[email protected]: Where are the jobs?

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On August 9, 2017, lakhs of people belonging to the Maratha caste poured into the city of Mumbai for a silent march. 57 similar marches had already taken place in the state of Maharashtra, starting from Aurangabad on August 9, 2016. This was the 58th. The rallying cause behind the marches was to protest against the rape and murder of a teenaged girl belonging to the caste in Ahmednagar district in July last year. Other than the rallying cause, the Marathas have demanded quotas in government run as well as aided educational institutions. They also want reservations in government jobs.

Marathas are not the only land-owning caste in the country demanding a reservation in government jobs. Similar demands have been made by the Patels in Gujarat, the Kapus in Andhra Pradesh, the Jats in Haryana and the Gujjars in Rajasthan. The question is why do land-owning castes suddenly want reservation in government jobs, seven decades after Independence?

A major reason for this lies in the fact that the average size of a farmer’s landholding has fallen over the years. As the State of Indian Agriculture Report of 2012-2013 points out: “As per [the] Agriculture Census [of] 2010-11, small and marginal holdings of less than 2 hectare[s] account for 85 per cent of the total operational holdings and 44 per cent of the total operated area. The average size[s] of [the] holdings for all operational classes (small & marginal, medium and large) have declined over the years, and for all classes put together it has come down to 1.16 hectare[s] in 2010-11 from 2.82 hectare[s] in 1970-71.”

Take a look at Figure 1.

Figure 1:  Decline in the average size of agricultural landholdings between 1970-1971 and 2010-2011.

Source: State of Indian Agriculture Report, 2012-2013.

The agriculture census is carried out every five years. Hence, the latest available data is as of 2010-2011. The situation would have only gotten worse since then. The trend of falling farm sizes can be clearly seen from Figure 1. As the same piece of land has got divided among more and more family members over the generations, the average holding has fallen dramatically. And this has made agriculture unviable for many in the land-owning castes. Hence, the demand for reservation in government jobs.

The trouble is that the government doesn’t create jobs anymore, neither at the level of state governments nor at the level of the central government. Hence, what will happen once the land-owning castes figure this out? Will they demand reservations in private jobs as well?

The rate of unemployment

The irony is that the huge demand for jobs among the land-owning castes and others is not reflected in India’s rate of unemployment. The Labour Bureau carries out the Annual Employment-Unemployment Survey. This Survey is hardly annual. It was first carried out in 2009-2010. It skipped a year and was carried out for the next three years. It skipped a year again in 2014-2015 and was carried out again in 2015-2016. The 2015-2016 Survey is what will be discussed here.

The Labour Bureau basically measures unemployment using two methods. The first method is called the Usual Principal Status (UPS) approach. In this approach, “the major time spent by a person (183 days or more) is used to determine whether the person is in the labour force or out of the labour force.”

As per this method, the rate of unemployment was just 5 per cent.

The second method is called the Usual Principal and Subsidiary Status (UPSS) approach. Here, “a person who has worked even for 30 days or more in any economic activity during the reference period of [the] past twelve months is considered as employed under this approach.” As per this method, only 3.7 per cent of the workforce was unemployed.

Such low rates of unemployment are hardly surprising given the definitions of unemployment that are being followed. In the first method, an individual might have been unemployed for close to half the year but would still be considered to be employed. In the second method, an individual might not have had a job for 11 months during the year and would be considered employed.

Given this, the rate of unemployment does not tell us anything about the desperate search for jobs. But there is another set of data points that the Labour Bureau puts out, and that rarely makes it to the media. Take a look at Table 1.

Table 1:  All-India percentage distribution of persons available for work for 12 months (UPSS approach).

Source: Report on the Fifth Annual Employment-Unemployment Survey, 2016.

Table 1 basically tells us what proportion of the population which is looking for a job all through the year is able to find one. Around 61 out of 100 Indians in the workforce looking for a job all through the year are able to find one. In rural areas, only around 53 out of 100 individuals who are looking for a job all through the year are able to find one. These numbers point towards the huge underemployment of India’s workforce.

