# Measuring social norms: direct and indirect methods and the role of financial incentives in reducing misreporting

Ann-Kristin Reitmann, Clotilde Mahé, Micheline Goedhuys, Eleonora Nillesen

► To cite this version:

Ann-Kristin Reitmann, Clotilde Mahé, Micheline Goedhuys, Eleonora Nillesen. Measuring social norms: direct and indirect methods and the role of financial incentives in reducing misreporting. Journal of Economic Behavior and Organization, 2026, 243, pp.107465. 10.1016/j.jebo.2026.107465. hal-05496799

HAL Id: hal-05496799

https://hal.science/hal-05496799v1

Submitted on 6 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Research Paper

# Measuring social norms: direct and indirect methods and the role of financial incentives in reducing misreporting

Ann-Kristin Reitmann<sup>a,b,c,*</sup>, Clotilde Mahé<sup>d,e</sup>, Micheline Goedhuys<sup>f,g</sup>,
Eleonora Nillesen<sup>f,g</sup>

ARTICLE INFO

JEL classification:

C83

C99

D91

J29

O12

Keywords:

Survey experiment

Social norms

Female labor force participation

Opinion-matching method

List experiment

A B S T R A C T

We test two common features of the opinion-matching method to elicit personal beliefs and perceived social norms regarding female labor force participation among a sample of young Tunisian men and women. Our survey experiment contains two orthogonal treatment arms. In the first treatment arm, respondents were randomly assigned to answer a direct question about their personal beliefs regarding women working outside the home and an indirect questioning technique that provides more cover for the respondent: the list experiment. We find significant discrepancies between the two, particularly among male respondents – suggesting either biased personal beliefs in the direct question or non-strategic misreporting in the list experiment. It follows that, depending on which method is deemed more credible and which gender sample is considered, the prevalence of pluralistic ignorance varies widely in our setting. For the second treatment arm, we provided half of our sample with a financial incentive when eliciting perceived social norms regarding the appropriateness of women working. We find that financial incentives are necessary for both men and women to distinguish their personal beliefs from their perception of what others believe.

1. Introduction

Social norms shape everyday human interactions, and are crucial to explain behaviors in social and economic situations. Next to the long-standing concepts of rationality and self-interest (Basu, 2003; Bicchieri, 2006; Elster, 1989), the notion of social norms has long been incorporated into economic theory and models (see e.g., Andreoni and Bernheim, 2009; Bénabou and Tirole, 2006; Bernheim, 1994), as well as empirical economics (see e.g., Bicchieri and Xiao, 2009; Bursztyn et al., 2020; Fehr and Fischbacher, 2004; Fehr and Gächter, 2002; Fehr and Schurtenberger, 2018; Krupka and Weber, 2013; La Ferrara et al., 2012).

<footnote><sup>a</sup> Université de Rennes CNRS, CREM-UMR 6211, 7 Place Hoche, Rennes, 35065, France</footnote>

<footnote><sup>b</sup> Universität Passau, Innstraße 41, Passau, 94032, Germany</footnote>

<footnote><sup>c</sup> Leibniz Universität Hannover, Welfengarten 1, Hannover, 30167, Germany</footnote>

<footnote><sup>d</sup> School of Business and Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, Netherlands</footnote>

<footnote><sup>e</sup> Amsterdam Institute for Global Health and Development, Paasheuvelweg 25, Amsterdam, 1105 BP, Netherlands</footnote>

<footnote><sup>f</sup> United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology, Boschstraat 24, Maastricht, 6211 AX, Netherlands</footnote>

<footnote><sup>g</sup> School of Business and Economics, Maastricht University, Tongersestraat 53, Maastricht, 6211 LM, Netherlands</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: ann-kristin.reitmann@univ-rennes.fr (A.-K. Reitmann), c.n.mahe@vu.nl (C. Mahé), m.goedhuys@maastrichtuniversity.nl (M. Goedhuys), eleonora.nillesen@maastrichtuniversity.nl (E. Nillesen).</footnote>

<footnote>https://doi.org/10.1016/j.jebo.2026.107465</footnote>

<footnote>Received 19 February 2025; Received in revised form 10 December 2025; Accepted 29 January 2026</footnote>

<footnote>Available online 6 February 2026</footnote>

<footnote>0167-2681/© 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>
Social norms are broadly defined as shared understandings within a social group about what is considered acceptable or unacceptable behavior in a given situation (Bicchieri, 2017, 2006; Fehr and Schurtenberger, 2018; Sugden, 1998). More specifically, injunctive social norms are commonly held standards of what one *should* do – or not do – in a given situation (see e.g., Charness et al., 2025; Görges and Nosenzo, 2020b).<fnref n="1" /> However, this type of social norms is difficult to measure for at least two reasons. First, people may find it cognitively difficult to report what they think the social norm is, as this requires them to form beliefs about what others consider appropriate or inappropriate behavior. Second, people may be unwilling to report truthfully what they think the social norm is if they perceive this to be different from the researcher's beliefs and values. To mitigate these issues, several methods – among them the opinion-matching method pioneered by Bicchieri and Xiao (2009) – have been developed. They rely on eliciting personal beliefs (first-order beliefs, FOBs), alongside perceptions of what others believe (second-order beliefs, SOBs) (Charness et al., 2025; Görges and Nosenzo, 2020b) – the latter serving as a proxy for social norms.

Concerns about social desirability may, however, compromise the validity of FOBs on sensitive topics when asked directly. We experimentally test whether responses on FOBs differ by elicitation method. For half of our sample, we elicit FOBs using a direct question, and for the other half we use a list experiment that provides more cover for the respondent to anonymously state their FOBs. Importantly, our experiment is embedded in a face-to-face survey where issues of social desirability may loom large and discrepancies between elicitation methods may be expected.

Eliciting SOBs can also be challenging. Guessing what others believe plausibly requires a certain degree of cognitive effort, and runs the risk that respondents that are unwilling to make that effort simply report their FOBs as SOBs. To mitigate such concerns, the elicitation of SOBs is often financially incentivized in the opinion-matching method. Participants are compensated in proportion to the accuracy of their guesses. To test whether financial incentives are necessary, and for whom, a random subset of our sample receives financial incentives when eliciting SOBs, and the other does not.

We use a novel two-arm survey experiment to test how sensitive FOBs and SOBs are to different experimental manipulations in an area in which social norms are gaining increased traction: female labor force participation (FLFP). Despite educational gaps diminishing, or even closing, labor market participation rates of women are typically much lower than those of men across the world (ILOSTAT, 2021). Stringent social norms that hold negative views about women working outside the house have been suggested as a possible explanation to the gender employment gap across the world (Bursztyn et al., 2020; Görges and Nosenzo, 2020a; Heath, 2024; Jayachandran, 2021).

Our contribution is twofold. We are the first to experimentally test the sensitivity of FOBs to different elicitation methods for the opinion-matching method in a face-to-face survey, where respondents may feel more strongly inclined to give socially desirable responses to questions on sensitive topics. Responses that differ depending on the elicitation methods used, are interesting in its own right, but also have implications for the assessment of *pluralistic ignorance* (Bursztyn and Yang, 2022; Miller, 2023; Sargent and Newman, 2021) – a key outcome measure resulting from the opinion-matching method, which is derived by comparing average FOBs to SOBs.

Second, we test the role of financial incentives in improving belief accuracy of perceived social norms. While researchers routinely use financial incentives (Burdea and Woon, 2022; Gächter and Renner, 2010; Schotter and Trevino, 2014), they substantially increase the material and organizational costs in experiments, and may, under certain conditions, even decrease belief accuracy (Danz et al., 2022). This motivates a second, and orthogonal to the first, treatment arm, where we randomize financial incentives when eliciting SOBs. This helps researchers to decide if and how to use them.

The survey experiment was conducted in late 2021 and early 2022 among adolescents and young adults living in disadvantaged neighborhoods across Tunisia. Tunisia is among the most advanced Middle East and North African (MENA) countries in terms of women's rights and legislation (Chambers and Cummings, 2014; Kashina, 2021; Nazir, 2005). According to the Global Gender Gap Report (World Economic Forum, 2021), for instance, Tunisia is close to parity in educational attainment between boys and girls. Yet, narrowing the gender gap in education has not translated into the economy, where gender inequalities persist. The labor market participation rate of the female population (aged 15 plus) amounted to only 25% in 2021, as opposed to 67% for the male population (aged 15 plus) (International Labour Organization, 2021).<fnref n="2" /> Conservative social norms are believed to be a key driver of low levels of female inclusion in the Tunisian labor market (Chambers and Cummings, 2014; Clark et al., 1991; Diwan and Vartanova, 2017; Haghighat-Sordellini, 2009; Sinha, 2011).<fnref n="3" /> These norms for example prescribe that young women are expected to marry, and assume domestic responsibilities, whereas the normative role of men is that of the breadwinner who provides for the family with income while assuming low responsibility within the household (Assaad and Krafft, 2015; Assaad et al., 2017, 2022; Lassassi and Tansel, 2020; Nazier and Ezzat, 2022).

Our results are as follows. In our first treatment arm, where we compare responses on FOBs across two randomized elicitation methods, we find that a direct question on the appropriateness of women working outside the house elicits favorable responses of about 90%, while this figure is substantially lower, at 68%, for respondents in the list experiment. When separating by gender, we find that women hold overall more progressive personal beliefs in both methods: the support is 96% in the direct question and 80% in the list experiment. For men, personal beliefs are more conservative and the gap in support for the two elicitation methods is much

<footnote n="1">¹ This study investigates the measurement of injunctive social norms, referred to here from now on as simply 'social norms'. These are different from descriptive norms, which rather refer to 'what is commonly done' in a situation as opposed to 'what one should do'.</footnote>

<footnote n="2">² As a reference, FLFP in the MENA region was only 19% in 2022, against 71% for men (International Labour Organization, 2022).</footnote>

<footnote n="3">³ There are also other barriers that constrain female labor supply. The lack of proper institutional support through paid maternity leave, on-site child care facilities, social security or long-term job perspectives are discouraging working mothers to remain in the labor market, or to join the labor force in the first place (Moghadam, 2019).</footnote>
larger: 84% (direct question) versus 55% (list experiment). The significant discrepancy between direct and indirect elicited personal beliefs may indicate social desirability or experimenter demand effects in the direct questioning method (Aycinena et al., 2024b), particularly among men, and (or) document non-strategic misreporting in the list experiment (Haber et al., 2018; Kuhn and Vivyan, 2022).

In the second treatment arm of the experiment, where we elicit and compare SOBs with and without financial incentives, we find no average effect. Still, we observe treatment effects differentiated by gender. Without financial incentives, respondents tend to align their guesses of others' beliefs (SOBs) with their own personal beliefs (FOBs), resulting in more conservative responses for men and more progressive responses for women. With financial incentives, SOBs of men and women tend to converge. Both genders thus respond to financial incentives, and state SOBs that are closer aligned than without financial incentives. This suggests that financial incentives improve cognitive effort to make an accurate guess.

Our findings show that, when using the opinion-matching method in face-to-face surveys, FOBs can be very different, depending on the elicitation method used. We find no evidence that the sensitivity to reporting social desirable personal beliefs can be explained with (respondent nor enumerator) gender, education, wealth or self-efficacy. We provide a number of auxiliary results in support of the existence of social desirability bias in our setting. Our results furthermore indicate that the provision of financial incentives, while having no average effect, still seem to increase individuals' guess accuracy by better distinguishing second- from first-order beliefs. In our setting, distinguishing personal opinions from perceived social norms is particularly relevant for groups affected by the sensitivity of the topic, i.e., men and women. Lastly, with FOBs varying between 90% when elicited using a direct question and only 68% when using the list experiment, its comparison with SOBs of 71% suggests pluralistic ignorance is negligible when trusting in the list experiment measure, but substantial when trusting the direct questioning measure. This is different from findings by Bursztyn et al. (2020), who elicit FOBs using the direct and indirect elicitation method alongside SOBs on women working outside their home among men in Saudi Arabia. Their results show no discrepancy between FOBs using direct or indirect elicitation efforts and hence, find that men consistently underestimate the true support for working women. The discrepancies in our study may be due to stronger social desirability bias or experimenter demand effects that may compromise response validity through direct questioning. One important difference between the study by Bursztyn et al. (2020) and this study is that, in our setting, the direct question was asked during an in-person survey with the enumerator reading out the questions to the participant, as is common for household surveys in low- and middle-income countries. In turn, respondents in Bursztyn et al.'s (2020) study filled out the questionnaire on their mobile phones, which provided higher levels of privacy and anonymity.

The remainder of the paper is structured as follows. In Section 2, we explain the opinion-matching method in more detail. Section 3 presents the data, the experimental set-up as well as descriptive statistics and validity tests. Section 4 presents the results of the two-step assessments. Section 5 discusses our findings and their implications for assessing pluralistic ignorance. Section 5 concludes.

## 2. Conceptual framework

The measurement of social norms is far from trivial and suffers from many potential biases. A common method is to aggregate the FOBs of a social group. Yet, this method creates a measure that might confound norms and other relevant determinants of the issue under consideration (Görges and Nosenzo, 2020a). In addition, there might be a misalignment between average FOBs and perceived social norms, caused by a phenomenon called 'pluralistic ignorance' (Bursztyn et al., 2020, 2024; Bursztyn and Yang, 2022; Görges and Nosenzo, 2020a), which makes average FOBs a poor proxy for the prevailing social norms.

Another approach for approximating social norms is to ask about SOBs. Görges and Nosenzo (2020a) and Harness et al. (2025) provide reviews of different (experimental) methods for these elicitation and measurement approaches, and describe their respective benefits and challenges. While coordination-game approaches based on Krupka and Weber (2013) are the most widely adopted and tested, the opinion-matching method pioneered by Bicchieri and Xiao (2009) has also been adopted in recent studies (e.g., Aycinena et al., 2024b; Bursztyn et al., 2020; Lane et al., 2023), but fewer tests have been implemented to validate and test the robustness of the method (Charness et al., 2025).<fnref n="4" />

The opinion-matching method consists of a two-step elicitation procedure. In a first step, a group of respondents are asked to report their (first-order) beliefs about the appropriateness of a certain behavior (e.g., *Do you think it is appropriate that...*). In the second step, the same (or a new) group of respondents is asked to guess the most common response of the first step (i.e., to report their SOBs of appropriateness).<fnref n="5" /> In order to incentivize a distinction from their own FOBs in step two, which requires a certain degree of cognitive effort, respondents receive financial incentives proportional to the accuracy of their response in the second step (Bicchieri et al., 2022; Bicchieri and Xiao, 2009; Görges and Nosenzo, 2020a). With the two-step procedure providing data on FOBs and SOBs, this method also enables measuring to what extent there exist misconceptions about social norms. In the context of FLFP, for example, these norms are possibly misperceived in the sense that men and women privately find that women are well placed to work outside the house, but they think others may disapprove (see also Aloud et al., 2020; Barigozzi and Montinari, 2025; Bursztyn et al., 2020, 2024; Cameron et al., 2024; Cortés et al., 2024; Gauri et al., 2019).

