<?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.178466.1</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>Regulatory Design and Long-Term Growth: Insights from China&#x2019;s Experience for Latin America</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 1; peer review: 2 approved with reservations]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Valdiglesias</surname>
                        <given-names>Jhon</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Funding Acquisition</role>
                    <role content-type="http://credit.niso.org/">Investigation</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; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Center for Asian Studies, National University of San Marcos, Lima, Peru</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:jvaldiglesiaso@unmsm.edu.pe">jvaldiglesiaso@unmsm.edu.pe</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>2</day>
                <month>4</month>
                <year>2026</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2026</year>
            </pub-date>
            <volume>15</volume>
            <elocation-id>469</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>12</day>
                    <month>3</month>
                    <year>2026</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Valdiglesias J</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/15-469/pdf"/>
            <abstract>
                <sec>
                    <title>Background</title>
                    <p>Regulatory efficiency is increasingly recognized as a key determinant of long-term economic growth. China&#x2019;s institutional consolidation offers a benchmark for understanding how predictable enforcement, streamlined procedures, and coordinated governance can reduce transaction costs and foster development. In contrast, many Latin American countries face persistent administrative complexity that may hinder growth.</p>
                </sec>
                <sec>
                    <title>Methods</title>
                    <p>This study applies Institutional Theory and Transaction Cost Economics to conceptualize regulatory efficiency. Using panel-data estimations with fixed- and random-effects models, the analysis examines the relationship between regulatory indicators&#x2014;procedural requirements, compliance costs, and business-environment scores&#x2014;and GDP per capita growth. Macroeconomic controls such as trade openness, inflation, capital formation, and rule of law are included to isolate the effect of regulatory efficiency. China serves as a benchmark case against selected Latin American countries.</p>
                </sec>
                <sec>
                    <title>Results</title>
                    <p>The results show a robust and statistically significant association between lower regulatory frictions and stronger economic performance. China&#x2019;s coherent and strategically aligned regulatory framework is linked to higher and more stable GDP per capita growth. In contrast, Latin America exhibits persistent procedural delays, higher compliance costs, and lower regulatory efficiency, which correspond to weaker and more volatile growth outcomes. Panel-data and dummy-variable regressions consistently confirm China&#x2019;s superior performance as a reference for effective regulatory design.</p>
                </sec>
                <sec>
                    <title>Conclusions</title>
                    <p>This study provides quantitative evidence that regulatory design and institutional coordination are decisive for long-term economic growth. By highlighting the growth costs of administrative inefficiency and the benefits of coherent regulation, the findings inform policy strategies aimed at improving institutional quality and fostering sustainable development. Comparative benchmarks, such as China, demonstrate how regulatory efficiency can enhance productivity and macroeconomic stability in emerging economies.</p>
                </sec>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>regulatory efficiency</kwd>
                <kwd>GDP growth</kwd>
                <kwd>China</kwd>
                <kwd>Latin America</kwd>
                <kwd>benchmarking</kwd>
            </kwd-group>
            <funding-group>
                <award-group id="fund-1" xlink:href="https://doi.org/10.13039/501100008786">
                    <funding-source>Universidad Nacional Mayor de San Marcos</funding-source>
                </award-group>
                <funding-statement>This research was supported by Universidad Nacional Mayor de San Marcos, Peru, under its institutional research support program: R.R. N.&#x00b0; 005446-2025-R/UNMSM and Project number D25123681 &#x2013; Project type: PCONFIGI, 2025.</funding-statement>
            </funding-group>
        </article-meta>
    </front>
    <body>
        <sec id="sec5" sec-type="intro">
            <title>1. Introduction</title>
            <p>Regulatory efficiency is increasingly recognized as a fundamental driver of economic growth and business development. Countries that maintain clear, predictable, and well-enforced regulations create an environment in which private enterprises can thrive, attracting investment and promoting innovation. China provides a striking example of such a system. Firms operating in China face minimal bureaucratic obstacles, obtain operating licenses and construction permits in just a few days, and encounter very low barriers to accessing land or other essential inputs (
                <xref ref-type="table" rid="T1">
Table 1</xref>). This efficiency has not only streamlined business operations but has also strengthened investor confidence and facilitated sustained economic expansion.</p>
            <table-wrap id="T1" orientation="portrait" position="float">
                <label>
Table 1. </label>
                <caption>
                    <title>Regulatory environment indicators for selected latin american economies.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Indicator</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Peru (2023)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Chile (2010)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Colombia (2023)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Brazil (2009)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Argentina (2017)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Mexico (2023)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
China (2024)</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Average senior management time spent dealing with the requirements of government regulation (%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">12.3</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">9.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">26.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">14.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">20.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">15.8</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">0.8</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Average days to obtain an operating license</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">118.3</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">68.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">39.3</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">79.7</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">88.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">18</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">6.5</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Days to obtain a construction-related permit [median]</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">180</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">45</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">30</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">60</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">30</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">20</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">10</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Percent of firms identifying business licensing and permits as a major or very severe constraint</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">27.8</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">7.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">8.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">48.4</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">25</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">16.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">0.8</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="middle">Percent of firms identifying access to land as a major or very severe constraint</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">25.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">17</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">7.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">35.3</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">14.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">16.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="middle">2.6</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Note. Data come from the World Bank Enterprise Surveys (various years). Different years appear because only one survey year is available per country for many indicators. Japan is excluded from this descriptive table but included in the regressions. Values are for descriptive context only. Source: World Bank Enterprise Surveys, 
                        <ext-link ext-link-type="uri" xlink:href="https://www.enterprisesurveys.org">https://www.enterprisesurveys.org</ext-link>
                    </p>
                </table-wrap-foot>
            </table-wrap>
            <p>By contrast, many Latin American economies continue to experience slow and fragmented regulatory processes. In countries such as Peru, Brazil, and Argentina, firms spend a considerable portion of management time navigating compliance requirements, often waiting months for operating licenses or construction permits. In addition, a substantial share of businesses perceives licensing, permits, and access to land as major constraints. These inefficiencies increase transaction costs, slow business formation, and create uncertainty that limits investment and innovation, particularly for small and medium-sized enterprises.</p>
            <p>The differences between China and Latin America illustrate a substantial regulatory gap. While China&#x2019;s system enables rapid decision-making and predictable enforcement, Latin American regulatory frameworks often generate uncertainty and operational delays. This gap raises important questions about how institutional efficiency shapes economic performance and how lessons from high-performing systems can inform policy reforms. Specifically, understanding the mechanisms by which China achieves regulatory efficiency may help Latin American countries identify strategies to simplify processes, strengthen enforcement, and reduce barriers to private sector growth.</p>
            <p>This study aims to explore the relationship between regulatory efficiency and economic outcomes, focusing on how variations in institutional performance affect investment, competitiveness, and GDP per capita. It examines the constraints imposed by slow, fragmented regulatory processes in Latin America and contrasts these challenges with China&#x2019;s streamlined, predictable system. Furthermore, it considers how elements of China&#x2019;s regulatory model might be adapted to improve governance and foster a more supportive business environment in Latin American contexts.</p>
            <p>The objectives of this research are threefold. First, it evaluates the efficiency of regulatory processes in China and selected Latin American countries, including the time required to obtain licenses and permits and the severity of perceived constraints. Second, it assesses the effects of bureaucratic fragmentation on private sector performance, investment decisions, and the ability to innovate. Third, it explores policy insights from China&#x2019;s experience, highlighting opportunities to enhance regulatory frameworks in Latin America to reduce uncertainty and foster sustainable economic development.</p>