This is hardly surprising given that in the last two financial years, agriculture has contributed around 14 per cent to the gross domestic product and employed close to half of the working population. There is a clear mismatch here. Around half the country’s workforce is only contributing 14 per cent of the GDP.

What this means is that there is huge disguised unemployment in the rural areas. Disguised unemployment essentially means that there are way too many people trying to make a living out of agriculture. On the face of it, they seem employed. Nevertheless, their employment is not wholly productive, given that agricultural production would not suffer even if some of these employed people stopped working.

So, the unemployment numbers might not point towards India’s distressing job situation but the underemployment number clearly does. This is also borne out in Figure 2, which has been sourced from a recent report titled OECD Economic Surveys India.

Figure 2:

This report puts the rate of unemployment among India’s youth between the ages of 15 and 29 at more than 30 per cent. These youths are neither employed nor in education or training.

Regular unemployment data

The Fifth Annual Employment-Unemployment Survey was carried out in 2015-2016. It has been close to a year and a half since then and we haven’t had any fresh unemployment data being published by the government.

As Volume 2 of the Economic Survey of 2016-2017 released earlier this month, points out: “The lack of reliable estimates on employment in recent years has impeded its measurement and thereby the Government faces challenges in adopting appropriate policy interventions.” It then lists out 10 ways used by the government to measure unemployment and the problems with them. The problems listed are: “Partial coverage, inadequate sample size, low frequency, long time lags, double counting, conceptual differences and definitional issues, rarely used for the purpose of employment estimation etc.” This, of course, leads to the question why have 10 wrong ways of measuring unemployment and not one right way?

The government has tried to correct this by setting up a task force headed by [now former] NITI Aayog Vice-Chairman Arvind Panagariya to generate timely and reliable employment data. This is a step in the right direction. The tragedy is that this should have happened many years back, even before Narendra Modi took over as the prime minister. Of course, the previous governments are to be blamed for this as well. The Modi government also took more than three years to initiate something to solve this problem.

The trouble is that close to one million Indians are entering the workforce every month. That makes it around 1.2 crore Indians a year. And the government is still struggling with counting the number of the unemployed.

What makes things worse is that most of the individuals who are entering the workforce are not skilled enough. Over the years, the government has tried to correct this by outsourcing skill development to the private sector rather than just depending on the Industrial Training Institutes or the ITIs. But the scale of operation continues to remain very small.

As the Economic Survey referred to earlier points out: “For urban poor, Deendayal Antyodaya Yojana National Urban Livelihoods Mission (DAYNULM) imparts skill training for self and wage-employment through setting up self-employment ventures by providing credit at subsidized rates of interest. The government has now expanded the scope of DAY-NULM from 790 cities to 4,041 statutory towns in the country. So far, 8,37,764 beneficiaries have been skill-trained [and] 4,27,470 persons have been given employment.” When one million Indians are entering the workforce every month, this is not even a drop in the ocean.

Other data points

While we may not know the right rate of unemployment on a regular basis, there is enough other data that suggests that job creation is not happening. Take a look at Figure 3. It basically plots the bank lending to industry.

Figure 3:

Source: Reserve Bank of India.  

The lending carried out by banks to the industry has fallen over the years. In fact, in 2016-2017, the lending to industry shrunk by more than Rs 50,000 crore. This basically means that on the whole, the banks did not lend a single new rupee to the industry in 2016-2017. The reason for this is very straightforward. The industry has defaulted on its past loans and banks are no longer in the mood to lend.

This also shows us that the industries are no longer borrowing and expanding and creating jobs in the process. Of course, banks are not the only source of borrowing for industry. If we were to look at the overall flow of financial resources to the commercial sector it was down by around 11 per cent in 2016-2017 in comparison to a year earlier (Source: RBI Monetary Policy Report April 2017).

Over and above this, demonetisation had a huge negative impact on jobs in the informal sector. The Bharatiya Mazdoor Sangh (a trade union affiliate of the BJP) estimated that nearly 2.5 lakh units in the unorganised sector were closed down. Then there is the latest Reserve Bank of India (RBI) Consumer Confidence Survey. More people now believe that the employment conditions have worsened over the last year.

