Bihar may be an uphill battle for the BJP

An average of three recent opinion polls (here, here and here) shows that the Bharatiya Janata Party (BJP)- and Janata Dal (United)(JD(U))-led alliances are locked in a statistical dead heat in the 2015 Bihar state election.

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This is a very creditable performance for the BJP. The party was long overshadowed by its partner of two decades, Nitish Kumar’s JD(U), even though it played a crucial supporting role in bringing upper caste votes to the alliance. Today, it appears to have fought Bihar’s two dominant parties to a standstill, and could yet form a government with its political allies. Indeed, the formation of a “grand coalition” (महागठबंधन) by the JD(U), Rashtriya Janata Dal (RJD) and Indian National Congress (INC) could ironically elevate the BJP to political primacy in Bihar, as Yogendra Yadav argued in a recent TV interview.

But it won’t be easy for the BJP to overcome the grand coalition’s raw arithmetic power. At the height of the Modi wave in the 2014 general election, the BJP and its allies outpolled the combined votes of these three parties in only 85 of 243 assembly segments. Most of these lay within 11 Lok Sabha constituencies where heavyweight BJP alliance candidates were contesting. The corollary: the then-hypothetical grand coalition “won” 158 assembly segments.

The big change since then is that the RJD and INC are now in alliance with the JD(U). So why does the race appear to be so tight? One theory is that disaffection with the RJD and Lalu Yadav will encourage many JD(U) supporters who might otherwise have voted for Nitish to switch to the the BJP. Furthermore younger and more educated Yadavs are also said to be weighing support for the BJP, which means that the RJD could also lose votes to the BJP.

Who knows, right? Well, not quite. We have a test of this theory in ten state by-elections fought in August 2014, only three months after the general election. The main difference was that the RJD+INC and JD(U) fought separately in the general election, but presented a unified front in the by-elections. A comparison of vote shares in the two elections gives us an opportunity to gauge the effect of the grand coalition.

So what happened? There was a swing of 2.1% towards the JD(U)-led front and a swing of 6.2% away from the BJP alliance (using simple averages). The BJP and its allies won four assembly seats and the JD(U) alliance took six. This would seem to show that the unification of the RJD, JD(U) and INC had a (weak) positive effect, while the BJP lost substantial vote share compared with the general election, only three months later. If this is a foretaste of how the grand coalition might fare against the BJP, the latter has much to be concerned about.

There are, as usual, caveats. This measure is imperfect because (1) issues and candidates differ between national and state elections and (2) ruling parties tend to win by-elections about two-thirds of the time because voters like their constituencies to receive favourable treatment. This means that any alliance effect — political contradictions that forced some RJD and JD(U) voters into the arms of the BJP — could have been hidden in the by-elections by such an incumbent effect. Furthermore, ten is admittedly a small sample in a state that has 243 assembly constituencies, although as the map below shows they were reasonably diversified in geographical terms (156-Bhagalpur in the southeast seems to not have been properly highlighted).

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Most importantly, of course, there was no chance that the by-elections would result in a change in government, whereas the BJP and its partners present a credible alternative in the state election. So a BJP wave or the move of key swing constituencies towards it (be they Mahadalits or Yadavs) could still produce victory for the BJP. However, the recent historical record and the sheer quantum of votes available to the JD(U) and RJD suggest this won’t be so easy.

Some proof required

There’s been much heated debate about Surjit Bhalla’s August 29 and September 5 Indian Express op-eds in which he argued: “the only explanation for the constancy of the share of the Christian population between 1991 and 2011 (2.32% and 2.3% respectively) is large scale conversion to Christianity”.

The crux of Bhalla’s argument is that because Sikhs and Christians had comparable income and fertility levels in the early 1990s, their respective population growth rates in between 1991 and 2011 should have been similar. Instead, “the Sikh population grew at an average rate of 1.2% per annum, while the population growth rate of Christians was a relatively higher 1.9% per annum… despite [Christians] having the highest per capita consumption, the highest level of female education and the lowest fertility.” The gap is accounted for, says Bhalla, by religious conversions to Christianity, which he quantifies at 1,70,000 per year between 1991 and 2011.

