<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.166262.2</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Research Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Counterbalance Economics (CBE): Harnessing Aggregate Markups to Finance Voucher-Based Inequality Reduction</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 2; peer review: 1 approved, 1 approved with reservations]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Malliaros</surname>
                        <given-names>Peter</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0001-7947-9015</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Pacheco-Jaramillo</surname>
                        <given-names>W Alejandro</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-4208-5546</uri>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Research, UrCommunity Ltda, Melbourne, VIC, 3051, Australia</aff>
                <aff id="a2">
                    <label>2</label>Economics, Universidad Anahuac Mexico, Huixquilucan de Degollado, State of Mexico, 52786, Mexico</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:peter@urcommunity.com.au">peter@urcommunity.com.au</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>5</day>
                <month>2</month>
                <year>2026</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2025</year>
            </pub-date>
            <volume>14</volume>
            <elocation-id>980</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>2</day>
                    <month>2</month>
                    <year>2026</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Malliaros P and Pacheco-Jaramillo WA</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/14-980/pdf"/>
            <abstract>
                <sec>
                    <title>Background</title>
                    <p>Corporate markups have risen markedly since the 1980s, widening the gap between profits and wages and fuelling income inequality. Counterbalance Economics (CBE) converts a portion of those markup rents into firm-redeemable vouchers for low-income households, promising redistribution without incurring large public budgets. This paper tests whether in-kind financed voucher scheme can neutralise the inequality effect of markups across advanced and emerging OECD economies.</p>
                </sec>
                <sec>
                    <title>Methodology</title>
                    <p>We assemble an unbalanced panel of 12 countries (1982&#x2013;2016) that combines Study/World Bank Gini indices with De Loecker&#x2013;Eeckhout aggregate markups, macro controls (log GDP per capita, terms of trade, R&amp;D intensity, unemployment) and a newly coded CBE dummy capturing six staggered adoptions of mark-up-funded voucher programmes. A two-way fixed-effects difference-in-differences model estimates the causal impact of markups, CBE, and their interaction on inequality, controlling for country&#x2013;clustered errors.</p>
                </sec>
                <sec>
                    <title>Results</title>
                    <p>In the absence of a policy, a 10-percentage-point rise in markups increases the Gini coefficient by 1.23 points. Where CBE is in force, the interaction term turns significantly negative implying the same markup increase now lowers the Gini by 2.9 points. Disaggregating the data reveals a more minor but still significant reduction in developed economies and a larger effect in emerging economies, consistent with higher initial inequality and weaker traditional safety nets. Placebo and pre-trend tests detect no spurious effects, and synthetic-control replications for early adopters confirm the magnitude of the effect.</p>
                </sec>
                <sec>
                    <title>Conclusions</title>
                    <p>Markup rents are a potent driver of inequality, yet a modest, rule-based in-kind mechanism that recycles those rents into vouchers can more than offset their distributive harm. CBE therefore emerges as a market-compatible and potentially scalable complement to progressive taxation, whose effectiveness depends on institutional capacity, regulatory oversight, and interaction with existing redistributive systems&#x2014;particularly in emerging economies facing fiscal and political constraints. Future research should pair industry-level markups with micro-data on voucher use to refine targeting and assess long-run growth effects.</p>
                </sec>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Income inequality (D31)</kwd>
                <kwd>aggregate mark-ups and market power (D43</kwd>
                <kwd>L13)</kwd>
                <kwd>voucher-based redistribution and in-kind transfers (H23)</kwd>
                <kwd>corporate ESG and social responsibility (M14)</kwd>
                <kwd>panel econometrics and staggered difference-in-differences (C23)</kwd>
                <kwd>innovation and R&amp;D intensity (O32).</kwd>
            </kwd-group>
            <funding-group>
                <funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement>
            </funding-group>
        </article-meta>
        <notes>
            <sec sec-type="version-changes">
                <label>Revised</label>
                <title>Amendments from Version 1</title>
                <p>Summary of Key Revisions Compared to the Previously Published Version In this revised version, we have substantially expanded and refined the conceptual, institutional, and policy framing of Counterbalance Economics (CBE) in response to external review and to strengthen the manuscript&#x2019;s clarity and contribution. First, the policy implications have been reframed with greater conditional precision, making explicit that the effectiveness of CBE depends on institutional capacity, regulatory oversight, and interaction with existing redistributive systems. Language suggesting transformative or universal applicability has been replaced with wording that situates CBE as a complementary mechanism, not a substitute for traditional taxation, transfers, or universal public services. Second, we have deepened the discussion of analogies with existing programs &#x2014; most notably Bolsa Fam&#x00ed;lia &#x2014; by more clearly distinguishing CBE&#x2019;s institutional logic, financing, and normative foundations from state-led social programmes. This includes explicit acknowledgements of fundamental differences while preserving Bolsa Fam&#x00ed;lia as an illustrative reference point. Third, the literature review and discussion have been expanded to incorporate additional evidence on the redistributive effectiveness of universal public services, grounded in established research (e.g., Verbist, F&#x00f6;rster, &amp; Vaalavuo, 2012), thereby situating CBE within the broader inequality-reduction literature and tempering any implied claims of market-based superiority. Fourth, the manuscript now contains an expanded institutional limitations section that foregrounds the conditions under which CBE can generate meaningful redistribution, including the necessity of credible enforcement, firm participation incentives, and regulatory oversight. Corresponding revisions have also been made to the Abstract, Discussion, and Conclusions to ensure consistent framing throughout. Collectively, these revisions preserve the original empirical contributions while aligning interpretation, policy relevance, and normative statements more closely with both the evidence and the broader literature.</p>
            </sec>
        </notes>
    </front>
    <body>
        <sec id="sec5" sec-type="intro">
            <title>Introduction</title>
            <p>Income inequality has become a central public policy challenge in both developed and developing economies. Empirical research since the mid-2000s links high inequality to slower and less durable growth (
                <xref ref-type="bibr" rid="ref8">Berg &amp; Ostry, 2011</xref>; 
                <xref ref-type="bibr" rid="ref12">Cingano, 2014</xref>) as well as to reduced intergenerational mobility (
                <xref ref-type="bibr" rid="ref10">Chetty et al., 2014</xref>) and greater social tensions and health costs (
                <xref ref-type="bibr" rid="ref57">Wilkinson &amp; Pickett, 2009</xref>). When income and wealth are concentrated in a few hands, aggregate demand can weaken, and macro-financial fragility may rise (
                <xref ref-type="bibr" rid="ref32">Kumhof &amp; Ranci&#x00e8;re, 2010</xref>), while trust and social cohesion erode (
                <xref ref-type="bibr" rid="ref40">OECD, 2015</xref>). Addressing inequality is, therefore, not only a matter of social justice but also of economic prudence: increased disparities undermine productivity, political stability and long-run growth potential (
                <xref ref-type="bibr" rid="ref46">Piketty, 2014</xref>). However, despite broad agreement on its importance, achieving a more equitable income distribution has proved difficult. Conventional tools&#x2014;such as progressive income taxes, cash transfers, and minimum wages&#x2014;have delivered only limited and sometimes temporary, reductions in inequality (
                <xref ref-type="bibr" rid="ref3">Atkinson, 2015</xref>). Political constraints and concerns about efficiency often curb the ambition of these policies, echoing Arthur Okun&#x2019;s classic &#x201c;leaky bucket&#x201d; analogy, in which redistribution may lose efficiency (water) as it moves resources from the rich to the poor.</p>
            <p>In the face of these challenges, policy debate has shifted toward market-based mechanisms that enlist private firms as active agents of redistribution. A rich corporate-responsibility literature finds that companies increasingly embrace social objectives&#x2014;whether to secure reputational capital, satisfy ESG (Environmental, Social, and Governance)-oriented investors, or expand future consumer demand (
                <xref ref-type="bibr" rid="ref7">B&#x00e9;nabou &amp; Tirole, 2010</xref>; 
                <xref ref-type="bibr" rid="ref17">Eccles, Ioannou, &amp; Serafeim, 2014</xref>). Building on this trend, we introduce Counterbalance Economics (CBE), an inter-corporate voucher architecture in which firms issue and accept Countertrade Credits (CCs) that lower-income workers can redeem for goods and services. CCs create a parallel, non-cash income channel that boosts purchasing power at the bottom of the distribution without relying on higher taxes or inflationary finance, echoing the efficiency arguments advanced for well-designed in-kind transfers and complementary currencies (
                <xref ref-type="bibr" rid="ref27">Johnson, 2018</xref>; 
                <xref ref-type="bibr" rid="ref56">Teasdale &amp; Aiken, 2019</xref>). By embedding redistribution within regular commercial exchange, CBE aligns with the &#x201c;shared-value&#x201d; paradigm that sees profitable opportunities in meeting social needs (
                <xref ref-type="bibr" rid="ref47">Porter &amp; Kramer, 2011</xref>) and with evidence that firms with credible ESG commitments enjoy lower capital costs and stronger long-run performance (
                <xref ref-type="bibr" rid="ref30">Kr&#x00fc;ger, 2015</xref>; 
                <xref ref-type="bibr" rid="ref34">Liang &amp; Renneboog, 2020</xref>). The core hypothesis, therefore, is that voucher-mediated contributions from the private sector can reduce inequality at scale while preserving market incentives and macro-stability&#x2014;a proposition that complements, rather than replaces, traditional government programmes. In this sense, CBE should not be interpreted as an alternative welfare-state architecture, but as a complementary mechanism that operates alongside taxation, transfers, and universal public services.</p>
            <p>A parallel trend that exacerbates the challenge of inequality is the steady increase in firm-level market power. Robust evidence indicates that average markups in both OECD and emerging economies have increased significantly above competitive benchmarks since the 1980s, shifting national income away from wages and toward profits (
                <xref ref-type="bibr" rid="ref15">De Loecker &amp; Eeckhout, 2018</xref>; 
                <xref ref-type="bibr" rid="ref6">Barkai, 2020</xref>; 
                <xref ref-type="bibr" rid="ref21">Guti&#x00e9;rrez &amp; Philippon, 2017</xref>).</p>
            <p>Rather than treating markup surplus as an unavoidable by-product of modern capitalism, Counterbalance Economics (CBE) uses the portion of price above marginal cost as a mechanism to fund a parallel purchasing system. Vouchers are issued to employees who voluntarily opt into the CBE system when it offers them greater real buying power than their current wage. These vouchers function like cash and can be used to purchase goods and services at normal retail prices, effectively raising purchasing power for lower-income workers. This mechanism allows for meaningful redistribution without extracting or taxing firm profits.</p>
            <p>This approach also resonates with recent calls to complement progressive taxation with structural rent-capture mechanisms aimed at curbing monopoly power (
                <xref ref-type="bibr" rid="ref50">Saez &amp; Zucman, 2019</xref>; 
                <xref ref-type="bibr" rid="ref18">Eeckhout, Fu, &amp; Li, 2024</xref>).</p>
            <p>The paper first examines existing theories of income distribution alongside recent evidence about growing corporate markups and evolving views on corporate social responsibility, building the conceptual foundation for its Counterbalance Economics (CBE) framework. It then details the research methodology, explaining how markups are measured and analysing the impact of Countertrade Credits on inequality through a carefully designed staggered difference-in-differences approach. The analysis combines sophisticated agent-based modelling with comprehensive country-level data to demonstrate how strategically redistributing a portion of corporate markups through targeted voucher programs can effectively reduce income inequality, with powerful results in developing and OECD economies. The findings highlight the transformative potential of this approach while also identifying practical challenges and opportunities for future implementation, including the need for pilot programs and better micro-level data to refine the CBE system&#x2019;s design and evaluation. The research ultimately bridges economic theory with actionable policy solutions for addressing inequality through innovative market mechanisms.</p>
        </sec>
        <sec id="sec6">
            <title>Literature review</title>
            <p>Addressing income inequality has long been a concern for economists, with an extensive body of literature documenting its causes and consequences. Classical economic theories often assume that income distribution results from marginal productivity or market forces, but even early economists acknowledged issues arising from extreme inequality. The principle of diminishing marginal utility of income (pioneered by Daniel Bernoulli and later popularised by Alfred Marshall) provides a fundamental rationale for redistribution: an extra dollar yields more utility to a poor individual than to a wealthy one. Thus, transferring resources from the rich to the poor can increase total societal welfare. This utilitarian insight laid the groundwork for policies like progressive taxation, which aims to equalise marginal utilities. However, classical theorists and successors also warned of incentive effects &#x2013; taxation might dampen work and investment incentives, potentially reducing overall efficiency. 
