Introduction Empirical Framework Results Discussion Appendix

Good Intentions Gone Bad?The Dodd-Frank Act and Conflict in Africa’s Great Lakes Region

Jeffrey R. Bloem

Environmental and Resource Economics SeminarDepartment of Applied Economics

University of Minnesota

November 5, 2018

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

‘Conflict Minerals’ 101

Background

I ‘3TG Minerals’I Tin: Used in circuit boards and as coating to prevent corrosionI Tantalum: Helps store electricity and also used in dental surgery instrumentsI Tungsten: Enables phones to vibrate and used in heavy metal alloysI Gold: Coats electrical wiring and is commonly used in jewelry

I Untapped raw minerals in the DRC worth ≈ US$24 trillion

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

‘Conflict Minerals’ 101

Mine to Table Minerals

I 3TG minerals are abundant in the DRCI The DRC contributes to between 5 and 20 percent of the global supplyI Exported by smelting companies and mixed with minerals from around the world

I These minerals are found in a variety of popular productsI For example: mobile phones, laptops, jewelry, automobiles, and medical

equipmentI Very likely all of us use minerals originally mined in the DRC on a daily basis

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

‘Conflict Minerals’ 101

Minerals and Armed Conflict

I Strong evidence that revenues from the extraction of minerals fuel conflict(Berman et al. 2017)

I Armed groups fund themselves by taxing mineral mines

I These groups enact violent conflict and sexual violenceI Estimates between 2 and 6 million people killed over the past 20 yearsI Stalls and reverses economic development and poverty alleviation (Collier et al.

2003)

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Dodd-Frank Act

Section 1502 of the Dodd-Frank Act

I In 2010, US lawmakers passed legislation with the intentions of reducingconflict in the DRC and surrounding countries

I Regulates reporting on supply chain links of tin, tantalum, tungsten, and gold(3TG) to armed groups

I Any company registered with the US SEC must perform due diligence and file areport (“Form SD”)

I The legislation was—and remains—controversialI US companies claim compliance costs impose an undue burdenI Other critics claim the policy is built on faulty assumptions about the causes of

conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Dodd-Frank Act

Policy CoverageDRC and Surrounding Countries

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Dodd-Frank Act

Policy Implementation

I The Dodd-Frank Act was officially passed by the US Congress in July 2010I Direct consequence: In Sept. 2010 the DRC shut down its entire mineral export

industry (re-opened in 2011)I Real effects: In some areas exports of tin dropped by 90 percent (Seay 2012)

I In August 2012 the “final rules” of the legislation are agreed upon by the USSEC

I In July 2013 a lawsuit is in place arguing that the regulation violates USconstitutional rights

I Companies required to file first “due diligence” reports in May 2014

I In April 2015 US appeals court decides companies must still file annual reports

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Dodd-Frank Act

Policy ImplementationContinued

I In April 2017, the US SEC suspended enforcement of the legislationI The Financial CHOICE Act of 2017 would have officially abolished the

regulationsI Ultimately, dismissed by the US Senate

I Many companies still complying with the rulesI The law can be enforced again quite quicklyI Some companies—such as Apple, Intel, and Tiffany & Co.—have publicly stated

they intend to follow the rules even if they are abolishedI Responding to a “market expectation” for “conflict free” minerals

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Dodd-Frank Act

Theoretical Mechanisms of Conflict and Minerals

Mechanism Description Effect ofDodd-Frankon Conflict

Feasibility Revenue from informal taxation funds rebel groups and conflict ⇓

Greed Formal taxation by central government increases the “prize” of political power m

Weak state capacity Poorly developed state capacity increases vulnerability to coups m

Capital input intensity Mineral extraction is relatively more capital intensive leading to excess labor m

Grievances Frustrations from socio-economic inequality leads to conflict m

Migration Changing demographic composition leads to conflict ⇑

Opportunity cost Increased average income reduces incentive to perpetuate conflict ⇑