            <p>By highlighting China as a benchmark for regulatory efficiency and comparing it to Latin American experiences, this study contributes to the literature on institutional quality and economic growth. It provides detailed evidence of how regulatory design impacts business activity, clarifies the mechanisms linking efficient regulation to higher investment and growth, and offers practical guidance for policymakers seeking to improve governance and competitiveness. The analysis demonstrates that closing the regulatory gap is not merely a technical challenge, but a critical pathway for enhancing private sector development and long-term economic performance in Latin America.</p>
        </sec>
        <sec id="sec6">
            <title>2. Literature review and hypothesis</title>
            <sec id="sec7">
                <title>2.1. Regulatory institutions and growth in China and Lain America</title>
                <p>Mainstream growth theory identifies institutional quality as central to sustained development, yet the Latin American experience reveals the limits of this premise when reforms lack depth and coherence. Although dynamic panel evidence links growth to macroeconomic stability and institutional credibility (
                    <xref ref-type="bibr" rid="ref5">Araujo et al., 2014</xref>), the commodity super-cycle demonstrated that much of the region&#x2019;s convergence was externally driven and temporary, with structural regulatory weaknesses resurfacing once prices declined.</p>
                <p>In contrast, China&#x2019;s trajectory challenges the assumption that liberal-democratic institutions are necessary for efficiency gains: stochastic frontier estimates show significant improvements in productive efficiency relative to Brazil (
                    <xref ref-type="bibr" rid="ref41">Nazmi &amp; Revilla, 2008</xref>), achieved within a system defined by disciplined state coordination rather than liberalization. Subsequent regulatory consolidation under Xi Jinping&#x2014;marked by a structural break in 2012 and reinforced anti-corruption enforcement&#x2014;suggests deliberate institutional recalibration to enhance coherence and reduce rent-seeking (Valdiglesias, 2026), with measurable governance improvements associated with stronger economic performance compared to fragmented Latin American reforms (
                    <xref ref-type="bibr" rid="ref63">Valdiglesias, 2025</xref>).</p>
                <p>Sectoral evidence reinforces this asymmetry: infrastructure and electricity regulation in Latin America remain undermined by weak oversight, corruption, and limited enforcement capacity despite formal autonomy (
                    <xref ref-type="bibr" rid="ref56">Su&#x00e1;rez-Alem&#x00e1;n et al., 2019</xref>; 
                    <xref ref-type="bibr" rid="ref44">Ravillard et al., 2010</xref>; 
                    <xref ref-type="bibr" rid="ref4">Andres, 2014</xref>), whereas China&#x2019;s coordinated regulatory approach&#x2014;even in environmental policy&#x2014;demonstrates how initially restrictive measures can ultimately foster green growth and productivity spillovers (
                    <xref ref-type="bibr" rid="ref11">Chen et al., 2024</xref>).</p>
                <p>
China&#x2019;s expansion reinforced Latin America&#x2019;s commodity dependence and revealed structural weaknesses in industrial and competition policy (
                    <xref ref-type="bibr" rid="ref78">Wise &amp; Quiliconi, 2007</xref>; 
                    <xref ref-type="bibr" rid="ref76">Wise, 2016</xref>). Although often framed as liberalizing reforms (
                    <xref ref-type="bibr" rid="ref24">Hamner, 2002</xref>), China&#x2019;s model remains anchored in state coordination through SOEs and strategic planning (
                    <xref ref-type="bibr" rid="ref25">Hearn and Le&#x00f3;n-Manr&#x00ed;quez, 2011</xref>), diverging sharply from Latin America&#x2019;s fragmented post-2009 regulatory adjustments. Ongoing deficiencies in corporate governance, investor protection, SME finance, and enforcement capacity (
                    <xref ref-type="bibr" rid="ref13">Chong and Lopez de Silanes, 2007</xref>; 
                    <xref ref-type="bibr" rid="ref60">Valdiglesias, 2012</xref>; 
                    <xref ref-type="bibr" rid="ref9">Cardoza et al., 2016</xref>) further contrast with the centralized coordination and transaction-cost efficiency observed in East Asia (
                    <xref ref-type="bibr" rid="ref43">OECD Development Centre, 2015</xref>).</p>
                <p>Empirical evidence reinforces this divergence. Independent regulatory agencies with credible enforcement powers are associated with higher investment and improved infrastructure outcomes (
                    <xref ref-type="bibr" rid="ref23">Gutierrez and Berg, 2000</xref>). East Asian&#x2014;particularly Chinese&#x2014;firms operating in Latin America, such as in Peru&#x2019;s mining sector, benefit from alignment between host-country openness and home-country strategic coordination (
                    <xref ref-type="bibr" rid="ref39">Moran et al., 2012</xref>). At the same time, East Asian investment abroad often prioritizes pragmatic market access over strict adherence to global governance norms (
                    <xref ref-type="bibr" rid="ref16">Dollar, 2017</xref>), highlighting a selective application of regulatory discipline. Domestically, East Asian private firms have historically navigated weak property rights and shifting policy landscapes through dense state-business networks (
                    <xref ref-type="bibr" rid="ref2">Ahlstrom et al., 2008</xref>; 
                    <xref ref-type="bibr" rid="ref61">Valdiglesias, 2018</xref>), underscoring that regulatory efficiency in the region is neither purely liberal nor institutionally neutral.</p>
                <p>
China&#x2019;s expanding presence in Latin America illustrates how regulation can function as an instrument of strategic statecraft. Policy banks, bilateral agreements, and coordinated investment frameworks reinforce regulatory alignment with national development objectives (
                    <xref ref-type="bibr" rid="ref17">Fornes and Mendez, 2018</xref>; 
                    <xref ref-type="bibr" rid="ref27">Jenkins, 2022</xref>). Although private governance mechanisms increasingly complement formal rules (
                    <xref ref-type="bibr" rid="ref34">Mayer and Gereffi, 2010</xref>), their effectiveness ultimately depends on enforcement capacity&#x2014;an area where many Latin American countries remain deficient. Historical privatization experiences further reveal that liberalization alone does not guarantee efficiency gains when regulatory oversight is weak or fragmented (
                    <xref ref-type="bibr" rid="ref12">Chong and Lopez de Silanes 2005</xref>).</p>
                <p>Foreign direct investment from East Asia generates ambivalent outcomes in Latin America. While contributing to employment, structural change, and technological transfer (
                    <xref ref-type="bibr" rid="ref21">Gir&#x00f3;n, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref47">Rocha, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref19">Freites, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref80">Zambrano-Monserrate, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref35">Maz&#x00e9; et al., 2024</xref>), it also operates within regulatory environments characterized by limited monitoring and enforcement (
                    <xref ref-type="bibr" rid="ref33">Lopez and Munoz, 2020</xref>; 
                    <xref ref-type="bibr" rid="ref14">De Barrios et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref3">Albright, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref67">Vieira et al., 2023</xref>). Allegations of labor and environmental violations (
                    <xref ref-type="bibr" rid="ref26">Irwin and Gallagher, 2013</xref>) should therefore be understood within systemic regulatory weaknesses rather than attributed solely to foreign actors. More broadly, institutional fragility tends to deter sustained productive investment, raise transaction costs, and constrain SME expansion (
                    <xref ref-type="bibr" rid="ref13">Chong and Lopez de Silanes, 2007</xref>; 
                    <xref ref-type="bibr" rid="ref62">Valdiglesias, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref36">McKay et al., 2018</xref>; 
                    <xref ref-type="bibr" rid="ref66">Vassolo et al., 2012</xref>).</p>
                <p>Recent scholarship emphasizes that East Asia&#x2019;s regulatory advantage lies in the integration of industrial policy, pilot free trade zones, coordinated oversight, and infrastructural investment (
                    <xref ref-type="bibr" rid="ref52">Shen et al., 2025</xref>; 
                    <xref ref-type="bibr" rid="ref53">Sims et al., 2025</xref>). Stronger governance coherence and policy integration reduce uncertainty and facilitate long-term planning (
                    <xref ref-type="bibr" rid="ref28">Kastner and Pearson, 2021</xref>; 
                    <xref ref-type="bibr" rid="ref29">Kim and Dong, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref72">Weng and Li, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref81">Zhang et al., 2022</xref>). In contrast, Latin America&#x2019;s fragmented enforcement structures, informal practices, and uneven institutional quality undermine predictability and corporate decision-making (
                    <xref ref-type="bibr" rid="ref70">Walsh and Ferro, 2020</xref>; 
                    <xref ref-type="bibr" rid="ref55">Stallings, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref20">Gaganis et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref46">Reyes, 2021</xref>; 
                    <xref ref-type="bibr" rid="ref37">Mechelli and Cimini, 2020</xref>). Even reforms in state-owned enterprises have delivered only partial efficiency gains without resolving systemic governance constraints (
                    <xref ref-type="bibr" rid="ref6">Bello et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref31">Kri&#x017e;i&#x0107;, 2021</xref>; 
                    <xref ref-type="bibr" rid="ref1">Abreo et al., 2021</xref>).</p>
                <p>Infrastructure and digital integration further accentuate the gap. East Asia&#x2019;s coordinated investments in logistics, transportation, and digital systems enhance regulatory effectiveness and lower operational costs, whereas Latin America&#x2019;s fragmented frameworks limit the developmental impact of FDI (
                    <xref ref-type="bibr" rid="ref54">Soto and Martinez-Cobas, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref32">Lei, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref59">Urrego-Sandoval and Pardo, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref33">Lopez and Munoz, 2020</xref>; 
                    <xref ref-type="bibr" rid="ref28">Kastner and Pearson, 2021</xref>). Informal institutions and public perceptions also shape compliance and reputational risk differently across regions, reinforcing regulatory outcomes in East Asia while amplifying volatility in Latin America (
                    <xref ref-type="bibr" rid="ref20">Gaganis et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref50">Rousham et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref38">Mihut et al., 2025</xref>; 
                    <xref ref-type="bibr" rid="ref7">Bezuidenhout et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref71">Wang and Feng, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref3">Albright, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref82">Zhou, 2023</xref>).</p>
                <p>Importantly, methodological differences in the literature mirror these institutional asymmetries. Research on East Asia frequently employs longitudinal and quantitative approaches capable of identifying causal relationships between regulation and firm-level performance, whereas studies on Latin America remain more descriptive and fragmented, limiting systematic evaluation of welfare effects (
                    <xref ref-type="bibr" rid="ref69">Vrontis et al., 2024</xref>; 
                    <xref ref-type="bibr" rid="ref47">Rocha, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref29">Kim and Dong, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref37">Mechelli and Cimini, 2020</xref>; 
                    <xref ref-type="bibr" rid="ref46">Reyes, 2021</xref>; 
                    <xref ref-type="bibr" rid="ref55">Stallings, 2024</xref>).</p>
                <p>Taken together, the literature suggests that China&#x2014;and East Asia more broadly&#x2014;offers not a replicable blueprint but a benchmark illustrating how regulatory efficiency emerges from coherent governance, strategic coordination, and credible enforcement. For Latin America, strengthening institutional capacity, ensuring consistent rule application, and preventing regulatory capture are prerequisites for translating reform into sustained economic growth (
