1This appendix provides additional information on our coding procedures. It thenpresents supplementary model results and visualizations of data from the RPGMD Version 1.
We display our visualizations alongside data from the PGMD of Carey et al. (2013). The twodata sources use different coding methods for counting PGMs, which readers should note as
they consider the comparison. Despite the differences, however, the two data sources are
generally consistent in identifying which states have smaller or larger numbers of PGMs.

Additional Detail on the Coding Procedures
The main text describes the inclusion criteria for PGMs into the RPGMD and states
which variables are coded for the analysis. While the main text discusses theories behind eachvariable, space considerations prevent us from fully discussing their operationalization. In thissection, we therefore discuss how we operationalized the variables in our analysis.

Variables in the dataset:
The RPGMD variables discussed in the main text are divided into four categories: ethnic
relationships, alliance relationships, violence against civilians, and context. Additional variables
are included for reference and ease of data analysis.
As we worked with the ACLED events data, it became clear that the same group was notalways listed in one way. For example, the Patriots Militia in Algeria is included in ACLED as
both “Patriot Militia of Algerian Government” and “Patriots Militia.” This is likely a result ofdifferent news sources referring to groups in different ways, which should not be surprising.
ACLED may use multiple spellings for a single group as well, but we did not find this issue with2our PGMs. Some entries, however, were entered differently. Specifically, Janjaweed has anentry with an extra space that is listed separately from the rest of the events committed by the“Janjaweed.” Therefore, we chose to include “ACLED_multiple_entries,”“ACLED_number_entries,” and “ACLED_listings” in order to be transparent about our coding
decisions. These variables provide an indicator of whether we combined multiple ACLED
entries, the number of ACLED entries we combined, and the specific listings that we used fromACLED, respectively.
Next, we include indicators of whether the PGM is an umbrella group or a subgroup ofan umbrella group. These indicators allowed us to aggregate or disaggregate the umbrellagroups for our analysis. We chose to disaggregate the umbrella groups. Subgroups werespecified when it was clear that they had their own independent agendas. This distinction ismost relevant for the Mayi Mayi in the Democratic Republic of the Congo. The Mayi Mayi havea lot of subgroups, so the choice of whether to aggregate or disaggregate the umbrella group
has the potential to affect results. Supporting the robustness of our results, however, model
specifications with umbrella groups aggregated and disaggregated yielded similar results, asTables A1-A4 in this appendix show.
Several indicator variables also help quickly identify PGMs that appear in the PGMD
only, the RPGMD only, or both. First, “pgm_added” is an indicator of whether we added thePGM. When this is coded as a 1, the PGM is in the RPGMD only. Second, “pre_1997” is anindicator of whether all of the group’s activity occurred before 1997. Since 1997 is the first year
included in the RPGMD, all PGMs coded as a 1 for this variable are only in the PGMD. Third,

3“overlap_period” is an indicator of whether the PGM was active during the overlap period
between the PGMD and the RPGMD of 1997-2007.
To address a concern with ACLED’s standard procedure of coding multi-day events as
multiple events, we also created a list of ACLED violence against civilians (VAC) that extend overmultiple days. This list is available upon request. Critics would suggest that these events areduplicated in ACLED, so we used our list of multi-day VAC events to create the “Dup_VAC” and“VAC_noDup” variables. These variables show which groups are involved in multi-day eventsand provide a measure of VAC where multi-day events are treated as a single event,respectively. Results are robust to using the “VAC_noDup” variable as a dependent variable.

Ethnic relationships:
We then code characteristics of PGM ethnic relationships. This begins with a dummy
variable for ethnic recruiting. This variable is in line with the Carey et al. (2013) dataset. ForPGMs that engage in ethnic recruiting, we code them as one of two types. One type is Defector
PGMs. This variable is coded as 1 when the PGM comes from the same ethnic group as therebels it is fighting and 0 otherwise.1 The second type is Rival PGMs. This variable is coded as 1
when the PGM recruits from a different ethnic group than the one from which rebels are

