NODEs)" earlier this year. NODEs are defined as:

open and secure delivery platforms, anchored by transparent governance mechanisms, which enable a community of partners to unlock solutions and thereby transform social outcomes.

The NODES framework will allow the opening up, and sharing of personal and non-personal data held in various sectors (such as healthcare, agriculture, and skills development). Each NODE will consist of infrastructure developed and operated by the government. The private sector will utilise the common infrastructure and data to provide solutions to the public. Per the CW, this will enable greater intra-government and public-private coordination and create efficiency gains. This framework will promote access to innovative e-governance and other services for citizens while enabling robust governance processes to be implemented.

We wrote a detailed response to MeitY. In our submission, we make suggestions on four key issues with the CW:

• Role of the state: The CW needs to demonstrate clarity on the need for government intervention on the scale proposed. The market failures that require State intervention must be identified on a sectoral basis.
• Centralisation of governance and technical systems: The CW envisages establishing monolithic, stack-based digital systems in a variety of sectors. The government would be responsible for establishing and operating the technology infrastructure as well as the governance of such systems. However, excessive centralisation can reduce competition, innovation, and produce unsecure systems.
• Alignment with the existing government policies: The CW needs to consider existing government policies on the adoption of open source software (OSS), open APIs and open standards. Further, the CW needs to account for existing open data and e-governance related initiatives in the identified sectors, and how these would interact with the NODEs framework.
• Preserving and protecting Constitutional norms: The CW needs to ensure the protection of fundamental rights, democratic accountability, and transparency in the creation and regulation of NODEs. Further, it also needs to account for the federal division of competencies enshrined in the Constitution.

This article summarises our comments and suggestions on the above mentioned issues.

Role of the State

The CW adopts a 'solutionist' approach, in that it does not undertake sufficient analysis of the circumstances and problems in each sector. For instance, the CW identifies two market failure in the skills sector: (i) information asymmetry amongst the stakeholders, and (ii) a lack of trust in the information that is available. It proposes a Talent (Skilling and Job) NODE as a one-stop solution to connect employers, job seekers, counsellors and skilling institutes. Instead of the approach undertaken by the CW, one should consider if private entities can or are already innovating to bridge the information asymmetry and trust issues in the sector, and what policies could provide an environment where such information asymmetry may be reduced. If the problem in the skills sector is a lack of trust, it is unclear why this cannot be solved by interventions such as certification standards.

As a general rule, the State should be involved with building technological systems only for essential state activities (Kelkar and Shah, 2019). It is therefore critical to differentiate between sectors where the State has a legitimate role (say in the provision of its welfare and statutory functions), from sectors where private sector solutions could suffice. For example, the State could have a role in providing access to Public Distribution System (PDS), but need not be a player in building a platform for access to rail reservations.

The responsible ministry should analyse if the NODE is serving welfare or other essential function of government. In case there is no such element, the government should not use its finances on creating infrastructure for such a NODE. Such an approach would promote innovation, prevent the emergence of a state-centric technological mono-culture, and allow the private sector to respond appropriately to requirements of any particular sector. Entities would not be forced to build on top of state-mandated infrastructure, which may not always be necessary or appropriate.

In the context of the NODEs framework, the State should primarily have three roles:

1. Open up data: The government must focus on building databases and providing access to the public, in a non-discriminatory manner. The benefits of enabling free flows of information are well known. That said, it is important to keep in mind the need to ensure non-discriminatory access to ensure data quality, and to prevent against privacy and other downstream harms. For instance, the Delhi government recently shared locations for COVID-19 relief centers on Google maps, thereby giving Google a competitive edge over other mapping solutions. We believe that an appropriate approach would involve the Delhi government making the relevant information open. This can be done by providing the geo-tagged locations on its open data governance website. Methods to embed this data in third-party apps and services could be provided to enable non-discriminatory access. Similarly, the benefits of opening up railways related data, which is currently monopolised by the IRCTC can enable the provision of customised travel solutions. Greater linkages could be formed with private players in the hospitality and tourism sectors, leading to mutual benefits to the railways as well as the private sector and consumers.
2. Implement regulatory frameworks: The government should institute regulatory processes and norms based on the need to protect and promote fundamental rights and correct market failures. Interventions must be designed to (a) promote effective competition and the maintenance of a level playing field, (b) avoid function creep, (c) protect and promote fundamental rights, (d) ensure appropriate apportioning of functions, obligations and responsibilities/liabilities.
3. Ensure democratic accountability: It is now well-established that "code is law" (Lessig, 1999). This makes it imperative for the government to establish systems of democratic accountability, transparency and openness in the creation and regulation of public digital systems. Transparency and accountability measures should be implemented both at the conceptualisation stage as well as thereafter. This should involve:

• An open and transparent consultation process in the design of the NODEs, similar to the the Report of the Financial Sector Legislative Reforms Commission recommendations for regulation making.

