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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Behav. Econ.</journal-id>
<journal-title>Frontiers in Behavioral Economics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Behav. Econ.</abbrev-journal-title>
<issn pub-type="epub">2813-5296</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/frbhe.2025.1612211</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Behavioral Economics</subject>
<subj-group>
<subject>Brief Research Report</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>The power of the purse: gender, cooperation, and socially responsible consumption</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>V&#x000E1;squez</surname> <given-names>Alejandra</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Becchetti</surname> <given-names>Leonardo</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Pelligra</surname> <given-names>Vittorio</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c002"><sup>&#x0002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>Department of Economics, Faculty of Economics and Business Administration, University of Santiago</institution>, <addr-line>Santiago de Chile</addr-line>, <country>Chile</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Management and Law, Faculty of Economics, University of Rome Tor Vergata</institution>, <addr-line>Roma</addr-line>, <country>Italy</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Economic and Business Sciences, Faculty of Legal, Economic and Political Sciences, University of Cagliari, Cagliari</institution>, <addr-line>Sardinia</addr-line>, <country>Italy</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Vanessa Valero, Institut Mines-T&#x000E9;l&#x000E9;com Business School, France</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Chaoqian Wang, George Mason University, United States</p>
<p>Silvio St&#x000E4;dter, University of Wismar, Germany</p>
<p>Chowdhury Mohammad Sakib Anwar, University of Winchester, United Kingdom</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Alejandra V&#x000E1;squez <email>alejandra.vasquez.a&#x00040;usach.cl</email></corresp>
<corresp id="c002">Vittorio Pelligra <email>pelligra&#x00040;unica.it</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>15</day>
<month>10</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>4</volume>
<elocation-id>1612211</elocation-id>
<history>
<date date-type="received">
<day>15</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>15</day>
<month>09</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2025 V&#x000E1;squez, Becchetti and Pelligra.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>V&#x000E1;squez, Becchetti and Pelligra</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>We test for gender effects in the &#x0201C;vote with your wallet&#x0201D; game, a multi- person version of the prisoner&#x00027;s dilemma that models responsible consump- tion decisions. We find that women cooperate significantly more (have more responsible consumption decisions) than men in the baseline version of the game. This baseline excludes three additional elements tested in companion treatments: i) a legality frame, where the ethical product is labeled as being certified for compliance with anticorruption standards; ii) an ex-post redistribution scheme, where those who buy the less ethical product compensate those who choose the more responsible option; and iii) a conformity treatment, where participants are informed of prior players&#x00027; choices to simulate social influence. Without these added interventions, women still show significantly greater cooperation, revealing a baseline preference for prosocial behavior in this strategic consumption setting.</p></abstract>
<kwd-group>
<kwd>cooperation</kwd>
<kwd>socially responsible consumption behavior</kwd>
<kwd>gender</kwd>
<kwd>vote with the wallet game</kwd>
<kwd>social preferences</kwd>
</kwd-group>
<counts>
<fig-count count="5"/>
<table-count count="4"/>
<equation-count count="9"/>
<ref-count count="32"/>
<page-count count="10"/>
<word-count count="6524"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Culture and Ethics</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>1 Introduction</title>
<p>This paper aims to test the significance of the gender effect in the &#x0201C;Vote-with-the-Wallet&#x0201D; game (VWG). This game is a multiperson version of the prisoner&#x00027;s dilemma (PD), with a binary choice that we use to describe the dilemma typically faced by consumers confronted with more or less socially and/or environmentally sustainable consumption options.<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref></p>
<p>In our experiment, players (consumers) choose between a standard product that costs less and a product that costs more and is advertised as &#x0201C;responsible.&#x0201D; The purchase of the responsible product creates a monetary externality for all other players, functioning as a public good and making cooperation difficult (i.e., achieving a high share of responsible choices). In this context, we are interested in studying the effect of gender on the dynamics of these choices, which aligns with evidence that consumers need help identifying sustainable options and that gender norms can influence their perception (<xref ref-type="bibr" rid="B29">Th&#x000F8;gersen, 2021</xref>; <xref ref-type="bibr" rid="B10">Bloodhart and Swim, 2020</xref>).</p>
<p>Focusing on gender is particularly relevant because differences are strongly associated with variations in social preferences, ethical sensitivities, and responses to contextual cues. Women, on average, tend to exhibit stronger other-regarding preferences, greater aversion to inequality, and more prosocial behavior in consumption contexts (<xref ref-type="bibr" rid="B17">Croson and Gneezy, 2009</xref>; <xref ref-type="bibr" rid="B2">Arnocky and Stroink, 2010</xref>; <xref ref-type="bibr" rid="B32">Zelezny et al., 2000</xref>; <xref ref-type="bibr" rid="B10">Bloodhart and Swim, 2020</xref>). Previous experimental evidence shows that gender differences often emerge in risk attitudes (women tend to be more risk-averse; <xref ref-type="bibr" rid="B16">Costa, 1994</xref>), responses to incentives and competition (women are generally less attracted or responsive), and, to a lesser extent, in altruistic and cooperative behavior (<xref ref-type="bibr" rid="B17">Croson and Gneezy, 2009</xref>). While meta-analyses suggest that overall cooperation rates are practically indistinguishable across genders globally (<xref ref-type="bibr" rid="B28">Spadaro et al., 2022</xref>), women tend to display greater sensitivity to contextual cues, stronger other-regarding preferences, and a higher likelihood of conditional cooperation (<xref ref-type="bibr" rid="B17">Croson and Gneezy, 2009</xref>; <xref ref-type="bibr" rid="B23">Furtner et al., 2021</xref>). These traits are particularly relevant for ethical consumption dilemmas such as the VWG, where cooperation relies on recognizing and acting upon socially desirable options without external enforcement (<xref ref-type="bibr" rid="B10">Bloodhart and Swim, 2020</xref>; <xref ref-type="bibr" rid="B32">Zelezny et al., 2000</xref>).</p>
<p>Studying gender-based behavioral patterns in controlled experimental settings enriches the behavioral economics of gender and provides insight for designing effective interventions and policies that promote sustainable and ethical consumption (<xref ref-type="bibr" rid="B17">Croson and Gneezy, 2009</xref>). These tendencies are especially relevant in consumption choices with moral or public-good dimensions, such as environmental sustainability or ethical sourcing. Marketing and policy strategies often target &#x0201C;ethical consumption&#x0201D; by appealing to values commonly associated with femininity, such as care, responsibility, and fairness (<xref ref-type="bibr" rid="B10">Bloodhart and Swim, 2020</xref>).</p>
