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Who are Our Experts? Predictors of Participation in Expert Surveys

Christoph Valentin Steinert EMAIL logo and Andrea Ruggeri


Who are the colleagues participating when asked to complete expert surveys? This research note investigates which individuals’ characteristics associate with positive responses. Drawing on an expert survey dedicated to post-conflict trials, we collect data on various attributes of both respondents and non-respondents such as their age, sex, academic positions, disciplines, and research outputs. We expect that decisions to participate result from an interplay of (1) individuals’ levels of context-specific expertise, (2) the value attached to their expert role, (3) their confidence in making authoritative statements, and (4) resource constraints. Employing logistic regression models and statistical simulations (n = 414), we find that context-specific expertise is the primary, but not the only determinant of participation. On the one hand and luckily, individuals whose research corresponds closely to the object of study are most likely to participate. On the other hand and unfortunately, individuals with high citation outputs, female experts, and Area Studies-scholars are less likely to respond. Consequently, certain groups are under-represented in expert evaluations frequently considered as authoritative source of knowledge.

Corresponding author: Christoph Valentin Steinert, Department of Political Science IV, University of Mannheim, Parkring 47, 1. OG, 68159Mannheim, Germany, E-mail:

Appendices A. Overview of Predictors

Personal characteristics:

Female: Binary indicator of the sex of the contacted expert.

Year of birth: Continuous indicator of the year of birth of the contacted expert. If available, we used information in CVs and on homepages of scholars. Otherwise, we contacted them asking for this information.

Stages of academic careers:

Year of PhD: Continuous indicator capturing the year when an expert completed her/his PhD.

Professor: Binary measure capturing whether the contacted expert holds a full professorship.

Post-PhD: Binary variable indicating whether an expert holds a position as post-Doc, Assistant/ Associate/ Junior Professor. Coded as 0, if an expert holds a full professorship.

Emeritus: Binary indicator recording whether an expert is a retired professor.

Academic output:

Number of publications: Ordinal measure of the number of publications. If an expert has 1–5 publications, coded as 0. If s/he has 6−15 publications, coded as 1. If s/he has more than 15 publications, coded as 2.

Number of citations: Continuous variable indicating the number of citations in academic journals. We used Google Scholar profiles to collect these data. If no Google Scholar profiles were available, we added up the number of citations of authors’ identifiable publications ourselves using references in Google Scholar.

Location of research:

Academic: Binary measure indicating whether an expert works currently at a university. If s/he conducts research at a non-academic institution or s/he has a non-research job, coded as 0.

Western institution: Binary measure indicating whether an expert works at a US or European university or institution.

US institution: Binary measure capturing whether an expert works at a US institution.

Ivy League class university: Binary variable capturing whether an expert is employed at a ’self-declared’ top university. The following universities are deemed as Ivy League class universities: US Ivy League, Oxford, Cambridge, Science Po.

Academic discipline/ Research approach:

Anthropology: Binary indicator capturing whether an expert is primarily trained as anthropologist.

Area Studies: Binary measure signifying whether an expert is primarily trained as a specialist for area studies.

Economics: Binary variable capturing whether an expert is primarily trained as economist.

History: Binary measure indicating whether an expert is primarily trained as historian.

Law: Binary indicator denoting whether an expert is primarily trained as a lawyer.

Political Science: Binary variable indicating whether an expert is primarily trained in the field of political science.

Quantitative scholar: Binary measure capturing whether an expert works primarily with quantitative methods.

Specific expertise for object of research:

Match of publication: Ordinal variable capturing whether the selection publication pertains directly to the post-conflict trial. We hand-coded this variable reading Abstracts and screening full texts of scholars’ publications. Coded as 2, if the selection publication contains the respective post-conflict trial already in its title or abstract. Coded as 1, if the does not address the trial in its title or abstract but pertains directly to the political situation in the country during trial implementation. If the selection publication is only loosely connected to the post-conflict trial, coded as 0.

