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 Seminar Department of Applied Economics University of Minnesota November 5, 2018 Jeffrey R. Bloem University of Minnesota Good Intentions Gone Bad?
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Good Intentions Gone Bad? · 2018. 11. 5. · Good Intentions Gone Bad? Introduction Empirical Framework Results DiscussionAppendix Dodd-Frank Act Policy Implementation Continued
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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
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
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.
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
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
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
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
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)
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
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
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
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.
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
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.
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
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
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.
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.
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.
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.
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.
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.
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.