<?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.177965.2</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Research Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Market Diversification and Mango Exports in Emerging Economies: Evidence from Peru and Colombia, 2004-2024</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 2; peer review: 1 approved, 1 not approved]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Cosio Borda</surname>
                        <given-names>Ricardo Fernando</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/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0009-0005-7259-608X</uri>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Ancajima Vel&#x00e1;squez</surname>
                        <given-names>Janel Janelvia</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/">Software</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>
                    <uri content-type="orcid">https://orcid.org/0000-0001-6481-2671</uri>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Quispe-Farf&#x00e1;n</surname>
                        <given-names>Percy Hugo</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Resources</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</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>
                    <uri content-type="orcid">https://orcid.org/0000-0002-8921-486X</uri>
                    <xref ref-type="aff" rid="a3">3</xref>
                </contrib>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Gamarra-Vargas</surname>
                        <given-names>Natalie Margarita</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Funding Acquisition</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Resources</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</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>
                    <uri content-type="orcid">https://orcid.org/0000-0002-7289-6462</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a3">3</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Saenz Arenas</surname>
                        <given-names>Esther Rosa</given-names>
                    </name>
                    <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/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a4">4</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Pati&#x00f1;o Castro</surname>
                        <given-names>Omar Alonso</given-names>
                    </name>
                    <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/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Cajavilca-Gonz&#x00e1;les</surname>
                        <given-names>Berenice</given-names>
                    </name>
                    <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/">Software</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="aff" rid="a3">3</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Cardich Pulgar</surname>
                        <given-names>Jorge Luis</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Formal Analysis</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="aff" rid="a5">5</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Facultad de Administraci&#x00f3;n, Finanzas y Ciencias Econ&#x00f3;micas, Universidad EAN, Bogot&#x00e1;, Colombia</aff>
                <aff id="a2">
                    <label>2</label>Universidad C&#x00e9;sar Vallejo, Piura, Peru</aff>
                <aff id="a3">
                    <label>3</label>Universidad Peruana de Ciencias Aplicadas, Lima, Peru</aff>
                <aff id="a4">
                    <label>4</label>Universidad Tecnol&#x00f3;gica del Per&#x00fa;, Lima, Peru</aff>
                <aff id="a5">
                    <label>5</label>Universidad San Ignacio de Loyola, Lima, Peru</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:natalie.gamarra@upc.pe">natalie.gamarra@upc.pe</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>23</day>
                <month>6</month>
                <year>2026</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2026</year>
            </pub-date>
            <volume>15</volume>
            <elocation-id>363</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>3</day>
                    <month>6</month>
                    <year>2026</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Cosio Borda RF et al.</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/15-363/pdf"/>
            <abstract>
                <p>Within global agri-food trade, mango has become a tropical fruit of strategic relevance. However, export growth is sustainable only when it is supported by diversified markets, competitive stability, and access to destinations with effective purchasing capacity. This study examines market diversification and mango export competitiveness in Peru and Colombia, two emerging economies, during 2004&#x2013;2024. A quantitative, longitudinal, and non-experimental design was applied using trade data from Trade Map, macroeconomic indicators from the World Bank, geographic distance data from CEPII GeoDist, and information on trade agreements from official sources. The analysis combined export performance indicators, normalized revealed comparative advantage, normalized relative purchasing capacity, the temporal stability of international demand, and a gravity model estimated through EGLS with PCSE correction. The results reveal a clear structural difference between the two countries. Peru increased its mango exports from USD 42.028 million in 2004 to USD 316.986 million in 2024 and maintained positive values of normalized revealed comparative advantage throughout the period. Colombia expanded its exports from USD 806 thousand to USD 15.037 million, although its competitive position became positive only from 2020 onward. The normalized relative purchasing capacity index identified the Netherlands, France, Spain, and the United States as the most important demand markets, while the stability analysis showed greater persistence among importers after 2018. The gravity model indicated that exporter GDP, destination population, and trade agreements were positively associated with exports. Overall, Peru has a consolidated export structure, whereas Colombia remains in an expansion phase and requires greater market diversification and consolidation to strengthen its international export sustainability.</p>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Agroindustry</kwd>
                <kwd>exports</kwd>
                <kwd>diversification</kwd>
                <kwd>market</kwd>
                <kwd>price.</kwd>
            </kwd-group>
            <funding-group>
                <award-group id="fund-1">
                    <funding-source>Universidad Peruana de Ciencias Aplicadas</funding-source>
                </award-group>
                <funding-statement>Universidad Peruana de Ciencias Aplicadas / UPC-EXPOST-2026-1. </funding-statement>
                <funding-statement>
                    <italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic>
                </funding-statement>
            </funding-group>
        </article-meta>
        <notes>
            <sec sec-type="version-changes">
                <label>Revised</label>
                <title>Amendments from Version 1</title>
                <p>Based on the reviewers&#x2019; comments, the research was reoriented to address the following points: 1. A more in-depth literature review was conducted, identifying previous studies and the theoretical foundations of the models applied. 2. The methodology was reoriented, employing the Herfindahl-Hirschman Index, the Trade Competitiveness Index, the Revealed Comparative Advantage Index (RCA) and its Symmetric Version (RSCA), the Gravity Model of International Trade, Estimator Selection and the Hausman Test. 4. The database used was updated and uploaded to an open repository. 3. The results obtained were more robust due to the methodological procedure employed. 4. The conclusions were reoriented to reflect the new results obtained. 5. The references were strengthened and updated.</p>
            </sec>
        </notes>
    </front>
    <body>
        <sec id="sec5" sec-type="intro">
            <title>1. Introduction</title>
            <p>Mango occupies a prominent position among tropical fruits with a strong presence in global agri-food markets, driven by sustained demand, a broad range of cultivars, and its capacity to participate in both fresh-consumption channels and value-added agro-industrial chains. The structural limitations of this perishable product condition its internationalization capacity, because it is highly vulnerable to mechanical damage, rapid quality deterioration, difficult-to-control ripening patterns, and increasingly strict sanitary and phytosanitary protocols (
                <xref ref-type="bibr" rid="ref33">Ntsoane et al., 2019</xref>; 
                <xref ref-type="bibr" rid="ref37">Sivakumar et al., 2011</xref>). Within this framework, competitiveness does not refer only to exported volume, but also to maintaining traceability, food safety, logistical timeliness, and continuity of supply in highly demanding markets (
                <xref ref-type="bibr" rid="ref22">Le et al., 2022</xref>; 
                <xref ref-type="bibr" rid="ref29">Matulaprungsan et al., 2019</xref>).</p>
            <p>Mango represents a strategic alternative for diversifying the agro-export supply of emerging economies such as Colombia and Peru, while also generating foreign exchange and stimulating producing territories. However, export expansion does not by itself guarantee a sustainable trajectory (
                <xref ref-type="bibr" rid="ref4">Ayyaz et al., 2019</xref>). Evidence from agri-food trade shows that export growth may occur despite structural weaknesses when countries depend on a limited number of destinations, face non-tariff barriers, or show low logistical sophistication (
                <xref ref-type="bibr" rid="ref31">Mizik, 2021</xref>; 
                <xref ref-type="bibr" rid="ref42">Wickrama et al., 2024</xref>). Therefore, the performance of Colombian and Peruvian mango exports should be assessed not only from the perspective of trade volume, but also through the robustness of their market structure and the stability of their competitive position (
                <xref ref-type="bibr" rid="ref20">L&#x0103;daru et al., 2024</xref>; 
                <xref ref-type="bibr" rid="ref41">Wang et al., 2022</xref>).</p>
            <p>The difference between the two economies is reflected in the accumulated evidence. According to 
                <xref ref-type="bibr" rid="ref39">Tatiana Gonzales Cabrera et al. (2022)</xref>, Peru is among the world&#x2019;s leading exporters of fresh mango, and non-traditional agro-exports continue to be led by this fruit, which maintained growth despite the pandemic. Likewise, 
                <xref ref-type="bibr" rid="ref34">Santa Mar&#x00ed;a &amp; Chac&#x00f3;n (2023)</xref> reports that the average for 2013&#x2013;2022 shows annual growth of 10% in FOB value, with the Netherlands and the United States predominating as destination markets. Even so, a certain degree of market concentration is evident. In turn, 
                <xref ref-type="bibr" rid="ref32">Montes Ninaquispe et al. (2024)</xref> identifies a strong export specialization indicator in Peruvian fruits, together with a problem of market and exporter diversification. In the case of Colombia, 
                <xref ref-type="bibr" rid="ref15">Iba&#x00f1;ez and Colinan (2024)</xref> documented the productive growth of mango in areas such as Cesar and its effect on the local economy, although exports remain marginal compared with productive potential. 
                <xref ref-type="bibr" rid="ref12">Gaitan et al. (2022)</xref> warned that international competitiveness in mango requires transforming the structure of varieties, seasonality, and phytosanitary access, limitations that are not exclusive to Colombia but also affect other emerging producers. The scarcity of scientific literature on Colombian mango exports contrasts with the extensive evidence available for Peru. This, in itself, reflects the lower level of export development in the sector 
                <xref ref-type="bibr" rid="ref4">Ayyaz et al. (2019)</xref>. The large gap between an economy with a consolidated trajectory and another in an early stage of insertion justifies an analytical comparison that evaluates the structural conditions, market diversification, relative competitiveness, and gravity-related determinants under which sustainable insertion in perishable tropical fruits can be built.</p>
            <p>Market diversification constitutes a critical dimension for agricultural products. Concentrating exports in a small number of importers exposes value-chain actors to regulatory changes, price increases, and logistical disruptions (
                <xref ref-type="bibr" rid="ref6">Balavac &amp; Pugh, 2016</xref>). Greater structural diversity distributes risks, reduces commercial dependence, and strengthens the resilience of the export profile. This relationship has been widely documented through the Herfindahl-Hirschman index and market-share measures 
                <xref ref-type="bibr" rid="ref6">Balavac and Pugh (2016)</xref>, and its relevance for the analysis of fresh fruits has been confirmed in studies on destination concentration and export sustainability (
                <xref ref-type="bibr" rid="ref19">Ju&#x00e1;rez et al., 2025</xref>).</p>
            <p>In Latin America, agro-exports have grown significantly in recent decades, while also revealing vulnerabilities associated with dependence on traditional markets and supply concentration (
                <xref ref-type="bibr" rid="ref3">Ayuda et al., 2022</xref>; 
                <xref ref-type="bibr" rid="ref30">Melo et al., 2014</xref>). Colombia and Peru are emerging economies with agro-export potential, but they differ in infrastructure, export experience, current trade agreements, and the degree of market consolidation. This distinction makes it relevant to compare their mango exports in order to determine which country presents a more diversified structure and a more advantageous competitive position (
                <xref ref-type="bibr" rid="ref23">Leit&#x00e3;o, 2024</xref>; 
                <xref ref-type="bibr" rid="ref43">Xu et al., 2023</xref>).</p>
            <p>The problem becomes more complex when considering that export flows do not depend solely on domestic supply. Bilateral trade is influenced by several factors, including economic size, geographic distance, transportation costs, and tariff barriers (
                <xref ref-type="bibr" rid="ref23">Leit&#x00e3;o, 2024</xref>). In this sense, diversification analysis cannot be limited to a description of current destinations; it must also explain why exports are concentrated in certain markets and which opportunities remain underused. Gravity models are particularly useful because they connect export performance, market structure, and the economic determinants of trade (
                <xref ref-type="bibr" rid="ref3">Ayuda et al., 2022</xref>; 
                <xref ref-type="bibr" rid="ref41">Wang et al., 2022</xref>; 
                <xref ref-type="bibr" rid="ref43">Xu et al., 2023</xref>).</p>
            <p>Therefore, the central question is not whether Colombia and Peru export mango, but whether they do so through a diversified, competitive, and economically grounded commercial trajectory. Interpreting export growth as a commercial success without considering concentration vulnerabilities or low relative competitiveness could lead to partial conclusions (
                <xref ref-type="bibr" rid="ref9">Bojnec &amp; Fert&#x0151;, 2017</xref>; 
                <xref ref-type="bibr" rid="ref35">Seyoum, 2007</xref>). Accordingly, it is necessary to jointly assess market share, destination concentration, net exports, revealed symmetric comparative advantage, revealed symmetric relative purchasing capacity, and gravity-related determinants of mango trade in both economies.</p>
            <p>The literature on agri-food competitiveness has moved from approaches focused on static comparative advantages toward multidimensional perspectives that incorporate quality, productivity, innovation, and performance in international markets. 
                <xref ref-type="bibr" rid="ref31">Mizik (2021)</xref> notes that there is no universal measure of competitiveness in agri-food trade; therefore, it is essential to combine measures that capture specialization, trade performance, and export structure. 
                <xref ref-type="bibr" rid="ref41">Wang et al. (2022)</xref> and 
                <xref ref-type="bibr" rid="ref20">L&#x0103;daru et al. (2024)</xref> emphasize that this competitiveness also depends on technical complexity, institutional quality, and logistical infrastructure, while 
                <xref ref-type="bibr" rid="ref38">Suroso et al. (2023)</xref> highlights the importance of adapting to changing market requirements. This provides producers with a broader perspective for generating value from mango exports (
                <xref ref-type="bibr" rid="ref18">Jambor &amp; Babu, 2016</xref>).</p>
            <p>In the specific case of mango, 
                <xref ref-type="bibr" rid="ref4">Ayyaz et al. (2019)</xref> used revealed comparative advantage indices to compare exporting countries and found important asymmetries in competitive positions. Fruit exports are significantly affected by non-tariff barriers; according to 
                <xref ref-type="bibr" rid="ref42">Wickrama et al. (2024)</xref>, mango export competitiveness depends on storage technologies, preventive treatments, and effective logistical coordination, as emphasized in the literature on postharvest quality (
                <xref ref-type="bibr" rid="ref33">Ntsoane et al., 2019</xref>; 
                <xref ref-type="bibr" rid="ref37">Sivakumar et al., 2011</xref>). Despite these advances, no studies have compared Colombia and Peru from an integrated perspective that links diversification, competitiveness, and trade gravity.</p>
            <p>Another relevant research stream has examined export diversification as a mechanism for reducing vulnerability. 
                <xref ref-type="bibr" rid="ref6">Balavac &amp; Pugh (2016)</xref> associate lower volatility in external performance with openness, diversification, and institutional quality. 
                <xref ref-type="bibr" rid="ref7">Barbieri et al. (2024)</xref> analyze diversification as a territorial dimension of export performance. Recent studies also indicate that the HHI can be used to evaluate market dependence and the commercial sustainability of Peruvian fresh fruits (
                <xref ref-type="bibr" rid="ref19">Ju&#x00e1;rez et al., 2025</xref>). These studies agree that diversification is not only a matter of increasing the number of destinations, but also of strengthening the export structure against external shocks.</p>
            <p>Finally, gravity-model analysis indicates that economic size, distance, trade agreements, and access barriers affect agri-food flows. 
                <xref ref-type="bibr" rid="ref1">Altamirano-Gonzales and Mor&#x00e1;n-Santamar&#x00ed;a (2026)</xref> applied this approach to Latin American agri-food exports; 
                <xref ref-type="bibr" rid="ref30">Melo et al. (2014)</xref> showed that sanitary standards affect fruit trade flows; and 
                <xref ref-type="bibr" rid="ref23">Leit&#x00e3;o (2024)</xref> systematized the contributions of the gravity model to the analysis of international trade. This evidence supports the inclusion of this model in the analysis of Colombian and Peruvian mango exports, as it allows the identification of current market-access determinants and destinations with untapped potential (
                <xref ref-type="bibr" rid="ref41">Wang et al., 2022</xref>; 
                <xref ref-type="bibr" rid="ref43">Xu et al., 2023</xref>).</p>
            <p>The theory of comparative advantage and its empirical operationalization through international trade indicators provide the basis for this study. The Balassa index indicates whether a country has positive export specialization in a given product. When compared across countries, revealed symmetric comparative advantage corrects the asymmetries of the traditional index and facilitates comparisons between countries and periods (
                <xref ref-type="bibr" rid="ref21">Laursen, 2015</xref>). In this study, the RSCA is particularly suitable for determining whether Colombia and Peru hold a favorable competitive position in global mango trade during 2004&#x2013;2024 (
                <xref ref-type="bibr" rid="ref4">Ayyaz et al., 2019</xref>).</p>
            <p>
Complementarily, market share approximates each country&#x2019;s relative importance in world trade; the Herfindahl-Hirschman index measures the degree of concentration or diversification of export destinations; and the net export index distinguishes clearly exporting profiles from importing profiles (
                <xref ref-type="bibr" rid="ref9">Bojnec &amp; Fert&#x0151;, 2017</xref>; 
                <xref ref-type="bibr" rid="ref31">Mizik, 2021</xref>). Combining these indicators with the gravity model enables a more comprehensive reading of mango exports by incorporating observed performance, market structure, relative competitiveness, and bilateral trade determinants (
                <xref ref-type="bibr" rid="ref3">Ayuda et al., 2022</xref>).</p>
            <p>Despite the advances noted above, a research gap remains. The literature has already examined mango competitiveness, export diversification, market concentration, and the gravity determinants of agricultural trade; however, comparative evidence that integrates market share, the Herfindahl-Hirschman index, the net export index, revealed symmetric comparative advantage, normalized relative purchasing capacity, and the gravity model within the same analytical framework remains scarce for evaluating mango exports from Colombia and Peru during 2004&#x2013;2024. This absence prevents a clear determination of whether both emerging economies have built a diversified and competitive international insertion or whether their performance still depends on vulnerable trade structures. Accordingly, this study aims to evaluate market diversification and the export competitiveness of mango in Colombia and Peru. Through trade indicators, it analyzes the commercial performance of these emerging economies; through trade-based models, it evaluates export concentration and revealed comparative advantage; and, finally, through a gravity model, it assesses the diversification of destination markets for exports made by these emerging economies during 2004&#x2013;2024.</p>
        </sec>
        <sec id="sec6" sec-type="methods">
            <title>2. Methods</title>
            <sec id="sec7">
                <title>2.1 Approach, Type, Design, and Scope</title>
                <p>The study adopted a quantitative approach because it analyzes the competitive performance of mango exports from Colombia and Peru through the objective measurement of trade variables, the estimation of standardized indicators, and the empirical verification of relationships using statistical and econometric techniques, which allows replicable and comparable results across countries and periods (
                    <xref ref-type="bibr" rid="ref25">Lim, 2025</xref>). Its purpose was applied, as it was oriented toward generating useful evidence for decision-making in the agro-export sector of both countries (
                    <xref ref-type="bibr" rid="ref113">Hern&#x00e1;ndez et al., 2024</xref>). The design was non-experimental and longitudinal, with variables observed without manipulation during 2004&#x2013;2024, allowing trends and structural changes that are not observable in cross-sectional studies to be captured (
                    <xref ref-type="bibr" rid="ref24">Li et al., 2024</xref>). The scope combined a descriptive dimension, aimed at characterizing export behavior and market structure, with an explanatory dimension, focused on estimating bilateral trade determinants through a gravity model, following an appropriate approach for studies on export sustainability (
                    <xref ref-type="bibr" rid="ref26">Llerena Tapia et al., 2025</xref>).</p>
            </sec>
            <sec id="sec8">
                <title>2.2 Unit of Analysis, Population, and Sample</title>
                <p>The unit of analysis was international trade in mango exports from Colombia and Peru; the unit of observation corresponded to annual flows to each destination market during 2004&#x2013;2024. The population comprised all export records under tariff subheading 0804502000 (fresh or dried mangoes and mangosteens) in Trade Map. Because the study used the complete set of available observations, a census sample was employed, which ensures the exact calculation of the HHI, TC, VCRN, and CRCN without selection bias and allows the gravity model to capture all active bilateral trade relationships in the period (
                    <xref ref-type="bibr" rid="ref36">Shukla et al., 2023</xref>).</p>
            </sec>
            <sec id="sec9">
                <title>2.3 Product Delimitation and Information Sources</title>
                <p>The study focused exclusively on fresh and dried mangoes, excluding juices, preserves, and other processed forms, whose trade dynamics, distribution channels, and international prices differ substantially (
                    <xref ref-type="bibr" rid="ref8">Barmon et al., 2025</xref>). The empirical information was obtained from the sources described in 
                    <xref ref-type="table" rid="T1">Table 1</xref>.</p>
                <table-wrap id="T1" orientation="portrait" position="float">
                    <label>
Table 1. </label>
                    <caption>
                        <title>Data, variables, and sources.</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>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Exp</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Mango exports from Colombia/Peru to the destination country (USD FOB).</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Trade Map (ITC, 2024)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">PIB_origen</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">GDP of the exporting country (Colombia or Peru), constant dollars.</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Banco Mundial (2024)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">PIB_destino</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">GDP of the destination importing country, constant dollars.</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Banco Mundial (2024)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Pop_dest</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Total population of the destination country.</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Banco Mundial (2024)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Dist</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Geographic distance (km) between the economic centers of the exporter and the destination.</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">CEPII GeoDist (Mayer &amp; Zignago, 2011)</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">TLC</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Dummy: 1 if an FTA is in force between the exporter and the destination; 0 otherwise.</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">MINCETUR/MINCIT/OMC</td>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
            </sec>
            <sec id="sec10">
                <title>2.4 Variables and Indicators</title>
                <p>To evaluate market diversification and the competitive performance of mango exports from Colombia and Peru, four complementary indicators were combined: market concentration (HHI), trade profile (TC), revealed comparative advantage (RCA and RSCA), and geographic potential (gravity model). This combination responds to the fact that no individual index fully captures the complexity of agri-food competitiveness (
                    <xref ref-type="bibr" rid="ref8">Barmon et al., 2025</xref>).</p>
                <p>

