# Attitudes and behaviors in a fragile state. A list experiment in Mali

Olivia Bertelli, Thomas Calvo, Emmanuelle Lavallée, Marion Mercier, Sandrine Mesplé-Somps

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Olivia Bertelli, Thomas Calvo, Emmanuelle Lavallée, Marion Mercier, Sandrine Mesplé-Somps. Attitudes and behaviors in a fragile state. A list experiment in Mali. World Development, 2025, 196, pp.107151. 10.1016/j.worlddev.2025.107151. hal-05418637

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Regular Research Article

# Attitudes and behaviors in a fragile state. A list experiment in Mali*

Olivia Bertelli<sup>ID</sup>, Thomas Calvo<sup>ID</sup>, Emmanuelle Lavallée<sup>ID</sup>, Marion Mercier<sup>ID*</sup>,
Sandrine Mesplé-Somps<sup>ID</sup>

DIAL-LEDa, UMR CNRS 8007 & IRD 260, Université Paris-Dauphine, PSL Research University, France

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Dataset link: https://doi.org/10.23708/RBYQR
O

JEL classification:
C83
D74
O55

Keywords:
Autocratic regimes
List experiment
Response bias
Conflict
Mali

## ABSTRACT

Understanding individual behaviors and attitudes towards political regimes is key to explaining their persistence. However, scantily available micro-level indicators are likely to be biased, especially in non-democratic settings where people might refrain from answering sensitive questions truthfully. We study behaviors and attitudes in Mali, at a time of conflict and military rule, by running an experimental survey on a nationwide sample of individuals. We elicit the prevalence rates of two attitudes towards the military situation in Mali and three behaviors associated with the ongoing conflict (firearms possession, willingness to engage in violence and victimization). Results indicate that support for the military regime is overreported and trust in the foreign armed forces deployed in the country is underreported when elicited by means of direct questions as opposed to a list experiment. Behaviors are not significantly misreported on average, but they are misreported by certain population segments particularly vulnerable to violence. This impacts directly on the estimation of correlations between respondent characteristics and sensitive outcomes when measured by direct questions.

## Introduction

The survival of political regimes and the likelihood of political violence depend on whether agents are willing and able to engage in collective action. Leaders are therefore urged to elevate their popularity and/or prevent coordination among agents (Bueno De Mesquita, 2010; Bueno De Mesquita & Smith, 2010; Guriev & Treisman, 2020b). That makes information crucial for people to form their own opinions about the regime and assess their fellow citizens' views, and for the regime to attract and evaluate popular support. This is particularly the case in non-democratic regimes, with their high information asymmetries and political uncertainty, alongside the omnipresent risk of different types of violence, e.g. state repression, coups and popular uprisings

(Gehlbach et al., 2016). Although information about others' actions and perceptions is of critical importance in autocracies, both citizens and leaders are reluctant to provide truthful information which could make them vulnerable to state repression, local violence or popular uprisings.

Empirical evidence on citizens' behaviors under authoritarian rule and support for non-democratic regimes is particularly scant, despite their being key factors in the persistence of autocracies – a prevalent regime type in around half of the world's countries (Garfias & Magaloni, 2018; Guriev & Treisman, 2020a). Not surprisingly, studying political attitudes in autocratic regimes is challenging. Conducting surveys in these settings can pose serious safety concerns and, even where surveys can be conducted, answers are likely to be affected by response bias (Blair et al., 2020). The same could also be said of democratic

<footnote>* This work was made possible thanks to the funding of the French National Research Institute for Sustainable Development (IRD), Project GDRI-MOV (Groupement de recherche international - Sud "Mesurer et Observer les Violences, Violences de genre et de conflits dans les pays en voie de développement"). We would like to express our gratitude to Prof. Massa Coulibaly and Moussa Coulibaly of the GREAT team for the survey management in Mali, and to Ousmane Z. Traoré for the coding of the questionnaire. We sincerely thank all the enumerators and the men and women who accepted to answer our survey. Many thanks to Olivier Bargain, Tanguy Bernard, Graeme Blair, Anne Boring, Delphine Boutin, Margherita Comola, José de Sousa, Kai Gehring, Marine Jouvin, Florence Kondylis, Camille Laville, Pierre Mandon and Carole Treibich for their helpful comments, as well as to the participants of the 2022 ICDE, HiCN and ASWEDE conferences, Tours Workshop on Conflicts, Crimes and Terror, PSE workshop on the Economics of Conflict zones, and seminars at DIAL, ENS Lyon, Erasmus University of Rotterdam, Bordeaux School of Economics and Paris-Saclay for the insightful discussions. We would also like to thank the Paris School of Economics internal review board for approving our experimental protocol. The Pre- analysis plan is registered under: Bertelli, Olivia et al., (2021) "Violence-related experiences and opinions in Mali: a list-experiment based analysis". AEA RCT Registry. The usual disclaimer applies.</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: olivia.bertelli@dauphine.psl.eu (O. Bertelli), thomas.calvo@ird.fr (T. Calvo), emmanuelle.lavallée@dauphine.psl.eu (E. Lavallée), marion.mercier@cnrs.fr (M. Mercier), sandrine.mesple-somps@ird.fr (S. Mesplé-Somps).</footnote>
regimes suffering from democratic backsliding (Hyde, 2020) and weak institutions (EIU, 2025).

This article provides the first empirical evidence on support for a military regime in a conflict-affected setting while accounting for response bias. We study the prevalence of five indicators of political attitudes, behaviors and experience in Mali using original data collected in July 2021 from a nationwide sample of men and women. Our survey was conducted a year after a military coup which overturned the previous democratically elected government, and weeks after a second “coup within the coup”. At that time foreign armed forces had been stationed in the country for eight years fighting active rebel groups on the ground.

We investigate respondents' recent behaviors and experience under the military regime – whether they own a firearm, whether they are willing to engage in armed conflict, and whether they have been a victim of physical assault – and their political attitudes – whether they consider Mali should be ruled by the army and whether they trust the foreign armed forces stationed in the country. To limit response bias, we elicited answers using a list experiment (LE) in addition to standard direct questions (DQ).

In a context of widespread insecurity, people might refrain from answering truthfully. Respondents might fear repression, punishment or social stigmatization if they were to voice a negative assessment of the situation, criticize the regime, express their trust in other actors, or reveal their own involvement in violence. The list experiment technique makes for a higher level of confidentiality, since respondents do not openly disclose their answer, thereby limiting the risk of social desirability and the fear of a lack of confidentiality which often affects answers to direct questions (Tourangeau & Yan, 2007). Any significant difference between the answers obtained by the two survey techniques thus arguably provides evidence of response bias, due to respondents lying or refusing to answer standard direct questions.

We identify three main findings. Firstly, we observe substantial average biases in the reporting of political attitudes. Respondents over-report their support for the military regime by 10 percentage points, while they underreport their trust in the foreign armed forces by 11 percentage points. In particular, we find that 63.2% of respondents support the military regime, which is about 14.5% lower than the DQ-prevalence (74%), whereas the list experiment finds that 36% of respondents trust the foreign armed forces, which is almost 50% higher than the DQ-prevalence (24.7%). The opposite direction of misreporting of these two political attitudes is consistent with the military regime's strong opposition to the international coalition led by France in Mali at the time of our survey. Interestingly enough, even net of response bias, our results find a high level of support for the military regime (63.2%). This is in line with studies conducted in other non-democratic regimes, such as China and Russia, where popular support for the government is found by both the list experiment technique (Frye et al., 2017; Li et al., 2017) and an Implicit Association Test (Huang et al., 2022).

A second set of results explores potential heterogeneity in misreporting across respondent characteristics (gender, education level, and exposure to conflict-related violence). As far as behaviors and experience are concerned, we find suggestive evidence that response biases could differ depending on the extent of vulnerability of respondents. In particular, respondents living in conflict-torn localities underreport their firearms possession and their willingness to take up arms, while those in conflict-free localities overreport these two items. In contrast, support for the military regime and trust in the foreign armed forces are generally misreported by all respondents, irrespective of their characteristics, although in varying proportions.

Our final set of results explores the repercussions of response biases in a standard regression setting. We show that asymmetric response biases across sub-groups can give rise to fake (non)significant correlations between respondent characteristics and DQ-measured outcomes, even when average response bias is null. For instance, respondents

in conflict-free and conflict-torn localities do not appear to differ in terms of their willingness to take up arms when asked directly, but a significant difference appears between them when the same outcome is measured using the LE technique. We find similar inconsistencies with respect to respondent education and gender. These results caution against measurement error in the outcome variable leading to biased estimates, even when the outcome is free on average from response bias.

This paper contributes to three strands of the existing literature. Firstly, we add to the literature investigating support for non-democratic regimes (see among others, Dimitrov, 2009; Guriev & Treisman, 2020a). Whereas the literature explores mainly the reasons for popular support for autocracies from a theoretical or descriptive perspective – to a large extent in post-communist countries – our analysis provides new quantitative evidence regarding the level of popular support for a military regime in West Africa. Importantly, our study raises concerns about the use of declarative survey data, generally used by the literature.