<footnote n="4">⁴ For the sake of brevity, we refrain from providing a detailed account of all methods and refer the interested reader to Görges and Nosenzo (2020a) and Harness et al. (2025).</footnote>

<footnote n="5">⁵ Note that in its original form, the first step consists of respondents being asked if a dictator should split money equally in a dictator game, with the response format being binary (i.e., 'yes/no'). In the second step, they then have to guess how many other respondents replied 'yes' to the first question (Bicchieri and Xiao, 2009). In subsequent studies, the approach was adopted in different ways in terms of framing the first-step question (e.g., Bicchieri and Chavez, 2010) and allowing for multiple response categories (e.g., Lane et al., 2023).</footnote>
However, despite its strength (see also Charness et al., 2025), the opinion-matching method comes with some disadvantages. One is that, by construction, the first step cannot be incentivized, because it measures personal opinions. Responses therefore are prone to various biases (Aycinena et al., 2024b). Survey participants may provide socially desirable responses that may not reflect their true personal opinion. Here, respondents may align their answer with what is perceived to be the social norm – causing social desirability bias (DeMaio, 1984) – or they may respond in line with what they believe the interviewer or experimenter wants to hear, commonly known as experimenter demand effects. More precisely, considering the often sensitive nature of topics like beliefs about FLFP, social experimenter demand effects could occur where respondents perceive social pressure to respond what constitutes appropriate behavior according to the experimenter (Zizzo, 2010).<fnref n="6" /> Aycinena et al. (2024b) experimentally explore how social desirability bias could impact the measurement of social norms under the opinion-matching method. They elicit norms in different variants of the dictator game in a laboratory setting, and induce biased disclosure of beliefs in the first step. Their pre-registered results indicate only small and non-significant effects. However, the authors conclude that, when applying the method, researchers should evaluate how a potential bias is likely to materialize. The awareness of information asymmetry between the respondent and a third-party (e.g., the enumerator) may be stronger in the field with face-to-face interviews than the laboratory or online settings.

Such biases can in turn undermine the measurement of SOBs if second-step respondents anticipate this and provide answers that align with the belief distortion in the first step instead of perceived social norms (Aycinena et al., 2024b; Görges and Nosenzo, 2020a).<fnref n="7" />

A second disadvantage is that the opinion-matching method comes along with substantial financial requirements because of response-contingent payments for the accuracy of SOBs. While researchers routinely use financial incentives for belief elicitation to improve belief accuracy (see Burdea and Woon, 2022; Gächter and Renner, 2010; Schotter and Trevino, 2014, for evidence of incentives improving belief accuracy), new evidence suggests that information on the offered financial incentives could, in some settings, increase false reporting, while withholding information on incentives could, in fact, reduce it (Danz et al., 2022).

Taken together, this motivates our study on testing (i) the sensitivity of FOBs in the first step to different elicitation methods, and (ii) assessing the need for financial incentives in the second step.

# 3. Data and validity of survey experiment

## 3.1. Survey context and experimental design

The opinion-matching survey experiment was embedded in the baseline survey data collection of a larger study that aims to measure the impact of an improved nation-wide infrastructure program for disadvantaged neighborhoods in Tunisia. The survey was implemented in late 2021 and early 2022 by an independent survey firm.<fnref n="8" /> The survey experiment was part of the questionnaire administered to a sample of 2659 young adults aged 16 to 30 years.<fnref n="9" /> Due to the focus on low-income neighborhoods, our findings pertain to disadvantaged areas in Tunisia. Yet, as Fig. 1 indicates, these areas are distributed across the entire country, including areas in the relatively prosperous flat coastal zone, which is perceived to have a less conservative mindset, and areas located in the poorer remote non-coastal zone.

Fig. 2 presents the experimental design. We followed a stratified randomization approach, where we stratified at the area level, assuming that social norms are uniform within, but not necessarily between areas. Within an area, the 40 households were randomly allocated to the respective treatment and control groups according to their household ID (HH ID). The following two sub-sections describe the nature of the two treatment arms in detail and the randomized allocation process. Moreover, they contain balance and validity tests.

## 3.2. First-order beliefs

The first step of this study's application of the opinion-matching method is to measure FOBs on the appropriateness of women working outside their home, which are beliefs about what survey participants personally consider (in)appropriate. We measure these personal beliefs in two ways: directly, and indirectly through a list experiment. For this, we randomly divided the sample into two groups (see Fig. 2). This division took place between households with HH ID 1 to 20 (Group FOB_DQ) and 21 to 40 (Group FOB_LE). The total sample size for the first experimental arm amounts to 2637 young Tunisians, where 1351 responded to the direct question and 1286 took part in the list experiment (with 654 in the control and 632 in the treatment group).<fnref n="10" /> Table 1 presents the

<footnote n="6">⁶ Social desirability bias and bias induced by social experimenter demand effects can be understood as the same construct and will be used as such from now on.</footnote>

<footnote n="7">⁷ We believe that this concern is more salient in settings where respondents know each other and are better equipped to assess each others' response biases. Yet, in our setting, respondents in each area are not aware about who else is interviewed for the survey at the time of the interview.</footnote>

<footnote n="8">⁸ The implementation of the infrastructure program, *Programme de Réhabilitation et d'Intégration des Quartiers d'Habitation* (PRIQH 2), only started in July 2023. The selection of treatment areas was not final at the time of our data collection. The possibility of any anticipation effects among our survey respondents is thus minimal.</footnote>

<footnote n="9">⁹ The survey experiment was conducted as part of the first youth questionnaire module asking about respondents' values, and targeted all eligible youth respondents residing in the sampled households in the same way.</footnote>

<footnote n="10">¹⁰ 22 observations (i.e., 1.6%) did not respond to the direct question. Due to the small number of observations, both in absolute and relative terms, we do not believe that a different outcome would alter our results.</footnote>
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Location of sampled low-income neighborhood youth across Tunisia. Note: This figure presents the GPS coordinates (gray dots) of the respective household of youth questionnaire respondents.</figcaption></figure>

<table><caption>**Fig. 2.** Experimental design. Note: "The direct question was asked to 1373 respondents, but 22 respondents refused to answer the direct question. "Second-order beliefs were elicited from 2659 respondents (1219 without financial incentives and 1340 with financial incentives), but 297 respondents in the group without financial incentive refused to answer the question.</caption><thead><tr><th colspan="4">Group FOB: N=2,637</th></tr></thead><tbody><tr><td colspan="2">Group FOB_DQ: HH ID 1-20</td><td colspan="2">Group FOB_LE: HH ID 21-40</td></tr><tr><td colspan="4">List experiment (N = 1,286)</td></tr><tr><td colspan="2">HH ID [21,30]</td><td colspan="2">HH ID [31,40]</td></tr><tr><td rowspan="3">FOB: Direct question (N = 1,351*)</td><td>LE_C:</td><td>LE_T:</td></tr><tr><td>Control group;</td><td>Treatment group;</td></tr><tr><td>3 statements<br>(N = 654)</td><td>4 statements<br>(N = 632)</td></tr></tbody><thead><tr><th colspan="4">Group SOB: N=2,362</th></tr></thead><tbody><tr><td colspan="4">Experimental arm with financial incentives</td></tr><tr><td>Even HH ID<br>(2, 4, ..., 18, 20)</td><td>Uneven HH ID<br>(1, 2, ..., 17, 19)</td><td>Even HH ID<br>(22, 24, ..., 38, 40)</td><td>Uneven HH ID<br>(21, 23, ..., 37, 39)</td></tr><tr><td>Group SOB_NI:<br>Control group<br>= no incentive<br>(N = 518°)</td><td>Group SOB_FI:<br>Treatment group =<br>financial incentive<br>(N = 706)</td><td>Group SOB_NI:<br>Control group<br>= no incentive<br>(N = 504°)</td><td>Group SOB_FI:<br>Treatment group =<br>financial incentive<br>(N = 634)</td></tr></tbody></table>
<table><caption>**Table 1**
List experiment (LE) and direct question (DQ) sample description.</caption><thead><tr><th rowspan="2"></th><th colspan="2">LE</th><th colspan="2">DQ</th><th colspan="3">LE - DQ</th></tr><tr><th>(1)<br>Mean</th><th>(2)<br>SD</th><th>(3)<br>Mean</th><th>(4)<br>SD</th><th>(5)<br>Diff.</th><th>(6)<br>p-value</th><th>(7)<br>Norm. diff.</th></tr></thead><tbody><tr><td>Age (years)</td><td>23.36</td><td>4.46</td><td>23.33</td><td>4.39</td><td>0.03</td><td>0.84</td><td>-0.01</td></tr><tr><td>Female</td><td>0.53</td><td>0.50</td><td>0.52</td><td>0.50</td><td>0.01</td><td>0.69</td><td>-0.01</td></tr><tr><td>Married</td><td>0.10</td><td>0.30</td><td>0.08</td><td>0.27</td><td>0.02</td><td>0.06</td><td>-0.05</td></tr><tr><td>Primary education</td><td>0.08</td><td>0.28</td><td>0.07</td><td>0.25</td><td>0.01</td><td>0.19</td><td>-0.04</td></tr><tr><td>Secondary education</td><td>0.62</td><td>0.48</td><td>0.66</td><td>0.48</td><td>-0.04</td><td>0.09</td><td>0.05</td></tr><tr><td>Vocational education</td><td>0.11</td><td>0.31</td><td>0.11</td><td>0.32</td><td>0.00</td><td>0.54</td><td>0.02</td></tr><tr><td>Tertiary education or higher</td><td>0.19</td><td>0.39</td><td>0.16</td><td>0.37</td><td>0.03</td><td>0.08</td><td>-0.05</td></tr><tr><td>Unemployed, work w/o pay</td><td>0.36</td><td>0.48</td><td>0.35</td><td>0.48</td><td>0.01</td><td>0.75</td><td>-0.01</td></tr><tr><td>Student</td><td>0.37</td><td>0.48</td><td>0.37</td><td>0.48</td><td>0.00</td><td>0.66</td><td>-0.01</td></tr><tr><td>Paid worker</td><td>0.14</td><td>0.35</td><td>0.14</td><td>0.35</td><td>0.00</td><td>0.83</td><td>0.01</td></tr><tr><td>Independent worker</td><td>0.05</td><td>0.22</td><td>0.05</td><td>0.23</td><td>0.00</td><td>0.62</td><td>0.01</td></tr><tr><td>Housewife</td><td>0.08</td><td>0.27</td><td>0.09</td><td>0.28</td><td>-0.01</td><td>0.51</td><td>0.02</td></tr><tr><td>Household size (members)</td><td>4.68</td><td>1.47</td><td>4.71</td><td>1.43</td><td>-0.03</td><td>0.61</td><td>0.01</td></tr><tr><td>Asset index (score)</td><td>0.12</td><td>1.00</td><td>0.14</td><td>0.97</td><td>-0.02</td><td>0.68</td><td>0.01</td></tr><tr><td>Interviewer is female</td><td>0.64</td><td>0.48</td><td>0.73</td><td>0.44</td><td>-0.09</td><td>0.00</td><td>0.15</td></tr><tr><td>Interviewer has veil</td><td>0.38</td><td>0.49</td><td>0.38</td><td>0.49</td><td>0.00</td><td>0.94</td><td>-0.00</td></tr><tr><td>N</td><td colspan="2">1286</td><td colspan="2">1351</td><td colspan="3"></td></tr><tr><td colspan="8">F-test of joint significance:</td></tr><tr><td colspan="8">F-stat</td></tr><tr><td colspan="8">p-value</td></tr><tr><td colspan="8">1.59</td></tr><tr><td colspan="8">0.09</td></tr></tbody></table>

Note: This table presents individual, household and interviewer characteristics of respondents receiving the direct FOB question and the list experiment FOB question. Columns (1) and (3) report the mean values for each group. Columns (2) and (4) report the respective standard deviation. Column (5) shows the difference between the two group means, and Column (6) shows the *p*-values for the associated test of equality of means. Column (7) presents normalized differences. The joint null of equal means is rejected at standard levels.

summary statistics for each sample, and gives information about whether observable characteristics (i.e., respondent socio-economic characteristics, household characteristics such as gender and perceived religiosity of the interviewer) are balanced between the two groups. Respondents in the list experiment sample are on average more likely to be married, less likely to have secondary education, more likely to have tertiary or higher education and less likely to be interviewed by a female enumerator. These statistically significant differences mean that the null of equal means is rejected at conventional levels. Yet, all, except one, differences are only borderline significant at the 10%-level. Moreover, the respective normalized differences in Column (7) indicate that these differences are not necessarily of economic importance.<fnref n="11" />

Respondents in Group A were directly asked about their FOBs towards women and work in Tunisia. More specifically, they were asked the following question “In general, do you think it is appropriate that women work outside the home?”, which they had to answer on a 4-point Likert-scale ranging from 1 = very inappropriate, 2 = inappropriate, 3 = appropriate to 4 = very appropriate. In our analysis, we combine the latter two categories to infer support for women working outside their home (i.e., ‘finding it (very) appropriate’). This first step is by construction not incentivized and may therefore be vulnerable to response bias. For instance, instead of responding in line with their true, personal opinion, they might answer in line with what is perceived to be the social norm, or with what they perceive the interviewer wants to hear (DeMaio, 1984; Zizzo, 2010).

In order to understand the potentially different responses to the FOB-question depending on which measure we use, we conducted a list experiment with the other half of the sample. In a list experiment, respondents are randomly assigned to either a control or treatment group (Blair et al., 2014; Glynn, 2013; Rosenfeld et al., 2016). Both groups receive a list of opinion statements, but only the treatment group sees an additional, potentially stigmatized and sensitive item. Participants report how many statements they agree with, but not which ones. By comparing the average number of agreements between groups, researchers can infer average support for the sensitive item. In this study, statement four is the item of interest, which is shown only to the treatment group:

1. In my opinion, Tunisian youth should receive privileged access to job vacancies.

2. In my opinion, working in the public sector is better than working in the private sector.

3. In my opinion, vocational training is good for finding a job.

4. In my opinion, it is appropriate that women work outside of the home.

<footnote n="11">¹¹ We assess the normalized difference additional to the *t*-statistic in order to account for the relatively high sample size. All normalized differences are below the critical threshold of 0.25 (Imbens and Wooldridge, 2009).</footnote>
To derive the share of respondents who find it appropriate that women work outside the house, the average number of stated agreements in the control group needs to be subtracted from the average number of stated agreements in the treatment group. In order for the derived share to be valid, however, the applied list experiment in itself needs to be valid. The validity is based on three assumptions: (i) successful randomization, (ii) no design effect, and (iii) no lying (Lépine et al., 2020; Porter et al., 2021).

