India’s Big Government
Page 54
What we have seen until now are examples of the MGNREGS both working and not working in different states and with regard to different contexts. How do things look on an all-India basis? In order to get a feel of that, we will have to look into the report issued by the Comptroller and Auditor General (CAG) of India in 2013.
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The CAG carried out the first performance audit of the MGNREGS between February 2006 and March 2007. The second audit was carried out between April 2007 and March 2012. The implementation of the scheme was checked in 3,848 gram panchayats across 28 states and 4 union territories. The results were clearly not very encouraging. Some of the most important findings are discussed here. (Wherever possible, I will try and provide updated data alongside the CAG findings.)
The MGNREGA guarantees 100 days of work per household. But the MGNREGS has never managed to achieve that. The CAG found that the per rural household employment under the scheme had stood at 54 days in 2009-2010.895
This further declined to around 46.7 days in 2012-2013 and then fell to 41.3 days in 2013-2014. The number of working days per household had come down further to 40 days in 2014-2015 and again rose to 49 days in 2015-2016.896
Interestingly, in 2013-2014, only 6.4 per cent of the households completed 100 days of work, as mandated under the MGNREGA. The figure had been at 10.5 per cent in 2012-2013.897 In 2015-2016, it was at 10.1 per cent.898 This trend is also clearly visible in the total number of person-days of work generated by the MGNREGS (see Figure 13.1).
As can be seen from Figure 13.1, the work generated under the MGNREGS peaked in 2009-2010 and has been falling since then. In 2009-2010, 283.6 crore person-days of work were generated. By 2014-2015, this had fallen, by 41.3 per cent, to 166.3 crore person-days.
The question is: Why is this happening? The answer can be found in the allocations made to the MGNREGS by the central government over the years. Take a look at Figures 13.2(a) and 13.2(b). They show the total money spent on the MGNREGS and the total wage expenditure on it, respectively.
Figure 13.1: Total number of person-days worked under the MGNREGS (in crore) till 2014-2015.
* Up to December 31, 2015.
Source: The Mahatma Gandhi National Rural Employment Guarantee Act, 2005—The Journey of a Decade, Ministry of Rural Development.
Figure 13.2(a): Total government expenditure on the MGNREGS (in Rs. crore) over the years.
* Budgeted central outlay.
Source: The Mahatma Gandhi National Rural Employment Guarantee Act, 2005— The Journey of a Decade, Ministry of Rural Development; and www.indiabudget.nic.in.
The total expenditure on the MGNREGS was higher in 2009-2010 than the allocations have been in 2015-2016 and 2016-2017 (See Figure 13.2(a)). Once inflation is factored in, we see that the spending on the MGNREGS in real terms has come down dramatically. In 2009-2010, Rs. 37,905 crore was spent on the MGNREGS. It was in this year that the number of days of work per household peaked at 54 days.
So how much would the government have had to spend in 2015-2016 in inflation-adjusted terms for the amount to be the equivalent of Rs. 37,905 crore? The amount works out to Rs. 61,676 crore.xxi And what was the allocation towards the MGNREGS in 2015-2016? Rs. 34,699 crore. And how much money was finally spent during the course of the year? Rs. 36,645 crore.899
Figure 13.2(b): Wage expenditure on the MGNREGS (in Rs. crore) till 2014-2015.
* Up to December 31, 2015.
Source: The Mahatma Gandhi National Rural Employment Guarantee Act, 2005— The Journey of a Decade, Ministry of Rural Development.
What about 2014-2015? Around Rs. 59,060 crore would have had to be spent in inflation-adjusted terms. However, the total expenditure on the MGNREGS in 2014-2015 was at Rs. 36,032 crore, which was significantly lower.