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Dodd-Frank Act

(Incomplete) Theory of Change

I Theory of change rests on the strength of the link minerals and conflictI Key assumption: Reducing the revenue earned by armed groups from minerals

will reduce conflictI In theory, this tightens the budget constraint of armed groups (e.g. Fearon 2004;

Collier et al. 2009; Dube and Naidu 2015)

I In practice, it is not clear this mechanism dominatesI For example, consider the “opportunity cost” mechanism (e.g. Becker 1963;

Collier and Hoeffler 1998; Grossman 1991; Dube and Vargas 2013)I A reduction in mineral extraction decreases incomes and the opportunity cost of

joining a rebel groupI This could increase conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Results Preview

I Find evidence of unintended consequences of the conflict minerals legislationin the DRC

I May be more dramatic than previously reportedI Roughly a 100 percent increase in the probability of conflictI Compare to between 30 to 50 percent increase of various types of conflict (Stoop

et al. 2018)

I Find no evidence of any reduction (or increase) in the probability of conflictwithin all covered countries pooled together

I Pooling all countries together estimates the overall effect of the policy, but hidesimportant heterogeneity

I The recent suspension of enforcement by the US SEC has had little effect bothin the DRC and in all covered countries pooled together

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Related Literature

I Qualitative studies on the effects of the Dodd-Frank Act on livelihoods in theDRC

I See Greenen (2012); Cuvelier et al. (2014); Radley and Vogel (2015); Vogel andRaeymaekers (2016)

I Struggle to quantify the causal relationship

I Quantitative studies compare outcomes in geographic areas within the DRCI See Parker et al. (2016); Parker and Vadheim (2017); Stoop et al. (2018)I Important methodological improvement, but still may suffer from endogeneity

issuesI The presence of spillovers between geographical regions—a potential SUTVA

violationI Spillovers are relevant in this context (Maystadt et al. 2014)

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

The Potential Problem of SpilloversPanel A of Figure 1 from Stoop et al. (2018)

I If the Dodd-Frank Act increased conflict in both the “treated” and“non-treated” territories, then these estimates are biased

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Empirical Method

I Compare the prevalence of conflict:I Over time (monthly) at the second sub-national administrative levelI Across countries covered by the Dodd-Frank Act and other sub-Saharan African

countriesI Use a difference-in-differences estimation strategy

I Benefits of this approach:I Avoids concerns with spillovers present in within-DRC analysisI Allows impact estimation on the full list of covered countriesI Extends the study period through 2016

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Data

I Armed Conflict Location and Event Data (ACLED) projectI Subset includes data from 38 sub-Saharan African countries from 2004 through

2016I Construct a monthly panel dataset: 156 time periods and 3,681 administrative

regions

I Outcome variables:I (A) All conflictI (B) Violence against civiliansI (C) Rebel group battlesI (D) Riots and protestsI (E) Deadly conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Conflict Events in Africa

Before July 2010 vs. After July 2010

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Conflict Trends by Type

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Estimation Specification (1)

I Linear regression model:

yrct = αrc + γt + β · 1{c = DRC} · 1{t ≥ July 2010}+ εrct (1)

I yrct type of conflict in administrative area r in country c in month tI αrc and γt are geographic and month fixed effectsI β is the coefficient of interest and is the DID estimate of the effect of the

Dodd-Frank ActI εrct is an error term

I Implement a variant of Fisher’s permutation test (Fisher 1935) for robustnesscheck on inference

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Estimation Specification (2)

I Linear regression model:

yrct = ηrc + λt + δt · 1{c = DRC} · 1{t = 2005, 2006, 2007, ..., 2016}+ ξrct (2)

I yrct type of conflict in administrative area r in country c in month tI ηrc and λt are geographic and month fixed effectsI δt is a vector of coefficients and is the year-specific DID estimate of the effect of

the Dodd-Frank ActI ξrct is an error term

I Tests the assumption that conflict would not have evolved differently in theabsence of the Dodd-Frank Act

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Core Results

Effect of the Dodd-Frank Act on Conflict

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

Panel A: DRC Only

Effect of Dodd-Frank 0.143*** 0.076*** 0.063*** 0.113*** 0.068***(0.007) (0.004) (0.002) (0.005) (0.005)