The first assumption requires that individuals allocated to the list experiment treatment and control group are comparable and hence, on average, likely to agree with the same number of non-sensitive statements in any given list. Evidence for the comparability of both groups within the list experiment sample is given in Table A1 in Appendix A. Respondents in the treatment group (i.e., the group receiving an additional stigmatized statement) are on average less likely to be female, more likely to work for pay and less likely to be housewives. Moreover, respondents in the treatment group are on average significantly less likely to be interviewed by a female enumerator who is wearing a veil. Although these statistically significant differences mean that the null of equal means is rejected, the normalized differences in Column (7) indicate that the differences are small in size.

The second assumption is necessary so that the inclusion of the sensitive item does not change the number of positive answers to the non-sensitive items. We test this by implementing a statistical test developed by Blair and Imai (2012). The test estimates the joint probabilities of the responses to the stigmatized item and the non-stigmatized items. The null hypothesis of the test states that all estimated joint probabilities are positive ($\pi_v > 0$), which indicates that there is no design effect. In turn, the alternative hypothesis states that any of the estimated joint probabilities are negative, which would render the estimates based on the list experiment questionable. Probabilities cannot be negative by definition, so if some of the joint probabilities are, the test checks whether they have arisen by chance. To increase the power of the test, Blair and Imai (2012) propose using the method of generalized moment selection (GMS). As indicated in Table A2 in Appendix A, one joint probability is negative. However, we are confident that the assumption of no design effect still holds, because the Bonferroni-corrected p-value indicates that we cannot reject the null hypothesis of no design effect (at all conventional significance levels).<fnref n="12" />

The third assumption requires the absence of ceiling (a respondent would honestly answer “yes” to all non-sensitive items) and floor (a respondent would honestly answer “no” to all non-sensitive items) effects, so that individuals do not become reluctant to provide truthful answers if they believe they no longer benefit from the privacy of their responses (Glynn, 2013; Kuklinski et al., 1997). Thus, the existence of ceiling and (or) floor effects would lead to an underestimation of the true population supporting the sensitive item (Blair and Imai, 2012). Table A3 in Appendix A summarizes the number of statements that respondents in the control and treatment groups on average stated to agree with. The responses are well distributed in both groups and mainly concentrated around the two middle statements. Close to none of the respondents disagree with all statements, that is reporting “0”. By agreeing with all four statements in the treatment group, 26.6% are candid about revealing their support for women working outside the home. Yet, there are quite some respondents who report to agree with three statements in the control group, which may suggest a ceiling effect. We analyze this potential ceiling effect by running a regression of an indicator variable equal to one if the respondent in the control group reports to agree with three statements on observable individual (age, gender, marital status, educational level, employment status) and household characteristics (household size and wealth).<fnref n="13" /> Significant differences among these characteristics would allow seeing who is most likely under-reporting support for women working outside the home by not reporting to agree with four statements – i.e., most likely to hit the ceiling. The results are reported in Appendix A (see Table A4); we find no statistically significant effect of relevant characteristics on the indicator variable except for a marginally positive effect for housewives. This could indicate a ceiling effect for this group of respondents, implying that we slightly underestimate the true support for the sensitive item (Asadullah et al., 2021; Blair and Imai, 2012).

To conclude, while our list experiment satisfies the assumptions of (i) successful randomization and (ii) no design effect, we have to interpret results with caution regarding the (iii) “no liar” assumption. Therefore, when deriving the share of respondents who find it appropriate that women work outside the home when respondents are being asked indirectly (through the list experiment), as opposed to directly, we have to consider a slight underestimation of the true support.

### 3.3. Second-order beliefs

The second step of the opinion-matching method is to measure SOBs. We introduce a second, and orthogonal to the first, treatment arm where we split the sample again. This split took place within Group FOB_DQ and Group FOB_LE (see Fig. 2). Half of each group (those with even HH IDs) were asked about their SOBs without receiving any financial incentive (Group SOB_NI). The question was posed as follows: “If we ask 30 other young people of your age living in the area if they find it appropriate that women work outside the home, how many do you think will find it appropriate?” The other half of each group (those with uneven HH IDs) were asked about their SOBs and were incentivized financially according to the accuracy of their guess (Group SOB_FI): “We are interviewing a number of other young people in the village. You will be asked to guess how many of these people will find it appropriate that women work outside the home. If you make a very good estimate, you can win TND 60.<fnref n="14" /> The two respondents with the best estimates win the prize of TND 60 in the form of call credit that you can obtain at [SHOP]. If we ask 30 other young people of your age living in the area if they find it appropriate that women work outside the home, how many do you think will find it appropriate?”

<footnote n="12">¹² We follow Blair and Imai (2012) and use the Bonferroni corrected p-value to test the null hypothesis of no design effect.</footnote>

<footnote n="13">¹³ This test was not pre-specified, but is warranted due to the possibility of ceiling effects in our setting. See Online Appendix for further explanations of deviations from the pre-analysis plan.</footnote>

<footnote n="14">¹⁴ In 2022, the hourly minimum wage for a 40-hour week in the non-agricultural sector was TND 2.11. Hence, TND 60 amounts to approximately 28 hours of work paid under minimum wage.</footnote>
<table><caption>**Table 2**
No incentive (NI) and financial incentive (FI) sample description.</caption><thead><tr><th rowspan="2"></th><th colspan="2">NI</th><th colspan="2">FI</th><th colspan="3">NI - FI</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr><tr><th></th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Diff.</th><th>p-value</th><th>Norm. diff.</th></tr></thead><tbody><tr><td>Age (years)</td><td>23.32</td><td>4.36</td><td>23.43</td><td>4.47</td><td>-0.11</td><td>0.54</td><td>0.02</td></tr><tr><td>Female</td><td>0.55</td><td>0.50</td><td>0.50</td><td>0.50</td><td>0.05</td><td>0.03</td><td>-0.06</td></tr><tr><td>Married</td><td>0.09</td><td>0.29</td><td>0.09</td><td>0.29</td><td>0.00</td><td>0.84</td><td>-0.01</td></tr><tr><td>Primary education</td><td>0.07</td><td>0.26</td><td>0.08</td><td>0.27</td><td>-0.01</td><td>0.75</td><td>0.01</td></tr><tr><td>Secondary education</td><td>0.64</td><td>0.48</td><td>0.66</td><td>0.48</td><td>-0.02</td><td>0.33</td><td>0.03</td></tr><tr><td>Vocational education</td><td>0.10</td><td>0.31</td><td>0.11</td><td>0.32</td><td>-0.01</td><td>0.50</td><td>0.02</td></tr><tr><td>Tertiary education or higher</td><td>0.19</td><td>0.39</td><td>0.15</td><td>0.36</td><td>0.04</td><td>0.04</td><td>-0.06</td></tr><tr><td>Unemployed, work w/o pay</td><td>0.34</td><td>0.48</td><td>0.36</td><td>0.48</td><td>-0.02</td><td>0.38</td><td>0.03</td></tr><tr><td>Student</td><td>0.38</td><td>0.49</td><td>0.36</td><td>0.48</td><td>0.02</td><td>0.28</td><td>-0.03</td></tr><tr><td>Paid worker</td><td>0.13</td><td>0.34</td><td>0.15</td><td>0.36</td><td>-0.02</td><td>0.16</td><td>0.04</td></tr><tr><td>Independent worker</td><td>0.05</td><td>0.22</td><td>0.06</td><td>0.23</td><td>-0.01</td><td>0.50</td><td>0.02</td></tr><tr><td>Housewife</td><td>0.10</td><td>0.30</td><td>0.08</td><td>0.27</td><td>0.02</td><td>0.06</td><td>-0.06</td></tr><tr><td>Household size (members)</td><td>4.69</td><td>1.43</td><td>4.75</td><td>1.49</td><td>-0.06</td><td>0.33</td><td>0.03</td></tr><tr><td>Asset index (score)</td><td>0.12</td><td>0.99</td><td>0.13</td><td>0.97</td><td>-0.01</td><td>0.86</td><td>0.01</td></tr><tr><td>Interviewer is female</td><td>0.70</td><td>0.46</td><td>0.68</td><td>0.46</td><td>0.02</td><td>0.43</td><td>-0.02</td></tr><tr><td>Interviewer has veil</td><td>0.39</td><td>0.49</td><td>0.37</td><td>0.48</td><td>0.02</td><td>0.36</td><td>-0.03</td></tr><tr><td>N</td><td colspan="2">1022</td><td colspan="2">1340</td><td colspan="3"></td></tr><tr><td colspan="8">F-test of joint significance:</td></tr><tr><td colspan="8">F-stat</td></tr><tr><td colspan="8">p-value</td></tr></tbody></table>

Note: This table presents individual, household and interviewer characteristics of respondents receiving the SOB question with and without financial incentives. Columns (1) and (3) report the mean values for each group. Columns (2) and (4) report the respective standard deviation. Column (5) shows the difference between the two group means, and Column (6) shows the p-values for the associated test of equality of means. Column (7) presents normalized differences. The joint null of equal means is not rejected at standard levels.

Table 2 presents the summary statistics for the sample without financial incentives (i.e., 1022 respondents) and the sample with financial incentives (i.e., 1340 respondents).<fnref n="15" /> Survey participants with financial incentives are on average less likely to be female and have tertiary or higher education, and female respondents are less likely to be housewives. Again, the normalized differences render these imbalances to be negligible.

Given the order of the two experimental treatment arms, it is possible that being exposed to different FOB questions in the first experimental arm may affect how survey participants respond to the SOB question in the second experimental arm. We test for these experimental treatment spillover effects (Gaines et al., 2007; Transue et al., 2009) in two ways. First, we test whether having been exposed to the direct question or list experiment in the first step of the experiment has led to differential responses in the second step where we elicit SOBs. As indicated in Table B1 in Appendix B, we find no effect, neither for the full sample nor when splitting the sample according to having received financial incentives or not. Second, we also test for differential responses within the list experiment sample as the control group did not see the “sensitive” item about women working outside their home. As summarized in Table B2 in Appendix B, we again find no significant effect. We interpret the lack of significant differences as supportive of the idea that our two experimental treatment arms are self-contained, and that neither having been asked the direct question nor the appearance of the sensitive item in the list experiment acts as some form of prime, that increases salience of the topic and affects cognitive effort and/or beliefs about others.<fnref n="16" />

# 4. Experimental results

## 4.1. How robust are first-order beliefs?

When asked directly, 90% of the respondents on average state that they find it (very) appropriate that women work outside the home. This number paints a rather progressive picture of the personal beliefs of young Tunisians, and raises questions on whether

<footnote n="15">¹⁵ 297 observations (i.e., 11.2%) did not respond to the SOB question without financial incentives. We investigate the sample of missing observations further and compare them to the remaining sample of 2362 observations with valid responses to the SOB question. Table E1 in Appendix E contains the comparison in observable characteristics. Based on the results from the *t*-test, the missing sample is less likely to have secondary education and live in slightly smaller households. Yet, the respective normalized differences in Column (7) indicate that these differences are not of economic importance. Moreover, the joint null of equal means is not rejected at standard levels. We conclude that the missing sample of 297 observations is overall very comparable in observable characteristics to the remaining sample of 2362 observations. Hence, we do not believe that the missing observations are cause for concern.</footnote>

<footnote n="16">¹⁶ We thank one of the anonymous reviewers for the suggestion to test for the experimental treatment spillover effects. The tests for Tables B1 and B2 were not pre-specified. See Online Appendix for further explanations of deviations from the pre-analysis plan.</footnote>
<table><caption>**Table 3**

Comparing first-order beliefs (FOBs) using different measurement techniques.</caption><thead><tr><th>Share of respondents who find it (very) appropriate that women work outside their home:</th><th>(1) Full sample</th><th>(2) Female</th><th>(3) Male</th></tr></thead><tbody><tr><td>FOBs</td><td></td><td></td><td></td></tr><tr><td>Direct question (DQ)</td><td>0.90</td><td>0.96</td><td>0.84</td></tr><tr><td>List experiment (LE)</td><td>0.68</td><td>0.80</td><td>0.55</td></tr><tr><td>Difference in means (DQ - LE)</td><td>0.22</td><td>0.16</td><td>0.29</td></tr><tr><td><i>p-value</i></td><td>0.00</td><td>0.02</td><td>0.00</td></tr><tr><td><i>N</i></td><td>2637</td><td>1382</td><td>1255</td></tr></tbody></table>

Note: This table compares FOBs inferred from direct question (DQ) or list experiment (LE). Column (1) shows the share of respondents who find it (very) appropriate that women worked outside their home either reported directly (DQ) or as derived from the list experiment (LE). Column (2) shows the respective shares for the subsample of women and Column (3) for the subsample of men. The *p*-values stem from a test of equality of means between FOBs of the DQ and the LE group.

these FOBs suffer from any type of response bias. Although enumerators were both men and women from Tunisia, and responses were promised to be kept anonymous, respondents may have felt social pressure to answer the question about their own views in a more progressive way when being asked directly in a face-to-face interview. We assess this concern by comparing the stated degree of support for women working outside the home with the degree of support inferred from the list experiment.

As indicated in Table 3, 68% of the respondents in the list experiment sample on average state that they find it appropriate that women work outside the home.<fnref n="17" /> Even when factoring in a slight underestimation due to possible ceiling effects, this share is significantly lower than the share of the sample of respondents that has been asked directly. We do not assume the slight underestimation in the list experiment to explain the entire observed difference in personal beliefs measured with the two elicitation methods. Hence, the stated degree of support is much more progressive than opinions revealed through our list experiment. Independently of the method, we observe that, overall, women report more personal support towards women working outside the home than men. Such gender differences are also observed in Bursztyn et al. (2024), who study gender norms (including norms on women working outside of the home) in 60 countries worldwide. While Tunisia is not part of their country sample, they find the largest gender differences in the share of support for women working outside of the home in other MENA countries such as Jordan and Algeria.<fnref n="18" />

The application of both methods shows the potential range that personal beliefs can take within our sample of young Tunisians. A plausible explanation for the difference in FOB measures is that the FOBs derived from the direct question may be biased upward, with the bias being induced by social experimenter demand effects.<fnref n="19" /> In fact, our finding is consistent with evidence from a supplementary qualitative study conducted to understand social norms perceived by Tunisian youth in the disadvantaged neighborhoods surveyed (Ziadi et al., 2023). In the focus group discussions (FGDs) conducted in Hkaima (Mahdia) and Métouia (Gabès), the two areas where the survey indicated opposing norms, with Hkaima being the most conservative and Métouia being most progressive, participants generally said that it could be appropriate that women worked outside the home when asked.<fnref n="20" /> Yet, when further probing into their responses, it became apparent that there were several conditions attached to women working outside the home. For instance, participants considered it to be appropriate only if co-workers were also female, if they managed to fulfill their duties as a housewife and to the children, if they worked close to home, if they used safe transport to get to work, and if women's work was considered “decent” on a number of contractual and social protection aspects. These conditions effectively lower the true support for women working outside their home. Similar patterns are observed in a study conducted in Jordan (Gauri et al., 2019).

