Wednesday, June 1, 2016

Public Infrastructure: A misplaced emphasis on highways and expressways

(The following piece appeared in India Together ; reproduced below )

Why does the government continue with its blind focus on highways and expressways when infrastructure for water supply, waste management and mass transit system is in desperate need of attention? Kannan Kasturi criticises the government's prioritization and says there is more to public infrastructure than just highways and expressways.

The usual economic justification for investing in highways is that they will have a “multiplier effect”. The argument is along these lines: highways will lead to greater efficiencies in trade and industry by allowing faster transport of goods. On the other hand, investment in “social infrastructure” – such as water or waste management - may not offer immediate benefits to the economy.
The government’s lopsided emphasis on highways is not entirely without support. It has the approval of a section of the car owning public that has discovered the joys of fast inter-city travel without the constraints imposed by public transport. But more significantly, it has had the approval of corporate India for whom it opened up another area of business.
Creating “non-performing” assets
Corporate involvement in highways was initially limited to executing government contracts. Setting itself ambitious targets for highway addition, the government came up with a new form of “Public Private Partnership” (PPP) that allowed corporations to invest in and own public highway infrastructure and earn toll income. The justification of this form of PPP – the “build operate and transfer” (BOT) toll model - was that it would bring in private investment into infrastructure and thus make possible infrastructure addition at a faster rate than would be possible with the government’s own resources.
The BOT toll model became popular very quickly. The government had in parallel made changes to policy that made it easier for banks to finance infrastructure. Corporate investments were made on the back of large scale borrowing from banks, exploiting the banking norms that allowed up to 70% debt financing for infrastructure.
Under government prodding, public sector banks who were the major corporate financiers (they account for 73% of all lending by banks in India) channeled national savings reserved for lending to infrastructure disproportionately to highway projects. According to RBI data, outstanding bank loans to the roads sector is Rs 1.8 lakh crore as of Mar 2016. 80% of bank lending for infrastructure is in just two areas - roads and power. The remaining 20% is shared between infrastructure for telecom, airports, ports and “social infrastructure”.
In the last few years, private ownership has turned the spotlight on the economics of highways. A large number of projects are unable to service their debt taken mainly from public sector banks. They have become non-performing assets (NPA’s) for the banks and this is reflected in the statistic that 50% of bank NPA’s are in the infrastructure sector which is largely just roads and power. The major role of public investment even in the BOT toll model stands exposed. The government will ultimately have to pay for the non-performing ‘highway assets’ held by the public sector banks, one way or another.
While delays in land acquisition and environmental clearance leading to cost overruns are commonly held to be the cause for non-performance, many of these projects have not had the expected initial traffic or traffic growth at the rates envisaged during planning. In particular, goods traffic growth on toll roads has been abysmal. This has resulted in toll collection – despite yearly increase in rates – falling short of debt servicing requirements. The poor traffic growth shows up the specious nature of the economic multiplier argument for highways.
The unattractive revenue from toll has pushed the government in recent times to resort to forms of engagement with private corporations where the revenues of the private partner are independent of toll collected. The contract model is once again in vogue. The government is also pushing various “annuity” models where the infrastructure companies cover the investment requirements fully or with government participation, build and operate a highway and receive an agreed amount as annual income.
In this arrangement, the government collects the toll and bears the traffic risk. In other words, the government is back to encouraging “risk free capitalism” in the roads sector. The need for these forms of engagement is telling; private corporations do not see economic justification in these highways but the government will nevertheless build them.
A recently completed mega highway project – the Yamuna Expressway - for which detailed information is available in the public domain, corroborates the general picture of the performance of recent highway projects in India. It also illustrates an alternate model of financing – “land value capture” - that has been used to build grandiose economically unviable highways.
The Yamuna Expressway – a case study
The Yamuna Expressway project connecting Noida (on the outskirts of Delhi) to Agra was initiated in 2001 by the BJP Government in UP lead by Rajnath Singh. It was to be a six lane access controlled highway. The economic justification provided for the project was typical: besides encouraging tourism, the highway would “open up avenues for industrial and urban development” of the region.
The Jaypee Group, one of only two bidders, was selected for the project during Mayawati’s tenure as CM of UP. The private partner would make the entire investment and have rights to the toll. Anticipating that toll revenues would be inadequate, the government came up with a scheme where the builder could recover his investment in the expressway through “land value capture”.
The government would acquire land along the expressway and pass this on to the builder for developing urban townships. The value of the land was expected to go up for several reasons - the change in land use from agricultural to urban (residential, commercial, industrial) decreed by the government and the speedy access to Delhi and Agra via the expressway from these pockets.
A CAG audit of the partnership agreement carried out in 2012 was extremely damning. It found that the government of the day accepted the cost and revenue projections of the developer without any independent analysis. It also recorded that the government allowed the developer to actually select the location of the land parcels! Nearly three fifths of the 2500 hectares of the land selected was in the National Capital Region. 500 hectares was prime real estate in Noida.
It took till 2007 for the UP Government to decide on the alignment of the expressway after which the newly formed Jaypee Infratech began project execution. The project was completed ahead of time and what is touted as India’s most advanced highway was opened to traffic in Aug 2012.
As is the case with most highway projects, the traffic projections for Yamuna Expressway turned out to be wild. An average of 16490 vehicles used the highway per day in 2014-15 against the expected 100,000. The annual revenue from toll for that year was only Rs 168 crore on a highway on which the company claimed to have invested Rs 9962 crore. (For comparison, the annual investment in the railways which transports 13 million passengers a day is now around Rs 40,000 crore.)
True to the general pattern, the project expenses were met mainly through borrowing from banks. Jaypee Infratech’s debt stood at more than Rs 8700 crore as of Sept 2015.
The low traffic indicates that the hopes of the access controlled “world class” expressway spurring economic development in the region were misplaced. Some of the reasons for the low traffic are related to the very conception and design of the highway. Access control has meant that people of towns and villages along the Yamuna banks cannot use the highway and will not reap the benefits of faster travel and transport to the metros. The high toll on the expressway has kept potential users away despite heavy congestion on the alternative NH 2.
Even the Taj tourist does not seem to have benefited greatly. While the 165 km length of the expressway can be negotiated in 100 minutes, the congested roads from Delhi to Noida (where the expressway begins) and the drive through Agra to the Taj bring home the reality of roads in urban India.
Financing highways through land capture
The land along the course of the expressway is fertile canal irrigated three crop farmland. Farmers co-operated with the government when land was being acquired for the highway. However the government’s move to acquire land to hand over to the company for urban enclaves met with resistance. The government used time tested methods to harass farmers – disconnecting electricity supply, stopping cultivation and implicating them in false cases.
The government agency acquiring land has the high sounding name of “Yamuna Expressway Industrial Development Authority” (YEIDA). It has plans of ‘developing’ much more land along the Expressway - a draft master plan of Feb 2011 shows no less than 58000 hectares earmarked for development! Among the first acts of ‘development’ of YEIDA was acquiring and transferring 2500 acres of land in Greater Noida to the Jaypee Group for a race course and a “sports city".
In the run up to the inaugural Formula 1 race at the racecourse built on their land, farmers in the villages of Bhatta & Parsaul at the outskirts of Delhi protested demanding that their land be returned. Clashes took place between farmers and the police in which four people lost their lives. Subsequently, the government let loose a reign of terror on the hapless villagers. Thus was acquired the land which would pay for the expressway.
Farmers were paid an average of Rs 35 lakh/acre by the government for the 6175 acres of land acquired along the expressway. The expressway builder paid the government on average Rs 44 lakh/acre. By 2013, the company had sold a 300 acre piece of this land lying near the race track to a real estate company at Rs 5 crore/acre!
Fifteen years after the project was conceived even the urban enclaves along the expressway have not materialized, let alone industry. However, the rural agricultural economy has been made poorer with the loss of large tracts of fertile irrigated land.
Why not social infrastructure?
The overall experience of highway development in India over the last 15 years is of low traffic volumes and slow traffic growth. Toll revenues do not provide an adequate return on the investment in these highways. Low traffic growth also exposes the specious nature of the “economic multiplier” argument. 
The question that presents itself is this:  why does the government continue with its blind focus on highways and expressways despite this negative experience?
There is no shortage of infrastructure outside highways that is in desperate need of attention. The low priority for water infrastructure has made the country extremely vulnerable to vagaries of nature. This is seen in the terrible scarcity of even drinking water affecting a large part of the country today. The lack of adequate waste management has polluted all the lakes and rivers near our urban areas and is rapidly making our cities unlivable.
The central government cannot absolve itself of responsibility by claiming that “social infrastructure” is in the domain of the states. It is high time it changes priorities from “world class expressways” that will benefit only corporate interests to infrastructure that has an immediate impact on the life of the average citizen.