• A cost-benefit analysis that takes into account the economic costs and benefits of operationalising a NODE within a sector. This would also allow for suitable alternative approaches to be explored.

• Integration of principles of participatory and democratic governance into the implementation and operation phases. This would promote citizen-centric governance, particularly in the context of privatisation of regulatory functions. For example, the National Payments Corporation of India (NPCI) functions as a quasi-regulatory agency due to the scope of its powers, functions, and de-facto regulatory monopoly. However, being a private entity, it has not been brought under the purview of the Right to Information Act, 2005. This limits citizen engagement with governance processes.

• Mechanisms to enable allocation of responsibilities and coordination between government entities at different levels (local, state, and central). This is especially important when dealing with common issues (such as tagging of data sets, instituting grievance redress mechanisms, etc.) without usurping constitutional and statutory functions.

Centralisation of governance and technical systems

Enabling the government to pick technological winners and losers or enabling a technical monoculture would decrease innovation and competition. It is well-recognised that centralisation can lead to increased security concerns. One must also be wary of unintended consequences of even the best planned regulation in the technology space. Technology moves too fast and has multiple possible future use cases. Over-regulation or excessive centralisation could have negative effects on expected outcomes.

In cases where the government is required to create digital systems, these must be federated and decentralised to the extent possible. The creation of monolithic technical architectures, which are often de facto mandatory, must be avoided. For instance, the creation of a centralised identification system -Aadhaar- which was thereafter mandated for use across different sectors has caused various problems ranging from exclusions, intrusions into privacy rights of citizens and inhibiting innovation (i.e. such a system is preferred over other possible forms of identification that could suffice in any particular use-case). Implementing a centralised system of 'public infrastructure' may therefore not be necessary and may in fact reduce competition and civil liberties protections.

Instead, the focus of the government should be on enabling the private sector to develop relevant platforms and technologies that compete with one another on a level playing field, albeit with due consideration for regulatory, human rights and other problems that may arise in any given context. Such a system would also promote greater security. The use of federated databases, enabling the development of alternative technical solutions to be built on data, etc., would mean that problems associated with having a single source of truth or a single source of failure can be avoided.

Alignment with existing government policies

The CW proposes principles of open and interoperable delivery platforms. There are two concerns in the manner in which these are described in the CW.

1. The CW does not refer to existing government policies on the use of OSS in e-Governance projects. Various policies specifically deal with the issue at hand (for example, National Policy on IT, 2012, the policy on Adoption of Open Source Software for Government of India, and the policy on Open Standards for e-Governance).

2. The scope of the word 'open' as used in the CW is vague and appears to confuse concepts of "open access" and "open source". The CW suggests that each NODE will require a different degree of openness to adhere to specific objectives, context, or mitigate potential risks. This approach can dilute existing policies (mentioned above) that contain clear definitions and mandates on the use of open source solutions by the government.

It is imperative that the NODEs framework build on and strengthen existing government commitments towards the use of OSS solutions. This will unlock the benefits of OSS/Open APIs/open standards such as enhanced security and verifiability, no vendor lock-in, etc.

Preserving and promoting constitutional safeguards

The creation of NODEs platforms would significantly impact fundamental rights. We envisage three instances where the NODES environment needs to be careful about preserving constitutional safeguards.

1. Right to equality, right to life, and personal liberty: Digitisation at the scale contemplated by the CW may lead to concerns about access to services and possible exclusions therefrom. Ensuring rights protection may be particularly important in the context of the use of AI-based solutions and possible discrimination that may arise as a result. The understanding of what amounts to discrimination must be evolved by each NODE distinctly and will depend on the sector.

2. Right to privacy: Each of the NODEs will invariably result in the collection and processing of personal data and non-personal data by both government and private entities. The collection and use of personal data by different state entities must necessarily satisfy the tests laid down by the Supreme Court in the Puttaswamy decisions (2017 and 2018). Similarly, principles relating to the use of data by the private sector as laid down in the context of the Aadhaar judgment (Puttaswamy, 2018) must also be adhered to. Due regard must also be given to (the developing) regulatory frameworks concerning personal and non-personal data.