<footnote n="17">¹⁷ Note that the share of 0.68 is derived by subtracting the average number of stated agreements in the control group (1.97) from the average number of stated agreements in the treatment group (2.65). Another method to derive the share that supports the sensitive item is to regress the number of statements on the treatment group indicator. This approach also allows to control for possible imbalances in observable characteristics between the treatment and control group. In Table A5, we show the results of this exercise. When we include the characteristics that have been found to be imbalanced between the treatment and control group based on the *t*-test (see Table A1), the share that supports the sensitive item increases to 71%. The difference between the two coefficients (i.e., 0.68 in Column (1) and 0.71 in Column (2)) is borderline significant at the 10%-level. Hence, we consider the 68% to be the most conservative level of support reported from the list experiment. Note that this test was not pre-specified. See Online Appendix for further explanations of deviations from the pre-analysis plan.</footnote>

<footnote n="18">¹⁸ The gap in shares between genders and the magnitude of support by gender in Jordan and Algeria is also similar to what we observe in our sample with the FOB list experiment measure. Bursztyn et al. (2024), however, elicit the FOBs in their sample with a direct question through a phone survey.</footnote>

<footnote n="19">¹⁹ As indicated in Section 3.2, we pool those respondents who find it appropriate that women work outside the home and those who find it very appropriate. Of the 90% supporting women working outside the home, 36% even find it very appropriate. This relatively high share may be further perceived as an indicator for respondents feeling social pressure to answer more progressively.</footnote>

<footnote n="20">²⁰ A total of 12 FGDs were held, with six in Hkaima and six in Métouia. The groups were further stratified by gender - as it was considered that participants could more freely express themselves on the topic in single gender groups - and by age cohort (15–18, 19–24 and 25–30 years old) to allow maximum homogeneity within the groups to observe changes in perceived norms at this stage in life. The participants were out-of-sample participants from the main survey.</footnote>
Another interpretation is that the FOBs derived from the list experiment may be biased downward. The tests of the list experiment reveal a small deviation from the “no liar” assumption, which can induce bias in the personal beliefs elicited through the list experiment (Ahlquist, 2018). In our case, this might result in an underestimation of true beliefs. Furthermore, a recent strand of literature warrants caution of the general validity of list experiments due to non-strategic misreporting caused by the complexity of the approach (Haber et al., 2018; Kuhn and Vivyan, 2022). We also follow Cullen (2023) and explore whether there are characteristics that potentially explain misreporting under either of the two survey methods – for instance, whether certain traits of the respondent or enumerator explain sensitivity to social desirability under the direct question.<fnref n="21" /> Here, we explore the correlations between the characteristics of the respondents and the support reported for women working outside their home according to each survey method. For the direct question, we regress the stated support FOB of individual i living in area a and interviewed by enumerator e on the respective respondent/interviewer characteristic X<sub>iae</sub>. Given that both personal beliefs and social norms are influenced by the social network, we include area fixed effects (μ<sub>a</sub>). Furthermore, since interviewer characteristics have been found to influence response patterns towards sensitive issues (Nillesen et al., 2021; Reitmann et al., 2020) we control for whether the interviewer is female and if so, whether she is wearing a hijab (τ<sub>e</sub>).

$$ FOB_{iae} = \alpha + \delta_{DQ}X_{iae} + \mu_a + \tau_e + \epsilon_{iae} \quad (1) $$

For the list experiment, we regress the number of supported statements S<sub>iae</sub> on the respective respondent/interviewer characteristic X<sub>iae</sub>, the treatment status T<sub>iae</sub> and an interaction term of the two:

$$ S_{iae} = \alpha + \beta T_{iae} + \gamma X_{iae} + \delta_{LE}(T_{iae} \times X_{iae}) + \mu_a + \tau_e + \epsilon_{iae} \quad (2) $$

For each method, δ is the parameter of interest as it indicates the average support for women working outside the home for the respective type of respondent as depicted by the characteristics. Here, we consider gender, level of education, wealth and self-efficacy using Chen et al.’s (2001) New General Self-Efficacy (eight-item) Scale.<fnref n="22" /> We moreover include enumerator gender to assess whether female enumerators affect responses under either method. To assess potential misreporting across the methods, we test the difference between the coefficients δ<sub>DQ</sub> and δ<sub>LE</sub>. The results are summarized in Table C1 in Appendix C. We observe and show that while having secondary education and higher levels of self-efficacy are positively correlated with more favorable gender attitudes under the direct question, we cannot reject the null of equality of coefficients under the direct questioning and the list experiment. Hence, while these characteristics may influence the reported support for women working outside their home under the direct question, their effect is not significantly different from how these characteristics affect reporting under the list experiment.

A comparison of the FOB measures by method and gender reveals an interesting pattern: the gap in FOBs reported directly and indirectly is larger for men than for women (29 versus 16 percentage points). A possible explanation is that men are more prone to provide socially desirable answers to questions on sensitive topics when being asked directly. Given the nature of the topic around gender, men may feel a greater pressure to hide gender-stereotypical preferences than women. This pattern has also been observed in studies on other gender-related topics such as views towards transgender people in the labor market (Aksoy et al., 2025), attitudes towards sexism in the workplace (Boring and Delfgaauw, 2024) and views towards female political leaders (Burden et al., 2017; Setzler, 2019).<fnref n="23" />

The overall discrepancy between the stated support for women working outside the home (derived by the direct question) and the degree of support inferred using the list experiment is different to what a recent study in a similar context found. In their survey with a nationally representative sample of about 1500 married Saudi men, for instance, Bursztyn et al. (2020) also compare the stated degree of support for women working outside the home (82%) with the degree of support inferred using a list experiment (80%). The difference is only minimal, indicating that in their setting social desirability bias or experimenter demand effects are low.<fnref n="24" /> The authors compare the FOBs to the SOBs elicited among the same sample and find an average wedge of 25 percentage points, implying that a substantial number of Saudi men experiences pluralistic ignorance. That is, they hold a rather progressive private opinion towards women working outside the home, but they incorrectly believe that most other Saudi men hold a more traditional opinion. Yet, if we follow the same strategy, we find different levels of pluralistic ignorance depending on which FOB measure we are following. We return to this issue after analyzing the SOBs.

<footnote n="21">²¹ This test was not pre-specified. See Online Appendix for further explanations of deviations from the pre-analysis plan.</footnote>

<footnote n="22">²² Self-efficacy – a person’s belief in their ability to plan and carry out actions to achieve goals – could influence FOBs in (at least) two ways. First, individuals with high self-efficacy are more likely to challenge stigmatized views, despite social or family pressure, as those are seen as hurdles to overcome, not personal failures. They might thus be more likely to believe women can work outside the home, since no barrier feels insurmountable, in contrast to individuals with low self-efficacy, who would tend to accept stigmatized views as unavoidable. Second, when asked sensitive questions, self-efficacy is expected to affect whether people give socially desirable answers. Individuals with high self-efficacy are more confident in their beliefs, and would not change their responses to fit social norms, even if their views are stigmatized, while people with low self-efficacy fear judgment and conflict, and tend to give answers that match social expectations, even if they do not reflect their true opinions.</footnote>

<footnote n="23">²³ Aksoy et al. (2025) find that men are more likely to report socially desirable views regarding transgender people in the labor market when asked directly, as opposed to being asked indirectly via a double list experiment. Boring and Delfgaauw (2024) find substantially more social desirability bias among men in directly reported attitudes towards sexism in the workplace. Burden et al. (2017) and Setzler (2019) find that men are more likely to hide their concerns about female political leaders through a measure that provides more cover, than when being asked explicitly.</footnote>

<footnote n="24">²⁴ The sample in Bursztyn et al. (2020) differs strongly from ours. While we interview adolescent male and female Tunisians face-to-face, Bursztyn et al. (2020) conduct an online survey with a national sample of married Saudi men only.</footnote>
<table><caption>**Table 4**
Average second-order beliefs (SOBs) with and without financial incentives.</caption><thead><tr><th>Perceived share of respondents finding it appropriate that women work outside their home:</th><th>(1) Full sample</th><th>(2) Female</th><th>(3) Male</th></tr></thead><tbody><tr><td>SOBs (full sample)</td><td>0.72</td><td>0.76</td><td>0.67</td></tr><tr><td>No incentive (NI)</td><td>0.72</td><td>0.78</td><td>0.65</td></tr><tr><td>Financial incentive (FI)</td><td>0.71</td><td>0.75</td><td>0.68</td></tr><tr><td>Difference in means (NI - FI)</td><td>0.01</td><td>0.03</td><td>-0.03</td></tr><tr><td><i>p</i>-value</td><td>0.67</td><td>0.02</td><td>0.03</td></tr><tr><td><i>N</i></td><td>2362</td><td>1233</td><td>1129</td></tr></tbody></table>

Note: This table presents SOBs elicited with or without financial incentives. Column (1) shows the mean stated perceived share of respondents who find it appropriate that women worked outside their home for the entire sample, and, separately, for the sample that received no financial incentive (NI) and the sample that received financial incentives (FI). Column (2) reports respective means for the subsample of women and Column (3) for the subsample of men. The *p*-values stem from a test of equality of means between SOBs of the NI and FI group.

## 4.2. Are financial incentives necessary for accurate second-order beliefs?

We assess SOBs by asking respondents how many of 30 other young people of the same age in the area find it appropriate that women work outside the home. For the analysis, we will express the stated number in shares. A higher share indicates more progressive perceived social norms towards women working outside the home. In order to test whether financial incentives yield different SOBs using the opinion-matching method, we conducted the second step with a group receiving an incentive and another group receiving none. Researchers routinely use financial incentives to encourage cognitive effort among respondents (Burdea and Woon, 2022; Gächter and Renner, 2010; Schotter and Trevino, 2014), but recent evidence suggests that having precise knowledge on incentives may in fact reduce belief accuracy in some contexts including different methods of belief elicitation (Danz et al., 2022).

Table 4 summarizes the average guesses of respondents for the full sample, and separately for the group without and the group with financial incentives. We do not find a significant difference in responses between the two groups; on average, young people believe that between 71–72% of young people of the same age in the area find it appropriate that women work outside their home.

Our findings could indicate that financial incentives are not necessary to incentivize cognitive effort.<fnref n="25" /> One reason may however have to do with respondent characteristics, that predict higher or lower FOBs and therefore also lead to group-specific differences in SOBs and the resulting impact of financial incentives. We address this issue below where we investigate the impact of incentives for different subgroups.

In line with the framing literature (see e.g., Brewer, 2003; Haider-Markel and Joslyn, 2001; Reitmann et al., 2020; Van Gorp et al., 2009), we hypothesize that the need for financial incentives to induce cognitive effort for separating personal from second-order beliefs may co-vary with specific socio-demographic characteristics (e.g., education and wealth), personal traits (e.g., self-efficacy) or the level of involvement with the issue under investigation (e.g., gender). Therefore, we run the following regression:

$$SOB_{iae} = \alpha + \beta FI_{iae} + \gamma X_{iae} + \delta(FI_{iae} \times X_{iae}) + \mu_a + \tau_e + \epsilon_{iae}, \quad (3)$$

where we regress the stated perceived share of respondents finding it appropriate that women work outside their home (SOB<sub>iae</sub>) on the respective respondent characteristic (X<sub>iae</sub>), an indicator variable that takes on the value of one if the respondent received a financial incentive (FI<sub>iae</sub>), and an interaction of the two. In all regressions, we control for interviewer characteristics and include area fixed effects. Additionally, we include controls for respondent and household characteristics. We will focus on the effect of gender in Table 5, and list the findings for the remaining characteristics in Table D1 in Appendix D.<fnref n="26" />

Without financial incentives, men state that 64.7% other young Tunisians in the area would find it appropriate that women work outside the home. Women, on the other hand, state a higher share of 77.8% without financial incentives (see also coefficient “Female” in Table 5). Given that women overall hold more progressive personal views about FLFP than men (see Table 3), it seems that without financial incentives, respondents align their responses to the SOB-question with their personal views (FOBs). Yet, once financial incentives come into place, men report a higher share compared to no financial incentives (see coefficient “FI” in Table 5), and women report a lower share (see interaction term between “FI” and “Female” in Table 5). Since these opposing effects – financial incentives lead men and women to move away from non-incentivized SOBs in opposite directions – are similar in magnitude, the

<footnote n="25">²⁵ Although ex-post calculations on the Minimum Detectable Effect (MDE) size reveal that we lack power to distinguish an insignificant effect from a very small effect (calculations available upon request), we believe that a difference of 0.01 – even if significant – is economically negligible.</footnote>

<footnote n="26">²⁶ We find that wealthier respondents and more self-efficacious respondents report a higher perceived share of respondents finding it appropriate that women work outside their home – they report more progressive perceived social norms. One explanation, as detailed earlier, is that such types of respondents are more likely to challenge stigmatized views, as those are considered a challenge to overcome. A second reason is that scoring higher on a self-efficacy scale should amplify the expected effect of the financial incentives, as it is associated with greater effort when performing a task, a dynamic that is explicitly nurtured by the financial incentives.</footnote>
<table><caption>**Table 5**
Correlations between second-order beliefs (SOBs) and respondent gender.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr><tr><td></td><td colspan="2">SOB: Perceived share of respondents finding it appropriate that women work outside their home</td></tr></thead><tbody><tr><td>Financial incentive (FI)</td><td>0.04<sup>**</sup><br>(0.02)</td><td>0.04<sup>**</sup><br>(0.02)</td></tr><tr><td>Female</td><td>0.14<sup>***</sup><br>(0.01)</td><td>0.14<sup>***</sup><br>(0.01)</td></tr><tr><td>FI × Female</td><td>-0.07<sup>***</sup><br>(0.02)</td><td>-0.07<sup>***</sup><br>(0.02)</td></tr><tr><td>Constant</td><td>0.66<sup>***</sup><br>(0.03)</td><td>0.59<sup>***</sup><br>(0.06)</td></tr><tr><td>N</td><td>2362</td><td>2362</td></tr><tr><td>R<sup>2</sup></td><td>0.20</td><td>0.21</td></tr><tr><td>Individual controls</td><td>NO</td><td>YES</td></tr><tr><td>Household controls</td><td>NO</td><td>YES</td></tr><tr><td>Interviewer controls</td><td>YES</td><td>YES</td></tr><tr><td>Area fixed effects</td><td>YES</td><td>YES</td></tr></tbody></table>