Thursday, March 3, 2016

How private thermal projects have fared?

This piece appeared in EPW. Reproduced in full below. The figures and tables have been copied from EPW.

Private Thermal Power in a Liberal Policy Regime

The Electricity Act, 2003 and the National Electricity Policy, 2005 put in place a highly liberal policy regime for private thermal electricity generators.

Licensing was done away with. Techno-economic clearance from the Central Electricity Authority was no longer necessary. Generators were provided open access to the transmission network, owned with but a few exceptions by state and central utilities. They could supply power to any part of India. Generators were also freed from having to enter into long term (12-25 year) power purchase agreements with distribution companies which limited profit margins. They could opt for shorter term contracts as well as sell in the power markets through traders and power exchanges.

The liberal regime resulted in an explosion of interest from private companies. This paper follows the development of private thermal power projects over a decade to determine the major impacts of this policy regime and to critique it.

Sources of data

Comprehensive data on private thermal projects is not available from a single source. With the end of the licensing regime, the Central Electricity Authority (CEA) has stopped monitoring projects except when they are close to becoming operational. Power projects however have to obtain Environmental Clearance (EC) and the Ministry of Environment, Forest, and Climate Change (MoEF) keeps track of projects that have begun the process leading to EC. Even before starting the EC process, companies typically sign a Memorandum of Understanding (MOU) with the government of the state where their project is located. Additionally, companies intending to access the inter-state transmission network register access requests with the central transmission utility. A composite picture of projects in different stages of development can be pieced together from all this disparate data.

It is a painstaking task to obtain clean summary data from the MoEF database. The task can be simplified by limiting it to a subset of all India data. The choice of the subset is explained below.

India is divided into five regions in the context of electricity generation and distribution – the Southern, Northern, Western, Eastern and North Eastern Regions - defined by a transmission infrastructure that allows power generated in any state in a region to be conveyed to any other state in the same region. Electricity generated in any of these regions is largely consumed within the same region.

The highest build up of private thermal capacity in the period from 2008 (about when the earliest thermal plants conceived in the new regime would have become operational) to 2015 has been in the Western Region - composed of the states of Chhattisgarh, Madhya Pradesh, Gujarat, Maharashtra and Goa - accounting for over 57% of the all India addition to private thermal generation capacity (Table 1).This justifies the use of data limited to the Western Region for the analysis in this paper.

The great thermal power rush

The extant of private interest in thermal power projects can be gauged from the number of projects with EC. Companies with EC for a project would have tied up with the state government for public land and water and have a plan for fuel supply. They would also have completed the mandatory “public hearing” in the project area – a gathering that is often an outlet for public opposition to a project. Only serious players would have obtained EC for their projects. (Kasturi, 2011:10)

There are two things noteworthy about private thermal projects that have obtained EC in the Western Region.

One is the sheer magnitude of the capacity planned - 79 private thermal power projects, with a combined generation capacity of 92 GW (Table 2). The latter figure can be better appreciated if it is kept in mind that all India addition of private thermal generation in the 11th plan (2007-2012) was 19 GW and the Planning Commission deemed 64 GW of thermal power addition (private and public included) during the 12th plan period (2012-2017) sufficient to meet the requirements of the country with GDP growing at nine percent (Planning Commission, 2012:1.4.1)! The government of the day appears to have been fully aware that many of these proposals would not fructify.

Second is the bunching of proposals between 2006 and 2010 (Table 2). The interest in projects rapidly peaked and had all but petered out by 2011. There has been no fresh private interest in thermal plants in the Western Region since 2011 (with but one or two exceptions who do not have EC yet). The negative consequences of this bunching are briefly touched upon later. The changing interest in thermal power strongly relates to the rapidly changing economics of thermal power production of this period.

With permission to sell electricity in the market, captive generators made good profits in the prevailing conditions of electricity scarcity, early on under the liberal regime established by the Electricity Act, 2003. Jindal Steel and Power (JSP), a captive generator itself, went on to establish a thermal plant in 2007 operating exclusively as a merchant supplier without any long term power purchase agreements (PPA’s) and made super profits during the period 2007 to 2010 (Joshi, 2009). The success of merchant producers and JSP in particular very likely attracted many entrants into thermal power.

From 2010, the situation turned unfavourable. With the production of coal stagnating, the government stopped giving long term coal linkages to power plants from 2011. International coal prices increased rapidly all of 2010, peaking in early 2011 and importing fuel was not a good option. The biggest dampener was that merchant electricity rates dropped sharply during the second half of 2010 and thereafter stayed low in the Western Region (Figure 1). Other regions with the sole exception of the Southern Region also showed similar falling prices.

Under these changed circumstances, many thermal projects were put on hold and others abandoned.

The cost of stalled and abandoned projects

Chhattisgarh in the Western Region is a case study of some of the excesses and “externalities” of the new policy regime. The state advertised itself as the upcoming “power hub”, an exporter of electricity to the rest of the country, and signed as many as 61 MOU’s for thermal power.