                    <xref ref-type="bibr" rid="ref68">Vorotnikova, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref6">Bello et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref57">The World Bank, 2025</xref>). The central analytical question, therefore, is not whether Latin America can imitate East Asia, but how regulatory efficiency&#x2014;understood as predictable, enforceable, and strategically aligned governance&#x2014;can be constructed within its distinct institutional constraints.</p>
            </sec>
            <sec id="sec8">
                <title>2.2. Theoretical framework and hypothesis</title>
                <p>Institutional Theory provides the analytical foundation for understanding how regulatory structures shape economic outcomes. Economic performance does not emerge solely from market forces, but from the configuration of formal rules&#x2014;laws, regulatory agencies, enforcement mechanisms&#x2014;and informal norms that stabilize expectations and structure incentives (
                    <xref ref-type="bibr" rid="ref42">North, 1990</xref>; 
                    <xref ref-type="bibr" rid="ref51">Scott, 2008</xref>). Institutions reduce uncertainty by making behavior predictable; when they fail to do so, economic actors face higher risks, shorter planning horizons, and lower incentives to invest. In developing economies, where enforcement is often uneven, informal arrangements may temporarily compensate for weak formal governance. However, such compensatory mechanisms rarely substitute for credible regulation; instead, they tend to entrench opacity, increase discretion, and elevate transaction costs. Latin America exemplifies this institutional dualism: regulatory instability, politicized oversight, and fragmented enforcement undermine predictability, discouraging long-term capital formation and innovation.</p>
                <p>New Institutional Economics (NIE) strengthens this argument by explicitly linking institutional quality to efficiency and welfare outcomes (
                    <xref ref-type="bibr" rid="ref73 ref75">Williamson, 1975, 1985</xref>). Institutions determine the cost structure of economic exchange. When regulatory frameworks are coherent and consistently enforced, they lower negotiation, monitoring, and compliance costs, facilitating productive investment. Conversely, weak or inconsistently applied rules raise the cost of contracting, expand opportunities for rent-seeking, and distort resource allocation. Transaction Cost Economics (TCE) sharpens this logic by conceptualizing firms as adaptive responses to institutional constraints (
                    <xref ref-type="bibr" rid="ref74 ref75">Williamson, 1979, 1985</xref>). In environments characterized by regulatory unpredictability, firms internalize uncertainty through vertical integration, informal arrangements, or strategic non-compliance. While such strategies may ensure survival, they reduce aggregate efficiency and constrain productivity growth.</p>
                <p>Within this framework, regulatory efficiency is not synonymous with deregulation. Rather, it refers to the capacity of the state to design clear rules, enforce them credibly, and coordinate policy objectives across sectors. Efficient regulation minimizes administrative burdens without sacrificing oversight, reduces discretionary enforcement, and aligns incentives between public authorities and private actors. The critical variable, therefore, is not the size of the state but its organizational coherence and enforcement credibility.</p>
                <p>
China represents a benchmark case of regulatory consolidation within a state-led model. Its institutional architecture integrates bureaucratic discipline, strategic coordination, and policy continuity, reducing uncertainty and transaction costs despite limited political liberalization. Regulatory predictability&#x2014;particularly in licensing, property formalization, and industrial policy&#x2014;creates stable expectations that support investment and long-term planning. In contrast, many Latin American economies exhibit fragmented regulatory regimes in which formal reforms coexist with weak enforcement and politicized implementation. This disjunction generates higher compliance costs, regulatory volatility, and diminished investor confidence, ultimately constraining productivity and per capita growth.</p>
                <p>Building on these theoretical foundations, this study advances the proposition that regulatory efficiency&#x2014;understood as institutional coherence, credible enforcement, and reduced transaction costs&#x2014;constitutes a decisive determinant of economic performance. Where regulatory systems provide stability and predictability, they enable capital accumulation, entrepreneurial expansion, and sustained growth. Where they remain fragmented and inconsistent, they amplify uncertainty and limit development potential.</p>
                <p>

                    <bold>Hypotheses</bold>
                </p>
                <p>

                    <bold>Central Hypothesis (H
                        <sub>0</sub>)</bold>
                </p>
                <p>Differences in regulatory efficiency&#x2014;reflected in administrative costs, procedural complexity, and overall business-environment quality&#x2014;are systematically associated with differences in GDP per capita growth between China and selected Latin American economies, with China&#x2019;s more efficient regulatory framework linked to superior growth performance.
                    <statement id="state1">
                        <label>H1.</label>
                        <p>Higher administrative burdens&#x2014;measured by greater costs of construction permits and a higher number of property registration procedures&#x2014;are negatively associated with GDP per capita growth, controlling for inflation, trade openness, gross capital formation, and rule of law.</p>
                    </statement>

                    <statement id="state2">
                        <label>H2.</label>
                        <p>Higher regulatory quality&#x2014;captured by ease-of-doing-business scores and property registration scores&#x2014;is positively associated with GDP per capita growth, reflecting the growth-enhancing effects of reduced transaction costs and greater institutional predictability.</p>
                    </statement>

                    <statement id="state3">
                        <label>H3.</label>
                        <p>After controlling for macroeconomic and institutional factors, Latin American countries exhibit systematically lower growth performance relative to China, indicating that China&#x2019;s regulatory efficiency and institutional coherence account for a significant share of the observed growth differential.</p>
                    </statement>
                </p>
            </sec>
        </sec>
        <sec id="sec9" sec-type="methods">
            <title>3. Method</title>
            <p>This study uses a panel data approach to analyze the impact of regulatory efficiency and institutional quality on economic growth. The dataset covers seven countries&#x2014;Argentina, Brazil, Chile, Colombia, Mexico, Peru, and China&#x2014;over the period 2003&#x2013;2019. The five Doing Business indicators&#x2014;costs for construction permits, construction permit scores, ease of doing business, property registration score, and number of property registration procedures&#x2014;represent the broader variable of regulatory efficiency, capturing administrative burdens, procedural complexity, and the overall conduciveness of the business environment.</p>
            <p>Panel data models allow control for unobserved heterogeneity across countries (country-specific characteristics) and over time (temporal shocks), providing more accurate estimates of the effect of regulatory and institutional variables on GDP growth. Both fixed-effects (FE) and random-effects (RE) specifications are estimated. Model selection is guided by the Hausman test, ensuring appropriate handling of correlation between regressors and unobserved effects.</p>
            <sec id="sec10">
                <title>3.1 Econometric model</title>
                <p>

                    <bold>Dependent variable:</bold>
                </p>
                <p>GDP growth_it&#x00a0;=&#x00a0;Annual GDP growth for country i in year t.</p>
                <p>

                    <bold>Treatment variable:</bold>
                </p>
                <p>Regulatory Efficiency_it&#x00a0;=&#x00a0;The 5 variables of Doing Business.</p>
                <p>

                    <bold>Control variables:</bold>

                    <list list-type="bullet">
                        <list-item>
                            <label>&#x2022;</label>
                            <p>Trade openness (% of GDP): captures benefits of international integration and technology diffusion.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>Inflation (annual %): reflects macroeconomic stability; high inflation is expected to negatively impact growth.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>Gross capital formation (annual %): represents domestic investment in productive capacity.</p>
                        </list-item>
                        <list-item>
                            <label>&#x2022;</label>
                            <p>Rule of Law (percentile rank): proxies institutional quality and enforcement of contracts and property rights.</p>
                        </list-item>
                    </list>
                </p>
                <p>The fixed-effects model accounts for time-invariant country characteristics, denoted as &#x03b1;_i. In this specification, i&#x00a0;=&#x00a0;1, &#x2026;,7 represents the countries in the sample, t&#x00a0;=&#x00a0;2003, &#x2026;,2019 represents the time period, and u_it is the idiosyncratic error term. By using this approach, the model isolates the effect of within-country variation over time, effectively controlling for all time-invariant factors that might otherwise bias the estimates.</p>
                <p>The random-effects model assumes that the country-specific effects, denoted as &#x03bc;_i, are uncorrelated with the regressors. These effects are modeled as normally distributed with mean zero and variance &#x03c3;_&#x03bc;
                    <sup>2</sup>, while the idiosyncratic error term u_it is also normally distributed with mean zero and variance &#x03c3;_u
                    <sup>2</sup>. The &#x03bc;_i term captures unobserved heterogeneity across countries. When this assumption of no correlation holds, the random-effects model is more efficient than the fixed-effects model, providing more precise estimates:</p>
                <p>

                    <bold>Hausman test</bold>
                </p>
                <p>In this context, 
                    <italic toggle="yes">&#x03b2;&#x0302;{FE} and &#x03b2;&#x0302;</italic>{RE} represent the vectors of estimated coefficients from the fixed-effects and random-effects models, respectively, and 
                    <italic toggle="yes">k</italic> denotes the number of regressors. The Hausman test evaluates the null hypothesis (
                    <italic toggle="yes">H
                        <sub>0</sub>
                    </italic>) that the random-effects estimator is consistent, which requires that the country-specific effects (
                    <italic toggle="yes">&#x03bc;_i</italic>) are uncorrelated with the regressors. A p-value greater than 0.05 indicates that the random-effects model is preferred, while a p-value below 0.05 favors the fixed-effects specification. This test ensures that the selected model produces unbiased and consistent coefficient estimates.</p>
            </sec>
            <sec id="sec11">
                <title>3.2 Sources and causal identification</title>
                <p>The analysis uses data from the World Bank&#x2019;s WDI and WGI (2003&#x2013;2019), including GDP growth, inflation, trade openness, gross capital formation, and Rule of Law. Regulatory indicators&#x2014;construction permit costs and scores, ease of doing business, and property registration procedures&#x2014;come from Doing Business (
                    <xref ref-type="table" rid="T2">
Table 2</xref>). These standardized sources ensure consistent, comparable measures across the seven countries.</p>
                <table-wrap id="T2" orientation="portrait" position="float">
                    <label>
