Communal realignment and support for the BJP, 2009–2019

Table of contents :
Abstract
1. Introduction
2. Data and measures
3. The 2019 LS election: exploring turnout
4. The 2019 LS election: analysis of the results
5. Conclusion
Note
Acknowledgements
Disclosure statement
Notes on contributor
ORCID
References

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Contemporary South Asia

ISSN: 0958-4935 (Print) 1469-364X (Online) Journal homepage: https://www.tandfonline.com/loi/ccsa20

Communal realignment and support for the BJP, 2009–2019 Oliver Heath To cite this article: Oliver Heath (2020): Communal realignment and support for the BJP, 2009–2019, Contemporary South Asia, DOI: 10.1080/09584935.2020.1765986 To link to this article: https://doi.org/10.1080/09584935.2020.1765986

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CONTEMPORARY SOUTH ASIA https://doi.org/10.1080/09584935.2020.1765986

Communal realignment and support for the BJP, 2009–2019 Oliver Heath Department of Politics, International Relation and Philosophy, Royal Holloway, University of London, Surrey, UK ABSTRACT

KEYWORDS

To what extent has the BJP managed to capture the rural vote? And how does the party’s level of support relate to poverty and literacy? Is the Hindu-Muslim divide becoming more pronounced – or has it faded as the party seeks to broaden its appeal? Using constituency level data linked to census data, this article examines support for the BJP and patterns of social realignment at the constituency level from 2009 to 2019. In particular, I examine the impact of social cleavages related to the level of urbanisation, poverty, and literacy within a constituency, as well as its religious and caste profile. Using OLS regression with statelevel fixed effects I present a detailed analysis of the social profile of places where the BJP has prospered over the last ten years and the major socio-geographical fault lines that run through the country. The results show that over the last three election cycles support for the BJP has become increasingly polarised along communal lines.

India; communalism; realignment

1. Introduction The 2019 elections marked an historic victory for the BJP. Not only was the party re-elected to power, but against many expectations it was done so with an increased majority. The BJP won 303 seats (up from 282 in 2014) on 37% of vote (up from 31% in 2014). Moreover, in those seats where the BJP contested, the party received an almost insurmountable 46% of the vote. After a period when coalition governments were the norm, 2019 was also the second time in succession that the BJP managed to secure an overall majority by itself, without reliance on its ally partners. Following the 2014 elections, many commentators thought the result represented a ‘one-off’ for the BJP, which the party would not be able to repeat (Verma 2019). But now after a resounding victory the BJP is more dominant than ever and their position now rivals that of the Congress party in earlier eras, which has led to a ‘second dominant party’ system (Vasihnav and Hinston 2019). The resurgence of the BJP into a genuinely national party that is capable of ruling at the centre on its own has sparked a lively debate about the changing nature of Indian politics and the extent to which the new found dominance of the BJP represents a major ‘realignment’ (Sridharan 2014; Tillin 2015; Verma 2019; Vasihnav and Hinston 2019). Writing in the aftermath of the 2014 election, Palshikar (2014) suggested the result represented a ‘critical election’ which heralded ‘a new phase in the life of the post-Congress polity’. According to this perspective, critical realignments can be understood as periods of dramatic rather than incremental change. They refer to an ‘abrupt, large, and enduring form of change in prevailing electoral patterns’ (Nardulli 1995, 11). And whereas it was clear in 2014 that the first two of these components had been realised – with an abrupt and large-scale change in the partisan balance of the electorate, it was less clear whether or not these changes would endure. Now, after 2019, there is evidence that these changes may be more durable than previously thought. CONTACT Oliver Heath

[email protected]