Note: This table shows OLS estimates, with robust standard errors in parentheses, clustered at the area level. The outcome is the stated perceived share of respondents finding it appropriate that women work outside their home (i.e., SOBs). Column (1) displays coefficient estimates, controlling for interviewee characteristics (female, wears a veil) and area fixed effects. In Column (2), additional controls include individual (age, marital status, education, employment) and household characteristics (household size, asset index score). *** $p < 0.001$, ** $p < 0.005$, * $p < 0.01$.

sample average effect of financial incentives is attenuated towards zero. We further illustrate this in Fig. 3, where we map the shift in reported SOBs induced by financial incentives for the entire sample (circle), and men (diamond) and women (square) separately. While there is no effect for the combined sample, the split sample effects indicate that men on average report a 4 percentage points higher share and women report a 4 percentage points lower share. Thus, when being inquired "If we ask 30 other young people of your age living in the area if they find it appropriate that women work outside the home, how many do you think will find it appropriate?", young male Tunisians that received the financial incentive respond on average one person more, and young female respondents that got the incentive one person less.<fnref n="27" /> With financial incentives, the stated perceived social norms of young Tunisian men and women become more aligned, possibly because both men and women exert more effort to separate their personal beliefs from the perceived prevailing social norm. This suggests financial incentives induce more accurate measures of perceived social norms.<fnref n="28" />

# 5. Discussion

## 5.1. Differences in first-order beliefs

The first part of the survey experiment tests the sensitivity of FOBs to using different elicitation methods: the direct question and the list experiment. This test has also been implemented by Bursztyn et al. (2020) on the same topic in the MENA region. They do not find meaningful differences between elicitation methods, whereas our study shows differences in support for women working outside their home that are as large as 22 percentage points for the pooled sample, 16 percentage points for the female sample and 29 percentage points for the male sample. By finding these discrepancies between elicitation methods, our findings are in line with the findings of other studies investigating personal beliefs around gender-related issues by comparing the responses from a direct question to those from a list experiment. Studies that elicited personal beliefs towards sexism and diversity, equity, and inclusion policies in the workplace (Boring and Delfgaauw, 2024), transgender people in the labor market (Aksoy et al., 2025), intimate partner/gender-based violence, female genital mutilation and child marriage (Asadullah et al., 2021; Bertelli et al., 2025; De Cao and Lutz, 2018; Gibson et al., 2018, 2022) find significant discrepancies between elicitation measures – even those that were conducted online. Depending on the topic, stated support for the respective issue is significantly lower or higher than the support derived from the list experiment. The direction of the difference always points toward social desirability bias, in which respondents may feel pressured to respond in

<footnote n="27">²⁷ An effect of (-)0.04 corresponds to 4 percentage points, which translates into around 1 person, with 30 people representing 100%.</footnote>

<footnote n="28">²⁸ In this population of young, educated Tunisians, it is reasonable to assume that men and women have access to similar information sources and networks, that help shape these perceptions. We should not expect large differences in perceived social norms unless respondents simply base their SOBs on their FOBs, which seems to be the case in the non-incentivized group.</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 3.** Shift in second-order beliefs (SOBs) with financial incentives, by gender. Note: This figure presents coefficient estimates illustrating a shift in stated SOBs when financially incentivized for the full sample (circle), men (diamond) and women (square) with 95% confidence intervals. Coefficient estimates stem from an OLS regression where standard errors are clustered at the area level. The outcome is the stated perceived share of respondents finding it appropriate that women work outside their home (i.e., SOBs). Included controls are interviewer characteristics (female, has veil) and area fixed effects.</figcaption></figure>

line with social norms, the local legal framework, or the experimenter's belief as perceived by the respondent (see e.g., Bertelli et al., 2025).

We discuss three potential reasons for the differences in our FOB measures, starting with the one that we consider most plausible based on findings from the above-mentioned studies.

The first-best explanation is a difference in the level of (perceived) coverage. In other studies, for example Bursztyn et al. (2020) and Bursztyn et al. (2024), respondents could privately record their responses on their phone or online, while in our setting elicitation was done during a face-to-face survey. Kadam et al. (2025) test for differential responses to sensitive questions by randomizing the survey mode (phone or in-person) and enumerator gender among ultra-poor women in Ethiopia. They find that specific subgroups (in their case the least empowered women) were more likely to oppose domestic violence – thereby going against the social norm – when interviewed through a phone survey that provided more privacy than when interviewed in-person. If people gave socially desirable answers when asked directly, but not in the list experiment, there would be a difference in responses between both methods and in the direction that we observe (i.e., with lower approval for women working outside their home in the list experiment). We do not find that (respondent nor enumerator) gender, education, wealth or self-efficacy explains the differences in FOBs.

Second, due to a potential violation of the “no liar” assumption, there is the possibility that respondents may have under-reported their true FOBs in the list experiment. We find no strong evidence of this, except for a marginally significant ceiling effect for housewives (which comprise only 9% of our sample), which would not explain the full discrepancy of 22 percentage points.

Lastly, respondents could have made non-strategic reporting errors in the list experiment due to the complexity of the task as shown by Haber et al. (2018) and Kuhn and Vivyan (2022). While we cannot rule this out, our population is relatively well educated, and there is no systematic observation from the field teams that would suggest respondents did not understand the instructions of the list experiment.

Despite not being able to pin down the exact reason(s) for having divergent responses on FOBs, the significant range in responses have important implications for assessing the level of pluralistic ignorance. The short answer to this question is that pluralistic ignorance may or may not be present in our sample, depending on which method of eliciting FOBs we deem more credible. We illustrate this in Fig. 4 that displays the two measures of personal beliefs and the measure of perceived social norms.

The figure presents the cumulative distribution function (CDF) of financially incentivized respondents' guesses about the share of young Tunisians in their area agreeing with the statement that it is appropriate that women work outside the home. The vertical line shows the average share of young Tunisians agreeing with the statement. Dashed/dotted vertical lines show the true proportion of respondents agreeing with the statement; the dashed line represents the degree of support derived from the direct question and the dotted line the degree of support inferred from the list experiment. If we believe that strategic and (or) non-strategic misreporting are of low concern in the list experiment and, hence, FOBs elicited through the list experiment are unbiased, one would conclude personal beliefs and perceived social norms are well aligned. In this case, pluralistic ignorance is not an issue. However, if we instead belief
<figure><img src="image_4.png" /><figcaption>**Fig. 4.** (Mis)perceptions about others' beliefs. Note: This figure presents the cumulative distribution function (CDF) of financially incentivized respondents' guesses about the share of young Tunisians in their area agreeing with the statement that it is appropriate that women work outside the home. The vertical line shows the average perceived share (SOBs w/ FI). Dashed/dotted vertical lines show the true proportion of respondents agreeing with this statement; the dashed line represents the degree of support derived from the direct question (DQ) and the dotted line the degree of support inferred from the list experiment (LE). The difference in mean shares of 19 percentage points between SOBs w/ FI and DQ is statistically significant ($p$-value = 0.00). The difference in mean shares of 3 percentage points between SOBs w/ FI and LE is not statistically significant ($p$-value = 0.49).</figcaption></figure>

what people report when asked the direct question (hence, assuming low concerns of social desirability), we see a wedge between FOBs and SOBs of about 19 percentage points (statistically significant at the 1%-level), indicating high levels of pluralistic ignorance among our target population.

Other studies that investigate differences between first- and second-order beliefs regarding FLFP consistently find that people tend to underestimate societal support for women working outside their home (Aloud et al., 2020; Barigozzi and Montinari, 2025; Bursztyn et al., 2020, 2024; Cameron et al., 2024; Cortés et al., 2024; Gauri et al., 2019). Apart from Bursztyn et al. (2020), all of these studies elicit FOBs using a direct question. The majority of studies conduct their surveys online (Aloud et al., 2020; Barigozzi and Montinari, 2025; Bursztyn et al., 2020; Cameron et al., 2024; Cortés et al., 2024) or via telephone (Bursztyn et al., 2024). Some studies ask more nuanced questions. For instance, Gauri et al. (2019) ask whether married (as opposed to unmarried) women should be allowed to work, and Cortés et al. (2024) ask whether women with children should return to work. Gauri et al. (2019) found that people in Jordan underestimate support for unmarried women's right to work but overestimate support for married women's right to work. Cameron et al. (2024) elicited social norms separately by gender and found that men and women in Indonesia significantly underestimated the extent of women's support but accurately perceived men's support.

Knowing whether there is pluralistic ignorance in our sample is further complicated when taking into account the differences in first- and second-order beliefs by gender.<fnref n="29" /> While we still observe an underestimation of the support for women working outside their home when relying on the direct question for both genders, we find that men overestimate support when using the list experiment. This can be explained by the fact that when following the list experiment measure, the male sample holds more conservative personal beliefs about women working than the female sample. It follows that men believe that the level of support of others is more progressive than their own personal beliefs. Women's estimates of the level of support, in turn, are relatively accurate. A potential explanation is that this topic concerns women themselves; they may discuss this issue with others and are therefore better informed.

## 5.2. Financial incentives and belief accuracy

The second feature we address, which has never been tested, is the need for financial incentives for eliciting perceived social norms with the opinion-matching method. The rationale for incentives is the underlying idea that thinking about others' beliefs

<footnote n="29">²⁹ For more details, see Appendix F for a replication of Fig. 4 split by gender. We thank one of the anonymous reviewers for the suggestion to investigate the differences in gender in more detail. Fig. F1 was not pre-specified. See Online Appendix for further explanations of deviations from the pre-analysis plan.</footnote>
requires a certain cognitive effort that needs to be incentivized to prevent respondents from making no effort and just report their FOBs as SOBs. The incentive usually takes the form of financially remunerating respondents proportional to the accuracy of their guesses. We randomly assigned respondents to a group that received financial incentives and a group that received none, and find no significant difference on average. One may conclude that incentivizing belief elicitation does not lead to greater belief accuracy. Yet, once we separate by gender, we observe that incentives lead both men and women to revise their SOBs. Non-incentivized SOBs for women are higher than those for non-incentivized men. Once incentives come into play, we see both men and women deviating from non-incentivized SOBs and providing answers that are more similar. Incentivized men report more progressive norms than non-incentivized men. Incentivized women report more conservative norms than non-incentivized women. Hence, incentivized men and women report SOBs that are less aligned with their FOBs. Since the order of magnitude is similar for men and women, and the net effect is close to zero, one would falsely conclude that average SOBs do not change with incentives. In fact, men and women's beliefs become more aligned, which lends credibility to the idea that financial incentives in our settings are necessary to improve belief accuracy.

## 6. Conclusion

Social norms have been receiving increased attention in the literature as a key driver to explain the inertia of several societal challenges – including gender equality and FLFP (see e.g., Alesina et al., 2013; Bursztyn et al., 2020; Bertrand et al., 2015; Bernhardt et al., 2018; Fernández, 2013). This has spurred research on how to measure social norms, with an emphasis on using incentive-compatible methods (see e.g., Aycinena et al., 2024a; Bicchieri and Xiao, 2009; Bicchieri and Chavez, 2010; Dimant, 2023; Kimbrough and Vostroknutov, 2016; Krupka and Weber, 2013).

The opinion-matching method is one of such methods. It uses a two-step approach to elicit first- and second-order beliefs about the appropriateness of a certain behavior. We use it to test two of its common experimental features, in a survey on support for women working outside the home among young Tunisians living in disadvantaged neighborhoods. First, we find that personal beliefs about working women differ strongly by elicitation method; stated support is up to 22 percentage points higher when asked using a direct question than when asked indirectly through a list experiment. Second, financial incentives increase the accuracy of guesses on perceived social norms; incentivized men and women are less inclined to align their stated SOBs with their FOBs.

Our findings have implications for assessing the level of pluralistic ignorance. The prevalence of pluralistic ignorance in our study strongly depends on the elicitation method applied in the first step of the opinion-matching method. Using the direct question, we find a strong misalignment between average personal beliefs and perceived social norms, indicating high levels of misperception among young Tunisian men and women. In contrast, there is no pluralistic ignorance when using results of the list experiment – at least among the female sample, where average personal beliefs are closely aligned with perceived social norms.

Policy solutions would also be very different depending on which result one believes. If the former, potential powerful and low-cost policy instruments include information provision interventions to correct misperceptions as in Aloud et al. (2020), Bursztyn et al. (2020), Cameron et al. (2024) and Cortés et al. (2024). If the latter, among the female sample, social norms are well aligned with personal beliefs, and indicate, on average, low support for women working outside of the house. This support is even lower among the male sample, and personal beliefs seem to be far less influenced by what others think. If policymakers are concerned with increasing FLFP, they would have to carefully investigate whether beliefs and behaviors are amenable by, for instance, improving working conditions for women as demonstrated by Kondylis et al. (2020) and Garlick et al. (2025), or creating an environment in which it is acceptable for women to work (Gauri et al., 2019).

Does the elicitation of perceived social norms require financial incentives? While providing incentives could have significant cost implications for the experiment, our findings suggest this trade-off might be necessary when there is reason to suspect that subgroups within a population may hold systematically different or stronger personal views on a sensitive topic, as in our case where men and women hold different personal views related to gender roles. Although our average SOB measure appeared unbiased, unincentivized gender specific measures of SOBs differ for men and women in our sample, whereas incentivized SOBs actually converge. In future work, researchers on limited budgets could test lower levels of incentives, or explore options to increase people's internal motivation to exert cognitive effort in providing accurate beliefs of others.

## Data availability

Data will be made available on request.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Micheline Goedhuys, Eleonora Nillesen and Clotilde Mahé report financial support was provided by Agence Française de Développement (AFD). Micheline Goedhuys and Eleonora Nillesen received funds from AFD to finance the data collection. Part of these funds were also used to finance Clotilde Mahé as a consultant to support the data collection. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements

This research was supported by funds from Agence Française de Développement (AFD). AFD had no involvement in the preparation of this article; the findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of AFD. We thank Center of Arab Women for Training and Research (CAWTAR) for valuable comments on the study design. The authors also thank Samy Kallel, Director General of BJKA Consulting, and his team for the professional implementation of the survey. We thank seminar participants at the German Development Economics Conference (Dresden), Nordic Conference in Development Economics (Gothenburg), Leibniz University Hannover and UNU-MERIT (Maastricht) for valuable comments. We also thank the co-editor, Daniele Nosenzo, and two anonymous referees for their constructive feedback and suggestions. Part of this paper was written when Nillesen was visiting the Vancouver School of Economics, University of British Columbia, Canada, during the academic year 2024/2025. This study was pre-registered at the Open Science Framework (OSF) under https://osf.io/sf6n5/. The Online Appendix contains a detailed account of deviations from the pre-analysis plan, explanations for the deviations and robustness checks.

Appendix A. Validity of list experiment

<caption>Table A1</caption>

List experiment sample description; control (LE_C) and treatment (LE_T) group.