40 projects, two thirds of the number proposed, have not completed the formalities needed for environment clearance (Table 3). All these projects were announced many years ago and the overriding reason for them not to have progressed appears to be the changed economics of thermal power generation described earlier. Of the 21 projects with EC, 10 are operational, mostly with only partial capacity on stream (Table 3). A few are under construction but extremely delayed. A few others appear to be stalled. In all, only about a quarter of the proposed thermal projects in Chhattisgarh may materialize.

For proponents of competition the failure of some projects may not be of concern. The failed projects however not just cost their investors. They come at great cost to the agricultural communities amidst whom they are located. Almost all stalled projects with EC have acquired all the land they would have needed and the land requirement for thermal plants is substantial – 700 to 900 acres for every 1000 MW plant. At least 17 of the projects that have not even obtained EC have acquired part or all of the land for their proposed plants. The early land acquisition has been encouraged by government itself in the past for providing coal linkages which in turn was necessary for obtaining EC (Kasturi, 2011:11).

Skewed addition of generation capacity

Private thermal generation plants that have proliferated under the new policy regime have aggravated the imbalance between installed generation capacity and energy requirement in the different regions (Figure 2). The Western Region has become relatively capacity surplus while the North and South regions have a capacity deficit. Given that nearly half of the increase in total all India generation capacity came from private thermal plants and that nearly 64% of this was in the Western Region, this region was bound to become relatively over endowed with generation capacity (Table 1).

The surplus capacity in some regions and capacity deficit in others may not be an issue if there is adequate inter-regional transmission capacity. Starting from 2010, the market has existed for export of electricity from Western Region to the Southern Region. The relative electricity surplus in the Western Region (WR) and deficit in the Southern Region (SR) is seen in the different prices registered for the two regions at the IEX (Figure 1). The problems of inter-regional transmission are illustrated by the WR-SR energy exchanges.

Actual imports from WR into SR increased only marginally from 2010 through 2013, limited by the nominal transmission capacity (Table 4). The nominal transmission capacity cannot be entirely utilized in practice as margins need to be kept aside for technical reasons; the table also shows actual imports.

Capacity enhancements happened in 2014 with the opening of the first of two 2100 MW links between Sholapur in Maharashtra and Raichur in Karnataka. However, even with the much higher nominal transmission capacity available, the average power transferred from Western to the Southern region went up only by small amounts.

The reason behind this is that power transfers require end to end transmission capacity between generating centres and load centres. Even if the inter-regional transmission capacity - which is the capacity of links across the region borders – is adequate, there may be bottlenecks elsewhere on the end-to-end corridor. In the present case, the transmission capacity from WR generating clusters (such as those in Chhattisgarh) to Sholapur and from Raichur to the SR load centres is lacking. In 2014-15, only 1172 MW of power, equivalent to a capacity transfer of 1500 MW or about 0.6% of all India generation capacity for the year, could be transferred from WR to SR. [2] This was far below Southern Regions requirement and Western Regions available surplus (Figure 3).

Idle generators amidst electricity scarcity

The skewed regional addition of generation capacity in the new policy regime has its direct consequences. The plant load factor (PLF) has serially decreased in the Western Region except for Gujarat, and is now at 43% (Table 5).

While coal availability would have been considered the problem some years back, it appears that it is no longer so. In FY15, coal India production increased by 32 MT, more than the cumulative increases in production in the previous four years. Coal stocks in state plants have gone up. International coal prices have come down. The problem appears to be that there are no customers for the power generated by these power producers.

The electricity distribution companies (discoms) in the Western Region cannot absorb more power in their current situation. This of course does not mean that electricity has reached every household in this region or that there is round the clock supply. It only means that the discoms have met their stated requirements – limited by their transmission network, their distribution reach and their financial ability to buy more power. Export to the Southern Region – where Karnataka is facing a severe electricity crisis because of a deficit monsoon crippling its hydropower generation – is not possible because of lack of transmission capacity. These power producers have been stranded.

Issues in developing generation and transmission capacity in step

Before the advent of the new regime, electricity generation in India was planned to keep each region self sufficient. States developed their generation and transmission infrastructure in tandem. The centre, while establishing new generating units in a state also developed the inter-state and inter-regional transmission systems required to deliver the power to the states allocated power from the unit. In addition, a few transmission links were built by the centre across region boundaries specifically to exchange power.

The National Electricity Policy, 2005 declares that network planning and implementation should be based on the transmission needs arising from the open access regime and not contingent on a prior agreement with the users (MoP, 2005:5.3.2). Considering that generating units can target customers anywhere in the country and transmission systems are expensive and have to be built with long term needs in mind, this appears to be wishful thinking under present conditions of India.

Optimal design of transmission systems requires knowledge of the location, capacity and time frame of commissioning of each new generation plant as well as its intended customers. Multiple agencies must work to enhance intra-state, inter-state and inter-regional networks in a coordinated manner to ensure the required transmission capacity end to end (CEA 2012:7.4.2).

Transmission planning has become extremely difficult in the new regime as power plants are no longer required to enter into long term PPA’s with distribution utilities. Many private generation plants staking claims for long term access to the transmission network have not specified end users for their power as they have not (on purpose) or could not (because of lack of tenders) enter into long term PPAs with distribution utilities. Further, they are not accountable for their schedule of commissioning (PGCIL, 2010).

For the transmission utilities, as of now almost entirely owned by the states and the centre, the above uncertainties put at risk the investment in transmission infrastructure and can lead to a situation where there is sub-optimal utilization of the network. Generation plants on the other hand can be denied access because of congestion, as is happening today (Planning Commission 2012:2.2.2).

Concluding remarks

The extremely liberal regime ushered in by the Electricity Act 2003 allowed the few existing private captive thermal generators to make handsome profits. This attracted a large number of private companies to venture into thermal power generation, particularly in certain regions with perceived advantages in terms of availability of coal and water. The changing economics of thermal power production however quickly lead to this interest petering out.

The majority of proposed projects were abandoned, but not without cost to the communities of the area they were to be located in. Of the rest, only a few are operational with partial capacity while others are under construction with delayed schedules or have gone into limbo.

The location of the functional plants serves to further exacerbate the regional imbalance between demand and generation capacity. Not being able to sell their electricity locally because of lack of immediate demand and in power deficit regions because of the lack of adequate transmission capacity to load centres, these plants idle or run at low PLF’s even as parts of the country reel under severe electricity shortage. The overall development of the private thermal power sector shows a far from optimal utilization of national resources.

There are yet other negative consequences for the electricity sector which are not detailed in this paper. The rush to build thermal plants created a spurt in demand for capital equipment that was taken advantage of by foreign manufactures at the cost of domestic manufacturing. This is apparent in the details of executing agencies and equipment suppliers of private plants captured by CEA (CEA, 2015). State owned banks, the main lenders to dysfunctional power projects are burdened with huge non-performing assets (Acharya, 2012). This also makes it harder for newer entrants into the power sector to obtain financing for their projects.