Our results tie in with studies that, like ours, assess unbiased rates of support for non-democratic regimes using experimental surveys. For instance, Frye et al. (2017) find no robust evidence of response bias in support for Vladimir Putin while Li et al. (2017) and Nicholson and Huang (2023) both document overreported trust or support for the Chinese central government. Overall and highly consistent with our results, the Blair et al. (2020) meta-analysis shows that support for authoritarian regimes is over-reported by 14 percentage points on average. Our setting also links in with the work by Corstange (2022), who uses a list experiment to show that displaced Syrians in Lebanon tended to misreport their support for different actors engaged in the conflict in Syria heterogeneously across self-declared political preferences. We provide new empirical evidence by studying support for a military regime in West Africa in a conflict-torn setting, highlighting relevant heterogeneity in misreporting and its repercussions for classical statistical inference. Our work is also similar to the study by Blair et al. (2014) who explore support for the International Security Assistance Force during the war in Afghanistan, by conducting an LE and an endorsement experiment.<fnref n="1" /> While both indirect survey methods are found to yield consistent estimates of a 16% level of support for ISAF, the authors do not explore possible response biases. We differ from their study, as we adopt a systematic comparative approach to the LE-based and DQ-based measures.

In that respect, our approach relates directly to the literature using LEs to measure the prevalence of sensitive behaviors or opinions and quantify response bias. As such, misreporting is documented in a wide variety of attitudes e.g., homophobia (Coffman et al., 2017), acceptance of female genital mutilation (Bertelli, Calvo, Lavallée, et al., 2025; De Cao & Lutz, 2018; Gibson et al., 2018), intimate partner violence and child marriage (Asadullah et al., 2021; Bertelli, Calvo, Lavallée, et al., 2025), and behaviors such as voting (Holbrook & Krosnick, 2010; Rosenfeld et al., 2016), loan use (Karlan & Zinman, 2012), physical violence (Porter et al., 2021), sexual behavior (Coffman et al., 2017; Jamison et al., 2013), the use of condoms among female sex workers (Lépine et al., 2020; Treibich & Lépine, 2019), child labor (Jouvin, 2023), and domestic violence against women (Agüero & Frisancho, 2022; Bertelli, Calvo, Coulibaly, et al., 2024; Bulte & Lensink, 2019; Chuang et al., 2021; Cullen, 2023; Joseph et al., 2017; Peterman et al., 2018; Traunmüller et al., 2019; Treibich & Lépine, 2019). To our knowledge, our paper is the first to use an indirect survey technique applied to political attitudes and behaviors under a military

<footnote n="1">¹ In an endorsement experiment, treated respondents rate their support for policies endorsed by socially sensitive actors, whereas control respondents rate the same policies without endorsement by any particular actor. The difference between the two groups is interpreted as the support (or lack of) for the actor of interest. See also, for instance, Bullock et al. (2011) or Lyall et al. (2013).</footnote>
regime. In addition, we provide new insightful evidence with regard to the heterogeneity of misreporting across sub-groups and illustrate the consequences of using biased self-reported data in a standard regression analysis.

More broadly, our paper relates to the literature on fragile states and conflict-torn or post-conflict settings. One particular strand of literature works on documenting behaviors and preferences, highlighting specific attitudes among individuals who have been exposed to violence (see among others Bellows & Miguel, 2009; Bertelli & Kurdi, 2022; Calvo et al., 2020; Cassar et al., 2013; Gilligan et al., 2014; Rohner et al., 2013; Voors et al., 2012). We contribute to this body of literature using direct and indirect survey techniques to measure political attitudes and behaviors in a fragile state governed by a military regime. Another related strand of literature focuses on the challenges raised by data collection and research conducted in sensitive contexts – for ethical and security reasons, and in terms of data quality. In particular, a number of papers discuss suitable qualitative and quantitative investigation protocols when the research objective concerns potentially traumatic events and/or when the research setting is prone to violence (see for instance Brück et al., 2016; Di Maio & Fiala, 2020; Harris & van der Windt, 2023; Nordås, Østby, Biaba, et al., 2016; Nordås, Østby, Rustad, et al., 2016; Wood, 2006). Our approach contributes to this literature by documenting the reliability of a specific, indirect survey technique to measure a range of political attitudes, behaviors and experiences in a fragile context.

The rest of the paper is organized as follows. The following section describes the Malian context. In Section 2 we discuss the survey data collected, and the external data we use on conflict-related violence. Section 3 explains the empirical strategy and discusses the experimental validity of our setting. We present the main results in Section 4. The final section provides our main conclusions.

# 1. The Malian context

This study was conducted in July 2021 in a particularly tense political climate. On May 24, 2021 Mali saw its second coup in less than a year. After deposing President Ibrahim Boubacar Keïta in August 2020, the military junta, under the leadership of Colonel Assimi Goïta, arrested and removed from office the president and prime minister of the interim government, before delaying the holding of presidential elections.<fnref n="2" />

These coups occurred in the midst of an armed conflict that had broken out in January 2012 when a number of armed groups challenged the government's authority. The insurgents were mainly Tuareg rebels and jihadist groups.<fnref n="3" /> They launched their first attacks on military positions in the north and quickly took control of major northern cities including Timbuktu, Gao and Kidal. Meanwhile, President Amadou Toumani Touré, held responsible for the crisis, was ousted by a military coup in March 2012. As armed groups moved south, the French government launched Operation *Serval*, soon followed by a UN peacekeeping mission and supported by an African-led mission to Mali. By the end of January 2013, most cities had been retaken and new democratic elections were held in July. Despite two peace agreements, in 2013 and 2015 (in Ouagadougou and Algiers, respectively), the conflict gradually

escalated and spread to central Mali, and subsequently throughout the entire country. The nature of the conflict shifted with the emergence of new non-state armed actors with ethnic and separatist demands. To date, the Malian conflict has taken the lives of thousands and displaced hundreds of thousands more, in addition to considerably weakening the Malian state. It currently appears to be in an impasse: responses from national and international authorities have fallen short of the mark (Guichaoua & Pellerin, 2017).

The Malian political environment is also marked by repeated accusations against the foreign armed forces of supporting the armed insurgents. With the Malian government making its first request for Russian military aid in late 2021, France and its allies announced the departure of their troops from Mali at the beginning of 2022.

The Malian context is not a unique and isolated case. Above and beyond the significant human, diplomatic, and economic ramifications of the Malian crises, violence has spread across West Africa. The “three-border region” of Burkina Faso, Mali, and Niger ranks among the most violent area in Africa (Raleigh et al., 2023), involving both transnational actors (notably terrorist groups) and local non-state armed groups. Violent unrest continues to spread through West Africa, where, following Mali’s 2020 coup, nine other coups have overthrown incumbent presidents. Although the immediate causes of these coups and international responses differ from one country to the next, newly appointed leaders have adopted common rhetoric and political decisions that could have similar effects on survey response biases. In many Sahelian states, new self-proclaimed leaders have justified their new regime as an expression of popular demand even as they curtail freedoms of expression and the press (FIDH, 2025; HRW, 2024). And, in Burkina Faso, Mali, and Niger, they have accused the UN and European military forces of propagating violence and collusion with non-state armed groups.<fnref n="4" /> Given the features that the Malian situation shares with other countries in the Sahelian region, we expect our findings on misreporting of victimization, behaviors with respect to violence and attitudes towards key actors to most likely apply beyond the Malian context.

# 2. Data

## 2.1. The survey

The list experiment, also known as the unmatched control or item count technique, is a popular survey tool for eliciting truthful responses to sensitive questions, as it guarantees a much higher level of confidentiality than standard direct survey techniques. A list of statements is administered to respondents, who are asked to report the number of statements with which they agree or that apply to their situation, without naming which one(s). The statements are either neutral (i.e., concern everyday life) or sensitive. Respondents are randomly allocated to answer either a list of purely neutral statements (control group), or the same list of neutral statements augmented with one sensitive item (treatment group). In that way, the difference in average response (i.e. the number of statements with which the respondent agrees) between the two groups provides an accurate measure of the prevalence rate of the sensitive item (Blair & Imai, 2012). This survey technique reduces declarative bias insofar as sensitive information is not revealed directly to the interviewer, making for a much higher level of confidentiality than direct questioning (Blair et al., 2020).

In our study, respondents in the control group were administered five lists of three neutral items each. In the treatment group, each list included a fourth item referring to a politically sensitive attitude.

<footnote n="2">² The decision to postpone the presidential elections to 2026 was made in December 2021, i.e., after our survey took place. This triggered sanctions from the Economic Community of West African States (ECOWAS) with the closure of Mali's borders with the other Member States and an economic and financial embargo. Sanctions were lifted following the junta's June 2022 announcement that elections would be held in February 2024. However, in September 2023, the elections were postponed again indefinitely.</footnote>

<footnote n="3">³ Although Mali is no stranger to Tuareg uprisings – four major uprisings in 1963, 1991, 2006 and 2012 were all followed by peace agreements – jihadist groups only started to thrive in the late 2000s, as the central government gradually disengaged from the Sahelian regions (Julien, 2011).</footnote>

<footnote n="4">⁴ See, for instance, S. Mihoubi brief (2025). La communication des dirigeants de l'Alliance des États du Sahel : un régime narratif sahélien ? *Fabulae Mundi*. Accessed on April, the 17th at https://fabulaemundi.hypotheses.org/1353.</footnote>
Respondents in the control group were also asked about the same sensitive item, but in a direct way, with a “Yes/No” question towards the end of the questionnaire. They were given the option of skipping the question and moving directly to the next one if they did not want to answer.<fnref n="5" /> The full lists of items and direct questions forming the subject of this analysis are provided in Table A1 in Appendix A.

The five sensitive items focus on two dimensions. Firstly, three items relate to behaviors and experience in a context of insecurity:

(Q1) In the last 12 months, a household member or I have been physically assaulted outside the home;

(Q2) A household member or I own a firearm;

(Q3) A household member or I am prepared to take up arms to defend our community.