<table><thead><tr><th rowspan="2"></th><th colspan="2">LE_C</th><th colspan="2">LE_T</th><th colspan="3">LE_C - LE_T</th></tr><tr><th>(1)<br>Mean</th><th>(2)<br>SD</th><th>(3)<br>Mean</th><th>(4)<br>SD</th><th>(5)<br>Diff.</th><th>(6)<br>p-value</th><th>(7)<br>Norm. diff.</th></tr></thead><tbody><tr><td>Age (years)</td><td>23.52</td><td>4.54</td><td>23.20</td><td>4.39</td><td>0.32</td><td>0.20</td><td>-0.05</td></tr><tr><td>Female</td><td>0.56</td><td>0.50</td><td>0.50</td><td>0.50</td><td>0.06</td><td>0.05</td><td>-0.08</td></tr><tr><td>Married</td><td>0.10</td><td>0.31</td><td>0.10</td><td>0.30</td><td>0.00</td><td>0.73</td><td>-0.01</td></tr><tr><td>Primary education</td><td>0.08</td><td>0.27</td><td>0.09</td><td>0.28</td><td>-0.01</td><td>0.56</td><td>0.02</td></tr><tr><td>Secondary education</td><td>0.63</td><td>0.48</td><td>0.62</td><td>0.49</td><td>0.01</td><td>0.68</td><td>-0.02</td></tr><tr><td>Vocational education</td><td>0.10</td><td>0.30</td><td>0.11</td><td>0.32</td><td>-0.01</td><td>0.51</td><td>0.03</td></tr><tr><td>Tertiary education or higher</td><td>0.19</td><td>0.39</td><td>0.18</td><td>0.39</td><td>0.01</td><td>0.67</td><td>-0.02</td></tr><tr><td>Unemployed, work w/o pay</td><td>0.37</td><td>0.48</td><td>0.34</td><td>0.47</td><td>0.03</td><td>0.14</td><td>-0.06</td></tr><tr><td>Student</td><td>0.37</td><td>0.48</td><td>0.38</td><td>0.49</td><td>-0.01</td><td>0.64</td><td>0.02</td></tr><tr><td>Paid worker</td><td>0.11</td><td>0.31</td><td>0.17</td><td>0.37</td><td>-0.06</td><td>0.00</td><td>0.12</td></tr><tr><td>Independent worker</td><td>0.05</td><td>0.22</td><td>0.05</td><td>0.22</td><td>0.00</td><td>0.91</td><td>-0.00</td></tr><tr><td>Housewife</td><td>0.10</td><td>0.30</td><td>0.07</td><td>0.25</td><td>0.03</td><td>0.05</td><td>-0.08</td></tr><tr><td>Household size (members)</td><td>4.66</td><td>1.44</td><td>4.71</td><td>1.49</td><td>-0.05</td><td>0.54</td><td>0.02</td></tr><tr><td>Asset index (score)</td><td>0.13</td><td>0.96</td><td>0.11</td><td>1.05</td><td>0.02</td><td>0.69</td><td>-0.02</td></tr><tr><td>Interviewer is female</td><td>0.56</td><td>0.50</td><td>0.71</td><td>0.45</td><td>-0.15</td><td>0.00</td><td>0.22</td></tr><tr><td>Interviewer has veil</td><td>0.33</td><td>0.47</td><td>0.44</td><td>0.50</td><td>-0.11</td><td>0.00</td><td>0.17</td></tr><tr><td>N</td><td colspan="2">654</td><td colspan="2">632</td><td colspan="3"></td></tr><tr><td colspan="8">F-test of joint significance:</td></tr><tr><td colspan="8">F-stat</td></tr><tr><td colspan="8">p-value</td></tr><tr><td colspan="8">2.47</td></tr><tr><td colspan="8">0.00</td></tr></tbody></table>

Note: This table presents individual, household and interviewer characteristics of respondents in the list experiment of the treatment and control group. Columns (1) and (3) report the mean values for each group. Columns (2) and (4) report the respective standard deviations. Column (5) shows the difference between the two group means, and Column (6) shows the p-values for the associated test of equality of means. Column (7) presents normalized differences. The joint null of equal means is rejected at standard levels.
<table><caption>**Table A2**
Testing for design effects.</caption><thead><tr><th></th><th colspan="4">No. of statements</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>0</th><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><th>π<sub>y1</sub></th><td>0.00</td><td>0.15</td><td>0.26</td><td>0.27</td></tr><tr><th>π<sub>y0</sub></th><td>0.00</td><td>0.10</td><td>0.25</td><td>-0.03</td></tr><tr><th>Bonferroni-corrected p-value</th><td colspan="4">0.17</td></tr></tbody></table>

Note: This table presents evidence confirming the absence of design effects in conducting the list experiment. $π_{y_t}$ indicates the joint probabilities, with $t=0$ reflecting the control group, and $t=1$ the treatment group. They were calculated using the *kict deff* command in Stata developed by Tsai (2019). The null hypothesis is no design effect, which cannot be rejected at conventional levels on the basis of the Bonferroni-corrected *p*-value (Blair and Imai, 2012).

<table><caption>**Table A3**
Summary of list experiment (LE) results.</caption><thead><tr><th></th><th colspan="5">No. of statements</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr><tr><th></th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th></tr></thead><tbody><tr><th>LE control group (%)</th><td>0.46</td><td>25.38</td><td>50.92</td><td>23.24</td><td></td></tr><tr><th>LE treatment group (%)</th><td>0.00</td><td>10.44</td><td>40.66</td><td>22.31</td><td>26.58</td></tr><tr><th>Both groups (%)</th><td>0.23</td><td>18.04</td><td>45.88</td><td>22.78</td><td>13.06</td></tr></tbody></table>

Note: This table synthesizes results stemming from the list experiment (LE). Columns (1)-(5) show the percentage of respondents that agree with 0–4 statements in the list experiment for the entire LE sample, and separately for the LE control (that received 3 statements) and treatment (that received 4 statements) group.
<table><caption>**Table A4**
Ceiling effects in list experiment (LE).</caption><thead><tr><th></th><th>(1)<br>Ceiling LE_C:<br>Respondent in LE<br>control group agrees<br>with three statements</th></tr></thead><tbody><tr><td>Age (years)</td><td>-0.01<br>(0.01)</td></tr><tr><td>Female</td><td>-0.03<br>(0.03)</td></tr><tr><td>Married</td><td>0.00<br>(0.07)</td></tr><tr><td>Primary education</td><td>Ref.</td></tr><tr><td>Secondary education</td><td>0.07<br>(0.06)</td></tr><tr><td>Vocational education</td><td>0.11<br>(0.07)</td></tr><tr><td>Tertiary education or higher</td><td>0.07<br>(0.07)</td></tr><tr><td>Unemployed, work without pay</td><td>Ref.</td></tr><tr><td>Student</td><td>-0.05<br>(0.05)</td></tr><tr><td>Paid worker</td><td>0.01<br>(0.05)</td></tr><tr><td>Independent worker</td><td>-0.08<br>(0.07)</td></tr><tr><td>Housewife</td><td>0.12<sup>*</sup><br>(0.07)</td></tr><tr><td>Household size (members)</td><td>0.00<br>(0.01)</td></tr><tr><td>Asset index (score)</td><td>-0.02<br>(0.02)</td></tr><tr><td>Constant</td><td>0.12<br>(0.16)</td></tr><tr><td>N</td><td>654</td></tr><tr><td>R<sup>2</sup></td><td>0.59</td></tr><tr><td>Interviewer controls</td><td>YES</td></tr><tr><td>Area fixed effects</td><td>YES</td></tr></tbody></table>

Note: This table presents OLS estimates with robust standard errors in parentheses, clustered at the area level. The outcome is an indicator variable equal to one if the respondent in the list experiment (LE) control group reports to agree with three statements; and 0, otherwise. The regression controls for interviewer characteristics (female, wears a veil) and area fixed effects. The reference category for education is “primary education”. The reference category for employment is “unemployed/work without pay”. *** $p < 0.001$, ** $p < 0.005$, * $p < 0.01$.
<table><caption>**Table A5**

Correction for imbalances between list experiment (LE) groups.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr><tr><th></th><th>No. of statements</th><th></th></tr></thead><tbody><tr><td>LE treatment group</td><td>0.68<sup>***</sup><br>(0.07)</td><td>0.71<sup>***</sup><br>(0.08)</td></tr><tr><td>Female</td><td>-</td><td>0.06<br>(0.06)</td></tr><tr><td>Paid worker</td><td>-</td><td>0.11<br>(0.08)</td></tr><tr><td>Housewife</td><td>-</td><td>0.23<sup>**</sup><br>(0.10)</td></tr><tr><td>Interviewer is female</td><td>-</td><td>-0.16<br>(0.12)</td></tr><tr><td>Interviewer has veil</td><td>-</td><td>-0.00<br>(0.08)</td></tr><tr><td>Constant</td><td>1.97<sup>***</sup><br>(0.05)</td><td>1.99<sup>***</sup><br>(0.10)</td></tr><tr><td>N</td><td>1286</td><td>1286</td></tr><tr><td>R<sup>2</sup></td><td>0.14</td><td>0.15</td></tr></tbody></table>

Note: This table presents OLS estimates with robust standard errors in parentheses, clustered at the area level. The outcome is the number of supported statements in the list experiment (LE).***

$p < 0.001$, ** $p < 0.005$, * $p < 0.01$.

## Appendix B. Experimental treatment spillover effects

<table><caption>**Table B1**

Test for experimental treatment spillover effects; list experiment (LE) and direct question (DQ) sample.</caption><thead><tr><th rowspan="2">Perceived share of respondents finding it appropriate<br>that women work outside their home:</th><th colspan="2">LE</th><th colspan="2">DQ</th><th colspan="3">LE - DQ</th></tr><tr><th>(1)<br>Mean</th><th>(2)<br>SD</th><th>(3)<br>Mean</th><th>(4)<br>SD</th><th>(5)<br>Diff.</th><th>(6)<br>p-value</th><th>(7)<br>N</th></tr></thead><tbody><tr><td>Second-order beliefs (full sample)</td><td>0.71</td><td>0.27</td><td>0.72</td><td>0.23</td><td>-0.01</td><td>0.52</td><td>2362</td></tr><tr><td>No incentive (NI)</td><td>0.72</td><td>0.25</td><td>0.71</td><td>0.26</td><td>0.01</td><td>0.49</td><td>1022</td></tr><tr><td>Financial incentive (FI)</td><td>0.70</td><td>0.28</td><td>0.72</td><td>0.21</td><td>-0.02</td><td>0.13</td><td>1340</td></tr></tbody></table>

Note: The table presents a comparison of responses to the SOB question in the second experimental arm between respondents receiving the direct FOB question and the list experiment FOB question in the first experimental arm. Columns (1) and (3) report the mean values for each group. Columns (2) and (4) report the respective standard deviations. Column (5) shows the difference between the two group means, and Column (6) shows the *p*-values for the test of equality of means. Column (7) indicates respective sample sizes.

<table><caption>**Table B2**

Test for experimental treatment spillover effects; list experiment control (LE_C) and treatment (LE_T) sample.</caption><thead><tr><th rowspan="2">Perceived share of respondents finding it appropriate<br>that women work outside their home:</th><th colspan="2">LE_C</th><th colspan="2">LE_T</th><th colspan="3">LE_C - LE_T</th></tr><tr><th>(1)<br>Mean</th><th>(2)<br>SD</th><th>(3)<br>Mean</th><th>(4)<br>SD</th><th>(5)<br>Diff.</th><th>(6)<br>p-value</th><th>(7)<br>N</th></tr></thead><tbody><tr><td>Second-order beliefs (full sample)</td><td>0.71</td><td>0.27</td><td>0.71</td><td>0.26</td><td>0.00</td><td>0.83</td><td>1138</td></tr><tr><td>No incentive (NI)</td><td>0.73</td><td>0.24</td><td>0.72</td><td>0.26</td><td>0.01</td><td>0.70</td><td>504</td></tr><tr><td>Financial incentive (FI)</td><td>0.70</td><td>0.29</td><td>0.71</td><td>0.26</td><td>-0.01</td><td>0.54</td><td>634</td></tr></tbody></table>

Note: This table presents a comparison of responses to the SOB question in the second experimental arm between respondents of the control and treatment group receiving the list experiment FOB question in the first experimental arm. Columns (1) and (3) report the mean values for each group. Columns (2) and (4) report the respective standard deviations. Column (5) shows the difference between the two group means, and Column (6) shows the *p*-values for the test of equality of means. Column (7) indicates respective sample sizes.
Appendix C. Exploration of first-order beliefs

<caption>Table C1</caption>

Correlations between first-order beliefs (FOBs) and respondent/interviewer characteristics across survey methods.

<table><thead><tr><th rowspan="2"></th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th>FOB_DQ<br>(Very) appropriate<br>that women work<br>outside their home</th><th>FOB_LE<br>No. of statements</th><th>Method differences<br>p-value</th></tr></thead><tbody><tr><td>Female</td><td>0.12***<br>(0.02)</td><td>0.20*<br>(0.10)</td><td>0.42</td></tr><tr><td>Secondary education or higher</td><td>0.07<br>(0.04)</td><td>-0.01<br>(0.16)</td><td>0.63</td></tr><tr><td>HH in third or fourth wealth quartile</td><td>0.01<br>(0.02)</td><td>-0.00<br>(0.09)</td><td>0.93</td></tr><tr><td>Self-efficacy (scale)</td><td>0.21***<br>(0.07)</td><td>0.55<br>(0.40)</td><td>0.38</td></tr><tr><td>Interviewer is female</td><td>0.01<br>(0.05)</td><td>0.02<br>(0.16)</td><td>0.96</td></tr></tbody></table>

Note: Column (1) shows $\delta_{DQ}$ coefficient estimates of Equation (1); Column (2) shows $\delta_{LE}$ coefficient estimates of Equation (2). Robust standard errors, clustered at the area level, are in parentheses. Controls include interviewer characteristics (female, wears a veil) and area fixed effects. The p-values for the method difference in Column (3) stems from a test of difference of the respective $\delta_{DQ}$ and $\delta_{LE}$ coefficient estimates. *** $p < 0.001$, ** $p < 0.005$, * $p < 0.01$.
Appendix D. Exploration of second-order beliefs