This seems at first like a smart way to isolate the impact of conversion using demographic data. But as the following critique by Rohini Prabha Pande, expert on gender and population dynamics, shows, Bhalla may be too clever by half:

Guest post by Rohini Prabha Pande

Why Bhalla’s argument is half-baked 

The most likely explanation for Bhalla’s “puzzle” might be the simplest one. Even with a slightly lower fertility rate, the Christian population may have grown larger than that of Sikhs because of a larger population base to which this fertility rate is applied, rather than lakhs of conversions. In particular, if the age distribution among Christians favors the reproductive age groups, then even with lower fertility rates the population could grow. This is related to the concept of population momentum and, at minimum, Bhalla needs to examine the age distribution and age-specific fertility rates of Christian versus other groups to rule this out as an alternative explanation.

In another sleight of hand, Bhalla summarily rules out migration – a key component of population growth – as an explanation of why the Sikh population might have grown more slowly than the Christian population. Here too he needs to explain why he is correct, and others – like Aswini Nanda – are wrong.

There are other flaws in his reasoning. I agree with Tony Joseph’s argument that Bhalla’s results depend on which period you pick. To that I would like to add that Bhalla’s focus on 20-year time periods brushes under the rug some key aspects of the relative growth of these two groups’ population, because the more you collapse data, the more information you lose. A quick calculation shows that if you simply parse 1991-2011 into two 10-year census periods (even without going further back in time to 1971, as Joseph does) you get a somewhat different picture: the (compounded) rate of growth of the Christian population across the two periods is slowing faster than the rate of growth of the Sikh population (see Table 1 below).

In other words, while the Christians may have a higher population growth rate than the Sikhs, both groups’ rates are dropping, and the Christians’ growth rate is declining faster than that of the Sikhs. The difference between the Christian and Sikh populations’ growth rates has narrowed from 0.9 percentage points between 1991-2001 to 0.6 percentage points between 2001-2011.

Table 1: Changes in population growth rates of different religious groups in India, 1991-2011
Table 1: Changes in population growth rates of different religious groups in India, 1991-2011

Bhalla then asks “what caused Christian population growth to accelerate from 1.39% (1971-91) to 1.93% a year (1991-2011)?” and concludes that the answer lies in religious conversions. One could equally and legitimately ask what caused Christian population growth to drop from 2% between 1991-2001 to 1.5% between 2001-2011. Framed this way, the question does not – unfortunately for Bhalla – lead to the conclusion that conversions have risen.

So one has to take Bhalla’s assertions with a large pinch of salt, if one were to pay any attention to them at all.

Finally, in his 4 September rebuttal of Joseph, Bhalla asserts that: “The excess Sikh males do not marry; excess Christian women do marry and produce Christian kids.” Aside from the parochialism implicit in this statement, is he assuming that the excess Sikh males don’t marry because there are no Sikh women? His own occasional coauthor, sociologist Ravinder Kaur, is the foremost researcher in India (look here) on the increasing phenomenon of across-region marriage, showing in particular how excess males in Punjab (presumably including Sikhs) import brides from Andhra Pradesh and Assam.

To sum up, Bhalla’s calculations of conversion are at best inconclusive, at worst, disingenuous.

Why the BJP should worry about Rajasthan’s local election results

Both the Bharatiya Janata Party (BJP) and the Indian National Congress (INC) claimed victory in the August 17 urban local body elections in Rajasthan.

Both sides seem to have a point: the BJP won decisively while the INC strongly improved its performance in comparison with the 2013 state and the 2014 general elections.