3. Federal structure: The NODEs framework must also consider the impact on the division of subject matter competencies under the Constitution. One could envisage benefits arising from NODEs in areas such as agriculture, judicial services, healthcare, etc. However, these sectors fall under the State List in the Seventh Schedule to the Constitution. Implementation of NODEs in these sectors should not result in de facto centralisation of federated competencies. Instead, mechanisms to ensure coordination and cooperation between different levels of government must be considered.

We, therefore, recommend that each NODE be backed by an appropriate statute, to the extent possible. This would ensure greater democratic deliberations, prevent excessive and arbitrary executive action, set out the rights of citizens and private entities, and clarify the scope/ limits of any particular project. Providing statutory backing would also limit mission creep, while delineating rights and obligations and governance processes. For instance, despite its various faults, the statutory mandate provided to the Unique Identification Authority of India and the restrictions on data sharing in the Aadhaar Act have proven invaluable in ensuring that biometric and other data is not made freely available for non-Aadhaar purposes by the public sector, including for instance, in criminal investigations. In contrast, projects such as FASTags (which aims to digitise highway toll systems) are being gradually expanded with plans to integrate the system with criminal tracking networks, amongst others.

Conclusion

The CW provides a basic overview of the concept of a NODE and identifies certain sectors in which such a system could lead to gains (such as the skills and health sectors). For various reasons outlined in our submission, our recommendation is to not proceed with implementing the NODEs framework in the manner currently outlined in the CW. We believe that the CW should be seen as an exploratory document. Greater clarity is required on the need for interventions on the scale envisaged in the document, particularly in view of the proposed centralised, stack-based approach. The NODEs framework should consider the need for openness at lower layers of the stack (infrastructural layers), adhere to existing government policies on the use of OSS, Open APIs and Open Standards, and consider policy developments concerning the regulation of personal and non-personal data. The CW should also ensure greater transparency and democratic accountability of governance frameworks and the processes for the creation of a NODE.

References

Bailey et al, 2020: Rishab Bailey, Vrinda Bhandari, Smriti Parsheera and Faiza Rahman, Comments on the draft Personal Data Protection Bill, 2019: Part I, LEAP blog, 2020.

Centre for Digital Built Britain, 2018: A Bolton, M Enzer, J Schooling et al, The Gemini Principles: Guiding values for the national digital twin and information management framework, Centre for Digital Built Britain and Digital Framework Task Group, 2018.

FICCI & KPMG, 2014: FICCI and KPMG, Skilling India: A look back at the progress, challenges and the way forward, 2014.

Kelkar and Shah, 2019: Vijay Kelkar and Ajay Shah, In service of the republic: The art and science of economic policy, Penguin Allen Lane, 2019.

Lessig, 1999: Lawrence Lessig, Code and other laws of cyberspace, Basic Books, 1999.

Puttaswamy, 2018: Justice K.S. Puttaswamy v. Union of India (Aadhaar case), 2019 (1) SCC 1.

Leblanc, 2020: David Leblanc, E-participation: A quick overview of recent qualitative trends, DESA Working Paper No. 163, United Nations Department of Economic and Social Affairs, 2020.

Michealson, 2017: Rosa Michealson, Is Agile the answer? The case of UK universal credit, in Grand Successes and Failures in IT - Public and Private Sector, IFIP Advances in Information and Communication Technology, Springer, 2017.

Ministry of Rural Development, 2013:Ajeevika skills guidelines, Ministry of Rural Development, Government of India, 2013.

Puttaswamy, 2017: Justice K.S. Puttaswamy v. Union of India (Right to privacy case), 2017 (10) SCC 1.

Raghavan and Singh, 2020: Malavika Raghavan and Anubhutie Singh, Building safe consumer data infrastructure in India: Account aggregators in the financial sector - Part I, Dvara Research, 2020.

Steinberg and Castro, 2017: Michael Steinberg and Daniel Castro, The state of open data portals in Latin America, Centre for Data Innovation, 2017.

Zambrano, Lohanto and Cedac, 2009: Raul Zambrano, Ken Lohanto and Pauline Cedac, E-governance and citizen participation in West Africa: Challenges and opportunities, The Panos Institute, West Africa and the United Nations Development Programme, 2009.

The authors are researchers at NIPFP.