<p>The VWG experiment is particularly suitable for gender analysis because it models a clear moral trade-off in a controlled setting, allowing intrinsic prosocial tendencies to emerge. The absence of social cues in the baseline isolates behavioral differences without external influence. Its repeated, binary structure with a gender-balanced sample enhances internal validity and reveals stable patterns in cooperative behavior, making it an effective tool to study gendered responses in ethical consumption contexts.</p>
<p>The treatments considered beyond the baseline systematically vary institutional cues that may interact with gendered preferences. The redistribution mechanism introduces fairness and punishment, potentially amplifying women&#x00027;s aversion to inequality. The legality frame activates normative expectations and moral signaling, to which women may be more responsive due to social-role internalization. The conformity treatment tests sensitivity to peer behavior, aligning with evidence that women exhibit greater social norm compliance. These manipulations help uncover conditional patterns of cooperation that would remain hidden in a neutral setting.</p>
<p>In this study, we make an original contribution to the debate on gender differences in social preference experiments. In the baseline treatment without redistribution women exhibit a higher propensity to cooperate. Unlike studies suggesting women are &#x0201C;conditional&#x0201D; cooperators (<xref ref-type="bibr" rid="B23">Furtner et al., 2021</xref>), our experimental design&#x02014;lacking group information in the baseline indicates that women start from a higher baseline level of cooperation even without external cues.</p>
<p>We analyze data collected by <xref ref-type="bibr" rid="B4">Becchetti et al. (2024)</xref>, which includes multiple VWG treatments with and without redistribution, a &#x0201C;legality frame,&#x0201D; and a conformity treatment. The companion study investigated an ex-post redistribution mechanism, the impact of a legality frame for the responsible product, and the effect of a conformity information design. The main findings of the complementary study are:</p>
<list list-type="bullet">
<list-item><p>Both the legality frame and the conformity treatment induce a significant increase in the cooperation rate relative to the baseline.</p></list-item>
<list-item><p>The redistributive mechanism significantly increases cooperation.</p></list-item>
<list-item><p>The combination of frame, redistribution, and conformity produces an approximately 63% increase in cooperative choices relative to baseline.</p></list-item>
<list-item><p>Behavior homogenization under redistribution aligns with evidence that economic incentives can mitigate biases based on social norms (<xref ref-type="bibr" rid="B29">Th&#x000F8;gersen, 2021</xref>).</p></list-item>
</list>
<p>The remainder of the paper is structured as follows: Section 2 details the experimental design, the formulation of the VWG, and the general hypotheses of the study. Section 3 presents the initial empirical findings from the static tests, followed by an in-depth econometric analysis and a Bayesian multilevel probit model to assess the robustness of the gender effect. Section 4 discusses our results, contextualizing them within the existing literature and addressing the study&#x00027;s implications and limitations. Section 5 provides the main conclusions and final remarks.</p>
</sec>
<sec id="s2">
<title>2 Experimental design and hypotheses</title>
<p>The theoretical benchmark for this experiment is the VWG, a multiperson version of the PD. The PD is one of the most studied games in the social sciences as it formalizes the conflict between what is socially efficient and what is individually optimal, a theme that underlies many interesting interactions, economic and otherwise (<xref ref-type="bibr" rid="B19">Embrey et al., 2017</xref>).</p>
<p>The <italic>n</italic> players choose between the &#x0201C;responsible&#x0201D; product A, which costs more but produces a positive (environmental, social, legal) externality, and the conventional product B, less costly but without externality.</p>
<p>Following the original formulation in <xref ref-type="bibr" rid="B7">Becchetti and Salustri (2015)</xref>, the game is defined as</p>
<disp-formula id="E1"><label>(1)</label><mml:math id="M1"><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:mi>G</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>N</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msup></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>&#x02208;</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>U</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>&#x02208;</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mtext>&#x02003;</mml:mtext><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy="false">{</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>&#x02026;</mml:mo><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow><mml:mo stretchy="false">}</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mtext>&#x02003;</mml:mtext><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy="false">{</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mo>,</mml:mo><mml:mi>B</mml:mi></mml:mrow><mml:mo stretchy="false">}</mml:mo></mml:mrow><mml:mtext>&#x02003;</mml:mtext><mml:mo>&#x02200;</mml:mo><mml:mi>i</mml:mi><mml:mo>&#x02208;</mml:mo><mml:mi>N</mml:mi><mml:mo>,</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>and the <italic>i</italic>th player payoff function</p>
<disp-formula id="E2"><label>(2)</label><mml:math id="M2"><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:msub><mml:mrow><mml:mi>U</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msup></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:mfrac><mml:mrow><mml:mi>x</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:mfrac><mml:mi>&#x003B2;</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B1;</mml:mi><mml:mo>-</mml:mo><mml:mi>&#x003B3;</mml:mi></mml:mtd><mml:mtd><mml:mtext class="textrm" mathvariant="normal">if&#x000A0;</mml:mtext><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mi>A</mml:mi><mml:mo>,</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mfrac><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:mfrac><mml:mi>&#x003B2;</mml:mi></mml:mtd><mml:mtd><mml:mtext class="textrm" mathvariant="normal">if&#x000A0;</mml:mtext><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mi>B</mml:mi><mml:mo>,</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>with <italic>U</italic> being the player&#x00027;s utility function, <italic>S</italic> being the chosen strategy, and <italic>x</italic> the number of players buying product A excluding player <italic>i</italic>. The crucial parameters of the game are the price differential between the two products (&#x003B3; &#x02208; [0, &#x0002B; &#x0221E;)), the other-regarding preference component (&#x003B1; &#x02208; [0, &#x0002B; &#x0221E;)) satisfied by the purchase of the &#x0201C;responsible&#x0201D; product,<xref ref-type="fn" rid="fn0002"><sup>2</sup></xref>, and the positive externality (&#x003B2; &#x02208; [0, &#x0002B; &#x0221E;)) accruing to the utility of every player (irrespective of her/his product choice) in proportion to the share of players buying the responsible product A.</p>
<p>The game <italic>G</italic><sub><italic>n</italic></sub> always has a unique (<xref ref-type="bibr" rid="B25">Nash, 1950</xref>) equilibrium (<italic>B, B</italic>) when <inline-formula><mml:math id="M3"><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:mfrac><mml:mi>&#x003B2;</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B1;</mml:mi><mml:mo>&#x0003C;</mml:mo><mml:mi>&#x003B3;</mml:mi></mml:math></inline-formula>, and (<italic>A, A</italic>) otherwise, and we are in the PD area for intermediate values of &#x003B3; where <inline-formula><mml:math id="M4"><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:mfrac><mml:mi>&#x003B2;</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B1;</mml:mi><mml:mo>&#x0003C;</mml:mo><mml:mi>&#x003B3;</mml:mi><mml:mo>&#x0003C;</mml:mo><mml:mi>&#x003B2;</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B1;</mml:mi></mml:math></inline-formula>. This condition implies a large PD region as the number of players increases. The implication is that, in consumer markets where &#x0201C;responsible&#x0201D; products involve a public goods dilemma similar to the VWG, this PD-type problem may be highly relevant. However, this inference is based on data from a specific sample of Italian students, highlighting the need to investigate its generalizability to other markets and cultures.<xref ref-type="fn" rid="fn0003"><sup>3</sup></xref></p>