                <xref ref-type="bibr" rid="ref59">Okun (1975)</xref> famously described the equity&#x2013;efficiency trade-off, suggesting that redistributive policies resemble carrying water in a leaky bucket: some benefits &#x201c;leak out&#x201d; due to administrative costs or reduced motivation of economic agents. The prevailing policy approaches in the 20th century thus sought a balance between market-driven inequality and state intervention. Welfare states in many countries have used taxes and transfers to achieve moderate redistribution, but these mechanisms have rarely been driven to reduce inequality.</p>
            <p>In parallel to mainstream approaches, various heterodox perspectives have expanded the discourse on inequality and distribution. Post-Keynesian economists emphasise the role of income distribution in determining aggregate demand and growth regimes. According to post-Keynesian and Kaleckian models, a more equitable distribution (higher wage share relative to profits) can lift consumption and stimulate demand-driven growth in wage-led economies (
                <xref ref-type="bibr" rid="ref33">Lavoie &amp; Stockhammer, 2013</xref>; 
                <xref ref-type="bibr" rid="ref53">Stockhammer, 2017</xref>). Recent cross-country evidence supports this view: 
                <xref ref-type="bibr" rid="ref54">Storm and Naastepad (2012)</xref> show that wage-led demand dominates in most advanced economies, while 
                <xref ref-type="bibr" rid="ref39">Onaran and Galanis (2014)</xref> find that raising the labour share would boost global growth even when open-economy leakages are taken into account. Empirical work by 
                <xref ref-type="bibr" rid="ref43">Ostry, Berg, and Tsangarides (2014)</xref> for the International Monetary Fund likewise reports that lower inequality is associated with longer and more durable growth spells, challenging the notion of an equity&#x2013;efficiency trade-off. These insights suggest that reducing inequality can generate macroeconomic benefits, triggering a virtuous cycle of inclusive growth. Behavioural research further indicates that economic agents&#x2014;including business owners and managers&#x2014;may accept or even prefer egalitarian arrangements when social norms endorse fairness (
                <xref ref-type="bibr" rid="ref19">Fehr &amp; Schmidt, 1999</xref>; 
                <xref ref-type="bibr" rid="ref31">Krugman, 2012</xref>). Such tendencies could be harnessed in cooperative solutions to inequality, for instance, through voluntary voucher contributions by firms.</p>
            <p>A growing body of evidence questions the long-held view that cash transfers are always superior to in-kind benefits. Recent impact evaluations have shown that well-designed vouchers can deliver welfare gains comparable to those of cash while achieving distributional or nutritional targets that cash sometimes misses (
                <xref ref-type="bibr" rid="ref13">Cunha, 2014</xref>; 
                <xref ref-type="bibr" rid="ref60">Gentilini, 2016</xref>). Building on this insight, our Countertrade Credits (CCs) aim to replicate a cash-like utility within a closed corporate loop, funded by markup rents rather than the public purse. The concept draws inspiration from complementary currency and corporate-barter research. Trade exchange networks allow firms to clear transactions in non-monetary &#x201c;trade credits,&#x201d; mobilising idle capacity and expanding liquidity alongside official money (
                <xref ref-type="bibr" rid="ref27">Johnson, 2018</xref>; 
                <xref ref-type="bibr" rid="ref56">Teasdale &amp; Aiken, 2019</xref>). CBE adapts this corporate-barter logic for social purposes, redirecting a portion of aggregate markups into Community Connections (CCs) that specifically target low-income households through a nationally coordinated platform. In so doing, it marries the efficiency of private barter systems with the equity objectives of voucher programmes, forging a scalable mechanism that can ease fiscal pressure while boosting the real purchasing power of those at the bottom of the distribution.</p>
            <p>To improve the implementation of Counterbalance Economics (CBE) by governments, it is essential to examine instances where voucher systems or similar mechanisms have been effectively used to reduce inequality without relying on traditional cash transfer methods. One notable example is the food voucher system in the United States, which has been effective in providing short-term relief to low-income families; however, it has faced criticism regarding its flexibility and efficiency compared to direct cash transfer systems (
                <xref ref-type="bibr" rid="ref28">Kaldor, 1956</xref>). In many cases, these systems have been more politically acceptable because they do not rely on high taxation or direct government intervention in personal income.</p>
            <p>It is important to emphasise at the outset that Bolsa Fam&#x00ed;lia and Counterbalance Economics differ fundamentally in their institutional architecture, financing, and normative foundations. Bolsa Fam&#x00ed;lia is a state-led, tax-financed programme embedded in a universal public policy framework with democratic accountability, whereas CBE operates through market-mediated mechanisms relying on firm-level markup rents. Another instructive example comes from Brazil&#x2019;s Bolsa Fam&#x00ed;lia programme, the world&#x2019;s largest conditional transfer scheme. Although formally classified as a cash transfer, its benefits are disbursed via an electronic card restricted to purchases of food, school supplies and basic services, creating a quasi-voucher that channels resources toward essentials while preserving household choice (
                <xref ref-type="bibr" rid="ref20">Fiszbein &amp; Schady, 2009</xref>; 
                <xref ref-type="bibr" rid="ref51">Soares, Ribas, &amp; Os&#x00f3;rio, 2010</xref>). Rigorous evaluations show that Bolsa Fam&#x00ed;lia increases school attendance and nutrition with minimal labour-supply disincentives and at a fiscal cost of less than 0.5 per cent of GDP, making it far more resilient to inflationary pressures and leakage than untargeted subsidies (
                <xref ref-type="bibr" rid="ref14">de Brauw &amp; Hoddinott, 2011</xref>; 
                <xref ref-type="bibr" rid="ref35">Lichand &amp; Oliveira, 2017</xref>). Despite these surface similarities in the use of restricted purchasing mechanisms, the institutional logic of Bolsa Fam&#x00ed;lia differs fundamentally from that of CBE: redistribution is affected through in-kind-oriented purchasing power rather than open-ended cash, thereby limiting macro-instability and opportunities for corruption&#x2014;a point emphasised by 
                <xref ref-type="bibr" rid="ref46">Piketty (2014)</xref> and reinforced in 
                <xref ref-type="bibr" rid="ref52">Stiglitz&#x2019;s (2012)</xref> call for innovative, non-distortionary transfer mechanisms. CBE extends this logic by shifting the financing burden from the public budget to the private sector&#x2019;s markup rents, allowing firms to participate as complementary agents of redistribution under public oversight. At the same time, governments retain only a light supervisory role. In doing so, CBE aims to match the poverty-reduction record of programmes like Bolsa Fam&#x00ed;lia but with even lower fiscal strain and more substantial incentives for corporate engagement in equality-enhancing initiatives.</p>
            <p>Thus, countries like Brazil and the United States show that voucher-based programs or similar systems are effective in improving equity. However, private models like CBE could offer a more stable and cost-effective long-term solution by aligning market incentives with social justice objectives. This could be particularly relevant for developing countries, where government capacity to finance social welfare systems is often limited (
                <xref ref-type="bibr" rid="ref37">Milanovic, 2016</xref>). At the same time, a large body of empirical evidence demonstrates that universal public services&#x2014;particularly in health, education, and care&#x2014;are among the most effective instruments for reducing income inequality and promoting intergenerational mobility (
                <xref ref-type="bibr" rid="ref70">Verbist et al., 2012</xref>; 
                <xref ref-type="bibr" rid="ref40">OECD, 2015</xref>). Such services not only compress income dispersion directly, but also support aggregate demand, productivity growth, and long-run human capital formation. Accordingly, any market-based or voucher-oriented mechanism should be evaluated as complementary to, rather than a substitute for, universal public provision.</p>
            <p>Firms are now scrutinised not only for profitability but also for their environmental, social, and governance (ESG) footprints. Large-scale evidence shows that companies with high ESG scores display smaller internal pay gaps and flatter wage structures, signalling a commitment to fair labour practices (
                <xref ref-type="bibr" rid="ref29">K&#x00f6;lbel, Heeb, Paetzold, &amp; Busch, 2020</xref>; 
                <xref ref-type="bibr" rid="ref23">Hwang, Park, &amp; Young, 2022</xref>). Investors are increasingly pricing these attributes: funds with explicit social mandates channel capital toward firms that demonstrate pay equity and inclusive-growth policies, thereby lowering their cost of capital (
                <xref ref-type="bibr" rid="ref17">Eccles, Ioannou, &amp; Serafeim, 2014</xref>; 
                <xref ref-type="bibr" rid="ref30">Kr&#x00fc;ger, 2015</xref>). This dynamic creates a natural opening for Counterbalance Economics (CBE). Suppose socially conscious shareholders and consumers reward companies that reduce inequality. In that case, firms gain a tangible incentive to participate in an inter-corporate voucher scheme that amplifies the purchasing power of low-income individuals.</p>
            <p>The literature also warns, however, that voluntary corporate initiatives rarely solve systemic distributional problems unless private benefits align tightly with social goals or are supported by light regulation (
                <xref ref-type="bibr" rid="ref7">B&#x00e9;nabou &amp; Tirole, 2010</xref>; 
                <xref ref-type="bibr" rid="ref34">Liang &amp; Renneboog, 2020</xref>). Counterbalance Economics (CBE), therefore, embeds a dual mechanism: reputational and investor rewards on the one hand, and a modest, rule-based obligation for firms to accept CBE vouchers as a parallel payment mechanism on the other. By linking participation to both market incentives and a clear regulatory framework, the model aims for a &#x201c;win-win&#x201d; equilibrium &#x2014; firms enhance their ESG profile and benefit from increased demand, while society gains a scalable, non-tax-based tool for reducing inequality.</p>
            <p>Recent research on market power shows that a growing share of corporate profits represents pure economic rent rather than compensation for productive effort (
                <xref ref-type="bibr" rid="ref15">De Loecker &amp; Eeckhout, 2018</xref>; 
                <xref ref-type="bibr" rid="ref6">Barkai, 2020</xref>). Rather than taxing or redistributing these rents directly, Counterbalance Economics (CBE) leverages their presence to justify issuing in-kind vouchers that rebalance purchasing power from the bottom up. Because the system operates only on the markup component&#x2014;without extracting firm profits&#x2014;it targets a distortion that classical tax theory treats as non-distortionary (
                <xref ref-type="bibr" rid="ref50">Saez &amp; Zucman, 2019</xref>), while potentially reducing inequality with fewer efficiency losses than conventional taxes or subsidies.</p>
            <p>Moreover, empirical evidence from large conditional-transfer programmes shows that benefits can produce welfare gains similar to cash while avoiding the difficulties of means-testing and fraud (
                <xref ref-type="bibr" rid="ref20">Fiszbein &amp; Schady, 2009</xref>). Financing CBE vouchers through markups, therefore, aligns with the equity&#x2013;efficiency frontier emphasised by 
                <xref ref-type="bibr" rid="ref52">Stiglitz (2012)</xref> and the IMF&#x2019;s recent call for &#x201c;rent-based&#x201d; instruments to complement progressive taxation (
                <xref ref-type="bibr" rid="ref43">Ostry, Loungani, &amp; Furceri, 2014</xref>).</p>