                    <bold>

                        <italic toggle="yes">2.4.1 Procedures</italic>
</bold>
                </p>
                <p>

                    <italic toggle="yes">Herfindahl-Hirschman Index (HHI)</italic>
                </p>
                <p>The HHI simultaneously measures the concentration and geographic diversification of export flows, as it is sensitive both to the number of destinations and to the equity of their distribution (
                    <xref ref-type="bibr" rid="ref14">Hu et al., 2023</xref>). It is calculated as follows:
                    <disp-formula id="e1">

                        <mml:math display="block">
                            <mml:msub>
                                <mml:mi mathvariant="italic">HHI</mml:mi>
                                <mml:mi>t</mml:mi>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:mo>&#x2211;</mml:mo>
                            <mml:msubsup>
                                <mml:mi>s</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                                <mml:mn>2</mml:mn>
                            </mml:msubsup>
                        </mml:math>
</disp-formula>where:</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi mathvariant="italic">HHI</mml:mi>
                                <mml:mi>t</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = market concentration index of the exporting country in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>s</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                            </mml:msub>
                        </mml:math>
</inline-formula> = share of destination country 
                    <italic toggle="yes">i</italic> in total mango exports in year 
                    <italic toggle="yes">t</italic>, calculated as:
                    <disp-formula id="e2">

                        <mml:math display="block">
                            <mml:msub>
                                <mml:mi>s</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:mfrac>
                                <mml:msub>
                                    <mml:mi>X</mml:mi>
                                    <mml:mrow>
                                        <mml:mi>i</mml:mi>
                                        <mml:mo>,</mml:mo>
                                        <mml:mi>t</mml:mi>
                                    </mml:mrow>
                                </mml:msub>
                                <mml:mrow>
                                    <mml:mo>&#x2211;</mml:mo>
                                    <mml:msub>
                                        <mml:mi>X</mml:mi>
                                        <mml:mrow>
                                            <mml:mi>i</mml:mi>
                                            <mml:mo>,</mml:mo>
                                            <mml:mi>t</mml:mi>
                                        </mml:mrow>
                                    </mml:msub>
                                </mml:mrow>
                            </mml:mfrac>
                        </mml:math>
</disp-formula>
                </p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>X</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                            </mml:msub>
                        </mml:math>
</inline-formula> = value of mango exports to destination 
                    <italic toggle="yes">i</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mi>n</mml:mi>
                        </mml:math>
</inline-formula> = total number of active destination markets in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>The index ranges from 0 to 1. For interpretation, the criteria of the U.S. Department of Justice (DOJ) are used: HHI&#x00a0;&lt;1,500, low concentration; 1,500 to 2,500, moderate concentration; and&#x00a0;&gt;2,500, highly concentrated market (Antitrust 
                    <xref ref-type="bibr" rid="ref2">Division, 2026</xref>).</p>
                <p>

