Data | Why BJP gave 10% seats to turncoats in Gujarat Assembly elections and will it work?

Hardik Patel (second from right), the face of Patidar quota agitation and former working president of the Gujarat Congress was also considered a turncoat for this analysis, though he never contested in polls before
| Photo Credit: PTI

Ten per cent of candidates fielded by the Bharatiya Janata Party (BJP) in the 2022 Gujarat polls are turncoats. This is the highest such share of BJP candidates in at least the last six elections, including 2022. Between 1998 and 2012, less than 5% of the BJP’s candidates were turncoats; in 2017, the share increased to 9%.

Turncoats are candidates who contested from another party in the previous election and are being fielded by the BJP this time. For this analysis, persons such as Hardik Patel, who did not contest before but were prominent members of other parties and are BJP candidates in 2022, have also been counted as turncoats.

Table 1 shows the percentage of BJP candidates who are/were turncoats, new faces, repeaters and incumbents in 2022, 2017 and 2012. For instance, in 2022, 10% of BJP candidates are turncoats, 37% are new faces, 63% are repeaters, and 41% are incumbents. New faces are those who have not contested polls before. Repeaters are those who have contested polls in the past, either as BJP candidates or otherwise. Incumbents are sitting MLAs and may belong to the BJP or other parties. So, the turncoat list is made up of repeaters (other party candidates from previous polls), incumbents (sitting MLAs from other parties) and exceptions like Mr. Patel, who is a new face from another party. The sum of the four candidate types exceeds 100% because these categories are not mutually exclusive (for example, all incumbents are repeaters but not all repeaters are incumbents).

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As shown in Table 1, the BJP is planning to counter anti-incumbency by reducing the share of incumbent MLAs. However, it has not gone with new faces — their share has also decreased. Instead, the party is banking on turncoats and experienced candidates who are not sitting MLAs (they could have fought and lost in 2017 or were part of elections before that).

This strategy is not new for the BJP. As shown in  Table 2, in 2018, 26% of the BJP’s candidates in Karnataka were turncoats and in 2019, 74% of them in Maharashtra were repeaters. However, in Uttar Pradesh (2022) and Rajasthan (2018), the party banked more on new faces.

But past results suggest that fielding turncoats has not worked for the BJP in Gujarat when compared to fielding new faces. To assess the success of these candidate types, their strike rates were considered. Strike rate is the number of seats won as a share of seats contested. For instance, in  Table 3, 29% of the BJP’s turncoat candidates won in the 2017 elections in Gujarat compared to 52% of new faces. In 2012, too, the strike rate of turncoat candidates was the lowest of all four candidate types.

However, in Karnataka (2018) and Maharashtra (2019) — the two other instances where the BJP fielded 10% or more turncoats — the plan worked, with the strike rate among turncoats ranging between 47% and 62% respectively, as shown in  Table 4. It is this strategy that the BJP is trying to emulate in Gujarat.

There seems to be a method to the BJP’s candidate selection. Of all the seats allocated to turncoats in 2022, 89% were lost by the BJP in 2017. In 53% of these seats, the party’s vote share reduced in 2017 compared to 2012. Also, 21% of these seats were won by the BJP in 2012 but were lost in 2017. So, the BJP seems to have used turncoats to try its luck in the seats where it is weakest.

Similarly, new faces too have been fielded in these weaker seats. On the other hand, repeaters and incumbents have been fielded in seats where the party is relatively strong, as shown in  Table 5.

Source: Election Commission of India, news reports, Trivedi Centre for Political Data

nihalani.j@thehindu.co.in | vignesh.r@thehindu.co.in

Also read: Poll-bound Gujarat ranks among the lowest in socio-environmental measures, regressed in last 15 years



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