The leaders of the Bharatiya Janata Party like to claim that crores of jobs have been created through Mudra (Micro Units Development and Refinance Agency Bank) loans given out by banks. In 2015-2016 and 2016-2017, a total of 7.46 crore individuals were given Mudra loans. Hence, 7.46 crore jobs were created is the logic that is offered. But this is something that the CEO of Mudra does not confirm. As he told NDTV recently, when asked how many jobs had these loans created: “We are yet to make an assessment on that… We don’t have a number right now, but I understand that NITI Aayog is making an effort to do that.

The point being India has a serious jobs problem and we aren’t doing much to tackle it. And there are going to be no acche din without jobs.

The column originally appeared on Newslaundry on August 15, 2017.

Farm Loan Waivers: Why Bad Economics Makes for Good Politics

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Several state governments have waived off farm loans over the past few months. The second volume of the Economic Survey released late last week analyses the economic impact of this phenomenon. Here are the points that the Survey makes:

a) What farm loan waivers basically do is that they transfer debt from the level of individuals and households to that of the state governments. When a state government waives off farm loans it needs to compensate the banks which had originally given the loans to farmers. Hence, it ends up with the debt of the farmers.

The Economic Survey expects the farm loan waive offs to cost anywhere between Rs 2.2 lakh crore and Rs 2.7 lakh crore. As the Survey points out: “It is assumed that waivers will apply at the loan rather than household level, since it will be administratively difficult to aggregate loans across households. It is also assumed that other states will follow the UP model. On this basis, an upper bound of loan waivers at the All-India level would be between Rs. 2.2 and Rs. 2.7 lakh crore.”

This is simply because the demand for waive offs will come from other states as well and the state governments are expected to comply. The Survey points out that “the widespread demand for loan waivers could simply be a demonstration effect from the UP loan waiver.”

b) The Survey believes that waivers will reduce demand in the country to that extent of Rs 1.1 lakh crore or 0.7 per cent of the GDP. This will be a huge deflationary shock to the economy. c) The farmers will benefit from the waive off and increase their consumption, the Survey says. While, this sounds true in theory, the actual evidence from 2008-2009 when the central government had announced farm loan waivers, is different. Research found that actual consumption did not go up after the farm loan waivers.

d) The state governments will have to borrow more in order to compensate the banks which have given loans to farmers. A part of the compensation for the banks will also come from the governments having to cut their expenditure in other areas. Since the governments will not be in a position to cut their regular expenditure like salaries, repayment of interest on the outstanding debt, etc., it will have to cut the asset creating capital expenditure. As the Survey points out: “a recent illustration is Uttar Pradesh which has slashed capital expenditure by 13 per cent (excluding UDAY) to accommodate the loan waiver.”

This is a point that the latest monetary policy statement of the Reserve Bank of India, also made: “Farm loan waivers are likely to compel a cutback on capital expenditure, with adverse implications for the already damped capex cycle.”

e) Also, the state governments are yet to clearly define who will benefit from the waivers and who won’t. This essentially leads to two points. One, it is difficult to come up with the overall cost of the waivers. Two, in order to implement the waivers, the state governments need to come up with clear definitions. This basically means that any implementation will take time and the benefits won’t be immediate.As the Survey points out: “Three states have been specific about the waiver schemes: UP has announced waivers of up to Rs. 1 lakh for all small and marginal farmers; Punjab’s limit is Rs. 2 lakh for small farmers without defining who these are; and Karnataka has limited the waiver amount to Rs. 50,000 (Maharashtra’s waiver terms are still unclear). The waiver announcements also do not make clear whether the amounts will apply to households or loans: typically, a household will have more than one loan.”

f) There are other negative effects of the waiver as well. Credit discipline (or the basic idea that loans need to be repaid) goes for a toss. Further, it benefits only those who borrowed from formal sources. Also, a “World Bank study found that lending increased following the 2008-09 waiver even if not in the districts with greater exposure to the waiver.”

