Politicians, Criminality and Mining Booms in India Sam Asher and - PowerPoint PPT Presentation

Politicians, Criminality and Mining Booms in India Sam Asher and Paul Novosad March 24, 2015 Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 1 / 20

Overview Question: Does mineral wealth lead to bad political outcomes? Context: India, 1980-present Empirical strategy: Instrument local mineral wealth with geological deposits, local production, and global prices Outcomes: Election results, politician criminality Results Mining booms lead to criminal politicians Electoral competition falls: Bigger win margins Incumbency advantages increase Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 2 / 20

Motivation Could resource wealth be bad for growth? Economic Factors Dutch Disease Volatility Political factors Rent-seeking Conflict Why is resource extraction special? Spatial concentration → ownership concentration High fixed cost, low variable cost: rents Highly regulated Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 3 / 20

Our approach Focus on testing one mechanism of the political resource curse Resource studies often measure the sum of multiple positive and negative effects Results do not rule out mechanisms in other direction Different mechanisms imply different solutions Outcomes Politician behavior Voter behavior Context Democracy Institutionally weak: significant corruption Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 4 / 20

Mechanisms for a political resource curse Resource wealth shocks increase potential rents Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 5 / 20

Mechanisms for a political resource curse Resource wealth shocks increase potential rents Adverse Selection Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 6 / 20

Mechanisms for a political resource curse Resource wealth shocks increase potential rents Adverse Selection Election Success Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 7 / 20

Mechanisms for a political resource curse Resource wealth shocks increase potential rents Adverse Selection Election Success Moral Hazard Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 8 / 20

Mining in India Half of districts have at least one large mineral deposit 2.5% of GDP Mix of private / public Taxes and royalties to state and federal government only No local profit-sharing Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 9 / 20

Empirical strategy Challenge: mineral wealth is largely static Compensating differentials suggest mineral-rich places will lack other natural advantages Our approach: Sample: places with production at baseline Define point source of wealth with geological deposits Predict change in value over time using district production and global price changes Natural experiment: Value of subsurface minerals increases in treatment regions, remains unchanged in control Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 10 / 20

Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 11 / 20

Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 12 / 20

Cross section: Mining places vs non-mining places Criminal Serious Criminal Criminal Count Assets Graduate Deposit -0.004 0.007 -0.018 -0.152 -0.003 (0.009) (0.006) (0.036) (0.038)*** (0.014) Log population -0.001 -0.000 -0.008 -0.070 -0.002 (0.005) (0.003) (0.023) (0.029)** (0.006) Rural pop share 0.003 -0.002 -0.006 0.016 -0.001 (0.010) (0.006) (0.049) (0.044) (0.011) Employment share 0.003 -0.065 0.480 1.452 0.046 (0.046) (0.034)* (0.540) (0.489)*** (0.147) Firm size -0.005 0.001 0.014 0.059 -0.012 (0.005) (0.003) (0.040) (0.053) (0.009) Rural electrification -0.003 0.001 0.007 0.404 0.099 (0.023) (0.021) (0.076) (0.110)*** (0.037)** Primary schools per capita -8.284 -2.750 -43.325 -81.313 -17.385 (6.120) (3.800) (26.072) (40.783)* (6.398)** Government employment share -0.067 -0.021 -0.125 -0.578 0.164 (0.034)* (0.029) (0.169) (0.219)** (0.073)** N 4983 4983 4983 4980 4943 r2 0.21 0.13 0.12 0.34 0.14 ∗ p < 0 . 10 , ∗∗ p < 0 . 05 , ∗∗∗ p < 0 . 01 Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 13 / 20

Dependent variable: MLA has at least one criminal case 1 2 3 4 5 6 Price shock 0.094 0.099 0.101 (0.037)** (0.036)** (0.035)*** Price shock (large deposits) 0.079 0.085 0.085 (0.029)** (0.028)*** (0.028)*** Deposit count -0.001 -0.001 -0.000 (0.002) (0.002) (0.002) Large deposit count -0.002 -0.001 -0.000 (0.002) (0.002) (0.002) Log population -0.021 -0.023 -0.032 -0.030 (0.019) (0.019) (0.021) (0.021) Rural pop share -0.003 -0.002 0.030 0.029 (0.033) (0.032) (0.038) (0.038) Employment share 0.195 0.109 0.250 0.159 (0.272) (0.276) (0.349) (0.382) Firm size -0.029 -0.029 -0.034 -0.033 (0.019) (0.023) (0.023) (0.028) Rural electrification -0.027 -0.002 (0.098) (0.134) Primary schools per capita -50.581 -52.274 (21.805)** (26.166)* Government employment share -0.119 -0.119 (0.187) (0.217) N 1812 1755 1755 1453 1408 1408 r2 0.12 0.12 0.12 0.11 0.11 0.12 ∗ p < 0 . 10 , ∗∗ p < 0 . 05 , ∗∗∗ p < 0 . 01 Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 14 / 20