© 2020 Informa UK Limited, trading as Taylor & Francis Group

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A number of recent studies indicate that part of the reason for the BJP’s success is that they managed to shed their old image of an ‘upper caste’ party and embraced a new form of ethno-political majoritarianism, delinked from a religious Hindu nationalism (Chhibber and Verma 2019). In doing so, the BJP adopted an aggressive form of majoritarianism, which specifically targeted minorities. According to Sardesai (2019) examples of this were manifold: During the campaign, Modi frequently invoked ‘Hindu anger’ directed at Muslims, and promised to implement the National Register for Citizens, with the implicit threat of removing Muslims and Christians from the country. Since returning to office, this issue has sparked widespread violence targeted at Muslims, particularly in those areas which the BJP now controls. To understand this process of political realignment in this article I examine how the social base of the BJP has changed from 2009 to 2019. To what extent has the BJP managed to capture the rural vote? And how does its level of support relate to poverty and literacy? Is the Hindu-Muslim divide becoming more pronounced – or has it faded as the party seeks to broaden its appeal? By utilising demographic data derived from the Census and linked to parliamentary constituency boundaries I analyse the extent to which a major social realignment has taken place, and whether there is evidence that this has hardened over the last election cycle or whether the party has broadened its socio-geographic appeal. In doing so I examine the extent to which a new communal cleavage, based on Hindu majoritarianism now structures political conflict, replacing the more internally differentiated caste cleavages that structured electoral competition during the multi-party era. I also consider the impact of social divides related to urbanisation, poverty, and education.

2. Data and measures Whereas a great deal is known about the individual level correlates of vote choice in India from survey data, we currently have very limited knowledge about the social geography of electoral support and the major faultiness that run through the country. This is an important gap in our knowledge since in first past the post electoral systems, as used in India, MPs are elected at the constituency level. And although it is important to know how individuals vote, it is the concentration of these individuals within constituencies that determines which party wins seats, and ultimately which party or parties form the government. To understand why parties prosper in some constituencies, but struggle in others, we, therefore, need to understand how electoral support is related to social geography. This has important implications for how we think about representation. If people living in some types of constituency are represented by the governing party at the centre, but people living in other types of constituency are excluded from political power, then the difference between winners and losers may become more pronounced. In the context of nationalist majoritarianism this could be potentially divisive. In order to examine the social alignments that structure contemporary patterns of political conflict I make use of an innovative new dataset constructed by Jensenius (2016) that links census data to parliamentary constituencies. While all of India’s electoral results are reported at the constituency level, the socio-economic characteristics of constituencies that are available from the Census need to be estimated from other levels of analysis. This poses a challenge for linking census data to constituency data. Administratively, Census data is collated for states, districts, blocks, villages (and towns with wards) that all fit nicely into each other, but these levels of analysis do not map onto political geography in a straightforward way. Politically, India is divided into more than 4000 State Assembly Constituencies (ACs) that fit into administrative districts (but cross block boundaries), and several of these ACs are then aggregated into 543 Parliamentary Constituencies (PCs) that often cross district boundaries. A number of different approaches have been developed in order to link census data to parliamentary constituency boundaries. Using geocoded mapping technology (GIS) it is now possible to match both villages and blocks to political constituencies with some precision. In a recent work, Bhavnani and Jensenius (2015) created area-weighted estimates of India’s pre-delimitation ACs from blocklevel

