Trial by water? The impact of the 2013 Amur flood on the outcomes of subnational

elections in Russia

Sokolov Boris (LCSR),Shcherbak Andrey (LCSR),

Ukhvatova Maria (HSE SPb)30.10.2014

Introduction

• Natural disasters as an exogenous shock for government and population – Hurricanes Katrina and Betsy (Gomes and Wilson 2008; Healy and

Malhotra 2009); shark attacks on the East Coast of the USA (Bartels and Achen 2004); floods in Germany (Bechtel and Hainmuller 2011) and Pakistan (Fair et al. 2013); Russian wildfires (Szakonyi 2011; Lazarev et al. 2014)

• How do disasters affect popular attitudes toward various aspects of political system? It is still not clear!

• July – October 2013 – unpresedented flood in Russian Far East

Theoretical framework• Blame attribution theory

– Blaming government for a disaster makes people feel safer by ‘regaining a sense of control’ and believing in chance to avoid of any further disasters

• Theory of retrospective voting – People blindly punish government for suffering losses from disaster– People reward government for relief spending– People decide whether punish or reward government relying on evaluation of

government performance in preventing and managing of a disaster.

• Political support in non-democracies– Whether people’s support for government in authoritarian and semi-authoritarian

regimes follows the same logic as in developed democracies?– Under the conditions of semi-authoritarian rule, elections may be considered as not a

form of political competition but instead as a referendum on support for government– The 2010 Pakistani flood: higher voter turnout (in 2013), higher incumbent support less support for militant groups and niche-oriented parties. Voters in affected electoral districts demanded for higher quality of government services

and accountability; and had higher level of political awareness

Amur flood comparing to previous cases analyzed in literature

• In Khabarovsk, the flood peaked by September 4, just four day before elections!

• This allows for isolating the effect of post-flood governmental aid (which was combined with the effect of disasters in previous research)

• However, additional measures are required to distinguish between the effect of disaster and the effect of government response during the flood (i.e. between psychological factors and rational choice as determinants of vote decision)

The 2013 Amur River Flood• Since the end of July 2013 - an unprecedented

flood in the Far East• The water level - daily growth reached 15 cm• August 8 – the state of emergency declared• BUT: no victims!

The 2013 Amur River Flood

The 2013 Amur River FloodYear Water level

In Khabarovsk , (cm) Date

1872 722 -

1902 630 9.06

1912 390 18.09

1922 390 30.07

1932 620 1.09

1936 500 12.08

1965 480 19.09

1975 301 13.08

1979 221 15.05

1982 371 -

1998 523 4.09

2001 261 17.08

2013 808 4.09

The 2013 Amur River FloodDamage estimate from the 2013 flood in Khabarovsk

Settlements 73

Living houses 3046

Household plots 3494

Agricultural land 45 thousands ha

Roads 66 km

Bridges 4

Evacuated 5844 persons

Including children 1511 persons

The 2013 Amur River Flood

The Khabarovsk Krai governor’s election – September 8, 2013

• The winner of the governor election was the acting governor V.I. Shport – He had not been previously elected, but appointed by the Russian president

V. Putin in 2009

• The opposition was presented by three candidates: Furgal, MP from the LDPR; Postnikov, the head of regional Communist Party branch; and Yashchuk from the ‘Justice Russia’ party.

• On the ballot day, Shport gained 63.92% of popular vote while the tally of the second-best candidate was only 19.14%

• Turnout was 33,88 %

The Khabarovsk Krai governor’s election

Candidate (party) Votes % of votes of

registered voters

% votes from real

voter turnout

Shport (United

Russia)

223 542 21.66 63.92

Furgal (LDPR) 66 920 6.47 19.14

Postnikov (CPRF) 34 020 3.29 9.73

Yashchuk (Justice

Russia)

13 943 1.35 3.99

Hypotheses• H1. Growth of political activism:

– Voter turnout increases in affected areas• H2. Voters’ gratitude:

– Voters do reward authorities in affected areas

On the one hand, the immediate government reaction – the rescue operation and disaster management – can be treated as an example of government effectiveness. The flood caused no victims. One may expect that Khabarovsk Krai residents would appreciate it and would not punish the authorities too much.