This explains, to a large extent, why the average number of days of work available to workers under the MGNREGS has fallen over the years. The money allocated towards the scheme/Act has been more or less constant, whereas the average wages have been going up. The average wages of a labourer working under the MGNREGS in 2006-2007 had stood at Rs. 65 per day. 900 The average wages per day in 2015-2016 were at Rs. 154.901
Nevertheless, in 2016-2017, the wages have jumped to more than Rs. 200 per day in several states. In 2016-2017, the highest per day wages under the MGNREGS is in the state of Haryana, at Rs. 259 per day (against Rs. 251 per day in 2015-2016). In the states of Kerala, Karnataka, Tamil Nadu and Punjab, it is at, respectively, Rs. 240 per day (Rs. 229 per day in 2015-2016), Rs. 240 per day (Rs. 220 per day in 2015-2016), Rs. 203 per day (Rs. 183 per day in 2015-2016), and Rs. 218 per day (Rs. 200 per day in 2015-2016). In the eastern states, the wages are lower than Rs. 200 per day.902
The point is that, if the per day wages go up (as they should) and the expenditure on the MGNREGS does not correspondingly go up, it is but natural that the number of days of work available under the MGNREGS would come down.
As Aggarwal writes in the context of Jharkhand:903
Due to the budget caps and delays in receiving funds from the ministry in 2014-2015, Jharkhand was left with very little money from October 2014 to the [end] of the financial year to provide employment and ensure [the] timely payment of wages. Given the uncertainty [in] receiving additional funds [for] the rest of the financial year, local officials became reluctant to open works; they preferred violating [the] workers’ right to work than dealing with the hassles of delays in wage payments. Further, the ministry exempted the states from paying compensation to workers who did not receive their wages on time due to [the] shortage of funds.
The allocation of money towards the MGNREGS went up for the first few years and since then has almost come to a standstill. It seems as if the central government has lost interest in the scheme. When the current Finance Minister, Arun Jaitley, made an allocation of Rs. 37,000 crore towards the MGNREGS in 2016-2017, a section of the media went to town saying that this had been the highest allocation towards the scheme ever. This isn’t true, since higher allocations had been made in the past. Also, as discussed earlier, if we were to adjust the figures for inflation, then the current allocations towards the MGNREGS are significantly lower than in comparison to the past.
Figure 13.3: Food subsidies as a percentage of the total government expenditure between 2010-2011 and 2015-2016.
Source: Centre for Monitoring Indian Economy. * Revised estimates.
So what led to the allocation towards the MGNREGS remaining flat over the years? The government has offered no explanation for this. It is easy, however, to figure out why this has been the case. Every year, there is only a given amount of money that the government can spend. If it is spending more money on a few things, then it has to cut down on expenditure somewhere else. And the MGNREGS just happens to be that somewhere else.
Take a look at Figure 13.3. It shows the food subsidies as a percentage of the total government expenditure during the course of the year for the past few years.
Figure 13.4: MGNREGS expenditure as a percentage of the total government expenditure between 2009-2010 and 2015-2016.
* Budgeted central outlay.
Source: Centre for Monitoring Indian Economy; The Mahatma Gandhi National Rural Employment Guarantee Act, 2005—The Journey of a Decade, Ministry of Rural Development; and www.indiabudget.nic.in.
As is clear from the graph, the food subsidies as a percentage of the total government expenditure have risen over the years. And this has meant that the government has had to cut expenditure towards other allocations, like the MGNREGS.
Figure 13.4 shows the expenditure on the MGNREGS as a percentage of the total government expenditure over the years.
The ratio has fallen over the years. In 2009-2010, the ratio was at 6.49 per cent. It has since fallen to 2.49 per cent. Other than food subsidies, other Big Government expenditure areas, like mounting losses of the public sector enterprises, the recapitalisation of the public sector banks, the abundance of black money, etc., have also had a role to
play in the amount of money being spent towards the MGNREGS coming down over the years.
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What were the other major findings of the CAG audit? The audit found that close to 7.7 lakh works, amounting to Rs. 4,071 crore, were incomplete even after one to five years. It also found that, in fourteen states and one union territory, 1.29 crore works, amounting to close to Rs. 1,27,000 crore, were approved during the annual plans. Nevertheless, of these, only 38.65 lakh works, or around 30 per cent of the planned works, amounting to Rs. 27,792 crore, had been completed during the period of the audit. This showed significant inefficiency in the implementation of the annual plans.
