Good Intentions Gone Bad? The Dodd Frank Act and Conflict in Africas - - PowerPoint PPT Presentation

Introduction Policy Implementation Empirical Framework Results Appendix

Good Intentions Gone Bad?

The Dodd Frank Act and Conflict in Africa’s Great Lakes Region Jeffrey R. Bloem

AAEA Annual Meeting - Washington DC

August 7, 2018

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

Minerals and Conflict

◮ ‘Conflict minerals’ find there way into a host of popular

consumer products

◮ Cell phones, laptops, jewelry, eyeglasses, cars, airplanes, and

medical equipment

◮ Revenues from the extraction of these minerals fuel conflict

across Africa

◮ See Berman et al. (2017)

◮ Conflicts are often deadly

◮ Estimates vary between 2 and 6 million people killed due to

violent conflict over the last two decades in the region.

◮ Violent conflict reverses economic development and efforts

to alleviate poverty

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

Section 1502 of the Dodd Frank Act

◮ In 2010, US lawmakers passed legislation with the

intentions of reducing conflict in the DRC and surrounding countries

◮ Regulates reporting on supply chain links of tin, tantalum,

tungsten, and gold (3TG) to armed groups

◮ Any company registered with the US SEC must perform

due diligence — and file a report (“Form SD”)

◮ The legislation was, and remains, controversial

◮ US companies claim compliance costs impose an undue

burden

◮ Other critics claim the policy is build on faulty assumptions

about the causes of conflict

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

Related Literature

◮ Qualitative studies on the effects of the Dodd Frank Act on

livelihoods in the DRC

◮ See Greenen (2012); Cuvelier et al. (2014); Radley and

Vogel (2015); Vogel and Raeymaekers (2016)

◮ Struggle to quantify the causal relationship

◮ Quantitative studies compare outcomes in geographic areas

within the DRC

◮ See Parker et al. (2016); Parker and Vadheim (2017); Stoop

et al. (2018)

◮ Important methodological improvement, but still may suffer

from endogeneity issues

◮ The presence of spillovers between geographical regions — a

potential SUTVA violation

◮ Spillovers are relevant in this context (Maystadt et al. 2014) Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

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Empirical Method

◮ Compare the prevalence of conflict:

◮ Over time (monthly) at the second sub-national

administrative level

◮ Across countries covered by the Dodd Frank Act and other

sub-Saharan African countries

◮ Use a difference-in-differences estimation strategy

◮ Benefits of this approach:

◮ Avoids concerns with spillovers present in within-DRC

analysis

◮ Allows impact estimation on the full list of covered countries ◮ Extends the study period through 2016 Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

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Results Preview

◮ Find evidence of unintended consequences caused by the

Dodd Frank Act

◮ Roughly doubled the probability of conflict within the DRC

compared to other not-covered countries

◮ This result qualitatively persists across disaggregated types

• f conflict

◮ Violence against civilians, rebel group battles, riots and

protests, and deadly conflict

◮ Find little evidence of any systematic change within all

covered countries pooled together

◮ Pooling all covered countries together hides important

heterogeneity

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

(Incomplete) Theory of Change

◮ Theory of change rests on the strength of the link minerals

and conflict

◮ Key assumption: Reducing the revenue earned by armed

groups from minerals will reduce conflict

◮ In theory, this tightens the budget constraint of armed

groups (e.g. Fearon 2004; Collier et al. 2009; Dube and Naidu 2015)

◮ In practice, it is not clear this mechanism dominates

◮ For example, consider the “opportunity cost” mechanism

(e.g. Becker 1963; Collier and Hoeffler 1998; Grossman 1991; Dube and Vargas 2013)

◮ A reduction in mineral extraction decreases incomes and

the opportunity cost of joining a rebel group

◮ This could increase conflict Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

Background

◮ The Dodd Frank Act was officially passed by the US

Congress in July 2010

◮ Direct consequence: In Sept. 2010 the DRC shut down its

entire mineral export industry (re-opened in 2011)

◮ Real effects: In some areas exports of tin dropped by 90

percent (Seay 2012)

◮ In August 2012 the “final rules” of the legislation are

agreed upon by the US SEC

◮ In July 2013 a lawsuit is in place arguing that the

regulation violates US constitutional rights

◮ Companies required to file first “due diligence” reports in

May 2014

◮ In April 2015 US appeals court decides companies must

still file annual reports

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

Background (continued)

◮ In April 2017, the US SEC suspended enforcement of the

legislation

◮ The Financial CHOICE Act of 2017 would have officially

abolished the regulations

◮ Ultimately, dismissed by the US Senate

◮ Many companies still complying with the rules

◮ The law can be enforced again quite quickly ◮ Some companies — such as Apple and Intel — have

publicly stated they intend to follow the rules even if they are abolished

◮ Responding to a “market expectation” for “conflict free”

minerals

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

Data

◮ Armed Conflict Location and Event Data (ACLED)

project

◮ Subset includes data from 39 sub-Saharan African countries

from 2004 through 2016

◮ Construct a monthly panel dataset: 156 time periods and

3,764 administrative regions

◮ Outcome variables:

◮ (A) All conflict ◮ (B) Violence against civilians ◮ (C) Rebel group battles ◮ (D) Riots and protests ◮ (E) Deadly conflict Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

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Conflict Events in Africa

Before July 2010 vs. After July 2010

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Conflict Trends by Type

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Estimation Specification (1)

◮ Linear regression model: yrct = αrc + γt + β · 1{rc = DRC} · 1{t ≥ July 2010} + ǫrct (1)

◮ yrct type of conflict in administrative area r in country c in

month t

◮ αrc and γt are geographic and month fixed effects ◮ β is the coefficient of interest and is the DID estimate of the

effect of the Dodd Frank Act

◮ ǫrct is an error term

◮ Implement a variant of Fisher’s permutation test (Fisher

1935) for robustness check on inference

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

Estimation Specification (2)

◮ Linear regression model: yrct = ηrc+λt+δt·1{rc = DRC}·1{t = 2005, 2006, 2007, ..., 2016}+ξrct (2)

◮ yrct type of conflict in administrative area r in country c in

month t

◮ ηrc and λt are geographic and month fixed effects ◮ δt is a vector of coefficients and is the year-specific DID

estimate of the effect of the Dodd Frank Act

◮ ξrct is an error term

◮ Tests the assumption that conflict would not have evolved

differently in the absence of the Dodd Frank Act

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Policy Implementation Empirical Framework Results Appendix

Effect of the Dodd Frank Act on Conflict

Table: Effect of Dodd Frank on Conflict

Conflict, All Violence Against Rebel Group Riots and Protests Deadly Conflict Types Civilians Battles (1) (2) (3) (4) (5) Panel A: DRC Only 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 432,432 432,432 432,432 432,432 432,432 Basline DRC mean 0.140 0.084 0.082 0.050 0.072 Geographic and time FEs Yes Yes Yes Yes Yes R-squared 0.141 0.098 0.084 0.125 0.074 Panel B: All Covered Countries Effect of Dodd Frank 0.0009 0.008***

• 0.001

0.003

• 0.004*

(0.003) (0.002) (0.001) (0.003) (0.002) Observations 572,676 572,676 572,676 572,676 572,676 Basline Covered mean 0.030 0.015 0.013 0.010 0.015 Geographic and time FEs Yes Yes Yes Yes Yes R-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 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 Policy Implementation Empirical Framework Results Appendix

Placebo Estimates from Permutation Tests

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

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Year-Specific Effects, DRC Only

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

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Robustness Check: Synthetic Control Estimation

All Conflict

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

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Discussion and Conclusion

◮ Find evidence of unintended consequences of the Dodd

Frank Act

◮ Strongest effects within the DRC — increased conflict ◮ No evidence of a systematic reduction of conflict in other

covered countries

◮ Results are qualitatively consistent across all types of

conflict events

◮ Minerals not necessarily the only cause conflict

◮ Additional factors are also important (e.g. poverty,

inequality, weak political institutions)

◮ Repealing the ‘conflict mineral’ legislation unlikely to

reverse trends

◮ Need to support human rights and economic opportunities

in the region

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

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Year-Specific Effects, All Covered Countries

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

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Country-Specific Effects

Table: Country-Specific Effects of Dodd Frank on Conflict

Conflict, All Violence Against Rebel Group Riots and Protests Deadly Conflict Types 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 432,432 432,432 432,432 432,432 432,432 R-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 450,060 450,060 450,060 450,060 450,060 R-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 Policy Implementation Empirical Framework Results Appendix

Country-Specific Effects (continued)

Table: Country-Specific Effects of Dodd Frank on Conflict

Conflict, All Violence Against Rebel Group Riots and Protests Deadly Conflict Types 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 448,812 448,812 448,812 448,812 448,812 R-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 436,020 436,020 436,020 436,020 436,020 R-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 Policy Implementation Empirical Framework Results Appendix

Country-Specific Effects (continued)

Table: Country-Specific Effects of Dodd Frank on Conflict

Conflict, All Violence Against Rebel Group Riots and Protests Deadly Conflict Types 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 432,276 432,276 432,276 432,276 432,276 R-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 429,468 429,468 429,468 429,468 429,468 R-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 Policy Implementation Empirical Framework Results Appendix

Country-Specific Effects (continued)

Table: Country-Specific Effects of Dodd Frank on Conflict

Conflict, All Violence Against Rebel Group Riots and Protests Deadly Conflict Types 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 453,336 453,336 453,336 453,336 453,336 R-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 450,996 450,996 450,996 450,996 450,996 R-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 Policy Implementation Empirical Framework Results Appendix

Country-Specific Effects (continued)

Table: Country-Specific Effects of Dodd Frank on Conflict

Conflict, All Violence Against Rebel Group Riots and Protests Deadly Conflict Types 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 436,332 436,332 436,332 436,332 436,332 R-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
• 0.020

95th 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 Policy Implementation Empirical Framework Results Appendix

Synthetic Control Estimation

Violence Against Civilians

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Synthetic Control Estimation

Rebel Group Battles

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Synthetic Control Estimation

Riots and Protests

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Synthetic Control Estimation

Deadly Conflict

Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?