The first two items are similar to those found in standard surveys, such as the Malian Continuous, Modular Household Survey (EMOP). Items 2 and 3 address citizens' responses to violent threats and their potential involvement in violent actions. Firearms possession is regulated in Mali. Some categories of arms are strictly prohibited from civilian use, and others can be legally obtained provided a proper license is issued by the authorities. Jihadist groups were probably the main potential threat faced by Malians at the time of the survey, but other threats include armed rebel groups (including Tuareg rebels), local and ethnic militias and, depending on standpoint, the Malian army and foreign armed forces. We argue that in a non-democratic regime with widespread insecurity, respondents might be reluctant to report these types of (potential) involvement in violence.

Secondly, two items address attitudes towards key actors:

(Q4) Today's Mali should be ruled by the army;

(Q5) I trust the foreign armed forces in Mali.

The focus of these two items is explained by the fact that, at the time of our survey, a military junta was in place and several foreign armed forces, including the French military (the former colonial power), were still stationed in the country and engaged in the decades-long conflict.<fnref n="6" /> Anecdotal evidence suggests strong support for the Malian army and mistrust of the foreign military forces operating in the country (Haidara, 2020, 2021). It is however plausible that social desirability and/or the fear of possible reprisals affected respondents' answers to these direct questions.

It is worth noting that, although worded in a context-specific manner, our set of sensitive items is relevant to conflict-affected areas in general. Victimization, firearms possession and readiness to take up arms all reflect key aspects of conflict dynamics, and trust in armed groups engaged in the conflict is crucial to facilitate and underpin the effectiveness of their operations (Berman et al., 2011; Karlan & Zinman, 2012).<fnref n="7" />

## 2.2. Sample selection and randomization

We selected our sample from a national household survey, the “Harmonized Household Living Standards” Survey (Enquête harmonisée sur les conditions de vie des ménages, hereafter EHCVM), conducted by

the Malian Institute of Statistics in 2018–2019. This survey is representative at national, regional and urban/rural levels.<fnref n="8" /> It provides surveyed households’ contact details in the form of a mobile phone number for the household head and another household member (in general, the head’s spouse). Contact information is available for 5835 of the 6602 households and for 6415 individuals. Our sample is representative at national and regional levels of households with a mobile phone in Mali (see Fig. 2 for the survey’s geographic coverage), given that 89.9% of households owned a mobile phone in Mali in 2018 (INSTAT, 2019).

We set out to interview a sample of 1500 individuals, men and women in equal proportions from among those who provided a telephone number. In anticipation of potentially high non-response rates, we randomly selected 2000 individuals, one aged 18 or over per household, 1400 out of whom were assigned to the main list with 600 retained as replacements.<fnref n="9" /> Our selected sample contains the same region and gender proportions as the original sample of individuals with phone numbers (i.e. mobile phone owners). The targeted sample size was determined by power computation carried out using data on the prevalence of firearms possession obtained from the 2019 EMOP survey.<fnref n="10" />

We randomly assigned half of the sample to the treatment group and the other half to the control group, stratified by gender and region. The interviewers contacted by phone 1719 individuals (874 in the control group and 845 in the treated group), a total of 1509 of whom agreed to answer our questionnaire (764 in the control group and 745 in the treated group). Section 2.3 further discusses the issue of non-response.

The survey was conducted by a gender-balanced team of twelve Malian enumerators hired by the Malian Research Group in Applied and Theoretical Economics (GREAT), with the authors leading the training sessions. Data collection was carried out by telephone in July and August 2021 to accommodate the health and security situation at the time.<fnref n="11" /> Interviews were conducted in the respondents' chosen language. Each enumerator conducted both LE and DQ surveys, following random assignment.

## 2.3. Randomization balance check and non-response

The randomization balance test was conducted on the basis of the respondent characteristics collected by our survey: age, gender, primary school completion, marital status, ethnic group, migration status, and region of residence.<fnref n="12" /> In addition, we included information on respondent exposure to conflict-related violence at commune level based on Armed Conflict Location and Event Data (Raleigh et al., 2010).

<footnote n="5">⁵ Respondents in each group did not have the option of skipping an entire list and moving on to the next one, but they were entitled to stop the questionnaire at any time.</footnote>

<footnote n="6">⁶ The Russian-funded private military company, the Wagner Group, was not yet operating in Mali at the time of the survey. The very first reports of its presence on Malian soil date back to the end of 2021 (Elischer, 2022; Thompson et al., 2022).</footnote>

<footnote n="7">⁷ We discuss ethical and practical concerns relating to our survey in detail in Appendix B.</footnote>

<footnote n="8">⁸ The EHCVM household sample is drawn from a traditional two-way stratified random sampling design. Firstly, enumeration areas are randomly drawn with selection proportional to regional population size, taking into account urban/rural distribution. Secondly, 6602 households are drawn from the sample of enumeration areas and interviewed in one of the two rounds of data collection (between October and December 2018 and between April and June 2019).</footnote>

<footnote n="9">⁹ Details of the sampling and survey protocol are further discussed in Bertelli et al. (2022). See Appendix C for more details on the replacement protocol.</footnote>

<footnote n="10">¹⁰ See Appendix F for a more detailed discussion of sample size and the bias-variance trade-off in our list experiments.</footnote>

<footnote n="11">¹¹ Telephone surveys provide a higher level of confidentiality with respect to household members within earshot of the interview. Robust evidence on how social desirability bias differs between face-to-face and telephone surveys is still inconclusive. However, list experiments are found to reduce social desirability bias in telephone surveys compared to face-to-face interviews (Holbrook & Krosnick, 2010).</footnote>

<footnote n="12">¹² Note that we cannot include information from the original EHCVM survey, since we surveyed the adult person who answered the phone irrespective of whether they were part of the EHCVM survey.</footnote>
<table><caption>**Table 1**
Randomization balance check.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Control</th><th colspan="4">Treatment</th></tr><tr><th>(1)<br>Obs.</th><th>(2)<br>Mean</th><th>(3)<br>s.d.</th><th>(4)<br>Obs.</th><th>(5)<br>Mean</th><th>(6)<br>s.d.</th><th>(7)<br>Diff.</th></tr></thead><tbody><tr><td>Age</td><td>764</td><td>42.00</td><td>13.72</td><td>745</td><td>42.53</td><td>14.34</td><td>0.505</td></tr><tr><td>Primary education completed</td><td>764</td><td>0.41</td><td>0.49</td><td>745</td><td>0.39</td><td>0.49</td><td>-0.018</td></tr><tr><td>Married</td><td>764</td><td>0.88</td><td>0.33</td><td>745</td><td>0.88</td><td>0.33</td><td>-0.003</td></tr><tr><td>Mother tongue Bambara</td><td>764</td><td>0.47</td><td>0.50</td><td>745</td><td>0.51</td><td>0.50</td><td>0.039*</td></tr><tr><td>Mother tongue Songhai</td><td>764</td><td>0.19</td><td>0.40</td><td>745</td><td>0.18</td><td>0.39</td><td>-0.007</td></tr><tr><td>Mother tongue Other</td><td>764</td><td>0.15</td><td>0.35</td><td>745</td><td>0.13</td><td>0.34</td><td>-0.013</td></tr><tr><td>First year in place of residence</td><td>764</td><td>1986.83</td><td>18.92</td><td>745</td><td>1987.60</td><td>19.42</td><td>0.278</td></tr><tr><td>Migrant</td><td>764</td><td>0.30</td><td>0.46</td><td>745</td><td>0.35</td><td>0.48</td><td>0.020</td></tr><tr><td>Lived in current place of residence prior to 2012</td><td>764</td><td>0.87</td><td>0.33</td><td>745</td><td>0.86</td><td>0.35</td><td>-0.002</td></tr><tr><td>Living in commune with violent events</td><td>764</td><td>0.74</td><td>0.44</td><td>745</td><td>0.69</td><td>0.46</td><td>-0.043**</td></tr><tr><td>Average number of violent events</td><td>764</td><td>31.59</td><td>53.40</td><td>745</td><td>31.11</td><td>52.83</td><td>-0.570</td></tr><tr><td>Living in com. with the Govt. involved in VEs</td><td>764</td><td>0.68</td><td>0.47</td><td>745</td><td>0.62</td><td>0.49</td><td>-0.050**</td></tr><tr><td>Living in com. with foreign AFs involved in VEs</td><td>764</td><td>0.39</td><td>0.49</td><td>745</td><td>0.38</td><td>0.49</td><td>-0.013</td></tr><tr><td>Joint F-test</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.11</td></tr><tr><td>P-value</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.343</td></tr></tbody></table>

Notes: Control variables include region fixed-effects, respondent gender and enumerator fixed-effects. “Living in a commune with violent events” is a dummy equal to one if the respondent lives in a commune exposed to a conflict-related violent event at least once since 2012 (ACLED); “Living in commune with the Govt. involved in violent events” is a dummy equal to one if the respondent lives in a commune where the Malian government was involved in a conflict-related violent event at least once and zero otherwise (ACLED). “Living in commune with foreign AFs involved in violent events” is a dummy equal to one if the respondent lives in a commune where foreign Armed Forces (AFs) were involved in a conflict-related violent at least once event and zero otherwise (ACLED).

Table 1 reports the main characteristics of our sample. Respondents are aged 42 on average, some 40% of them completed primary education, most of them (88%) are married, half of them speak Bambara as their mother tongue, and roughly a third were not born in their current place of residence, even though most (86%) were living in their current place of residence before the start of the conflict in 2012, suggesting that the change of residence was mostly unrelated to the outbreak of the conflict or the national coup. Finally, two-thirds of respondents live in communes that have witnessed at least one violent conflict-related event since 2012. Most often, governmental forces were involved in those events, whereas the involvement of foreign armed forces was rarer.