<p>The design of the experiment is presented in <xref ref-type="table" rid="T1">Table 1</xref>, which details the number of sessions and the different treatments.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Experimental design.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Treatment</bold></th>
<th valign="top" align="center"><bold>No. of Sessions</bold></th>
<th valign="top" align="left"><bold>Phase 1 (10 Rounds)</bold></th>
<th valign="top" align="left"><bold>Phase 2 (10 Rounds)</bold></th>
<th valign="top" align="left"><bold>Phase 3</bold></th>
<th valign="top" align="center"><bold>No. of Players</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Baseline</td>
<td valign="top" align="center">1 &#x02013; 3</td>
<td valign="top" align="left">Baseline</td>
<td valign="top" align="left">Redistribution</td>
<td valign="top" align="left">Questionnaire</td>
<td valign="top" align="center">30</td>
</tr>
<tr>
<td valign="top" align="left">Baseline</td>
<td valign="top" align="center">4 &#x02013; 6</td>
<td valign="top" align="left">Redistribution</td>
<td valign="top" align="left">Baseline</td>
<td valign="top" align="left">Questionnaire</td>
<td valign="top" align="center">30</td>
</tr>
<tr>
<td valign="top" align="left">Frame</td>
<td valign="top" align="center">7 &#x02013; 9</td>
<td valign="top" align="left">Frame</td>
<td valign="top" align="left">Frame &#x0002B; Redistribution</td>
<td valign="top" align="left">Questionnaire</td>
<td valign="top" align="center">30</td>
</tr>
<tr>
<td valign="top" align="left">Frame</td>
<td valign="top" align="center">10 &#x02013; 12</td>
<td valign="top" align="left">Frame &#x0002B; Redistribution</td>
<td valign="top" align="left">Frame</td>
<td valign="top" align="left">Questionnaire</td>
<td valign="top" align="center">30</td>
</tr>
<tr>
<td valign="top" align="left">Conformity</td>
<td valign="top" align="center">13 &#x02013; 15</td>
<td valign="top" align="left">Frame (conformity)</td>
<td valign="top" align="left">Frame (conformity) &#x0002B; Redistribution</td>
<td valign="top" align="left">Questionnaire</td>
<td valign="top" align="center">30</td>
</tr>
<tr>
<td valign="top" align="left">Conformity</td>
<td valign="top" align="center">16 &#x02013; 18</td>
<td valign="top" align="left">Frame (conformity) &#x0002B; Redistribution</td>
<td valign="top" align="left">Frame (conformity)</td>
<td valign="top" align="left">Questionnaire</td>
<td valign="top" align="center">30</td>
</tr></tbody>
</table>
<table-wrap-foot>
<p>This table describes the experimental design, including the number of sessions, phases, and players per treatment group.</p>
</table-wrap-foot>
</table-wrap>
<p>In each experimental session, a group of 10 players chooses between products A and B over 20 rounds. Product A costs 10 Experimental Currency Units (ECUs), while product B costs 5 ECUs. Each player buying product A generates 3 ECUs for every player in the given round (2 ECUs=1 euro). In each round players are given an endowment of 20 ECUs. The sequence of actions in each round is as follows: i) the <italic>i</italic>th player formulates her/his expectation on the number of players choosing product A; ii) chooses the product to buy; iii) is informed about the number of players choosing product A; iv) is asked about her/his satisfaction about the game, her/his behavior and the behavior of the other players in the game with three different questions on a 0&#x02013;10 scale. Half of the rounds (the first or the last 10, depending on the session considered (see <xref ref-type="table" rid="T1">Table 1</xref> for details), present a redistribution phase in which at the end of each round players choosing product B know that they have to transfer 1 ECU in a pool that will be divided into equal parts among players choosing product A at the end of each round.<xref ref-type="fn" rid="fn0004"><sup>4</sup></xref></p>
<p>This mechanism simulates a redistributive fiscal policy intended to encourage responsible (environmental, social, legal) consumption choices. The above-described baseline game has been played with two variations.</p>
<list list-type="bullet">
<list-item><p>The &#x0201C;framed game&#x0201D;: players are informed that the &#x0201C;responsible&#x0201D; product A is a product awarded with a 3-star legality rating from the Italian Competition Authority (ICA).<xref ref-type="fn" rid="fn0005"><sup>5</sup></xref></p></list-item>
<list-item><p>The &#x0201C;conformity treatment&#x0201D; is about giving information to the players in each round, about the choices of the previous session players, with the same characteristics. That is the average of what happened in the corresponding rounds of sessions (7&#x02013;9) for sessions (10&#x02013;12), and sessions (13&#x02013;15) for sessions (16&#x02013;18).<xref ref-type="fn" rid="fn0006"><sup>6</sup></xref></p></list-item>
</list>
<p>The experiment was run at the University of Cagliari, Italy, with 180 volunteers (with an exact gender balance in each session) in November 2015. The z-Tree platform (<xref ref-type="bibr" rid="B22">Fischbacher, 2007</xref>) was used to program the experiment. Given the payoffs described, the experiment gives the following values to the crucial VWG model parameters: <italic>n</italic> = 10, &#x003B2;= 30, &#x003B3;=5, &#x003B1;=0. Based on these values, if we assume that the player has no other-regarding preferences (i.e., &#x003B1;=0) the unique (inefficient) Nash Equilibrium (NE) of the multiplayer game in non-redistribution treatments is obtained when all players chose B, since <inline-formula><mml:math id="M5"><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:mfrac><mml:mi>&#x003B2;</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B1;</mml:mi><mml:mo>&#x0003C;</mml:mo><mml:mi>&#x003B3;</mml:mi><mml:mo>&#x0003C;</mml:mo><mml:mi>&#x003B2;</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B1;</mml:mi></mml:math></inline-formula> (i.e., 3 &#x0003C; 5 &#x0003C; 30). However, in redistribution treatments, buying product B yields a lower payoff when there is only one cooperator and the same payoff as buying product A when there are two cooperators. The redistribution mechanism renders it more convenient to buy product B as the number of &#x0201C;responsible&#x0201D; choices gets larger. However, it is still always better (equilibrium of the game) to keep buying product B.</p>
<p>To test the gender effect, we formulate the following general hypothesis:</p>
<disp-formula id="E3"><label>(3)</label><mml:math id="M6"><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:mtable style="text-align:axis;" equalrows="false" columnlines="none" equalcolumns="false" class="array"><mml:mtr><mml:mtd><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mo>:</mml:mo><mml:mtext>&#x000A0;</mml:mtext><mml:mi>S</mml:mi><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mi>f</mml:mi></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>S</mml:mi><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub><mml:mo>:</mml:mo><mml:mtext>&#x000A0;</mml:mtext><mml:mi>S</mml:mi><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mi>f</mml:mi></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>&#x02260;</mml:mo><mml:mi>S</mml:mi><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>where <italic>S</italic>(<italic>f</italic>)<sub><italic>t, s</italic></sub>[<italic>S</italic>(<italic>m</italic>)<sub><italic>t, s</italic></sub>] is the share of cooperative choices (buy product A) selected by female [male] participants in round <italic>t</italic> of treatment <italic>s</italic>. The six considered treatments are those shown in <xref ref-type="table" rid="T1">Table 1</xref>. Each treatment is considered in full and separately when it occurs in the first 10 or the last 10 rounds (i.e., baseline before in sessions 1&#x02013;3 and baseline after in sessions 4&#x02013;6). Under the null, the share of cooperating females is not significantly different from that of cooperating males.</p>