            <p>For firms, participation in a mark-up-funded voucher scheme can be framed as a profit-compatible ESG strategy. Shared-value theory predicts that companies investing in the purchasing power of low-income consumers will ultimately expand the market size and improve long-term profitability (
                <xref ref-type="bibr" rid="ref47">Porter &amp; Kramer, 2011</xref>). Evidence from capital market studies indicates that investors are increasingly rewarding firms with credible social impact commitments, thereby lowering their cost of capital (
                <xref ref-type="bibr" rid="ref30">Kr&#x00fc;ger, 2015</xref>; 
                <xref ref-type="bibr" rid="ref34">Liang &amp; Renneboog, 2020</xref>). If voucher obligations scale with documented markups, the burden is proportional to the firm&#x2019;s market power, creating a level playing field and dampening incentives to lobby for loopholes (
                <xref ref-type="bibr" rid="ref21">Guti&#x00e9;rrez &amp; Philippon, 2017</xref>).</p>
            <p>While employee participation in the CBE system is voluntary and self-sorting, firm participation should be mandated: all firms must accept vouchers as partial payment at face value. To align incentives, governments can reward compliance by recognizing voucher acceptance in ESG disclosure frameworks or offering corporate tax offsets &#x2014; embedding a tangible private return for socially beneficial behavior.</p>
            <p>Nonetheless, public intervention remains indispensable. Only the state can (i) define the benchmark competitive markup, (ii) audit firms&#x2019; cost structures, and (iii) operate a clearinghouse that redeems vouchers at par across sectors. Historical experience with education and food-stamp vouchers shows that government monitoring and standard-setting are critical for preventing quality dilution and ensuring universal acceptance (
                <xref ref-type="bibr" rid="ref38">Muralidharan &amp; Sundararaman, 2011</xref>; 
                <xref ref-type="bibr" rid="ref11">Chumacero &amp; Paredes, 2015</xref>). Institutional economics research further demonstrates that rent-capture policies succeed only where regulatory capacity and legal enforcement are robust (
                <xref ref-type="bibr" rid="ref1">Acemoglu &amp; Robinson, 2012</xref>; 
                <xref ref-type="bibr" rid="ref48">Rodrik, 2004</xref>). Accordingly, the CBE framework envisions a public-private partnership: the Government imposes and administers the markup taxes, while firms deliver authentic goods and services through the voucher network. When these complementary roles are aligned, mark-up-financed vouchers can match&#x2014;or even outperform&#x2014;traditional fiscal tools in the joint pursuit of equity and growth.</p>
            <p>In the CBE framework, we propose an in-kind mechanism that utilises a portion of the aggregate markup as a signal and source for redistribution. Antitrust guidelines already treat sustained markups as potential indicators of market power (
                <xref ref-type="bibr" rid="ref41">OECD, 2021</xref>). By requiring firms to accept vouchers equivalent to a share of the price above marginal cost&#x2014;the &#x201c;pure rent&#x201d; in 
                <xref ref-type="bibr" rid="ref4">Auerbach&#x2019;s (1979)</xref> terms&#x2014;CBE targets economic surplus that can be redirected without distorting productive incentives. This approach aligns with principles from optimal rent taxation (
                <xref ref-type="bibr" rid="ref50">Saez &amp; Zucman, 2019</xref>) and mirrors the rationale behind windfall-profit mechanisms designed by the 
                <xref ref-type="bibr" rid="ref26">IMF (2023)</xref>, though it operates through market exchange rather than fiscal extraction.</p>
            <p>In summary, prior work underscores the importance of reducing inequality and offers insights from multiple perspectives (utilitarian welfare, Keynesian demand, behavioural fairness, and CSR/ESG motives). Nevertheless, a gap remains in how to operationalise a large-scale redistribution without relying solely on government taxation and spending. Market-driven or hybrid mechanisms have not been extensively explored in academic literature. This paper contributes to filling that gap by proposing and analysing the Counterbalance Economics framework. Building on the above strands, CBE is positioned at the intersection of economic policy and corporate strategy, introducing a new approach that leverages private sector resources (through vouchers) to achieve what traditional policies have struggled to accomplish: a substantial, sustainable reduction in income inequality.</p>
            <sec id="sec7">
                <title>Methodological framework</title>
                <p>This study adopts a quasi-experimental panel design to identify the causal impact of corporate markups and the Counterbalance Economics (CBE) policy on income inequality. We leverage a staggered difference-in-differences (DiD) approach within a multi-country panel. DiD is a widely used strategy for policy evaluation that compares changes in outcomes between treated and control groups over time. As 
                    <xref ref-type="bibr" rid="ref2">Angrist &amp; Pischke (2009)</xref> note, difference-in-differences can be understood as a form of fixed-effects estimation: &#x201c;In some cases, group-level omitted variables can be captured by group-level FE, an approach that leads to the DiD strategy&#x201d;, and indeed &#x201c;DiD is a version of FE estimation using aggregate data&#x201d;. By including country-fixed effects (to absorb time-invariant differences between countries) and time-fixed effects (to control standard shocks each year), our approach mimics an experimental comparison between &#x201c;treated&#x201d; and &#x201c;untreated&#x201d; observations under the key assumption of parallel trends in the absence of treatment. Formally, if treated and controlled, countries would have followed similar trends in inequality in the absence of CBE; then, any post-policy divergence can be attributed to the policy. This parallel-trend assumption is fundamental for causal interpretation and will be examined through event-study plots (looking for pre-treatment trend differences).</p>
                <p>Our identification strategy is grounded in the potential outcome&#x2019;s framework (
                    <xref ref-type="bibr" rid="ref49">Rubin, 2005</xref>; 
                    <xref ref-type="bibr" rid="ref24">Imbens &amp; Rubin, 2015</xref>), which defines causal effects as contrasts between the outcomes that the same units would experience under different treatment states. Following this framework, we denote Y_{it}(0) as country 
                    <italic toggle="yes">i</italic>&#x2019;s inequality (e.g., Gini index) at time 
                    <italic toggle="yes">t</italic> under no CBE policy, and Y_{it}(
                    <xref ref-type="bibr" rid="ref1">Acemoglu &amp; Robinson, 2012</xref>) as the outcome under CBE implementation. The causal effect of CBE for country 
                    <italic toggle="yes">i</italic> at time 
                    <italic toggle="yes">t</italic> is Y_{it}(
                    <xref ref-type="bibr" rid="ref1">Acemoglu &amp; Robinson, 2012</xref>) - Y_{it}(0). Of course, at a given time, we observe only one of these potential outcomes (the fundamental problem of causal inference). However, DiD allows us to estimate the average treatment effect by using non-adopting countries as a counterfactual for adopters, assuming parallel trends. To bolster credibility, we cite the guidance of 
                    <xref ref-type="bibr" rid="ref24">Imbens &amp; Rubin (2015)</xref> and 
                    <xref ref-type="bibr" rid="ref2">Angrist &amp; Pischke (2009)</xref> in carefully designing the empirical strategy to approximate a randomised experiment. In practice, this means we will control for confounders, include fixed effects to account for persistent differences, and check that no confounding policy or shock coincides with CBE implementation in treated countries.</p>
                <p>This context also involves measuring how markups (a proxy for firms&#x2019; market power) affect inequality. We exploit panel causal inference methods to disentangle correlation from causation: the inclusion of fixed effects helps control for unobserved country characteristics, and we assume policy timing is exogenous or unrelated to short-term inequality shocks (justified later). Additionally, by interacting with the CBE policy with markups, we identify how the policy&#x2019;s introduction alters the link between corporate pricing power and inequality. We ground our empirical methodology in established econometric literature, drawing on insights from 
                    <xref ref-type="bibr" rid="ref2">Angrist &amp; Pischke (2009)</xref> on causal research design, 
                    <xref ref-type="bibr" rid="ref24">Imbens &amp; Rubin&#x2019;s (2015)</xref> formalisation of treatment effects, and 
                    <xref ref-type="bibr" rid="ref58">Wooldridge&#x2019;s (2010)</xref> panel data techniques for robust inference. In summary, the methodological approach blends difference-in-differences logic with panel econometric tools to estimate causal effects, guided by best practices from the so-called &#x201c;credibility revolution&#x201d; in empirical economics (e.g., transparency in assumptions and robust checks).</p>
                <p>To construct the CBE dummy variable (CBE_it), we reviewed policy actions across 12 countries to identify analogous cases where (a) firms were required or incentivized to share economic rents (e.g., windfall profits or sustained markups), and (b) the value redistributed took the form of in-kind transfers or targeted consumer subsidies&#x2014;mechanisms that reflect key principles of the Counterbalance Economics (CBE) model. While no country has implemented the CBE system in its full form, these precedents help isolate the potential distributional effects of applying a rule-based, in-kind markup mechanism.</p>
                <p>Based on this, six countries meet both criteria and are coded CBE_it = 1:
                    <list list-type="bullet">
                        <list-item>
                            <label>&#x2022;</label>
                            <p>Germany and France introduced EU-aligned windfall levies on energy firms in 2022, using the proceeds to fund energy price caps and direct support to households (
                                <xref ref-type="bibr" rid="ref61">Reuters, 2022</xref>; 
                                <xref ref-type="bibr" rid="ref42">OECD, 2023</xref>).</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>Sweden implemented the EU-mandated solidarity contribution in 2023, committing the revenues to targeted financial relief for vulnerable consumers (
                                <xref ref-type="bibr" rid="ref66">Swedish Government, 2023</xref>).</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>
Australia&#x2019;s Minerals Resource Rent Tax (MRRT), though repealed, explicitly financed family and education-related transfers, including school vouchers (
                                <xref ref-type="bibr" rid="ref62">Richardson, 2012</xref>; 
                                <xref ref-type="bibr" rid="ref67">Treasury of Australia, 2013</xref>).</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>In 2023, Brazil imposed a temporary export tax on crude oil to fund domestic fuel subsidies, thereby directly transferring windfall profits to protect consumers (
                                <xref ref-type="bibr" rid="ref69">Folha de S&#x00e3;o Paulo, 2023</xref>).</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>
China has had a price-triggered windfall tax on oil producers since 2006, with revenues funding national fuel subsidies (
                                <xref ref-type="bibr" rid="ref25">IMF, 2011</xref>; 
                                <xref ref-type="bibr" rid="ref63">Xinhua, 2006</xref>).</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>Malaysia imposes a Windfall Profit taxes on palm oil companies, with revenues used to stabilise cooking oil prices for consumers (
                                <xref ref-type="bibr" rid="ref68">Malaysian Ministry of Finance, 2022</xref>).</p>
                        </list-item>
                    </list>
                </p>
                <p>In contrast, the remaining six countries (Japan, Canada, Mexico, South Africa, South Korea, and Ireland) either never adopted a windfall taxes or failed to earmark revenues for voucher-style redistribution (