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census data. Similarly, Jensenius (2013) created AC-level estimates of census variables based on mapping village-level census data to constituencies. This article draws on Jensenius’ most recent work that creates area-weighted estimates of census variables at the PC level by overlaying GIS maps of India’s almost 6000 administrative blocks (usually called Tehsils, Mandals, or Police Circles) and maps of India’s PCs from after the 2008 delimitation. This makes it possible to identify what proportion of the area of each block overlaps with each PC. These proportions are then used to create area-weighted PC-level estimates of the variables in the Indian Census from 2001. The resulting dataset has PC-level estimates of the variables included in the Indian 2001 census, including the literacy rate, occupational distribution, and proportion of urban population of each of India’s PCs. These social profiles are then linked to electoral constituency data from the 2009, 2014 and 2019 Lok Sabha elections, using data compiled by the Trivedi Centre (Jensenius and Verniers 2017). Constituency level data of this type provides a valuable way to examine electoral change, since it relies on actual vote tallies rather than survey-based self-reports which are subject to all sorts of measurement error (see Heath and Johns 2010). Nonetheless, it does come with some limitations. Since the analysis is based on aggregate, constituency-level data, we have to be cautious about what if anything we can infer about individual level behaviour. For example, if support for the BJP is lower in constituencies with a large number of Muslims, this could be because Muslims are less likely to vote for the BJP than Hindus, and so the more Muslims there are in a constituency the lower the vote share tends to be for the BJP. Given what we know from survey data this is not an unreasonable assumption to make. However, the same pattern could also be observed if Hindus in Muslim areas are less likely to vote for the BJP than Hindus in predominantly Hindu constituencies. This could be because Hindus who live in close proximity to Muslims are more likely to establish inter-communal ties, which in turn makes these less susceptible to majoritarian impulses. In either case though, the same general finding applies: support for the BJP is concentrated in predominantly Hindu areas. To measure this communal divide I use the percentage of Hindus within a constituency and the percentage of Muslims within a constituency. I also consider the percentage of Scheduled Castes. Although it is not possible to disaggregate further by caste using Census data, there are other measures available that indirectly link to cultural practices associated with different castes and may also be related to support for the BJP. One such measure is the sex ratio within a given constituency. In an important paper, Dyson and Moore (1983) argued that kinships system based on village exogamy led to lower autonomy of women, lower age at marriage, higher fertility, higher childhood female mortality, and higher sex ratios. By contrast, kinship systems based on cross-cousin marriages increased the autonomy of women and contributed to sex ratios that favoured females rather than males. As Chakraborty and Kim (2010) show, sex ratios vary by region, and are highest in the North and lowest in the South, but even within regions they also vary systematically by caste, in a way that is more consistent with cultural factors than economic ones. According to Chakraborty and Kim (2010), even within each region, the higher religious or landowning castes possessed the highest sex ratios, and the lower artisan and menial service castes had the lowest. This is particularly evident among Rajput-like castes with preferential hypergamous marriage practices, which historically had a propensity to practice female infanticide and/or sex selective abortion, and so had very high sex ratios (Vishwanath 1994). Central to these castes sovereignties that underpin this practice is the maintenance of honour through the control of women. These sovereignties have an affinity with Hinduvta gender ideology and patriarchal nationalist discourse. Under Modi, there has been a revival of ‘traditional’ understandings of women, modesty, honour and shame (Sarkar and Butalia 1995; Sen 2018), and this gendered ideology of Hinduvta goes hand in hand with a revival of honour and bias against girl children. To examine the link between these practices and support for the BJP I measure the sex-ratio within a constituency using the percentage of females. I then examine, using state-level fixed effects, whether support for the BJP is stronger in those communities with patriarchal gender cultures, where the sex ratio is worse. In doing so I also control for education and development. The indicator for poverty is the proportion of ‘marginal workers’ within a constituency. Marginal workers are

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defined in the census as those who had worked less than 6 months during the previous year (but had been engaged in some work during the period). These are workers who are typically poor and probably dependent on local land-owners and other local strong-men to get work (Jensenius 2016). To measure education I use the average literacy rate within a constituency. Lastly, I also consider the rural-urban divide. In the past the BJP has tended to perform more strongly in urban areas than rural areas. Yet India remains a predominantly rural country. Any party that seeks to do well nationally, therefore, needs to capture the rural vote. Historically, the BJP has struggled to do this, though in recent years it has targeted poor – often rural voters through the provision of low-cost social services by its organisational affiliates (Thachil 2014). To investigate whether support for the BJP in rural areas has changed I examine the association between support for the BJP and Jensenius’ measure of the size (as a percentage) of the rural population within a constituency, which comes from block-level census data aggregated to the PC level as described above. By examining this wide range of socio-geographic we can get a much more detailed picture of the sort of places where the BJP has prospered over the last ten years. In the first empirical section, I consider how these social divides are related to turnout and in the second empirical section, I examine how they are related to the vote share of the BJP and how this has changed over time.

3. The 2019 LS election: exploring turnout Before considering how these social alignments have changed over time, I first examine the sources of turnout. One big unknown of the 2019 Lok Sabha elections was the extent to which citizens would participate. After the record turnout in 2014, would voters continue to turn out at the polls with the same enthusiasm? And how would turnout affect the electoral performance of the incumbent government? The overall rate of turnout was 67.1 per cent, the highest since Independence and 0.5 percentage points up on the previous record set in 2014. There was also considerable variation across states. In Madhya Pradesh turnout on average increased by 11 percentage points. In Rajasthan turnout increased by an average of 3 percentage points and in Bihar, Chhatisgarh and Karnataka and Andhra Pradesh and Uttar Pradesh turnout increased by an average of around 2 percentage points. On the face of it this high level of turnout would appear to be good news for the BJP. In the 2014 Loks Sabha elections, Chhibber and Ostermann (2014) show that local activists mattered greatly in shaping vote choice and turning out votes for the BJP. Similarly, Heath (2015) shows that increases in turnout at the constituency level correlated with increased vote share for the BJP. Turnout is not only an important barometer for the health of democracy, but also for the health of the major political parties that contest elections. The mobilisation capacity of political parties in India is strongly linked to turnout (Heath and Ziegfeld 2018). Campaigns not only mobilise those they directly contact; they also benefit from ‘secondary mobilisation’ (Cox 1999, 2015), when the voters they contact in turn mobilise members of their own social networks. As Figure 1 shows, since the BJP burst on to the national scene in 1991, breaking the 20 point barrier for vote share for the first time, increases in turnout have tended to go hand in hand with increases in the BJP’s vote share. Figure 1 shows the overall time trend (on the left) and the association between turnout and support for the BJP since 1991 (on the right). There is a very strong and positive relationship between turnout and support for the BJP since 1991. In elections with a high turnout the BJP has tended to do relatively well. One explanation for this pattern between turnout and support for the BJP is to do with the mobilisation capacity of the party, and that when the BJP are able to run an effective ground campaign they succeed not only in getting new voters to the polls but in persuading existing voters to support them. So in what sort of places did turnout increase the most in 2019? And in what sort of places is turnout generally highest? Answers to these questions have been hampered by a lack of high quality data. Survey data routinely vastly overestimates the reported level of turnout, and so is not a very reliable source of information. And up until now we have only had limited information