This seems to be the general trend. Data from the Ministry of Rural Development suggests that around 71 per cent of the work that had been initiated under the MGNREGS since the scheme started has been completed. In actual fact, in 2015-2016, the work completion rate under the MGNREGS was only 23 per cent.904
The CAG audit also found that the expenditure on works amounting to a total of Rs. 6,547 crore did not lead to the creation of any durable assets. In a large number of cases, money was diverted for uses which are not allowed under the MGNREGS.
Furthermore, the audit found that job cards had not been issued to 12,455 households in six states. But, more worryingly, photographs were not pasted onto 4.33 lakh job cards in seven states. If there are no photos on the job cards, how can it be ensured that only those entitled to work under the MGNREGS are actually doing the work?
Also, in the gram panchayats which were audited, irregularities regarding the registration of workers were found. At the same time, it was noticed that there were delays as well as the non-issuing of job cards. Furthermore, obtaining a job card did not automatically translate into employment. The CAG report points out that “in over 47,687 cases, the beneficiaries were neither provided employment nor received an unemployment allowance”.
This basically meant (as we have seen earlier) that work was not available on demand as it should have been. What was even more worrying was the fact that the poorest states did not get the highest allocations under the MGNREGS. The three states of Bihar, Maharashtra and Uttar Pradesh accounted for 46 per cent of the rural poor in India during the period of the audit, yet they accounted for only 20 per cent of the funds released under the MGNREGS. Thus, as the CAG report puts it rather disturbingly, the poorest of the poor were not able to exercise their rights under the MGNREGS.
In four states, namely, Arunachal Pradesh, Kerala, Tamil Nadu and Manipur, the governments hadn’t appointed GRSs. Over and above this, there were widespread shortages of GRSs spread across different states. These shortages ranged from 20 per cent to 93 per cent. Indeed, this is worrying, because the MGNREGS is ultimately implemented by the GRSs on a day-to-day basis.
As mentioned earlier, the MGNREGA envisages vigilance at various levels, starting at the village level with social audits. Over and above this, there are district- as well as state-level vigilance committees. As the CAG report pointed out:
Gaps in the envisaged monitoring mechanism were noticed in a large number of states. There were significant shortages in [the] verification of works by state officials. Quality Monitors and Vigilance and Monitoring Committees had not been appointed in several states. Social audit[ing] remained ineffective to a large extent, as social audits had not been established. Shortfalls in social audit[s] were noted in a number of states.
This again is an excellent example of Big Government acting as a funnel, a funnel which is very broad at the top. When a policy is designed at the central government level, a perfect policy with all the requisite check-ins and monitoring systems built in is created. But, at the implementation level, the funnel is very, very narrow. The same set of local government employees needs to implement many government programmes. And this is where things start to go wrong.
And if all this wasn’t enough, in 23 states there were widespread instances of the non-payment of wages as well as the delayed payment of wages. The CAG could not measure the scale of these inadequacies because of the extensive irregularities in record maintenance at all levels. This finding is in line with other Big Government schemes where leakages are high and the money or a cheaper good never reaches those it is intended for.
What can be done (or, more importantly, is being done) in order to ensure that money reaches those who have worked under the MGN-REGS on time and without hassles? As Karthik Muralidharan, Paul Niehaus and Sandip Sukhtankar write in the research paper titled ‘Building State Capacity: Evidence from Biometric Smart-cards in India’:905
Building a secure payments infrastructure, … [which] makes it easier for governments to accurately identify beneficiaries and transfer benefits directly into their bank accounts, may significantly improve state capacity for programme implementation. This view has gained momentum from recent technological advances, which have made it feasible to issue payments via bank accounts linked to biometrically authenticated unique IDs. Biometric technology is seen as especially promising in developing countries, where high illiteracy rates make it unrealistic to universally deploy traditional forms of authentication, such as passwords or PIN numbers.