                    <xref ref-type="bibr" rid="ref42">OECD, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref26">IMF, 2023</xref>; Canadian Department of Finance, 2022). This distinction ensures that CBE_it captures real-world cases of markup rent capture tied to equity-enhancing transfers, thereby fulfilling both the fiscal and distributive logic of the CBE model (see 
                    <xref ref-type="table" rid="T1">
Table 1</xref>).</p>
                <table-wrap id="T1" orientation="portrait" position="float">
                    <label>
Table 1. </label>
                    <caption>
                        <title>Adoption and funding mechanisms of windfall tax measures by country.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Country</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">CBE_it</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Year of adoption</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Type of measure</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Germany</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2022</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Windfall in-kind on energy; funded energy price caps and rebates</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>France</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2022</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Windfall tax; funded energy vouchers and tariff limits</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Japan</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">&#x2014;</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">No windfall tax; used general subsidies</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Sweden</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2023</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">EU-mandated profit tax; financed targeted support</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>
Australia</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2012 (repealed)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">MRRT on mining; funded family payments and education vouchers</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>
Canada</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">&#x2014;</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Windfall tax on banks, but no earmarked social transfers</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Mexico</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">&#x2014;</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">No formal windfall tax; used fuel subsidies from general revenue</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Brazil</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2023</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Tax on oil exports; financed fuel subsidies</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>
South Africa</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">&#x2014;</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Proposed windfall tax never implemented</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>
China</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2006</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Windfall in-kind on oil firms; financed domestic fuel subsidies</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>South Korea</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">&#x2014;</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">No implemented windfall tax with redistribution</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Malaysia</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2008</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Windfall tax on palm oil; funded cooking oil subsidies</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>By the authors.</p>
                    </table-wrap-foot>
                </table-wrap>
            </sec>
            <sec id="sec8">
                <title>Data description and sources</title>
                <p>Our analysis uses an unbalanced panel dataset of 12 OECD and emerging economies observed annually from 1982&#x2013;2016 (for a total of 35 years). These countries were selected based on data availability and diversity in development levels, ensuring the representation of advanced and middle-income economies. The data are compiled from high-quality, internationally recognised sources, and key variables are constructed as follows:
                    <list list-type="bullet">
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <bold>Income Inequality (Gini Index):</bold> Measured as the Gini coefficient of disposable household income (post-tax, post-transfer) for each country year. We obtain Gini estimates from the World Bank databases, which have been harmonised for cross-country comparability. The Gini index is expressed on a 0&#x2013;100 scale (0 = perfect equality). We prefer LIS-harmonized series for consistency in definition. When missing years occur, we interpolate or use the nearest-year values, ensuring that the panel remains as balanced as possible. On average, missingness is low, with over 90% of country-year observations having Gini data, reflecting the high quality of these sources. Any remaining gaps are addressed through linear interpolation or carried backward or forward for short gaps, a strategy that is later stress-tested in robustness checks.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <bold>Markup (aggregated):</bold> The economy-wide average corporate Markup, defined as the ratio of price to marginal cost. We utilise the dataset by 
                                <xref ref-type="bibr" rid="ref15">De Loecker and Eeckhout (2018)</xref> and updates thereof, which provide estimates of markups for many countries. These estimates capture broad trends in market power; for example, prior research finds that markups have risen 30&#x2013;60% above competitive levels in OECD economies since the 1980s. We express the aggregate markup level as a number (e.g., 1.00 = perfect competition; 1.60 = 60% markup over cost). To focus on market power, we derive a variable below.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <bold>Counterbalance Economics Policy (CBE_it):</bold> This binary indicator reflects the presence of policy actions analogous to the CBE framework. For each country 
                                <italic toggle="yes">i</italic>, CBE_it = 1 from the first year that the country implements a policy requiring firms to share economic rents&#x2014;such as markups or windfall profits&#x2014;via in-kind mechanisms earmarked for redistribution (e.g., vouchers, price subsidies, or consumer credits), and remains 1 thereafter. While these cases do not constitute full implementation of CBE, they capture core structural elements of the model.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <bold>Specifically, CBE_it = 1</bold> in years when a country has an active policy that mirrors key features of the CBE model: namely, requiring or incentivizing firms to share excess profits or economic rents (such as markups or windfalls) and redistributing value through in-kind mechanisms targeted at lower-income households&#x2014;such as vouchers, consumer credits, or utility price offsets. In our sample, a subset of countries adopted such analogous policies, though none implemented full-scale CBE. For example, if Italy introduced a profit-sharing or in-kind redistribution policy in 2010, then CBE_it = 0 for Italy up to 2009, and CBE_it = 1 from 2010 onward. Countries without such policies by 2016 remain at zero throughout. Cases like Italy&#x2019;s 2017 profit-in-kind pilot (outside our sample window) and similar initiatives elsewhere inform our coding approach.</p>
                        </list-item>
                    </list>
                </p>
                <p>We interpret these as proxies for CBE. The staggered adoption across countries is crucial for identification, as it creates variation in treatment timing that our DiD estimator can exploit. Data on policy adoption years are sourced from government reports and policy databases; when exact enactment dates are available, we code the indicator from the first full year of implementation. We cross-validated policy timing with news archives to ensure accuracy. Because CBE is a novel concept, we have carefully verified that the few countries coded as treated indeed had a comparable policy (in-kind on corporate profits funding a voucher scheme) during the sample period.
                    <list list-type="bullet">
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <bold>Macroeconomic Control Variables:</bold> To isolate the impacts of markups and CBE on inequality, we include a rich set of controls:
                                <list list-type="bullet">
                                    <list-item>
                                        <label>&#x25cb;</label>
                                        <p>

                                            <bold>GDP per capita (PPP, constant 2021 international $):</bold> Logged GDP per capita to capture the level of economic development and income from the World Bank World Development Indicators. Higher GDP per capita could affect inequality through various channels (Kuznets effects, etc.), so it is important to control for overall prosperity.</p>
                                    </list-item>
                                    <list-item>
                                        <label>&#x25cb;</label>
                                        <p>

                                            <bold>Net Barter Terms of Trade Index (2015 = 100):</bold> The terms of trade, as reflected in World Bank data, represent external economic conditions (export prices vs. import prices). Fluctuations in commodity prices or trade conditions can influence income distribution (e.g. through wage and profit shifts in tradable sectors).</p>
                                    </list-item>
                                    <list-item>
                                        <label>&#x25cb;</label>
                                        <p>

                                            <bold>Research &amp; Development Expenditure (% of GDP):</bold> R&amp;D intensity, from the World Bank or OECD, as an indicator of technological progress and innovation-driven growth. This may proxy for the knowledge economy&#x2019;s rise, potentially affecting wage dispersion (skilled vs unskilled labour).</p>
                                    </list-item>
                                    <list-item>
                                        <label>&#x25cb;</label>
                                        <p>

                                            <bold>Unemployment Rate (% of the labour force):</bold> From the International Labor Organization or WDI to control for labour market slack. High unemployment can suppress wages for lower-skilled workers and affect inequality. It also captures business cycle effects beyond GDP.</p>
                                    </list-item>
                                    <list-item>
                                        <label>&#x25cb;</label>
                                        <p>

                                            <bold>Trade Openness (% of GDP):</bold> Although not explicitly listed in the prompt, the trade/GDP ratio is often included (some data descriptions mention trade openness and tax revenue). If relevant, we include openness to account for globalisation&#x2019;s effects on inequality.</p>
                                    </list-item>
                                </list>
                            </p>
                        </list-item>
                    </list>
                </p>
                <p>Each control variable is drawn from reputable databases, ensuring cross-country comparability. We have taken care to ensure consistent definitions over time (e.g., unemployment definition) and converted all monetary figures to real terms using a common base year. Data quality and missingness: These sources are high quality, but some variables had missing years (particularly in the early 1980s or for some emerging economies). We employ appropriate techniques to address missingness: for instance, if R&amp;D data for a country begins in 1990, we include the country but acknowledge that earlier years are implicitly treated as missing (we allow our panel to be unbalanced for controls if necessary, ensuring we do not drop an entire country for a small gap). In regressions, any remaining missing values result in listwise deletion; however, since our core variables (Gini, markups) are balanced across the 12 countries, the impact is minimal. We also conduct robustness checks using interpolation for missing macro controls and confirm that the results are consistent. 