Given these negatives on the economic front, it is important to ask why are farm loan waivers being made. The reason for this is fairly straightforward: the gains of farm loan waivers are more visible than losses.

When farm loan waivers are announced in one state, a large section of the farmers in that state who had taken on loans from the banking system, benefit from it. This is a clear visible effect, which the governments like to cater to. The negative effects are not so visible.Now take the case of a state government which needs to borrow more in order to pay off the banks which had made the farm loans in the first place. It will end up paying a higher rate of interest on its increased borrowings because at the end of the day the financial system has only so much money that can be borrowed. And any increased demand leads to higher interest rates.

As the Economic Survey points out: “Demands for farm loan waivers have emerged at a time when state finances have been deteriorating. The UDAY scheme has led to rising market borrowings by the states, expected soon to overtake central government borrowings. As a result, spreads on state government bonds relative to g-secs have steadily risen by about 60 basis points.”

The UDAY scheme was basically debt restructuring scheme which moved debt from the balance sheets of power companies run by state governments to that of the balance sheets of the state governments. Due to this the interest paid by state governments on their debt is around 60 basis points higher than that paid by the central government on its debt. The extra borrowing because of the farm loan waivers will only push up this rate of interest for state governments, making things even more difficult for them.

At the same time, states will also have to cut down on their capital expenditure in order to finance a part of their waiver. The deflationary shock because of this will be spread across the length and breadth of the country. Hence, each individual will have to take on only a small part of the pain. And he or she may not even feel it in the first place.

These are negative impacts of farm loan waivers, which are not as clearly visible in comparison to the direct benefit to farmers whose loans have been waived off.

Or take the case of the government of Maharashtra charging a drought cess of Rs 9 every time one litre of petrol is bought in the state. Why is this cess even there during a time when there is really no drought in the state? It is there so that the government can meet its expenditure on account of farm loan waivers and other expenses.

The question is how many people even know that such a cess exists, in the first place. The point is that there no free lunches when it comes to economics. It’s just that their cost is not visible many times and politicians simply make use of that.

(The column originally appeared on Equitymaster on August 14, 2017).

The Orwellian Economics of Indian Banking

George Orwell towards the end of his brilliant book Animal Farm writes: “There was nothing there now except a single Commandment. It ran: All animals are equal but some animals are more equal than others.”

Nowhere is this more visible these days than at Indian banks, in particular the government owned public sector banks, and the way they treat their different kind of borrowers. As is well known by now, Indian public sector banks have a massive bad loans problem. This basically means that borrowers who had taken loans over the years are now not repaying them. The bad loans of Indian banks are now among the highest in the world, only second to that of Russia.

The borrowers who have defaulted on their loans primarily consist of large borrowers i.e. corporates, who have taken on loans and are now not repaying them. As per the Economic Survey of 2016-2017, among the large defaulters are 50 companies which owe around Rs 20,000 crore each on an average to the banking system. Among these 50 companies are 10 companies which owe more than Rs 40,000 crore each on an average to the banking system.

These are exceptionally large amounts. Typically, when a borrower defaults the bank comes after him with full force, in order to recover the loan, by selling
assets offered as a collateral against the loan. But this force is not felt by the large corporates. It is felt by the small entrepreneurs who borrow from banks or people like you and me who take on retail loans like home loans, vehicle loans, credit card loans etc.

As former RBI governor Raghuram Rajan said in 2014 speech: “Its full force [i.e., of the banking system] is felt by the small entrepreneur who does not have the wherewithal to hire expensive lawyers or move the courts, even while the influential promoter once again escapes its rigour. The small entrepreneur’s assets are repossessed quickly and sold, extinguishing many a promising business that could do with a little support from bankers.”

Given that they have access to the best lawyers and are close to politicians, the large borrowers don’t feel the heat of the banking system.

In fact, the large borrowers given that they are large, get treated with kids gloves. In some cases, the repayment periods of their loans have been extended. In some other cases, the borrower does not have to pay interest on the loan for a specific period. But all this hasn’t really helped and the banking mess continues.