                    <italic toggle="yes">Trade Competitiveness Index (TC)</italic>
                </p>
                <p>The TC examines the net position of Colombia and Peru in mango trade based on their exports and imports. This makes it possible to determine whether the country is a net exporter or a net importer, a dimension not captured by the RCA, and allows evidence to be triangulated from different methodological perspectives (
                    <xref ref-type="bibr" rid="ref27">Long, 2021</xref>).
                    <disp-formula id="e3">

                        <mml:math display="block">
                            <mml:msub>
                                <mml:mi mathvariant="italic">TC</mml:mi>
                                <mml:mi>t</mml:mi>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:mfrac>
                                <mml:mrow>
                                    <mml:msub>
                                        <mml:mi>X</mml:mi>
                                        <mml:mi>t</mml:mi>
                                    </mml:msub>
                                    <mml:mo>&#x2212;</mml:mo>
                                    <mml:msub>
                                        <mml:mi>M</mml:mi>
                                        <mml:mi>t</mml:mi>
                                    </mml:msub>
                                </mml:mrow>
                                <mml:mrow>
                                    <mml:msub>
                                        <mml:mi>X</mml:mi>
                                        <mml:mi>t</mml:mi>
                                    </mml:msub>
                                    <mml:mo>+</mml:mo>
                                    <mml:msub>
                                        <mml:mi>M</mml:mi>
                                        <mml:mi>t</mml:mi>
                                    </mml:msub>
                                </mml:mrow>
                            </mml:mfrac>
                        </mml:math>
</disp-formula>where:</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi mathvariant="italic">TC</mml:mi>
                                <mml:mi>t</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = trade competitiveness index of the exporting country in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>X</mml:mi>
                                <mml:mi>t</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = value of mango exports of the exporting country in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>M</mml:mi>
                                <mml:mi>t</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = value of mango imports of the same country in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>The TC takes values in the interval [&#x2212;1, 1]. Values close to +1 indicate a consolidated net exporter, whereas values close to &#x2212;1 indicate a net importer.</p>
                <p>Revealed Comparative Advantage Index (RCA) and Symmetric Version (RSCA).</p>
                <p>The RCA determines whether export specialization is favorable relative to the world average (
                    <xref ref-type="bibr" rid="ref5">Balassa, 1965</xref>), but it presents statistical asymmetry because it has no upper bound, which prevents comparisons between countries and periods (
                    <xref ref-type="bibr" rid="ref21">Laursen, 2015</xref>). Therefore, the RSCA is adopted as the key measure, because it corrects this asymmetry by limiting the index to [&#x2212;1, 1] and improves comparability.
                    <disp-formula id="e4">

                        <mml:math display="block">
                            <mml:msub>
                                <mml:mi mathvariant="italic">RCA</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:mfrac>
                                <mml:mfrac>
                                    <mml:msub>
                                        <mml:mi>X</mml:mi>
                                        <mml:mrow>
                                            <mml:mi>i</mml:mi>
                                            <mml:mo>,</mml:mo>
                                            <mml:mi>t</mml:mi>
                                        </mml:mrow>
                                    </mml:msub>
                                    <mml:msub>
                                        <mml:mi>X</mml:mi>
                                        <mml:mi>t</mml:mi>
                                    </mml:msub>
                                </mml:mfrac>
                                <mml:mfrac>
                                    <mml:msub>
                                        <mml:mi>X</mml:mi>
                                        <mml:mrow>
                                            <mml:mi>i</mml:mi>
                                            <mml:mo>,</mml:mo>
                                            <mml:mi>t</mml:mi>
                                        </mml:mrow>
                                    </mml:msub>
                                    <mml:msub>
                                        <mml:mi>X</mml:mi>
                                        <mml:mi>t</mml:mi>
                                    </mml:msub>
                                </mml:mfrac>
                            </mml:mfrac>
                        </mml:math>
</disp-formula>where:</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi mathvariant="italic">RCA</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                            </mml:msub>
                        </mml:math>
</inline-formula> = revealed comparative advantage index for product 
                    <italic toggle="yes">i</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msubsup>
                                <mml:mi>X</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                                <mml:mi>p</mml:mi>
                            </mml:msubsup>
                        </mml:math>
</inline-formula> = exports of product 
                    <italic toggle="yes">i</italic> by country 
                    <italic toggle="yes">p</italic> (Colombia or Peru) in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>X</mml:mi>
                                <mml:mi>t</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = total exports of country 
                    <italic toggle="yes">p</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msubsup>
                                <mml:mi>X</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                                <mml:mi>w</mml:mi>
                            </mml:msubsup>
                        </mml:math>
</inline-formula> = world exports of product 
                    <italic toggle="yes">i</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>X</mml:mi>
                                <mml:mi>t</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = total world exports in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>Values greater than 1 indicate revealed comparative advantage. Subsequently, the RCA was transformed into RSCA as follows:
                    <disp-formula id="e5">

                        <mml:math display="block">
                            <mml:msub>
                                <mml:mtext mathvariant="italic">RSCA</mml:mtext>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:mfrac>
                                <mml:mrow>
                                    <mml:msub>
                                        <mml:mi mathvariant="italic">RCA</mml:mi>
                                        <mml:mrow>
                                            <mml:mi>i</mml:mi>
                                            <mml:mo>,</mml:mo>
                                            <mml:mi>t</mml:mi>
                                        </mml:mrow>
                                    </mml:msub>
                                    <mml:mo>&#x2212;</mml:mo>
                                    <mml:mn>1</mml:mn>
                                </mml:mrow>
                                <mml:mrow>
                                    <mml:msub>
                                        <mml:mi mathvariant="italic">RCA</mml:mi>
                                        <mml:mrow>
                                            <mml:mi>i</mml:mi>
                                            <mml:mo>,</mml:mo>
                                            <mml:mi>t</mml:mi>
                                        </mml:mrow>
                                    </mml:msub>
                                    <mml:mo>+</mml:mo>
                                    <mml:mn>1</mml:mn>
                                </mml:mrow>
                            </mml:mfrac>
                        </mml:math>
</disp-formula>where:</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mtext mathvariant="italic">RSCA</mml:mtext>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                            </mml:msub>
                        </mml:math>
</inline-formula> = revealed symmetric comparative advantage of product 
                    <italic toggle="yes">i</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi mathvariant="italic">RCA</mml:mi>
                                <mml:mrow>
                                    <mml:mi>i</mml:mi>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                </mml:mrow>
                            </mml:msub>
                        </mml:math>
</inline-formula> = revealed comparative advantage index calculated in the previous equation.</p>
                <p>The RSCA ranges from &#x2212;1 to +1. Positive values indicate revealed comparative advantage, whereas negative values indicate relative disadvantage (
                    <xref ref-type="bibr" rid="ref40">T&#x00f6;r&#x00f6;k et al., 2020</xref>).</p>
                <p>

                    <italic toggle="yes">Gravity Model of International Trade</italic>
                </p>
                <p>The gravity model identifies the structural factors that determine mango export flows from Colombia and Peru and quantifies untapped export potential in each destination, a dimension that static indicators cannot reveal (Ismaiel Ali Ismaiel et al., 2023). Its theoretical foundation derives from Anderson, Helpman, and Tinbergen, and it has been widely applied in studies of Latin American agro-exports (
                    <xref ref-type="bibr" rid="ref17">Jadhav &amp; Ghosh, 2024</xref>). The baseline specification is:
                    <disp-formula id="e6">

                        <mml:math display="block">
                            <mml:msub>
                                <mml:mi>X</mml:mi>
                                <mml:mi mathvariant="italic">ij</mml:mi>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:mi>A</mml:mi>
                            <mml:mo>&#x00b7;</mml:mo>
                            <mml:mfrac>
                                <mml:mrow>
                                    <mml:msub>
                                        <mml:mi>Y</mml:mi>
                                        <mml:mi>i</mml:mi>
                                    </mml:msub>
                                    <mml:mo>&#x00b7;</mml:mo>
                                    <mml:msub>
                                        <mml:mi>Y</mml:mi>
                                        <mml:mi>j</mml:mi>
                                    </mml:msub>
                                </mml:mrow>
                                <mml:msub>
                                    <mml:mi>D</mml:mi>
                                    <mml:mi mathvariant="italic">ij</mml:mi>
                                </mml:msub>
                            </mml:mfrac>
                        </mml:math>
</disp-formula>where:</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>X</mml:mi>
                                <mml:mi mathvariant="italic">ij</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = value of exports from country 
                    <italic toggle="yes">i</italic> to country 
                    <italic toggle="yes">j</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>Y</mml:mi>
                                <mml:mi>i</mml:mi>
                            </mml:msub>
                            <mml:mo>,</mml:mo>
                            <mml:msub>
                                <mml:mi>Y</mml:mi>
                                <mml:mi>j</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = GDP of the exporting country 
                    <italic toggle="yes">i</italic> and importing country 
                    <italic toggle="yes">j</italic>, respectively.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>D</mml:mi>
                                <mml:mi mathvariant="italic">ij</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = bilateral geographic distance between 
                    <italic toggle="yes">i</italic> and 
                    <italic toggle="yes">j</italic>, representing trade costs.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mi>A</mml:mi>
                        </mml:math>
</inline-formula> = constant grouping factors common to all trade relationships.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mi>&#x03b1;</mml:mi>
                            <mml:mo>,</mml:mo>
                            <mml:mi>&#x03b2;</mml:mi>
                            <mml:mo>,</mml:mo>
                            <mml:mi>&#x03b3;</mml:mi>
                        </mml:math>
</inline-formula> = empirically estimated parameters reflecting trade sensitivity to changes in economic size and distance.</p>
                <p>Transformed into its log-linearized form for econometric estimation:</p>
                <p>Estimated log-linear specification
                    <disp-formula id="e7">

                        <mml:math display="block">
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">EXP</mml:mi>
                                    <mml:mi mathvariant="italic">ijt</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                            <mml:mo>=</mml:mo>
                            <mml:msub>
                                <mml:mi>&#x03b2;</mml:mi>
                                <mml:mn>0</mml:mn>
                            </mml:msub>
                            <mml:mo>+</mml:mo>
                            <mml:msub>
                                <mml:mi>&#x03b2;</mml:mi>
                                <mml:mn>1</mml:mn>
                            </mml:msub>
                            <mml:mspace width="0.25em"/>
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">PIB</mml:mi>
                                    <mml:mi mathvariant="italic">it</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                            <mml:mo>+</mml:mo>
                            <mml:msub>
                                <mml:mi>&#x03b2;</mml:mi>
                                <mml:mn>2</mml:mn>
                            </mml:msub>
                            <mml:mspace width="0.25em"/>
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">PIB</mml:mi>
                                    <mml:mi mathvariant="italic">jt</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                            <mml:mo>+</mml:mo>
                            <mml:msub>
                                <mml:mi>&#x03b2;</mml:mi>
                                <mml:mn>3</mml:mn>
                            </mml:msub>
                            <mml:mspace width="0.25em"/>
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">POP</mml:mi>
                                    <mml:mi mathvariant="italic">jt</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                            <mml:mo>+</mml:mo>
                            <mml:msub>
                                <mml:mi>&#x03b2;</mml:mi>
                                <mml:mn>4</mml:mn>
                            </mml:msub>
                            <mml:mspace width="0.25em"/>
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mtext mathvariant="italic">DIST</mml:mtext>
                                    <mml:mi mathvariant="italic">ij</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                            <mml:mo>+</mml:mo>
                            <mml:msub>
                                <mml:mi>&#x03b2;</mml:mi>
                                <mml:mn>5</mml:mn>
                            </mml:msub>
                            <mml:msub>
                                <mml:mi mathvariant="italic">TLC</mml:mi>
                                <mml:mi mathvariant="italic">ij</mml:mi>
                            </mml:msub>
                            <mml:mo>+</mml:mo>
                            <mml:msub>
                                <mml:mi>u</mml:mi>
                                <mml:mi mathvariant="italic">ij</mml:mi>
                            </mml:msub>
                            <mml:mo>+</mml:mo>
                            <mml:msub>
                                <mml:mi>&#x03b5;</mml:mi>
                                <mml:mi mathvariant="italic">ijt</mml:mi>
                            </mml:msub>
                        </mml:math>
</disp-formula>where:</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">EXP</mml:mi>
                                    <mml:mi mathvariant="italic">ijt</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                        </mml:math>
</inline-formula> = natural logarithm of mango exports from country 
                    <italic toggle="yes">i</italic> to country 
                    <italic toggle="yes">j</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">PIB</mml:mi>
                                    <mml:mi mathvariant="italic">it</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                        </mml:math>
</inline-formula> = logarithm of GDP of the exporting country (Colombia or Peru) in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">PIB</mml:mi>
                                    <mml:mi mathvariant="italic">jt</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                        </mml:math>
</inline-formula> = logarithm of GDP of destination importing country 
                    <italic toggle="yes">j</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">POP</mml:mi>
                                    <mml:mi mathvariant="italic">jt</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                        </mml:math>
</inline-formula> = logarithm of the total population of destination country 
                    <italic toggle="yes">j</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mo mathvariant="italic">ln</mml:mo>
                            <mml:mrow>
                                <mml:mo stretchy="true">(</mml:mo>
                                <mml:msub>
                                    <mml:mtext mathvariant="italic">DIST</mml:mtext>
                                    <mml:mi mathvariant="italic">ij</mml:mi>
                                </mml:msub>
                                <mml:mo stretchy="true">)</mml:mo>
                            </mml:mrow>
                        </mml:math>
</inline-formula> = logarithm of geographic distance in kilometers between exporting country 
                    <italic toggle="yes">i</italic> and destination 
                    <italic toggle="yes">j</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi mathvariant="italic">TLC</mml:mi>
                                <mml:mi mathvariant="italic">ij</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = dummy variable: it takes the value of 1 if an FTA is in force between 
                    <italic toggle="yes">i</italic> and 
                    <italic toggle="yes">j</italic>, and 0 otherwise.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>u</mml:mi>
                                <mml:mi mathvariant="italic">ij</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = unobserved specific effect of bilateral pair 
                    <italic toggle="yes">i-j</italic> (fixed or random effect).</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi>&#x03b5;</mml:mi>
                                <mml:mi mathvariant="italic">ijt</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = idiosyncratic error term.</p>
                <p>