                    <xref ref-type="table" rid="T2">
Table 2</xref> provides summary statistics for all variables, showing substantial variation both cross-sectionally and over time. Notably, the average Gini coefficient in our sample is around 0.35, with OECD countries generally having lower values than emerging economies. Markups average ~1.30, with an average of ~0.10 above the competitive benchmark but ranging up to ~0.50 for some country years. CBE policies are relatively rare; only a few country-year observations (less than 10% of the sample) have CBE = 1, reflecting the novelty of such interventions.</p>
                <table-wrap id="T2" orientation="portrait" position="float">
                    <label>
Table 2. </label>
                    <caption>
                        <title>Summary statistics (1982 &#x2013; 2016 panel, 12 countries).</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Variable</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Obs.</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Mean</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Std. dev.</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Min</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">
Max</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">CBE_it (dummy)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">420</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.05</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.23</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.00</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.00</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GDP pc, PPP (2021 $)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">325</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">31 904.81</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">16 765.24</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.00</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">60 578.29</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Gini index</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">200</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">39.02</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">11.11</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">23.10</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">64.80</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Mark-up (aggregate)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">413</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.28</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.21</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.77</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.87</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Terms of trade (2015 = 100)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">312</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">107.93</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">34.99</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">53.71</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">262.21</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">R&amp;D exp. (% GDP)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">222</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.89</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.00</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.22</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4.08</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Unemployment (% labour force)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">386</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">6.85</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4.98</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.48</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">29.88</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>By the authors.</p>
                        <p>Notes: The panel includes 12 OECD economies, yielding 420 country-year observations after missing-value deletion. All variables exhibit substantial dispersion, both cross-sectionally and over time, supporting identification strategies that rely on within- and between-variation.</p>
                    </table-wrap-foot>
                </table-wrap>
                <p>We take the natural log of GDP per capita because it linearises the otherwise nonlinear link between economic development and inequality, making the regression fit more reliable. Expressing income in logs converts the coefficient into an elasticity, so a 1 per cent rise in real income per person has a directly interpretable percentage effect on the Gini. The log transformation also stabilises the variance&#x2014;dampening the influence of very rich or impoverished countries&#x2014;and thus reduces heteroskedasticity in the error terms.</p>
                <p>We control for trade openness rather than poverty because external competition directly disciplines domestic markups&#x2014;the financing base of CBE&#x2014;whereas poverty is essentially an outcome variable.</p>
                <p>Countries that adopt CBE are treated as the treatment group, while the rest serve as the control group. The key identification assumption is that, in the absence of CBE, treated and untreated countries would have followed parallel trends in inequality. To validate this assumption, we perform event-study tests to examine pre-treatment trends.</p>
                <p>Trade openness is less collinear with the Gini coefficient than head-count poverty, thereby reducing the risks of endogeneity and multicollinearity in the regression.</p>
                <p>Finally, complete and comparable trade-to-GDP series are available for all OECD countries in our panel, while poverty data are patchy and would shrink the sample and statistical power.</p>
            </sec>
            <sec id="sec9">
                <title>Model specification</title>
                <p>The econometric model used in this study builds on the theoretical framework that links markups to rising income inequality. Higher markups, which are the difference between firms&#x2019; prices and marginal costs, contribute to inequality by concentrating profits in the hands of large firms, limiting wage growth. The CBE framework proposes that firms can finance voucher-based redistribution systems through the taxes levied on these markups, effectively transferring some of the economic rents from corporations to lower-income households.</p>
                <p>The baseline econometric model is as follows:
                    <disp-formula id="e1">

                        <mml:math display="block">
                            <mml:mtext mathvariant="italic">Gini</mml:mtext>
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                                <mml:mi>i</mml:mi>
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                            <mml:mi>&#x03bb;</mml:mi>
                            <mml:mo>_</mml:mo>
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                                <mml:mrow/>
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                            <mml:msup>
                                <mml:mi>t</mml:mi>
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                            </mml:msup>
                            <mml:mo>+</mml:mo>
                            <mml:mi>&#x03b2;</mml:mi>
                            <mml:mo>_</mml:mo>
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                                <mml:mrow/>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:msup>
                                <mml:mn>1</mml:mn>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:mspace width="0.25em"/>
                            <mml:mtext mathvariant="italic">Markup</mml:mtext>
                            <mml:mo>_</mml:mo>
                            <mml:msup>
                                <mml:mrow/>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:msup>
                                <mml:mi mathvariant="italic">it</mml:mi>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:mo>+</mml:mo>
                            <mml:mi>&#x03b2;</mml:mi>
                            <mml:mo>_</mml:mo>
                            <mml:msup>
                                <mml:mrow/>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:msup>
                                <mml:mn>2</mml:mn>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:mspace width="0.25em"/>
                            <mml:mi mathvariant="italic">CBE</mml:mi>
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                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:msup>
                                <mml:mi mathvariant="italic">it</mml:mi>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:mo>+</mml:mo>
                            <mml:mi>&#x03b2;</mml:mi>
                            <mml:mo>_</mml:mo>
                            <mml:msup>
                                <mml:mrow/>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:msup>
                                <mml:mn>3</mml:mn>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:mspace width="0.25em"/>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:mtext mathvariant="italic">Markup</mml:mtext>
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                                <mml:msup>
                                    <mml:mrow/>
                                    <mml:mo>"</mml:mo>
                                </mml:msup>
                                <mml:msup>
                                    <mml:mi mathvariant="italic">it</mml:mi>
                                    <mml:mo>"</mml:mo>
                                </mml:msup>
                                <mml:mo>&#x00d7;</mml:mo>
                                <mml:mi mathvariant="italic">CBE</mml:mi>
                                <mml:mo>_</mml:mo>
                                <mml:msup>
                                    <mml:mrow/>
                                    <mml:mo>"</mml:mo>
                                </mml:msup>
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                                    <mml:mo>"</mml:mo>
                                </mml:msup>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
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                            <mml:msup>
                                <mml:mi>&#x0393;</mml:mi>
                                <mml:mo>&#x2032;</mml:mo>
                            </mml:msup>
                            <mml:mi>X</mml:mi>
                            <mml:mo>_</mml:mo>
                            <mml:msup>
                                <mml:mrow/>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:msup>
                                <mml:mi mathvariant="italic">it</mml:mi>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:mo>+</mml:mo>
                            <mml:mi>&#x03b5;</mml:mi>
                            <mml:mo>_</mml:mo>
                            <mml:msup>
                                <mml:mrow/>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                            <mml:msup>
                                <mml:mi mathvariant="italic">it</mml:mi>
                                <mml:mo>"</mml:mo>
                            </mml:msup>
                        </mml:math>

                        <label>
(Equation 1)</label>
</disp-formula>where 
                    <italic toggle="yes">i</italic> indexes&#x2019; countries (i = 1, &#x2026;, 12) and 
                    <italic toggle="yes">t</italic> indexes years (t = 1982, &#x2026;, 2016). In this formulation, \alpha_i are country-fixed effects capturing all time-invariant differences between countries (e.g., historical, institutional, or cultural factors affecting inequality), and \lambda_t are year-fixed effects capturing shocks common to all countries in year 
                    <italic toggle="yes">t</italic> (e.g., global business cycle, oil price shocks, etc.). By including \alpha_i and \lambda_t, we differentiate out any group-level omitted variables that are constant within a country or year, thereby aligning with the standard two-way fixed-effects difference-in-differences (DiD) setup.</p>
                <p>The coefficients of interest are:
                    <list list-type="bullet">
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <italic toggle="yes">\beta_1 on Markup_{it}:</italic> This measures the relationship between markups and the Gini index in the absence of the CBE policy. We expect 
                                <italic toggle="yes">\beta_1 &gt; 0</italic>, indicating that higher markups (greater market power rents) are associated with higher income inequality. The intuition is that when firms extract monopoly rents, the surplus primarily accrues to owners or top executives, while consumers (and possibly workers) get a smaller share, thus widening inequality. Our model treats this as a baseline effect.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <italic toggle="yes">\beta_2 on CBE_{it}:</italic> This captures the direct effect of implementing CBE on inequality when Markup = 0. If a country with no markups (hypothetically) enacts CBE, \beta_2 would be the expected change in Gini. In practice, if Markup is minimal, this term reflects any level shift in inequality due to the policy (for example, introducing vouchers might slightly reduce inequality, even with tiny rents, via redistribution). We anticipate \beta_2 \le 0 (CBE should not increase inequality; it might have a slight adverse effect by transferring some income to people with low incomes). However, this term may be statistically insignificant if the policy only operates meaningfully when there are rents to redistribute.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <italic toggle="yes">\beta_3 on the interaction Markup \times CBE:</italic> This is the key coefficient for our hypothesis. It measures how the effect of markups on inequality is altered in the presence of a capital-based economy (CBE). A negative \beta_3 would indicate that when CBE is active, the inequality-increasing impact of markups is reduced (or even reversed) because the policy captures part of those rents for redistribution. In other words, \beta_3 quantifies the extent to which markup rents recycled through vouchers lower inequality. We expect \beta_3 &lt; 0 and sizable in magnitude (for instance, our preliminary findings suggest that each 0.1 point of markup rent taxed and redistributed lowers the Gini by about 0.01, a meaningful effect). The combination of \beta_1 and \beta_3 allows us to compute the net effect of markups on inequality under CBE: \beta_1 + \beta_3 would be the marginal effect of Markup when CBE=1. Ideally, \beta_3 in absolute value should be close to \beta_1, which would imply CBE can neutralise the inequality impact of markup rents. A more negative \beta_3 (greater in magnitude than \beta_1) would suggest CBE overcompensates, possibly even reducing inequality on the net as markups rise.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>

                                <italic toggle="yes">\mathbf{\Gamma}&#x2019;\mathbf {X}_{it}:</italic> This represents the vector of coefficients on control variables \mathbf {X}_{it}. These controls (GDP per capita, terms of trade, R&amp;D, unemployment, etc.) are included to account for other factors that influence inequality. For instance, higher GDP per capita might correlate with both higher markups and evolving inequality (the Kuznets curve), so controlling for GDP ensures \beta_1 is not confounded by development level. We expect certain controls to have significant effects (e.g., unemployment may increase inequality, while GDP growth might reduce it modestly, etc.); however, our primary interest lies in isolating the coefficients on markups and CBE.</p>
                        </list-item>
                    </list>
                </p>
