Political Punishment and Financial Safety Nets: Evidence from Indias - - PowerPoint PPT Presentation

Introduction Literature Empirical Strategy Results Discussion

Political Punishment and Financial Safety Nets: Evidence from India’s Demonetization

Gaurav Khanna Priya Mukherjee

UCSD William & Mary

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Introduction Literature Empirical Strategy Results Discussion

Motivation

Are Politicians Rewarded/Punished for policies?

Are voters rational? Who is blamed/gets credit? Does salience matter?

Unique Context: India’s Demonetization

Sudden announcement: 86% of currency deemed illegal (Negative) economic consequences immediately afterwards Highly salient, and no ambiguity in who implemented policy Narratives exist on how citizens responded and why - but no clear evidence

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Demonetization suddenly announced by PM

November 8, 2016: invalidate 86% of currency in circulation

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Demonetization suddenly announced

PM Modi on November 8, 2016:

Demonetized 500 and 1000 rupee notes. 86% of the total currency in circulation Issuance of new 500 and 2000 rupee notes in exchange Could only draw rupee 4000 (USD 62) per person-day Deposit notes by end of year

Government-stated goal: Curtail shadow economy, fight terrorism, tackle counterfeit Stock Market crash next day. GDP growth projections lowered

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“Demonetisation was a very big mistake...this was a shock to 86 percent of the value of currency in the market...it was no surprise that the economy went into a short recoil” - Kaushik Basu

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Introduction Literature Empirical Strategy Results Discussion Praveen Kumar/HT The man who became the face of demonetisation misery last year questioned the Bharatiya Janata Party-led government’s decision to make Rs 500 and Rs 1,000 notes illegal, the Hindustan Times reported on

• Wednesday. Nand Lal, a retired soldier who lives in Gurugram, was photographed crying after failing to

collect his pension despite standing for three days in a queue outside a bank. Gaurav Khanna, Priya Mukherjee Political Punishment and Financial Safety Nets 10 / 42

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Electoral Consequences?

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Electoral Consequences?

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What We Show

Was BJP punished for ‘poorly implemented’ demonetization?

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What We Show

Was BJP punished for ‘poorly implemented’ demonetization? Those in regions with fewer banks adversely affected

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What We Show

Was BJP punished for ‘poorly implemented’ demonetization? Those in regions with fewer banks adversely affected Leverage policy experiment: (by the opposition Congress party) Congress (opposition party) increased access to banks/cash, encouraged private sector banks to enter RD design: Selected unbanked districts Implementing regime not rewarded (Less salient, slow policy)

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What We Show

Was BJP punished for ‘poorly implemented’ demonetization? Those in regions with fewer banks adversely affected Leverage policy experiment: (by the opposition Congress party) Congress (opposition party) increased access to banks/cash, encouraged private sector banks to enter RD design: Selected unbanked districts Implementing regime not rewarded (Less salient, slow policy) After demonetization: (by the BJP party), less banked regions: Negative economic outcomes (nightlights) (replicate Chodorow-Reich et al) Less support for policy (survey data) Lower BJP vote share (election outcomes) Significant heterogeneity: Little to No Effects in Stronghold Areas

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Economists on Demonetization

Lahiri (2019): Negative economic impacts; not achieve goals Chodorow-Reich et al 2018: contractions in

employment, nightlights-based output, bank credit effects dissipate over time

Banerjee & Kala 2017: surveyed 400 wholesale/retail traders

20% reported a fall in sales > 40%. Sales were 20% lower on average.

Supply side effects, Subramaniam (2019); increase in deposits, Chanda and Cook (2019); perhaps other effects: for example ↑ in bank a/c or card take-up (?)

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Are Politicians Rewarded/Punished by Citizens?

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Are Politicians Rewarded/Punished by Citizens?

Rational Voters

Targeted programs (Manacorda et al 2011, De La O (2013) Switch to Oppositions (Blattman et al 2017)

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Introduction Literature Empirical Strategy Results Discussion

Are Politicians Rewarded/Punished by Citizens?

Rational Voters

Targeted programs (Manacorda et al 2011, De La O (2013) Switch to Oppositions (Blattman et al 2017)

Random Shocks (?)