Each of these problems can be seen as caused by a failure of coordination, adequate due diligence and so on. Taken together, they point to the infirmities in the legal and policy framework. The framework acknowledges the heavily capital intensive nature of the industry and the need for a planned approach to electricity for the optimal utilization of national resources to serve the economy. Yet it allows private generation companies unfettered freedom to set up plants without reference to timing, location or quantity all in the name of efficiency through competition.

The present government meanwhile has shifted its focus to solar energy. It is pushing humungous targets for solar generation capacity addition - reminiscent of the previous governments push for thermal energy - without addressing any of the issues that have severely impacted thermal energy development.


— (2015): “Monthly Report on Broad Status of Thermal Projects in the Country, April 2015,” Central Electricity Authority, Ministry of Power, 29 May.
Kasturi, Kannan (2011): “New Thermal Power Clusters,” Economic & Political Weekly, 1 October.
MoP (2005): “National Electricity Policy 2005,” Ministry of Power, 12 February, available in
Joshi, Rishi (2009): “Merchant of Power,” Business Today, 4th October


[1] Average actual utilized capacity (MW)  = (Actual transfer in a year (MU)) * (1000/(24*365))
[2] The equivalent generation capacity has been arrived at by assuming a plant operating at 75% PLF to generate  1172 MW

Tuesday, March 1, 2016

Modi Government's solar policy - 1:

(A piece I wrote on the Modi Government's solar plans that has been carried in The Wire ; reproduced below )

Solar Energy: Too much, too fast ?

The Narendra Modi government’s aggressive push for renewable energy, seeking to increase its share in the electric supply from the current 7% to nearly 19% by 2022, has been greeted with much enthusiasm all round. What is most striking is the target for solar capacity, which has gone up five-fold to an eye catching 100 GW. Till recently, 100 GW was the official estimate of India’s solar potential in the “medium-term” – till 2032. As justification for its aggressive target, the government points to the price of solar approaching “grid parity” and the international commitments it has made to increase the use of non-fossil-fuel sources of energy.
While energy generated without emissions is generally welcome, the rapid penetration of solar energy into the grid on the scale planned will present major problems for the state distribution utilities. In particular, it will make it difficult for them to meet another commitment made by the government to the people of India of providing “24 hours supply of adequate and uninterrupted power”.
The problems associated with solar electricity on the grid stem from the variability of solar generation. The output of a solar power plant varies with the movement of the sun and follows a bell shaped curve with a peak around noon. Cloudy or foggy conditions lower output and moving clouds can cause rapid fluctuations.
The variability of solar generation (and more generally of all renewable energy generation) is something the utilities need to handle as they must maintain a balance at all times between electricity supply and demand on the grid. Balancing becomes more challenging with increasing penetration of renewable energy.
Balancing supply and demand
Balancing is not a new requirement.  Even if generation on the grid is not variable, balancing requirements will arise from daily variations in demand. The average all India pattern of demand variation is a higher day time demand compared to the night time load and a sharp peak in the late evening. Load balancing is achieved by varying supply from conventional – coal, gas, hydro – power plants. These plants come with a range of characteristics which define their use in balancing.
Older subcritical coal-fired plants have low flexibility as they were designed to provide a steady output. Frequent output changes in these plants leads to wear and tear with attendant costs. Newer supercritical coal-fired plants are more flexible and resilient than the older subcritical plants by design. Gas-fired and hydro power plants with reservoirs are the most flexible and their output can be changed rapidly to handle changing load.
On an all India scale, coal-fired plants provide a steady output – to meet what is called the base load – while gas-fired plants and hydro plants with storage are managed to respond to variations in demand. With gas being expensive, hydro generators are used, wherever possible, to meet demand peaks.
A problem for the states
However, it is at the state level that balancing problems come to the fore.
That is because in India’s federal structure, states (and utilities within the state) are responsible for balancing supply and demand on their own grid. For this, they require a mix of conventional generators with different levels of flexibility that is adequate to meet the variations in load and renewable supply on their grid. However, all states do not have access to the different resources in the right measure.
Tamil Nadu, which has a large wind power capacity, is a good case study. Balancing requirements arise from both load and supply variations. The uncertainty associated with wind power generation adds to the complexity. While the state has significant reservoir-based hydro capacity, its ability to use this for balancing is restricted by irrigation release schedules and periods of high inflows into reservoirs when hydro power generation cannot be curtailed.
When wind power generation is greater than expected, the state utility, lacking flexible balancing resources, has to back down power from coal plants or refuse wind power. Either option results in objections from the other party – violating contract provisions in one case and not respecting the “must-run” status in the other – and the dispute is now in the courts. Legal issues aside, there are negative economic consequences either way. Varying power from coal plants means underutilisation of capacity and higher costs related to wear and tear. Backing down wind power means wasted energy.
Are we prepared for large-scale renewable energy?
The different types of long gestation infrastructure needed to handle large scale penetration of renewable energy on the grid are known from government sponsored studies dating to 2012 and 2013. These are broadly inter-state transmission lines, grid level storage and adequate flexible generation resources.
Transmission corridors carrying renewable energy across state boundaries would bring balancing resources over a larger area into play. In storage, these studies identified pumped storage projects – where energy is stored in water pumped from lower reservoirs into upper reservoirs – as a good option as there were a number of potential sites in India. Additionally, these projects would also support flexible generation.
Based on 12th plan targets for capacity addition (which included 56 GW of renewable energy), it was estimated that by 2017 only 60% of the balancing power requirements would be met by flexible generation from the planned pumped storage, hydro and gas plants. The remaining would have to be met using the less flexible supercritical coal plants. After steeply raising the renewable energy target to 175 GW by 2022, how is the government preparing the grid for it?
The only preparation underway is in the area of establishing inter-state transmission corridors (termed ‘Green Energy Corridors’). These were already under implementation for existing renewable energy sites in Tamil Nadu, Gujarat and Rajasthan and the present government has enlarged their scope to include its “ultra mega solar parks”.
There is no visible movement on augmenting pumped storage capacity. Among new grid-level storage technologies, electro-chemical technologies (batteries, capacitors) may be the most suitable for Indian requirements. These technologies are however still five to ten years from commercialisation and as yet only one large battery storage plant (>10 MW with 4 hrs supply) is in operation worldwide.
Even if the government projections for addition of flexible generation capacity hold, the planned growth in renewable generation will far outpace it. In fact, capacity addition is likely to fall short by a large margin with problems with both gas-fired plants and hydro plants – expensive imported fuel in one case and environmental issues in the other.
Given the above, flexible generation resources will be grossly inadequate to meet balancing requirements by 2022. Coal-fired plants, both supercritical and subcritical, will have to be used for balancing and utilities, lacking adequate flexibility to handle supply variations, will be forced to resort to supply interruptions and load shedding.
The real cost of solar
Problems of balancing aside, there is also the issue of cost. Utilities are extremely price conscious and will not contract solar energy if it is more expensive than other sources. After solar producers offered to sell NTPC electricity at rates of Rs 4.63/unit in AP last year and at Rs 4.34/unit in Rajasthan early this year, the Minister of Power was quick to announce that solar energy prices had reached “near grid parity”. This statement is misleading.
NTPC is able to sell this power to utilities only because it “bundles” it with cheaper power from its old coal-fired plants and offers them power at the “bundled” rate lower than its purchase price for solar electricity. It is this bundled price that must approach “grid parity” – the average contracted price of electricity on the grid for the utility – for NTPC to be able to find buyers.
Then again, even after reaching “grid parity”, solar can prove to be expensive. Its true cost to the utility is not just the price at which it has been purchased. The cost of balancing – be it the cost of storage or the cost of coal capacity held in reserve – must also be attributed to solar generation.
Future shocks
The government’s announcement of massive solar energy targets therefore appears to be an impetuous decision. States will be unwilling to allow high penetration of solar energy into their grids considering its cost and the problems of addressing its variability. That is why the central government is all set to force the issue by increasing renewable purchase obligations. The new tariff policy states that solar electricity must constitute 8% of non-hydro power consumed by every utility by 2022.
Forcing the issue will have consequences for the quality of electricity supply. The limited arsenal in their hands to deal with supply variability will make it difficult for state utilities to fulfil the commitment of adequate and uninterrupted power supply 24 hours a day. The central government anticipates the need for curtailing demand. That is why it is pushing for the large-scale installation of smart meters that can support time-of-day tariff and facilitate demand reduction.
Savings on carbon emission would come from capacity underutilisation of coal plants, an expensive strategy for India. With no storage available on the grid, coal plants will be needed as backup, and installation of new solar plants will not lower the requirement for new coal plants.
India may be better served by a plan that looks at developing solar and other renewable energy generation in step with cost effective storage and flexible generation that is available to all states. Such a combination would reduce carbon emission by reducing the need for new coal plants. If such a plan entails slower adoption of solar generation, that may not altogether be bad; solar plants, as long term trends suggest, will only get cheaper with time.