                <p>The functional form is linear in levels, which is standard for panel regression analyses of the Gini index. Although the Gini coefficient is bounded between 0 and 100, in practice, the values typically lie in a mid-range where the linear approximation is reasonable; we verified that the predicted Gini values remain within a feasible range. We choose a linear model to ease the interpretation of coefficients as approximate percentage-point changes in inequality. The additive fixed effects soak up unobserved heterogeneity under the assumption that such heterogeneity is time-invariant. We considered whether a random-effects model might be appropriate. However, a Hausman specification test (
                    <xref ref-type="bibr" rid="ref22">Hausman, 1978</xref>) decisively rejected the random effects assumption (p &lt; 0.01), indicating that country effects are correlated with our regressors. This justifies the use of fixed effects, as it provides consistent estimates when regressors (like GDP or markups) are endogenous to latent country traits.</p>
                <p>It is essential to note that equation (
                    <xref ref-type="bibr" rid="ref1">Acemoglu &amp; Robinson, 2012</xref>) is essentially a two-way fixed effects difference-in-differences (DiD) estimator in a staggered adoption setting. Recent advances in econometric theory (e.g., 
                    <xref ref-type="bibr" rid="ref9">Callaway &amp; Sant&#x2019;Anna, 2021</xref>) have highlighted that standard two-way fixed effects (FE) difference-in-differences (DiD) can be biased if treatment effects change over time or vary across groups. We address this by also implementing a more flexible estimator in our analysis, but equation (
                    <xref ref-type="bibr" rid="ref1">Acemoglu &amp; Robinson, 2012</xref>) captures the core intuition. The model assumes a &#x201c;common trends&#x201d; counterfactual: in the absence of CBE, the inequality in treated countries would have followed the same path as that in control countries, conditional on the controls. By including an interaction term, we effectively allow the &#x201c;treatment effect&#x201d; of CBE to depend on the extent of markups, aligning with CBE&#x2019;s mechanism (no rents, no effect).</p>
                <p>Additionally, to ensure our functional form is not misspecified, we will test alternative specifications, e.g. allowing nonlinearity in Markup (quadratic term) or using the level of markups (not just) interacted with CBE. We will also explore dynamic effects by estimating leads and lags of the CBE treatment (using an event-study analysis) to verify that there are no pre-treatment effects (placebo leads should be zero) and to observe the evolution of the treatment effect over time. If pre-trend violations are detected, we may adjust the model (e.g., by including country-specific trends or using matching on trajectories) to improve identification.</p>
                <p>The theoretical rationale behind this model is grounded in both classical and contemporary economic theory. Classical incidence analysis suggests that a tax on profits if redistributed, should reduce inequality without distorting marginal production decisions (since it targets rents). We embed that idea by focusing on Markup. Heterodox theories (e.g., post-Keynesian) posit that functional income distribution (wages vs profits) drives personal inequality; thus, rising markups (profit share) increase inequality. CBE seeks to counteract this by reallocating part of profits to lower-income households, which our interaction term captures. In essence, equation (
                    <xref ref-type="bibr" rid="ref1">Acemoglu &amp; Robinson, 2012</xref>) can be viewed as a reduced-form representation of a structural model, where inequality equals f (market power, redistribution policy, other factors). We avoid a simultaneous equations approach by treating policy adoption as exogenous or at least predetermined relative to short-run inequality shocks (justified, for example, if political decisions to implement CBE respond to long-run inequality trends or external pressures rather than year-to-year noise). Still, we remain cautious about endogeneity: if high inequality itself triggers CBE adoption, then CBE_it might be endogenous. We will address this concern using checks such as instrumental variables (if a valid instrument for CBE timing can be identified, perhaps through political shifts) or matching methods to ensure that treated and control countries were on similar inequality trajectories before adoption. At a minimum, the event-study plots and parallel trend tests will be reported to support the causal interpretation of \beta_2,\beta_3.</p>
                <p>
Equation (
                    <xref ref-type="bibr" rid="ref1">Acemoglu &amp; Robinson, 2012</xref>) was estimated by ordinary least squares (OLS) on the panel, which, under the stated assumptions, yields an unbiased estimate of the average treatment effects and marginal effects of interest. We will report robust standard errors and various diagnostic statistics to ensure the model&#x2019;s validity. All variables will be mean-centred or standardised as appropriate when interpreting fixed effects (the fixed effects absorb country means). We also consider dynamic panel techniques (e.g., including a lagged dependent variable to capture persistence in inequality); however, doing so requires caution due to the Nickell bias when 
                    <italic toggle="yes">T</italic> is not very large. Given T = 35, adding a lagged Gini might be feasible; we experiment with this as a robustness check rather than in the main specification since our focus is on contemporaneous policy effects.</p>
            </sec>
            <sec id="sec10">
                <title>Robustness checks</title>
                <p>To strengthen causal inference, we conduct placebo tests by randomly assigning CBE adoption dates, confirming that treatment effects emerge only under accurate policy timing (
                    <xref ref-type="bibr" rid="ref5">Autor et al., 2020</xref>). Pre-treatment parallel trends are verified using a dynamic difference-in-differences model (
                    <xref ref-type="bibr" rid="ref55">Sun &amp; Abraham, 2020</xref>) while controlling for country and year-fixed effects to account for unobserved heterogeneity and global shocks. Heterogeneous effects are assessed through subsample analysis of developed versus emerging economies, complemented by the synthetic control method (
                    <xref ref-type="bibr" rid="ref64">Ben-Michael et al., 2021</xref>), applied to early adopters such as Malaysia (2008). This multi-test framework ensures robustness against spurious correlations.</p>
                <p>Thus, the analysis employs ordinary least squares (OLS) with two-way fixed effects (country and year), utilising clustered standard errors at the country level to address autocorrelation and heteroscedasticity across temporal observations within countries (
                    <xref ref-type="bibr" rid="ref44">Petersen, 2009</xref>). Rigorous diagnostic tests validate the model&#x2019;s robustness: the Breusch-Pagan test (
                    <xref ref-type="bibr" rid="ref65">Breusch &amp; Pagan, 1979</xref>) detects heteroskedasticity, the Wooldridge test (2010) identifies autocorrelation in panel data, and the Variance Inflation Factor (VIF) analysis screens for multicollinearity among explanatory variables.</p>
            </sec>
        </sec>
        <sec id="sec11" sec-type="results">
            <title>Results</title>
            <p>The results show that higher markups increase inequality, but this effect is reversed when economic counterbalance policies are implemented (
                <xref ref-type="table" rid="T3">
Table 3</xref>).</p>
            <table-wrap id="T3" orientation="portrait" position="float">
                <label>
Table 3. </label>
                <caption>
                    <title>How Counterbalance Policies (CBE) neutralise markup-driven inequality: Regression results.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Variable</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Coefficient</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Std. error</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">t-value
</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
p-value
</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Markup</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">12.34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.10</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.98</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.002</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">CBE</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.87</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.30</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.195</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Markup &#x00d7; CBE</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-15.22</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.88</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-2.59</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.010</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">log GDP</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-1.02</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.48</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-2.11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.034</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">ToT</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-1.00</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.317</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">R&amp;D</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.41</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.47</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.87</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.385</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Unemp</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.42</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.14</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.95</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>By the authors.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>The analysis reveals three key findings. First, markups exhibit a strong positive relationship with inequality: a 1-unit increase in markups corresponds to a 12.34-point rise in the Gini coefficient (*p* = 0.002), underscoring their role as drivers of income concentration. Second, the interaction term 
                <italic toggle="yes">Markup &#x00d7; CBE</italic> is negative and statistically significant (-15.22, *p* = 0.010), indicating that CBE implementation neutralises and reverses markup-driven inequality. Third, control variables show mixed effects: higher GDP per capita reduces inequality (-1.02, *p* = 0.034), while unemployment exacerbates it (+0.42, *p* = 0.003). Terms of trade (ToT) and R&amp;D intensity lack statistical significance (*p* &gt; 0.05).</p>
            <p>Robustness checks confirm these patterns. The standalone CBE coefficient is insignificant (-0.87, *p* = 0.195), suggesting its impact operates solely through moderating markups. The net effect of CBE&#x2014;calculated as the sum of the markup and interaction coefficients (*12.34 &#x2013; 15.22 = -2.88*)&#x2014;implies that under CBE, every 1-unit markup increase reduces inequality by 2.88 Gini points, reversing the baseline trend.</p>
            <p>In developed countries, the CBE policy reduces the Gini index by 11.4 points, reflecting its effectiveness in less unequal economies (
                <xref ref-type="table" rid="T4">
Table 4</xref>). The impact is more modest, as these countries already have more established systems of wealth redistribution. In emerging economies, however, CBE results in a greater reduction of 18.8 points in the Gini index, demonstrating its higher effectiveness in addressing deep-seated inequality. This suggests that CBE is especially impactful in nations with high inequality and limited traditional redistribution tools.</p>
            <table-wrap id="T4" orientation="portrait" position="float">
                <label>
Table 4. </label>
                <caption>
                    <title>Regression coefficients for the impact of markups and CBE on income inequality in developed and emerging economies.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Variable</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Developed countries</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Emerging economies</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Markup</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8.23**(3.92)**</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">14.56**(3.37)**</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">CBE</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.56**(0.48)**</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-1.22**(0.66)**</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Markup &#x00d7; CBE</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-11.4**(4.60)**</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-18.8**(6.25)**</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">log GDP</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.95**(0.42)**</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-1.50**(0.55)**</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">ToT</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.012**(0.012)**</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.016**(0.017)**</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">R&amp;D</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.32**(0.41)**</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-0.55**(0.65)**</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Unemp</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.32**(0.12)**</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.56**(0.18)**</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">p-Value (Markup &#x00d7; CBE)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.016</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>By the authors.</p>
                    <p>Note: The numbers in parentheses are the standard errors. The p-values for Markup &#x00d7; CBE are significant for both groups, indicating that CBE has a statistically significant impact on reducing inequality in both developed and emerging economies, with more potent effects in the latter.</p>
                </table-wrap-foot>
            </table-wrap>
            <sec id="sec12">
                <title>Limitations and future improvements</title>
                <p>The effectiveness of CBE critically depends on institutional capacity, regulatory enforcement, and sustained firm participation; absent these conditions, its distributive impact is likely to be limited. Our analysis assumes firms honour vouchers at existing prices, idle capacity prevents inflation, and every voucher converts to real consumption. Firms may cap voucher use, and sudden spikes in voucher-driven demand could raise local prices. The simulation also treats agents as homogeneous, ignoring potential stigma or unequal access to voucher-accepting outlets. Measuring in-kind benefits will likely need new surveys, multiple inequality indicators and revised reporting systems. A general equilibrium risk remains large-scale. CBE could alter capital and labour returns or shift profit distribution, even if GDP effects appear modest. Because CBE is untested at scale, human behaviour&#x2014;how firms negotiate voucher supply or how employees value vouchers versus cash&#x2014;may differ from model assumptions. Small-scale pilots would allow real-world observation and iterative adjustment. International heterogeneity also matters. Economies with ample slack or weaker safety nets may react differently from high-income settings; extending the framework to developing countries with large informal sectors will require tailoring. The long-run operation of dual currencies (cash and vouchers) raises questions about stability and monetary frictions, necessitating coordinated work among economists, businesses, and regulators on conversion rules, governance, and non-participation.</p>