Table 2. </label>
                    <caption>
                        <title>Variables, definitions, sources, and construction.</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">Definition</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Source</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Years</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">GDP growth (annual %)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Income level in current dollars</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">WDI</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Inflation (%)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Annual consumer price inflation</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">WDI</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Trade (% of GDP)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Trade openness index</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">WDI</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Gross capital formation (growth %)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Investment growth</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">WDI</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Rule of Law (percentile rank)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Institutional quality</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">WGI</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Costs for construction permits</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Cost of obtaining a construction permit</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Doing Business</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Costs for construction permits - Score</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Scaled score reflecting the relative cost of construction permits</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Doing Business</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Ease of Doing Business</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Overall score measuring how easy it is to do business in a country</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Doing Business</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Registering property procedures - Score</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Scaled score representing the number of steps required to register property</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Doing Business</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Registering property procedures (number)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Actual number of steps needed to register property</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Doing Business</td>
                                <td align="char" char="&#x2013;" colspan="1" rowspan="1" valign="middle">2003&#x2013;2019</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>Notes. All variables come from the World Bank&#x2019;s WDI and WGI databases, except 
                            <italic toggle="yes">Time to start a business</italic>, taken from 
                            <italic toggle="yes">Doing Business</italic> (available only for 2003&#x2013;2019). GDP per capita and its growth, inflation, trade openness, gross capital formation, and Rule of Law follow standard World Bank definitions. 
                            <italic toggle="yes">Log GDP per capita (2015)</italic> is the natural log of each country&#x2019;s 2015 value. The start-up days variable is used only for descriptive purposes. Source: World Bank.</p>
                    </table-wrap-foot>
                </table-wrap>
                <p>Causal inference is ensured through several strategies. The treatment variable&#x2014;days required to start a business&#x2014;is lagged by one year to reduce simultaneity bias. Fixed-effects models control for unobserved, time-invariant country characteristics, while standard errors are clustered at the country level to address heteroskedasticity and within-country correlation. Counts of Doing Business reforms are used as quasi-exogenous shocks to regulatory quality, strengthening identification.</p>
            </sec>
        </sec>
        <sec id="sec12" sec-type="results">
            <title>4. Results</title>
            <p>The analysis reveals clear structural differences between China and selected Latin American countries. Descriptive statistics and figures show that China consistently achieves higher GDP growth, lower inflation volatility, stronger governance, and more efficient regulatory processes, including lower costs and fewer procedures for business registration and property formalization.</p>
            <p>Panel-data regressions demonstrate that these regulatory and institutional advantages are significantly linked to stronger per capita growth, with lower administrative burdens and higher ease-of-doing-business scores positively affecting outcomes across both fixed- and random-effects models. Hausman tests confirm the appropriate model choice, while dummy-variable regressions benchmark China against Latin America, showing that Latin American countries systematically experience lower growth relative to China.</p>
            <sec id="sec13">
                <title>4.1. Descriptive analysis</title>
                <p>
                    <xref ref-type="table" rid="T3">
Table 3</xref> presents key macroeconomic and institutional indicators for selected Latin American countries and China for the period 2003&#x2013;2019. The indicators include annual GDP growth, inflation rates, trade openness (as a percentage of GDP), gross capital formation growth, and rule of law percentile ranks. These measures capture both economic performance and institutional quality, highlighting structural differences between China and Latin American economies.</p>
                <table-wrap id="T3" orientation="portrait" position="float">
                    <label>
Table 3. </label>
                    <caption>
                        <title>Descriptive statistics &#x2013; All key variables (2003&#x2013;2019).</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">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>
                                <th align="left" colspan="1" rowspan="1" valign="top">Observations</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">GDP growth (annual %) overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">4.22</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">3.68</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">-6</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">14</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">2.37</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">2.9</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">9.06</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">2.95</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">&#x2212;5.02</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">10.98</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Inflation (%) overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">4.71</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">6.09</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;1</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">54</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">15.19</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">2.71</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">44</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">2.25</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">&#x2212;5.29</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">14.71</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Trade (% of GDP) overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">46.19</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">15.44</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">22</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">81</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">15.01</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">26.01</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">66.75</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">6.62</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">32.21</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">63.51</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Rule of Law (percentile rank) overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">44.78</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">18.07</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">20</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">88</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">18.65</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">32.16</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">85.3</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">5.08</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">31.68</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">58.81</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Gross capital formation (growth %) overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">6.53</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">12.09</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;25</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">40.2</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">3.38</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">2.14</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">11.7</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">11.67</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">&#x2212;25.27</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">39.78</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>

                            <bold>Note:</bold> 
                            <xref ref-type="table" rid="T3">
Table 3</xref> reports key macroeconomic and institutional indicators for China and selected Latin American countries (2003&#x2013;2019). China shows stronger governance, higher and more stable growth, lower inflation, and more predictable investment, while Latin America displays greater variability and weaker institutions. Data from the World Bank WDI and WGI.</p>
                    </table-wrap-foot>
                </table-wrap>
                <p>The data shows that China consistently exhibits higher GDP growth, more stable macroeconomic conditions, and stronger governance indicators, while Latin American countries display greater variability and lower rule-of-law scores. The sources for these data are the World Bank&#x2019;s World Development Indicators and the Worldwide Governance Indicators, which provide standardized, cross-country comparable measures for macroeconomic and institutional analysis.</p>
                <p>
                    <xref ref-type="fig" rid="f1">
Figure 1</xref> presents the analysis of GDP growth and macroeconomic controls. The figure illustrates that China exhibits strong growth alongside higher rule-of-law scores, lower inflation volatility, and more stable investment patterns, whereas Latin American countries show greater dispersion in economic performance and governance quality. This variation highlights the structural differences in institutional and macroeconomic environments.</p>
                <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                    <label>
Figure 1. </label>
                    <caption>
                        <title>Graphic analysis of economic growth and control variables.</title>
                        <p>Note: Visual comparison of macroeconomic performance and governance indicators. China exhibits stable growth and high rule-of-law scores, while Latin American countries display dispersed economic and institutional outcomes, emphasizing regional heterogeneity.</p>
                    </caption>
                    <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/196855/1531637d-0039-4ee2-989b-254701d3e330_figure1.gif"/>
                </fig>
                <p>
                    <xref ref-type="table" rid="T4">
Table 4</xref> reports regulatory efficiency indicators, including costs and scores for construction permits, ease of doing business, and property registration procedures. China consistently demonstrates lower costs, higher scores, and fewer procedures, highlighting a more efficient business environment, whereas Latin American countries face higher costs, lower scores, and more administrative complexity. These contrasts establish China as a benchmark for regulatory efficiency and underscore the institutional gaps facing Latin American firms. 