The results of the balance check and the joint F-test on the set of observable characteristics show that treatment and control groups are statistically similar. Nevertheless, we observe two main significant differences. The share of respondents whose mother tongue is Bambara is 3.9 percentage points higher in the treatment group, while the share of respondents living in communes with at least one violent conflict-related event (or with at least one violent event in which governmental forces were involved) since 2012 is lower in the treatment group. Table G1 (Panel 1) in Appendix G.1 shows that our results are robust to controlling for this set of covariates.

Moving on to the non-response rate – defined as the share of individuals who were not reached or did not consent to participate in the survey – our survey reports a rate of 12.2% due mainly to the inability to reach people.<fnref n="13" /> Table C2 in Appendix C tests whether non-response is correlated with known individual characteristics (namely treatment status, gender and region). Most importantly, we show that non-response is uncorrelated with treatment assignment (11.8% in the treated group and 12.6% in the control group, Table C1 in Appendix C). Note that all those who were not reached by phone or who did not consent to the survey were replaced by another adult person living in the same region (see Appendix C).

## 2.4. Design, ceiling and floor effects

In addition to randomization, the validity of a list experiment is based on: (a) the no design effect assumption, meaning that the inclusion of a sensitive item should not change the sum of affirmative answers to the baseline items; and (b) the no liars assumption, meaning that respondents answer the questions truthfully.

To explore the risk of violating the no design effect assumption, we compare the proportions of respondents in the treatment and control groups reporting at least *J* (*J* = 1, 2, 3) items. Answers to the baseline items are expected to follow a normal distribution in the population and the inclusion of the sensitive item should not affect it. The only difference in the proportions estimated for the two groups should be due to the prevalence of the sensitive item. Consequently, the share of respondents reporting at least one/two/three items should be larger in the treatment group, which received a longer list of items, compared to the control group. The opposite could indicate the presence of a design effect, meaning that the answers to the three baseline items are impacted by the inclusion of the sensitive item (Glynn, 2013).

Table E2 in Appendix E shows a larger proportion of respondents reporting at least one item in the control group for the first list. However, the *p*-value of the *t*-test of the difference between the two proportions is 0.336, meaning that we cannot reject the null hypothesis that this negative difference is down to chance, while all the other differences are positive as expected.<fnref n="14" /> That is reassuring as regards the absence of any design effects.

The second main assumption behind list experiments is that respondents answer the survey truthfully and do not conceal their true answer to the sensitive items. This assumption is violated if respondents lie about their true preferences and misreport the number of items with which they agree, giving rise to biased LE prevalence estimates. Two main threats can challenge this assumption: a ceiling effect and a floor effect.

A ceiling effect can occur when treated respondents agree with all the items, including the sensitive item, possibly fearing that answering truthfully would reveal their (affirmative) preference for the sensitive item. Consequently, they might prefer to answer $Y_i = J$ rather than $Y_i = J + 1$ to conceal their true affirmative preference for the sensitive item.

<footnote n="13">¹³ Respondents who refused to answer one (or more) question(s) and skipped straight to the next question, but eventually completed the questionnaire, are disregarded here and are addressed later in the analysis. There are no cases of incomplete questionnaires (beyond the consent question) in our sample.</footnote>

<footnote n="14">¹⁴ The same result is found with the Bonferroni-corrected minimum *p*-values obtained using the R List package designed by Blair and Imai (2012).</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Share of respondents agreeing with *j* items, by list and treatment status.</figcaption></figure>

That would yield an underestimation of the sensitive item prevalence rate.

A floor effect can occur when treated respondents disagree with all the baseline items. In this case, they might adopt two different strategies, depending on whether or not they agree with the sensitive item. Those who agree with the sensitive item may (mistakenly) think that their true preference for the sensitive item would be revealed if they reported $Y_i = 1$. They would then choose to report $Y_i = 0$, yielding an underestimation of the LE-based prevalence rate. By contrast, those who disagree with all items including the sensitive one may choose to conceal their true answer ($Y_i = 0$) and report $Y_i = 1$ instead, leading to an overestimation of the LE-based prevalence. Whether the first or second type of scenario occurs arguably depends on what the socially desirable answer to the sensitive item is. If answering ‘No’ is socially desirable (as, in our case, for all lists but the fourth one), the presence of a floor effect would underestimate the prevalence of the sensitive item. By contrast, if ‘Yes’ is the most socially desirable answer (as in the fourth list in our case), the risk is that prevalence will be overestimated.

It is particularly useful to look at the proportions for each response across groups to gauge the plausibility of the assumptions of no ceiling or floor effects. To do so, as per Blair and Imai (2012) and Glynn (2013), we run two empirical exercises. Firstly, we look at the shares of respondents agreeing with *j* items (with *j* = 0, 1, 2, 3, 4) for each list. Fig. 1 provides a graphic representation of these shares for each list, for both the treatment and the control groups (see Table E1 in Appendix E for the exact proportions). We are particularly interested in the tails of the control group's distributions, as we want to avoid the two extreme cases of a large share of respondents (dis)agreeing with all the baseline items. The share of control group respondents who do not agree with any item or who agree with all three items is always substantially lower than the proportion reporting one or two items and is fairly similar across lists. In particular, the share of 0s or 3s in the control group is quite small (around 10%) and in line with that of

other studies, suggesting the absence of any substantial ceiling or floor effects.

However, in the case of the second list, we do observe a relatively large share of three items (21.3%) in the control group, suggesting a potential ceiling effect and, therefore, a risk of underestimating the sensitive item. Moreover, the 0s amount to 14% to 15% of control group responses for both the fourth and fifth lists, indicating a possible risk of a floor effect. For the fourth item ("Today's Mali should be ruled by the army"), answering 'Yes' is likely to be the socially desirable strategy. Some treated respondents might therefore answer one instead of zero to avoid revealing their true preference, which would yield an overestimation of prevalence, as explained above. Reassuringly, however, the share of treated respondents who report one is very similar across lists and is no larger for list 4 than for the other lists. For the fifth item ("I trust the foreign armed forces in Mali"), the 'No' answer is probably the most socially desirable. In this case, some treated respondents who would normally pick one might choose to answer zero instead, yielding a risk of underestimation of the true prevalence rate. Again, the share of treated respondents reporting zero remains similar across lists and is relatively low (9%).<fnref n="15" />

Regarding the statistical validity of the list experiment design, it is worth mentioning that the choice of baseline items aims to limit the risks of design, ceiling and floor effects (Blair & Imai, 2012; Chuang et al., 2021; Glynn, 2013). In keeping with the dedicated literature, we included items which are neither too common nor too uncommon and which are likely to be negatively correlated with the sensitive items and among themselves. Moreover, we chose items that are neither

<footnote n="15">¹⁵ As additional tests we re-estimate LE-prevalence rates and response biases under extreme scenarios of no ceiling and no floor effects, similarly to Cullen (2023). Relative to these scenarios, our results are lower-bound estimates. Results available from the authors upon request.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Survey coverage and commune exposure to violent events since 2012.</figcaption></figure>

*Notes:* The map shows the communes where respondents in our sample were living at the time of the survey. For a given commune, we highlight whether treatment and/or control respondent lived there and whether violent events had occurred there since the beginning of the conflict (2012), based on ACLED data.

*Source:* GDRI-Sud MOV survey data, ACLED, authors calculations.

too innocuous, which would make the sensitive item visible to the respondent, nor too sensitive.<fnref n="16" />

In this regard, for a given list, we included baseline items on topics related to the sensitive item, such as security and public life, with harmonized wording across all the items. Nevertheless, baseline items too closely related to the sensitive item's topic might have been perceived as sensitive themselves and/or have primed respondents and influenced their answer with respect to the sensitive item. To limit this risk, we avoided directly relating our baseline items to the ongoing conflict or to any specific violent political conflict. We approached the subject of security from an angle that had as little as possible to do with political violence, by referring to safety on the streets and at home and to tensions between neighbors; and we addressed the collective dimension by means of reference to public goods such as the power grid and the public health system, which were not directly the subject of the ongoing violence. Moreover, our baseline items made no mention of any other local actors than the police – in particular, we did not evoke political or militant groups – and, at the aggregate level, we only referred to the political class in general terms. Finally, we simplified the wording of each item as far as possible to avoid both memory overload and to facilitate translation into Malian languages (Bambara, Soninke, Tamashek, Fula and Songhai).

Nevertheless, our approach has two main limitations. Firstly, we cannot test for a negative correlation between the baseline items as we did not administrate them in DQ form. Despite our careful choice of baseline items, some of them might have primed respondents and

affected their answer. However, it is hard to fathom whether and how that could bias our results, as priming could affect respondents' answers in an unpredictable and heterogeneous way. Secondly, we did not randomize the order of lists across respondents, potentially raising a higher risk of fatigue when it came to the final lists. To mitigate this risk to the maximum, we made the survey as short as possible (interviews were generally between 15 and 20 min long). Importantly, no respondent dropped out of the survey after giving their consent to participate. To encourage respondents to complete the questionnaire, we gave them with CFAF 1000 worth of phone credit at the end of the interview, as promised immediately following their consent to answer our survey.

## 2.5. Violence data

We use the Armed Conflict and Location and Event Data (ACLED) to explore heterogeneity in misreporting by exposure to conflict-related violence.<fnref n="17" /> We merge ACLED measures of violence computed at commune level (equivalent to administration level 4) with our survey data based on respondents' communes of residence.