But it also seems true that the BJP hasn’t done as well as it might have hoped in urban constituencies that have long been its stronghold (as pointed out here and here). The fact of the BJP’s victory tell us little: the party has been so dominant in urban Rajasthan that it even outperformed the then-ruling INC in 2010 by a single percentage point.

So how to evaluate these election results? Direct comparisons with state and general elections are a bit crude because local elections are driven considerably by local issues and candidates. Indeed the BJP’s winning margin in 2015 over the INC (3.4 percentage points) was pretty much the same as in 2005 (3.3 percentage points), the previous time it was in power. The results, in this context, are unremarkable and at best mark the return of “normal” politics to Rajasthan.

Yet things aren’t quite that simple. The separation of urban local elections into two phases since 1994 provides us with a “natural experiment” that allows us to compare changes in urban support for the BJP between 2014 and 2015. In November 2014, 1,696 wards in 46 urban local bodies had elections, while the August 2015 elections covered 3,351 wards in 129 municipal bodies.

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The chart above shows that, in November 2014, the BJP gained a sizeable vote swing of 6.8 percentage points in comparison with 2009, and the INC experienced a corresponding negative swing of of 7.2 percentage points. In August 2015, the swing towards the BJP compared with 2010 was only 0.8 percentage points, and the swing away from the INC 1.6 percentage points. Differences in absolute levels of support between the two phases don’t matter here: we are concerned only with vote swings.

This isn’t a perfect measure because it assumes that, as of 2014-15, the choice of whether a ward had elections in November or August was essentially random (any evidence, for or against, would be welcome). Plus some of the candidate qualification rules changed prior to the August election and many INC and BJP candidates run as independents in municipal elections.

Even so, this is us the closest we have to a yardstick of the change in urban support for the BJP between November 2014 and August 2015. Another way to think about this is in terms of the vote foregone by the BJP between the two rounds: a hefty 6 percentage points.

Something seems to have changed between November 2014 and August 2015. Rajasthan, long a swing state, is back in play.

Could prison save your life?

The 62nd anniversary of the death of the Bharatiya Janata Party founder Shyama Prasad Mukherjee while in custody in Srinagar sparked discussion about the party’s alliance with the quasi-separatist Peoples Democratic Party. But a more interesting tangent for me is the still grim reality of custodial deaths in Indian prisons and police stations.

The numbers appear bleak: drawing on data from the official National Human Rights Commission, the New Delhi-based Asian Centre for Human Rights (ACHR) reported in 2011 that 14,231 persons died in police and judicial custody between 2001 and 2010. Of this, 1,504 deaths occurred in policy custody and 12,727 in judicial custody, with the ACHR’s director Suhas Chakma stating that:

A large majority of these deaths are a direct consequence of torture in custody. These deaths reflect only a fraction of the problem with torture and custodial deaths in India as not all the cases of deaths in police and prison custody are reported to the NHRC. Further, the NHRC does not have jurisdiction over the armed forces and the NHRC also does not record statistics of torture not resulting into death.

This is certainly consistent with the image many of us have about policing in India being a brutal business. Even so it is also clear that some proportion of prisoners in custody would have died even if they had not gone to prison. The right way to think about the harm that prison does is to look for “excess mortality” i.e. whether being in prison caused people to die at a greater rate than they would have otherwise.

According to the National Crime Records Bureau’s Prison Statistics India 2013, 1,597 of 411,992 prisoners died in 2013, which translates to a mortality rate of 388 per 100,000 prisoners. In the ten years between 2004 and 2013, the prison mortality rate ranged between 353 and 395, and averaged 377 (shown below).