Shark attacks (Achen and Bartels 2012) Oil shocks (Wolfers 2002) Rainfall shocks (Cole et al 2012)

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Introduction Literature Empirical Strategy Results Discussion

Are Politicians Rewarded/Punished by Citizens?

Rational Voters

Targeted programs (Manacorda et al 2011, De La O (2013) Switch to Oppositions (Blattman et al 2017)

Random Shocks (?)

Shark attacks (Achen and Bartels 2012) Oil shocks (Wolfers 2002) Rainfall shocks (Cole et al 2012)

Who is blamed/gets credit?

Information/salience matters (Ferraz and Finan 2008, 2011) Claiming credit (Guiteras and Mobarak 2014, Dietrich and Winters 2014, Zimmerman 2018)

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Introduction Literature Empirical Strategy Results Discussion

Are Politicians Rewarded/Punished by Citizens?

Rational Voters

Targeted programs (Manacorda et al 2011, De La O (2013) Switch to Oppositions (Blattman et al 2017)

Random Shocks (?)

Shark attacks (Achen and Bartels 2012) Oil shocks (Wolfers 2002) Rainfall shocks (Cole et al 2012)

Who is blamed/gets credit?

Information/salience matters (Ferraz and Finan 2008, 2011) Claiming credit (Guiteras and Mobarak 2014, Dietrich and Winters 2014, Zimmerman 2018)

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Empirical Strategy

Voter Behaviorc = β0 + β1Exposure to Demonetizationc + ǫc Need exogenous variation in exposure to demonetization

Access to banks/cash/ATMs

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Empirical Strategy

Voter Behaviorc = β0 + β1Exposure to Demonetizationc + ǫc Need exogenous variation in exposure to demonetization

Access to banks/cash/ATMs

Leverage the Congress’ 2005 Banking Reform Made bank branch licensing easier in underbanked districts

Underbanked: branches per person < average Regression Discontinuity in growth of banking + can use diff-in-disc.

Young (2018): Improves agriculture, manufacturing, GDP

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Data

Banking Data:

Central bank of India (RBI) 2002-2016 Bank branches, accounts, credit at the branch level Branch addresses (including district)

Election Data:

Votes by candidate name and party: states that had elections after November 2016, through Dec. 2018 Create coalitions variable (based on 2016)

Nightlights: August 2016-May 2017 Mood of the Nation Survey:

May 2017 across 19 states, 146 constituencies Support for Modi, economic conditions, bank access Views on demonetization

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Probability of Receiving Banking Policy

(a) First Stage (b) District Density at RD cutoff

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First Stage

Table: Unbanked Status, and growth in branches at the RD cutoff P(Unbanked Status) ∆Log(New Branches) RD Cutoff 0.971*** 0.968*** 0.960*** 1.757*** (0.0179) (0.0182) (0.0369) (0.553) Bandwidth [-2; 2] [-2; 2] [-1.3 ;1.3] [-.6 ;.6] Specification Linear Quadratic MSE MSE

Optimal Bandwidths (Cattaneo et al 2017)

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Did the Banking Policy Increase Access to Banks?

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(a) New branches (2006-10) (b) New branches MOF data (c) Growth in branches (d) Old Branches 2002-5

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Branches

Panel A ∆Log(Branches) RD Estimate 0.960** 1.275** 0.737** 0.729** (0.425) (0.523) (0.314) (0.316) Bandwidth [-2.3 ;.7] [-1.8 ;.6] [-2; 2] [-2; 2] Mean 1.751 1.755 1.761 1.761 Specification MSE2 CER2 Linear Quadratic Panel B Log(Old Branches) RD Estimate

• 0.141
• 0.189

0.0403 0.0447 (0.225) (0.250) (0.198) (0.198) Bandwidth [-2.4 ;1.3] [-1.9 ;1] [-2; 2] [-2; 2] Mean 0.391 0.396 0.363 0.363 Specification MSE2 CER2 Linear Quadratic

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Accounts and Credit Limit by 2012