                    <italic toggle="yes">(See</italic> 
                    <xref ref-type="table" rid="T4">
Table 4</xref>
                    <italic toggle="yes">).</italic>
                </p>
                <table-wrap id="T4" orientation="portrait" position="float">
                    <label>
Table 4. </label>
                    <caption>
                        <title>Descriptive statistics of regulatory efficiency: construction permits, ease of doing business, and property registration procedures.</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">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>
                                <th align="left" colspan="1" rowspan="1" valign="top">Observations</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Costs for construction permits overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">4.49</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3.48</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.8</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">11.8</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">3.71</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">1.14</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">10.21</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">1.22</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.37</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7.47</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Costs for construction permit. Score overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">77.53</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">17.42</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">40.84</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">95.80</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">18.54</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">48.91</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">94.27</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">6.08</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">62.54</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">97.89</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;19</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Ease of Doing Business overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">65.32</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">5.95</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">55.53</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">73.55</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">6.41</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">55.53</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">72.03</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">1.13</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">63.00</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">66.85</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Registering property proce. - Score overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">49.73</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">17.48</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">78.75</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">22.55</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">70.12</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">5.18</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">39.32</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">60.15</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Registering property proc. (number) overall</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">7.08</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">2.24</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3.55</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">13.61</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N&#x00a0;=&#x00a0;119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Between</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">2.92</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">4.59</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">13.61</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">n&#x00a0;=&#x00a0;7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2003;Within</td>
                                <td align="char" char="." colspan="1" rowspan="1" valign="middle">0.62</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">5.83</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">8.33</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">T&#x00a0;=&#x00a0;17</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>Note: Descriptive statistics on regulatory efficiency, including construction permit costs and scores, ease of doing business, and property registration procedures for China and selected Latin American countries. China consistently shows lower costs, higher scores, and fewer procedures, highlighting a more efficient regulatory environment, while Latin American countries face higher costs, lower scores, and more complex procedures. Data are primarily from the World Bank&#x2019;s Doing Business database, complemented by the World Bank Enterprise Surveys.</p>
                    </table-wrap-foot>
                </table-wrap>
                <p>
                    <xref ref-type="fig" rid="f2">
Figure 2</xref> illustrates these regulatory indicators. China emerges as a clear benchmark with minimal procedural delays, lower costs, and higher scores across all measures. Latin American countries display higher administrative burdens, more costly procedures, and lower scores, confirming a persistent efficiency gap. These descriptive patterns form the basis for subsequent econometric analysis linking regulatory efficiency to economic performance.</p>
                <fig fig-type="figure" id="f2" orientation="portrait" position="float">
                    <label>
Figure 2. </label>
                    <caption>
                        <title>Graphic analysis of variables of regulatory efficiency.</title>
                        <p>Note: Graphical analysis of regulatory efficiency indicators, including construction permit costs and scores, ease of doing business, and property registration procedures. China serves as a benchmark with minimal procedural delays, lower costs, and higher scores, whereas Latin American countries face higher administrative burdens, more costly procedures, and lower regulatory efficiency. Data are primarily from the World Bank&#x2019;s Doing Business database, complemented by the World Bank Enterprise Surveys.</p>
                    </caption>
                    <graphic id="gr2" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/196855/1531637d-0039-4ee2-989b-254701d3e330_figure2.gif"/>
                </fig>
            </sec>
            <sec id="sec14">
                <title>4.2. Regression results</title>
                <p>To assess the impact of regulatory efficiency on economic performance, we conducted panel-data regressions covering China and selected Latin American countries. Both fixed-effects (FE) and random-effects (RE) models were used to account for country-specific and temporal heterogeneity. The regression analysis focuses on key indicators of regulatory efficiency: construction permit costs, construction permit scores, ease of doing business, and property registration procedures (score and number of steps). These variables reflect administrative burdens, procedural complexity, and the overall conduciveness of the business environment.</p>
                <p>The results, summarized in 
                    <xref ref-type="table" rid="T5">
Table 5</xref>, indicate that higher regulatory efficiency is consistently associated with stronger GDP growth. In the FE specifications, lower construction permit costs and higher ease-of-doing-business scores positively correlate with growth, while in the RE specifications, fewer property registration procedures are linked to better economic performance. China consistently displays lower administrative burdens, higher regulatory scores, and fewer required procedures, which positions it as a benchmark for an efficient regulatory environment compared with the Latin American countries in the sample.</p>
                <table-wrap id="T5" orientation="portrait" position="float">
                    <label>
Table 5. </label>
                    <caption>
                        <title>GDP growth and regulatory environment: fixed effects (FE) and random effects (RE) specifications.</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">FE: Costs for construction permits (1)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">FE: Costs for construction permits Score (2)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">FE: Ease of doing business (3)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">RE: Registering property proce. &#x2013; Score (4)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">RE: Registering property proc. (number) (5)</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Regulatory Efficiency</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.1585
                                    <xref ref-type="table-fn" rid="tfn2">**</xref> (0.062)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.0312
                                    <xref ref-type="table-fn" rid="tfn2">**</xref> (0.012)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.4743
                                    <xref ref-type="table-fn" rid="tfn2">**</xref> (0.153)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0587
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.013)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.456
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.106)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Inflation (annual %)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0001 (0.109)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0005 (0.109)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.048 (0.230)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.0810
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.015)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.082
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.016)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Trade (% of GDP)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0185 (0.021)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0187 (0.021)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.102
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.019)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.0308
                                    <xref ref-type="table-fn" rid="tfn2">**</xref> (0.015)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.0318
                                    <xref ref-type="table-fn" rid="tfn2">**</xref> (0.016)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Rule of Law (Percentile)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0311 (0.021)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0311 (0.021)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.173
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.040)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.0113
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.0039)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.011
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.0040)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Gross Capital Formation (annual %)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.1536
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.018)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.1536
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.018)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.1163
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.012)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.1746
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.016)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.1740
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.016)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Constant</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.316 (0.573)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">2.803
                                    <xref ref-type="table-fn" rid="tfn2">**</xref> (1.085)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;42.919
                                    <xref ref-type="table-fn" rid="tfn2">**</xref> (12.566)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">2.491
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (0.288)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">8.656
                                    <xref ref-type="table-fn" rid="tfn1">***</xref> (1.709)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">N</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">119</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">119</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">119</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">119</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Groups</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>Note: Regression results using fixed-effects (FE) and random-effects (RE) panel models. Variables include regulatory efficiency indicators (construction permits, procedures, ease of doing business), inflation, trade openness, gross capital formation, and Rule of Law percentile. Significance levels:</p>
                        <fn-group content-type="footnotes">
                            <fn id="tfn1">
                                <label>***</label>
                                <p>p&#x00a0;&lt;&#x00a0;0.01,</p>
                            </fn>
                            <fn id="tfn2">
                                <label>**</label>
                                <p>p&#x00a0;&lt;&#x00a0;0.05,</p>
                            </fn>
                            <fn id="tfn3">
                                <label>*</label>
                                <p>p&#x00a0;&lt;&#x00a0;0.1,</p>
                            </fn>
                            <fn id="tfn4">
                                <p>+ marginal p&#x00a0;&#x2248;&#x00a0;0.05&#x2013;0.1. Source: World Bank Enterprise Surveys, 2015&#x2013;2023; World Bank WDI and WGI.</p>
                            </fn>
                        </fn-group>
                    </table-wrap-foot>
                </table-wrap>
                <p>Macroeconomic control variables behave as expected. Gross capital formation contributes positively to GDP growth across all models, while inflation shows a generally negative effect. Trade openness presents mixed results but is significant in certain specifications. Rule of Law percentile ranks generally have a positive or modest effect, highlighting the relevance of institutional quality alongside regulatory efficiency.</p>
                <p>To determine the appropriate panel-data specification, we conducted a series of Hausman tests comparing FE and RE models across different model specifications. The Hausman test evaluates whether the RE estimator is consistent by testing the null hypothesis that the individual effects are uncorrelated with the regressors.</p>
                <p>As shown in 
                    <xref ref-type="table" rid="T6">
Table 6</xref>, Tests 1 and 2 yield Chi
                    <sup>2</sup> values of 2.30 and 2.31, with p-values exceeding 0.80, indicating no evidence to reject the null hypothesis. These results suggest that, for these specifications, the random-effects estimator is both consistent and efficient. Test 3, with a Chi
                    <sup>2</sup> of 11.02 and a marginal p-value of approximately 0.05, suggests caution: while the RE model may still be acceptable, fixed effects could also be considered depending on robustness checks. In contrast, Tests 4 and 5 produce highly significant results (Chi
                    <sup>2</sup>&#x00a0;=&#x00a0;24.19 and 23.78, p&#x00a0;&lt;&#x00a0;0.001), leading to rejection of the null hypothesis and indicating that fixed-effects models are required for these specifications.</p>
                <table-wrap id="T6" orientation="portrait" position="float">
                    <label>
Table 6. </label>
                    <caption>
                        <title>Hausman test results for panel-data model selection.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Test</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Chi
                                    <sup>2</sup> (Hausman)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Prob &gt; Chi
                                    <sup>2</sup>
                                </th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Recommended model</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Notes</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">2.30</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.8058</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Random effects</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">p&#x00a0;&gt;&#x00a0;0.05&#x00a0;&#x2192;&#x00a0;cannot reject Ho, RE is consistent &amp; efficient</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">2.31</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.8045</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Random effects</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">p&#x00a0;&gt;&#x00a0;0.05&#x00a0;&#x2192;&#x00a0;same as above</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">11.02</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0510</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Random effects (cautious)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">borderline p&#x00a0;&#x2248;&#x00a0;0.05; RE may be acceptable</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">4</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">24.19</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0002</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Fixed effects</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">p&#x00a0;&lt;&#x00a0;0.05&#x00a0;&#x2192;&#x00a0;reject Ho, FE needed</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">5</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">23.78</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.0002</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Fixed effects</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">p&#x00a0;&lt;&#x00a0;0.05&#x00a0;&#x2192;&#x00a0;reject Ho, FE needed</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>Note: The Hausman test checks if random-effects are consistent. Tests 1&#x2013;3 suggest RE may be adequate; Tests 4&#x2013;5 indicate FE is required. Data include GDP per capita growth, regulatory efficiency (permits, ease of doing business, property registration), macro controls (trade, inflation, capital formation), and Rule of Law for China and selected Latin American countries, from World Bank Enterprise Surveys (2015&#x2013;2023), WDI, WGI, and Ease of Doing Business.</p>
                    </table-wrap-foot>
                </table-wrap>
                <p>These results highlight the heterogeneity of the data structure across models and confirm that model selection must be guided by both statistical tests and theoretical considerations. For most specifications with high p-values, RE is adequate, but for those with significant Hausman tests, FE better accounts for correlation between the individual effects and regressors, ensuring unbiased and consistent coefficient estimates.</p>
                <p>To further assess the differential impact of regulatory and institutional conditions on economic performance, we conducted a panel-data analysis using dummy variables to distinguish China from selected Latin American countries. 