Observations 433,992 433,992 433,992 433,992 433,992Baseline DRC mean 0.140 0.084 0.082 0.050 0.072Geographic and time FEs Yes Yes Yes Yes YesR-squared 0.141 0.097 0.084 0.125 0.074

Panel B: All Covered Countries

Effect of Dodd-Frank 0.001 0.008 -0.001 0.003 -0.004(0.016) (0.010) (0.007) (0.012) (0.010)

Observations 574,236 574,236 574,236 574,236 574,236Baseline Covered mean 0.030 0.015 0.013 0.010 0.015Geographic and time FEs Yes Yes Yes Yes YesR-squared 0.129 0.087 0.076 0.116 0.067

Notes: The dependent variable is a binary variable indicating the existence of a conflict event at the second sub-nationaladministrative area within a given month. Standard errors clustered at the country level are in parentheses. Bonferroniadjusted p-values are noted as follows *** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Core Results

Placebo Estimates from Permutation TestsEffect of Dodd-Frank on Conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Core Results

Year-Specific Effects, DRC Only

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Robustness Test

Question

I Are other non-covered sub-Saharan African countries a reasonable comparisongroup?

I The factors causing conflict may be very different in these countries—they arenot covered for a reason

I The synthetic control method is similar to diff-in-diff estimation, but relies onan alternative comparison group (Abadie et al. 2010; 2015)

I A convex combination of administrative areas that best match thepre-intervention trend in the outcome

I Robustness of results should address concerns about the appropriateness of thecomparison group

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Robustness Test

Robustness Check: Synthetic Control EstimationAll Conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Secondary Analysis

Enforcement Suspension

I In April 2017 the US SEC suspended enforcement of the conflict mineralslegislation

I Some are hopeful this will lead to positive outcomes in the DRC andsurrounding countries

I Others are less than optimistic, and point out:I The Dodd-Frank Act is still US lawI The conflict minerals legislation can be enforced again quite quicklyI Some companies intend to comply with the regulations even if the law is officially

removed from US law

I Perform similar estimation strategy (as in core analysis), from May 2014through September 2018

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Secondary Analysis

Effect of Enforcement Suspension on Conflict

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

Panel A: DRC Only

Effect of Enforcement Suspension 0.007 0.027*** 0.010*** -0.012 0.014***(0.007) (0.004) (0.003) (0.005) (0.003)

Observations 147,976 147,976 147,976 147,976 147,976Basline DRC mean 0.357 0.179 0.156 0.247 0.184Geographic and time FEs Yes Yes Yes Yes YesR-squared 0.181 0.116 0.135 0.164 0.131

Panel B: All Covered Countries

Effect of Enforcement Suspension -0.002 0.005 -0.006 -0.014 -0.006(0.0111) (0.010) (0.005) (0.008) (0.004)

Observations 195,676 195,676 195,676 195,676 195,676Basline Covered mean 0.092 0.052 0.022 0.051 0.037Geographic and time FEs Yes Yes Yes Yes YesR-squared 0.177 0.129 0.125 0.153 0.122

Notes: The dependent variable is a binary variable indicating the existence of a conflict event at the second sub-nationaladministrative area within a given month. Standard errors clustered at the country level are in parentheses. Bonferroniadjusted p-values are noted as follows *** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Secondary Analysis

Placebo Estimates from Permutation TestsEffect of Enforcement Suspension on Conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Potential Mechanisms

I This study is not able to rigorously test the specific mechanisms leading tothese results

I Likely that the “opportunity cost” and “migration” mechanisms dominate the“feasibility” mechanism

I Or (perhaps less likely) ambiguous mechanisms end up increasing conflict

I Supportive anecdotal evidenceI “When his father could no longer make enough money from the tin mine, when

he could no longer pay for school, Bienfait Kabesha ran off and joined a militia.It offered the promise of loot and food, and soon he was firing an old rifle on thefront lines of Africa’s deadliest conflict. He was 14.” - Raghavan, S. (2014) TheWashington Post

