How the Election Commission aided the BJP in its phasing of the 2019 election

As soon as voting closed on 19 May, Congress President Rahul Gandhi accused the Election Commission of India (ECI) of capitulating to pressure from Prime Minister Narendra Modi on a number of fronts:

This is a serious set of allegations. Is there any truth to the claim that the ECI designed an election schedule that aided the Bharatiya Janata Party (BJP) campaign?

The political challenge for the BJP in 2019 was to retain as many seats as possible from its 2014 sweep of northern, western and central India, and to compensate for any losses with gains from the large eastern states of West Bengal and Odisha. Given its weakness in southern India the BJP’s main task there was to hold on its 2014 Karnataka tally as far as possible.

The 2019 election schedule has seven phases, three fewer than 2014, but this is somewhat misleading. The average number of phases per state and union territory has increased slightly from 1.9 in 2014 to 2.2 in 2019. However there are 12 states with 20+ seats each that account for 79% of 543 Lok Sabha seats. It is these states that will determine who forms the next government. Here, the number of phases has risen from 2.5 in 2014 to 3.2 in 2019, an average increase of 0.7 phases.

It’s clear from the analysis below that the ECI’s schedule has evolved in a direction that appears tailor-made for the BJP. The number of voting phases has gone up (in comparison with 2014) on those states where the BJP would like to maximise its campaigning time.

Let’s categorise states into three categories from the BJP’s perspective: “attack”, “defend” and “ignore”. States under “attack” by the BJP include West Bengal and Odisha where it won only three out of 63 seats in 2014, but saw potential for big gains in 2019. States being “defended” are the seven states where the BJP and its allies outperformed in 2014, and where the party hopes to hold on to as many seats as possible. And “ignore” includes Tamil Nadu, Andhra Pradesh and Kerala where the BJP alliance has poor prospects.

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The picture is as follows: the number of phases that “attack” states undergo has gone up by an average of two between 2014 and 2019, while the number of phases in states the BJP needs to “defend” has risen by an average of 0.7 phases. In states where the BJP sees little prospect of gains, the average has gone down by 0.3 phases. The fractions may seem small, but the gap between “defend” and “ignore” states is equivalent to a full phase, giving BJP leaders one extra phase in which to campaign there.

Things are rather different from the perspective of the Indian National Congress (INC). The INC alliance is on the “attack” in Maharashtra, Bihar, Tamil Nadu, Madhya Pradesh, Karnataka, Gujarat and Rajasthan, and is “defending” Kerala. It has limited prospects in Uttar Pradesh, West Bengal, Andhra Pradesh and Odisha.

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And what we see is a 0.6-phase increase in the INC’s “attack” states, mostly because six of the seven states are those that the BJP happens to be “defending”. In states where the INC is not in contention, the ECI has increased campaigning by a full phase, while there is no increase in the one state the INC is “defending”. All in all, not a sequence particularly helpful to the INC.

Rahul Gandhi’s charge that the election phases were designed to help the BJP appears supported by the evidence. Unless the ECI has a more convincing argument that we are yet to see.

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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.

The return of the gender gap

If women alone were to vote in the current elections, the Bharatiya Janata Party (BJP) would win 88 more Lok Sabha seats than the Indian National Congress (INC) (using CSDS data — see Table 3a). If only men voted, the BJP would get 126 more seats than the INC (if we stubbornly assume a constant ratio of voteshare-to-seats conversion). This hypothetical example highlights how gendered voting choices can have a big effect on election outcomes.

As Rajeshwari Deshpande wrote in September 2009:

The Congress has enjoyed an advantage among women voters since the 1996 general election. The party’s popularity among women reached a high point in 1999 when the gender gap in favour of the Congress was 5 percentage points. Since then, the gap has been closing. In the 2009 election, the Congress enjoyed a slight tilt in women’s votes towards it.