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Surprisingly, that’s about half of India’s average mortality rate in 2011 of 710 per 100,000 persons. Shock and horror — could going to prison in India actually help you live longer? There are two obvious problems with jumping to this conclusion:

  1. Underreporting. NGOs such as ACHR say that there is severe underreporting of prison mortality, and that the true figure is much higher. While this sounds intuitive, it’s much harder to hide deaths than it is, for instance, to hide rapes. You could cover up torture or mistreatment cases as having occurred due to natural causes (official figures say only 115 of 1,597 prisoners died unnatural deaths in 2013), but you can’t really pretend that a dead person didn’t die.
  2. Wrong measure. The mortality of the general population is perhaps not the appropriate baseline with which to evaluate prisoner mortality. The first thing to note is that only 4.4% of the prison population consists of women, which means we should focus on male mortality rates. Second, the age distribution of the prison population is different from that of the general population, with a concentration of prisoners in the 18-50 age group (as Table 1.5 from the 2013 prisons report shows below), which is the more appropriate reference group.

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The mortality rate for the general male population (in 2011), weighted by the same age distribution as India’s prison population, turns out to be in the vicinity of 430 per 100,000, which is still higher than India’s prison mortality rate (keep in mind this is a back-of-the-envelope calculation). The number drops to 390 if we (somewhat arbitrarily) exclude all persons above the age of 65, but that’s still higher than the comparable prison mortality rate of 357 in 2011. Narrowing the sample yet further to men in the 20-50 age group, from whom 87% of the prison population is drawn, gives us 330, which at last produces a number lower than the mortality rate of the prison population. What this tells us is that India’s prison mortality rate is still surprisingly low.

So what gives? It could be that the mortality-increasing effects of prisoner mistreatment are being swamped by the mortality-lowering effects of providing inmates with shelter, food, healthcare and physical security.

Another way to think about this would be to compare India’s prison population’s characteristics with that of another country. A comparison with the United States is illustrative, since that country’s overall mortality rate is similar to India’s even though its crime and incarceration rates are much higher. The male mortality rate of the 15-54 age group (pdf) in the United States is about 420, higher than the 330 for Indian men in the broadly comparable 20-50 age group. Yet the prison mortality rate (pdf) in the United States averages 184 per 100,000, about half of India’s. It turns out that the gap between the mortality of the prison-going male population and of actual prisoners is much more favourable for prisoners in the United States than in India, which means there could still be something producing excess mortality in Indian prisons.

What’s your beef, Mr Naqvi?

The Minister of State for Parliamentary Affairs Mukhtar Abbas Naqvi seems to have very specific views about Indians who wish to to eat beef.

His boss, Prime Minister Narendra Modi, famously criticised the United Progressive Alliance government for facilitating beef exports (which he termed a “pink revolution”), and repeatedly visited this theme during his 2014 election campaign.

When accused of practicing “dog whistle politics” against religious minorities, Modi insisted that his motivation was only to prevent farmers from being deprived of valuable livestock assets, because it is as much of a sin to deprive a farmer of cattle as it is to take away his land (no really — watch the interview here).

With farmers’ very futures at stake, the Modi government must have cracked down on beef exports, right? Wrong. Indian beef exports have grown apace since the BJP came to power and show no signs of slowing (data from the Ministry of Commerce, HS codes 0201 and 0202).

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In value terms too beef exports have grown despite an overall export slowdown in 2014-15.

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The government clearly feels that if you want to eat Indian beef, you should do it outside the country.

Selection bias and land acquisition data

What proportion of industrial projects are being held up by land acquisition challenges? A somewhat abstruse debate entered the mainstream after Rahul Gandhi cited Centre for Monitoring Indian Economy (CMIE) data (provided by the Finance Ministry) in a 12 May Lok Sabha speech:

Although I have argued elsewhere that the 8% figure may be an exaggeration in the context of Narendra Modi’s land amendments, the more common view is that it understates the extent to which land acquisition difficulties inhibit manufacturing in India, as Maitreesh Ghatak has explained most clearly in Quartz:

“It’s a classic underreporting problem,” said Maitreesh Ghatak, a professor at the London School of Economics who has studied land acquisition law in India. “There may be projects that never got started because they anticipated these problems. Also, the ones that did get started are likely to have been selected because the risk of land acquisition problems was low for them for whatever reason.”