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Conclusion

I Find evidence of unintended consequences of the Dodd-Frank ActI Strongest effects within the DRC — increased conflictI No evidence of a systematic reduction of conflict in other covered countriesI Results are qualitatively consistent across all types of conflict events

I Minerals not necessarily the only cause conflictI Additional factors are also important (e.g. poverty, inequality, weak political

institutions)

I Officially repealing the ‘conflict mineral’ legislation may be unlikely to reversetrends

I Need to support human rights and economic opportunities in the region

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Year-Specific Effects, All Covered Countries

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Country-Specific Effects

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

Panel A: Democratic Republic of Congo

Effect of Dodd-Frank 0.143*** 0.0756*** 0.0627*** 0.113*** 0.068***(0.023) (0.016) (0.016) (0.021) (0.018)

Observations 432,432 432,432 432,432 432,432 432,432R-squared 0.141 0.098 0.084 0.125 0.074

Panel B: Angola

Effect of Dodd-Frank -0.0308*** -0.0108*** -0.00535*** -0.0229*** -0.0141***(0.00307) (0.00143) (0.000759) (0.00247) (0.00110)

Observations 450,060 450,060 450,060 450,060 450,060R-squared 0.115 0.071 0.042 0.111 0.047

Notes: The dependent variable is a binary variable indicating the existence of a conflict event at the 2nd subnational admin-istrative area within a given month. Standard errors clustered by the 2nd subnational administrative area in parentheses*** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Country-Specific Effects (continued)

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

Panel C: Burundi

Effect of Dodd-Frank 0.0339*** 0.0325*** 0.000903 0.0363*** 0.00503(0.00659) (0.00487) (0.00262) (0.00624) (0.00342)

Observations 448,812 448,812 448,812 448,812 448,812R-squared 0.112 0.069 0.040 0.109 0.046

Panel D: Central African Republic

Effect of Dodd-Frank 0.0715*** 0.0601*** 0.0297*** 0.0223** 0.0544***(0.0143) (0.0107) (0.00894) (0.00958) (0.0109)

Observations 436,020 436,020 436,020 436,020 436,020R-squared 0.116 0.074 0.045 0.112 0.051

Notes: The dependent variable is a binary variable indicating the existence of a conflict event at the 2nd subnational admin-istrative area within a given month. Standard errors clustered by the 2nd subnational administrative area in parentheses*** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Country-Specific Effects (continued)

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

Panel E: Republic of Congo

Effect of Dodd-Frank -0.0272*** -0.0112*** -0.00459*** -0.0178*** -0.0133***(0.00504) (0.00165) (0.00121) (0.00585) (0.00132)

Observations 432,276 432,276 432,276 432,276 432,276R-squared 0.115 0.071 0.042 0.112 0.047

Panel F: Rwanda

Effect of Dodd-Frank -0.00351 0.00452 -0.00395** -0.0119** -0.0156***(0.0144) (0.0118) (0.00179) (0.00513) (0.00417)

Observations 429,468 429,468 429,468 429,468 429,468R-squared 0.113 0.071 0.041 0.111 0.047

Notes: The dependent variable is a binary variable indicating the existence of a conflict event at the 2nd subnational admin-istrative area within a given month. Standard errors clustered by the 2nd subnational administrative area in parentheses*** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Country-Specific Effects (continued)

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

Panel G: Tanzania

Effect of Dodd-Frank -0.0216*** -0.00762*** -0.00362*** -0.0182*** -0.0101***(0.00268) (0.00132) (0.000880) (0.00219) (0.00132)

Observations 453,336 453,336 453,336 453,336 453,336R-squared 0.113 0.070 0.041 0.110 0.046

Panel H: Uganda

Effect of Dodd-Frank -0.0353*** -0.0163*** -0.0275*** -0.00668 -0.0342***(0.00722) (0.00350) (0.00478) (0.00406) (0.00456)

Observations 450,996 450,996 450,996 450,996 450,996R-squared 0.114 0.071 0.045 0.114 0.049