(a) Number of Accounts (b) Credit Limit

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Credit

Panel A Total Credit Limit RD Estimate 886.9** 937.3** 1,002* 1,105* (377.4) (432.6) (580.4) (591.2) Bandwidth [-.7 ;1.8] [-.5 ;1.3] [-2; 2] [-2; 2] Mean 658.03 766.321 938.6 938.6 Specification MSE2 CER2 Linear Quadratic Panel B Total Credit Outstanding RD Estimate 523.4** 519.3* 710.1* 761.4* (255.0) (282.7) (381.0) (388.3) Bandwidth [-.8 ;1.5] [-.5 ;1.1] [-2; 2] [-2; 2] Mean 521.537 586.171 650.2 650.2 Specification MSE2 CER2 Linear Quadratic

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Financial Access - CSDS Household Survey

Table: Survey Data: Bank Accounts and Credit Access

Bank/Post Office Acc. Debit/Credit Card RD Estimate 0.0853*** 0.00271 0.212*** 0.304*** (0.0208) (0.0274) (0.0472) (0.0655) Observations 1970 1385 2504 1715 BW Type MSE2 CER2 MSE2 CER2 Mean DV .883 .862 0.553 .545 BW [-1 ;.5] [-.6 ;.3] [-1.6 ;.5] [-1 ;.3] Robust p-value 0.002 0.909 0.000 0.000

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Was the Congress rewarded for banking expansion?

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Electoral Effects of Bank Policy Pre-Demonetization

Prob(Winning) Congress UPA Received Banks

• 0.0880

0.00739

• 0.0372
• 0.0155

(0.0645) (0.0416) (0.0449) (0.0380) BW Type MSE1 MSE2 MSE1 MSE2 Robust p-value 0.230 0.891 0.297 0.559 BW [-.7 ;.7] [-2.6 ;1] [-1.2 ;1.2] [-2.2 ;1.4] Vote Shares Congress UPA Received Banks

• 0.0315

0.0215 0.00726 0.0158 (0.0214) (0.0171) (0.0219) (0.0153) BW Type MSE1 MSE2 MSE1 MSE2 Robust p-value 0.120 0.320 0.993 0.581 BW [-.6 ;.6] [-1.5 ;.6] [-.7 ;.7] [-1.3 ;1]

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No Discontinuity in Pre-Demonetization Vote Shares

(a) Vote Shares in Pre-period (b) Number of votes in pre-period

Why was the Congress party not rewarded? Salience? Attributing credit? Gains manifest over time?

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Economic Impacts of Demonetization

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Economic Impacts of Demonetization

Difference-in-Discontinuities: Lightspt = β(Postt × 1RD) + µt + ψp + ǫ in bandwidth

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Economic Impacts of Demonetization

Difference-in-Discontinuities: Lightspt = β(Postt × 1RD) + µt + ψp + ǫ in bandwidth

Log Nightlights Post*Banks 0.0977*** 0.0895*** (0.0202) (0.0199) Observations 4,591 4,839 R-squared 0.894 0.900 Mean DV .0.537 0.576 BW [-5; 5] [-10; 10]

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Correlates of Support for Demonetization

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Correlates of Support for Demonetization

CSDS Survey: 12K respondents, 146 constituencies in May 2017 40% believed policy was “right move” 16% thought it wasn’t. 32% right move, but done in hurry

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Correlates of Support for Demonetization

CSDS Survey: 12K respondents, 146 constituencies in May 2017 40% believed policy was “right move” 16% thought it wasn’t. 32% right move, but done in hurry

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Views on Demonetization

Right move but Was right move w/ bad prep. RD Estimate 0.0867 0.168**

• 0.123*
• 0.156**

(0.0748) (0.0728) (0.0655) (0.0606) Observations 10,318 10,882 10,318 10,882 R-squared 0.018 0.011 0.015 0.013 BW [-5; 5] [-10; 10] [-5; 5] [-10; 10] Mean DV 0.452 0.458 0.318 0.317 CSDS survey in May 2017: 11,835 respondents across 146 constituencies

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Was the BJP punished for demonetization?

