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

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 Ph.D. Candidate Department of Applied Economics University of Minnesota March 17, 2019 Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix Minerals and Conflict ◮ ‘Conflict minerals’ find their 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 Empirical Framework Results Discussion 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 Empirical Framework Results Discussion Appendix Policy Implementation (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) ◮ This reduces the “feasibility” to perpetuate conflict ◮ 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 Empirical Framework Results Discussion Appendix Policy Implementation 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 Empirical Framework Results Discussion Appendix Policy Implementation 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 Empirical Framework Results Discussion Appendix Existing Research ◮ 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?

Introduction Empirical Framework Results Discussion Appendix 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 core results through 2016 Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix Research Questions ◮ Did the Dodd-Frank Act Increase or Decrease Conflict? ◮ In the DRC? ◮ In all covered countries (DRC + surrounding countries)? ◮ What are the underlying mechanisms of the effects? ◮ Specifically: “feasibility” vs. “opportunity cost” ◮ What is the effect of the recent enforcement suspension by the US SEC? Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?

Introduction Empirical Framework Results Discussion Appendix Data ◮ Armed Conflict Location and Event Data (ACLED) project ◮ Subset includes data from 39 sub-Saharan African countries from 2004 through 2016 ◮ Enforcement suspension analysis: May 2014 - October 2018 ◮ 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?

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) ◮ Linear regression model: y rct = α rc + γ t + β · 1 { rc = DRC } · 1 { t ≥ July 2010 } + ǫ rct (1) ◮ y rct type of conflict in administrative area r in country c in month t ◮ α rc and γ t 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 ◮ ǫ rct 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 Empirical Framework Results Discussion Appendix Estimation Specification (2) ◮ Linear regression model: y rct = η rc + λ t + δ t · 1 { rc = DRC } · 1 { t = 2005 , 2006 , 2007 , ..., 2016 } + ξ rct (2) ◮ y rct type of conflict in administrative area r in country c in month t ◮ η rc and λ t are geographic and month fixed effects ◮ δ t is a vector of coefficients and is the year-specific DID estimate of the effect of the Dodd-Frank Act ◮ ξ rct 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 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 Conflict Types 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 433,992 433,992 433,992 433,992 433,992 Baseline 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 0.097 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 574,236 574,236 574,236 574,236 574,236 Baseline 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 second sub-national administrative area within a given month. Standard errors clustered at the country level 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 Core Results Placebo Estimates from Permutation Tests Effect of Dodd-Frank on Conflict Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?