                    <xref ref-type="table" rid="T7">
Table 7</xref> reports the results across four specifications: three random-effects models (columns 1&#x2013;3) and one fixed-effects model (column 4). The dummy variables are coded as 1 for Latin American countries and 0 for China, using China as a benchmark for efficient regulatory practices and institutional quality.</p>
                <table-wrap id="T7" orientation="portrait" position="float">
                    <label>
Table 7. </label>
                    <caption>
                        <title>Impact of regulatory, institutional, and dummy variables on GDP growth.</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">Dummy
                                    <xref ref-type="table-fn" rid="tfn7">*</xref>costs for construction permits (RE)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Dummy
                                    <xref ref-type="table-fn" rid="tfn7">*</xref> costs for construction permits score (RE)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Dummy
                                    <xref ref-type="table-fn" rid="tfn7">*</xref> ease of doing business (RE)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Dummy
                                    <xref ref-type="table-fn" rid="tfn7">*</xref>regist. property proce. &#x2013; score (FE)</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Gross capital formation</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.262
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.262
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.253
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.175
                                    <xref ref-type="table-fn" rid="tfn6">**</xref>
                                </td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Trade/GDP</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.130
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.081
                                    <xref ref-type="table-fn" rid="tfn6">**</xref>
                                </td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Rule of law percentile</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.058
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.026</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Inflation</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.015</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Dummy</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;3.658
                                    <xref ref-type="table-fn" rid="tfn7">*</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">- 27.82
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2013;</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Dummy</bold>
                                    <xref ref-type="table-fn" rid="tfn7">

                                        <bold>*</bold>
                                    </xref>
                                    <bold>regulatory efficiency</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.178
                                    <xref ref-type="table-fn" rid="tfn7">*</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.036</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.397</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;0.004</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Constant</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3.234
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3.316
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">8.258
                                    <xref ref-type="table-fn" rid="tfn5">***</xref>
                                </td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">&#x2212;2.030</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>R</bold>
                                    <sup>

                                        <bold>2</bold>
                                    </sup> 
                                    <bold>Overall</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.647</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.647</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.855</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.348</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Observations</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">119</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">119</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="middle">71</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">
                                    <bold>Groups</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                            </tr>
                        </tbody>
                    </table>
                    <table-wrap-foot>
                        <p>Note: Models 1&#x2013;3 use random effects, Model 4 uses fixed effects. Significance:</p>
                        <fn-group content-type="footnotes">
                            <fn id="tfn5">
                                <label>***</label>
                                <p>p&#x00a0;&lt;&#x00a0;0.01,</p>
                            </fn>
                            <fn id="tfn6">
                                <label>**</label>
                                <p>p&#x00a0;&lt;&#x00a0;0.05,</p>
                            </fn>
                            <fn id="tfn7">
                                <label>*</label>
                                <p>p&#x00a0;&lt;&#x00a0;0.1,</p>
                            </fn>
                            <fn id="tfn8">
                                <p>+ marginal p&#x00a0;&#x2248;&#x00a0;0.05&#x2013;0.1. Missing (&#x2212;) indicates excluded variables. Diagnostics confirm panel robustness. Dummies: 1&#x00a0;=&#x00a0;Latin America, 0&#x00a0;=&#x00a0;China (benchmark). Data: World Bank Enterprise Surveys (2015&#x2013;2023), WDI, WGI, Ease of Doing Business.</p>
                            </fn>
                        </fn-group>
                    </table-wrap-foot>
                </table-wrap>
                <p>The results in 
                    <xref ref-type="table" rid="T7">
Table 7</xref> show that China consistently achieves more favorable growth outcomes relative to Latin America. Coefficients for the Latin American dummies are generally negative or smaller than the baseline (China), with column 3 reaching statistical significance at the 1% level (&#x2212;27.82), underscoring the substantial gap in growth performance linked to differences in regulatory efficiency, institutional quality, and governance structures.</p>
                <p>Control variables behave as expected: gross capital formation remains positively and strongly associated with growth, trade and Rule of Law show mixed but coherent effects, and inflation has a modest negative effect in the FE specification. Overall, these findings confirm that China serves as a practical benchmark in the region, with lower administrative burdens, more predictable regulatory processes, and stronger governance translating into more stable and higher per capita growth compared to Latin America.</p>
            </sec>
        </sec>
        <sec id="sec15" sec-type="discussions">
            <title>5. Discussions</title>
            <p>The empirical results align with and deepen existing debates on institutions and development. In line with 
                <xref ref-type="bibr" rid="ref24">Hamner (2002)</xref>, who underscored the competitiveness gains associated with China&#x2019;s gradual market opening, the evidence here indicates that what matters most is not liberalization in isolation but the administrative and regulatory conditions under which markets operate. Lower procedural complexity, reduced compliance costs, and stronger business-environment scores are consistently associated with higher GDP per capita growth. Conversely, the heavier administrative burdens observed in Latin America are not neutral frictions; they are systematically correlated with weaker macroeconomic performance. Regulatory inefficiency, therefore, carries tangible growth costs.</p>
            <p>The findings also challenge the convergence argument advanced by 
                <xref ref-type="bibr" rid="ref25">Hearn and Le&#x00f3;n-Manr&#x00ed;quez (2011)</xref>. Although both regions experienced post-2009 institutional adjustments, the data do not support sustained institutional alignment. Instead, panel estimations and benchmark regressions point to persistent divergence. China&#x2019;s regulatory consolidation&#x2014;particularly the strengthening of enforcement and institutional coordination noted in Valdiglesias (2026; 
                <xref ref-type="bibr" rid="ref63">2025</xref>)&#x2014;appears to have enhanced predictability and policy coherence. In contrast, Latin American reforms remain uneven and frequently undermined by fragmented implementation. This pattern is consistent with evidence of structural weaknesses in corporate governance and SME productivity documented by 
                <xref ref-type="bibr" rid="ref13">Chong and Lopez de Silanes (2007)</xref> and 
                <xref ref-type="bibr" rid="ref9">Cardoza et al. (2016)</xref>. Even after accounting for inflation, trade openness, capital formation, and rule of law, administrative delays and procedural burdens retain a statistically significant association with lower growth.</p>
            <p>Sector-specific research reinforces this macro-level divergence. Persistent weaknesses in infrastructure and electricity oversight in Latin America (
                <xref ref-type="bibr" rid="ref56">Su&#x00e1;rez-Alem&#x00e1;n et al., 2019</xref>; 
                <xref ref-type="bibr" rid="ref44">Ravillard et al., 2010</xref>; 
                <xref ref-type="bibr" rid="ref4">Andres, 2014</xref>), together with broader institutional fragility affecting SMEs (
                <xref ref-type="bibr" rid="ref13">Chong and Lopez de Silanes, 2007</xref>; 
                <xref ref-type="bibr" rid="ref62">Valdiglesias, 2024</xref>; 
                <xref ref-type="bibr" rid="ref36">McKay et al., 2018</xref>; 
                <xref ref-type="bibr" rid="ref66">Vassolo et al., 2012</xref>), illustrate how regulatory gaps translate into structural constraints. By contrast, East Asia&#x2019;s integration of industrial policy, coordinated supervision, and infrastructure investment (
                <xref ref-type="bibr" rid="ref52">Shen et al., 2025</xref>; 
                <xref ref-type="bibr" rid="ref53">Sims et al., 2025</xref>; 
                <xref ref-type="bibr" rid="ref28">Kastner and Pearson, 2021</xref>; 
                <xref ref-type="bibr" rid="ref29">Kim and Dong, 2025</xref>; 
                <xref ref-type="bibr" rid="ref72">Weng and Li, 2024</xref>; 
                <xref ref-type="bibr" rid="ref81">Zhang et al., 2022</xref>) reduces uncertainty and supports long-term planning. Even when outward investment strategies are pragmatically oriented (
                <xref ref-type="bibr" rid="ref16">Dollar, 2017</xref>; 
                <xref ref-type="bibr" rid="ref39">Moran et al., 2012</xref>), domestic regulatory coherence remains a distinguishing advantage. Overall, the evidence supports the view that predictable, enforceable, and strategically aligned regulation is a structural determinant of divergent growth trajectories.</p>
            <p>This study contributes to comparative political economy by providing systematic quantitative evidence that links regulatory efficiency to GDP per capita growth across regions. Through a panel-data design that incorporates fixed- and random-effects models with Hausman diagnostics, it moves beyond descriptive comparisons and estimates the magnitude of regulatory costs. By treating China as an empirical benchmark rather than a normative template, the analysis refines debates on institutional quality, state capacity, and transaction-cost efficiency.</p>