1 This coding procedure is in line with Stanton’s (2015) approach.

4recruiting and 0 otherwise. All other PGMs are coded as non-ethnic. In our dataset, 83 PGMs
use ethnic recruiting, where 68 PGMs are Rival PGMs and 15 PGMs are Defector PGMs.2
The determinations for these coding decisions were made based on several types ofsources. First, Google searches using varying combinations of PGM names and country names
yielded media sources that provided initial background information about each PGM. This
information was in some cases sufficient to determine whether the PGM had engaged in ethnic
recruiting and then whether it was a Rival PGM or Defector PGM. Media sources are mostly
English-language sources, but when investigating events in Francophone countries weoccasionally used French language sources. When these media sources were insufficient tomake these determinations, we consulted NGO reports and academic publications for further
in-depth information. The most valuable NGO reports tended to come from Human Rights
Watch, Amnesty International, Small Arms Survey, and the Enough Project. Academic
publications from journals such as African Affairs were used in some other cases, such as our
examination of the Civil Defense Forces in Sierra Leone (Hoffman, 2007).

2 These numbers reflect the numbers when we disaggregate PGM umbrella groups into sub-groups (e.g. Mayi Mayi). For more on this see: Stearns J. (2011) Dancing in the Glory ofMonsters, New York: Public Affairs branch of Perseus Books Group, Stearns J. (2012) From
CNDP to M23: The Evolution of an Armed Movement in Eastern Congo. London: Rift Valley
Institute, Stearns J. (2013) Raia Mutomboki: The flawed peace process in the DRC and the birthof an armed franchise. London: Rift Valley Institute.

5Alliance relationships:
Carey et al. (2013) differentiate between informal and semi-official PGMs. Informal
militias are more likely to commit human rights abuses than semi-official PGMs because they
allow governments to use repression for strategic benefits while evading accountability (Careyet al., 2014). We include this variable, Government Ties, coded 1 for semi-official PGMs and 0for informal PGMs. While government acknowledgment of its relationship with a PGM isimportant, it is also necessary to assess the strength of that relationship.
Recognizing the potential for PGMs to be unreliable allies for governments, we add adummy variable coding PGMs as a 1 if they ever fight government forces and 0 otherwise. We
refer to this variable as Ever Fight Government. The coding for this variable is based on theACLED coding of event descriptions and actors involved in violent events. An event must list thePGM and government forces as opposing actors or state that the PGM is fighting government
forces in an event description in order to code this variable as a 1.3
Committed PGMs were identified from our list of PGMs who had ever fought thegovernment. PGMs are coded as committed if: (1) Their violent events in ACLED display distinct
periods of being pro-government and anti-government; and (2) Secondary sources, following
the previously specified procedure of Google searches for media sources and then consulting

3 There are certainly PGMs with the potential to fight the government who do not do so.

However, this measure is valuable precisely because armed groups may exhibit fundamentallydifferent behavior as a result of actually being on both pro-government and anti-government
sides.

6NGO reports and academic articles for further information, reveal that the group had a close
link to a specific leader or ethnic group and that the leader or ethnic group was not willing tosever that link.
Opportunist PGMs were also identified from our list of PGMs who had ever fought thegovernment. PGMs are opportunist if: (1) Their violent events in ACLED display distinct periodsof being pro-government and anti-government; and (2) Secondary sources, following thepreviously specified procedure of Google searches for media sources and then consulting NGO
reports and academic articles for further information, reveal that the group lacked a close link
to a specific leader or ethnic group or that the leader or ethnic group was willing to sever thatlink.
Autonomous PGMs were also identified from our list of PGMs who had ever fought thegovernment. PGMs are autonomous if their violent events in ACLED do not display distinct
periods of being pro-government and anti-government. Instead, they target both progovernment and anti-government groups during the same time period.
PGMs can fall into more than one of these three categories. Out of the 149 PGMs in ourdataset, 59 of them have ever fought the government. There are 11 Opportunist PGMs, 43
Autonomous PGMs, and 12 Committed PGMs. While the Opportunist and Committed categories
contain relatively few PGMs, we contend that they remain substantively relevant. Furthermore,
the large and statistically significant coefficient for the Committed category when Levels of VAC
is the dependent variable—discussed in the main text and shown in Table 4—suggests that
Committed PGMs are a very important PGM category.