<caption>Table D1</caption>

Correlations between second-order beliefs and respondent characteristics.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><td colspan="7">SOB: Perceived share of respondents finding<br>it appropriate that women work outside their home</td></tr></thead><tbody><tr><td>Financial incentive (FI)</td><td>0.01<br>(0.05)</td><td>0.02<br>(0.05)</td><td>-0.04<sup>*</sup><br>(0.02)</td><td>-0.03<sup>*</sup><br>(0.02)</td><td>-0.20<sup>***</sup><br>(0.07)</td><td>-0.19<sup>***</sup><br>(0.07)</td></tr><tr><td>Primary education</td><td>Ref.</td><td>Ref.</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Secondary education</td><td>0.01<br>(0.05)</td><td>0.01<br>(0.05)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Vocational education</td><td>-0.00<br>(0.06)</td><td>-0.00<br>(0.06)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Tertiary (plus) education</td><td>0.05<br>(0.05)</td><td>0.02<br>(0.05)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>FI × Secondary education</td><td>-0.01<br>(0.05)</td><td>-0.02<br>(0.05)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>FI × Vocational education</td><td>0.01<br>(0.06)</td><td>0.00<br>(0.06)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>FI × Tertiary education</td><td>-0.02<br>(0.06)</td><td>-0.03<br>(0.06)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>First wealth quartile</td><td>-</td><td>-</td><td>Ref.</td><td>Ref.</td><td>-</td><td>-</td></tr><tr><td>Second wealth quartile</td><td>-</td><td>-</td><td>0.01<br>(0.03)</td><td>0.00<br>(0.03)</td><td>-</td><td>-</td></tr><tr><td>Third wealth quartile</td><td>-</td><td>-</td><td>-0.01<br>(0.03)</td><td>-0.01<br>(0.02)</td><td>-</td><td>-</td></tr><tr><td>Fourth wealth quartile</td><td>-</td><td>-</td><td>0.03<br>(0.03)</td><td>0.03<br>(0.03)</td><td>-</td><td>-</td></tr><tr><td>FI × second quartile</td><td>-</td><td>-</td><td>0.01<br>(0.04)</td><td>0.02<br>(0.04)</td><td>-</td><td>-</td></tr><tr><td>FI × third quartile</td><td>-</td><td>-</td><td>0.07<sup>**</sup><br>(0.03)</td><td>0.06<sup>**</sup><br>(0.03)</td><td>-</td><td>-</td></tr><tr><td>FI × fourth quartile</td><td>-</td><td>-</td><td>0.05<sup>*</sup><br>(0.03)</td><td>0.05<sup>*</sup><br>(0.03)</td><td>-</td><td>-</td></tr><tr><td>Self-efficacy (scale)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-0.05<br>(0.06)</td><td>-0.06<br>(0.06)</td></tr><tr><td>FI × Self-efficacy</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.27<sup>***</sup><br>(0.09)</td><td>0.26<sup>***</sup><br>(0.08)</td></tr><tr><td>Constant</td><td>0.71<sup>***</sup><br>(0.06)</td><td>0.61<sup>***</sup><br>(0.07)</td><td>0.74<sup>***</sup><br>(0.03)</td><td>0.63<sup>***</sup><br>(0.06)</td><td>0.76<sup>***</sup><br>(0.06)</td><td>0.66<sup>***</sup><br>(0.08)</td></tr><tr><td>N</td><td>2362</td><td>2362</td><td>2362</td><td>2362</td><td>2264</td><td>2264</td></tr><tr><td>R<sup>2</sup></td><td>0.17</td><td>0.20</td><td>0.17</td><td>0.21</td><td>0.17</td><td>0.21</td></tr><tr><td>Individual controls</td><td>NO</td><td>YES</td><td>NO</td><td>YES</td><td>NO</td><td>YES</td></tr><tr><td>Household controls</td><td>NO</td><td>YES</td><td>NO</td><td>YES</td><td>NO</td><td>YES</td></tr><tr><td>Interviewer controls</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Area fixed effects</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr></tbody></table>

Note: This table presents OLS estimates with robust standard errors, clustered at the area level, in parentheses. The outcome is the stated perceived share of respondents finding it appropriate that women work outside their home (i.e., SOBs). Columns (1), (3) and (5) display the respective coefficient estimates when controlling for interviewer characteristics (female, wears a veil) and area fixed effects. In Columns (2), (4) and (6) additional controls include individual characteristics (age, marital status, education, employment) and household characteristics (household size, asset index score). The reference category for education is “primary education”. The reference category for the wealth quartiles employment is “first wealth quartile”. *** $p < 0.001$, ** $p < 0.005$, * $p < 0.01$.
Appendix E. Missing observations in second-order beliefs

<caption>Table E1</caption>

Comparison of second-order belief (SOB) sample and missing observations (SOB_MISSING).

<table><thead><tr><th rowspan="2"></th><th colspan="2">SOB</th><th colspan="2">SOB_MISSING</th><th colspan="3">SOB - SOB_MISSING</th></tr><tr><th>(1)<br>Mean</th><th>(2)<br>SD</th><th>(3)<br>Mean</th><th>(4)<br>SD</th><th>(5)<br>Diff.</th><th>(6)<br>p-value</th><th>(7)<br>Norm. diff.</th></tr></thead><tbody><tr><td>Age (years)</td><td>23.38</td><td>4.43</td><td>23.10</td><td>4.34</td><td>0.28</td><td>0.29</td><td>-0.05</td></tr><tr><td>Female</td><td>0.52</td><td>0.50</td><td>0.52</td><td>0.50</td><td>0.00</td><td>0.82</td><td>-0.01</td></tr><tr><td>Married</td><td>0.09</td><td>0.29</td><td>0.09</td><td>0.29</td><td>0.00</td><td>0.99</td><td>0.00</td></tr><tr><td>Primary education</td><td>0.08</td><td>0.26</td><td>0.08</td><td>0.28</td><td>0.00</td><td>0.59</td><td>0.02</td></tr><tr><td>Secondary education</td><td>0.65</td><td>0.48</td><td>0.59</td><td>0.49</td><td>0.06</td><td>0.07</td><td>-0.08</td></tr><tr><td>Vocational education</td><td>0.11</td><td>0.31</td><td>0.12</td><td>0.33</td><td>-0.01</td><td>0.55</td><td>0.03</td></tr><tr><td>Tertiary education or higher</td><td>0.17</td><td>0.37</td><td>0.20</td><td>0.40</td><td>-0.03</td><td>0.14</td><td>0.06</td></tr><tr><td>Unemployed, work w/o pay</td><td>0.35</td><td>0.48</td><td>0.35</td><td>0.48</td><td>0.00</td><td>0.98</td><td>-0.00</td></tr><tr><td>Student</td><td>0.37</td><td>0.48</td><td>0.39</td><td>0.49</td><td>-0.02</td><td>0.46</td><td>0.03</td></tr><tr><td>Paid worker</td><td>0.14</td><td>0.35</td><td>0.14</td><td>0.35</td><td>0.00</td><td>0.91</td><td>-0.01</td></tr><tr><td>Independent worker</td><td>0.05</td><td>0.22</td><td>0.04</td><td>0.20</td><td>0.01</td><td>0.52</td><td>-0.03</td></tr><tr><td>Housewife</td><td>0.09</td><td>0.28</td><td>0.08</td><td>0.27</td><td>0.01</td><td>0.57</td><td>-0.03</td></tr><tr><td>Household size (members)</td><td>4.73</td><td>1.46</td><td>4.46</td><td>1.31</td><td>0.27</td><td>0.00</td><td>-0.13</td></tr><tr><td>Asset index (score)</td><td>0.13</td><td>0.98</td><td>0.12</td><td>1.03</td><td>0.01</td><td>0.89</td><td>-0.01</td></tr><tr><td>Interviewer is female</td><td>0.69</td><td>0.46</td><td>0.66</td><td>0.48</td><td>0.03</td><td>0.23</td><td>-0.05</td></tr><tr><td>Interviewer has veil</td><td>0.38</td><td>0.49</td><td>0.41</td><td>0.49</td><td>-0.03</td><td>0.23</td><td>0.05</td></tr><tr><td>N</td><td colspan="2">2362</td><td colspan="2">297</td><td colspan="3"></td></tr><tr><td colspan="8">F-test of joint significance:</td></tr><tr><td colspan="8">F-stat</td></tr><tr><td colspan="8">p-value</td></tr><tr><td colspan="8">1.55</td></tr><tr><td colspan="8">0.10</td></tr></tbody></table>

Note: This table presents individual, household and interviewer characteristics of respondents that reported second-order beliefs and observations that did not. Columns (1) and (3) report the mean values for each group. Columns (2) and (4) report the respective standard deviations. Column (5) shows the difference between the two group means, and Column (6) shows the *p*-values for the associated test of equality of means. Column (7) presents normalized differences. The joint null of equal means is not rejected at standard levels.
Appendix F. First- and second-order beliefs by gender

<figure><img src="image_5.png" /><figcaption>Fig. F1. (Mis)perceptions about others' beliefs, by gender. Note: This figure presents the cumulative distribution function (CDF) of financially incentivized respondents' guesses about the share of young Tunisians in their area agreeing with the statement that it is appropriate that women work outside the home. Panel (a) consists of the female sample and Panel (b) of the male sample. The vertical line shows the average perceived share (SOBs w/ FI). Dashed/dotted vertical lines show the true proportion of respondents agreeing with this statement; the dashed line represents the degree of support derived from the direct question (DQ) and the dotted line the degree of support inferred from the list experiment (LE). For Panel (a): The difference in mean shares of 21 percentage points between SOBs w/ FI and DQ is statistically significant ($p$-value = 0.00). The difference in mean shares of 5 percentage points between SOBs w/ FI and LE is not statistically significant ($p$-value = 0.40). For Panel (b): The difference in mean shares of 16 percentage points between SOBs w/ FI and DQ is statistically significant ($p$-value = 0.00). The difference in mean shares of 13 percentage points between SOBs w/ FI and LE is statistically significant ($p$-value = 0.07).</figcaption></figure>

Supplementary material

Supplementary material associated with this article can be found in the online version at 10.1016/j.jebo.2026.107465.

References

Ahlquist, J.S., 2018. List experiment design, non-strategic respondent error, and item count technique estimators. Polit. Anal. 26, 34–53. https://doi.org/10.1017/pan.2017.31

Aksoy, B., Carpenter, C.S., Sansone, D., 2025. Understanding labor market discrimination against transgender people: evidence from a double list experiment and a survey. Manage. Sci. 71 (1), 659–677. https://doi.org/10.1287/mnsc.2023.02567

Alesina, A., Giuliano, P., Nunn, N., 2013. On the origins of gender roles: women and the plough. Q. J. Econ. 128 (2), 469–530. https://doi.org/10.1093/qje/qjt005

Aloud, M.E., Al-Rashood, S., Ganguli, I., Zafar, B., 2020. Information and Social Norms: Experimental Evidence on the Labor Market Aspirations of Saudi Women. NBER Working Paper Series (No. 26693), National Bureau of Economic Research, Cambridge, MA.

Andreoni, J., Bernheim, B.D., 2009. Social image and the 50-50 norm: a theoretical and experimental analysis of audience effects. Econometrica 77 (5), 1607–1636. https://doi.org/10.3982/ecta7384

Asadullah, M.N., De Cao, E., Khatoon, F.Z., Siddique, Z., 2021. Measuring gender attitudes using list experiments. J. Popul. Econ. 34 (2), 367–400. https://doi.org/10.1007/s00148-020-00805-2

Assaad, R., Ghazouani, S., Krafft, C., 2017. Marriage, fertility, and women's agency in Tunisia. Working Paper Series (No. 1157), Economic Research Forum, Dokki, Giza.

Assaad, R., Krafft, C., 2015. The economics of marriage in north Africa: a unifying theoretical framework. In: Monga, C., Lin, J.Y. (Eds.), The Oxford Handbook of Africa and Economics: Contexts and Concepts. Oxford University Press, Oxford, 1 ed. pp. 72–85.
Assaad, R., Krafft, C., Selwaness, I., 2022. The impact of marriage on Women's employment in the Middle East and North Africa. Fem. Econ. 28 (2), 247-279.
https://doi.org/10.1080/13545701.2021.2007415

Aycinena, D., Bogliacino, F., Kimbrough, E.O., 2024a. Measuring norms: a comparison of the predictive and descriptive power of three methods. Mimeo. https://doi.org/10.2139/ssrn.4663919

Aycinena, D., Bogliacino, F., Kimbrough, E.O., 2024b. Measuring norms: assessing the threat of social desirability bias to the Bicchieri and Xiao elicitation method. J. Econ. Behav. Organiz. 222, 225-239. https://doi.org/10.1016/j.jebo.2024.04.002

Barigozzi, F., Montinari, N., 2025. Social norms in survey experiments: personal beliefs versus normative expectations. J. Econ. Behav. Organiz. 239, 107292. https://doi.org/10.1016/j.jebo.2025.107292

Basu, K., 2003. Analytical Development Economics - The Less Developed Economy Revisited. MIT Press, Cambridge, MA.

Bénabou, R., Tírole, J., 2006. Incentives and prosocial behavior. Am. Econ. Rev. 96 (5), 1652-1678. https://doi.org/10.1257/aer.96.5.1652

Bernhardt, A., Field, E., Pande, R., Rigol, N., Schaner, S., Troyer-Moore, C., 2018. Male social status and women's work. AEA Papers Proc. 108, 363-367. https://doi.org/10.1257/pandp.20181086

Bernheim, B.D., 1994. A theory of conformity. J. Polit. Econ. 102 (5), 841-877. https://doi.org/10.1086/261957

Bertelli, O., Calvo, T., Lavallée, E., Mercier, M., Mesplé-Somps, S., 2025. What one thinks, what one says and what one does: male justifications and practices of gender-based violence in Mali. J. Dev. Econ., 103479. https://doi.org/10.1016/j.jdeveco.2025.103479

Bertrand, M., Kamenica, E., Pan, J., 2015. Gender identity and relative income within households. Q. J. Econ. 130 (2), 571-614. https://doi.org/10.1093/qje/qjv001

Bicchieri, C., 2006. The Grammar of Society: The Nature and Dynamics of Social Norms. Cambridge University Press, New York, NY.

Bicchieri, C., 2017. Norms in the Wild: How to Diagnose, Measure, and Change Social Norms. Oxford University Press, Oxford, UK.

Bicchieri, C., Chavez, A., 2010. Behaving as expected: public information and fairness norms. J. Behav. Decis. Mak. 23 (2), 161-178. https://doi.org/10.1002/bdm.648

Bicchieri, C., Das, U., Gant, S., Sander, R., 2022. Examining norms and social expectations surrounding exclusive breastfeeding: evidence from Mali. World Dev. 153, 105824. https://doi.org/10.1016/j.worlddev.2022.105824

Bicchieri, C., Xiao, E., 2009. Do the right thing: but only if others do so. J. Behav. Decis. Mak. 22, 191-208. https://doi.org/10.1002/bdm.621

Blair, G., Imai, K., 2012. Statistical analysis of list experiments. Polit. Anal. 20 (1), 47-77. https://doi.org/10.1093/pan/mpr048

Blair, G., Imai, K., Lyall, J., 2014. Comparing and combining list and endorsement experiments: evidence from afghanistan. Am. J. Pol. Sci. 58 (4), 1043-1063. https://doi.org/10.1111/ajps.12086

Boring, A., Delfgaauw, J., 2024. Social desirability bias in attitudes towards sexism and DEI policies in the workplace. J. Econ. Behav. Organiz. 225, 465-482. https://doi.org/10.1016/j.jebo.2024.07.010

Brewer, P.R., 2003. Values, political knowledge, and public opinion about gay rights. Public Opin. Q. 67 (2), 173-201. https://doi.org/10.1086/374397

Burdea, V., Woon, J., 2022. Online belief elicitation methods. J. Econ. Psychol. 90, 102496. https://doi.org/10.1016/j.joep.2022.102496

Burden, B.C., Ono, Y., Yamada, M., 2017. Reassessing public support for a female president. J. Polit. 79 (3), 1073-1078. https://doi.org/10.1086/691799

Bursztyn, L., Cappelen, A.W., Tungodden, B., Voena, A., Yanagizawa-Drott, D.H., 2024. How Are Gender Norms Perceived? NBER Working Paper Series (No. 31049), National Bureau of Economic Research, Cambridge, MA, Cambridge, MA.

