SDG Metadata/16-4-1 — English @ Hosted Weblate

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Project website	github.com/worldbank/sdg-metadata
Instructions for translators	This project is limited to Russian translation only, for now. More detailed instructions to come.
Project maintainers	brockfanning
Translation license	MIT License
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Repository branch	master
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File mask	`translations-metadata/*/16-4-1.yml`
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Language code	en
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None Resource updated SDG Metadata 16-4-1 English	The “`translations-metadata/en/16-4-1.yml`” file was changed. a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <p><strong>URL:</strong></p> <p><a href="about:blank">www.unodc.org</a></p> <p>https://unctad.org/statistics/illicit-financial-flows<a href="https://sdgpulse.unctad.org/illicit-financial-flows/">https://sdgpulse.unctad.org/illicit-financial-flows/</a> and https://sdgpulse.unctad.org/unctad-leads-global-efforts-to-measure-illicit-financial-flows-with-unodc/</p> <p><a href="about:blank">https://dataunodc.un.org/</a></p> <p><a href="about:blank">https://unctadstat.unctad.org</a></p> <p>UNCTAD Stat Youtube Channel: https://www.youtube.com/channel/UCbRSDgH8NS-U6aAJ_Q6B14w</p> <p><a href="https://www.unodc.org/unodc/en/data-and-analysis/iff.html">https://www.unodc.org/unodc/en/data-and-analysis/iff.html</a></p> <p>UNCTAD-UNODC Conceptual Framework for the Statistical Measurement of Illicit Financial Flows (2020) <a href="https://unctad.org/publication/conceptual-framework-statistical-measurement-illicit-financial-flows"><u>https://unctad.org/publication/conceptual-framework-statistical-measurement-illicit-financial-flows</u></a> <br>UNCTAD Methodological Guidelines to Measure Tax and Commercial Illicit Financial Flows – Methods for pilot testing (2021). https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot</p> <p>UNODC-UNCTAD project on Latin America (2017-2020): <a href="https://www.unodc.org/unodc/en/data-and-analysis/iff_Lac.html">https://www.unodc.org/unodc/en/data-and-analysis/iff_Lac.html</a> </p> <p>UNCTAD-ECA project on Africa (2018-2022):<br><a href="https://unctad.org/project/defining-estimating-and-disseminating-statistics-illicit-financial-flows-africa">https://unctad.org/project/defining-estimating-and-disseminating-statistics-illicit-financial-flows-africa</a></p> <p>UNODC-ESCAP-UNCTAD project on Asia-Pacific (2020-2022): <a href="https://www.unodc.org/unodc/en/data-and-analysis/iff_Asia.html">https://www.unodc.org/unodc/en/data-and-analysis/iff_Asia.html</a> </p> <p>UNODC (2020) - Supply and value chains and illicit financial flows from the trade in ivory and rhinoceros horn (Chapter 8 – Second World Wildlife Crime Report) <a href="https://www.unodc.org/documents/data-and-analysis/wildlife/2020/WWLC20_Chapter_8_Value_chains.pdf">https://www.unodc.org/documents/data-and-analysis/wildlife/2020/WWLC20_Chapter_8_Value_chains.pdf</a> </p> English 962 characters edited Current translation Read only <p><strong>URL:</strong></p> <p><a href="about:blank">www.unodc.org</a></p> <p>~~<a href="about:blank~~https://unctad.org/statistics/illicit-financial-flows<a href="https://sdgpulse.unctad.org/illicit-financial-flows/">https://sdgpulse.unctad.org/statistics</a></p> <p>illicit-financial-flows/</a> and https://s~~tats~~dgpulse.unctad.org/~~iffs~~unctad-leads-global-efforts-to-measure-illicit-financial-flows-with-unodc/</p> <p><a href="about:blank">https://dataunodc.un.org/</a></p> <p><a href="about:blank">https://unctadstat.unctad.org</a></p> <p>UNCTAD Stat Youtube Channel: https://www.youtube.com/channel/UCbRSDgH8NS-U6aAJ_Q6B14w</p> <p><a href="https://www.unodc.org/unodc/en/data-and-analysis/iff.html">https://www.unodc.org/unodc/en/data-and-analysis/iff.html</a></p> <p>UNCTAD-UNODC Conceptual Framework for the Statistical Measurement of Illicit Financial Flows (2020) <a href="https://~~www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_C~~unctad.org/publication/conceptual_F-framework~~_for_publication_FINAL_16Oct_print.pdf">https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_C~~-statistical-measurement-illicit-financial-flows"><u>https://unctad.org/publication/conceptual_F-framework~~_for_publication_FINAL_16Oct_print.pdf~~-statistical-measurement-illicit-financial-flows</u></a> <br>UNCTAD Methodological Guidelines to Measure Tax and Commercial Illicit Financial Flows – Methods for pilot testing (2021). ~~<a href="~~https://unctad.org/~~system/files/non-off~~webflyer/methodologicial-document/20210917_IFFsGuidelinesForPilots_en_0.pdf">https://unctad.org/system/files/non-official-document/20210917_IFFsGuidelinesForPilots_en_0.pdf</a> </p> <p>UNCTAD-ECA capacity building projectguidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot</p> <p>UNODC-UNCTAD project on Latin America (2017-2020): <a href="https://www.unodc.org/unodc/en/data-and-analysis/iff_Lac.html">https://www.unodc.org/unodc/en/data-and-analysis/iff_Lac.html</a> </p> <p>UNCTAD-ECA project on Africa (2018-2022):<br><a href="https://unctad.org/project/defining-estimating-and-disseminating-statistics-illicit-financial-flows-africa">https://unctad.org/project/defining-estimating-and-disseminating-statistics-illicit-financial-flows-africa</a></p> ~~<p>UNODC-UNCTAD project on Latin America (2017-2020): <a href="https://www.unodc.org/unodc/en/data-and-analysis/iff_Lac.html">https://www.unodc.org/unodc/en/data-and-analysis/iff_Lac.html</a> </p>~~ <p>UNODC-ESCAP-UNCTAD project on Asia-Pacific (2020-2022): <a