                    <italic toggle="yes">Estimator Selection and Hausman Test</italic>
                </p>
                <p>Three specifications were estimated: Pooled OLS, which ignores individual heterogeneity; FE, which controls time-invariant unobserved heterogeneity; and RE estimated through EGLS under Swamy-Arora variance, which is more efficient when individual effects are not correlated with the regressors. The Hausman test (
                    <xref ref-type="table" rid="T2">Table 2</xref>) was applied, and its null hypothesis (p&#x00a0;&gt;&#x00a0;0.05) was not rejected. In the presence of heteroscedasticity or autocorrelation, standard errors were corrected using PCSE Period SUR (
                    <xref ref-type="bibr" rid="ref11">Davidescu et al., 2022</xref>). Robustness tests were also performed by excluding 2005&#x2013;2007 and by estimating specifications without the population variable; in both cases, the coefficients retained their sign and significance. Zero flows were replaced with a minimum positive constant to allow logarithmic transformation (
                    <xref ref-type="bibr" rid="ref16">Ismaiel Ali Ismaiel et al., 2023</xref>). All estimations were conducted in EViews 13.</p>
                <table-wrap id="T2" orientation="portrait" position="float">
                    <label>
Table 2. </label>
                    <caption>
                        <title>Hausman Test.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Test Summary</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Chi-Sq. Statistic</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Chi-Sq. d.f.</th>
                                <th align="left" colspan="1" rowspan="1" valign="top"/>
                                <th align="left" colspan="1" rowspan="1" valign="top">
Prob.</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Cross-section random</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">45.321017</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Variable</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Fixed</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Random</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Var (Diff.)</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">
                                    <bold>Prob.</bold>
</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">LN_GDP_ORIGEN</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5.322426</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">6.364009</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.106846</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.0014</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">LN_GDP_DEST</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.196279</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.284043</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.008701</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">LN_POP_DEST</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">9.020208</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.534887</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">3.511334</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
                <p>

                    <italic toggle="yes">Market Potential Calculation</italic>
                </p>
                <p>The coefficients of the RE model were used to project potential mango exports from Colombia and Peru to each destination. To correct logarithmic retransformation bias, the smearing estimator (Zhang et al., 2025) was applied, using the mean of the exponential of the residuals as a correction factor:
                    <disp-formula id="e8">

                        <mml:math display="block">
                            <mml:mover accent="true">
                                <mml:mi>E</mml:mi>
                                <mml:mo>-</mml:mo>
                            </mml:mover>
                            <mml:mi mathvariant="italic">X</mml:mi>
                            <mml:msub>
                                <mml:mover accent="true">
                                    <mml:mi>P</mml:mi>
                                    <mml:mo>&#x0302;</mml:mo>
                                </mml:mover>
                                <mml:mi mathvariant="italic">ijt</mml:mi>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:msup>
                                <mml:mover accent="true">
                                    <mml:mi>e</mml:mi>
                                    <mml:mo>&#x0302;</mml:mo>
                                </mml:mover>
                                <mml:mrow>
                                    <mml:mo mathvariant="italic">ln</mml:mo>
                                    <mml:mrow>
                                        <mml:mo stretchy="true">(</mml:mo>
                                        <mml:msub>
                                            <mml:mtext mathvariant="italic">EXP</mml:mtext>
                                            <mml:mtext mathvariant="italic">ijt</mml:mtext>
                                        </mml:msub>
                                        <mml:mo stretchy="true">)</mml:mo>
                                    </mml:mrow>
                                </mml:mrow>
                            </mml:msup>
                            <mml:mo>&#x00d7;</mml:mo>
                            <mml:msup>
                                <mml:mi>e</mml:mi>
                                <mml:msub>
                                    <mml:mi>u</mml:mi>
                                    <mml:mi mathvariant="italic">ij</mml:mi>
                                </mml:msub>
                            </mml:msup>
                        </mml:math>
</disp-formula>where:</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mover accent="true">
                                <mml:mi>E</mml:mi>
                                <mml:mo>-</mml:mo>
                            </mml:mover>
                            <mml:mi mathvariant="italic">X</mml:mi>
                            <mml:msub>
                                <mml:mover accent="true">
                                    <mml:mi>P</mml:mi>
                                    <mml:mo>&#x0302;</mml:mo>
                                </mml:mover>
                                <mml:mi mathvariant="italic">ijt</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = estimated potential exports from country 
                    <italic toggle="yes">i</italic> to destination 
                    <italic toggle="yes">j</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msup>
                                <mml:mover accent="true">
                                    <mml:mi>e</mml:mi>
                                    <mml:mo>&#x0302;</mml:mo>
                                </mml:mover>
                                <mml:mrow>
                                    <mml:mo mathvariant="italic">ln</mml:mo>
                                    <mml:mrow>
                                        <mml:mo stretchy="true">(</mml:mo>
                                        <mml:msub>
                                            <mml:mtext mathvariant="italic">EXP</mml:mtext>
                                            <mml:mtext mathvariant="italic">ijt</mml:mtext>
                                        </mml:msub>
                                        <mml:mo stretchy="true">)</mml:mo>
                                    </mml:mrow>
                                </mml:mrow>
                            </mml:msup>
                        </mml:math>
</inline-formula> = model prediction on the logarithmic scale, converted to levels using the exponential function.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msup>
                                <mml:mi>e</mml:mi>
                                <mml:msub>
                                    <mml:mi>u</mml:mi>
                                    <mml:mi mathvariant="italic">ij</mml:mi>
                                </mml:msub>
                            </mml:msup>
                        </mml:math>
</inline-formula> = smearing correction factor, calculated as the mean of the exponential of the residuals from the random-effects model.</p>
                <p>Market potential was calculated by comparing potential exports with observed exports:
                    <disp-formula id="e9">

                        <mml:math display="block">
                            <mml:msub>
                                <mml:mtext mathvariant="italic">Potencial</mml:mtext>
                                <mml:mi mathvariant="italic">ijt</mml:mi>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:mfrac>
                                <mml:mrow>
                                    <mml:mover accent="true">
                                        <mml:mi>E</mml:mi>
                                        <mml:mo>-</mml:mo>
                                    </mml:mover>
                                    <mml:mi mathvariant="italic">X</mml:mi>
                                    <mml:msub>
                                        <mml:mover accent="true">
                                            <mml:mi>P</mml:mi>
                                            <mml:mo>&#x0302;</mml:mo>
                                        </mml:mover>
                                        <mml:mi mathvariant="italic">ijt</mml:mi>
                                    </mml:msub>
                                    <mml:mo>&#x2212;</mml:mo>
                                    <mml:msub>
                                        <mml:mi mathvariant="italic">EXP</mml:mi>
                                        <mml:mi mathvariant="italic">ijt</mml:mi>
                                    </mml:msub>
                                </mml:mrow>
                                <mml:msub>
                                    <mml:mi mathvariant="italic">EXP</mml:mi>
                                    <mml:mi mathvariant="italic">ijt</mml:mi>
                                </mml:msub>
                            </mml:mfrac>
                            <mml:mo>&#x00d7;</mml:mo>
                            <mml:mn>100</mml:mn>
                        </mml:math>
</disp-formula>where:</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mtext mathvariant="italic">Potencial</mml:mtext>
                                <mml:mi mathvariant="italic">ijt</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = gap between potential and observed exports, expressed as a percentage.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:mover accent="true">
                                <mml:mi>E</mml:mi>
                                <mml:mo>-</mml:mo>
                            </mml:mover>
                            <mml:mi mathvariant="italic">X</mml:mi>
                            <mml:msub>
                                <mml:mover accent="true">
                                    <mml:mi>P</mml:mi>
                                    <mml:mo>&#x0302;</mml:mo>
                                </mml:mover>
                                <mml:mi mathvariant="italic">ijt</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = potential exports estimated by the gravity model.</p>
                <p>

                    <inline-formula>

                        <mml:math display="inline">
                            <mml:msub>
                                <mml:mi mathvariant="italic">EXP</mml:mi>
                                <mml:mi mathvariant="italic">ijt</mml:mi>
                            </mml:msub>
                        </mml:math>
</inline-formula> = exports actually observed from country 
                    <italic toggle="yes">i</italic> to destination 
                    <italic toggle="yes">j</italic> in year 
                    <italic toggle="yes">t</italic>.</p>
                <p>A positive value indicates that the country exports below its estimated potential (untapped opportunity). A value close to zero indicates trade consistent with the model fundamentals. A negative value reflects a saturated market. The market potential classification is in 
                    <xref ref-type="table" rid="T3">Table 3</xref>.</p>
                <table-wrap id="T3" orientation="portrait" position="float">
                    <label>
Table 3. </label>
                    <caption>
                        <title>Market Potential Classification.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Classification</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Criterion</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Interpretation</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Potencial &gt;50</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">High expansion capacity; exports far below the estimated potential.</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Emerging</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">10&#x00a0;&lt;&#x00a0;Potencial &#x2264;50</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Opportunityes de crecimiento moderadas.</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Consolidated</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;10&#x00a0;&#x2264;&#x00a0;Potencial &#x2264;10</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Balanced market; actual trade consistent with expected levels.</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Saturated</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Potencial &lt; &#x2212;10</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Overexploited market; limited room for expansion.</td>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
                <p>