            <p>For Latin America, the results point to clear institutional priorities. Simplifying business entry procedures, reducing construction and registration costs, and strengthening enforcement credibility are not merely technical adjustments but growth-enhancing reforms. The effectiveness of independent regulatory agencies depends on coordination capacity and insulation from fragmentation must occur alongside improvements in oversight and governance to prevent the inefficiencies observed in earlier privatization waves. Investments in infrastructure and digital regulatory systems can further reduce transaction costs and improve policy credibility.</p>
            <p>Further research should investigate firm-level transmission channels through which regulatory simplification affects productivity, innovation, and investment decisions, particularly among SMEs. Comparative longitudinal analyses could clarify how regulatory reform interacts with industrial policy and digital governance. Such work would help refine understanding of how institutional design translates into sustained and inclusive economic growth across diverse political and regulatory contexts.</p>
        </sec>
        <sec id="sec16">
            <title>Ethics and consent</title>
            <p>Ethical approval and consent were not required.</p>
        </sec>
    </body>
    <back>
        <sec id="sec19" sec-type="data-availability">
            <title>Data availability</title>
            <p>All datasets used in this study are publicly available. The datasets and processed data are cited below following the reference style used throughout the manuscript:
                <list list-type="bullet">
                    <list-item>
                        <label>&#x2022;</label>
                        <p>World Development Indicators (WDI)</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>World Bank. 
                            <italic toggle="yes">World Development Indicators.</italic> Washington, DC: World Bank; 2026. Available at: 
                            <ext-link ext-link-type="uri" xlink:href="https://databank.worldbank.org/source/world-development-indicators">https://databank.worldbank.org/source/world-development-indicators</ext-link>. Publicly accessible. Variables used: GDP growth, inflation, trade openness, gross capital formation.</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>Worldwide Governance Indicators (WGI)</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>World Bank. 
                            <italic toggle="yes">Worldwide Governance Indicators.</italic> Washington, DC: World Bank; 2026. Available at: 
                            <ext-link ext-link-type="uri" xlink:href="https://info.worldbank.org/governance/wgi/Home/Reports">https://info.worldbank.org/governance/wgi/Home/Reports</ext-link>. Publicly accessible. Variables used: rule of law, regulatory quality.</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>Enterprise Surveys</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>World Bank. 
                            <italic toggle="yes">Enterprise Surveys.</italic> Washington, DC: World Bank; 2026. Available at: 
                            <ext-link ext-link-type="uri" xlink:href="https://www.enterprisesurveys.org/">https://www.enterprisesurveys.org/</ext-link>. Publicly accessible. Variables used: firm-level regulatory data.</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>Processed dataset for replication (Extended Data)</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>Valdiglesias J. 
                            <italic toggle="yes">Processed dataset for replication of statistical analysis.</italic> Zenodo; 2026. DOI: 
                            <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.5281/zenodo.18727744">https://doi.org/10.5281/zenodo.18727744</ext-link>. (
                            <xref ref-type="bibr" rid="ref65">Valdiglesias, J. 2026b</xref>)</p>
                        <p>Licensed under 
                            <bold>

                                <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/deed.en">CC-BY 4.0</ext-link>
</bold>.</p>
                    </list-item>
                </list>
            </p>
        </sec>
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    <sub-article article-type="reviewer-report" id="report475912">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.196855.r475912</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Tzili Apango</surname>
                        <given-names>Eduardo</given-names>
                    </name>
                    <xref ref-type="aff" rid="r475912a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-8394-1767</uri>
                </contrib>
                <aff id="r475912a1">
                    <label>1</label>Universidad Autonoma Metropolitana, Mexico City, Mexico City, Mexico</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>2</day>
                <month>6</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Tzili Apango E</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="relatedArticleReport475912" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.178466.1"/>
            <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>The manuscript examines the relationship between regulatory efficiency and long-term economic growth through a comparative analysis of China and selected Latin American economies. The study draws on Institutional Theory and Transaction Cost Economics, and argues that coherent regulatory design, lower administrative burdens, and stronger enforcement capacity contribute to higher GDP growth. Using panel data for seven countries between 2003 and 2019, and estimating fixed-effects and random-effects models with standard macroeconomic controls, the article concludes that China&#x2019;s regulatory environment constitutes a benchmark associated with superior economic performance and that Latin American economies face persistent institutional and administrative constraints that weaken growth outcomes. The manuscript addresses an important and policy-relevant question and demonstrates familiarity with recent literature on institutions, governance, and development. The empirical strategy and the effort to combine theoretical discussion with quantitative analysis represent clear strengths.</p>
            <p> </p>
            <p> At the same time, several aspects of the manuscript require revision before the study can be considered fully scientifically sound. Although the article is generally well presented and cites recent literature, the presentation occasionally overstates both the coherence of empirical evidence and the degree to which the findings support causal claims. The literature review is extensive and up to date, but at times it moves beyond establishing analytical context and adopts evaluative interpretations of China&#x2019;s institutional performance that are not fully demonstrated by the empirical analysis. The manuscript would benefit from a clearer distinction between theoretical expectations, descriptive observations, and econometric findings. Several statements regarding China&#x2019;s regulatory advantages should be reformulated more cautiously and grounded directly in the variables that are measured.</p>
            <p> </p>
            <p> The study design is appropriate for an exploratory comparative analysis. Nevertheless, important methodological limitations remain insufficiently addressed. The panel includes only seven countries and combines indicators originating from different databases and time horizons. More importantly, the empirical framework assumes that observed growth differences can be attributed to regulatory efficiency after controlling for a limited set of macroeconomic variables, but the possibility of omitted-variable bias remains substantial. Structural factors such as industrial composition, export specialization, demographic dynamics, state capacity, financial development, and technological upgrading are not incorporated and may partially explain the estimated differences. The manuscript should therefore moderate claims of explanatory sufficiency and explicitly acknowledge the limits of cross-country benchmarking.</p>
            <p> </p>
            <p> The methods and analytical procedures are described in broad terms, but do not yet provide sufficient detail for full replication. Although the author reports publicly available datasets, and provides a processed dataset repository, the manuscript should specify the exact construction of each variable, indicate how missing observations were handled, provide the complete model equations, clarify whether variables were transformed or standardized, explain the clustering procedure in greater detail, and document the implementation of lag structures. The discussion of quasi-exogenous regulatory shocks through Doing Business reforms is currently underdeveloped and should explain precisely how reform counts were operationalized and incorporated into estimation.</p>
            <p> </p>
            <p> Statistical analysis is generally appropriate for the research question, but aspects of interpretation require revision. The use of fixed-effects and random-effects models with Hausman diagnostics is methodologically acceptable. However, the interpretation of statistical significance occasionally exceeds what the results support. Several coefficients display mixed signs across specifications, and some institutional variables behave inconsistently. The manuscript should report robustness checks, discuss coefficient instability, and provide stronger justification for model selection where Hausman results are borderline. In addition, the language of causality should be replaced with more cautious formulations emphasizing association rather than demonstrated causal impact unless stronger identification strategies are introduced.</p>
            <p> </p>
            <p> The availability of source data is a positive feature of the article. Public datasets and replication datasets are reported, which substantially improves transparency and reproducibility.</p>
            <p> </p>
            <p> The conclusions are directionally consistent with the empirical results, but currently extend beyond what the evidence can support. The findings justify the argument that regulatory indicators are associated with economic performance and that China provides a useful comparative reference case. However, the article does not demonstrate that regulatory efficiency is the decisive mechanism explaining divergent growth trajectories. The conclusions should therefore be narrowed and reformulated to reflect the observational nature of the design.</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>Partly</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>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>Partly</p>
            <p>Reviewer Expertise:</p>
            <p>China Studies, International Relations, China-Latin America Relations.</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="comment16343-475912">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Valdiglesias</surname>
                            <given-names>Jhon</given-names>
                        </name>
                        <aff>UNMSM, Peru</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>No competing interests.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>3</day>
                    <month>6</month>
                    <year>2026</year>
                </pub-date>
            </front-stub>