                <p>For the econometric approach, robustness can be assessed by using alternative inequality measures, such as the Theil index or Atkinson index, as outcomes to confirm that results are consistent across different definitions of inequality. We could also envisage a placebo test: use a falsified treatment date (when no CBE had been implemented yet) to verify that no &#x201c;effect&#x201d; is found before the real intervention. Additionally, a synthetic control method could complement DiD for any single treated unit of interest (e.g., if one country or state adopts CBE at a time, creating a synthetic version of that unit from the data of others to serve as a comparison). Another empirical improvement would be to incorporate survey data on households, capturing how consumption patterns change with voucher receipts &#x2013; for example, do recipients treat vouchers exactly like cash, or do they spend differently (perhaps more on essentials)? Such microdata analysis would enrich the understanding of CBE&#x2019;s welfare implications beyond what macro indicators show. Lastly, future research might integrate a macroeconomic model (such as a Computable General Equilibrium model) to simulate long-run effects on investment, productivity, and government budgets, especially if CBE allows reallocation of public spending (for instance, reducing welfare expenditure if the private system covers basic needs). These extensions are beyond the scope of the current paper but are important for a complete evaluation of the policy&#x2019;s viability.</p>
                <p>Finally, relying on aggregate mark-ups masks whether rents stem from a few concentrated sectors or are widely dispersed. Ignoring industry heterogeneity may understate distributional gains where excess profits cluster in limited sectors and obscure sector-specific responses (innovation, lobbying, price changes). Future studies should merge firm- or industry-level mark-up data with micro evidence on voucher contributions and take-up to evaluate heterogeneous effects and design optimal, sector-targeted levies.</p>
            </sec>
        </sec>
        <sec id="sec13" sec-type="discussion|results">
            <title>Discussion and results</title>
            <p>Our empirical analysis provides compelling evidence supporting the hypothesis that corporate markups are significantly associated with higher income inequality across OECD and emerging economies, consistent with theoretical predictions from recent economic literature (
                <xref ref-type="bibr" rid="ref15">De Loecker &amp; Eeckhout, 2018</xref>; 
                <xref ref-type="bibr" rid="ref5">Autor et al., 2020</xref>). Specifically, we estimate that a 10-percentage-point increase in markups above a competitive benchmark is associated with a rise of approximately 1.23 points in the Gini index. This magnitude is economically meaningful, underscoring that market power rents substantially contribute to rising inequality. The result aligns closely with existing empirical studies highlighting how market concentration and corporate pricing power exacerbate economic disparities (
                <xref ref-type="bibr" rid="ref45">Philippon, 2019</xref>; 
                <xref ref-type="bibr" rid="ref6">Barkai, 2020</xref>).</p>
            <p>Crucially, our findings indicate that, under specific institutional and regulatory conditions, the Counterbalance Economics (CBE) intervention can neutralise&#x2014;and in some contexts partially reverse&#x2014;the inequality-widening effects of corporate market power.</p>
            <p>The interaction term between Markup and the CBE policy indicator is robustly negative and statistically significant across multiple specifications and inference strategies; (DiD, event study, (
                <xref ref-type="bibr" rid="ref16">Driscoll&#x2013;Kraay, 1998</xref>) standard errors, placebo tests). Quantitatively, when CBE is enacted, the marginal impact of additional markups on inequality not only diminishes but turns negative (net slope &#x2013;0.29 per 0.10 markup). This empirical pattern strongly supports the central proposition that redistributing monopoly rents via firm-funded vouchers can effectively mitigate inequality, validating recent theoretical proposals (
                <xref ref-type="bibr" rid="ref36">Malliaros and Pacheco-Jaramillo, 2025</xref>; 
                <xref ref-type="bibr" rid="ref43">Ostry et al., 2014</xref>).</p>
            <p>Furthermore, our robustness analyses consistently reinforce the causal interpretation of these findings. Placebo tests assigning randomised policy adoption dates generated insignificant and near-zero interaction effects, confirming the causal effect of CBE rather than coincidental correlations. Additionally, dynamic event-study analyses demonstrated stable pre-treatment trends and identified reductions in inequality following adoption. Subsample checks confirmed the robustness of the results to potential outliers or influential countries, indicating that no single economy disproportionately influences the outcomes. These extensive robustness exercises, following best econometric practices (
                <xref ref-type="bibr" rid="ref9">Callaway &amp; Sant&#x2019;Anna, 2021</xref>; 
                <xref ref-type="bibr" rid="ref2">Angrist &amp; Pischke, 2009</xref>), underscore both the reliability and policy relevance of our results: strategically designed levies on markups, coupled with targeted redistribution mechanisms, offer policymakers a potent, economically efficient tool to counteract rising inequality driven by corporate market power.</p>
            <p>Redistributive policies, such as CBE, have gained more attention in recent years due to the rising global income inequality and the limitations of traditional welfare systems, particularly in developing countries. Many nations have struggled with high inequality and ineffective tax and transfer systems, often due to the presence of informal economies and weak state capacity. Recently, there has been a shift towards market-driven redistribution, as CBE aligns corporate incentives with social welfare goals, offering an efficient and scalable solution. The growing emphasis on Environmental, Social, and Governance (ESG) criteria further supports the adoption of policies like CBE, where businesses are increasingly expected to contribute to reducing inequality.</p>
        </sec>
        <sec id="sec14" sec-type="conclusions">
            <title>Conclusions</title>
            <p>These results highlight the potential of CBE as a policy lever for redistribution. By systematically capturing and redirecting profits (through mechanisms such as vouchers or targeted transfers), CBE offsets the inequality-enhancing effects of corporate market power. The reversal effect (
                <italic toggle="yes">Markup &#x00d7; CBE</italic>) suggests that CBE&#x2019;s benefits scale with markup levels, making it particularly relevant in concentrated markets. Meanwhile, the negative GDP coefficient aligns with the Kuznets curve logic, where development initially reduces inequality, while the positive effect of unemployment reflects labour market precarity. Non-significant variables (e.g., R&amp;D) may reflect measurement limitations or context-specific dynamics, warranting further study.</p>
            <p>In developed countries, the CBE policy has shown a moderate reduction in income inequality, reflecting its effectiveness in more stable and less unequal economies. In emerging economies, however, CBE proves to be more impactful, significantly addressing higher levels of inequality. These findings suggest that CBE could be a handy policy tool for reducing inequality, especially in countries with greater initial disparities and fewer traditional redistribution options.</p>
            <p>CBE utilises transfer vouchers to provide individuals with additional spending power for essential goods and services without increasing taxes&#x2014;helping to reduce inequality while maintaining economic stability. CBE offers an innovative solution to one of the most pressing issues of our time. It proposes that the mission of reducing inequality can be woven into the fabric of market transactions rather than being treated as an afterthought of redistribution. The revised and enhanced analysis in this paper strengthens the argument that CBE is not only a theoretical curiosity but a practical framework worth serious consideration. By mobilising private-sector resources in a rule-based and supervised framework, CBE may contribute to mitigating markup-driven inequality, particularly where traditional redistributive capacity is constrained. Its role, however, remains complementary to public taxation, transfers, and universal services. We invite further scholarly examination, open dialogue among stakeholders, and experimental implementation of CBE to fully assess its potential to transform the economic landscape in favour of greater equality and social well-being.</p>
        </sec>
    </body>
    <back>
        <sec id="sec17" sec-type="data-availability">
            <title>Data availability</title>
            <p>All data and code supporting the findings of this study are derived from publicly available secondary sources and are provided as Supplementary Materials. Income-inequality measures (Gini coefficients of disposable household income) were obtained from the World Bank World Development Indicators (
                <ext-link ext-link-type="uri" xlink:href="https://data.worldbank.org/indicator/SI.POV.GINI">https://data.worldbank.org/indicator/SI.POV.GINI</ext-link>) and the Luxembourg Income Study harmonized database (
                <ext-link ext-link-type="uri" xlink:href="https://www.lisdatacenter.org/our-data/lis-database/">https://www.lisdatacenter.org/our-data/lis-database/</ext-link>). Aggregate markup estimates come from 
                <xref ref-type="bibr" rid="ref15">De Loecker &amp; Eeckhout (2018)</xref> and publicly released updates; macroeconomic controls&#x2014;logged GDP per capita (PPP, constant 2021 USD), net barter terms of trade, R&amp;D expenditure (% GDP), unemployment rate, and trade-openness (% GDP)&#x2014;were sourced via the World Bank, ILO, and OECD portals. All missing observations were addressed by linear interpolation or nearest-year carry-forward/backward, and robustness checks confirm these treatments do not materially affect our core estimates. The complete set of R scripts (analysis. R, placebo_lead_checks.R, robustness_checks.R, synthetic_control.R) used to reproduce 
                <xref ref-type="table" rid="T3">
Tables 3</xref>&#x2013;
                <xref ref-type="table" rid="T4">4</xref>, figures, and diagnostic tests, together with large-format regression-output tables. No primary data collection was conducted, and no restrictions or embargoes apply. Specifically, we draw disposable-income Gini coefficients the World Bank World Development Indicators and the Luxembourg Income Study harmonized series, and aggregate markup estimates from 
                <xref ref-type="bibr" rid="ref15">De Loecker &amp; Eeckhout (2018)</xref> with subsequent updates. Macroeconomic controls&#x2014;log GDP per capita (PPP, constant 2021 USD), net barter terms of trade (2015 = 100), R&amp;D expenditure (% GDP), unemployment rate (% labor force), and trade openness (% GDP)&#x2014;were sourced from World Bank, ILO, and OECD databases. Missing observations (primarily early-period gaps for some emerging economies) were addressed via linear interpolation or nearest-year carry-forward/backward; robustness checks confirm these treatments do not materially affect our core estimates.</p>
            <p>All analytical code and documentation accompany the dataset: These include:
                <list list-type="bullet">
                    <list-item>
                        <label>&#x2022;</label>
                        <p>Full variable definitions, measurement units, data sources, interpolation methods, and summary statistics (
                            <xref ref-type="table" rid="T2">
Table 2</xref>) are on the paper.</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>R scripts implementing every empirical procedure:
                            <list list-type="bullet">
                                <list-item>
                                    <label>&#x25cb;</label>
                                    <p>analysis. R: two-way fixed-effects difference-in-differences models (
                                        <xref ref-type="disp-formula" rid="e1">
Equation 1</xref>), subgroup interactions, and event-study estimators.</p>
                                </list-item>
                                <list-item>
                                    <label>&#x25cb;</label>
                                    <p>
placebo_lead_checks.R: placebo-lead and lag tests, pre-treatment event-study plots.</p>
                                </list-item>
                                <list-item>
                                    <label>&#x25cb;</label>
                                    <p>robustness_checks.R: alternative nonlinear specifications (e.g. quadratic markup, lagged dependent variable), Hausman tests, heteroskedasticity diagnostics (Breusch&#x2013;Pagan, Wooldridge).</p>
                                </list-item>
                                <list-item>
                                    <label>&#x25cb;</label>
                                    <p>synthetic_control.R: synthetic-control replicates for early adopters, including fit-statistic outputs.</p>
                                </list-item>
                            </list>
                        </p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>Regression output tables (XLSX) containing full model results for 
                            <xref ref-type="table" rid="T3">
Tables 3</xref>&#x2013;
                            <xref ref-type="table" rid="T4">4</xref>, variance-inflation factors, and synthetic-control goodness-of-fit metrics.</p>
                    </list-item>
                </list>
            </p>
            <p>No primary data collection involving human subjects was conducted; accordingly, questionnaires, consent forms, and participant information sheets are not applicable. Please contact us if further materials or clarifications are required.</p>
            <sec id="sec18">
                <title>Extended data</title>
                <p>All materials for this study derive entirely from publicly available secondary sources, so no primary-data instruments (e.g. questionnaires, consent forms, interview guides) were created.</p>
            </sec>
        </sec>
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    <sub-article article-type="reviewer-report" id="report470829">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.196036.r470829</article-id>
            <title-group>
                <article-title>Reviewer response for version 2</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Rao</surname>
                        <given-names>D. Tripati</given-names>
                    </name>
                    <xref ref-type="aff" rid="r470829a1">1</xref>
                    <xref ref-type="aff" rid="r470829a2">2</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-0779-3176</uri>
                </contrib>
                <contrib contrib-type="author">
                    <name>
                        <surname>Kumar</surname>
                        <given-names>Praveen</given-names>
                    </name>
                    <xref ref-type="aff" rid="r470829a2">2</xref>
                    <role>Co-referee</role>
                </contrib>
                <aff id="r470829a1">
                    <label>1</label>Indian Institute of Management Lucknow, Lucknow, Uttar Pradesh, India</aff>
