Theory and Evidence from India’s General Elections
2025-07-30
Distribution of Modi and Rahul Gandhi’s Rallies Across Parliamentary Constituencies in 2019 and 2024
Two-Way FE Estimates of Rally Effects on Turnout Change
OLS Estimates of Rally Effects on Party Vote Swing
Share Voting Based on PM Candidate by Any Rally Presence (NES 2019)
Effect of Rally Presence on Awareness of Party’s Salient Issue (NES 2019)
Hypothesis 1: Mega Rallies by top leadership are less likely to be placed in party strongholds.
Hypothesis 2: Mega Rallies by top leadership are more likely to be placed in closely contested constituencies.
Logistic Regression Estimates of Rally Placement by Party Strength and Candidate Characteristics
Collecting Micro level Data: These findings are contingent on the data currently available, and we believe that a more fine-grained analysis—using smaller units such as assembly segments within parliamentary constituencies, may reveal a clearer and more accurate direct effect of political rallies. We are in the process of identifying exact rally locations to facilitate this.
Spatial Econometrics: We aim to further estimate the spillover effect of the rallies in neighboring segments by incorporating methods such as spatial regression.
Qualitative insights from interviews with relevant individuals.
Effect of Rallies on Turnout
Test:
\[ \Delta Turnout_{ct} = \beta_1 Rally_{ct} + \beta_2 Turnout Change_{ct} + \alpha_c + \lambda_t + \epsilon_{ct} \]
\(\Delta Turnout_{ct}\): Change in voter turnout in constituency \(c\) at time \(t\) compared to last election.
\(Rally_{ct}\): A binary variable = 1 if a top leader held a rally in constituency \(c\) during the campaign period.
\(TurnoutChange_{ct}\): Change in turnout between the two prior elections in constituency \(c\), used to account for pre-existing turnout trends.
\(\alpha_c\) (Constituency fixed effects): Controls for time-invariant characteristics of constituency \(c\) (e.g., demographics, geography).
\(\lambda_t\) (Year fixed effects): Controls for election-year-specific factors that affect turnout across all constituencies.
Test:
\[ \Delta VoteShare^{Party}_{ct} = \beta_1 Rally^{Leader}_{ct} + \beta_2 Stronghold^{Party}_{ct} + \mathbf{X}_{ct}'\boldsymbol{\gamma} + \epsilon_{ct} \]
\(\Delta VoteShare^{Party}_{ct}\): Change in the vote share of the party in constituency \(c\) at time \(t\) compared to the previous election.
\(Rally^{Leader}_{ct}\): A binary indicator equal to 1 if the party’s top leader held a rally in constituency \(c\) during the campaign period.
\(Stronghold^{Party}_{ct}\): A binary indicator equal to 1 if the constituency is a historical stronghold of the party.
\(\mathbf{X}_{ct}'\): A vector of control variables (e.g., turnout change, demographic controls).
\(\boldsymbol{\gamma}\): Coefficients for the control variables.
Test:
\[ \text{Issue Awareness}_{ic} = \beta_1 \, \text{Rally}_{c} + \mathbf{X}_{ct}'\boldsymbol{\gamma} + \epsilon_{ct} \]
\(\text{Issue Awareness}_{ic}\): A binary indicator equal to 1 if the voter was familiar with the flagship concern/narrative of the party.
\(\text{Rally}_{c}\): Indicates whether a high-profile rally by the party leader occurred in constituency (c).
\(\mathbf{X}_{ct}'\): A vector of individual covariates (e.g., interest in elections, rally attendance, and whether a party member visited their house).
\(\boldsymbol{\gamma}\): Coefficients for the control variables.
Test:
\[ \text{Pr}(\text{Rally}_{it} = 1) = \text{logit}^{-1}\Big( \beta_0 + \beta_1 \cdot \text{Stronghold}_{it} + \\ \beta_2 \cdot \text{CloseComp}_{it} + \beta_3 \cdot \text{Reservation}_{it} + \\ \beta_4 \cdot \text{StateGovt}_{it} + \beta_5 \cdot \text{Assets}_{it} + \\ \beta_6 \cdot \text{CriminalCases}_{it} + \varepsilon_{it} \Big) \]
\(\text{CloseComp}_{it}\): A binary indicator equal to 1 if the constiteuncy was a close competetion for the party in the last election
\(\text{Reservation}_{it}\): Indicates whether it was a reserved constituency.
\(\text{StateGovt}_{it}\): Indicates whether the party exercises majority in the state assembly.
\(\text{Assets}_{it}\): Log of total assets of the local candidate
\(\text{CriminalCases}_{it}\) : Log of total criminal cases against the local candidate
Delhi School on Political Analytics, 2024