B. Tables

Table 3:

The post-conflict trials expert survey.

# of itemItemExtreme poles of scale (continuous in integers)
1Were all perpetrators of violence treated in an equal way or were some groups systematically discriminated?0 = unequal treatment 10 = equal treatment
2Were there indications that the government justified repression with reference to the justice process?0 = occurred frequently 10 = never occurred
3Were there incidences of violence related to the justice process such as targeting of judges and witnesses or retribution violence directed at perpetrators?0 = widespread violence 10 = absence of violence
4Did the scope of the process mandate concern only human rights violations perpetrated by certain groups or was violence from all sides (including the current government) considered?0 = extremely narrow focus 10 = complete inclusiveness
5Was the justice process restricted to a singular event or period of time or did it also concern potential backlash violence after the conflict/ genocide under investigation?0 = timewise restricted 10 = timewise unrestricted
6Did the narrative created by the justice process serve the purpose to consolidate the government?0 = distorted narrative 10 = objective narrative
7On a continuum from 0 to 10, whereby 10 indicates post-conflict fairness and 0 indicates post-conflict injustice: How would you evaluate the respective justice process overall?0 = post-conflict injustice 10 = post-conflict justice
Table 4:

Descriptive statistics.

Ivy league class4140.0820.2750112%18%
Political science4140.4570.4990126%10%
Area studies4140.0700.256013%24%
# Of publications4141.7390.5300220% (at 2)10% (at 2)
Match of publication4140.5290.6980225% (at 2)0% (at 2)
Year of publication41420068.37619722017
Log # of citations4045.8921.51509.691
Year of birth222195612.9119261991
Year of PhD220199214.2319522017
Table 5:

Summary results statistical simulations.

Low bound (95% CI)Pr. meanUp bound (95% CI)
Model 1: Completed
 High match (2)0.12140.49070.8720
 Med. Match (1)0.04820.27880.6788
 Low match (0)0.01640.13010.4194
 Log citations (p25)0.01900.14840.4675
 Log citations (p75)0.01370.11130.3690
Model 2: Excused
 High match (2)0.00140.02280.1082
 Med. Match (1)0.00300.03860.1712
 Low match (0)0.00540.06760.2836
 Area studies0.12860.38880.7113
 Other discipline0.00540.06760.2836
Table 6:

Alternative model specifications (regressed on “Completed”).

Female−0.724* (−2.02)−0.759* (−2.15)−0.712* (−1.96)−0.712* (−1.97)−0.717* (−1.98)−0.724* (−2.00)
Western−0.480 (−1.18)−0.662 (−1.71)−0.179 (−0.49)−0.179 (−0.49)−0.192 (−0.53)−0.190 (−0.52)
Quantitative scholar−0.824 (−1.62)−0.934 (−1.87)−0.716 (−1.44)−0.716 (−1.43)−0.713 (−1.44)−0.700 (−1.41)
Match of publication1.080*** (5.05)1.151*** (5.51)1.079*** (5.04)1.079*** (5.04)1.083*** (5.03)1.095*** (5.09)
Year of publication0.0291 (1.48)0.0367 (1.81)0.0286 (1.47)0.0286 (1.47)0.0286 (1.46)0.0255 (1.35)
Political science0.664 (1.18)0.795 (1.40)0.586 (1.04)0.586 (1.04)0.626 (1.11)0.602 (1.08)
Anthropology1.263 (1.92)1.485* (2.29)1.172 (1.80)1.172 (1.81)1.213 (1.86)1.241 (1.90)
Area studies−1.229 (−0.99)−1.089 (−0.88)−1.505 (−1.23)−1.506 (−1.24)−1.489 (−1.25)−1.456 (−1.22)
Economics1.245 (1.29)1.292 (1.33)1.113 (1.14)1.112 (1.15)1.160 (1.20)1.100 (1.14)
History0.454 (0.77)0.515 (0.87)0.412 (0.70)0.412 (0.70)0.455 (0.77)0.495 (0.84)
Law−1.085 (−1.10)−1.095 (−1.12)−1.094 (−1.12)−1.094 (−1.12)−1.047 (−1.07)−1.125 (−1.17)
Emeritus0.488 (1.22)0.391 (0.95)0.480 (1.18)0.479 (1.19)0.464 (1.14)
Ivy league class−0.495 (−0.80)−0.570 (−0.87)−0.404 (−0.65)−0.402 (−0.65)
Academic−0.0246 (−0.05)0.00570 (0.01)0.0102 (0.02)
US0.490 (1.51)0.566 (1.77)
Post-PhD0.119 (0.40)
Log citations−0.174 (−1.90)−0.212* (−2.21)−0.212* (−2.22)−0.217* (−2.27)−0.209* (−2.18)
Professor−0.0867 (−0.27)0.184 (0.55)0.186 (0.57)0.205 (0.63)0.250 (0.78)
Num. of publications−0.116 (−0.39)
  1. t statistics in parentheses.