<sec>
<title>2.1 Empirical findings from static tests</title>
<p><xref ref-type="table" rid="T2">Table 2</xref> reports the results in terms of shares of cooperative choices by gender in each treatment. It shows that the gender effect is significant in the baseline treatment. When we decompose the latter, we find that the effect is concentrated in the baseline before treatment where the share of cooperators is significantly higher for women than for men (40.6 against 26.7 with &#x003C7;<sup>2</sup>6.58 <italic>p</italic>-value 0.01).</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Percentages of cooperative choices (by treatment and gender).</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Treatment</bold></th>
<th valign="top" align="center"><bold>Obs</bold></th>
<th valign="top" align="center"><bold>Share of cooperative choices (women vs. men)</bold></th>
<th valign="top" align="center"><bold>&#x003C7;<sup>2</sup></bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Base &#x0002B; redistribution</td>
<td valign="top" align="center">600</td>
<td valign="top" align="center">31.0 &#x02013; 24.7</td>
<td valign="top" align="center">2.9954</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution &#x0002B; base</td>
<td valign="top" align="center">600</td>
<td valign="top" align="center">39.7 &#x02013; 35.3</td>
<td valign="top" align="center">1.2018</td>
</tr>
<tr>
<td valign="top" align="left">Frame &#x0002B; redistribution</td>
<td valign="top" align="center">600</td>
<td valign="top" align="center">35.3 &#x02013; 37.7</td>
<td valign="top" align="center">0.3524</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution &#x0002B; frame</td>
<td valign="top" align="center">600</td>
<td valign="top" align="center">46.0 &#x02013; 37.3</td>
<td valign="top" align="center">4.6354</td>
</tr>
<tr>
<td valign="top" align="left">Frame (conformity) &#x0002B; redistribution</td>
<td valign="top" align="center">600</td>
<td valign="top" align="center">43.0 &#x02013; 36.7</td>
<td valign="top" align="center">2.5105</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution &#x0002B; frame (conformity)</td>
<td valign="top" align="center">600</td>
<td valign="top" align="center">47.3 &#x02013; 43.3</td>
<td valign="top" align="center">0.9684</td>
</tr>
<tr>
<td valign="top" align="left">Base before</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">40.7 &#x02013; 26.7</td>
<td valign="top" align="center">6.5824</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution base before</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">41.3 &#x02013; 36.0</td>
<td valign="top" align="center">0.8996</td>
</tr>
<tr>
<td valign="top" align="left">Frame before</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">42.0 &#x02013; 38.7</td>
<td valign="top" align="center">0.3463</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution frame before</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">42.0 &#x02013; 44.0</td>
<td valign="top" align="center">0.1224</td>
</tr>
<tr>
<td valign="top" align="left">Frame (conformity) before</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">39.3 &#x02013; 37.3</td>
<td valign="top" align="center">0.1269</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution frame (conformity) before</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">54.7 &#x02013; 40.0</td>
<td valign="top" align="center">6.4717</td>
</tr>
<tr>
<td valign="top" align="left">Base after</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">21.3 &#x02013; 22.7</td>
<td valign="top" align="center">0.0777</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution base after</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">38.0 &#x02013; 34.7</td>
<td valign="top" align="center">0.3602</td>
</tr>
<tr>
<td valign="top" align="left">Frame after</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">28.7 &#x02013; 36.7</td>
<td valign="top" align="center">2.1823</td>
</tr>
<tr>
<td valign="top" align="left">Frame (conformity) after</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">46.7 &#x02013; 36.0</td>
<td valign="top" align="center">3.5191</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution frame after</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">50.0 &#x02013; 30.7</td>
<td valign="top" align="center">11.6487</td>
</tr>
<tr>
<td valign="top" align="left">Redistribution frame (conformity) after</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">40.0 &#x02013; 46.7</td>
<td valign="top" align="center">1.3575</td>
</tr></tbody>
</table>
</table-wrap>
<p>Women also cooperate more when framed redistribution treatments are in the first 10 rounds and when framed treatments with conformist information and redistribution treatment with frame are in the second 20 rounds. This aligns with studies that observe higher female cooperation in one-shot dilemmas (<xref ref-type="bibr" rid="B26">Ortmann and Tichy, 1999</xref>), yet contrasts with meta-analyses that report no global differences (<xref ref-type="bibr" rid="B28">Spadaro et al., 2022</xref>).</p>
<p><xref ref-type="fig" rid="F1">Figure 1</xref> shows the proportions of cooperative choices by gender exclusively in the baseline condition, breaking down the results by round, prior to any treatment.</p>
<fig position="float" id="F1">
<label>Figure 1</label>
<caption><p>Cooperative choice rates in the baseline treatment: analysis by round and gender.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frbhe-04-1612211-g0001.tif">
<alt-text>Line graph showing the percentage of males and females choosing option A across ten rounds, with error bars indicating variability. Males are represented by a solid line and females by a dashed line.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec id="s3">
<title>3 Econometric analysis on the gender difference</title>
<p>We estimate the following logit specification for baseline &#x0201C;before&#x0201D; treatments in sessions 1&#x02013;3, where the dependent variable (VWGChoice) is a 0/1 dummy taking value 1 if the player chooses product A contributing to the production of the public good, Male is a (0/1) dummy for the male gender, Age is the player&#x00027;s age, DIncome is five income dummies picking up different income brackets and Round is the experiment round testing for dynamic effects in our game:</p>
<disp-formula id="E4"><label>(4)</label><mml:math id="M7"><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">VWGChoice</mml:mtext></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">Male</mml:mtext></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">Age</mml:mtext></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">Round</mml:mtext></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003F5;</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>To estimate the statistical power of the fixed-effects logit model under different effect sizes for the gender variable, we conducted a Monte Carlo simulation that replicates the structure of our panel dataset. Specifically, we examined the ability of the model to detect small, medium, and large effects by simulating datasets with hypothetical true coefficients of &#x003B2; &#x0003D; 0.2, 0.5, and 0.8, respectively. As shown in <xref ref-type="fig" rid="F2">Figure 2</xref> the results indicate that the current sample provides limited power to detect small effects (24.8%) but sufficient power for medium (83.2%) and large effects (97.9%).</p>
<fig position="float" id="F2">
<label>Figure 2</label>
<caption><p>Statistical power curve for the fixed-effects logit model estimating the gender effect. Estimated statistical power of detecting a gender effect in the fixed-effects logit model. Power analysis was conducted using a Monte Carlo simulation with 1,000 replications, based on the experimental panel structure (30 individuals observed over 10 rounds, for a total of 300 observations). Simulated datasets were generated under three assumed true values of the male coefficient (&#x003B2; &#x0003D; 0.2, 0.5, 0.8), corresponding, respectively, to small, medium, and large effect sizes. For each scenario, the model was re-estimated, and the proportion of simulations where the male coefficient was statistically significant at the 5% level was recorded.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frbhe-04-1612211-g0002.tif">