7

Measures of violence:
We add a variable that captures the abusive behavior of pro-government militias:
violence against civilians. This variable is constructed from ACLED Version 5. It only includes
events that ACLED designates as violence against civilians. This variable is a count variabledisplaying over-dispersion (Long and Freese, 2014). While it is subject to a variety of reporting
biases and urban bias (Davenport, 2010; Kalyvas, 2004), it does provide a valuable resource forcomparing relative violence levels (Tarrow, 2008; Tilly, 2008). Since our dataset uses PGMs as
the unit of analysis, we divide the total event count by the number of years in which the PGM
was active from 1997-2014.4
To calculate the number of years in which the PGM was active, we count the number ofyears between the year in which the PGM’s first and last activity occurred. The years in which
the first and last activity occurred are included. For instance, if the PGM’s first activity occurredin 1997 and its last activity occurred in 1999, then we code it as being active for three years. If
the first and last activity occurred in the same year, then we code the PGM as being active forone year. If the PGM begins to operate as a rebel group, we code the last activity as the lastactivity while the group fought on the government’s side. Thus, if a group fights on thegovernment’s side from 1997 to 1999, against the government from 2000 to 2003, and then on

4 The cross-sectional nature of the Relational PGM dataset precludes us from analyzing

temporal variation in PGM behavior, but it still allows us to analyze many aspects of the links
between PGM-government and PGM-civilian relationships, context, and behavior.

8

the government’s side again from 2004 to 2005, we code the group as being an active PGM forfive years.

Context:
Finally, we include two contextual variables: regime type and state vulnerability. Regime
type is an average of the country’s polity IV score for the period under analysis. From 19972014, regime type exhibited minimal variation for most African states, so this measurement
does not mask significant temporal variation. State vulnerability is a dichotomous measure ofwhether rebels have controlled territory within the country since the country obtainedindependence. PGM research is interested in the role of state capacity. Yet, we do not feel thattraditional variables used to measure state capacity, such as GDP per capita, tax collection,
infant mortality, and maternal mortality, adequately capture the components of state capacitythat are most relevant for a government’s decision to employ PGMs and a PGMs decision tocarry out human rights abuses (Fearon and Laitin, 2003; Thies, 2004; Thies, 2005). Instead, wechose to focus on the government’s perceived potential to lose control of territory. We
measure this by assessing whether the government has ever lost control of part of its territory
to rebels since gaining independence. Out of the 26 African states using PGMs, 18 states arecoded as vulnerable.

9Supplementary Model Results
Table A1 and Table A3 show the results with PGM sub-groups aggregated into umbrellagroups. Table A2 and Table A4 show the results with PGM umbrella groups disaggregated intosub-groups. Results are consistent across models using aggregated or disaggregated groups.
Model 8 and Model 16 are the same models that are reported in Table 4 of the main text.
Additionally, results are also robust to the exclusion of the Mayi Mayi, a particularly difficult
case to code given confusion over whether several groups that identify as Mayi Mayi are rebel
groups or PGMs.
These models also show that while the coefficients for the variables of whether thePGM ever fights the government are positive and significant across Tables A1-A4,
disaggregating the measure into Committed, Opportunist, and Autonomous PGMs reveals thatAutonomous PGMs are more likely to abuse civilians and abuse civilians at higher magnitudes.
Committed PGMs are no more or less likely to carry out violence against civilians, but when
they do they tend to do so at higher magnitudes. The lack of statistical significance forOpportunist PGMs with both Presence of VAC and Levels of VAC merits further study. Closer
inspection may reveal that Opportunist PGMs display varying behavior from other types ofPGMs on different measures, or it may be necessary to extend the geographical and temporal
scope to include more cases.

10

Table A1. Marginal Effects from Logit Models of Presence of Violence
Against Civilians -- Aggregated Groups
Model 1Model 2Model 3
Estimate SE
Estimate SE
Estimate SE
Vulnerable State-0.28*
0.14 -0.32*
0.14 -0.26*
0.13
Ever Fight the Government
0.28**
0.09 0.29**
0.08
Opportunist
0.05
0.20
Committed
0.10
0.15
Autonomous
0.35**
0.11
Government Ties
-0.02
0.10 -0.02
0.10 -0.02
0.11
Polity-0.03** 0.01 -0.03** 0.01 -0.03** 0.01
Ethnic Recruiting
-0.05
0.09
-0.02
0.09
Defector PGM
-0.16
0.13
Rival PGM
-0.01
0.10
N
108
108
108
+p<0.10, *p<0.05, **p<0.01, ***p<0.001