Notes: The dependent variable is a binary variable indicating the existence of a conflict event at the 2nd subnational admin-istrative area within a given month. Standard errors clustered by the 2nd subnational administrative area in parentheses*** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Country-Specific Effects (continued)

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

Panel I: Zambia

Effect of Dodd-Frank -0.00539 0.00332 -0.00325** -0.00621 -0.0113***(0.00553) (0.00322) (0.00152) (0.00514) (0.00141)

Observations 436,332 436,332 436,332 436,332 436,332R-squared 0.112 0.070 0.041 0.109 0.047

Placebo tests (other countries)5th percentile -0.042 -0.029 -0.010 -0.029 -0.02095th percentile 0.079 0.026 0.015 0.041 0.051

Geographic and time FEs Yes Yes Yes Yes Yes

Notes: The dependent variable is a binary variable indicating the existence of a conflict event at the 2nd subnational admin-istrative area within a given month. Standard errors clustered by the 2nd subnational administrative area in parentheses*** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Synthetic Control EstimationViolence Against Civilians

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Synthetic Control EstimationRebel Group Battles

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Synthetic Control EstimationRiots and Protests

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Synthetic Control EstimationDeadly Conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Spillover Effects within the DRC

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

DRC Non-mineral provinces only

Effect of Dodd-Frank 0.101*** 0.035*** 0.025*** 0.062*** 0.020***(0.007) (0.004) (0.002) (0.005) (0.00780)

Placebo tests (other countries)5th percentile -0.042 -0.029 -0.010 -0.028 -0.02095th percentile 0.080 0.026 0.015 0.041 0.051p-value (two-tailed) 0.13 0.13 0.13 0.13 0.40

Observations 430,560 430,560 430,560 430,560 430,560Baseline Covered mean 0.036 0.014 0.015 0.012 0.017Geographic and time FEs Yes Yes Yes Yes YesR-squared 0.116 0.071 0.041 0.113 0.047

Notes: “Non-mineral provinces” include Bandundu, Bas-Congo, Equateur, Kasai-Occidental, Kasai-Oriental, and Kinshasa.This definition intentionally excludes North and South Kivu, Maniema, Orientale, and Katanga which are usually associatedwith conflict minerals (Parker and Vadheim 2017). The dependent variable is a binary variable indicating the existence ofa conflict event at the 2nd subnational administrative area within a given month. Standard errors clustered at the countrylevel are in parentheses. Bonferroni adjusted p-values are noted as follows *** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Year-Specific Effects, All Covered CountriesEffect of Enforcement Suspension on Conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Year-Specific Effects, All Covered CountriesEffect of Enforcement Suspension on Conflict

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix

Alternative Dependent Variable Definitions

Conflict, All Violence Against Rebel Group Riots and Protests Deadly ConflictTypes Civilians Battles

(1) (2) (3) (4) (5)

Panel A: DV = 1 if > 5 Conflict Events

Effect of Dodd-Frank 0.039*** 0.017*** 0.009*** 0.013*** 0.019***(0.002) (0.000) (0.000) (0.001) (0.003)

Observations 433,992 433,992 433,992 433,992 433,992Baseline DRC mean 0.030 0.009 0.015 0.001 0.049Geographic and time FEs Yes Yes Yes Yes YesR-squared 0.132 0.117 0.070 0.067 0.059

Panel B: DV = 1 if > 10 Conflict Events

Effect of Dodd-Frank 0.019*** 0.005*** 0.003*** 0.002*** 0.013***(0.001) (0.000) (0.000) (0.001) (0.002)

Observations 433,992 433,992 433,992 433,992 433,992Baseline DRC mean 0.014 0.003 0.007 0.000 0.035Geographic and time FEs Yes Yes Yes Yes YesR-squared 0.085 0.048 0.047 0.040 0.049

Notes: The dependent variable is a binary variable indicating the existence of either more than 5 or ten conflict events atthe second sub-national administrative area within a given month. Standard errors clustered at the country level are inparentheses. Bonferroni adjusted p-values are noted as follows *** p<0.01, ** p<0.05, * p<0.1.

Jeffrey R. Bloem University of Minnesota

Good Intentions Gone Bad?