Sunday, February 21, 2016

Idle Generators and power deficit

India Together published this piece which deals with how thermal plants are idle in some parts of the country even while other parts are facing a severe power crisis. Reproduced below:


Idle generators in the midst of power deficit

The Southern region of India is expected to face high energy deficit this year while the Western and Eastern region will have a surplus of energy generation. Kannan Kasturi explains why such regional skew in energy generation and energy consumption exists and what it will take to resolve it.

Southern India is expected to face a severe electricity shortage this year. The Central Electricity Authority (CEA) in its latest annual forecast anticipates the energy deficit in the southern electricity grid to be over 11 percent, equivalent to a generation capacity deficit of 4000 MW. For Karnataka and Telangana, the forecasted energy deficit is greater than 16 percent.
Historically, India has been divided into five regional grids from an electricity distribution perspective – the Southern, Northern, Western, Eastern and North Eastern. The CEA forecasts a small deficit for the Northern region and a surplus for the Western and Eastern regions. The North Eastern region, while accounting for only a tiny fraction of all India electricity consumption, is also expected to have a large deficit.
The annual forecast results from an exercise, in which the electricity requirement and the available power for each state is collected and vetted by the CEA. The sources of power considered are both the public sector plants (belonging to the central or state governments) and private plants; however the forecast takes into account only firm contracts that are already in place between power producers and the distribution companies.
Energy deficit states may improve their power availability in the short term by buying electricity from surplus states during the year. It is also possible that actual demand turns out to be lower than projected demand, leading to a lower deficit. The numbers as they stand, however, predict a reversal of three years of decreasing power deficit in the southern region.
Before proceeding further, some clarification about CEA demand projections is in order. These projections are ultimately obtained from the electricity distribution companies (discoms) in the states and do not capture the full extent of demand for the following reasons.
The projections made by discoms are based on actual current usage and a projected rate of growth of demand for each category of customer, based on past trends. They are made assuming the limitations of the existing distribution network. The latent demand from a population left without service by the existing network, or from areas provided limited energy due to network limitations, do not figure in the demand projections.
Another issue with the demand projections is that discoms are supposed to compensate for the lower current usage figures resulting from scheduled and unscheduled power cuts (for example, the curtailed supply to irrigation pump sets).
Discoms by and large are not transparent about the power cuts they impose and the power outages due to faults, making independent estimation of the ‘unconstrained demand’ – the likely demand in the absence of power outages – very difficult. The CEA demand and deficit projections for the different regions need to be understood in the light of the above.
The regional skew in energy generation
Why do some regions have energy deficit and others have an energy surplus? Historically, electricity generation capacity was planned to keep each region self sufficient. Each state had its own power plants and the central government, while building plants in any region, also built the transmission systems to distribute power to the states in the region. Inter-regional transmission systems came up only on a case by case basis, typically if a part of the power from a central generating unit located in one region had been promised to a state in a neighbouring region.
The following graphic shows how power generation capacity and energy consumption have changed in recent years across the regions. Rather than absolute numbers, generation capacity and energy consumption for each region are shown as fractions of the all India capacity and consumption.
This shows the increasing imbalance between generation and consumption in some regions. (Note: The generation capacity pertains to Dec 2009 and Mar 2015 respectively. The energy requirement for 2015-16 is as per CEA projection).
The Eastern (including North East) and Western regions have a greater share of energy generation than their share of energy consumption. The opposite is true for the Northern and Southern regions. Moreover, this proportion (of electricity generated to electricity consumed) has significantly fallen in the southern region and significantly increased in the western region in recent years.
The missing transmission
Electricity surplus in some regions co-existing with electricity deficit in other regions points to problems in wheeling electricity between the surplus and deficit regions. The rapid build-up of generating capacity in the western region requires a simultaneous build-up of transmission capacity to evacuate the power to the energy deficient regions. That this has not happened will become clear from the price pattern of electricity traded in the market in the different regions.
Electricity producers sell their surplus electricity in the short term electricity market after they have met their long and medium term contractual obligations. Over 40 percent (2013-14 figures) of the electricity that is available for sale in the market is traded through the Indian Energy Exchange (IEX).
The graphic below shows the average price of electricity traded at IEX in the western region states of Maharashtra, MP and Gujarat and the southern region states of TN, Kerala and Pondicherry.
(Note: The units of the y axis are Rs/Mwh and not Rs/Kwh as incorrectly shown)
Since 2010, the prices in the southern region have ruled significantly higher than in the western region indicating that there are barriers to moving the electricity generated from the west to the south. The price in these western states has been range bound between Rs 2-4 /kwh from 2012 and below Rs 3/kwh since Nov 2014. The price in Chhattisgarh which is also part of the western electricity grid remains marginally lower. The range bound price over an extended period indicates that electricity supply may not be a constraint in this region.
IEX data shows that in the Northern region too, electricity tends to trade at a higher price than the western and eastern regions; however the price differences are not very large suggesting that the transmission capacity from east and west into the north may be nearly adequate. In fact, an examination of inter-regional flow data shows that power import into the northern region has been steadily increasing from an average 2800 MW in 2010-11 to 5300 MW in 2014-15, compensating for the deficit in energy generation capacity.
The power import into the southern region, on the other hand, has only marginally increased from an average 1200 MW to 1800 MW between 2009-10 and 2014-15 (This does not include about 1800 MW of power from an NTPC plant in Talcher, Orissa which is exclusively meant for the southern region and for which a dedicated transmission line exists).
The Central Government has periodically trumpeted the new inter-regional transmission capacity additions. Prime Minister Modi personally inaugurated the second of two Raichur-Sholapur transmission lines in August 2014. Together, the two lines were supposed to add 4200 MW of transmission capacity from the western to the southern region. As of June 2015, the increased capacity available has been less than a tenth of this at around 400 MW.
The reason behind this, it turns out, is that crucial transmission links from the generation clusters in the western region to its periphery with the southern region, which will allow the full utilisation of the Raichur-Sholapur lines, are delayed.
Other high capacity links from Chhattisgarh to Telangana and Tamil Nadu have also been stuck with the centre at the planning stage despite representations from the concerned states. The lack of urgency in implementing these projects, one suspects, may have something to do with political considerations.
After the government opened up power production to the private sector, the coal-bearing states of the western region – MP, Chhattisgarh, and Maharashtra – became the preferred destinations for private companies seeking to exploit the cost advantages of pit head power plants. This is the reason for the disproportionate build up of power generation capacity in the western region. The inability to deliver power to the load centres in the south has meant that many new plants are idling or operating much below capacity.
Even as the thermal (coal-based) generation capacity of private producers in these three states has gone up from 5.7 GW in April 2009 to 26.4 GW in April 2015, the Plant Load Factor (PLF) of these plants, the capacity usage indicator, has come down from 83.5 percent to 43.1 percent. Coal availability has not been an issue since 2014 because of higher production, adequate imports and low international prices. Low capacity utilisation is a consequence of the inability to sell the electricity.