This is a perfectly fair point, but it doesn’t necessarily follow that the 8% number is an understatement. Imagine a businessperson who is contemplating setting up a plant to manufacture wickets. One could easily imagine her investigating the feasibility of setting up such a plant, but giving up because she failed to procure land.

But one could also imagine her giving up because she couldn’t get a large enough bank loan, or a sufficient supply of workers, or even permission to cut trees to manufacture the wickets. To use Ghatak’s language, she may have chosen not to start because she anticipated any, or several of these problems. One simply does not have enough information about the universe of projects that were contemplated but not started, which doesn’t justify throwing out the data about projects that were started but subsequently ran into trouble. Because the ones that got started are also likely to have been selected (into the CMIE sample (pdf) of 804 projects) because the risk of labour, capital or market problems was low for whatever reason.

But let’s assume for argument’s sake that the number of unobserved projects delayed by land acquisition issues is in fact significantly high. It doesn’t follow that Modi’s land amendments will have any impact on their viability. The original 1894 land act was in operation until 31 December 2013, until which time the government was able to compulsorily purchase land for private companies without landowner consent or carrying out a social impact assessment, just as in the amended law. Yet land acquisition problems abounded. It would be ludicrous to argue that a law that was in operation for a single year — and excluded nuclear energy, mining, railways, national highways and petroleum pipelines from its purview — was responsible to delaying land acquisition in the prior decade.

Modi’s land amendment would do very little for stalled projects

Thanks to a Right to Information request to the Ministry of Finance by Venkatesh Nayak of the Commonwealth Human Rights Initiative, we have more detail regarding how many industrial projects are being held up by land acquisition difficulties. The data were collected by the Centre for Monitoring Indian Economy (CMIE) and analysed in Chapter 4 (PDF) in the first volume of the 2014-15 Economic Survey.

As low as the headline number of 8% appears, it may be an overstatement in the context of the government’s land acquisition amendments, since it includes government projects that do not fall under their purview. Remember that the hotly-debated consent clauses of the 2013 Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation and Resettlement Act applied only to land acquisition by the state on behalf of private companies (whether directly or via public-private partnerships). This means that the amendments will do nothing for public sector projects.

The 8% headline number comes from the list of 66 (of 804) projects that the CMIE has identified as held up by land acquisition difficulties, but 27 of these happen to be government-owned projects that will be unaffected by the land acquisition ordinance. Therefore the proportion of projects that stands to benefit from the Modi government’s land acquisition amendments is in fact 39 of 804, or 5% of the total.

If we take a stricter view and filter out real estate projects (townships, malls etc.), then the number of private industrial projects held up by land acquisition problems drops further to 26, around 3% of the total.

Now there is a view that the the observed number of projects could understate the effect of land acquisition difficulties on investment, since many projects will have been considered and dismissed without actually being launched. But this holds true of all factors of production (labour, capital, etc.), so it’s pretty hard to come up with a comprehensive and correctly weighted counterfactual.

So far as the current land act amendments are concerned, a quick scan of the list of 804 projects shows that many of them date back a decade (such as POSCO’s Paradip steel plant and Tata Steel’s Jharkhand plant). The 2013 land act came into force on 1 January 2014, which means that the consent and social impact assessment clauses can’t be really be blamed for the observed delays.

The National Pension Scheme’s bond funds may be riskier than its equity funds

A series of favourable budget announcements have revived interest in India’s National Pension Scheme (NPS), a “defined contribution” pension plan that invests in a diversified basket of financial assets such as stocks, corporate bonds and government securities. Its chief advantage over existing government-sponsored pension plans is that it allows subscribers to invest in equities (up to 50% of their funds) which should help them generate higher returns than they would with bonds alone.