Conflict-related violence concerns half (119) of the 234 communes covered by our survey (see Fig. 2). All surveyed communes located in northern Mali (in the Gao, Kidal and Timbuktu regions), as well as those in the regions of Mopti and Bamako, had been exposed to violence at least once since 2012. Violent events increased over time but remained concentrated in the center and the north of the country. The distribution of violent events in communes exposed at least once

<footnote n="16">¹⁶ We were unable to conduct a Double List Experiment, as recommended by Chuang et al. (2021), due to limited survey time.</footnote>

<footnote n="17">¹⁷ See Appendix D for further details on ACLED.</footnote>
since 2012 is highly right-skewed at both the extensive and intensive margins. The median number of violent events and associated fatalities by exposed commune is 3 and 5, respectively.

### 3. Empirical strategy

**Step 1. Detecting response bias.** The first step in our empirical analysis consists in measuring response bias affecting our five outcomes, by comparing the prevalence rates computed under the LE technique with those computed under the DQ technique.

The prevalence rate of sensitive item k (for $k \in \{1; 2; 3; 4; 5\}$) under DQ, $\hat{g}_k$ is obtained from a regression of the direct answer on a constant:

$$Z_{i,k} = g_k + \epsilon_{i,k}, \quad (1)$$

where $Z_{i,k}$ equals 1 if individual i answers 'Yes' to sensitive question k, and 0 otherwise.

The prevalence rate of sensitive item k under LE, $\hat{\gamma}_k$, is measured based on a comparison of control and treatment groups using the following equation:

$$Y_{i,k} = \alpha_k + \gamma_k T_i + \lambda_k W'_i + \epsilon_{i,k} \quad (2)$$

where $Y_{i,k}$ denotes the response given by individual i to the list of statements including item k ($Y_{i,k}$ ranges from 0 to 3 for respondents in the control group and from 0 to 4 for respondents in the treatment group), $T_i$ is a dummy variable that takes the value of 1 if i is in the treatment group and 0 if i is in the control group, and $\epsilon_{i,k}$ is the error term robust to heteroskedasticity. In addition, we introduce a vector of control variables $W'_i$ that includes the randomization strata, namely gender and region of residence, as well as enumerator fixed effects to account for enumerators' characteristics and for potential systematic differences in the quality of the data collected.<fnref n="18" />

Given the proposed notations, the first empirical step is to compare $\hat{g}_k$ and $\hat{\gamma}_k$, as estimated by Eqs. (1) and (2), for all five sensitive items.

**Step 2. Detecting heterogeneity in response bias.** The second step in our empirical analysis examines whether response biases are heterogeneous across sub-groups of the population. Specifically, we investigate whether response biases vary by gender, education and exposure to violence.<fnref n="19" />

Both social desirability bias and lack of confidentiality are likely to vary across sub-groups of the population. Individual characteristics are key to determining with which norm respondents identify themselves, how much they stick to it, and the feeling of shame they associate with deviating from it. Individual characteristics are also likely to affect respondents' trust in the enumerator, the desire to keep their personal information private, and expectations regarding the possible consequences of disclosing their answers to third parties. Men, driven by mainstream masculinity norms, can be expected to be less likely to conceal firearms possession, for example, than women. Similarly, educated respondents may be more informed about the illegal nature of firearms possession, and hence more likely to underreport it than uneducated respondents. In addition, respondents in violence-ridden areas may be relatively more likely to underreport firearms possession, to avoid being considered as potentially active participants in the ongoing violence. Along the same line of reasoning, the existing literature advocates an exploration of potential heterogeneity across the individual dimensions available in our data.<fnref n="20" />

Furthermore, Kramon and Weghorst (2019) point out that, in certain cases, list experiments were found to be inconsistent among respondents with a low level of education due to the cognitive load they require, leading people with cognitive difficulties or low education levels to give random answers to the enumerator.<fnref n="21" /> Verifying that response biases are not driven solely by uneducated respondents is, therefore, a useful indirect quality check.

We emulate the approach taken by Blair and Imai (2012) to test whether response bias (again measured by comparing prevalence rates under the two survey methods, LE and DQ) varies across sub-groups. The DQ prevalence rate of each of the five sensitive items is estimated by the following equation:

$$Z_{i,k} = g_k + \mu_k X_i + \epsilon_{i,k} \quad (3)$$

where $Z_{i,k}$ denotes the answer given by individual i to sensitive question k and $X_i$ is one of the three dummies for gender, primary education, and exposure to violence. We measure exposure to violence with a dummy that takes the value 1 for individuals living in a commune with at least one violent event since 2012 (the median number of violent event recorded at surveyed communes level), as documented by the ACLED data, and 0 otherwise. <fnref n="22" /> This analysis is enriched by probing heterogeneity in misreporting by the identity of the actors involved in violent events (Appendix G.3). Following our pre-analysis plan, we also explore heterogeneity in misreporting by region and ethnic group (proxied by mother tongue) in Appendix G.4.

The DQ prevalence rate of sensitive question k is given by $\hat{g}_k$ for the omitted category of variable $X_i$, and by $\hat{g}_k + \hat{\mu}_k$ for the category under study.

The LE prevalence rate of each of the five sensitive items is estimated by the following equation:

$$Y_{i,k} = \alpha_k + \beta_k X_i + \gamma_k T_i + \delta_k X_i \times T_i + \lambda_k W'_i + \epsilon_{i,k} \quad (4)$$

where we interact treatment status $T_i$ with one of the three dummies for gender, primary education, and exposure to violence. The estimated term $\hat{\gamma}_k + \hat{\delta}_k$ captures the prevalence rate as measured by LE among individuals with $X_i = 1$ while $\hat{\gamma}_k$ measures the prevalence for those with $X_i = 0$. As before, $W'_i$ is the vector of control variables at the individual level.

Given the proposed notations, the second empirical step consists in comparing $\hat{g}_k$ and $\hat{\gamma}_k$ on the one hand, and $\hat{g}_k + \hat{\mu}_k$ and $\hat{\gamma}_k + \hat{\delta}_k$ on the other, based on the parameters estimated in Eqs. (3) and (4).

<footnote n="18">¹⁸ Results are robust to the inclusion of an extended set of control variables, as shown in Panel 2 of Table G1 in Appendix G.1.</footnote>

<footnote n="19">¹⁹ Following our registered pre-analysis plan, we also study biases across age groups. Results, which are less conclusive, are available upon request from the authors.</footnote>

<footnote n="20">²⁰ In a similar setting to ours, Blair et al. (2014) highlight heterogeneous response biases regarding exposure to UN peacekeeping missions. In a less closely related research context, heterogeneous response biases are found based on respondent education levels (Agüero & Frisancho, 2022; Joseph et al., 2017), age (Joseph et al., 2017), risk of HIV infection (Treibich & Lépine, 2019), gender, marital status, urban/rural area and occupation (Koehler et al., 2022).</footnote>

<footnote n="21">²¹ As explained in Appendix A, we endeavored to limit this risk by first guiding respondents through an example of a list experiment, the answers to which were explicitly verified by the enumerator.</footnote>

<footnote n="22">²² Bi-variate correlation coefficients between pairs of respondent characteristics are statistically significant but of small size – 5.3% between the male dummy and exposure to violence, 6.5% between primary education and exposure to violence, and –6.6% between the male dummy and primary education. Controlling for region and enumerator fixed-effects, differences of means across sub-groups are also small in size, and most of them are not statistically significant, confirming little correlation between characteristics. It only appears that men are significantly less likely, by about 5 percentage points, to live in communes that were exposed to violence and, symmetrically, that respondents in exposed communes are about 6 percentage points more likely to be women.</footnote>
<table><caption>**Table 2**
Differences in prevalence rates, entire sample.</caption><thead><tr><th></th><th>Q1<br>Physical<br>assault</th><th>Q2<br>Firearms<br>possession</th><th>Q3<br>Prepared to<br>take up arms</th><th>Q4<br>Support for<br>military regime</th><th>Q5<br>Trust in<br>foreign AF</th></tr></thead><tbody><tr><td>LE</td><td>0.171***<br>(0.047)</td><td>0.169***<br>(0.044)</td><td>0.304***<br>(0.048)</td><td>0.632***<br>(0.045)</td><td>0.363***<br>(0.050)</td></tr><tr><td>DQ</td><td>0.122***<br>(0.012)</td><td>0.112***<br>(0.011)</td><td>0.309***<br>(0.017)</td><td>0.740***<br>(0.017)</td><td>0.247***<br>(0.016)</td></tr><tr><td>LE - DQ</td><td>0.049</td><td>0.057</td><td>-0.005</td><td>-0.108</td><td>0.116</td></tr><tr><td>χ<sup>2</sup></td><td>1.013</td><td>1.571</td><td>.009</td><td>5.031</td><td>4.689</td></tr><tr><td>p-value</td><td>0.314</td><td>0.210</td><td>0.924</td><td>0.025</td><td>0.030</td></tr><tr><td>Sharpened q-value</td><td>0.309</td><td>0.266</td><td>0.587</td><td>0.083</td><td>0.083</td></tr><tr><td>N LE</td><td>1509</td><td>1509</td><td>1509</td><td>1509</td><td>1509</td></tr><tr><td>N DQ</td><td>761</td><td>760</td><td>739</td><td>665</td><td>693</td></tr></tbody></table>

Notes: OLS estimations; AF: Armed Forces; LE equations include strata dummies (gender, and nine region fixed effects) and enumerator fixed effects as control variables.

Robust standard errors in brackets; \* *p* < 0.10, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

**Step 3. Detecting heterogeneity in prevalence rates.** Finally, the third part of our analysis explores the correlation between individual characteristics and prevalence rates measured using the DQ or LE survey technique. Individual characteristics might significantly correlate with prevalence rates measured using one technique but not the other, especially if response biases are heterogeneous across sub-groups of the population, as explored by Step 2 of the analysis. In other words, Step 3 will provide suggestive evidence regarding the possible repercussions of heterogeneous misreporting in regression analysis using the DQ-technique.