                    <italic toggle="yes">Data Processing and Statistical Analysis</italic>
                </p>
                <p>Mango export records from Colombia and Peru were organized and standardized in Microsoft Excel. The HHI and TC were calculated in IBM SPSS Statistics, a standard software package for descriptive and inferential analysis in economic and social research (
                    <xref ref-type="bibr" rid="ref28">Masuadi et al., 2021</xref>). The gravity model and panel-data estimations were processed in EViews version 13, a reference tool for estimating complex econometric models with panel data in international trade studies (
                    <xref ref-type="bibr" rid="ref11">Davidescu et al., 2022</xref>).</p>
                <p>The dataset can be found in the ZENODO repository, under the title &#x201c;Dataset: Market Diversification and Mango Exports in Emerging Economies: Evidence from Peru and Colombia, 2004&#x2212;2024&#x201d; (
                    <xref ref-type="bibr" rid="ref10">Cosio et al., 2026</xref>), 
                    <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.5281/zenodo.20496337">https://doi.org/10.5281/zenodo.20496337</ext-link>, License &#x201c;
                    <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</ext-link>&#x201d;.</p>
            </sec>
        </sec>
        <sec id="sec11" sec-type="results">
            <title>3. Results</title>
            <p>
                <xref ref-type="table" rid="T4">Table 4</xref> presents the evolution of mango exports from the emerging economies of Peru and Colombia during 2004&#x2013;2024. The results show a marked difference in the export scale of the two countries. Peru maintained a considerably higher export position throughout the period, increasing from USD 42.028 million in 2004 to USD 316.986 million in 2024. In terms of volume, Peruvian exports increased from 59,828 tons in 2004 to 177,670 tons in 2024, although the highest exported volume was recorded in 2021, with 253,042 tons.</p>
            <table-wrap id="T4" orientation="portrait" position="float">
                <label>
Table 4. </label>
                <caption>
                    <title>Mango exports from the emerging economies of Peru and Colombia to the world.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="2" valign="top">Year</th>
                            <th align="left" colspan="5" rowspan="1" valign="top">Exports - Peru</th>
                            <th align="left" colspan="5" rowspan="1" valign="top">Exports - Colombia</th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">FOB USD - Thousand</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Net Weight (MT)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Growth Rate (%)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">FOB Price (USD/kg)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">VCRN - Peru</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">FOB USD - Thousand</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Net Weight (MT)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Growth Rate (%)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">FOB Price (USD/kg)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
VCRN - Colombia</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2004</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">42,028</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">59,828</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.70</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.96</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">806</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2,275</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.35</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.16</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">38,396</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">57,618</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;9%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">360</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">115</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;55%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.13</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.59</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2006</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">59,325</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">82,685</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">55%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.72</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.95</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">455</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">126</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">26%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.60</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.55</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2007</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">63,360</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">82,675</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">7%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">626</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">220</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">38%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.84</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.48</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2008</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">64,129</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">82,696</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.78</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">691</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">374</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">10%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.85</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.54</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2009</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">70,928</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">69,189</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.03</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">340</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">131</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;51%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.60</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.71</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2010</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">90,037</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">97,504</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">27%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.92</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">434</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">198</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">28%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.19</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.63</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2011</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">115,414</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">124,051</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">28%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">512</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">354</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">18%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.45</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.71</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2012</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">117,277</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">99,609</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.92</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">258</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">52</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;50%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.96</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.88</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2013</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">132,681</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">127,186</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.04</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">631</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">210</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">145%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.01</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.80</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">139,347</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">120,777</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.15</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">684</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">110</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.21</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.79</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2015</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">195,874</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">133,930</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">41%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.46</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.96</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1,094</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">283</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">60%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.86</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.60</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2016</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">199,361</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">161,135</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.24</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.95</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2,207</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">864</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">102%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.55</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.34</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2017</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">191,772</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">162,938</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;4%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2,804</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">872</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">27%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.21</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.37</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2018</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">246,701</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">199,336</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">29%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.24</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3,722</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">889</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">33%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.19</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.25</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2019</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">252,049</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">191,417</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.32</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5,856</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1,649</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">57%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.55</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.10</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2020</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">280,542</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">239,391</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.17</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.95</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9,387</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2,281</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">60%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.20</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2021</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">315,636</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">253,042</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13,785</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2,396</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">47%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.75</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.32</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2022</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">296,211</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">249,786</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;6%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.19</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13,559</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2,393</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;2%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.26</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2023</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">254,734</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">196,856</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;14%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.29</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.92</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">14,434</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3,010</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.80</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.29</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2024</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">316,986</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">177,670</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">24%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.78</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.92</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">15,037</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3,312</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.54</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.28</td>
                        </tr>
                        <tr>
                            <td colspan="11" rowspan="1">
                                <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/203055/c725c8ad-de86-4a6b-8415-c650abd982c0_gra1.gif"/>
</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Note. International trade data records and calculations obtained from Trade Map of the International Trade Centre (ITC).</p>
                </table-wrap-foot>
            </table-wrap>
            <p>The Peruvian series shows an overall upward trend despite contractions in specific years. The largest annual increases were recorded first in 2006, with growth of 55%, and later in 2015, with 41%. By contrast, decreases occurred in 2005, 2017, 2022, and 2023. The decline in 2023 was particularly important, as FOB value fell by 14% compared with 2022, while in 2024 it recovered by 24%. The FOB price also increased over the period, rising from USD 0.70/kg in 2004 to USD 1.78/kg in 2024, the highest value in the entire series.</p>
            <p>Colombia followed a different export trajectory. Colombian mango exports increased from USD 806 thousand in 2004 to USD 15.037 million in 2024. Although Colombia&#x2019;s figure remained far below Peru&#x2019;s scale, the series shows more dynamic growth from 2015 onward. Colombian exports exceeded one million dollars in 2015 and reached USD 9.387 million in 2020, remaining above USD 13 million from 2021 onward. Exported volume also increased, from 2,275 tons in 2004 to 3,312 tons in 2024.</p>
            <p>The Colombian FOB price was higher than the Peruvian price in most years, although this did not translate into a comparable export scale. Colombia&#x2019;s price rose from USD 0.35/kg in 2004 to USD 4.54/kg in 2024, with a maximum of USD 6.21/kg in 2014. This behavior indicates that Colombia&#x2019;s export growth occurred in a context of lower volumes and higher unit values, whereas Peru combined higher volumes with a more consolidated export base.</p>
            <p>The normalized revealed comparative advantage values reported in 
                <xref ref-type="table" rid="T1">Table 1</xref> show a clear difference between Peru and Colombia. Peru maintained high positive VCRN values throughout the period, ranging from 0.92 to 0.96. This indicates a stable and persistent revealed comparative advantage in mango exports during 2004&#x2013;2024.</p>
            <p>Colombia followed a weaker trajectory, although with progressive improvement. Between 2004 and 2019, its VCRN values remained negative, moving from &#x2212;0.16 in 2004 to &#x2212;0.10 in 2019. The lowest value was recorded in 2012, at &#x2212;0.88. However, from 2020 onward, Colombia reported positive VCRN values: 0.20 in 2020, 0.32 in 2021, 0.26 in 2022, 0.29 in 2023, and 0.28 in 2024.</p>
            <p>These results indicate that Peru sustained a consolidated competitive position throughout the period, whereas Colombia moved from a position of revealed comparative disadvantage to a positive, although still moderate, position in recent years. The gap between the two countries remains considerable, especially in FOB value, exported volume, and comparative-advantage stability.</p>
            <p>
                <xref ref-type="table" rid="T5">Table 5</xref> reports the normalized relative purchasing capacity of the main international mango importers. The results show that the Netherlands maintained the highest and most stable positive values during the period, increasing from 0.74 in 2004 to 0.84 in 2024, with values above 0.80 in almost all years. This behavior positions the Dutch market as one of the most consistent demand destinations in the sample.</p>
            <table-wrap id="T5" orientation="portrait" position="float">
                <label>
Table 5. </label>
                <caption>
                    <title>Normalized relative purchasing capacity of international mango demand for exports from the emerging economies of Peru and Colombia (2004&#x2013;2024).</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">
N&#x00b0;</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Importers</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2004</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2005</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2006</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2007</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2008</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2009</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2010</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2011</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2012</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2013</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2014</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2015</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2016</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2017</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2018</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2019</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2020</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2021</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2022</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2023</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
2024</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">United States of America</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.50</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.50</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.40</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.47</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.51</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.38</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.42</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.44</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.40</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.42</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.41</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.32</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.30</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.24</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.30</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.32</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.36</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.39</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Netherlands</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.74</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.80</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.84</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.85</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.86</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.89</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.88</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.87</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.88</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.86</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.86</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.88</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.87</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.88</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.88</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.86</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.86</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.85</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.84</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.82</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.84</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">France</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.41</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.39</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.28</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.23</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.26</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.41</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.45</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.49</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.46</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.46</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.47</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.55</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.59</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.66</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.64</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.58</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.57</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.58</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.66</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">4</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Spain</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.32</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.09</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.21</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.24</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.15</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.17</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.23</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.23</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.16</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.17</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.35</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.37</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.43</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.46</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.45</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Germany</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.97</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;1.00</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.37</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.44</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.17</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.41</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.31</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.46</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.44</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.38</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.36</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">United Kingdom</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.06</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.22</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.31</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.29</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.13</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.27</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.40</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.43</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.42</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.46</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.39</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.35</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.16</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.19</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">7</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Canada</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.31</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.04</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.16</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.46</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.28</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.00</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.00</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.04</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.07</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.08</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.07</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.08</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.07</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.04</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.02</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.05</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">8</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Italy</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.74</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.63</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.97</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.97</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.81</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.47</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.09</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.06</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.44</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.35</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.30</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Russian Federation</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.98</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.93</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.65</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.02</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.54</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.56</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.59</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.74</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.66</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.72</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.72</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.70</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.76</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.70</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.50</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.68</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.66</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">10</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Polonia</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.57</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.89</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.20</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.87</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.71</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.69</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.45</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.66</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.50</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.70</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.65</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.56</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.60</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.68</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.87</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.45</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.06</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.08</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.24</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Japan</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.82</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.76</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.92</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.71</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.62</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.50</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.37</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.43</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.47</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.46</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.69</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.74</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.68</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.64</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.70</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">China</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;1.00</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.98</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.83</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.44</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.38</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.41</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.40</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.42</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.44</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.28</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.54</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">13</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Chile</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.99</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.99</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.98</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;1.00</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.98</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.98</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.87</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.91</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.76</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.36</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.04</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.23</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.60</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.54</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.47</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.43</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.39</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">14</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Belgium</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.90</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.98</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.92</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.82</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.89</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.89</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.60</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.37</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.41</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.47</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.55</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">15</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Switzerland</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.93</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.91</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.54</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.72</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.82</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.88</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.80</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.69</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.62</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.69</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.58</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.53</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.74</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.80</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">16</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">United Arab Emirates</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.99</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.99</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.99</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.98</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.99</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.97</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.93</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.92</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.91</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.92</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.62</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.67</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.65</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.49</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.61</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.65</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.75</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.81</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.83</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">17</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Norway</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.09</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.16</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.63</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.16</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.52</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.70</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.40</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.14</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.23</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.24</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.02</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.21</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.34</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Denmark</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.80</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.61</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.53</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.78</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.98</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.87</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.26</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.06</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.14</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.44</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.30</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.23</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">19</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Hong Kong, China</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.57</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.65</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.49</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.63</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.56</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.26</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.47</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.51</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.54</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.50</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.30</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.36</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.01</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.25</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.04</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.10</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.21</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.27</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.05</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">20</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Argentina</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.64</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.37</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.65</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.54</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.52</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.08</td>
                        </tr>
                        <tr>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="22" rowspan="1">
                                <graphic id="gr2" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/203055/c725c8ad-de86-4a6b-8415-c650abd982c0_gra2.gif"/>
</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Note. International trade data records and calculations obtained from Trade Map of the International Trade Centre (ITC).</p>
                </table-wrap-foot>
            </table-wrap>
            <p>The United States also reported positive values throughout the period, although with a downward trend, from 0.50 in 2004 to 0.39 in 2024. France showed a more favorable trajectory, increasing from 0.41 in 2004 to 0.66 in 2024, with particularly high values since 2016. Spain also improved over time, rising from 0.32 in 2004 to 0.45 in 2024, despite the negative value recorded in 2009.</p>
            <p>Other European markets presented heterogeneous patterns. Germany recorded missing or negative values in the early years, but reached positive values in the most recent period, closing at 0.36 in 2024. Italy also moved from negative to positive values after 2018, reaching 0.30 in 2024. Denmark showed positive values in the final years, including 0.23 in 2024.</p>
            <p>Several markets maintained persistent negative values or weak relative demand positions. Japan remained in negative territory throughout the period and closed at &#x2212;0.70 in 2024. China also maintained negative values in the available years, with &#x2212;0.54 in 2024. Chile, Belgium, Switzerland, and the United Arab Emirates mainly recorded negative values, indicating lower relative purchasing capacity for mango according to this indicator.</p>
            <p>
                <xref ref-type="table" rid="T6">Table 6</xref> evaluates the temporal stability of the CRCN index using 2024 as the reference year. The results show that the relationship between historical index values and the structure observed in 2024 was positive and statistically significant in all years analyzed. Beta coefficients remained positive throughout the period, with p-values below 0.05, indicating that the relative distribution of international purchasing capacity was not random but retained a certain degree of temporal persistence. Regarding stability properties, the levels were moderate in the early years from 2004 to 2017, with R
                <sup>2</sup> values ranging from 0.280 to 0.493. This suggests a demand structure less aligned with that observed in 2024. By contrast, in more recent years, CRCN stability clearly strengthened. R
                <sup>2</sup> values increased from 0.560 in 2018 to 0.926 in 2020 and remained at 0.807, 0.894, and 0.849 in 2021, 2022, and 2023, respectively. Likewise, the R coefficient exceeded 0.875 from 2019 onward, demonstrating greater persistence in the hierarchy of importing markets. These results are consistent with the previous CRCN analysis, in the sense that markets with greater relative purchasing absorption (the United Kingdom, France, Spain, and the United States) tend to retain relevant positions over time, whereas destinations with negative or weak values are less fluid and stable as strategic markets for export expansion.</p>
            <table-wrap id="T6" orientation="portrait" position="float">
                <label>
Table 6. </label>
                <caption>
                    <title>Summary of the multiple linear regression model applied to Peru.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Ref. 2024</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Lag</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Alpha</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Beta</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">P-Values</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">R
                                <sup>