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Figure 1. Comparing support for the BJP and turnout.

about the social profile of different constituencies. Table 1 reports two OLS regression models of turnout. The first model predicts the overall level of turnout in each constituency. The second model predicts the change in turnout from 2014-2019. Both models include a number of demographic variables related to the social profile of the constituencies based on the census data

Table 1. OLS Regressions – turnout in Lok Sabha constituencies.

% Rural % Scheduled caste % Muslim % Marginal workers % Female % Literate % Children under 6 Cons State FE N R2 Notes: Standard errors in parentheses. *p < .05. **p < .01. ***p < .001.

Model 1 Turnout 2019

Model 2 Turnout change

0.15*** (0.02) −0.11 (0.06) −0.02 (0.03) −0.01 (0.12) −0.71* (0.33) 0.04 (0.05) −0.17 (0.10) 72.51 (15.43) Yes 513 0.656

0.02* (0.01) 0.01 (0.03) −0.03* (0.01) 0.04 (0.06) 0.29 (0.17) −0.01 (0.02) −0.02 (0.05) −15.13 (7.75) Yes 513 0.544

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discussed in the measurement section and state-level fixed effects to account for unmeasured sources of variation between the different states. The first thing to notice from both models is that there is not much evidence that turnout varies very much according to the social profile of different constituencies. Although there is substantial between-state variation, turnout within states is largely unrelated to demographic factors. This is consistent with the observation that turnout to a large extent depends upon the mobilisation capacity of political parties (Heath and Ziegfeld 2018) rather than the sort of civic resources that are commonly thought to be important in Western democracies. Nonetheless, there are some demographic patterns that are worthy of comment. First, from model 1 we can see that people living in rural areas were much more likely to vote at the 2019 LS election than people living in urban areas. Moreover, from Model 2 we can also see that people living in rural areas were more likely to vote at the 2019 LS election than in the previous election. Figure 2 illustrates the magnitude of the effect of the relationship between rural constituencies and turnout in 2019 from Model 1. All other things being equal, turnout is about 60% in predominantly urban places where the rural population represents just 20% of the constituency. By contrast, turnout is about 70% in predominantly rural places where the rural population represents 80% of the constituency. This represents a substantial difference. Given that most constituencies in India are still predominantly rural – and people in rural constituencies are more likely to vote than people in urban constituencies – then any party that seeks to do well needs to capture the rural vote. The next section examines the extent to which the BJP was able to do this.

4. The 2019 LS election: analysis of the results In the Indian context there has been a great deal of research on processes of electoral change that have taken place since Independence and how this relates to the politicisation of different social divides (see for example Yadav 1996; Chhibber 1999; Heath 1999; Chhibber and Kollman 2004; Heath and Yadav 2010). To examine the electoral performance of the BJP and how social alignment have changed over the last ten years, I pay particular attention to the impact religions and caste, urbanisation, poverty, and literacy. Figure 3 shows the relationship between the percentage of Hindus within a constituency and the level of support for the BJP and how this has changed over the last three elections. We can see that the BJP has always tended to do better in Hindu majority constituencies. However, in 2009 this relationship was relatively weak (b = 0.17*). Across the country as a whole, in constituencies where

Figure 2. Predicted turnout and rural constituencies.

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Figure 3. Support for BJP and Hindu population density, 2009–2019.