• H3. Blind retrospection: – Voters do punish authorities in affected areas

On the other hand, voters had to make their choice before they would have received financial aid, compensations and new rebuilt houses.

Data and Variables• Dependent variables (at the level of polling station, 806 observations):

– Incumbent Vote, share of votes gained by the incumbent, in a given polling station, according to Central Election Commission of Russia.

– Voter turnout , voter turnout, in per cents, in a given polling station• “Treatment”:

– Affected , exposure of a given polling station area to the flood (0 – No exposure, 1 – Yes exposure). Of 806 polling station areas, 157 ones were affected by the flood

– Assignment is based on NASA satellite images; satellite maps were matched with CECR data on the borders of polling station areas by hands.

• Social-economic controls (RFSSS data for 2012; all available only on the municipal level):– Urban/rural , type of settlement, for a given polling station area (1 – urban, 0 – rural)– Log pop_density, logarithm of population density in a given municipality– Transfers, transfers from higher level budgets to the budget of a given municipality, in RUR– Budget deficit, budget deficit in a given municipality, in RUR– Income_pc, income per capita in a given municipality, in RUR– Mobile 3 G, the coverage of mobile 3G and 4G networks in a given polling station area. 3G network is

used as a proxy for accessibility of high-speed internet and control for a spillover effect. • Spillovers:

– Because all polling station are nested within municipalities which, in turn, nested within districts, it is likely that exposure of one polling station may also affect voter decisions in neighborhood stations within the same higher-level administrative units. To control for potential spillover effect we include two additional dummies

– Spillover 1, exposure of a given municipality to the flood (0 – No exposure, 1 – Yes exposure).– Spillover 2, exposure of a given rayon to the flood (0 – No exposure, 1 – Yes exposure).

Controlling for Election Fraud• It is widely believed that Russian elections are very fraudulent.• Whether inferences on manually corrected electoral data reflect true

dynamic of political support?• Inference using Rubin’s Causal Model still valid, if the probability of fraud

is balanced across affected and unaffected areas, that is, if fraud, when occurred, does not affect differences in outcomes between groups. We check this assumption using 2D histograms.

• We also control for possibility of fraud using several relevant indicators revealed in recent studies on the subject:

– Last digit in the number of ballots for winning candidate (incumbent)– Number of ballots for distant voting

• We exclude all observations with 100% turnout (there was no polling stations with 100% voting for incumbent or any other candidate)

0.00

0.25

0.50

0.75

1.00

40 60 80 100Vote for Incumbent, %

Tu

rno

ut,

%

2

4

6

count

Figure 3. 2D Correlation Between Vote for Incumbent and Turnout. Full Sample

0.00

0.25

0.50

0.75

1.00

40 60 80 100Vote for Incumbent, %

Tu

rno

ut,

%

2

4

6

count

Figure 4. 2D Correlation Between Vote for Incumbent and Turnout. Affected Areas

0.25

0.50

0.75

1.00

40 60 80 100Vote for Incumbent, %

Tu

rno

ut,

%

2

4

6

count

Figure 5. 2D Correlation Between Vote for Incumbent and Turnout. Unaffected Areas

0.25

0.50

0.75

1.00

40 60 80 100Vote for Incumbent, %

Tu

rno

ut,

%

2

4

6

count

Figure 6. 2D Correlation Between Vote for Incumbent and Turnout. Small Cities/Rural Areas

60

61

62

63

64

65

66

67

68

69

70

Overall Large Cities Small Cities and Rural Areas

Vo

tes fo

r In

cu

mb

en

t, %

Exposure to Flood

Unaffected Areas

Affected Areas

Figure 1. Incumbent's VoteShare inUnaffected and Affecteded Polling Stations

20

25

30

35

40

45

50

55

60

Overall Large Cities Small Cities and Rural Areas

Tu

rno

ut, %

Exposure to Flood

Unaffected Areas

Affected Areas

Figure 2. Turnout in Unaffected and Affecteded Polling Stations

Estimation strategy

• OLS regression• OLS with cluster-robust standard errors• Propensity score “nearest neighbor” matching