                                    <bold>2</bold>
                                </sup>
                            </th>
                            <th align="left" colspan="1" rowspan="1" valign="top">R</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Beta/R</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2004</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">20</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">20.584</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.421</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.010</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.315</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.561</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.750</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2005</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">19</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">21.414</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.426</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.009</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.323</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.568</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.750</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2006</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">18</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">20.944</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.403</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.017</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.280</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.529</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.762</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2007</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">17</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">24.705</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.487</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.392</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.626</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.778</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2008</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">16</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">24.806</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.545</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&lt;0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.479</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.692</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.788</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2009</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">15</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">28.853</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.592</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&lt;0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.493</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.702</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.843</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2010</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">14</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">21.262</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.506</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.002</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.430</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.656</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.771</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2011</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">17.642</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.442</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.008</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.334</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.578</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.765</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2012</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">16.810</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.422</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.008</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.327</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.572</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.738</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2013</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">14.561</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.394</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.016</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.280</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.529</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.745</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2014</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">10</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">14.432</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.394</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.020</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.267</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.517</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.762</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2015</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">14.639</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.402</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.020</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.266</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.516</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.779</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2016</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13.513</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.576</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.408</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.639</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.901</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2017</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">7</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13.292</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.556</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.392</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.626</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.888</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2018</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11.978</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.657</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&lt;0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.560</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.748</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.878</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2019</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9.140</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.900</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&lt;0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.765</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.875</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.029</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2020</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.650</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.977</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&lt;0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.926</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.962</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.016</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2021</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.491</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.926</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&lt;0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.807</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.898</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.031</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2022</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;4.742</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.012</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&lt;0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.894</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.946</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.070</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">2023</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;3.715</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.965</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&lt;0.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.849</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.921</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.048</td>
                        </tr>
                    </tbody>
                </table>
            </table-wrap>
            <p>
                <xref ref-type="table" rid="T7">Table 7</xref> presents the results of the EGLS estimator with PCSE correction for 2015&#x2013;2024. The coefficient of exporter-country GDP was positive and statistically significant (beta&#x00a0;=&#x00a0;6.364; p&#x00a0;=&#x00a0;0.000), indicating that the economic size of the exporting country was positively associated with mango export flows. The coefficient of destination-country GDP was also positive but not statistically significant (beta&#x00a0;=&#x00a0;0.284; p&#x00a0;=&#x00a0;0.256). Therefore, in this specification, destination GDP did not show a statistically significant relationship with mango exports. By contrast, the population of the destination country had a positive and significant coefficient (beta&#x00a0;=&#x00a0;1.535; p&#x00a0;=&#x00a0;0.0003), suggesting that more populous markets were associated with larger export flows.</p>
            <table-wrap id="T7" orientation="portrait" position="float">
                <label>
Table 7. </label>
                <caption>
                    <title>Results of the Gravity Model Estimated Using EGLS with PCSE Correction (2015&#x2013;2024).</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Variable</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Coefficient</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Std. Error</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">

