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Maulana Singh Yadav?

www.raphael-susewind.de
by

Raphael Susewind

on 18 March 2013

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Transcript of Maulana Singh Yadav?

Maulana Singh Yadav?
SP and the 'Muslim vote' in Uttar Pradesh 2012 Raphael Susewind, Universities of Bielefeld & Oxford
www.raphael-susewind.de Rumors Constituency-level results and Census data Survey data (CSDS / Lokniti) What's in a name? The algorithm behind it... Correlation analysis Reservation promises (Congress, SP)
MSY's low-key visits to 'influential' Ulema
Post-election demands (Khan vs Bukhari etc) Political Science No big difference in SP voteshare between 'Muslim-concentrated' constituencies and others
In survey, SP's share among Muslims declined vs 2007 BAD: Too coarse spatial resolution (taluqa)
BAD: Children, migrants, other non-electors
BAD: Only updated once every decade
BAD: Many Intercorrelations not eff. controllable
BAD: Level of measurement: area, not voter (and thus unclear who makes an effect) GOOD: Level of measurement: voter, not area
GOOD: Some controls for intercorrelations
BAD: No über-individual controls
BAD: Spatial disaggregation impossible But: what if all politics are local?! Boot level results and electoral roll data Rural / Urban comparison Thanks to NIC-UP (SHRISHTI), ECI and CEO UP for raw data,
Faculty and students at AMU for feedback on an earlier draft,
Oxford Supercomputing Centre for 1/4 million CPU hours Matches name, fathername and gender against database of indiachildnames.com
Provides a probabilistic "best bet" for religion Quality indicators for Muslim vs non-Muslim in UP:
sensitivity of 94.9%, specificity of 99.2%
PPV of 98.2% and NPV of 98.6% Natural experiment: link Muslim elector percentage to SP vote percentage between booths within each station

GOOD: Very high spatial resolution
GOOD: Electors rather than general population
GOOD: Updated on yearly basis
GOOD: Implicit controls for very many factors
GOOD: Level of measurement not an area
BAD: Level of measurement still not a voter
BAD: Urban bias (rural more one-booth-stations) 80% of names could be matched to religious community
(this varies from 65 to 100% across constituencies)
25% of stations (covering 51% of Muslim electors) have multiple booths (this varies from 2 to 97% and from 7 to 100% respectively across constituencies) Linear regression model Correlating "difference from station average" of
SP voteshare with that of Muslim elector percentage:

Pearson's r of . Explicit controls for female percentage and age average:
female percentage: coeff of -.80, age average in years: coeff of .02
Muslim percentage: coeff of .44, R² overall of .06 Spatial disaggregation Prelims .25 Correlation Rural: .13 Correlation Urban: .48 R² of lm Urban: .23 R² of lm Rural: .02 Rampur: .61
(Azam Khan) Behat: .49
(Bukhari's son) Implications Rural / urban angle: useful to explain spatial pattern in election dynamics
From identity- to issue-based pattern, esp. in cities? Rather not (at least not in this case)...
Local, local, local! Open threads How to convincingly generalize from stations with multiple booths to all stations, incl appropriate uncertainty margins
Same methods, different demographics? Yadavs...
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