The NPS does many things well. Its management fee is only 0.01% of assets, compared with the annual expense ratio of 1.75-2.75% for your typical balanced mutual fund. For people who have little inclination to figure out their optimal asset allocation, the NPS offers an “auto choice” (or “lifecycle”) option that steadily reduces the allocation to equities and increases those to bonds (particularly to safer government bonds) as the saver approaches retirement. The requirement that NPS equity investments (termed “Asset Class E”) be made in an index fund (benchmarked to either the BSE Sensex or the NSE Nifty) also eliminates fund manager risk. Bad fund manager choices can hurt fund returns: an S&P Dow Jones study found that more than half of Indian large-cap equity funds and bond funds failed to outperform their index over a five-year period (ending June 2014); four out of five government bond funds also fell short of their benchmark.

Even with fund manager risk minimised, stock market volatility is a reality, which is one reason why trade unionists have long resisted efforts to let the Employees Provident Fund Organisation invest a sliver of its Rs 650,000 crore (US$104 billion) corpus in equities. Because not all savers can stomach such volatility, the NPS limits equity exposure to 50% or less, and ensures investment in a diversified basket of assets.

So what might the problem be? Unlike equity investments in the NPS , the Pension Fund Regulatory and Development Authority (PFRDA) does not prescribe a particular index for investments in government securities (“Asset Class G”) or corporate bonds (“Asset Class C”), leaving it to fund managers to choose their investments (subject to detailed investment guidelines). This set up leaves NPS bond investors exposed to fund manager risk in a way that they are not with their equity investments.

Long-term bonds invariably expose investors to interest rate risk: the longer the tenor of a bond, the more its value will fluctuate with changes in interest rates. This can be good for investors when interest rates are declining (since bond values then rise) but can reduce the value of those bonds when interest rates rise. The sensitivity of a bond or a bond fund to interest rate changes is measured by its duration. The issue here is not that NPS subscribers have to deal with inevitable interest rate risk, but that NPS fund managers are effectively permitted to bet on interest rate movements and, to an extent, on credit quality. This exposes unaware savers to fund manager risk in their NPS bond investments.

The chart below measures the difference between the duration of HDFC Pension’s government securities fund and the CRISIL Gilt Index. It shows that, in June 2014, the interest rate sensitivity of HDFC’s government bond fund was very close to the index’s, but had increased greatly in comparative terms by December 2014. The fund manager appears to have chosen to take on greater interest rate risk than the broader market in an effort to juice returns on the assumption that interest rates are headed downwards.

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The chart below shows how NPS fund managers have taken on varying amounts of interest rate risk in their respective government securities portfolios. As on 28 February 2015, Kotak Mahindra had taken on the least risk vis-a-vis the CRISIL benchmark while ICICI Prudential had taken on the greatest. Screen Shot 2015-03-17 at 3.06.55 pm

Asset Class C represents corporate bonds. Corporate bond funds tend to take on less duration risk and more credit risk to generate higher returns, and the NPS is no exception. The chart below show the varying spread between the HDFC Pension corporate bond fund and an ad hoc index that is an 80-20 blend between the CRISIL AAA Long Term Bond Index and the CRISIL AA Long Term Bond Index. It would seem that HDFC started off more aggressively but has over time aligned its duration with the broader market.

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Below we have a comparison among different pension fund managers and the amount of interest rate risk they have taken on vis-a-vis our index. ICICI Prudential appears to be the most aggressive and Kotak Mahindra the most conservative here, and the problem is that NPS subscribers have no easy way of knowing this. Screen Shot 2015-03-17 at 3.02.53 pm

Finally, the chart below demonstrates how corporate bond fund managers have taken on varying levels of credit risk. Note that they are well within the prudential limits set by the PFRDA, but some funds appear more conservative than others. Screen Shot 2015-03-17 at 3.09.38 pm

To summarise, the argument is not that these funds are being managed poorly or irresponsibly, but that NPS bond investments are being exposed to fund manager risks in way that their equity investments are not. Savers should be, and indeed are, free to take on these risks if they want higher returns by voluntarily buying into actively-managed debt mutual funds, but a pension fund doesn’t seem to be the best place for an aggressive debt strategy. At a minimum NPS subscribers should be given the option to invest in bond index funds and provided all the information they need to make an informed choice.