Hindus comprised 90% of the population the BJP got on average 27% of the vote; but in places where Hindus comprised just 50% of the population the BJP got on average 21% of the vote. These differences are relatively modest. And even if there is some evidence that people in Hindu majority constituencies were more likely to vote BJP in 2009 than people in places with larger minority populations, the overall association between support for the BJP and the size of the Hindu population was pretty weak, indicating that Hindus were divided over which party to support perhaps because they were being mobilised along caste lines by a variety of other parties. However, in 2014 – when the BJP made its big breakthrough – we can see that the association between the size of the Hindu population and support for the BJP is much stronger (b = 0.36*). Across the country as a whole, in constituencies where Hindus comprised 90% of the population the BJP got on average 44% of the vote; but in places where Hindus comprised just 50% of the population the BJP got on average 29% of the vote. These differences are much more pronounced than they were in 2009. And whereas the BJP made relatively modest gains of 8 percentage points in places where Hindus comprised just 50% of the population; they made much stronger gains of 17 percentage points in places where Hindus were the large majority at 90% of the population. This indicates that the BJP may have had some success in uniting the Hindu vote in 2014. A similar picture emerges in 2019. The BJP performed very strongly in predominantly Hindu constituencies, getting on average 49% of the vote in places where Hindus comprised 90% of the population. But they also polled more strongly in places where Hindus were not so numerous, getting on average 38% of the vote in places where Hindus comprised 50% of the population. This indicates that the BJP has some success in consolidating their vote in predominantly Hindu constituencies, while also making some progress in places where religious minorities were in greater number. Figure 4 shows how support for the BJP varies according to the size of the Muslim population within a constituency. As expected there is a very similar pattern. People living in predominantly Muslim constituencies are much less likely to vote for the BJP than people living in places where there are few Muslims. Indeed, in 2019, the average vote share for the BJP in the 10 constituencies with the largest Muslim populations was just 11 percent.

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Figure 4. Support for BJP and Muslim population density.

Part of the reason for this consolidation of the Hindu vote is that the BJP has made great strides in constituencies with large Scheduled Caste populations, where it used to have relatively weak support. Figure 5 shows the relationship between the percentage of Scheduled Castes (SC) within a

Figure 5. Support for BJP and SC population density.

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constituency and the level of support for the BJP and how this has changed over the last three elections. We can clearly see that there has been a major realignment. In 2009 the BJP performed much worse in places with many Scheduled Castes than in places with just a small SC population (b = −0.70*). In 2014, the BJP still performed somewhat worse in places with large SC populations, but the difference was not so pronounced (b = −0.36). However, by 2019, the BJP performed just as strongly – if not better – in places with a large SC population as in places with a small SC population (b = 0.07). This is consistent with the idea that the BJP has consolidated the Hindu vote, and that internal caste divisions are not as politically important as they were previously (particularly in the era of multi-party competition). Lastly, Figure 6 shows the relationship between the percentage of Females within a constituency and the level of support for the BJP and how this has changed. On average, the percentage of females in a constituency is just 48% – though this sex ratio varies quite substantially between different places with a standard deviation of 1.58. We can see that the BJP does much better in places where the sex ratio is particularly bad and females comprise a smaller share of the population. In 2019, for every percentage point increase in the size of the female population the BJP vote declined by an average of just over 4 percentage points. It seems unlikely that this finding can be attributed to the difference in voting behaviour between men and women, since survey data indicates that there is very little difference between their level of support for the BJP.1 Moreover, the magnitude of the effect is far larger than could be explained purely with reference to differences in voting behaviour between men and women. It, therefore, seems plausible that the nature of the relationship relates to the cultural context of the places which have a bad sex ratio. To this end there are a number of different possibilities that require some unpacking. First, it could just be that sex ratios are worse in North India, where the BJP is traditionally strong, and so the relationship is driven by state level differences. Second, it could be that sex ratios are correlated with other demographic characteristics to do with poverty or education or migration and so the relationship is spurious. But it could also be that sex ratios tell us something about the dominant

Figure 6. Support for BJP and sex ratio.