<alt-text>Line graph showing the relationship between effect size (&#x003B2;) and statistical power. The x-axis represents effect size from 0.2 to 0.8, while the y-axis represents statistical power from 0 to 1. The line indicates increasing power with larger effect sizes, surpassing the 0.8 threshold.</alt-text>
</graphic>
</fig>
<p>Additionally, we evaluated the empirical power to detect the specific effect size estimated from the real data (<inline-formula><mml:math id="M8"><mml:mover accent="true"><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mo>^</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>839</mml:mn></mml:math></inline-formula>). We generated 1,000 simulated datasets using this coefficient as the true value and re-estimated the model in each case, recording the proportion of statistically significant results at the 5% level. This analysis yielded a power estimate of 98.7%, suggesting that the study design is highly capable of detecting effects of the magnitude observed.</p>
<p>After controlling for round and age, we find that the gender effect in the reference treatment remains significant. It is also observed that being male significantly reduces the probability of choosing product A by &#x0007E; 0.145 percentage points (<xref ref-type="table" rid="T3">Table 3</xref>). To verify the robustness of our econometric findings, we test whether gender significantly affects satisfaction with the game in the &#x0201C;before&#x0201D; baseline treatment. The selected ordered probit estimated specification is</p>
<disp-formula id="E5"><label>(5)</label><mml:math id="M9"><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">SatGame</mml:mtext></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">VWGChoice</mml:mtext></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mtext class="textrm" mathvariant="normal">VWGChoice</mml:mtext></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mo>&#x000D7;</mml:mo><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">Male</mml:mtext></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:mtext class="textrm" mathvariant="normal">AvgGroup</mml:mtext><mml:mo>&#x000D7;</mml:mo><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">VWGChoice</mml:mtext></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">Male</mml:mtext></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>5</mml:mn></mml:mrow></mml:msub><mml:mtext class="textrm" mathvariant="normal">Age</mml:mtext><mml:mo>&#x0002B;</mml:mo><mml:mstyle displaystyle="true"><mml:munder class="msub"><mml:mrow><mml:mo>&#x02211;</mml:mo></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:munder></mml:mstyle><mml:msub><mml:mrow><mml:mi>&#x003B4;</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">DIncome</mml:mtext></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mn>6</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext class="textrm" mathvariant="normal">Round</mml:mtext></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003F5;</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>where SatGame<sub><italic>i, t, s</italic></sub> is the satisfaction of the <italic>i</italic>th player about her/his behavior in round <italic>t</italic> of treatment <italic>s</italic>, VWGChoice &#x000D7; Male is the interaction between the choice of product A and male gender, and AvgGroup &#x000D7; VWGChoice is the share of players choosing good A in the given round. Other regressors are as in <xref ref-type="disp-formula" rid="E4">Equation 4</xref>.</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Econometric findings.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Variables</bold></th>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left" rowspan="2">VWGChoice</td>
<td/>
<td valign="top" align="center">&#x02212;0.311<sup>&#x0002A;&#x0002A;</sup></td>
</tr>
 <tr>
<td/>
<td valign="top" align="center">(0.157)</td>
</tr>
<tr>
<td valign="top" align="left" rowspan="2">Male</td>
<td valign="top" align="center">&#x02212;0.839<sup>&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">0.782</td>
</tr>
 <tr>
<td valign="top" align="center">(0.364)</td>
<td valign="top" align="center">(1.145)</td>
</tr>
<tr>
<td valign="top" align="left" rowspan="2">VWGChoice &#x000D7; male</td>
<td/>
<td valign="top" align="center">&#x02212;0.687<sup>&#x0002A;</sup></td>
</tr>
 <tr>
<td/>
<td valign="top" align="center">(0.368)</td>
</tr>
<tr>
<td valign="top" align="left" rowspan="2">AvgGroup &#x000D7; VWGChoice</td>
<td/>
<td valign="top" align="center">0.582<sup>&#x0002A;&#x0002A;</sup></td>
</tr>
 <tr>
<td/>
<td valign="top" align="center">(0.273)</td>
</tr>
<tr>
<td valign="top" align="left" rowspan="2">Age</td>
<td valign="top" align="center">&#x02212;0.047</td>
<td valign="top" align="center">0.030</td>
</tr>
 <tr>
<td valign="top" align="center">(0.058)</td>
<td valign="top" align="center">(0.048)</td>
</tr>
<tr>
<td valign="top" align="left">Round dummies</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Income dummies</td>
<td valign="top" align="center">No</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Observations</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">300</td>
</tr>
<tr>
<td valign="top" align="left">Wald &#x003C7;<sup>2</sup></td>
<td valign="top" align="center">25.81</td>
<td valign="top" align="center">38.26</td>
</tr></tbody>
</table>
<table-wrap-foot>
<p>Dependent variable: (1) choice of product A; (2) satisfaction about one&#x00027;s behavior in the game; standard errors (in parentheses) clustered at the session level. &#x0002A;&#x0002A;&#x0002A;<italic>p</italic> &#x0003C; 0.01, &#x0002A;&#x0002A;<italic>p</italic> &#x0003C; 0.05, &#x0002A;<italic>p</italic> &#x0003C; 0.1.</p>
</table-wrap-foot>
</table-wrap>
<p>Our findings show that &#x003B2;<sub>2</sub> is negative and significant, indicating a negative interaction between choosing VWGChoice and being male. In other words, men are significantly less satisfied than women when choosing the more expensive product that contributes to the public good. Specifically, choosing VWGChoice = 1 reduces the probability of men reporting the highest level of satisfaction (level 10) by &#x0007E; 0.144 percentage points.</p>
<p>None of the other treatments significant in static hypothesis testing are robust to the econometric checks of this section (results omitted for reasons of space).</p>
<p><xref ref-type="fig" rid="F3">Figure 3</xref> presents the marginal effects on the probability of VWGChoice being equal to 1. It can be seen that being male significantly reduces this probability by &#x0007E; 0.145 percentage points compared to being female. Starting in period 3, and particularly in periods 5, 6, and 10, there is a statistically significant decrease in the probability of VWGChoice being equal to 1 compared to the base period. The most pronounced effect is observed in period 6. Periods 4, 7, 8, and 9 also show a negative trend, albeit with marginal significance. Conversely, the age variable does not have a statistically significant effect on the probability of VWGChoice.</p>
<fig position="float" id="F3">
<label>Figure 3</label>
<caption><p>Marginal effects&#x02014;fixed-effects logit model.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frbhe-04-1612211-g0003.tif">
<alt-text>Dot and whisker plot showing effects on probability for different &#x0201C;SatGame&#x0201D; categories, numbered one to ten. Dots represent effect estimates, whiskers indicate confidence intervals. Categories near one and ten show wider intervals, suggesting more variability. The x-axis represents effect on probability ranging from negative point one to point one, with zero marked by a vertical dashed line.</alt-text>
</graphic>
</fig>