That no longer seems to be the case. According to the latest available CSDS data, the gender gap between the INC and BJP has reemerged with a vengeance (we define the gender gap as the difference between women and men voting for the BJP subtracted from the difference between women and men voting for the INC — note that the 2014 gap emerges from less reliable pre-poll data while the rest are from post-poll data):

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So why might such a gap exist? The United States has experienced a gender gap in favour of the Democratic Party since 1980, and in 2000 and 2012 Democratic presidential candidates had a 20 percentage point gap in their favour (that dwarfs the Congress Party’s record eight-point gap in 1998 and 1999). Explanations of the gender gap in the United States and other industrial societies fall into three general (and partly overlapping) categories:

  • Economic. Higher female participation in the labour force, and the fact that female workers face wage discrimination at the workplace, have pushed women to the left of the political spectrum in favour of parties that emphasise redistributive politics.
  • Family. In a similar vein, rising marriage ages, the widespread use of contraception, more out-of-wedlock childbearing and increased divorce rates have driven women voters to the political left, since they are no longer in a position to benefit from the incomes of male partners.
  • Values. The belief that a woman is entitled to reproductive autonomy (particularly salient in the United States) and the spread of feminist ideas have pushed women voters in the direction of political parties that share those values.

To what extent might these factors apply in the India case? At first blush, it would appear unlikely. India’s female labour force participation rate has fallen in recent years (from 44% in 2005 to 36% in 2012), which would undermine the case for an economic explanation. The mean effective age of marriage for Indian women has risen from 19.5 years in 1991 to 21.2 years in 2011 (a 9% increase), and contraception usage has increased between 1992-93 and 2005-06, but the divorce rate remains low and out-of-wedlock childbearing appears rare. So far as values go, abortion is not a partisan political issue in India, and there is no evidence that women’s rights issues shape voting behaviour.

Indeed that India even has a voting gender gap should be surprising: political scientists Ronald Inglehart and Pippa Norris found (using 1990s data) that only “postindustrial” societies have a gender gap that favours left parties; the gap favours ideologically conservative parties in post-Communist and developing societies. (Let’s leave aside the question of how generalisable categories such as “left” and “right” can be.)

But things get interesting when you look at the following chart (drawn from Table 2 in Deshpande’s article):

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The gender gap in favour of the INC in 2009 — when it was small by historical standards — was highest in precisely those groups that you would expect to be most influenced by the same economic and cultural trends that have created the gender gap in Western societies. If this holds up to further scrutiny (beyond the scope of this blog), then it could provide one answer to the puzzle that Deshpande posed about women and the INC: “There is no evidence that women find its policies more attractive than [do] men”.

There are other views. Deshpande argues that it makes more sense to look instead at regional variations, and sure enough, the INC’s gender advantage varies from state to state. She suggests that the INC’s gender gap is really the reflection of caste and class factors, and that there is no independent gender tilt in favour of the INC (although there probably is one against the BJP). Furthermore regional parties matter a great deal, and women in the latest CSDS surveys appear to be leaning towards Mamata Banerjee in West Bengal, Jayalalitha in Tamil Nadu and Mayawati in Uttar Pradesh.

That said, women’s issues are more salient in the 2014 national campaign than ever before (as Supriya Nair pursuasively points out), which has forced political parties to craft more specific appeals to women. An Association for Democratic Reforms-Daksh survey found that women’s safety is the third most important election issue (of ten) for urban voters. And Narendra Modi’s aggressive, patriarchal campaign clearly contrasts with Rahul Gandhi’s persistent outreach to women voters.

Political parties’ Facebook activity

In response to my post on Political parties’ Facebook “likes”, Pragya Tiwari correctly pointed out that an additional flaw in that measure is the likelihood that parties and politicians purchase “likes”, and that people “talking about this” might be a less manipulable figure.

So here is some data (an evening’s snapshot on Mar 1):

Politicians here appear to attract more chatter than political parties, although we don’t know what people are saying. No doubt there are commercial tools available to figure that out, and I’d be interested in getting such data were it to be available.