The problem clearly is that generation capacity has increased without adequate expansion in transmission capacity connecting it to power deficit load centres. The greed of the private promoters who did not tie up their production in long term contracts with discoms, hoping instead to make quick profits through merchant sales, is partly to blame.
Merchant selling - sales against short term contracts - allows the producer to charge prevailing market prices for electricity. Some years back, independent merchant producers made a killing in an environment of scarcity. Long term contracts, however, are based on competitive bidding by different producers and will not yield such high margins. But long term contracts would have kept the focus on building the necessary transmission links.
The major part of the blame must fall on the central government. Plants unable to sell electricity translates to banks, mainly public sector ones, saddled with non-performing assets. Further, operational plants are only a part of the problem. In the coal-bearing states in the same region, work is at a standstill in many more plants that are in various stages of development, awaiting better prices for electricity.
The government has clearly failed in its responsibility of overall planning of the electricity network and allowed scarce national capital to be wasted in generation capacity that cannot be utilised.

Comparing Census and NSS data on employment and unemployment

Reproduced from my article in the Economic and Political Weekly May 30, 2015

Unemployment rates in India are not particularly interesting. Available most frequently from the National Sample Survey (NSS), they are relatively low and have barely changed over the years.

Recently available census data provides far higher estimates of unemployment than the NSS.[1] Census unemployment numbers are especially high for youth aged 15 to 29 who are new entrants to the labour force. Over forty million youth are seeking work and the unemployment rate stands at above 15% and 30% for young men and women respectively.[2]These numbers make the case for a closer look at census and NSS employment and unemployment data to understand why they differ and what they represent.

This article begins with a brief discussion of the census and NSS methodologies to identify comparable data from the two data sets. This is followed by a comparison of aggregated employment, underemployment and unemployment data from the two sources to see how well they agree and how that agreement has changed over time. The last section looks at disaggregated data from the last census for clues to explain the wide difference in unemployment estimates.

Areas of broad compatibility

For estimates from two different data collection exercises to be comparable, they must at a minimum work with similar concepts and have the same reference period[3].  The areas of broad compatibility between the NSS and the census starting with the 1991 census are briefly captured below.[4]

Both the NSS and the census divide the country into rural and urban areas for data collection in a mutually consistent manner.[5] The census uses a reference period of one year for employment data and the NSS also provides data for such a period.

The census provides only limited data on employment and unemployment. It separates the population into ‘workers’ and ‘non-workers’ and provides estimates of the number of ‘workers’, the extent of underemployment among ‘workers’ and unemployment among ‘non-workers’. These then are the data of interest for a comparison between the census and NSS.

Both the census and the NSS have similar criteria for identifying ‘workers’ and the ‘unemployed’. ‘Workers’ are persons engaging in ‘economic activity’ and the set of production related activities accepted as ‘economic activity’ is almost the same in both exercises. The ‘unemployed’ among the non-working population are identified by determining if they are ‘seeking work or available for work if there is work available’. In urban areas, this test requires a person to be making tangible efforts to obtain work. In rural areas it is less stringent and only requires that a person be available for work if work is available. Both exercises estimate underemployment in workers and both identify students in the non-working population.

Getting to the specifics, Census B Series tables provide the count of ‘main workers’ - workers who have worked six months or more in the last year and ‘marginal workers’ – those who have worked less than six months.[6] The broadest count of workers is obtained by adding the count of ‘main workers’ and the count of ‘marginal workers’. From among the rest termed the ‘non-workers’, those who pass the test of ‘seeking or available for work’ make up the count of the census unemployed.[7] The census also measures underemployment among ‘marginal workers’ by applying the above test. Non-workers are additionally categorised by ‘main activity’ including as ‘students’ and persons ‘engaged in household duties’.

In the NSS, a person’s ‘activity status’ can be ‘worker’, ‘unemployed’ or ‘not in the labour force’ during the reference period and the ‘principal status’ is determined by  the ‘activity status’ in which relatively longer time has been spent (also referred to as the ‘major time’ criteria). [8] The broadest count of workers with a 1 year reference period is obtained from the tabulation referred to as ‘usual activity status (principal status + subsidiary status)’ in the NSS “Employment and Unemployment Situation” reports.[9] This includes persons whose ‘principal activity status’ is ‘worker’ as well as persons whose ‘principal activity status’ is other than ‘worker’, but who have worked for at least 30 days in the reference period. This tabulation also provides the categorization of non-working persons as ‘unemployed’, ‘students’ and as those ‘engaged in domestic duties’. The NSS additionally provides several indicators of underemployment among ‘workers’ which will be discussed later.