In fact, biometric identification has gained global popularity, and nearly 230 programmes in 80 countries deploy biometric identification payment systems. A good example in the Indian case is the smart-card programme that was implemented in 157 sub-districts in the undivided state of Andhra Pradesh to make payments to those working under the MGNREGS. By May 2012, the payments made through smart-cards had reached around 50 per cent. This is a very good ratio when one takes into account the fact that it took the United States of America over a decade and a half to move all social security payments to electronic transfers. In the Philippines, it took five years for a cash-transfer programme to reach 40 per cent of those it was intended for.906
The results of the smart-card biometric identification programme to make MGNREGS payments in Andhra Pradesh were thus fairly encouraging. After payments were made through this route, the workers spent 22 fewer minutes in collecting the money that was due to them. Furthermore, they collected their money 5.8 to 10 days sooner after finishing the work. The earnings through the MGNREGS increased by 24 per cent without the government having to increase its allocation towards the scheme. There was also a 7.1 per cent increase in the number of households which reported having worked under the MGNREGS. This happened primarily because there was a significant reduction in the number of quasi-ghost workers.907
As we have seen earlier, the officials in charge of running the MGNREGS on a day-to-day basis tended to over-report the total work done against a beneficiary’s name. This beneficiary had neither worked at the worksite nor received the payment made against his/her name. With the introduction of a biometric system of payment, it became more difficult for officials to fudge the actual work done. This essentially led to more actual work being done against the claimed wages, and, in the process, there was an increase in the payments made to the workers.908
In fact, the savings from the scheme were many times the money that was spent in getting the infrastructure in place. It cost the government $4 million to implement and operate the programme. In comparison, the value of the time savings of the workers was $4.5 million. At the same time, the leakage reduction in the MGNREGS worked out to $38 million per year, which is more than nine times the cost of implementation of the programme. Leakage in this case essentially means the officials creating work on paper and claiming payments against it from the government. One factor that needs to be kept in mind while discussing leakages is the fact that the number of job cards typically tends to be more than the number of households. This means that individual households typically tend to have more than one job card. When it comes to the sub-districts where the smart-card programme was launched, the ratio of job cards to households was 1.9:1. This basically means that the leakage is typically underestimated.909
Of course, all this went down well with the people who worked
under the MGNREGS. 90 per cent of the beneficiaries reported that the smart-card-based payment system was better than the earlier system. It is interesting to know how these payments were made. The government of Andhra Pradesh entered into contracts with banks. The banks, in turn, hired technology service providers to manage the technology required for the last-mile delivery and authentication. The banks were paid a commission of 2 per cent on the total amount of money that they paid out. The bank was supposed to pay the technology service provider out of this commission, depending on the contract it had entered into. In some cases, the technology service provider outsourced the last-mile delivery to a bank correspondent who, in turn, worked through customer service providers who made the actual payments to those working under the MGNREGS.910
The point is that the conventional system of the government officials, whose job it was to ensure that the payments were made, was totally done away with, and an entirely new system was created to implement the smart-card system. While this was one reason behind the success of the scheme, it also in its own way ensured that only 50 per cent of the payments were made through smart-cards and not 100 per cent, even though there was political will to do the same.911
As Muralidharan, Niehaus and Sukhtankar point out: “The poor gained significantly from the reform: beneficiaries received payments faster and more reliably, spent less time collecting payments, received a higher proportion of benefits, and paid less in bribes.” Nevertheless, despite this success, the government almost killed the programme.
The question is: Why? As Muralidharan, Niehaus and Sukhtankar write: “Local officials (whose rents were being reduced) were much more likely to relay negative anecdotes about smart-cards than positive ones, creating doubts among political leaders about the merits of the smart-card project. This bias was so pronounced that [the] government of Andhra Pradesh nearly scrapped the project in 2013, but ultimately decided not to do so, in part because of our results and data on the beneficiary preference for smart-cards.”