<p>To assess the robustness of the effect of gender on the probability of making a given choice, we estimated a series of xtlogit models with different combinations of controls. <xref ref-type="fig" rid="F4">Figure 4</xref> shows the specification curve corresponding to the coefficient of the male variable, together with its 95th percentile confidence intervals. As can be seen, the estimated coefficient is consistent in direction (negative) and reaches statistical significance in the most saturated models. This suggests that the gender effect is robust to reasonable model specifications.</p>
<fig position="float" id="F4">
<label>Figure 4</label>
<caption><p>Specification curve for the effect of being male on choice. Each point represents the estimated coefficient under a different combination of controls.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frbhe-04-1612211-g0004.tif">
<alt-text>Scatter plot showing estimated coefficients for six models, with error bars. The x-axis lists models one through six, and the y-axis indicates coefficient values ranging from -1.5 to 0.  Each point depicts a model&#x00027;s coefficient estimate with corresponding error bars.</alt-text>
</graphic>
</fig>
<p><xref ref-type="fig" rid="F5">Figure 5</xref> shows the marginal effects of a discrete change in VWGChoice from 0 to 1 on the probability of observing each of the 10 satisfaction levels. It is observed that VWGChoice = 1 is associated with a statistically significant increase in the probability of being in the lowest satisfaction categories (1, 2, 4, 5, and 6). Conversely, it is associated with a statistically significant decrease in the probability of being in the highest satisfaction categories (8, 9, and 10). The effects in categories 3 and 7 are not statistically significant. This suggests that VWGChoice = 1 shifts the satisfaction distribution toward lower value categories.</p>
<fig position="float" id="F5">
<label>Figure 5</label>
<caption><p>Marginal effects&#x02014;random-effects ordered probit model&#x02014;VWGChoice.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="frbhe-04-1612211-g0005.tif">
<alt-text>Dot plot showing effects on probability for categories labeled SatGame, ranging from 1 to 10. Horizontal axis measures effects from -0.1 to 0.1. Each point has error bars. Central vertical line marks zero effect.</alt-text>
</graphic>
</fig>
<sec>
<title>3.1 Bayesian hierarchical modeling</title>
<p>To control for false positives due to multiple hypothesis testing, we applied Laplace (Lasso-type) priors on the fixed effects. These shrinkage priors regularize coefficient estimates, penalizing non-informative predictors. This Bayesian approach offers simultaneous estimation and implicit multiplicity correction by favoring sparsity. The approach is more sophisticated and tailored than a raw conservative Bonferroni correction. Our main finding&#x02013;that women are significantly more cooperative in the baseline treatment&#x02013;remains robust under this procedure.</p>
<p>We estimate a Bayesian multilevel probit regression model to assess the probability of choosing the responsible product (VWGChoice = 1). The dependent variable is binary and indicates whether individual <italic>i</italic>, in session <italic>s</italic>, selected the responsible option in the task. The model incorporates both individual-level covariates and session-level random effects to account for unobserved heterogeneity across sessions.</p>
<p>Formally, the model is specified as</p>
<disp-formula id="E7"><mml:math id="M11"><mml:mtable columnalign="left"><mml:mtr><mml:mtd><mml:msubsup><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mo>*</mml:mo></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>&#x003B5;</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mtext>&#x02003;</mml:mtext><mml:msub><mml:mrow><mml:mi>&#x003B5;</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0007E;</mml:mo><mml:mrow><mml:mi mathvariant="script">N</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mtable style="text-align:axis;" equalrows="false" columnlines="none" equalcolumns="false" class="array"><mml:mtr><mml:mtd><mml:mn>1</mml:mn></mml:mtd><mml:mtd><mml:mtext class="textrm" mathvariant="normal">if&#x000A0;</mml:mtext><mml:msubsup><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mo>*</mml:mo></mml:mrow></mml:msubsup><mml:mo>&#x0003E;</mml:mo><mml:mn>0</mml:mn></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mn>0</mml:mn></mml:mtd><mml:mtd><mml:mtext class="textrm" mathvariant="normal">otherwise</mml:mtext></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>where <italic>y</italic><sub><italic>is</italic></sub> is the observed binary outcome, <italic>X</italic><sub><italic>is</italic></sub> includes the individual-level predictors (gender, age, income dummies, and round fixed effects), <inline-formula><mml:math id="M12"><mml:msub><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0007E;</mml:mo><mml:mrow><mml:mi mathvariant="script">N</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>&#x003C3;</mml:mi></mml:mrow><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math></inline-formula> is a random intercept for session <italic>s</italic>, and &#x003B5;<sub><italic>is</italic></sub> is the standard normal error term.</p>
<p>We place a Laplace prior with mean 0 and scale parameter &#x003BB; &#x0003D; 1 on all fixed-effect coefficients:</p>
<disp-formula id="E8"><mml:math id="M13"><mml:mrow><mml:msub><mml:mrow><mml:mi>&#x003B2;</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0007E;</mml:mo><mml:mtext class="textrm" mathvariant="normal">Laplace</mml:mtext><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mtext>&#x02003;</mml:mtext><mml:mtext class="textrm" mathvariant="normal">for all&#x000A0;</mml:mtext><mml:mi>j</mml:mi><mml:mo>&#x02208;</mml:mo><mml:mtext class="textrm" mathvariant="normal">covariates</mml:mtext><mml:mo>.</mml:mo></mml:mrow></mml:math></disp-formula>
<p>This prior induces a Lasso-type shrinkage that helps regularize the estimates and perform implicit variable selection, particularly useful in settings with many control variables.</p>
<p>The prior on the session-level random intercept is</p>
<disp-formula id="E9"><mml:math id="M14"><mml:mrow><mml:msub><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0007E;</mml:mo><mml:mrow><mml:mi mathvariant="script">N</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>&#x003C3;</mml:mi></mml:mrow><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mtext>&#x02003;</mml:mtext><mml:msubsup><mml:mrow><mml:mi>&#x003C3;</mml:mi></mml:mrow><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>&#x0007E;</mml:mo><mml:mtext class="textrm" mathvariant="normal">Inverse-Gamma</mml:mtext><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>01</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>01</mml:mn></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mo>.</mml:mo></mml:mrow></mml:math></disp-formula>
<p>Posterior inference is conducted using Metropolis&#x02013;Hastings Markov Chain Monte Carlo (MCMC), with 12,500 total iterations, discarding the first 2,500 as burn-in. We report posterior means, posterior standard deviations, Monte Carlo standard errors (MCSE), posterior medians, and 95% equal-tailed credible intervals. The magnitude of the male coefficient in <xref ref-type="table" rid="T4">Table 4</xref> (&#x02212;0.122) is slightly smaller than the maximum likelihood estimate in <xref ref-type="table" rid="T3">Table 3</xref> (&#x02212;0.839), also due to the different estimation method (Bayesian multilevel probit in <xref ref-type="table" rid="T4">Table 4</xref> vs. fixed effects logit in <xref ref-type="table" rid="T3">Table 3</xref>).</p>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p>Bayesian hierarchical modeling estimates.</p></caption>
<table frame="box" rules="all">
<thead>
<tr>
<th valign="top" align="left"><bold>Variable</bold></th>
<th valign="top" align="center"><bold>Mean</bold></th>
<th valign="top" align="center"><bold>SD</bold></th>
<th valign="top" align="center"><bold>MCSE</bold></th>
<th valign="top" align="center"><bold>Median</bold></th>
<th valign="top" align="center"><bold>95% Cred. Interval</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Male</td>