To run this analysis we consider the terms estimated separately for each survey technique: $\hat{\mu}_k$ (Eq. (3)) for DQ and $\hat{\delta}_k$ (Eq. (4)) for LE. Whereas $\hat{\mu}_k$ captures the correlation between respondent characteristics and DQ prevalence rates, $\hat{\delta}_k$ captures the same correlation for LE prevalence rates. In contrast to the two previous steps, this time we also include the full set of controls in the DQ-based analysis to make the two estimates as comparable as possible.

Caution should be taken when interpreting the results from Step 3 for two main reasons. Firstly, unobserved omitted variables correlated with respondent characteristics could bias the correlations. Secondly, the LE technique introduces larger variance and thereby less statistical precision than the DQ equivalent. Standard errors are therefore greater and it is harder to reject the null hypothesis of no correlation. However, we show in Appendix F that our sample size is large enough to detect significant biases as small as 4.9 percentage points.

# 4. Results

## 4.1. Prevalence rates and response biases

Table 2 presents the average prevalence rates for each sensitive item measured using the two techniques (i.e. $\hat{\delta}_k$ estimated from Eq. (1) and $\hat{\mu}_k$ estimated from Eq. (2)), along with the difference between them. As explained above, the information declared under DQ suffers from a reporting bias if the difference between LE and DQ is significantly different from zero.

We find a significant response bias affecting the two items related to political attitudes: support for the army (Q4) and trust in the foreign armed forces (Q5). When asked directly, 74.0% of respondents supported the current military regime and 24.7% trusted the foreign armed forces. In contrast, under the LE technique, 63.2% of respondents supported the current military regime and 36% trusted the foreign armed forces. These differences are statistically significant. They remain significant (at the 10% level) when adjusting q-values for the false

discovery rate as per the approach outlined in Benjamini et al. (2006) and Anderson (2008).

The size of these biases is considerable. Support for the military regime is overreported by 10.8 percentage points when asked directly,<fnref n="24" /> while trust in foreign armed forces is underreported by 11.6 percentage points. The bias size of the fourth item corresponds to 14.6% of the DQ prevalence rate, while the bias size of the fifth item corresponds to 45.7% of the DQ prevalence rate. Notice that, for both the fourth and fifth lists, the risk of a floor effect would imply that we underestimate misreporting. Indeed, as discussed in Section 2.4, a potential floor effect would yield an overestimation of support for the army (Q4) and an underestimation of trust in the foreign armed forces (Q5) under LE, widening the gap between the DQ and LE prevalence rates for both items.

In turn, questions about behaviors and victimization do not appear to be affected by significant response biases in the overall sample. Nevertheless, the prevalence rates are of interest in themselves. Experience of assault outside the home concerns 17% of respondents (or members of their families). Prevalence is greater under the LE technique than under DQ (12.2%), although the difference is not statistically significant. Similarly, the prevalence of firearms possession is greater albeit non-significantly when measured under LE (where 16.9% of respondents have a firearm at home) than when measured by the DQ technique (11.2%). The DQ share is close to the 10.5% share of household firearms possession measured by the 2021 EMOP survey using a similarly worded question.<fnref n="25" /> Note that, as discussed in Section 2.4, we might be underestimating the LE prevalence here due to a possible ceiling effect affecting our capacity to detect actual underreporting of firearms possession. Finally, under both interview techniques, almost one-third of the sample declares being prepared to take up arms.

It is worth mentioning that refusal to answer for the first two questions under DQ is very rare (respectively, 3 and 4 respondents, equating to 0.4% and 0.5% of the sample), but more frequent for the next three questions (respectively, 25, 99 and 71 respondents, or 3.3%, 13% and 9.3% of the sample).<fnref n="26" /> However, refusal to answer under DQ could affect the measurement of misreporting if refusals are not randomly distributed. Robustness checks reported in Appendix H show that

<footnote n="23">²³ Our results are very similar to those found by other surveys with similar direct questions. Data from Afrobarometer Round 8 conducted in July/August 2022 reports that 77% of Malian adults said they approve of the military rule.</footnote>

<footnote n="24">²⁴ These results align closely with the 14-percentage-point overreporting of support for authoritarian regimes documented in the meta-analysis by Blair et al. (2020).</footnote>

<footnote n="25">²⁵ The 2021 EMOP also asks respondents about victimization. Only 4% of EMOP respondents reported having suffered a physical assault, versus 12.2% in our sample.</footnote>

<footnote n="26">²⁶ Note that we are unable to disentangle the share of observed response bias due to respondents answering untruthfully in DQ from that due to respondents refusing to answer under DQ.</footnote>
<table><caption>**Table 3**
Response biases by sub-groups, Q1–Q3.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Q1<br>Physical assault</th><th colspan="3">Q2<br>Firearms possession</th><th colspan="3">Q3<br>Prepared to take up arms</th></tr><tr><th>Male<br>(1)</th><th>Primary<br>(2)</th><th>VE<br>(3)</th><th>Male<br>(4)</th><th>Primary<br>(5)</th><th>VE<br>(6)</th><th>Male<br>(7)</th><th>Primary<br>(8)</th><th>VE<br>(9)</th></tr></thead><tbody><tr><th>0</th><td>0.101<br>[0.134]</td><td>0.099<br>[0.102]</td><td>-0.024<br>[0.788]</td><td>0.126<br>[0.046]</td><td>0.010<br>[0.856]</td><td>-0.129<br>[0.135]</td><td>0.030<br>[0.668]</td><td>0.032<br>[0.604]</td><td>-0.146<br>[0.122]</td></tr><tr><th>p-value</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>1</th><td>0.004<br>[0.955]</td><td>-0.027<br>[0.728]</td><td>0.072<br>[0.200]</td><td>-0.003<br>[0.962]</td><td>0.125<br>[0.079]</td><td>0.115<br>[0.028]</td><td>-0.035<br>[0.609]</td><td>-0.066<br>[0.400]</td><td>0.046<br>[0.427]</td></tr><tr><th>p-value</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Diff (1 - 0)</th><td>-0.097<br>[0.309]</td><td>-0.126<br>[0.191]</td><td>0.096<br>[0.360]</td><td>-0.129<br>[0.145]</td><td>0.115<br>[0.204]</td><td>0.244<br>[0.014]</td><td>-0.065<br>[0.499]</td><td>-0.099<br>[0.320]</td><td>0.192<br>[0.079]</td></tr><tr><th>p-value</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Notes: Primary is a dummy equal to one for respondents having completed primary education; VE equals one if the respondent lives in a commune with at least one violent event since 2012. In each column, the first row reports the difference along with its *p*-value between the LE-based and the DQ-based prevalence rate for the sub-group of the omitted category (respectively, females, respondents with no primary education, and respondents not exposed to violence), i.e. $\hat{\gamma}_k - \hat{\delta}_k$. The second row reports the same difference along with its *p*-value for the non-omitted sub-group (respectively, males, respondents with at least primary education, and violence-exposed respondents), i.e. $(\hat{\gamma}_k + \hat{\delta}_k) - (\hat{\delta}_k + \hat{\mu}_k)$. The third row reports the difference in misreporting between the two sub-groups (response bias reported in row 2 minus response bias reported in row 1), and its *p*-value. LE equations include strata fixed-effects at the gender and regional level, and enumerator fixed-effects. Associated *p*-values are reported.

<table><caption>**Table 4**
Response biases by sub-groups, Q4–Q5.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Q4<br>Support for mil. regime</th><th colspan="3">Q5<br>Trust in foreign AF</th></tr><tr><th>Male<br>(1)</th><th>Primary<br>(2)</th><th>VE<br>(3)</th><th>Male<br>(4)</th><th>Primary<br>(5)</th><th>VE<br>(6)</th></tr></thead><tbody><tr><th>0</th><td>-0.134<br>[0.056]</td><td>-0.180<br>[0.002]</td><td>-0.122<br>[0.203]</td><td>0.054<br>[0.494]</td><td>0.105<br>[0.125]</td><td>0.120<br>[0.246]</td></tr><tr><th>p-value</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>1</th><td>-0.084<br>[0.197]</td><td>-0.004<br>[0.964]</td><td>-0.105<br>[0.057]</td><td>0.169<br>[0.018]</td><td>0.131<br>[0.108]</td><td>0.120<br>[0.051]</td></tr><tr><th>p-value</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Diff (1 - 0)</th><td>0.050<br>[0.601]</td><td>0.177<br>[0.067]</td><td>0.017<br>[0.875]</td><td>0.115<br>[0.267]</td><td>0.026<br>[0.800]</td><td>-0.001<br>[0.996]</td></tr><tr><th>p-value</th><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Notes: See Table 3.

observable characteristics explain a negligible proportion of variance in the probability of refusing to answer, and that the measurement of the response bias is robust to exclusion from the control group of respondents refusing to answer a direct question when computing the LE-based prevalence rate of the corresponding item.

## 4.2. Heterogeneity in misreporting

We now turn to investigating whether response bias is homogeneous in our sample by respondent gender, education level and past exposure to violence.<fnref n="27" /> The analysis is built on a comparison of the results of the estimation of Eqs. (3) and (4), as explained in Section 3. We present the results grouped by item and sub-divided by the three heterogeneity dimensions. Table 3 displays the results of the first three items by gender (columns 1, 4 and 7), education (columns 2, 5 and 8) and exposure to violence (columns 3, 6 and 9). For the sake of space, for each item we only report the difference between prevalence rates elicited by the two survey techniques for each sub-group, and the difference between sub-group misreporting. Table 4 then displays the results for the last two items regarding support for the military regime and trust in the foreign armed forces. Prevalence rates and the number of observations by sub-group for each of the two survey techniques are reported in Appendix G.2.