Model 4
Estimate SE-0.30*
0.13
0.01
0.07
0.39**-0.04-0.03**

0.19
0.15
0.11
0.10
0.01

-0.20
0.05

0.13
0.10
108

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Table A2. Marginal Effects from Logit Models of Presence of Violence
Against Civilians-- Disaggregated Groups
Model 5Model 6
Model 7
Estimate SE
Estimate SE
Estimate SE
Vulnerable State-0.31*
0.14 -0.36*
0.15 -0.29*
0.13Ever Fight the Government
0.28*** 0.07 0.28***
0.07
Opportunist
-0.03
0.15
Committed
0.11
0.15
Autonomous
0.37*** 0.08
Government Ties
-0.06
0.10 -0.06
0.10 -0.01
0.10
Polity-0.03** 0.01 -0.04*** 0.01 -0.03** 0.01
Ethnic Recruiting
-0.01
0.08
0.02
0.08
Defector PGM
-0.17
0.13
Rival PGM
0.02
0.08
N149
149
149
+p<0.10, *p<0.05, **p<0.01, ***p<0.001

Model 8Estimate SE-0.34*
0.14
-0.05
0.09
0.38***-0.02-0.03**

0.14
0.14
0.08
0.10
0.01

-0.18
0.07

0.13
0.08
149

Table A3. Marginal Effects from Hurdle Models of Levels ofViolence Against Civilians -- Aggregated Groups
Model 9Model 10Model 11
Estimate SE
Estimate SE
Estimate SE
Vulnerable State-2.33
2.07 -2.86
2.06 -0.65
2.10Ever Fight the Government
6.67*
2.68 6.67*
2.62
Opportunist
4.41
4.06
Committed
10.27*
5.23
Autonomous
4.69*
2.27
Government Ties
-5.17+
2.75 -5.14+
2.65 -4.85+
2.75
Polity-0.12
0.19 -0.12
0.18 -0.13
0.19
Ethnic Recruiting-3.49+
2.06-3.76+
2.21
Defector PGM-7.02*
3.33
Rival PGM
-1.88
1.86
N108
108
108+p<0.10, *p<0.05, **p<0.01, ***p<0.001

Model 12Estimate SE
-1.45
2.03
3.40
9.41*
5.51*
-4.86+
-0.14

3.76
4.76
2.34
2.62
0.18-7.55*
-1.89

3.47
1.96
108

12

Table A4. Marginal Effects from Hurdle Models of Levels of Violence
Against Civilians-- Disaggregated Groups
Model 13Model 14Model 15
EstimateSEEstimate SE
Estimate SE
Vulnerable State-2.13
1.63 -2.74
1.63 -0.67
1.65
Ever Fight the Government
5.15**
1.73 5.03**
1.65
Opportunist
1.66
2.41
Committed
8.00*
3.65
Autonomous
4.42**
1.59
Government Ties
-4.58*
2.06 -4.42*
1.96 -4.16*
2.07
Polity-0.14
0.14 -0.17
0.13 -0.11
0.14
Ethnic Recruiting-2.46+
1.43
-2.39
1.46
Defector PGM-5.77*
2.45
Rival PGM
-1.53
1.31
N149
149
149+p<0.10, *p<0.05, **p<0.01, ***p<0.001

Model 16
Estimate SE-1.44
1.59
1.15
7.63*
4.57**-3.93*
-0.15

2.25
3.37
1.52
1.92
0.13

-6.23*
-1.29

2.54
1.30
149

Visualizations of RPGMD Version 1 (1997-2014) vs. PGMD (1997-2007)
Since there are many different PGMs included in the PGMD and RPGMD, we also include
choropleth maps here to display the comparison between the two data sources for each year
1997-2007, 2008-2014 for the RPGMD, and country totals for the 1997-2007 overlap time
period.
Broadly, the choropleth maps show that the data sources are pretty consistent inidentifying which states have the most PGMs and the states have the fewest PGMs. The mostnotable differences are the PGMD’s inclusion of more PGMs from Algeria, Libya, and Ghana andthe RPGMD’s inclusion of more PGMs from Somalia and South Africa. Other differences
between the two data sources are minor.

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