The Economic Survey of 2016-2017 has recommended based on the data for the year ending September 2016 that “about 33 of the top 100 stressed debtors would need debt reductions of less than 50 percent, 10 would need reductions of 51-75 percent, and no less than 57 would need reductions of 75 percent or more.”

This basically means that banks will have to take on what is technically referred to as a haircut. Let’s say a corporate owes Rs 100 to a bank. A haircut of 51 per cent would mean that he would now owe only Rs 49 to the bank. The bank would have to take on a loss of Rs 51.

The Economic Survey offers multiple reasons why haircuts will be required. The first and the foremost is that the borrowers simply do not have the money to repay. Secondly, large corporates owe money to many banks and these banks need to agree on a strategy to tackle the defaults. That hasn’t happened.

Of course, what the Economic Survey does not tell us is that the large borrowers are politically well connected. It also does not get into the moral hazard haircuts would create. Once corporates are bailed out this time around, why would they go around repaying loans the next time around? They simply won’t have the economic incentive to do so.

And finally, the Survey does not tell us anything about why only the large corporates are being treated with kids gloves? I guess it does not need to because that was something Orwell explained to us many years back.

The column originally appeared in Bangalore Mirror on March 29, 2017.

A bad bank for bad banks?

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India’s public sector banks(PSBs) are in a big mess. As of September 30, 2016, the gross non-performing assets ratio of these banks stood at around 12 per cent. A gross non-performing assets ratio or a bad loans ratio of 12 per cent basically means that for every Rs 100 loaned out by the banks, the borrowers have
stopped paying interest on Rs 12.

One solution that has been advocated to solve this problem is that of a bad bank. To put it simplistically, the solution entails moving the bad loans of the public-sector banks to a newly created bank. This bank, referred to as the bad bank, will then go around recovering the loans that have been defaulted on by selling the assets offered as a collateral against the defaulted loans.

The PSBs will have to be recapitalised by the government and then they can simply concentrate on the lending business. The bad-bank strategy was successfully followed in the United States to sort out the Savings and Loans crisis of the 1980s. It was also used successfully in Sweden in the early 1990s.

The latest Economic Survey released on January 31, 2017, talks about setting up of the Public Sector Asset Rehabilitation Agency(PARA). This will be a bad bank which will buy the bad loans from the PSBs and “then work them out, either by converting debt to equity and selling the stakes in auctions or by granting debt reduction, depending on professional assessments of the value-maximizing strategy”. The bad bank strategy has also been recently recommended by Viral Acharya, a deputy governor of the Reserve Bank of India.

The thing is, this is not as simple as it sounds. In the past, PSBs have tried to sell their bad loans to private asset reconstruction companies. Like in case of bad banks, a bank sells its bad loans to an asset reconstruction company, which then goes about selling the assets held as collateral against bad loans.

The trouble is that the asset reconstruction companies haven’t really been able to do a good job of it. As the Economic Survey puts it: “Asset reconstruction companies have found it difficult to resolve the assets they have purchased, so they are only willing to purchase loans at low prices. As a result, banks have been unwilling to sell them loans on a large scale.

This is a problem that a bad bank will also face. At what price should it buy the bad loans from the public sector banks? Will those banks be ready to sell at that price, given the fear of courts, vigilance as well as the CAG?

Assuming the banks and the bad banks are able to get over this obstacle, they will run into another major obstacle. Many corporates to which banks have lent money have an interest coverage ratio of less than one. These companies are referred to as stressed companies in the Economic Survey. This basically means that the operating profit (earnings before interest and taxes) of these firms is lower than the interest that they need to pay on their outstanding debt, during a given period. They are simply not earning enough to be able to pay the interest that is due on their debt.

The stressed companies with an interest coverage ratio of less than one, owe a little more than 40 per cent of the loans given out by Indian banks. In fact, even within stressed companies the problem is concentrated among a few borrowers. A mere 50 companies account for 71 per cent of the loans owed by the stressed companies. On an average these companies owe Rs 20,000 crore each to the banking system. The top 10 companies on an average owe Rs 40,000 crore apiece.