Worker population ratios

Large sample surveys are conducted every five years and the NSS large sample rounds closest to census years have been selected for the comparison. Table 1 compares NSS and census worker population ratios across 3 censuses. The female worker population ratios in the NSS (1993-94) round show a large difference with the corresponding ratios of Census 1991.

Table 1. Workers in every 1000 persons aged 15 and above


Census - 1991
NSS (1993-94)
Census - 2001
NSS (1999-00)
Census - 2011
NSS (2011-12)

Source: Census of India[10] and NSS ‘Employment and Unemployment Situation in India’ reports[11]

A government committee of experts that was privy to the 1991 census data attributed the differences in census and NSS estimates to two main factors – a) differences in the set of activities that constituted ‘economic activity’ and b) differences in the minimum time spent working to be considered a ‘worker’.[12] 

More specifically, the National Statistical Commission (also known as the Dr Rangarajan Commission) was of the view that the exclusion of certain non-market economic activities from the definition of work adopted in the census could be the reason for the low female worker population ratios of Census 1991 in comparison with the NSS 50th round.[13]

The scope of non-market economic activities was expanded in Census 2001 by treating persons engaged in cultivation and growing of crops solely for domestic consumption and persons engaged in rearing of animals for production of milk for their own use as ‘workers’.[14] There is a dramatic improvement seen in the agreement between Census 2001 and NSS female workforce ratios, possibly as a consequence of this change. The differences in the male workforce ratios have also narrowed further compared to Census 1991.

Census 2011 shows a further narrowing of differences with NSS workforce estimates for rural men and urban men and women. The only spoiler is the count of rural working women where there is a large difference.[15]

The sharp fall in rural female workforce ratios seen in both the NSS 66th round (2009-10) and the NSS 68th round (2011-12) compared to NSS 61st round (2004-05) without any accompanying increase in unemployment has puzzled researchers following employment trends. There is an extensive literature attempting to throw light on this phenomenon termed the “missing labour force”.[16] If Census data is to be believed, much of the rural female labour force may not have gone missing at all.

The next two sections feature comparisons of underemployment and unemployment estimates. The comparisons will be confined to Census 2001 and Census 2011 which have better comparability with NSS data than Census 1991 for reasons stated earlier.

Underemployment in workers

The census, as mentioned earlier, counts the number of ‘marginal workers’ who are seeking or available for work.This is a measure of underemployment in workers who have worked for less than six months.

The NSS has several indicators of underemployment among usual workers. Among them, the one with a liberal interpretation of underemployment, is the one which measures usual workers (principal status+ subsidiary status) who were without work for at least 1 month and who sought or were available for work on at least some days during those month(s). Table 2 shows the underemployment numbers from the census and the range provided by this NSS indicator.

Table 2. Underemployed workers in every 1000 persons aged 15 and above


Census - 2001
NSS (1999-00)
Census - 2011
NSS (2011-12)

Source: Census of India and NSS ‘Employment and Unemployment Situation in India’ reports[17]

As the table shows, the census generally underestimates underemployment when compared with this NSS indicator. The probable reason for the wide margin of underestimation is that the census does not test ‘main workers’ – those who have worked greater than six months in the year - for underemployment. This means that the census population for the underemployment test is far smaller than the NSS population which includes all workers.

Unemployment and underemployment in non-workers

Table 3 compares census figures of non-working persons ‘seeking work’ with the NSS numbers of ‘unemployed’ for the last two censuses. NSS figures show dramatically lower unemployment than the census.

Table 3. Non-workers seeking work in every 1000 persons aged 15 and above


Census – 2001
NSS (1999-00)
Census – 2011
NSS (2011-12)

Note: Census number refer to non-workers seeking or available for work; NSS numbers are for those termed ‘unemployed’
Source: Census of India and NSS ‘Employment and Unemployment Situation in India’ reports

The National Statistical Commission noted many years back that NSS estimates of unemployment were low because of the definitions and concepts followed in its framework of employment and unemployment.[18]

What are the definitions and concepts in the NSS framework that makes its unemployment estimates low compared to the census?

The NSS uses the ‘major time’ criteria to decide if non-workers are ‘unemployed’ or outside the labour force.[19] Persons spending a major portion of their time in study or domestic duties are considered to be outside the labour force. They do not add to the count of the ‘unemployed’ even if they are seeking work.

In the census, on the other hand, the test of ‘seeking work’ is applied to all non-workers without any hard time criteria associated with it. Persons spending a major portion of their time in study or household duties can still qualify the test and be counted among the census unemployed.[20]

Students and unemployment

The above arguments, to the extent that they involve the double counting of students among the unemployed in the census, can be substantiated by examining age disaggregated data on non-working men (Table 4). The discussion can be limited to men in the age group 15-29 without compromising the analysis as this group accounts for 85% of the census unemployed.

Table 4. Age disaggregated profile of non-working men seeking work gathered from Census 2011 and NSS 2011-12


Age group
Not working
Of which

Seeking work
* Students seeking work





Notes: Numbers are ratios per 1000 population; * Lower bound on number of students seeking work
Source: Census of India[21] and NSS “Employment and Unemployment Situation in India” report

The census count of those seeking work far exceeds the NSS count of ‘unemployed’ for each age group as expected. Census numbers indicate that the bulk of non-working men of age 15-24 are students. Some of them can be expected to figure in the census count of those seeking work. A lower bound on the number of students seeking work has been computed and presented in Table 4 using the detailed breakup of non-workers by ‘main activity’ published as part of the census. [22]

The NSS count of male ‘unemployed’ agrees more closely with the census numbers after students seeking work have been excluded from the latter. It needs to be re-emphasised that what is listed in Table 4 is only a lower bound and exact numbers of students seeking work, if available, will further improve the fit between census and NSS data on unemployment among non-students. Even with the available data, it is clear that the census unemployment is much higher than NSS unemployment in males predominantly because it factors in students seeking work.

Women engaged in domestic duties and unemployment

Census figures indicate that one must consider women between 15 and 39 to account for 80% of female unemployment. Since disaggregated data is not available for this range from both census and NSS, the discussion below is with aggregated numbers. Table 5 presents data from the census and NSS for non-working women aged 15 and above. NSS data shows that these women are overwhelmingly students or engaged in domestic duties according to the ‘major time’ criteria.

Table 5. Profile of non-working women aged 15 and above seeking work gathered from NSS 2011-12 and Census 2011

Not working
Of which
Women in DD available for work^
Seeking work
of which
Engaged in domestic duties(DD)
‘Non-students’ seeking work *

Notes: Numbers are ratios per 1000 population; ^ Estimate of the number of non-working women usually engaged in domestic duties who are willing to take up regular full time work at home;* Upper bound on the number of ‘non-students’ seeking work
Source: Census of India[23] and NSS report “Participation of women in specified activities along with domestic duties”[24]

The census data allows a rough division of the total number of women ‘seeking work’ between students and ‘non-students’.[25] Table 5 lists the upper bound on the number of ‘non-students’ seeking work. From the NSS perspective, these non-students would almost entirely be women engaged in domestic duties.[26]

Again from the NSS perspective, non-working women who spend ‘major time’ in domestic duties would not qualify as ‘unemployed’ even if they were seeking work. However, the NSS recognizes this situation as a form of underemployment and provides several indicators of its extent. One such indicator – the number of non working women, engaged principally in domestic duties, that are willing to take up regular full time work if offered such work at home – is also shown in Table 5.