Some television commentary

Yours truly managed to inveigle himself into a few TV studio discussions around the Delhi state election, and here are the links for anyone interested.

Issues vs. drama in the Delhi election campaign

A focussed, non-partisan discussion (video here)

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Reading the Delhi exit polls

A heated political debate ensued (consisting mostly of ad hominem attacks by political spokespersons) before I got in my two bit in the last 15 minutes

Caste and class in Indian politics

A relaxed discussion in a longer format

Manufacturing a surprise

With all the flak that that the United Progressive Alliance (UPA) got for its “policy paralysis” and anti-growth policies (apparently both can simultaneously occur), one would hardly have expected a sharp economic revival in its final year in office. However, new and improved government economic data shows that in 2013-14 (i.e. the fiscal year ending March 2014), annual growth was a respectable 6.6%, up 1.7 percentage points from the previous year. This recovery is comparable in scale with the 1.9 percentage point growth spurt (to 8.6%) we saw in 2009-10 following the 2008 crash.

A tale of two base years (via Business Standard)
A tale of two base years (via Business Standard)

The chart shows that the biggest upward revisions (compared with the old data series that uses 2004-05 as base year) are in the areas of manufacturing and “trade, repair, hotels and restaurants”. For 2013-14, there is also a big upward revision in “mining and quarrying”. This is important: it tells us that despite what we have heard from lobbyists, industry associations and commentators, manufacturing performed reasonably well under the UPA. The mining industry, whose woes are only partly ascribable to UPA policy (other actors being the judiciary and state governments), also began a substantial recovery in 2013-14.

The share of manufacturing in the economy (under the gross value-added calculation) has also been revised from 13-15% under the old series (over the past three years) to 17-18% under the new series. This renders the government’s “Make in India” target of generating 25% of gross domestic product (GDP) from manufacturing by 2022 more achievable.

Are these revisions meaningful? R Jagannathan misses the point in attributing the growth jump mostly to the base effect created by a 2.2% reduction in the estimated size of the economy in 2011-12 (see table below). That did occur, but the 2012-13 estimate was also lowered by 1.3% while the 2013-14 estimate remained flat, none of which makes the growth numbers any less real.

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In fact, the new data are much more robust, particularly when it comes to the corporate sector. For instance, the old series relied on Reserve Bank of India data pertaining to about 2,500 companies, while the new one uses the MCA21 database at the Ministry of Corporate Affairs that covers 5 lakh firms. The obvious takeaway is that manufacturing continued to grow at a respectable pace despite the cramping effect of inflation, high interest rates and impaired balance sheets.

So do these numbers really vindicate the UPA (as ex-Finance Minister P Chidambaram has stated)?

There can be little question that the UPA went into policy paralysis between 2010, when serious allegations of corruption broke, and 2012 when it finally summoned up the will to raise fuel prices and permit FDI in retail. Most people would agree that it did some good things (such as institute a credible monetary policy under Reserve Bank Governor Raghuram Rajan, approve long-pending industrial projects and reduce fuel subsidies) but that its governance capacities were sapped by revelations of large-scale crony capitalism.

But as anger towards the UPA fades over time and its record is examined more dispassionately, the view that it seriously damaged India’s long-term growth prospects will ease into a more balanced critique.

Added on Feb 2

Morgan Stanley Research today issued a report that says that the new data showing a growth acceleration in 2013-14 are inconsistent with other indicators, such as the revenue growth of 0.9% clocked by 3,736 firms in the manufacturing and services sectors. This number, they say, has previously moved in sync with economic growth.

Let the games begin.