• Checking for the effect of flood within several subsamples a) Large urban areas: Khabarovsk and Komsomolsk-na-Amureb) Small cities and rural areas

OLS ResultsFull sample:• There is a significant effect of flood on incumbent’s voteshare, not on turnout• After adding control variables, the effect for voteshare becomes stronger; the

effect for turnout becomes significant: positive confounding?Subsamples: a) rural• For both dependent variables, effect becomes significant only after adding

controls: positive confounding again?• For both dependent variables, the effect of treatment disappears when controlling

on spillovers (at district-level)• For turnout, the effect of flood disappears when cluster-robust standard errors are

used instead conventional OLSSubsamples: b) Khabarovsk and Komsomolsk• The effect of flood on voting for incumbent is insignificant, and the effect of flood

on turnout is negative and robust for inclusion of ‘fraud’ controls. The negative sign is likely to be result of voter attrition due to evacuation of people

from affected areas.

OLS shortcomings

• Clustered data: standard OLS estimates are likely to be biased• Unbalanced cluster sizes: cluster-robust SE estimator are

likely to provide biased results• Assignment of treatment among units is not random – the

effect is not be considered as causal

Matching models• Model 1: full sample. 145 affected units + 145 control units

• Model 2: Khabarovsk and Komsomolsk excluded. 112 affected units + 112 control units

• Model 3: affected units/affected municipalities vs. unaffected units/unaffected municipalities. 145 affected units + 145 control units

• Covariates: last digit, transfers, budget deficit, number of ballots, number of ballots for distant voting, income per capita, 3G/4G covering

Sample balance before and after Matching

Variable Treatment mean Prematch control mean Postmatch control mean

Distance 0.27 0.169 0.224

Last Digit 5.828 5.561 5.821

Income_PC 13.314 16.899 14.16

Number of Ballotsfor Distant Voting 66.145 25.96 34.655

Number of Ballots 327.124 423.354 321.414

Budget_Def -110123.063 -191401.274 -94710.874

Mobile 1.6 1.49 1.559

Number of Polling Stations 145 627 145

Matching ResultsFull sample:• Effect of flood on incumbent’s voteshare and turnout is significant only at

0.1 level• After adding control variables which have not been included in propensity

score model, the effect of flood on both outcomes disappearsSubsamples: a) rural• Effect of flood on incumbent’s voteshare is significant at conventional

0.05 level (analysis for turnout was not conducted) even after adding controls omitted in PS model, but not in all specifications.

Subsamples: b) affected/affected vs. unaffected/unaffected• Effect of flood on incumbent’s voteshare is significant at conventional

0.05 level • After adding control variables which have not been included in propensity

score model, the effect of flood on both outcomes disappears

Discussion• Weak and contradictory evidence of positive effect of flood on vote for

incumbent in affected areas (especially beyond large cities)– When significant, the effect of flood on voting falls in interval from 1.5 to 3% (depending

of specification), that two times less then the effect of 2002 Elbe Floods in Germany (Hainmueller and Bechtel 2011) and 2010-11 Pakistani floods (Fair et al. 2013).

• Strength and significance of the effect of flood on voting for incumbent turnout depend on method of estimation and model specification

• Flood did not cause decrease either in incumbent’s voteshare or turnout: effective government response in the course of flood?

• Flood did not improve a lot percentage of votes for incumbent and turnout: voters had not received governmental aid before elections.

The absence of effect: true irrelevance of flood, positive confounding, or inappropriate modelling techniques?

Further Steps

• Look at municipal heads’ elections in Khabarovsk Krai and other affected regions.

• Create precise quantitative measure of exposure to flood within affected areas using satellite images and analyze this subsample separately

• Use negative control methods and placebo estimates for detecting source of positive confounding

• Conduct survey in affected areas… in our dreams :((

Thank you for your attention!