We find that most sub-groups do not exhibit significant misreporting, which is partly but not exclusively explained by a lack of statistical power.<fnref n="28" />,<fnref n="29" /> Our results find significant heterogeneity in misreporting with respect to firearms possession: women, respondents

with at least primary education and those living in conflict-affected areas significantly underreport firearms possession, with the bias size ranging between 11.5 and 12.6 percentage points (Table 3, columns 4–6). Misreporting of willingness to take up arms (columns 7–9) goes systematically in opposite directions across sub-groups, although it is never significantly different from zero. For these two items, the largest discrepancy in misreporting is found between respondents exposed or not exposed to conflict violence (24.4 and 19.2 percentage points), the difference being significantly different from zero. Last, although misreporting of physical assault by sub-group is not statistically significant at standard thresholds, it is worth noting that it goes in opposite directions across sub-groups for both primary education and exposure to violence.

These results show that misreporting is not systematically significantly heterogeneous, but response biases frequently go in opposite directions across sub-groups. Exposure to violence is the dimension where the difference in misreporting across sub-groups is the largest, with respondents in conflict-affected areas being subject to underreporting. Women and, to a lesser extent, uneducated respondents are also subject to a certain degree of underreporting. They are, along with

<footnote n="28">²⁸ In some cases, sub-group sample size is not large enough to detect small differences between prevalence rates measured under the two survey techniques. While total sample size allows us to detect response biases as small as 4.9 percentage points (Table F1), the smallest response bias we can detect in the sub-group analysis is around 6.6 percentage points (Tables F3 and F2). We are underpowered for 11 out of 30 sub-group × item combinations. As it happens, sub-group sample size is not an issue for coefficients with a *p*-value below 0.4 in Tables 3 and 4.</footnote>

<footnote n="29">²⁹ To address the issue of multiple hypothesis testing, we compute *p*-values controlling for the familywise error rate, in keeping with (Romano & Wolf, 2005a, 2005b), which captures the strong and likely negative dependence structure of tested hypotheses by means of bootstrapping (2500 iterations). We use (Clarke et al., 2020) *rwolf* Stata command. The response biases remain significant only for the gender dimension. Results for other sub-groups should be interpreted with caution.</footnote>

<footnote n="27">²⁷ Our results remain very robust to two sensitivity analyses addressing potential measurement error in exposure to violence. First, we use an alternative measure of exposure to violence that distinguishes communes with at least one fatality since 2012 from those with no fatalities. Second, we perform a placebo test by randomly assigning exposure to violence to 33% of communes with no recorded violent events over 500 iterations. Results are available upon request.</footnote>
respondents living in conflict-affected areas, those with the greatest experience of victimization as measured by our list experiment (see column 1 in Tables G2, G3 and G4 in Appendix G.2). That suggests that misreporting could be more prevalent among sub-groups that are vulnerable to violence. To complement this result, Appendix G.4 explores heterogeneity in misreporting across regions and languages (two dimensions that partially overlap). The results consistently suggest that sub-groups relatively more vulnerable to violence – people in the northern and central regions, and members of the largest ethnic groups in those regions – are more likely to misreport violence-related behaviors and experiences.

Turning to the set of results for attitudes (Table 4), most sub-groups overreport their support for the military regime when asked directly (columns 1–3). Significant misreporting is found among women (13.4 percentage points), uneducated respondents (18.0 percentage points) and those living in conflict-affected areas (10.5 percentage points). Uneducated respondents overreport their support significantly more (by 17.7 percentage points) than educated respondents, for whom the bias is close to zero, while the difference across sub-groups is not statistically significant for the other characteristics.

All sub-groups underreport their trust in the foreign armed forces, but not all are significantly different from zero (columns 4–6). Significant misreporting is found among men (16.9 percentage points), educated respondents (13.1 percentage points) and those exposed to violence (12 percentage points). The difference in misreporting across sub-groups is, however, very small as far as education and exposure to violence are concerned. The largest discrepancy in misreporting is found between men (16.9 percentage points) and women (5 percentage points and not significant), even though the difference is not significantly different from zero. When asked directly, 30% of women and 20% of men say they trust the foreign armed forces, whereas 35% of women and 37% of men do so under the list experiment technique (see Table G2 in Appendix G.2).

Irrespective of respondent exposure to violence, we find response biases of a similar size (in absolute value) affecting self-declared support for the military regime (between 10 and 12 percentage points) and trust in the foreign armed forces (12 percentage points). Response biases are significantly different from zero solely for respondents who live in conflict-affected communes, but are not significantly different across sub-groups (columns 3 and 6). To further explore this dimension, we investigate whether misreporting of these two items varies according to the type of actors involved in violent events, namely the government and foreign armed forces (Appendix G.3). Results show that support for the military regime is overreported essentially by respondents living in communes where the government was involved in at least one violent event since 2012, but not by respondents living in communes where the foreign armed forces had intervened. In turn, trust in the foreign armed forces is underreported by both types of respondents, although the magnitude of misreporting is 8 percentage points higher among those exposed to violent events involving the foreign armed forces than among respondents exposed to violent events in general.

Overall, we observe widespread fear of revealing truthful political attitudes towards the military government and the presence of foreign armed forces in Mali. All sub-groups overreport (respectively, underreport) their support for the Malian army and their trust in the foreign armed forces, albeit in different proportions. Moreover, the results for items relating to behaviors and experiences could suggest that population segments vulnerable to violence are particularly subject to misreporting, pointing to the likelihood of stronger social desirability bias and/or fear of revealing their true opinions among this type of respondent.

### 4.3. Characteristics associated with sensitive outcomes

In a standard OLS regression setting, measurement errors in the dependent variable can generate biased coefficient estimates if they are

systematically related to the explanatory variable (Wooldridge, 2015). Our previous results show that items 4 and 5 are significantly biased when measured using the DQ technique, with items 1, 2 and 3 presenting asymmetric biases across respondent characteristics. The final part of the empirical analysis investigates whether and how the correlation of respondent characteristics with our five sensitive outcomes varies depending on the survey technique used.

We report the results in Table 5. Columns 1, 3 and 5 display $\hat{\mu}_k$ (with the associated p-value) from Eq. (3), which reflects the estimated correlates of the DQ-based outcomes, while columns 2, 4 and 6 display $\hat{\delta}_k$ (with the associated p-value) from Eq. (4), which reflects the estimated correlates of the LE-based outcomes.

Respondent characteristics (gender and education in particular) on the whole significantly correlate with the prevalence rates measured using the DQ technique, and rarely with those measured by the LE technique. The reason for this divergence has to do with more than just the fact that the LE technique generates higher variance and lower precision. In a number of cases, we find correlation coefficients of opposite signs between the two survey techniques, possibly due to divergent misreporting across sub-groups. Furthermore, for the first two behavioral items, we would expect to find no significant differences across individual characteristics, as long as it is assumed that information is available to every adult household member. Gender and, to a lesser extent, education should not correlate with household firearms possession or household member victimization in the absence of any declarative bias. Reassuringly, that is never the case when sensitive items are measured under LE. Notwithstanding, in most instances, the correlation coefficients are not significantly different across survey techniques, meaning that the results should be interpreted with caution.

Starting with physical assault, we find that formally educated respondents are more likely to report physical assault under DQ (0.06, p-value = 0.03, column 4), but not under LE (column 5). That could be explained by the non-significant, but divergent pattern in misreporting between respondents with and without formal education (see Table 3, column 2).

Men seem to be more likely than women to own firearms when directly asked (0.07, p-value = 0.00, column 1), but not when using a list experiment (column 2). That is most likely due to opposite misreporting, whereby women significantly underreport firearms possession while men do not, as shown in Table 3 column 4. Interestingly, the correlation between education and firearms possession is much higher – although not significantly different from zero – under LE (column 5) than under DQ (column 4), which reflects the fact that formally educated respondents significantly underreport firearms possession under DQ while respondents with no formal education do not (Table 3, column 5). Similarly, respondents living in conflict-affected areas are more likely to report firearms possession in the list experiment (0.15, p-value = 0.11, column 8) than when directly asked (column 7), the difference between the two correlates being statistically significant. That can be related to the fact that respondents in conflict zones, who represent the majority of our sample, significantly underreport firearms possession, while those in conflict-free zones overreport it, as shown in Table 3, column 6.