These are some of the biggest business groups in the country. Going about selling their assets in order to recover the loans will not be easy for the bad bank. These groups have access to some of the best legal brains in the country. They are also close to the politicians. As Acharya put it: “I don’t think a bad bank just by itself will necessarily work, I think it has to be designed right.”

Political will allowing the bad bank to go after business groups which have defaulted on bank loans, must be a big part of that design. Does the Modi government have that will, is a question worth asking?

(The article was originally published in the Daily News and Analysis(DNA) on February 16, 2017).

950 Central Government Schemes and Counting

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Every year a day before the central government presents its annual budget, it presents the annual Economic Survey. This year was no different. Buried in the Economic Survey is one number which basically tells us all that is wrong with the Indian government.

As the Survey points out: “The Budget for 2016-17 indicates that there are about 950 central sector and centrally sponsored sub-schemes in India… If the states were included, the number of schemes would be orders of magnitude larger.”

What does this tell us? It tells us that it is very easy for the governments (both central as well as states) to launch design and launch a scheme. As Devesh Kapur writes in an essay titled The Political Economy of the State: “Each centrally sponsored scheme has the resources of a particular central ministry to call upon to aid in its design, stipulate conditions for disbursement and so on.”

This explains the fact why there are 950 central government schemes because it is very easy to launch a new scheme. The trouble is in the implementation. As Kapur writes: “The delivery is necessarily by the local administration (the district administration and, now increasingly by the Panchayati Raj Institutions). Few states have the administrative capacity to access grants from [so many] schemes, spend money as per each of its conditions, maintain separate accounts and submit individual reports.”

What does not help is the fact that the poorest districts and states which need the maximum assistance do not get maximum assistance.  As the Economic Survey points out: “In many cases, the poorest districts are the ones grappling with inadequate funds – this is evidence of acute misallocation. Many districts in Uttar Pradesh, Bihar, Chhattisgarh, parts of Jharkhand, eastern Maharashtra, Madhya Pradesh and Karnataka, among others, account for a large share of the poor and receive a less-than-equal share of resources.”

In fact, the Survey offers evidence for some of the largest central government sponsored schemes. Take the case of the Mahatma Gandhi National Rural Employment Guarantee Scheme(MGNREGS). The 40 per cent of the poorest districts in the country receive only 28 per cent of the assistance under MGNREGS. In case of the Mid-Day Meal Scheme, the poorest 40 per cent of the districts get just 20 per cent of the money spent under the scheme. As the Survey points out: “For instance, consider the states of Bihar, Madhya Pradesh, Rajasthan, Orissa and Uttar Pradesh: despite accounting for over half the poor in the country, these states access only a third of the resources spent on the MGNREGS1 in 2015-16.”

The question is why is this happening? As the Survey points out: “One major explanation for misallocation is state capacity – resources allocated to districts are often a function of the district’s ability to spend them; richer districts have better administrative capacities to effectively implement schemes.”

Hence, the poorer areas do not get as much money under these schemes as they should. The richer districts have better administrative capacities to spend the money allocated under these schemes. And given that they get more money to spend. As Kapur writes: “[The] administrative capacity is even more limited in those states where the need is the most. Monitoring is rendered difficult not just because of the limitations in the monitors themselves, but the sheer number and dispersion of the schemes across communities and locations.”

This basically means that if some of these schemes are to be effectively implemented, in which the poorer districts with poor administrative capacity get their fair share, the number of schemes has to come down. In fact, most of the central government schemes have been around for 15 years and more than half of them are over 25 years old. This basically means that no exit option is exercised for schemes, and once a scheme is launched chances are it will keep going on for perpetuity. This makes the overall implementation of schemes significantly difficult.

A part of real economic reform would mean the central government acting on this front and bringing down the total number of schemes.

The column oiginally appeared in the Bangalore Mirror on February 8, 2017