                                <italic toggle="yes">t-Statistic</italic>
</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Prob.</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">C</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;68.5126</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11.68403</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;5.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">LN_GDP_ORIGEN</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.364009</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.48719</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">LN_GDP_DEST</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.284043</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.250018</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.14</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.2561</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">LN_POP_DEST</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.534887</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.423127</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.63</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0003</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">LN_DIST</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;1.898413</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.147936</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;1.7</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0984</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">TLC</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.650881</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.579074</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.95</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0033</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="5" rowspan="1" valign="top">
                                <bold>Effects Specification</bold>
</td>
                        </tr>
                        <tr>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>S.D.</bold>
</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Rho</bold>
</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Cross-section random</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.37</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.4833</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Idiosyncratic random</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.59</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.5167</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="5" rowspan="1" valign="top">
                                <bold>Weighted Statistics</bold>
</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">R-squared</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.127455</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Mean dependent var</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.075595</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Adjusted R-squared</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.124782</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">S.D. dependent var</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">7.136237</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">S.E. of regression</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.676169</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Sum squared resid</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">72740.25</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">F-statistic</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">47.67832</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Durbin-Watson stat</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.949233</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Prob (F-statistic)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="5" rowspan="1" valign="top">
                                <bold>Unweighted Statistics</bold>
</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">R-squared</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.174611</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Mean dependent var</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">&#x2212;0.343481</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Sum squared resid</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">157223.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Durbin-Watson stat</td>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.439168</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Note. Obtained from the model estimation in EViews 9.0.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>The distance coefficient was negative, as expected in a gravity model, although only marginally significant at the 10% level (beta&#x00a0;=&#x00a0;&#x2212;1.898; p&#x00a0;=&#x00a0;0.0984). This result indicates that geo-graphic distance could reduce mango export flows, although the statistical evidence is weaker than that observed for exporter-country GDP, destination population, and trade agreements. The free trade agreement variable showed a positive and statistically significant coefficient (beta&#x00a0;=&#x00a0;4.651; p&#x00a0;=&#x00a0;0.0033), indicating that the existence of a trade agreement was associated with higher mango exports.</p>
            <p>The model was globally significant, with an F-statistic of 47.678 and p&#x00a0;=&#x00a0;0.000. However, the weighted R-squared was 0.127 and the adjusted R-squared was 0.125, indicating that the model explains only a limited proportion of the observed variability in mango export flows. Therefore, the estimated coefficients should be interpreted as evidence of statistical association rather than as a complete explanation of bilateral export performance.</p>
            <p>
                <xref ref-type="table" rid="T8">Table 8</xref> classifies the markets with the highest estimated expansion potential for 2024 according to the gravity model. The countries classified as &#x2018;Opportunity&#x2019; were Australia, Ireland, Norway, Sweden, Austria, Denmark, Venezuela, Finland, the Czech Republic, and India. These destinations showed positive gaps between estimated potential exports and observed exports.</p>
            <table-wrap id="T8" orientation="portrait" position="float">
                <label>
Table 8. </label>
                <caption>
                    <title>Ranking of Markets with the Highest Expansion Potential According to the Gravity Model (2024).</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Country_Dest</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Export_Fob</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Pred_Exp</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Potential</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Class_Cod</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Classification</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Australia</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.68E+29</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.68E+35</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Irlanda</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8.58E+28</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8.58E+34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Norway</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.08E+28</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.08E+34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Suecia</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.81E+28</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.81E+34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Austria</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.40E+28</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.40E+34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Denmark</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.97E+28</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.97E+34</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Venezuela</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8.43E+27</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8.43E+33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Finlandia</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.36E+27</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.36E+33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Rep&#x00fa;blica Checa</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.22E+27</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.22E+33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">India</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.41E+27</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.41E+33</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Opportunity</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>Note. Obtained from the model estimation in EViews 9.0.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>Australia ranked first, followed by Ireland, Norway, Sweden, and Austria. The other opportunity-potential markets were Denmark, Venezuela, Finland, the Czech Republic, and India. In all cases, the classification indicates that observed exports were below the potential estimated by the model.</p>
            <p>Nevertheless, the magnitude of the predicted values is extremely high; therefore, these results should be interpreted with caution. The values should be understood as exploratory signals of relative market potential, not as definitive commercial projections. Replacing zero with a minimum positive constant, applying logarithmic transformation, and retransfor-mation of predicted values may have amplified the estimated gaps. Consequently, these markets should be viewed as candidates for subsequent validation rather than as immediate export targets.</p>
        </sec>
        <sec id="sec12">
            <title>4. Discussion</title>
            <p>The results obtained show a clear divergence in the structure of international mango trade in Peru and Colombia during 2004&#x2013;2024. Peru&#x2019;s export trajectory reflects strengthening throughout the period analyzed, as well as a systematic increase in its FOB value. In Colombia, the dynamics of these flows are different, although the VCRN shows a more favorable performance by becoming positive from 2020 onward. This confirms that the two emerging economies are not at the same level of export maturity: Peru acts as a consolidated exporter, whereas Colombia is in a stage of expansion and initial positioning.</p>
            <p>This result is consistent with previous literature on the Peruvian agro-export sector. Studies on Peruvian mango exports highlight their continued growth, presence in international markets, and role within non-traditional agro-exports (
                <xref ref-type="bibr" rid="ref48">Mor&#x00e1;n-Santamar&#x00ed;a et al., 2026</xref>). Likewise, the literature on Peruvian agro-exports has identified significant specialization in fruits, although with limited diversification and dependence on a few markets, problems that still persist (
                <xref ref-type="bibr" rid="ref47">Garc&#x00ed;a-L&#x00f3;pez et al., 2025</xref>). In this sense, the results of the present study not only confirm Peru&#x2019;s export strength but also show that this strength should be analyzed not only through growth in exported value, but also through the structure of destination markets.</p>
            <p>In the Colombian case, the results show a different pattern. Although Colombia increased its export value between 2004 and 2024, its share remains low compared with Peru.</p>
            <p>The recent improvement in the VCRN indicates an evolution in the competitive position of Colombian mango; however, this progress has not yet translated into export consolidation. The literature had already noted that Colombia has productive potential; however, it faces limitations in converting that potential into solid international insertion, mainly due to issues related to varieties, seasonality, phytosanitary access, and export structure (
                <xref ref-type="bibr" rid="ref45">Cort&#x00e9;s-Cata&#x00f1;o et al., 2024</xref>). According to the results obtained in this study, that interpretation is confirmed: Colombia is growing, but it has not yet reached Peru&#x2019;s scale or competitive stability.</p>
            <p>There is an important difference between the two countries in terms of FOB unit prices. Colombia has higher unit prices than Peru in several years, but this does not mean that it is more competitive or has a stronger presence in the international market. The fact that a high price does not by itself guarantee a solid export position is an important finding. In mango, as in other perishable products, competitiveness also depends on maintaining volumes, ensuring supply continuity, and complying with sanitary standards. This is consistent with recent approaches that view agri-food competitiveness as multidimensional rather than merely as a relationship between price and exported volume (Bergman et al., 2025; 
                <xref ref-type="bibr" rid="ref46">Collantes-Barturen &amp; Mor&#x00e1;n-Santamar&#x00ed;a, 2025</xref>).</p>
            <p>The results of the normalized relative purchasing capacity indicator also show important differences among destination markets. The Netherlands appears as one of the most stable markets with the highest relative purchasing capacity, while the United States records positive values, although with a less expansive trajectory. France and Spain show gradual progress, indicating that some European markets have gained importance in international mango demand. In contrast, Japan, China, Chile, Switzerland, and the United Arab Emirates recurrently show negative or low values. This suggests that export diversification is not only about reaching more destinations, but also about building a market structure with real demand, sta-bility, and absorption capacity.</p>
            <p>According to 
                <xref ref-type="bibr" rid="ref49">Sangita (2018)</xref>, the literature on export diversification indicates that concentration in a few markets can increase exposure to regulatory changes, price fluctuations, and other risks. This is especially important for mango because it is a perishable product with quality requirements, transportation time constraints, and sanitary standards. Therefore, a sustainable export strategy cannot be limited to selling greater volume; it must also improve the solidity of the commercial structure. At this point, Peru faces the challenge of maintaining its leadership without depending excessively on a few markets, while Colombia needs to reach more destinations without losing consistency in quality, volume, and continuity.</p>
            <p>The model results show that exporter-country GDP, destination-country population, and the existence of trade agreements are positively associated with mango export flows. This is consistent with gravity trade theory, which argues that bilateral flows depend on economic size, potential demand, distance, and institutional access conditions (
                <xref ref-type="bibr" rid="ref1">Altamirano Gonzales &amp; Mor&#x00e1;n Santamar&#x00ed;a, 2026</xref>). The positive effect of trade agreements indicates that preferential-access conditions facilitate mango insertion into international markets, although they do not replace other factors such as logistics, phytosanitary compliance, and supply capacity.</p>
            <p>Destination-country GDP was not statistically significant in the model, indicating that the importer&#x2019;s aggregate economic size does not by itself explain the dynamics of mango exports. By contrast, destination population showed a positive and significant relationship. This may be interpreted as evidence that the size of the consumer market is more relevant than the total economic size of the country when analyzing a specific food product. However, this interpretation should be treated cautiously because the model has low explanatory power and therefore does not capture all determinants of bilateral mango trade.</p>
            <p>Geographic separation showed the expected negative sign, although with marginal significance. This is relevant because mango, as a perishable product, is subject to transportation costs, logistical times, and deterioration risks throughout the export chain. Marginal significance indicates that distance does not necessarily constitute an absolute barrier. In current agri-food markets, the effect of distance may be offset by trade agreements, logistical infrastructure, postharvest technology, and coordination capacity across the export chain. This observation is consistent with studies that highlight the importance of postharvest management and logistics for the international competitiveness of mango (
                <xref ref-type="bibr" rid="ref44">Cortez-Clavo et al., 2025</xref>; 
                <xref ref-type="bibr" rid="ref50">Zhang &amp; Mohammad, 2024</xref>).</p>
            <p>The report classifies Australia, Ireland, Norway, Sweden, Austria, and Denmark as the most recommended destinations, while Venezuela, Finland, the Czech Republic, and India appear as opportunity markets. However, the extremely high projected values should be interpreted cautiously. These results are not definitive commercial projections, but exploratory signals of relative potential. The treatment of zero flows, the use of logarithmic transformations, and the retransformation of estimated values may have widened the differences between observed and potential exports. Therefore, the markets mentioned should be considered starting points for further analysis using sanitary, logistical, tariff, commercial, and effective-demand criteria.</p>
            <p>From the perspective of export sustainability, the results suggest that mango performance should not be measured only by FOB-value growth. A sustainable pattern implies real diversification, competitive stability, access to markets with effective demand, and lower vulnerability to external shocks.</p>
            <p>The study has several limitations that should be considered. First, the analysis uses aggregate international trade data and does not consider differences by mango variety, quality, certifications, exporting firms, or marketing channels. Second, the subheading used groups fresh or dried mangoes and mangosteens, which may conceal within-product differences. Third, the gravity model identifies relevant associations but does not fully explain the complexity of export flows. Fourth, extreme values of market potential require methodological validation before being used as a basis for strategic decisions. Future research could include firm-level information, destination-concentration analysis, specific sanitary barriers, logistical costs, and differentiation between fresh, dried, and processed mango.</p>
        </sec>
        <sec id="sec13" sec-type="conclusions">
            <title>5. Conclusions</title>
            <p>This study examined market diversification and mango export competitiveness in two emerging countries, Peru and Colombia, during 2004&#x2013;2024. The results show that each country followed a different path within international mango trade. Peru maintains a consolidated export position: it has higher FOB values, exports larger volumes, and its normalized revealed comparative advantage (VCRN) remained positive throughout the period. Colombia, in contrast, shows a more recent and smaller-scale entry, although it has been improving progressively since 2020, when its VCRN values moved into positive territory.</p>
            <p>Peru is the country with the greatest competitive capacity in this comparison, and this is clearly reflected in its export performance. Its foreign sales grew steadily between 2004 and 2024, with some fluctuations in certain years, and reached their highest FOB value in 2024. This trajectory supports the idea of a more mature export structure, capable of maintaining international presence and generating higher trade revenues. Colombia also grew, especially in the last decade, but its performance remains more limited than Peru&#x2019;s, both in export scale and competitive stability.</p>
            <p>Comparing the VCRN of both countries reveals a structural difference between them. Peru recorded high and positive VCRN values throughout the period, reflecting persistent export specialization in mango. Colombia, by contrast, recorded negative values for most of the series. From 2020 onward, it moved into positive territory, indicating an improvement in its export positioning, although it still does not reach Peru&#x2019;s level of consolidation.</p>
            <p>The normalized relative purchasing capacity results reveal that international mango demand is concentrated in markets with different behaviors. The Netherlands stands out as one of the most stable markets with the highest relative purchasing capacity, while the United States maintains positive values, although with less dynamism in recent years. France and Spain show a favorable evolution, indicating real opportunities within the European market. By contrast, countries such as Japan, China, Chile, Switzerland, and the United Arab Emirates report negative or weak values, showing that not all destinations offer the same level of commercial opportunity.</p>
            <p>The gravity model identified the factors associated with mango export flows. Exporter-country GDP, destination-country population, and the existence of trade agreements showed a positive and significant relationship with exports. Distance had the expected negative sign, although with marginal significance. Importer-country GDP, in contrast, was not statistically significant. These results indicate that mango export growth does not depend only on the economic size of markets, but also on the exporting capacity of the country of origin, the population size of the destination, and institutional conditions for market access.</p>
            <p>For 2024, the ranking of expansion potential identified Australia, Ireland, Norway, Sweden, Austria, Denmark, Venezuela, Finland, the Czech Republic, and India as opportunity markets. However, the extremely high magnitude of the estimated values requires cautious interpretation. These are not definitive export projections, but exploratory signals of relative potential that require additional validation through logistical, sanitary, tariff, commercial, and effective-demand analysis.</p>
            <p>Overall, the results allow us to conclude that Peru has built a stronger and more competitive international insertion in mango trade, whereas Colombia is undergoing a growth stage with recent progress but still faces structural challenges. For Peru, the main challenge is to maintain its competitiveness and improve the effective diversification of its markets to reduce vulnerabilities to external shocks. For Colombia, the challenge is to convert its recent improvement into a more stable export strategy, with greater scale, supply continuity, and sustained access to international markets.</p>
            <p>Finally, the study indicates that mango export sustainability cannot be measured solely by FOB-value growth. Sustainable international insertion requires combining competitiveness, destination diversification, stability of comparative advantage, real purchasing capacity in destination markets, and favorable market-access conditions. Future research should deepen the analysis by mango varieties, exporting firms, certifications, phytosanitary barriers, logistical costs, and differences among fresh, dried, and processed mango in order to explain more precisely the conditions that determine the product&#x2019;s international competitiveness.</p>
        </sec>
    </body>
    <back>
        <sec id="sec14" sec-type="data-availability">
            <title>Data Availability Statement</title>
            <p>ZENODO repository. Dataset: Market Diversification and Mango Exports in Emerging Economies: Evidence from Peru and Colombia, 2004-2024. 
                <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.5281/zenodo.20496337">https://doi.org/10.5281/zenodo.20496337</ext-link> (
                <xref ref-type="bibr" rid="ref10">Cosio et al., 2026</xref>).</p>
            <p>This project contains the following underlying data:
                <list list-type="bullet">
                    <list-item>
                        <label>&#x2022;</label>
                        <p>DATASET. (It contains a database of the target markets from 2004 to 2024).</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>RESULTS. (It contains the statistical values of the results).</p>
                    </list-item>
                    <list-item>
                        <label>&#x2022;</label>
                        <p>TABLAS_GRAVITY_MODEL. (It contains the statistical values of the application of the gravity model).</p>
                    </list-item>
                </list>
            </p>
            <p>Data is available under the terms of the license &#x201c;