Much like firearms possession, men are also more likely than women to report willingness to engage in violence when directly asked (0.06, p-value = 0.07, column 1), but not in the list experiment (column 2). Although men and women's response biases are not significantly different from zero (Table 3 column 7), their opposite directions might explain this result. Similarly, educated respondents seem more willing to engage in conflict than uneducated respondents when directly asked (0.11, p-value = 0.00, column 4), but not under LE (column 5), which is consistent with response biases of opposite signs (see Table 3, column 8). By contrast, past exposure to conflict is positively correlated with willingness to take up arms measured using the LE (column 8), but not under the DQ technique (column 7). Importantly, this difference is statistically significantly different from zero. That is in line with the
<table><caption>**Table 5**
Correlation of respondent characteristics with sensitive item prevalence rates measured under DQ vs. LE.</caption><thead><tr><th rowspan="2">Item:</th><th colspan="3">Male</th><th colspan="3">Primary</th><th colspan="3">Violence Exp.</th></tr><tr><th>DQ (1)</th><th>LE (2)</th><th>Diff. (3)</th><th>DQ (4)</th><th>LE (5)</th><th>Diff. (6)</th><th>DQ (7)</th><th>LE (8)</th><th>Diff (9)</th></tr></thead><tbody><tr><td>Physical assault</td><td>0.02</td><td>-0.07</td><td></td><td>0.06</td><td>-0.07</td><td></td><td>0.03</td><td>0.13</td><td></td></tr><tr><td><i>p-value</i></td><td>[0.40]</td><td>[0.43]</td><td>[0.33]</td><td>[0.03]</td><td>[0.48]</td><td>[0.21]</td><td>[0.27]</td><td>[0.20]</td><td>[0.37]</td></tr><tr><td>Obs.</td><td>761</td><td>1509</td><td></td><td>761</td><td>1509</td><td></td><td>761</td><td>1509</td><td></td></tr><tr><td>Firearms possession</td><td>0.07</td><td>-0.06</td><td></td><td>0.01</td><td>0.12</td><td></td><td>-0.04</td><td>0.15</td><td></td></tr><tr><td><i>p-value</i></td><td>[0.00]</td><td>[0.51]</td><td>[0.16]</td><td>[0.68]</td><td>[0.18]</td><td>[0.24]</td><td>[0.23]</td><td>[0.11]</td><td>[0.05]</td></tr><tr><td>Obs.</td><td>760</td><td>1509</td><td></td><td>760</td><td>1509</td><td></td><td>760</td><td>1509</td><td></td></tr><tr><td>Prepared to take up arms</td><td>0.06</td><td>0.01</td><td></td><td>0.11</td><td>-0.00</td><td></td><td>0.00</td><td>0.21</td><td></td></tr><tr><td><i>p-value</i></td><td>[0.07]</td><td>[0.90]</td><td>[0.59]</td><td>[0.00]</td><td>[1.00]</td><td>[0.26]</td><td>[0.94]</td><td>[0.04]</td><td>[0.07]</td></tr><tr><td>Obs.</td><td>739</td><td>1509</td><td></td><td>739</td><td>1509</td><td></td><td>739</td><td>1509</td><td></td></tr><tr><td>Support for military regime</td><td>0.04</td><td>0.08</td><td></td><td>-0.05</td><td>0.12</td><td></td><td>0.06</td><td>0.02</td><td></td></tr><tr><td><i>p-value</i></td><td>[0.26]</td><td>[0.39]</td><td>[0.70]</td><td>[0.19]</td><td>[0.17]</td><td>[0.08]</td><td>[0.19]</td><td>[0.83]</td><td>[0.74]</td></tr><tr><td>Obs.</td><td>665</td><td>1509</td><td></td><td>665</td><td>1509</td><td></td><td>665</td><td>1509</td><td></td></tr><tr><td>Trust in foreign AF</td><td>-0.11</td><td>0.01</td><td></td><td>0.03</td><td>0.06</td><td></td><td>0.07</td><td>0.02</td><td></td></tr><tr><td><i>p-value</i></td><td>[0.00]</td><td>[0.88]</td><td>[0.23]</td><td>[0.37]</td><td>[0.55]</td><td>[0.79]</td><td>[0.13]</td><td>[0.83]</td><td>[0.69]</td></tr><tr><td>Obs.</td><td>693</td><td>1509</td><td></td><td>693</td><td>1509</td><td></td><td>693</td><td>1509</td><td></td></tr></tbody></table>

Notes: OLS estimations. All coefficients refer to a different estimation. All estimations control for region fixed-effects and enumerator fixed-effects.

Col. 1–4–7: Dep. var. = 1 if answer = ‘Yes’. Display the coefficients associated with, respectively, *Male*, *Primary* and *Violence exposure*.

Col. 2–5–8: Dep. var. = number of ‘Yes’ to each list. Display the coefficients associated with, respectively, *Treated* × *Male*, *Treated* × *Primary* and *Treated* × *Violence exposure*. The three columns also control for *Male* and *Treated*, Col. 5 for *Primary* and Col. 8 for *Violence exposure*.

results in Table 3 (column 9) indicating that respondents in conflict-affected zones slightly underreport their willingness to take up arms in DQ, while respondents in conflict-free zones overreport it (p-value = 0.122).

As shown in Table 4, all sub-groups tend to overreport their support for the military regime and underreport their trust in the foreign armed forces. However, since they do so in different proportions, misreporting can still affect the correlation of individual characteristics with self-reported attitudes. Table 5 shows that the correlation between education and support for the military regime switches sign – from negative to positive – when the outcome is free from response bias, with the difference between the two coefficients being significant (Table 5, columns 4–6). That is consistent with uneducated respondents overreporting their support more than educated respondents under DQ, albeit not significantly. Table 5 also shows that men are less likely than women to trust the foreign armed forces when directly asked, but not in a list experiment. This discrepancy mirrors the fact that men misreport their trust in the foreign armed forces in a larger proportion than women, although the difference is not statistically significant (Table 4, column 4).

All in all, Table 5 results show that, in the absence of an average response bias in the overall population, asymmetries in misreporting across sub-groups can still have substantial repercussions for the estimation of correlations between individual characteristics and sensitive attitudes or behaviors.

## 5. Discussion and conclusion

The existing literature shows that directly asking respondents about sensitive issues can give rise to misreporting, due to social desirability bias and fear of a lack of confidentiality. Reporting biases are likely to be even greater among respondents living under non-democratic regimes. Insecurity and/or limited freedom of speech can affect respondent trust in the enumerators, thus biasing responses to sensitive issues. Furthermore, a fragile context can shape the sensitivity of certain topics, in particular those related to the country’s political situation, as well as the prevailing norms defining social desirability. All of this can have important consequences for data collection and treatment effect estimates.

This is the first study to implement an experimental survey protocol to explore attitudes towards key actors and violence-related behaviors under a military regime – in our case, Mali. We find substantial

misreporting when individuals are directly asked sensitive questions about their attitudes. Respondents overreport their support for the military regime, and underreport their trust in the foreign armed forces. However, we find no average response bias concerning behaviors and experience-related outcomes. Nevertheless, close examination of respondent characteristics indicates that some population sub-groups, especially those who appear to be more vulnerable to violence, are more likely to misreport those items.

In documenting people’s tendency to truthfully report their victimization and behaviors with respect to violence, as well as their attitudes towards the military forces involved (be they Malian or not), our results tie in with the theoretical models that highlight the role of information for key political outcomes in autocratic settings. In particular, our results suggest that sensitive actions and perceptions are both of great importance and typically partially concealed in non-democratic regimes, pointing to the effective potential role of information in patterns of political violence and regime stability.

Our results can have important implications for data collection and policy evaluation. As asymmetric misreporting across population sub-groups (e.g. men versus women, and low-educated versus educated individuals) can result in biased correlations between respondent characteristics and outcomes measured by direct questions, administrating surveys using the DQ-technique in fragile contexts could significantly mismeasure the attitudes and behaviors of the target population. Consequently, interventions designed to improve local security conditions could yield misleading results if the outcomes are measured using survey methods affected by social desirability bias. Our results suggest that misreporting may be more common among the most vulnerable segments of the population, thereby pointing to the importance for development programs working with these populations to pay special attention to ensuring their freedom of speech. Over and above the specific focus of this survey, our findings highlight that significant (heterogeneous) treatment effects found by policy evaluations might (at least partly) reflect statistical artifacts due to asymmetric response biases across sub-groups, even when the average response bias is null. Analysis of the heterogeneous effects of development programs by recipient gender or education level, for instance, should be particularly attentive to these risks.

Our results also have relevant implications for policymaking at national and international levels. We argue that those are twofold. First, collecting accurate data on population’s experience of violence, as victims or perpetrators, provides highly valuable input for policies and
interventions in fragile states – whether implemented by international organizations, governments, or NGOs. Reliable information on victimization is relevant for the design and targeting of actions to mitigate the adverse effects of exposure to violence, and reliable information on the perpetration of violence is key to assessing peace-building efforts, such as Disarmament, Demobilization and Reintegration (DDR) programs. From this point of view, and looking beyond the Malian case, our finding that misreporting can be more prevalent among some segments of the population – especially those most vulnerable to violence – highlights the importance of considering possible asymmetric declarative biases to improve policy targeting and evaluation.

Second, accurate data on political attitudes provide highly valuable input for actors involved in security in fragile contexts and beyond. Gauging civilian support for different actors is key to assessing the political scenario and the possible dynamics of local violence. That is of interest to national governments, and also to the international community and international organizations. As a matter of fact, in 2008, NATO started sponsoring quarterly public opinion surveys in Afghanistan, notably concerning attitudes towards government and anti-government entities, “as part of its comprehensive assessment of the operating environment” (Powell & Eles, 2018). Were these data used to contextualize NATO actions, effectiveness of those actions may have suffered from a possible misalignment between real opinions and those overtly reported in the surveys. In that respect, our results point up the risk of political attitudes being subject to declarative biases. This is relevant for general public opinion, and especially for actors involved in governance and security to make better informed decisions and improve their operational effectiveness.

## CRediT authorship contribution statement

**Olivia Bertelli:** Project administration, Writing – review & editing, Writing – original draft, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Thomas Calvo:** Writing – review & editing, Writing – original draft, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Emmanuelle Lavallée:** Writing – review & editing, Writing – original draft, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Marion Mercier:** Writing – review & editing, Writing – original draft, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Sandrine Mesplé-Somps:** Writing – review & editing, Writing – original draft, Methodology, Funding acquisition, Formal analysis, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in the paper.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.worlddev.2025.107151.

## Data availability

The data and related documentation that support the findings of this study are openly available in DataSuds repository (IRD, France) at https://doi.org/10.23708/RBYQRO. Data reuse is granted under CC-BY license.

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