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cultural practices of different places – and that support for the BJP’s Hinduvta gender ideology and patriarchal nationalist discourse is greater in those societies where the sex ratio is worse. Each of these possibilities is investigated in the next section. These bivariate plots provide a useful insight into the sort of places where the BJP performs strongly and the sort of places where it tends to be weaker. There is some evidence that the religious composition of different constituencies is related to the BJP’s level of support – and that the BJP tends to fare better in places which are predominantly Hindu. There is also some evidence that the BJP now fares better in places with sizeable SC populations than it did previously. And finally, there is evidence that the BJP tends to perform more strongly in places where there is a bad sex-ratio. However, we need to treat these bivariate associations with some degree of caution. The BJP’s popularity in places with bad sex-ratios may just reflect its level of support in certain States. And places with larger Muslim populations may also tend to be more urban. To get a clearer idea of the independent impact of these different factors we must therefore control for other potentially important factors by carrying out a multivariate regression analysis. Table 2 reports three OLS regression models of support for the BJP from 2009 to 2019 using the same set of socio-demographic controls and state-level fixed effects. From model 3 we can see when we control for a range of different demographic variables and state level fixed effects, the BJP’s level of support in 2019 was much lower in places with sizeable Muslim communities than it was in predominantly Hindu places with few Muslims. Interestingly, we can see that the impact of the size of the Muslim population in the constituency on the BJP’s level of support has become much stronger over the last three elections. In 2009, the size of the Muslim population did not have a significant impact on the BJP’s level of support, indicating that it had little explanatory power once we take into account other more important variables. In 2014, the impact of Muslim population density is significant and negative, indicating that all other things being equal, the BJP performed worse in places which has relatively large Muslim populations. However, in 2019 we can see that the magnitude of the effect is more than twice the size that it was in 2014 (b = −0.330 vs −0.147) indicating that the religious cleavage has become much more important, and that Indian elections are increasingly polarised along communal lines relative to other social divides.

Table 2. OLS regressions – support for the BJP, 2009–2019.

% Rural % Scheduled caste % Muslim % Marginal workers % Female % Literate % Children under 6 State level FE Constant N Notes: Standard errors in parentheses. *p < .05. **p < .01. ***p < .001.

(1) BJP 2009 −0.086* (0.038) −0.269** (0.098) −0.054 (0.050) −0.012 (0.200) −0.454 (0.555) −0.005 (0.084) 0.100 (0.152) YES 49.306 (25.686) 409

(2) BJP 2014 −0.130*** (0.035) −0.205* (0.087) −0.147*** (0.042) 0.235 (0.171) −1.044* (0.477) 0.116 (0.075) 0.205 (0.135) YES 79.628*** (21.957) 409

(3) BJP 2019 −0.074* (0.034) −0.058 (0.085) −0.330*** (0.043) −0.334* (0.169) −1.017* (0.469) −0.069 (0.072) 0.101 (0.132) YES 109.343*** (21.599) 417

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From the other variables in the model we can see that one other notable change concerns the impact of the SC population on the BJP’s share of the vote. All other things being equal, in 2009 the size of the SC community used to have a significant and negative impact on the BJP’s vote, indicating that the BJP performed worse in places where there were more SCs. In 2014, the size of the SC population was still significant and negative, though it perhaps didn’t matter quite as much as it did in 2009 (b = −0.205 vs −0.269). However, in 2019 we can see that all other things being equal the BJP no longer performs significantly worse in places with sizeable SC communities. Taken together these findings imply that the Hindu vote has become more consolidated behind the BJP and less internally divided. There are a number of other findings that are worthy of comment. In 2019 the BJP performed significantly worse in constituencies with a bad sex ratios. This finding holds even when we control for state level fixed effects, and other developmental indicators such as literacy and population growth (measured by the percent of the population who are children under 6 years old) and urbanisation. We have to be a little careful how we interpret this finding, as there are a number of reasons why sex ratios may vary across different constituencies, to do with cultural practices, development, and migration. However, by controlling for literacy we have a reasonable proxy for development. And by controlling for urbanisation (or percentage Rural) we have a reasonable proxy for migration. This suggests that it may be more to do with the type of caste based cultural practices that are associated with bad sex-ratios. But further research on this topic is needed. Taking everything else into account we can see that the BJP still tends to be more popular in urban areas, but the relationship is not particularly strong, and if anything is somewhat weaker than it was in 2009. Of all the variables considered, it is the communal divide that stands out as the most important. We can get a clearer sense of the evolution of the communal divide by plotting the average marginal effect of the Muslim population density on support for the BJP across the last three election cycles. Figure 7 clearly illustrates how political polarisation between Hindu and Muslim communities has increased over the last 10 years. In 2009 the communal cleavage was not particularly strong once

Figure 7. Predicted support for BJP and Muslim constituencies.