Dependent var: share of candidates facing criminal cases 1 2 3 4 5 6 Price shock 0.026 0.026 0.028 (0.021) (0.021) (0.020) Price shock (large deposits) 0.023 0.025 0.025 (0.019) (0.019) (0.019) Deposit count -0.000 -0.000 -0.000 (0.001) (0.001) (0.001) Large deposit count -0.001 -0.000 -0.000 (0.001) (0.001) (0.001) Log population -0.015 -0.017 -0.018 -0.017 (0.009)* (0.009)* (0.010)* (0.010)* Rural pop share 0.003 0.004 0.014 0.014 (0.018) (0.017) (0.020) (0.019) Employment share 0.087 0.044 0.121 0.056 (0.106) (0.118) (0.144) (0.166) Firm size -0.007 -0.007 -0.008 -0.006 (0.008) (0.008) (0.008) (0.010) Rural electrification -0.035 -0.026 (0.052) (0.064) Primary schools per capita -24.293 -25.889 (12.400)* (16.305) Government employment share -0.064 -0.099 (0.073) (0.084) N 1812 1755 1755 1453 1408 1408 r2 0.24 0.24 0.24 0.25 0.24 0.25 ∗ p < 0 . 10 , ∗∗ p < 0 . 05 , ∗∗∗ p < 0 . 01 Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 15 / 20

Mineral price shocks and election outcomes Candidates ENOP Margin Turnout Price shock -0.019 0.006 0.017 0.006 (0.304) (0.029) (0.005)*** (0.006) Deposit count -0.003 0.006 -0.000 -0.000 (0.009) (0.002)** (0.000) (0.000) Log population -0.298 0.030 -0.002 0.004 (0.177) (0.028) (0.004) (0.005) Rural pop share -0.462 0.042 0.005 0.014 (0.248)* (0.044) (0.005) (0.005)** Employment share 2.300 -0.468 -0.095 0.085 (1.944) (0.363) (0.048)* (0.058) Firm size 0.835 0.023 0.007 -0.026 (0.313)** (0.031) (0.004)* (0.007)*** Rural electrification 2.424 -0.121 -0.014 0.059 (0.886)** (0.104) (0.014) (0.019)*** Primary schools per capita -343.153 22.384 9.847 3.021 (235.055) (23.307) (3.232)*** (7.943) Government employment share -5.490 -0.752 0.029 -0.184 (1.869)*** (0.329)** (0.038) (0.058)*** N 7421 8315 8326 7421 r2 0.51 0.40 0.20 0.66 ∗ p < 0 . 10 , ∗∗ p < 0 . 05 , ∗∗∗ p < 0 . 01 Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 16 / 20

Mineral price shocks and incumbent performance Local Inc Margin Local Inc Win State Inc Margin State Inc Win Price shock 0.034 0.084 0.026 0.042 (0.013)** (0.041)* (0.012)** (0.026) Deposit count -0.001 -0.002 -0.000 -0.001 (0.000) (0.001) (0.000) (0.001) Log population -0.013 -0.030 -0.000 -0.008 (0.010) (0.019) (0.006) (0.011) Rural pop share -0.003 0.004 0.004 0.004 (0.008) (0.029) (0.007) (0.018) Employment share -0.152 -0.185 -0.010 -0.005 (0.102) (0.260) (0.050) (0.127) Firm size 0.013 0.018 -0.002 -0.009 (0.007)* (0.016) (0.008) (0.014) Rural electrification 0.018 0.072 0.006 -0.032 (0.034) (0.060) (0.027) (0.052) Primary schools per capita 17.134 32.981 -1.980 -1.566 (9.275)* (22.589) (6.633) (16.299) Government employment share -0.011 0.096 0.021 0.040 (0.071) (0.152) (0.049) (0.100) N 4312 4312 4635 4635 r2 0.21 0.15 0.38 0.26 ∗ p < 0 . 10 , ∗∗ p < 0 . 05 , ∗∗∗ p < 0 . 01 Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 17 / 20

Discussion Main results More criminal MLA after a mining boom Incumbents are more entrenched and elections less competitive Voter turnout unaffected Mechanisms Selection into candidate pool: doesn’t explain full effect Moral Hazard: Timing of criminal cases is wrong Non-incumbents haven’t been exposed to mining rents yet Can test with candidate time series Criminals win more elections during mining booms Candidate effort Voter indifference Voter preference Sam Asher and Paul Novosad Politicians, Criminality and Mining Booms in India 18 / 20