Bursztyn, L., González, A.L., Yanagizawa-Drott, D., 2020. Misperceived social norms: women working outside the home in saudi arabia. Am. Econ. Rev. 110 (10), 2997-3029. https://doi.org/10.1257/aer.20180975

Bursztyn, L., Yang, D.Y., 2022. Misperceptions about others. Annu. Rev. Econom. 14, 425-452. https://doi.org/10.1146/annurev-economics-051520-023322

Cameron, L., Suarez, D.C., Setyonaluri, D., 2024. Leveraging Women's Views to Influence Gender Norms Around Women Working: Evidence from an Online Intervention in Indonesia. Policy Research Working Paper (No. 10681), World Bank, Washington, DC.

Chambers, V., Cummings, C., 2014. Building Momentum: Women's empowerment in Tunisia. ODI Development Progress (November), Overseas Development Institute, London.

Charness, G., Dimant, E., Gneezy, U., Krupka, E., 2025. Experimental methods: eliciting and measuring social norms. J. Econ. Behav. Organiz. 237, 107187. https://doi.org/10.1016/j.jebo.2025.107187

Chen, G., Gully, S.M., Eden, D., 2001. Validation of a new general self-Efficacy scale. Organ. Res. Methods 4 (1), 62-83. https://doi.org/10.1177/109442810141004

Clark, R., Ramsbey, T.W., Adler, E.S., 1991. Culture, gender, and labor force participation: a cross-national study. Gender Soc. 5 (1), 47-66.

Cortés, P., Koşar, G., Pan, J., Zafar, B., 2024. Should mothers work? how perceptions of the social norm affect individual attitudes toward work in the US. Rev. Econ. Stat., 1-28. https://doi.org/{10.1162/rest_a_01541}

Cullen, C., 2023. Method matters: the underreporting of intimate partner violence. World Bank Econ. Rev. 37 (1), 49-73. https://doi.org/10.1093/wber/lhac022

Danz, D., Vesterlund, L., Wilson, A.J., 2022. Belief elicitation and behavioral incentive compatibility. Am. Econ. Rev. 112 (9), 2851-2883. https://doi.org/10.1257/aer.20201248

De Cao, E., Lutz, C., 2018. Sensitive survey questions: measuring attitudes regarding female genital cutting through a list experiment. Oxf. Bull. Econ. Stat. 80 (5), 871-892. https://doi.org/10.1111/obes.12228

DeMaio, T.J., 1984. Social desirability and survey measurement: a review. In: Turner, C.F., Martin, E. (Eds.), Surveying Subjective Phenomena. Russell Sage Foundation, New York, NY. Vol. 2, pp. 257-282.

Dimant, E., 2023. Beyond average: a method for measuring the tightness, looseness, and polarization of social norms. Econ. Lett. 233, 111417. https://doi.org/10.1016/j.econlet.2023.111417

Diwan, I., Vartanova, I., 2017. The Effect of Patriarchal Culture on women's Labor Force Participation. Working Paper Series (No. 1101), Economic Research Forum, Dokki, Giza.

Elster, J., 1989. Social norms and economic theory. J. Econ. Perspect. 3 (4), 99-117. https://doi.org/10.1257/jep.3.4.99

Fehr, E., Fischbacher, U., 2004. Social norms and human cooperation. Trends Cogn. Sci. 8 (4), 185-190. https://doi.org/10.1016/j.tics.2004.02.007

Fehr, E., Gächter, S., 2002. Altruistic punishment in humans. Nature 415, 137-140. https://doi.org/10.1038/415137a

Fehr, E., Schurtenberger, I., 2018. Normative foundations of human cooperation. Nat. Hum. Behav. 2, 458-468. https://doi.org/10.1038/s41562-018-0385-5

Fernández, R., 2013. Cultural change as learning: the evolution of female labor force participation over a century. Am. Econ. Rev. 103 (1), 472-500. https://doi.org/10.1257/aer.103.1.472

Gächter, S., Renner, E., 2010. The effects of (incentivized) belief elicitation in public goods experiments. Exper. Econom. 13, 364-377. https://doi.org/10.1007/s10683-010-9246-4

Gaines, B.J., Kuklinski, J.H., Quirk, P.J., 2007. The logic of the survey experiment reexamined. Polit. Anal. 15 (1), 1-20. https://doi.org/10.1093/pan/mpl008

Garlick, R., Field, E., Vyborny, K., 2025. Women's Mobility and Labor Supply: Experimental Evidence from Pakistan. IZA Discussion Paper Series (No. 17883), Institute of Labor Economics, Bonn.

Gauri, V., Rahman, T., Iman, S., 2019. Measuring Social Norms about Female Labor Force Participation in Jordan. Policy Research Working Paper (No. 8916), World Bank, Washington, DC. https://doi.org/10.1596/1813-9450-8916

Gibson, M.A., Gurmu, E., Cobo, B., Rueda, M.M., Scott, I.M., 2018. Indirect questioning method reveals hidden support for female genital cutting in South Central Ethiopia. PLoS ONE 13 (5), e0193985. https://doi.org/10.1371/journal.pone.0193985

Gibson, M.A., Gurmu, E., Cobo, B., Rueda, M.M., Scott, I.M., 2022. Measuring hidden support for physical intimate partner violence: a list randomization experiment in South-Central Ethiopia. J. Interpers. Violence 37 (7-8), NP4238-NP4257. https://doi.org/10.1177/08862605209145

Glynn, A.N., 2013. What can we learn with statistical truth serum? Public Opin. Q. 77, 159-172. https://doi.org/10.1093/poq/nfs070

Görges, L., Nosenzo, D., 2020a. Measuring social norms in economics: why it is important and how it is done. Analyse und Kritik 42 (2), 285-312. https://doi.org/10.1515/auk-2020-0012

Görges, L., Nosenzo, D., 2020b. Social norms and the labor market. In: Zimmermann, K.F. (Ed.), Handbook of Labor, Human Resources and Population Economics. Springer Nature Switzerland AG, pp. 1-26. https://doi.org/10.1007/978-3-319-57365-6_131-1
Haber, N., Harling, G., Cohen, J., Mutevedzi, T., Tanser, F., Gareta, D., Herbst, K., Pillay, D., Bärnighausen, T., Fink, G., 2018. List randomization for eliciting HIV status and sexual behaviors in rural KwaZulu-Natal, South Africa: a randomized experiment using known true values for validation. BMC Med. Res. Methodol. 18, 1-12. https://doi.org/10.1186/s12874-018-0507-9

Haghighat-Sordellini, E., 2009. Determinants of female labor force participation: a focus on muslim countries. Int. Rev. Sociol. Revue Int. Sociol. 1, 103-125. https://doi.org/10.1080/03906700802613970

Haider-Markel, D.P., Joslyn, M.R., 2001. Gun policy, opinion, tragedy, and blame attribution: the conditional influence of issue frames. J. Polit. 63 (2), 520-543. https://doi.org/10.1111/0022-3816.00077

Heath, R., 2024. Female labour force participation. VoxDevLit 11 (1).

ILOSTAT, 2021. Gender gap in labour force participation rates. https://webapps.ilo.org/infostories/en-GB/Stories/Employment/barriers-women#global-gap/gap-labour-force.

Imbens, G.W., Wooldridge, J.M., 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47 (1), 5-86. https://doi.org/10.1257/jel.47.1.5

International Labour Organization, 2022. ILO Modelled Estimates and Projections database (ILOEST). https://ilostat.ilo.org/data/.

Jayachandran, S., 2021. Social norms as a barrier to Women's employment in developing countries. IMF Econ. Rev. 69 (3), 576-595. https://doi.org/10.1057/s41308-021-00140-w

Kadam, A., McCullough, E.B., McGavock, T.J., Magnan, N., 2025. Who is asking and how? effects of survey mode and enumerator gender on measuring women's life experience. World Dev. 195, 107078. https://doi.org/10.1016/j.worlddev.2025.107078

Kashina, A., 2021. Gender equality in tunisia: current trends. J. Soc. Sci. Transf. Trans. 1 (01), 1-18. https://doi.org/10.52459/josstt1140721

Kimbrough, E.O., Vostroknutov, A., 2016. Norms make preferences social. J. Eur. Econ. Assoc. 14 (3), 608-638. https://doi.org/10.1111/jeea.12152

Kondylis, F., Legovini, A., Vyborny, K., Zwager, A. M.T., Cardoso De Andrade, L., 2020. Demand for Safe Spaces: Avoiding Harassment and Stigma. World Bank Policy Research Working Paper (No. 9269), World Bank, Washington, DC.

Krupka, E.L., Weber, R.A., 2013. Identifying social norms using coordination games: why does dictator game sharing vary? J. Eur. Econ. Assoc. 11 (3), 495-524. https://doi.org/10.1111/jeea.12006

Kuhn, P.M., Vivyan, N., 2022. The misreporting trade-Off between list experiments and direct questions in practice: partition validation evidence from two countries. Polit. Anal. 30, 381-402. https://doi.org/10.1017/pan.2021.10

Kuklinski, J.H., Cobb, M.D., Gilens, M., 1997. Racial attitudes and the “New South”. J. Polit. 59 (2), 323-349. https://doi.org/10.1017/S0022381600053470

La Ferrara, E., Chong, A., Duryea, S., 2012. Soap operas and fertility: evidence from Brazil. Am. Econ. J. Appl. Econom. 4 (4), 1-31. https://doi.org/10.1257/app.4.4.1

Lane, T., Nosenzo, D., Sonderegger, S., 2023. Law and norms: empirical evidence. Am. Econ. Rev. 113 (5), 1255-1293. https://doi.org/10.1257/aer.20210970

Lassassi, M., Tansel, A., 2020. Female Labor Force Participation in Five Selected MENA Countries: An Age-Period-Cohort Analysis (Algeria, Egypt, Jordan, Palestine and Tunisia). Mimeo.

Lépine, A., Treibich, C., D'Exelle, B., 2020. Nothing but the truth: consistency and efficiency of the list experiment method for the measurement of sensitive health behaviours. Soc. Sci. Med. 266, 113326. https://doi.org/10.1016/j.socscimed.2020.113326

Miller, D.T., 2023. A century of pluralistic ignorance: what we have learned about its origins, forms, and consequences. Front. Soc. Psychol. 1 1260896. https://doi.org/10.3389/frsps.2023.1260896

Moghadam, V.M., 2019. Women and employment in Tunisia. Sociol. Develop. 5 (4), 337-359. https://doi.org/10.1525/sod.2019.5.4.337

Nazier, S., 2005. Challenging inequality: obstacles and opportunities towards Women's rights in the Middle East and North Africa. J. Inst. Just. Int. Stud. 5, 31-42.
Nillesen, E., Grimm, M., Goedhuys, M., Reitmann, A.K., Meysonnat, A., 2021. On the malleability of gender attitudes: evidence from implicit and explicit measures in Tunisia. World Dev. 138, 105263. https://doi.org/10.1016/j.worlddev.2020.105263

Porter, C., Favara, M., Sánchez, A., Scott, D., 2021. The impact of COVID-19 lockdowns on physical domestic violence: evidence from a list randomization experiment. SSM - Popul. Health 14, 100792. https://doi.org/10.1016/j.ssmph.2021.100792

Reitmann, A.-K., Goedhuys, M., Grimm, M., Nillesen, E.E.M., 2020. Gender attitudes in the Arab region - The role of framing and priming effects. J. Econ. Psychol. 80, 102288. https://doi.org/10.1016/j.joep.2020.102288

Rosenfeld, B., Imai, K., Shapiro, J.N., 2016. An empirical validation study of popular survey methodologies for sensitive questions. Am. J. Pol. Sci. 60 (3), 783-802. https://doi.org/10.1111/ajps.12205

Sargent, R.H., Newman, L.S., 2021. Pluralistic ignorance research in psychology: a scoping review of topic and method variation and directions for future research. Rev. Gen. Psychol. 25 (2), 163-184. https://doi.org/10.1177/1089268021995168

Schotter, A., Trevino, I., 2014. Belief elicitation in the laboratory. Annu. Rev. Econ. 6, 103-128. https://doi.org/10.1146/annurev-economics-080213-040927

Setzler, M., 2019. Measuring bias against female political leadership. Polit. Gender 15 (4), 695-721. https://doi.org/10.1017/S1743923X18000430

Sinha, S., 2011. Women's rights: tunisian women in the workplace. J. Int. Womens Stud. 12 (3), 185-200.

Sugden, R., 1998. Normative expectations: the simultaneous evolution of institutions and norms. In: Ben-Ner, A., Putterman, L. (Eds.), Economics, Values, and Organization. Cambridge University Press, Cambridge, chapter 2, pp. 73-100. https://doi.org/10.1017/CBO9781139174855.004

Transue, J.E., Lee, D.J., Aldrich, J.H., 2009. Treatment spillover effects across survey experiments. Polit. Anal. 17 (2), 143-161. https://doi.org/10.1093/pan/mpn012

Tsai, C.L., 2019. Statistical analysis of the item-count technique using stata. Stata J. 19 (2), 390-434. https://doi.org/10.1177/1536867X19854018

Van Gorp, B., Vettehen, P.H., Beentjes, J. W.J., 2009. Challenging the frame in the news: the role of issue involvement, attitude, and competing frames. J. Media Psychol. Theor. Methods Appl. 21 (4), 161-170. https://doi.org/10.1027/1864-1105.21.4.161

World Economic Forum, 2021. Global Gender Gap Report. World Economic Forum, Geneva. http://reports.weforum.org/global-gender-gap-report-2021/dataexplorer.

Ziadi, L., Goedhuys, M., Bouraoui, S., 2023. Normes sociales et accès des femmes au marché du travail en Tunisie: Une analyse qualitative Mimeo. UNU-MERIT, Maastricht University, Maastricht.

Zizzo, D.J., 2010. Experimenter demand effects in economic experiments. Exper. Econ. 13, 75-98. https://doi.org/10.1007/s10683-009-9230-z