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other socio-geographic divides were taken into account. Part of the reason for the bivariate association between Muslim population density and support for the BJP depicted in Figure 5 is that the BJP tended to be relatively strong in Hindi states with sizeable Muslim populations such as Gujarat, Rajasthan, Bihar and Madhya Pradesh. Yet once these state-level variations in support for the BJP are taken into account, constituencies did not vary much according to the size of their Muslim population. Yet in 2014 we can see that there were sharp differences in support for the BJP by size of the Muslim population within States and in 2019, this communal polarisation further intensified. Since the analysis is based on aggregate, constituency-level data, we have to be a little cautious about what if anything we can infer about individual level behaviour. One possibility is that voters have become increasingly polarised along religions lines. Since Hindus are more likely to vote for the BJP than Muslims, then the more Hindus there are in a constituency the higher the vote share tends to be for the BJP. There is almost certainly something in this. But another possibility that requires further investigation is that the voting behaviour of Hindus also depends upon the size of the local Muslim population. As the size of the Muslim population grows, Hindus may be even more likely to support the BJP as local tensions are stoked, making them more susceptible to majoritarian impulses. This possibility is also plausible, but requires individual level survey data linked to constituency level census data to properly investigate.

5. Conclusion In the space of ten years Indian politics has been dramatically reconfigured. In 2009 the BJP looked like a spent force, slumping to its lowest share of the vote since 1989 and registering a decline in its vote share for the third election in a row. Yet against expectation the 2014 elections marked an historic turnaround. The BJP swept to power, securing an outright majority on its own for the first time, off the back of an unprecedented vote swing, which took its share of the vote from 18.8 to 31.3 percent. This swing was larger than any of previous ‘waves’ that Congress enjoyed – bigger than the Indira wave of 1980 and bigger than both of the post-assassination waves of 1984 and 1991. Off the back of such a strong electoral performance, it was always likely that the BJP would experience some sort of drop in its popularity. Incumbent governments often do, particularly in India where anti-incumbency is common. Yet in 2019 once again the BJP managed to defy expectations, and not only returned to power but did so off the back of an even stronger performance – increasing its vote share and the size of its majority in parliament. The geographic sources of the BJP’s support reveal that it has become increasingly polarised along communal lines. Support for the BJP is also stronger in communities with patriarchal gender cultures, where the sex ratio is worse. Whereas it has long been known at the individual level that Hindus are much more likely than Muslims to support the BJP, up until now we have lacked an appreciation of how this translates into the geography of electoral support. The BJP has both broadened its appeal but in doing so has become more communally distinctive. In the past the BJP was more of a North Indian party than it was a purely Hindu party. But in becoming a national party it now also has a more distinctively Hindu base. As Vasihnav and Hinston (2019) write, the BJP’s brand of Hindu nationalism has allowed it to broaden its demographic base beyond a small sliver of Hindu upper castes and trading communities to include Dalits, OBCs, and Adivasis by using memes such as Ram Mandir, cow protection, and illegal immigration to transcend caste divisions among Hindus. Where this leaves religious minorities is open to question. India’s diversity has long been considered a source of political strength, as multiple cross-cutting cleavages mean that majorities can be formed along multiple lines, meaning that no single group (whether defined in terms of language, region, religion or caste) is permanently excluded from political power. In some sense then, the multi-party politics of the previous two decades provided a quasi-consociational framework in which different social cleavages were represented by different parties which then had to form coalitions to govern at the centre. But a consolidation of the

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Hindu vote threatens this arrangement and raises the prospect of a much more majoritarian form of democracy, where minority rights may be more in peril.

Note 1. https://scroll.in/article/893869/how-india-votes-has-the-bjp-gained-enough-women-voters-under-narendramodi-to-seal-2019.

Acknowledgements I would like to thank Francesca Jensenius for generously sharing data. Earlier versions of this paper were presented at workshops in Bristol and Paris. I’m grateful to the organisers Andrew Wyatt and Christophe Jaffrelot and participants for helpful comments and questions.

Disclosure statement No potential conflict of interest was reported by the author(s).

Notes on contributor Oliver Heath is Professor of Politics at Royal Holloway, University of London and co-director of the Democracy and Elections Centre.

ORCID Oliver Heath

http://orcid.org/0000-0002-1350-3040

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