<td valign="top" align="center">&#x02212;0.122<sup>&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">0.040</td>
<td valign="top" align="center">0.003</td>
<td valign="top" align="center">&#x02212;0.121</td>
<td valign="top" align="center">[&#x02212;0.198, &#x02212;0.051]</td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="center">0.019<sup>&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">0.005</td>
<td valign="top" align="center">0.001</td>
<td valign="top" align="center">0.019</td>
<td valign="top" align="center">[0.011, 0.028]</td>
</tr>
<tr>
<td valign="top" align="left">Intercept</td>
<td valign="top" align="center">&#x02212;0.781<sup>&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">0.131</td>
<td valign="top" align="center">0.025</td>
<td valign="top" align="center">&#x02212;0.772</td>
<td valign="top" align="center">[&#x02212;1.026, &#x02212;0.544]</td>
</tr>
<tr>
<td valign="top" align="left" colspan="6" style="background-color:#dee1e1"><bold>Random effects (session)</bold></td>
</tr>
<tr>
<td valign="top" align="left">Intercept</td>
<td valign="top" align="center">0.444</td>
<td valign="top" align="center">9.755</td>
<td valign="top" align="center">0.213</td>
<td valign="top" align="center">0.577</td>
<td valign="top" align="center">[&#x02212;18.490, 19.337]</td>
</tr>
<tr>
<td valign="top" align="left">&#x003C3;<sup>2</sup></td>
<td valign="top" align="center">0.035<sup>&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">0.017</td>
<td valign="top" align="center">0.0008</td>
<td valign="top" align="center">0.013</td>
<td valign="top" align="center">[0.013, 0.080]</td>
</tr>
<tr>
<td valign="top" align="left">Round dummies</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Income dummies</td>
<td valign="top" align="center">No</td>
<td valign="top" align="center">No</td>
<td valign="top" align="center">No</td>
<td valign="top" align="center">No</td>
<td valign="top" align="center">No</td>
</tr></tbody>
</table>
<table-wrap-foot>
<p>Dependent variable: choice of the responsible product A. Coefficients are posterior means from a Bayesian multilevel probit model estimated via Metropolis&#x02013;Hastings sampler. Laplace priors (&#x003BB; &#x0003D; 1) are used for fixed effects, and normal priors for random intercepts. Standard deviations (SD), Monte Carlo standard errors (MCSE), medians, and 95% credible intervals are reported. <sup>&#x0002A;&#x0002A;</sup> Credible interval does not include zero (95% significance).</p>
</table-wrap-foot>
</table-wrap>
<p>Nonetheless, the direction and significance remain consistent, strengthening the claim that gender (specifically, being male) is associated with a lower propensity for cooperative behavior in the experimental setting. The estimate in <xref ref-type="table" rid="T4">Table 4</xref> confirms the robustness of this effect even under stricter regularization and model uncertainty.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>Our results reveal that in our experimental study with Italian students, women cooperate significantly more than men in the baseline treatment of the &#x0201C;Vote with the Wallet Game&#x0201D; (VWG), a finding that contributes to the ongoing debate regarding gender differences in social preferences. Unlike studies suggesting that women are &#x0201C;conditional&#x0201D; cooperators (<xref ref-type="bibr" rid="B23">Furtner et al., 2021</xref>), our experimental design&#x02014;which lacks group information in the baseline&#x02014;suggests that, without external cues, women start from a higher baseline level of cooperation. This may reflect internalized prosocial preferences linked to gender norms that associate femininity with caregiving and collective responsibility (<xref ref-type="bibr" rid="B31">Xiao et al., 2019</xref>; <xref ref-type="bibr" rid="B10">Bloodhart and Swim, 2020</xref>).</p>
<p>This result contrasts with meta-analyses that do not identify significant differences between genders (<xref ref-type="bibr" rid="B28">Spadaro et al., 2022</xref>), underscoring the crucial role of the specific experimental context. The VWG is not only an abstract social dilemma; it simulates a consumption decision with explicit moral implications. In our study, the choice of the &#x0201C;responsible&#x0201D; product (A), especially in a cultural context like Italy&#x00027;s, which includes the &#x0201C;Legality Rating&#x0201D; system, could activate culturally rooted gender identities. In contexts like ours, where relative gender equality might be assumed (<xref ref-type="bibr" rid="B20">Falk and Hermle, 2018</xref>), women are able to express their prosocial preferences with fewer restrictions from traditional norms, which could explain the differences observed in our baseline treatment. Thus, we challenge the notion that gender gaps in cooperation arise solely from external factors (<xref ref-type="bibr" rid="B17">Croson and Gneezy, 2009</xref>), proposing that in morally charged contexts, women emerge as key agents in promoting the voluntary provision of public goods.</p>
<p>We are also aware that specific characteristics of our game can crucially affect outcomes. We model the VWG with positive externality. A different pay-off structure based on a negative externality from the conventional product could have made the gender effect stronger due to the existing evidence on higher women&#x00027;s loss aversion (<xref ref-type="bibr" rid="B30">Wang et al., 2017</xref>; <xref ref-type="bibr" rid="B18">Dawson, 2023</xref>).</p>
<p>Along the same lines, we also acknowledge that the decision to act cooperatively can be affected by the weight of the positive externality on the total players&#x00027; payoffs. Changes in the payoff-structure of the vote with the wallet game can be fruitful directions for future research to test whether our main findings are robust or change according to them.</p>
<p>An additional finding is how the redistribution of resources reduces gender disparities. The asymmetry between men and women is mitigated by penalizing free riding - similar to policies that internalize social costs (<xref ref-type="bibr" rid="B29">Th&#x000F8;gersen, 2021</xref>). This does not imply an innate female altruism, but rather distinct logics of cooperation: men seem to prioritize individual efficiency in the face of ambiguous incentives (<xref ref-type="bibr" rid="B13">Burnham, 2018</xref>), while women seek to avoid moral costs tied to irresponsible consumption, even without a guarantee of reciprocity. This behavior aligns with their greater aversion to violating social norms perceived as fair (<xref ref-type="bibr" rid="B10">Bloodhart and Swim, 2020</xref>).</p>
<p>Finally, the legality frame enhances cooperation but does not eliminate gender differences. This indicates that institutional frameworks influence prosocial preferences but do not nullify them. By labeling product A as &#x0201C;legal,&#x0201D; the decision shifts from the individual moral domain to the institutional realm, thereby reducing the weight of gender identity. This outcome aligns with studies linking institutional legitimacy (such as ethical certifications) to promoting prosocial behaviors and mitigating gender gaps (<xref ref-type="bibr" rid="B11">Bolderdijk et al., 2018</xref>).</p>
<p>While our findings point to a significant gender difference in cooperative behavior in the baseline condition, we emphasize that this analysis is post-hoc and exploratory. The sample consists exclusively of university students in Italy, which limits external validity and the generalizability of our results. Cultural context, socioeconomic background, and demographic homogeneity may all shape observed behaviors. As such, our results should be interpreted as suggestive rather than definitive, and they call for replication in more diverse and representative samples.</p>
</sec>
<sec sec-type="conclusions" id="s5">
<title>5 Conclusions</title>
<p>The original (<xref ref-type="bibr" rid="B4">Becchetti et al., 2024</xref>) experiment was designed to test the impact of corporate legality ratings on consumer choices and their willingness to pay for legality, testing whether consumers are willing to pay for this public good when properly informed about it or when a redistribution mechanism is introduced that makes the sustainable choice more convenient.</p>