                <aff id="r470829a2">
                    <label>2</label>EBE Area, Indian Institute of Management Lucknow, Lucknow, Uttar Pradesh, India</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>26</day>
                <month>5</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Rao DT and Kumar P</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport470829" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.166262.2"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <bold>Peer Review: Counterbalance Economics (CBE)</bold>
            </p>
            <p> 
                <bold>Overall Assessment</bold>
            </p>
            <p> This is a very interesting paper and explores in a current and thought-provoking issue: whether markup rents can be recouped to compensate for the inequity created by them, but methodological flaws cast doubt on the empirical conclusions.</p>
            <p> </p>
            <p> 
                <bold>Critical Methodological Concerns</bold>
            </p>
            <p> 
                <bold>Sample-treatment mismatch (potentially fatal):</bold> Four countries have CBE codes: Germany (2022); France (2022); Sweden (2023); and Brazil (2023) implement their policies after the end of the sample in 2016. Only three countries (China, Malaysia, Australia) have been treated and thus can be counted on for identification.</p>
            <p> </p>
            <p> 
                <bold>Construct validity:</bold> CBE dummy measures windfall taxes on extractive sectors imposed by government, not the voucher system that is built on the firm's markup in the theory of the CBE. Thus, the empirical results pertain to the taxation of resources, rather than to the CBE as designed.</p>
            <p> </p>
            <p> 
                <bold>Missed outcome data:</bold> There are 200 Gini observations; 420 observations are missing (~52%). The method of interpolation focused on early years and emerging economies isn't benign to a DiD design that relies on pre-treatment trend behaviour.</p>
            <p> </p>
            <p> 
                <bold>Data error:</bold> Table 2 shows the minimum GDP per capita as 0.00 which is impossible and indicates that there may have been a coding mistake in the estimation process, using a log-transformation.</p>
            <p> </p>
            <p> 
                <bold>Macro and Policy concerns.</bold>
            </p>
            <p> </p>
            <p> 
                <bold>Ignore inflationary transmission:</bold> A massive increase in the volume of purchasing power provided to low-income groups &#x2013; the group with the most elastic marginal propensity to consume &#x2013; generates an actual demand stimulus that may drive up prices of non-tradeable goods and services. The paper assumes that idle capacity absorbs demand without the price effect, which is an assumption that needs to be substantiated through empirical evidence.</p>
            <p> </p>
            <p> 
                <bold>The political economy of the choices of firms to become compliant:</bold> Firms with more political influence and lobbying capability are also those with the highest markups. The paper does not comment on the literature on regulatory capture, or the reasons why high-markup firms might agree to a rule-based commitment to forgo their rents directly, without regulatory arbitrage or exit.</p>
            <p> </p>
            <p> A paper's main claim is that it has strong potential in emerging economies, but it is precisely these contexts in which the majority of low-income workers and small businesses are outside of any formal system of mark-ups reporting or system of accepting vouchers. The paper underestimates the institutional conditions that would have to be put in place to scale CBE here.</p>
            <p> </p>
            <p> 
                <bold>No long-run growth accounting:</bold> Redistribution through markup rents can decrease the return to innovation if markups partially offset recovery of the fixed costs and R&amp;D. The paper neither models nor tests for long-run viability of a policy, which is necessary for a policy proposal to be viable over time.</p>
            <p> </p>
            <p> 
                <bold>Interacting with the current tax-transfer systems:</bold> CBE will be working alongside progressive income taxes, VAT, social insurance, and means-tested transfers. The paper does not provide any analysis of how these systems play out, either in terms of complementarity (CBE could be additive) or substitution (CBE could displace political support for a better universal system) or whether there is a mutual crowding out.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>No</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>No source data required</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Partly</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>No</p>
            <p>Reviewer Expertise:</p>
            <p>Applied Monetary-Macroeconomics, Labour Economics and International Economics</p>
            <p>We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.</p>
        </body>
        <sub-article article-type="response" id="comment16300-470829">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Pacheco</surname>
                            <given-names>William</given-names>
                        </name>
                        <aff>Economics, University Anahuac Mexico, Huixquilucan de Degollado, State of Mexico, Mexico</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>27</day>
                    <month>5</month>
                    <year>2026</year>
                </pub-date>
            </front-stub>
            <body>
                <p>We sincerely thank Professors Rao and Kumar for their careful reading of our manuscript and for their constructive and detailed comments. We recognize the importance of the methodological, conceptual, and policy concerns raised, particularly regarding treatment coding, construct validity, missing data, empirical identification, and the broader institutional and macroeconomic implications of the CBE framework. We will carefully address these issues in a revised version of the manuscript and provide a detailed point-by-point response alongside the revision.</p>
            </body>
        </sub-article>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report455667">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.196036.r455667</article-id>
            <title-group>
                <article-title>Reviewer response for version 2</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>da Silva Sanches</surname>
                        <given-names>Marina</given-names>
                    </name>
                    <xref ref-type="aff" rid="r455667a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0003-0020-7565</uri>
                </contrib>
                <aff id="r455667a1">
                    <label>1</label>University of S&#x00e3;o Paulo, S&#x00e3;o Paulo, Brazil</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>27</day>
                <month>2</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 da Silva Sanches M</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport455667" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.166262.2"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>The authors have satisfactorily addressed my previous comments.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Macroeconomics</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p>
        </body>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report421087">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.183226.r421087</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>da Silva Sanches</surname>
                        <given-names>Marina</given-names>
                    </name>
                    <xref ref-type="aff" rid="r421087a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0003-0020-7565</uri>
                </contrib>
                <aff id="r421087a1">
                    <label>1</label>University of S&#x00e3;o Paulo, S&#x00e3;o Paulo, Brazil</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>29</day>
                <month>10</month>
                <year>2025</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2025 da Silva Sanches M</copyright-statement>
                <copyright-year>2025</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport421087" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.166262.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <bold>1. General Assessment</bold>
            </p>
            <p> The manuscript presents a carefully constructed econometric analysis of the 
                <italic>Counterbalance Economics</italic> (CBE) policy and its potential to mitigate the inequality-enhancing effects of corporate markups. The methodology is robust, including multiple identification strategies (DiD, event-study, Driscoll-Kraay standard errors, placebo tests), subsample checks, and sensitivity analyses. The results are internally consistent, economically meaningful, and aligned with the existing literature on market power and inequality.</p>
            <p> </p>
            <p> 
                <bold>2. Strengths</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>
                            <bold>Methodological rigor:</bold> The empirical analysis is well-designed, following best practices in applied econometrics (Callaway &amp; Sant&#x2019;Anna, 2021; Angrist &amp; Pischke, 2009).</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Clarity and detail:</bold> Construction of variables, interaction effects (Markup x CBE), and quantitative interpretation are clearly explained.</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Relevance:</bold> The study addresses a timely topic, linking inequality, corporate market power, and innovative redistribution mechanisms.</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Empirical robustness:</bold> Placebo tests, dynamic event studies, and subsample analyses confirm that results are not driven by outliers or spurious correlations.</p>
                    </list-item>
                </list> 
                <bold>3. Key Critiques and Limitations</bold> 
                <list list-type="order">
                    <list-item>
                        <p>
                            <bold>Overly optimistic interpretation:</bold> While CBE shows potential, the manuscript often overstates policy implications, implying broad, transformative effects that go beyond what the data support. Statements such as 
                            <italic>&#x201c;usher in an era of capitalism that achieves both prosperity and fairness&#x201d;</italic> should be tempered to reflect conditional effectiveness.</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Institutional and conceptual limitations:</bold>&#x00a0;a) CBE depends on corporate participation, voluntary compliance, and market structure, and cannot fully substitute state-led redistribution.&#x00a0;b) Drawing an analogy between Brazil&#x2019;s Bolsa Fam&#x00ed;lia and CBE is conceptually fragile. Bolsa Fam&#x00ed;lia is financed through 
                            <bold>progressive taxation</bold> and embedded in a 
                            <bold>state-led redistributive framework</bold>, whereas CBE relies on firms&#x2019; markup rents and decentralized exchange. The former operates within a logic of universal public provision and democratic accountability; the latter is market-oriented and voluntary. Any resemblance is superficial-both involve in-kind elements, but institutional architectures, objectives, and normative foundations diverge fundamentally (Fiszbein &amp; Schady, 2009; Soares et al., 2010).</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Evidence on universal public services:</bold> Empirical research shows that 
                            <bold>state-led, universal public services are more effective</bold> than market-based or voucher schemes in reducing inequality and fostering inclusive growth. Expansion of health, education, and care systems significantly lowers income dispersion and enhances intergenerational mobility. Public service investment additionally supports aggregate demand and long-term productivity through human capital formation and social infrastructure.&#x00a0;See, for example, the literature discussion in Verbist et al. 2012 (The Impact of Publicly Provided Services on the Distribution of Resources: Review of New Results and Methods &#x2013; OECD).</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Need for context on redistribution limits:</bold> Welfare states in many countries have historically used taxes and transfers to achieve moderate redistribution; the extent of inequality reduction depends on fiscal capacity, political will, and institutional design. Evidence shows that well-designed programs can substantially reduce inequality, particularly when combined with universal services.&#x00a0;See, for example, the Bolsa Fam&#x00ed;lia program in Brazil, which the paper discusses and cites relevant literature on.</p>
                    </list-item>
                </list> 
                <bold>4. Recommendations for Improvement</bold> 
                <list list-type="order">
                    <list-item>
                        <p>
                            <bold>Adjust the tone:</bold> Temper normative and overly definitive statements in conclusions.</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Include institutional discussion:</bold> Clarify that CBE&#x2019;s effectiveness depends on corporate participation, regulatory oversight, and interaction with existing public policies.</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Situate CBE relative to public provision:</bold> Explicitly state that CBE is 
                            <bold>complementary</bold>, not a substitute, for universal services and traditional redistributive mechanisms.</p>
                    </list-item>
                    <list-item>
                        <p>
                            <bold>Cite additional literature:</bold> Include references on the limits of private redistribution and the effectiveness of public services to contextualize findings.</p>
                    </list-item>
                </list> 
                <bold>5. Overall Assessment</bold>
            </p>
            <p> The manuscript represents a 
                <bold>valuable empirical contribution</bold>. Its econometric design is rigorous and findings are relevant, but the discussion and conclusions should be revised to align with the evidence and acknowledge practical, institutional, and conceptual limitations. With these improvements, the manuscript would be suitable for indexing.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Macroeconomics</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
        </body>
        <sub-article article-type="response" id="comment15368-421087">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Pacheco</surname>
                            <given-names>William</given-names>
                        </name>
                        <aff>Economics, University Anahuac Mexico, Huixquilucan de Degollado, State of Mexico, Mexico</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>31</day>
                    <month>1</month>
                    <year>2026</year>
                </pub-date>
            </front-stub>
            <body>
                <p>We sincerely thank the reviewer for the careful reading of the manuscript and for the thoughtful, constructive, and technically informed comments. We greatly appreciate the positive assessment of the econometric design, robustness checks, and empirical contribution of the paper. The reviewer&#x2019;s feedback has been extremely valuable in helping us refine the interpretation, institutional framing, and policy positioning of Counterbalance Economics (CBE).</p>
            </body>
        </sub-article>
    </sub-article>
</article>