            <body>
                <p>Thank you for the detailed and constructive review. I have moderated the language throughout the manuscript to avoid strong causal interpretations and to clearly distinguish between theoretical expectations, descriptive evidence, and econometric results. The discussion of China has also been revised to ensure that it is presented strictly as a comparative empirical reference, without normative or overstated claims.</p>
                <p> In addition, the methodological section has been substantially expanded to improve transparency and replicability. This includes a clearer description of the construction of variables, the treatment of missing data, and the specification of the econometric models. I have also added more detail on the implementation of fixed and random effects models, as well as a clearer explanation of the Hausman test procedure and model selection.</p>
                <p> Furthermore, I have strengthened the discussion of limitations by explicitly acknowledging the risk of omitted variable bias and the constraints associated with the relatively small country sample. I believe the revised manuscript now provides a more balanced interpretation of the results, greater methodological clarity, and a clearer alignment between the empirical evidence and the conclusions.</p>
            </body>
        </sub-article>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report475905">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.196855.r475905</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Leng</surname>
                        <given-names>Ning</given-names>
                    </name>
                    <xref ref-type="aff" rid="r475905a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0001-9590-8641</uri>
                </contrib>
                <aff id="r475905a1">
                    <label>1</label>Georgetown University, Washington, District of Columbia, USA</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>19</day>
                <month>5</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Leng N</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="relatedArticleReport475905" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.178466.1"/>
            <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>This article tackles a highly relevant and pressing question in comparative political economy: how regulatory efficiency and institutional design impact long-term economic growth, particularly when contrasting the divergent trajectories of China and Latin America. The author does a commendable job grounding the study in institutional and transaction cost theory, providing a clear theoretical logic for why predictable regulations lower transaction costs and foster development.</p>
            <p> </p>
            <p> The author has collected an impressive range of data, which I applaud. By deploying panel-data, the author tests the impact of specific regulatory bottlenecks. And to directly benchmark China against specific Latin American countries provides a novel perspective into the structural divergence between the two regions.</p>
            <p> </p>
            <p> While the manuscript provides a quantitative assessment of formal regulatory metrics, it could benefit from deeper nuance&#x2014;specifically regarding the realities of informal politics and state capacity. Below I provide some main feedback for the author to consider.</p>
            <p> </p>
            <p> First,
                <bold> </bold>the primary limitation of the current manuscript is that it takes China's formal regulatory framework somewhat at face value, assuming a high degree of bureaucratic predictability and "disciplined state coordination". The author argues that in Latin America, "informal arrangements may temporarily compensate for weak formal governance" but ultimately "entrench opacity" and elevate transaction costs. However, the paper implicitly assumes that China has transcended these informalities through institutional consolidation. This dichotomy requires a more in-depth examination.</p>
            <p> </p>
            <p> China, much like Latin America, possesses a highly complex web of informal politics, discretionary regulation, and fragmented local governance. The "predictability" the author praises in China's licensing and property formalization often masks the reality that doing business in China relies heavily on dense, informal state-business networks to navigate shifting policy landscapes. Indeed, the author briefly acknowledges in the literature review that East Asian firms have historically navigated "weak property rights and shifting policy landscapes through dense state-business networks", but this crucial point is sidelined in the main analysis.</p>
            <p> </p>
            <p> The huge range of burdens on Chinese business and the highly unpredictable environment they face can be illustrated by works including my own book, &#x201c;Politicizing Business: How Firms Are Made to Serve the Party-State in China&#x201d; (Leng 2025), &#x00a0;&#x201c;Governing and ruling: the political logic of taxation in China&#x201d;(Zhang 2021) , &#x201c;Capitalism Without Democracy: The Private Sector in Contemporary China&#x201d; (Tsai 2007), &#x201c;Between Developmental and Clientelist States: Local State-Business Relationships in China&#x201d; (Ong 2012), &#x201c;The Private Sector in Public Office: Selective Property Rights in China&#x201d;,(Hou 2019), and many more. &#x00a0;</p>
            <p> </p>
            <p> Second, the manuscript highlights China's regulatory consolidation under Xi Jinping, specifically citing "reinforced anti-corruption enforcement" as a deliberate institutional recalibration that reduces rent-seeking and enhances coherence. However, the manuscript assumes the best regarding these initiatives. In practice, anti-corruption drives and regulatory crackdowns in authoritarian regimes are frequently discretionary, highly politicized, and selectively enforced against political rivals or specific sectors. By treating these structural shifts purely as "governance improvements", the article misses the fact that discretionary enforcement actually introduces a different, highly potent form of regulatory uncertainty for domestic and foreign actors, including companies. Some works that might help shed light on corruption and anti-corruption campaigns in China from different perspectives include &#x201c;Double Paradox: Rapid Growth and Rising Corruption in China&#x201d; (Wedeman 2012), &#x201c;Taking China&#x2019;s anticorruption campaign seriously&#x201d;(Manion 2016), &#x201c;Corruption and anti-corruption in reform China&#x201d; (He 2000), &#x201c;Corruption and anti-corruption in China: a review and future research agenda&#x201d; (Tong 2022), and &#x201c;Corruption stereotype and the unintended consequences of an anti-corruption campaign: evidence from the real estate sector in China&#x201d; (Fang and Zhang 2025).</p>
            <p> </p>
            <p> Lastly, isolating the exact determinants of GDP growth remains inherently complex, and there is a risk that the current econometric specifications do not adequately control for all relevant factors. As highlighted by foundational economic literature, such as &#x201c;I Just Ran Four Million Regressions&#x201d; (Sala-i-Martin 1997), cross-country growth regressions are highly sensitive to model specification and context-specific interaction. While the author controls for inflation, trade openness, gross capital formation, and rule of law, China differs structurally from the Latin American countries across numerous other critical dimensions. Factors such as educational attainment, the depth of the financial system, labor market regulations, and domestic security conditions could be equally, if not more, consequential for growth. Although the study relies on Fixed-Effects and Random-Effects models to account for unobserved time-invariant heterogeneity, many of these crucial omitted variables change over time and possess highly interactive natures. Consequently, without explicitly controlling for them, the model risks overstating&#x2014;or possibly understating&#x2014;the true explanatory power of the regulatory variables.</p>
            <p> </p>
            <p> All in all, to strengthen the paper, the author could consider revise the theoretical framing and the discussion section to avoid a binary where China equals "formal predictability" and Latin America equals "informal fragmentation." Instead, the author should explore how informality operates 
                <italic>differently</italic> in both regions. China's economic success may not be the result of a perfectly predictable, formal regulatory state, but rather a system where local discretionary politics and informal networks are temporarily aligned with growth and investment targets. Incorporating this political reality will make the comparison to Latin America significantly richer and more theoretically sound. I encourage the author to continue this important line of research.</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>Not applicable</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>Political economy, comparative politcs, state-business relations, China, Global China.</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>
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        <sub-article article-type="response" id="comment16269-475905">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Valdiglesias</surname>
                            <given-names>Jhon</given-names>
                        </name>
                        <aff>UNMSM, Peru</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>No competing interests to declare</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>22</day>
                    <month>5</month>
                    <year>2026</year>
                </pub-date>
            </front-stub>
            <body>
                <p>I thank the reviewer for the valuable comments and constructive suggestions. I&#x00a0;have revised the manuscript to address the main concerns: 
                    <list list-type="order">
                        <list-item>
                            <p>
                                <bold>Informality and China&#x2013;Latin America comparison:</bold>
                            </p>
                            <p> I now explicitly state that both regions are hybrid systems where formal and informal institutions coexist. The revised text avoids presenting China as fully formalized and instead emphasizes that informality operates differently across regions (more integrated in China&#x2019;s state capacity, more fragmented in Latin America).</p>
                        </list-item>
                        <list-item>
                            <p>
                                <bold>Anti-corruption and regulatory consolidation:</bold>
                            </p>
                            <p> I now clarify that anti-corruption campaigns in China may produce both governance improvements and regulatory uncertainty due to selective and discretionary enforcement.</p>
                        </list-item>
                        <list-item>
                            <p>
                                <bold>Methodological limitations:</bold>
                            </p>
                            <p> I added explicit discussion of omitted variables and clarify that results should be interpreted as associations, not fully structural causal estimates.</p>
                        </list-item>
                    </list>
                </p>
            </body>
        </sub-article>
    </sub-article>
</article>