<p>In this paper, we focus on the existence of gender differences in the VWG. The hypothesis of such an effect is grounded on a large literature that stresses a generalized higher sensitivity of women to contextual elements, such as frames (for a review, see <xref ref-type="bibr" rid="B17">Croson and Gneezy, 2009</xref>) and also on previous results with the VWG (<xref ref-type="bibr" rid="B9">Becchetti et al., 2018b</xref>).</p>
<p>Our findings document that women are inherently more cooperative - specifically, they are significantly more likely to choose the cooperative option in the baseline treatment, thereby deviating from the Nash equilibrium prediction of universal defection. This departure from the individually rational but socially inefficient equilibrium moves outcomes closer to the socially optimal scenario in which all players choose the responsible product and collectively benefit. This result may reflect differences in internalized prosocial preferences, as observed in studies on altruism and gender roles (<xref ref-type="bibr" rid="B31">Xiao et al., 2019</xref>), and it may be amplified in contexts characterized by greater gender equality (<xref ref-type="bibr" rid="B20">Falk and Hermle, 2018</xref>). This finding adds new evidence to the debate on gender differences in prosocial behavior with special reference to the VWG.</p>
<p>Our results seem to strengthen the position of those who consider women to be naturally more cooperative. From the policy-maker standpoint, these conclusions are good news since the same policy (legality frame and/or redistribution) does not have a differential impact depending on the gender of the consumer.</p>
<p>As a final caveat it is important to remark that the gender-based analyses emerged in the course of exploratory analysis rather than from a pre-registered hypothesis. This, along with the use of a student sample in a specific cultural setting, limits the broader applicability of our conclusions. Future studies with more heterogeneous populations and pre-registered designs would be essential to validate and extend these insights.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: <ext-link ext-link-type="uri" xlink:href="https://osf.io/ryu3v/files/osfstorage/67e46b3addc363adfa6b4247">https://osf.io/ryu3v/files/osfstorage/67e46b3addc363adfa6b4247</ext-link>.</p>
</sec>
<sec sec-type="ethics-statement" id="s7">
<title>Ethics statement</title>
<p>Ethical approval was not required for the studies involving humans because research context, the research presented in this manuscript was originally conducted in 2015, predating the current comprehensive ethical review protocols. We recognize the evolving standards of research ethics and are committed to transparency regarding our research methodology and participant protections. A. Participant engagement, at the time of data collection, our research team implemented rigorous participant management protocols: i) Informed consent: all participants provided either verbal. ii) Research transparency: participants received comprehensive briefings about research objectives. iii) Voluntary participation: clear communication of the right to withdraw was established. B. Data handling, Our data management approach prioritized participant privacy and confidentiality: i) Complete anonymization of individual participant data, ii) Secure and confidential data storage systems, iii) Strict protocols preventing retention of identifiable personal information. C. Methodological integrity, the research methodology aligned with the ethical standards prevalent during the period of data collection, with an unwavering commitment to: i) Participant welfare, research integrity, minimizing potential harm D. Request for consideration, we kindly request the journal&#x00027;s review and guidance regarding this retrospective ethical statement. We are fully prepared to provide any additional documentation or clarification needed to support our submission. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="s8">
<title>Author contributions</title>
<p>AV: Data curation, Methodology, Supervision, Investigation, Software, Writing &#x02013; review &#x00026; editing, Formal analysis, Writing &#x02013; original draft. LB: Writing &#x02013; review &#x00026; editing, Formal analysis, Investigation, Writing &#x02013; original draft, Data curation, Conceptualization. VP: Writing &#x02013; original draft, Funding acquisition, Supervision, Writing &#x02013; review &#x00026; editing, Resources, Project administration, Data curation, Methodology, Conceptualization.</p>
</sec>
<sec sec-type="funding-information" id="s9">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This work was funded by &#x0201C;Department of Economics and Business, University of Cagliari, Project Code. RICDIP_2017_PELLIGRA-Analisi sperimentale del vote-with-the-wallet game e degli effetti redistributive.&#x0201D;</p>
</sec>
<ack><p>We thank Airoon Torrealba and Carolina Carrillo for their research assistance. Financial support from the University of Cagliari, Italy, is kindly acknowledged.</p>
</ack>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s10">
<title>Generative AI statement</title>
<p>The author(s) declare that no Gen AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="s11">
<title>Publisher&#x00027;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="s12">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/frbhe.2025.1612211/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/frbhe.2025.1612211/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.pdf" id="SM1" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Data_Sheet_2.pdf" id="SM2" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<fn-group>
<fn id="fn0001"><p><sup>1</sup>The VWG is at the center of a research program investigating the conditions that facilitate responsible choices. Relevant theoretical and experimental results can be found in <xref ref-type="bibr" rid="B7">Becchetti and Salustri (2015</xref>, <xref ref-type="bibr" rid="B8">2016)</xref>; <xref ref-type="bibr" rid="B5">Becchetti et al. (2017</xref>, <xref ref-type="bibr" rid="B6">2018a</xref>,<xref ref-type="bibr" rid="B9">b</xref>, <xref ref-type="bibr" rid="B3">2020</xref>, <xref ref-type="bibr" rid="B4">2024)</xref>.</p></fn>
<fn id="fn0002"><p><sup>2</sup>The assumption is grounded in results from the literature on other regarding preferences such as (positive and negative) reciprocity (<xref ref-type="bibr" rid="B27">Rabin, 1993</xref>), inequity aversion (<xref ref-type="bibr" rid="B21">Fehr and Schmidt, 1999</xref>; <xref ref-type="bibr" rid="B12">Bolton and Ockenfels, 2000</xref>), social welfare preferences (<xref ref-type="bibr" rid="B14">Charness and Rabin, 2002</xref>) and various forms of pure and impure (warm glow) altruism (<xref ref-type="bibr" rid="B1">Andreoni, 1990</xref>). This other-regarding component is similar in spirit to the warm-glow component in the model developed by <xref ref-type="bibr" rid="B1">Andreoni (1990)</xref>.</p></fn>
<fn id="fn0003"><p><sup>3</sup>See <xref ref-type="bibr" rid="B7">Becchetti and Salustri (2015)</xref> for more details.</p></fn>
<fn id="fn0004"><p><sup>4</sup>See <xref ref-type="supplementary-material" rid="SM2">Appendix B</xref> for details about the instructions and the payoffs&#x00027; parametrization.</p></fn>
<fn id="fn0005"><p><sup>5</sup>Full details of the legality rating system are provided in the online <xref ref-type="supplementary-material" rid="SM1">Appendix A</xref>.</p></fn>
<fn id="fn0006"><p><sup>6</sup>In conformity treatments the information provided does not affect directly a player&#x00027;s payoff and therefore is intended to measure conformity, usually defined as the degree to which individuals in a group modify their behavior to fit the views of the society (<xref ref-type="bibr" rid="B24">Moscovici, 1985</xref>; <xref ref-type="bibr" rid="B15">Cialdini and Trost, 1998</xref>).</p></fn>
</fn-group>
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