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Elections

1979 constituencies across 21 states held elections post demonetization (2017 and 2018) RD and Difference-in-discontinuities: Votesp,t = β(Postt × 1RD) + µt + ψp + ǫ in bandwidth

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(a) Change in P(Ruling Party Victory) (b) Change in Vote Shares for Ruling Party (c) Changes in Votes for Ruling Coalition (d) Change in Vote Shares for Ruling Coalition

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Election Outcomes: Vote Shares

Regression Discontinuity Vote shares BJP or ally BJP Received Banks 0.113*** 0.103*** 0.101*** 0.0930** (0.0334) (0.0285) (0.0307) (0.0397) BW Type MSE1 MSE2 MSE1 MSE2 Robust p-value 0.003 0.002 0.001 0.010 BW [-.7 ;.7] [-2 ;.5] [-.6 ;.6] [-1.7 ;.4] Difference in Discontinuities Vote shares BJP or ally BJP Post*Banks 0.0973*** 0.0990*** 0.0485** 0.0476** (0.0234) (0.0226) (0.0209) (0.0200) Observations 9,021 9,465 10,633 11,220 R squared 0.662 0.660 0.520 0.515 BW [-5; 5] [-10; 10] [-5; 5] [-10; 10]

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Robustness

Bandwidth Selection Falsification tests (Diff-in-disc. analysis using some other year as demonetization year) Including all constituencies - even where parties did not field candidates (coded as zero)

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What happened in Strongholds?

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What happened in Strongholds?

Not much!

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What happened in Strongholds?

Not much!

Table: Impact on Elections: Heterogeneity NDA Vote Shares Post*Banks 0.171*** 0.176*** 0.193*** 0.205*** (0.0420) (0.0395) (0.0324) (0.0305) Post*Banks*NDA-Stronghold

• 0.0953**
• 0.101**

(0.0418) (0.0400) Post*Banks*BJP-Stronghold

• 0.177***
• 0.198***

(0.0342) (0.0326) Observations 10,289 10,853 10,289 10,853 R-squared 0.519 0.515 0.524 0.521 BW [-5; 5] [-10; 10] [-5; 5] [-10; 10] Mean DV 0.318 0.318 0.318 0.318

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Table: Impact on Elections: Heterogeneity BJP Vote Shares Post*Banks 0.137*** 0.146*** 0.123*** 0.134*** (0.0460) (0.0447) (0.0360) (0.0356) Post*Banks*NDA-Stronghold

• 0.106**
• 0.121***

(0.0436) (0.0422) Post*Banks*BJP-Stronghold

• 0.120***
• 0.139***

(0.0378) (0.0372) Observations 8,705 9,130 8,705 9,130 R-squared 0.667 0.665 0.669 0.667 BW [-5; 5] [-10; 10] [-5; 5] [-10; 10] Mean DV 0.265 0.266 0.265 0.266

Notes: Dependent variable is BJP’s vote share *** p < 0.01, ** p < 0.05, * p < 0.1.

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But it is not the case that stronghold areas were less badly impacted economically...

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But it is not the case that stronghold areas were less badly impacted economically...

Table: Economic Impact of Demonetization: Heterogeneity Log (Nightlights) Post*Banks 0.0791** 0.0405 0.106*** 0.0863*** (0.0401) (0.0418) (0.0301) (0.0312) Post*Banks*NDA-Stronghold 0.0443 0.0994 (0.0590) (0.0604) Post*Banks*BJP-Stronghold

• 0.0104

0.0203 (0.0490) (0.0500) Observations 4,543 4,771 4,543 4,771 R-squared 0.860 0.872 0.860 0.872 BW [-5; 5] [-10; 10] [-5; 5] [-10; 10] Mean DV

• 0.351
• 0.346
• 0.351
• 0.346

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Discussion

Using a unique episode in monetary policy, and an RD, we find Ruling party had growing support in the aggregate, but was punished: In districts with less access to cash / banks Ironically, ruling party rewarded more in districts with more banks - a result of opposition’s policy a decade prior Banking expansion vs demonetization: Salience matters? Effects driven by non-stronghold areas No impacts in BJP/ally stronghold areas (despite same econ. impacts), where voters are more aligned Multi-dimensional policy space + citizens get one vote (Besley and Coate, 2008) Other results: No effects on incumbents, similar (but not significant) effects on NDA in 2019 national elections

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Please email us your thoughts and suggestions. Thank you!

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