                <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</ext-link>&#x201d;.</p>
        </sec>
        <ack>
            <title>Acknowledgments</title>
            <p>A la Direcci&#x00f3;n de Investigaci&#x00f3;n de la Universidad Peruana de Ciencias Aplicadas por el apoyo brindado para realizaci&#x00f3;n de este trabajo de investigaci&#x00f3;n a trav&#x00e9;s del incentivo UPC-EXPOST-2026-1.</p>
        </ack>
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    <sub-article article-type="reviewer-report" id="report496057">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.203055.r496057</article-id>
            <title-group>
                <article-title>Reviewer response for version 2</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Ninaquispe</surname>
                        <given-names>Jose Carlos Montes</given-names>
                    </name>
                    <xref ref-type="aff" rid="r496057a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r496057a1">
                    <label>1</label>Universidad de San Mart&#x00ed;n de Porres, Chiclayo, Peru</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>27</day>
                <month>6</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Ninaquispe JCM</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="relatedArticleReport496057" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.177965.2"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve</meta-value>
                </custom-meta>
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        </front-stub>
        <body>
            <p>The manuscript addresses a relevant and timely topic for international trade and agro-export competitiveness, particularly by comparing mango exports from Peru and Colombia during the 2004&#x2013;2024 period. The study has potential, as it integrates market diversification, competitiveness indicators, relative purchasing capacity, demand stability, and a gravity model.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>No</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Partly</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>No</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>No</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>No</p>
            <p>Reviewer Expertise:</p>
            <p>My areas of expertise include international economics, international trade, export performance, market diversification, competitiveness, applied quantitative methods, statistics, econometrics, regression analysis, and time-series analysis. I also have experience in the methodological evaluation of scientific articles, particularly in assessing statistical consistency, the validity of empirical models, and the interpretation of results in economic and business studies.</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p>
        </body>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report467169">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.196290.r467169</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Hammami</surname>
                        <given-names>A.Malek</given-names>
                    </name>
                    <xref ref-type="aff" rid="r467169a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r467169a1">
                    <label>1</label>Food and Resource Economics Department, University of Florida Tropical Research and Education Center (Ringgold ID: 53709), Homestead, Florida, 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>9</day>
                <month>4</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Hammami AM</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="relatedArticleReport467169" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.177965.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>reject</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <underline>
                    <bold>Referee Report</bold>
                </underline>
            </p>
            <p> 
                <bold>Summary of the article</bold>
            </p>
            <p> This manuscript analyzes the relationship between market diversification, prices, and mango export performance in Peru and Colombia over the period 2004&#x2013;2024. Using Trade Map data and a quantitative, correlational approach implemented in SPSS, the authors conclude that market diversification is the main determinant of export performance, while price effects are less consistent and only significant in the case of Peru.</p>
            <p> The topic is relevant and policy-oriented, particularly for emerging economies seeking to improve export competitiveness and resilience. The use of publicly available data and the effort to address diversification are commendable. However, several important concerns related to 
                <bold>methodological rigor, statistical validity, and clarity of interpretation</bold>&#x00a0;might limit the scientific soundness of the study in its current form.</p>
            <p> </p>
            <p> 
                <underline>
                    <bold>comments&#x00a0;</bold>
                </underline>
            </p>
            <p> 
                <bold>1. &#x201c;impact&#x201d; vs. correlational design</bold>
            </p>
            <p> The manuscript repeatedly refers to the 
                <bold>&#x201c;impact&#x201d;</bold> of diversification and prices on exports. However, the study is explicitly defined as:</p>
            <p> &#x201c;non-experimental, correlational&#x201d; (Methods section)</p>
            <p> This creates a fundamental inconsistency. A correlational design does not allow causal inference.</p>
            <p> 
                <bold>Required revision:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>Replace &#x201c;impact&#x201d; with 
                            <bold>&#x201c;association&#x201d; or &#x201c;relationship&#x201d;</bold> throughout the manuscript</p>
                        <p> OR</p>
                    </list-item>
                    <list-item>
                        <p>Substantially strengthen the empirical strategy to support causal claims</p>
                    </list-item>
                </list> 
                <bold>2. Adhoc econometric framework and risk of spurious results</bold>
            </p>
            <p> The empirical analysis lacks sufficient detail and rigor: 
                <list list-type="bullet">
                    <list-item>
                        <p>AdHOC model equations are provided (not derived from a theoretical framework)</p>
                    </list-item>
                    <list-item>
                        <p>No discussion of functional form (e.g., levels vs. logs)</p>
                    </list-item>
                    <list-item>
                        <p>No control variables are included (e.g., exchange rates, production shocks, policy changes)</p>
                    </list-item>
                    <list-item>
                        <p>Time-series properties (trends, non-stationarity) are not addressed</p>
                    </list-item>
                </list> Given that the variables (exports, prices, number of markets) are likely trending over time, the very high R&#x00b2; values reported (&#x2248;90%) may reflect 
                <bold>spurious correlations rather than meaningful economic relationships</bold>.</p>
            <p> 
                <bold>Required revision:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>Clearly specify the empirical model</p>
                    </list-item>
                    <list-item>
                        <p>Address time-series properties (e.g., trends, stationarity)</p>
                    </list-item>
                    <list-item>
                        <p>Include control variables or at least discuss omitted variable bias</p>
                    </list-item>
                    <list-item>
                        <p>Provide a more cautious interpretation of results</p>
                    </list-item>
                </list> 
                <bold>3. Overinterpretation of results</bold>
            </p>
            <p> The conclusions state that diversification is the &#x201c;key factor&#x201d; and that price has a significant or insignificant &#x201c;impact.&#x201d; However, given the limitations above, these conclusions are 
                <bold>not sufficiently supported by the analysis</bold>.</p>
            <p> 
                <bold>Required revision:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>Temper conclusions</p>
                    </list-item>
                    <list-item>
                        <p>Align interpretation with the correlational nature of the analysis</p>
                    </list-item>
                    <list-item>
                        <p>Explicitly acknowledge limitations</p>
                    </list-item>
                </list> 
                <bold>4. Theoretical framework lacks integration</bold>
            </p>
            <p> The manuscript presents several theories (Ansoff, Krugman, Heckscher&#x2013;Ohlin, Porter), but they are largely descriptive and not clearly linked to: 
                <list list-type="bullet">
                    <list-item>
                        <p>Hypotheses</p>
                    </list-item>
                    <list-item>
                        <p>Model specification</p>
                    </list-item>
                    <list-item>
                        <p>Empirical findings</p>
                    </list-item>
                </list> 
                <bold>Required revision:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>Streamline the theoretical section</p>
                    </list-item>
                    <list-item>
                        <p>Explicitly connect theory &#x2192; hypothesis &#x2192; empirical test</p>
                    </list-item>
                </list> 
                <bold>5. Measurement of key variables</bold>
            </p>
            <p> The proxy for market diversification is the 
                <bold>number of export destinations</bold>, which is a very simplified measure and does not capture concentration.</p>
            <p> 
                <bold>Required revision:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>Justify the choice of this measure</p>
                    </list-item>
                    <list-item>
                        <p>Ideally, complement it with a concentration index (e.g., HHI or entropy index)</p>
                    </list-item>
                    <list-item>
                        <p>Clarify interpretation of FOB prices (which may reflect quality differences)</p>
                    </list-item>
                </list> 
                <bold>7. Writing and clarity</bold>
            </p>
            <p> The manuscript would benefit from significant language editing. Examples include: 
                <list list-type="bullet">
                    <list-item>
                        <p>&#x201c;has undergone a very changing dynamic&#x201d;</p>
                    </list-item>
                    <list-item>
                        <p>&#x201c;constant research of marketing strategies&#x201d;</p>
                    </list-item>
                    <list-item>
                        <p>incorrect punctuation such as &#x201c;However;&#x201d;</p>
                    </list-item>
                </list> 
                <bold>Recommendation:</bold> professional language editing is strongly advised.</p>
            <p> </p>
            <p> 
                <bold>7.1. Structure and repetition</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>The introduction is overly long and repetitive</p>
                    </list-item>
                    <list-item>
                        <p>Objectives and hypotheses are restated multiple times</p>
                    </list-item>
                </list> 
                <bold>Recommendation:</bold> streamline for clarity and conciseness.</p>
            <p> </p>
            <p> 
                <bold>8. Scope clarification</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>The manuscript should better justify: 
                            <list list-type="bullet">
                                <list-item>
                                    <p>Why Peru and Colombia were selected</p>
                                </list-item>
                                <list-item>
                                    <p>Why mango was chosen relative to other crops</p>
                                </list-item>
                            </list> </p>
                    </list-item>
                </list> 
                <underline>
                    <bold>Recommendation: </bold>
                </underline>Major revisions required.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>No</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>No</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>No</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>No</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Partly</p>
            <p>Reviewer Expertise:</p>
            <p>Agribusiness; Agricultural Trade; Production Economics</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.</p>
        </body>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report467170">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.196290.r467170</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Ninaquispe</surname>
                        <given-names>Jose Carlos Montes</given-names>
                    </name>
                    <xref ref-type="aff" rid="r467170a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r467170a1">
                    <label>1</label>Universidad de San Mart&#x00ed;n de Porres, Chiclayo, Peru</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>26</day>
                <month>3</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Ninaquispe JCM</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="relatedArticleReport467170" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.177965.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>reject</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p> From a statistical and methodological standpoint, I do not consider this manuscript suitable for indexing in its present version. The central problem is that the paper presents itself as an analysis of the &#x201c;impact&#x201d; of market diversification and prices on mango exports, yet the empirical strategy is not capable of identifying impact in any defensible statistical sense. The study is explicitly described as quantitative, non-experimental, correlational, and longitudinal, using annual data from 2004&#x2013;2024 and relying on Pearson correlations and linear regression in SPSS. That design is insufficient for causal inference, particularly when the data are clearly time series.</p>
            <p> </p>
            <p> The most serious flaw is the misuse of time-series data as if the observations were independent cross-sectional cases. The manuscript analyzes annual series for destination markets, FOB price, export volume, and FOB value over 21 years, yet it reports simple Pearson correlations and OLS regressions without any evidence of stationarity testing, autocorrelation diagnostics, cointegration analysis, structural-break testing, or time-series specification. This is a fundamental error. When variables display common upward trends over time, very high correlations such as 0.756, 0.925, 0.937, 0.951, and 0.957 can easily be spurious correlations driven by trend, not substantive economic relationships. In such a setting, the reported p-values and R-squared values are not persuasive.</p>
            <p> </p>
            <p> This problem becomes even more evident because the figures and narrative repeatedly describe sustained growth trends in the number of destination markets, export volume, and FOB value in both countries. Peru rises from 14 to 40 markets, and Colombia from 15 to 32 markets; export values also rise markedly over the study period. Under these conditions, strong contemporaneous correlations are expected even in the absence of a meaningful structural relationship. The authors never demonstrate that the residuals are stationary, that the regressions are not spurious, or that the estimated coefficients are not simply capturing shared temporal drift.</p>
            <p> </p>
            <p> A second major weakness is the systematic confusion between association and causation. The manuscript repeatedly uses the language of &#x201c;impact,&#x201d; &#x201c;influence,&#x201d; and &#x201c;predictor,&#x201d; and even concludes that diversification &#x201c;brought tangible and quantifiable benefits&#x201d; to export performance. That is not justified by the design. A correlational, non-experimental model using annual aggregate data cannot establish direction of causality. It is at least equally plausible that higher export success enables entry into more markets, rather than market expansion causing export success. In other words, reverse causality and simultaneity are obvious and unaddressed.</p>
            <p> </p>
            <p> There is also a serious endogeneity and identity problem in the specification. The dependent variable is FOB value, while one key regressor is FOB price, and the paper itself explicitly states that FOB value is logically linked to price and exported quantity. The manuscript even acknowledges that the increase in FOB value from higher FOB price is &#x201c;direct&#x201d; because total value is composed of quantity multiplied by unit price. This means the regression partly re-discovers a mechanical accounting relationship, not an independent explanatory effect. As a result, the coefficient on FOB price is not a clean substantive finding but is contaminated by the construction of the dependent variable itself.</p>
            <p> </p>
            <p> The operationalization of market diversification is also statistically weak. The paper defines diversification simply as the number of destination markets. That is not an adequate measure of diversification in trade analysis. A country can export to many destinations and still be highly concentrated if most exports go to one or two markets. The manuscript even cites prior work using more appropriate indicators such as the Herfindahl-Hirschman Index (HHI) and RCA, yet it does not apply any concentration index itself. Therefore, the paper&#x2019;s core explanatory variable is a crude count, not a valid measure of diversification intensity or dispersion.</p>
            <p> </p>
            <p> The paper is additionally weakened by small-sample overconfidence. All analyses are based on N = 21 annual observations. With such a small time-series sample, the effective information is even lower once serial dependence is considered. Yet the authors present extremely high R, R-squared, and adjusted R-squared values as if these were proof of robustness. In reality, with trending macro-style annual data, very high R-squared values are often a warning sign of model misspecification rather than evidence of explanatory power. The manuscript does nothing to reassure the reader that these are not inflated by common time trends.</p>
            <p> </p>
            <p> Equally problematic is the complete absence of basic regression diagnostics. The manuscript does not report any test or discussion of residual autocorrelation, heteroskedasticity, normality, linearity, influential observations, outliers, leverage, specification error, or model stability. No Durbin-Watson statistic is provided. No residual plots are discussed. No transformation of variables is attempted, despite the obvious scale differences and strong trending patterns in value and volume. Without these diagnostics, the regression results cannot be considered statistically credible.</p>
            <p> </p>
            <p> The inferential framework is also poorly justified. The authors state that the population is the universe of extracted data and that the sample is based on the period 2004&#x2013;2024. In other words, they are effectively using the full observed period they defined, not a sample drawn from a broader population in any meaningful sampling sense. Yet they rely on conventional significance testing without clearly articulating the stochastic process that would justify those inferential claims. This is conceptually weak and statistically underdeveloped.</p>
            <p> </p>
            <p> There are also internal inconsistencies that directly undermine confidence in the results section. The paper states that the Colombian price variable in the excluded-variables analysis had beta = 0.131, t = 1.683, p = 0.110, which would justify exclusion from the final model. However, Table 9 reports beta = 0.301, t = 2.893, p = 0.010, partial correlation = 0.563, tolerance = 0.429. Those values are not consistent with the text, and in fact they mirror the Peru coefficient pattern reported earlier. This strongly suggests either a copy-paste error, a reporting error, or loss of control over the analysis. For a quantitative paper, that is a major credibility failure.</p>
            <p> </p>
            <p> The reporting around coefficients is also careless to the point of implausibility. For Peru, the paper states that each additional market increases FOB value by 7,269.197 thousand dollars, while each extra dollar per kilogram increases FOB value by 104,375,426 thousand dollars. Even if one allows for unit-scaling complications, the manuscript does not explain these magnitudes clearly or consistently. The interpretation is presented as straightforward substantive evidence, but the scale handling is opaque and potentially misleading. This indicates weak statistical reporting discipline.</p>
            <p> </p>
            <p> There are further signs of inadequate quality control. The manuscript describes the study as covering &#x201c;20 years,&#x201d; yet the period 2004&#x2013;2024 inclusive corresponds to 21 annual observations, which is also what appears in the correlation tables. That inconsistency is basic but telling. Likewise, Figure 8, which is supposed to show Colombian FOB value, is captioned as if it referred to the item exported from Peru. These errors are not minor cosmetic issues; in a quantitative manuscript, they signal insufficient rigor in data handling and reporting.</p>
            <p> </p>
            <p> Substantively, the model also suffers from severe omitted-variable bias. Over a 21-year period, mango export value is likely affected by exchange rates, production shocks, weather events, phytosanitary access, freight costs, trade agreements, external demand, and domestic supply constraints. The paper itself mentions COVID-19, El Ni&#x00f1;o, U.S. market access, and logistics-related issues in the descriptive discussion, yet none of these factors are modeled. At that point, attributing most variation in FOB value to diversification and price alone is not statistically convincing.</p>
            <p> </p>
            <p> Finally, the manuscript overstates its comparative contribution. The title refers to &#x201c;emerging economies,&#x201d; but the analysis is simply two separate country-specific regressions. There is no panel model, no formal cross-country comparison of coefficients, no test of whether Peru and Colombia statistically differ, and no integrated comparative framework. The article promises a broader analytical contribution than the empirical design actually delivers.</p>
            <p> </p>
            <p> For all of these reasons, my formal judgment is that the manuscript should not be accepted for indexing in its current form. The problem is not a matter of minor revision. The statistical strategy is fundamentally inadequate for the claims being made. To become indexable, the study would require a substantial redesign: a valid measure of diversification, proper time-series treatment, explicit diagnostic testing, correction of internal reporting inconsistencies, more defensible model specification, and a clear abandonment of causal language unless a much stronger identification strategy is implemented.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>No</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Partly</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>No</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>No</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>No</p>
            <p>Reviewer Expertise:</p>
            <p>My areas of expertise include international economics, international trade, export performance, market diversification, competitiveness, applied quantitative methods, statistics, econometrics, regression analysis, and time-series analysis. I also have experience in the methodological evaluation of scientific articles, particularly in assessing statistical consistency, the validity of empirical models, and the interpretation of results in economic and business studies.</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.</p>
        </body>
    </sub-article>
</article>