Table 7:

Rare events logistic regression models.

(1) Completed(2) Excused
Female−0.676* (−1.97)0.178 (0.46)
Professor0.124 (0.39)0.697 (1.46)
Academic−0.0638 (−0.13)−1.032 (−1.90)
Log citations−0.188* (−2.06)0.112 (0.79)
Western−0.448 (−1.14)0.700 (1.08)
US0.429 (1.39)0.0274 (0.07)
Quantitative scholar−0.690 (−1.45)0.327 (0.57)
Match of publication1.022*** (4.95)−0.553* (−2.04)
Emeritus0.441 (1.13)0.279 (0.55)
Ivy league class−0.344 (−0.58)0.646 (1.20)
Year of publication0.0279 (1.50)−0.0004 (−0.02)
Political science0.377 (0.49)1.210 (1.22)
Anthropology0.955 (1.16)0.200 (0.14)
Area studies−1.049 (−0.82)2.066 (1.92)
Economics1.005 (0.94)1.710 (1.33)
History0.153 (0.20)1.008 (0.97)
Law−1.117 (−1.03)1.158 (0.84)
Sociology−0.219 (−0.23)
Constant−56.70 (−1.51)–3.086 (−0.07)
  1. t statistics in parentheses.

  2. *p < 0.05, **p < 0.01, ***p < 0.001.

Table 8:

Multinomial logistic regression.

(1) Excused(2) Completed
Female0.0525 (0.13)−0.729* (−2.01)
Professor0.763 (1.52)0.193 (0.57)
Academic−1.102 (−1.90)−0.151 (−0.28)
Citations_log0.0992 (0.66)−0.189 (−1.94)
Western0.789 (1.16)−0.407 (−0.99)
US0.127 (0.32)0.479 (1.46)
Quantitative scholar0.143 (0.24)−0.781 (−1.55)
Match of publication−0.385 (−1.26)1.039*** (4.74)
Emeritus0.341 (0.63)0.499 (1.22)
Elite university0.595 (1.06)−0.386 (−0.61)
Publication year0.00635 (0.28)0.0319 (1.61)
Political science1.837 (1.78)0.800 (1.44)
Anthropology0.425 (0.29)1.279* (1.98)
Area studies2.504* (2.23)−1.022 (−0.82)
Economics2.446 (1.82)1.490 (1.55)
History1.551 (1.42)0.525 (0.90)
Law1.037 (0.71)−1.013 (−1.04)
Constant−17.20 (−0.38)−65.07 (−1.63)
  1. t statistics in parentheses.

  2. *p < 0.05, **p < 0.01, ***p < 0.001.

C. Figure

Figure 3: Separation plot (based on Model 1).
Figure 3:

Separation plot (based on Model 1).


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Received: 2020-02-17
Accepted: 2020-07-28
Published Online: 2020-08-24

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