It can be seen that there is a fair agreement between the census numbers of ‘non-students’ seeking work and the aggregate of the NSS numbers of ‘unemployed’ and women primarily engaged in domestic duties who are available for work at home full time. Too much should not be read into the agreement as the NSS and census ‘tests’ of the availability of women engaged in domestic duties for work are not quite the same.

Nontheless, the available evidence makes it clear that census unemployment in women is far higher than NSS unemployment predominantly because it factors in students seeking work as well as women, primarily engaged in domestic duties, seeking work.

Concluding remarks

The comparison of NSS and census data reveals that the differences in employment estimates have narrowed over the last three censuses, the only exception being the estimates of rural women in Census 2011. This exception merits further study especially because the NSS numbers have not been on expected lines.

The NSS provides several indicators of underemployment among workers; however none of these are strictly comparable with census data as the coverage of workers is different in the two exercises.

Census unemployment estimates are far higher than the NSS estimates because they also include students and persons primarily engaged in domestic duties who are seeking work. The desire for work in students and in women primarily engaged in domestic duties is in some sense a reflection of underemployment in these sections. The census unemployment estimate, in this sense, is an aggregate estimate of the unemployed and underemployed.

While the underemployment among women engaged in domestic work is an ongoing part of NSS employment studies, it has been left to the census, with its relatively crude methodology, to uncover the extent of the latent Indian youth labour force.


Census (2011), “Instruction Manual for Updating of Abridged House list and Filling up of the Household Schedule”, Census of India, (

Hirway, Indira (2012): “Missing Labour Force, An Explanation”, Economic and Political Weekly, vol xlvii no 37, Sept 15, 2012

NSC (2001), “Report of National Statistical Commission”, Sept 5, 2001, (

NSSO (2008), “Review of Concepts and Measurement Techniques in Employment and Unemployment Surveys of NSSO”, National Sample Survey Organisation, Dec 2008, (

NSSO (2014a), “Employment and Unemployment Situation in India, NSS 68th round”, National Sample Survey Organisation, Jan 2014, (

NSSO (2014b), “Participation of Women in Specified Activities along with Domestic Duties”, NSS 68th round”, National Sample Survey Organisation, Sept 2014, (

Thorat Amit (2004), “NSS Employment Surveys; Problems with comparisons over time”, October 2004, Viewed on 10 Feb 2015 (

[1] Census 2011 tables containing data on workers were released in Sept 2014. The Census estimate of unemployment in urban men aged 15 and above exceeds the NSS estimate by a factor of 2.6. The factors for rural men and rural and urban women are much higher.
[2] Data from Table B1, Census 2011
[3] The ‘reference period’ is the period to which the data pertains
[4] For a brief historical overview of the problems of comparing NSS and census employment data starting from the 1951 census, see Thorat (2004)
[5] See Sec 1.5 NSSO (2014a) for a description of survey sampling in rural and urban areas
[6] Census B series tables for various years are available at the Census of India website (
[7] The census figures of ‘seeking or available for work’ are popularly interpreted as unemployment figures though it must be pointed out that the census itself refrains from using the term ‘unemployed’.
[8] A complete explanation of the ‘major time’ criteria may be found in Sec 2.21 of NSSO (2014a)
[9] These reports are available at the Ministry of Statistics and Program Implementation website (
[10] Ratios computed by author using population figures from the Table B1 for various years
[11] Data is from the ‘usual activity status (ps+ss)’ table 
[12] Sec 10.1, NSSO (2008)
[13] Sec 9.4.34 (xiv) , NSC(2001)
[14] See Metadata of Census 2001 at Census of India Website
[15] The census and NSS have different minimum time criteria for categorising persons as workers (30 days in NSS, none in census). Census data for 2011 shows that only 37 in 1000 rural women aged 15 and above were ‘marginal workers’ working less than 3 months. Even if all of these were missed out by the NSS, it would account for only a small part of the NSS underestimation.
[16] See Hirway (2012) for a summary of the various explanations attempted
[17] NSS data for 2011 is from Table 61 of NSSO (2014a). The table provides estimates of ‘usual status workers (ps+ss)’ who were without work for at least 1 month and who sought or were available for work on at least some days during those month(s). This number (N) is regardless of the age of the workers, and may include some workers below 15 years of age. ‘N’ serves as an upper bound of this indicator of underemployment among workers of age 15 and above. A lower bound is obtained by subtracting the total number of ‘usual status (ps+ss)’ workers aged below 15 from N
[18] Sec 9.4.27 (a) of NSC(2001)
[19] See note 8
[20] Sec 6.166, Census(2011)
[21] Raw data is from Tables B1 and B13 of Census 2011. Authors computation of  the number of students seeking work is explained in note 22
[22] Table B1of the census provides the total number of non-workers seeking work (U). Table B13 divides non-workers into categories by ‘main activity’ including the categories of ‘students’ (S) and ‘others’ (O). According to the census enumeration guidelines all ‘non-workers’ seeking work, other than students, are placed in the category ‘others’. The ‘others’ category also includes some persons who are not seeking work. (Sec 6.160, Census(2011)) ‘O’ is therefore an upper bound on the number of non-students seeking work and (U-O) represents a lower bound on the number of students seeking work
[23] ibid
[24] Table 6 of NSSO (2014b) provides the fraction of women aged 15 and above, usually (principal status) engaged in domestic duties and without subsidiary status work, who are ready to accept work at home. Table 5 provides the fraction of those willing to work at home (irrespective of subsidiary status) that are ready to accept regular full time work. The estimate of ‘women in domestic duties available for work’ is arrived at by applying these two fractions to the estimate of women usually engaged in domestic duties and without subsidiary status work (Table 2)
[25] See note 22
[26] As an example, after removing students from the rural ‘non-working’ women aged 15 and above, women engaged in domestic duties constitute nearly 90% of the rest (Table 2, NSSO(2014b))


Another paper analyzing the same data sets as above - India's rural transformation: A myth or reality?

Links to this article:
1. Changes_in_indias_rural_labour_market_in_the_2000s
2. Positives_outweighing_negatives_the_experiences_of_Indian_crowdsourced_workers
3. Emerging_Trends_and_Patterns_of_India%27s_Agricultural_Workforce_Evidence_from_the_Census
4. indias-unemployment-rate-conundrum
5. Changing Employment and Enterprise Structure in Gujarat: 1990-2005