href="https://www.unodc.org/unodc/en/data-and-analysis/iff_Asia.html">https://www.unodc.org/unodc/en/data-and-analysis/iff_Asia.html</a> </p> <p>UNODC (2020) - Supply and value chains and illicit financial flows from the trade in ivory and rhinoceros horn (Chapter 8 – Second World Wildlife Crime Report) <a href="https://www.unodc.org/documents/data-and-analysis/wildlife/2020/WWLC20_Chapter_8_Value_chains.pdf">https://www.unodc.org/documents/data-and-analysis/wildlife/2020/WWLC20_Chapter_8_Value_chains.pdf</a> </p> a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <p><strong>Data availability:</strong></p> <p>It is expected that first and preliminary statistics on IFFs will be reported globally in early 2023. It is further expected that the number of countries for which this indicator is available will gradually start increasing over time. According to inventories, over 60 per cent of countries globally already collect some data that can be used in the estimation of IFFs. However, notably efforts are planned to support countries in building their capacity to measure Indicator 16.4.1. Currently, 22 pioneering countries have pilot tested the indicator compilation with some in the final stages of producing early estimates of IFF statistics. Estimates will also be prepared in countries participating in UNCTAD and UNODC capacity building projects, carried out jointly with United Nations Regional Commissions in 2023-2026.</p> <p>It is expected that the estimates of IFFs are available as the (best) estimate, accompanied by a lower and an upper bound estimate. </p> <p><strong>Time series:</strong></p> <p>Availability of time series would be useful for the analysis of development over time. Feasibility of constructing historical time series data will be reviewed.</p> <p><strong>Disaggregation:</strong></p> <p>At the indicator level, the IFFs are to be reported separately as inward and outward IFFs. </p> <p>In addition, a disaggregated measurement approach is proposed. As a minimum, disaggregation of the index by relevant types of IFFs, should be published separately for the main elements. Furthermore, depending on data availability, each should be disaggregated to reflect specific IFFs categories (following the ones identified in the Conceptual Framework<sup><a href="#footnote-16" id="footnote-ref-16">[15]</a></sup>), for example: </p> <p>• IFFs from illicit tax and commercial practices (additionally, e.g., trade misinvoicing, tax evasion, aggressive tax avoidance by MNEs), </p> <p>• IFFs from illegal markets (additionally, e.g., drug trafficking, smuggling of migrants, wildlife trafficking), </p> <p>• IFFs from corruption, and</p> <p>• IFFs from exploitation-type and financing of crime and terrorism (additionally, e.g., trafficking in persons).</p> <p>Moreover, where possible and relevant, further disaggregation of IFF indicator is to be made in reference to:</p> <p>• Sector (e.g., as defined by economic sector or activity within the International Standard Industrial Classification of All Economic Activities)</p> <p>• Regions/Countries of origin/destination of the flows (to construct a country-flow matrix).</p> <p>Other possible disaggregation might be considered by countries regarding:• type of payment method (cash / trade flows / crypto currencies)</p> <p>• resulting assets (offshore wealth / real estate etc.)</p> <p>• actors (characters of individuals / types of businesses etc.)</p> <p>• industries, commodities or service categories.</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-16">15</sup><p> See page 13 at <a href="https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf">https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf</a> <a href="#footnote-ref-16">↑</a></p></div></div> English 1705 characters edited Current translation Read only <p><strong>Data availability:</strong></p> <p>~~Data collection has not yet started. It is~~It is expected that first and preliminary statistics on IFFs will be reported globally in early 2023. It is further expected that the number of countries for which this indicator is available will gradually start increasing over time. According to inventories, over 60 per cent of countries globally already collect some data that can be used in the estimation of IFFs. However, notably efforts are planned to support countries in building their capacity to measure Indicator 16.4.1. Currently, 22 pioneering countries arhave pilot test~~ing~~ed the indicator compilation with some in the final stages of producing early estimates of IFF statistics. Estimates will also be prepared in countries participating in UNCTAD and UNODC capacity building projects, carried out ~~currently~~ jointly with ~~ESCAP and ECA.~~United Nations Regional Commissions in 2023-2026.</p> <p>It is expected that the estimates of IFFs are available as the (best) estimate, accompanied by a lower and an upper bound estimate. </p> <p><strong>Time series:</strong></p> <p>Availability of time series would be useful for the analysis of development over time. Feasibility of constructing historical time series data will be reviewed.</p> <p><strong>Disaggregation:</strong></p> <p>~~Similar to Eurostat’s recommendations on measuring illegal economic activities~~At the indicator level, the IFFs are to be reported separately as inward and outward IFFs. </p> <p>In addition, a disaggregated measurement approach is proposed. As a minimum, disaggregation of the index by relevant types of IFFs, should be published separately for the main elements. Furthermore, depending on data availability: </p> <p>• IFFs from illicit tax and commercial practices, </p> <p>• IFFs from illegal markets, </p> <p>• IFFs from corruption, and</p> <p>• IFFs from exploitation-type and financing of crime and terrorism.</p> <p>In addition, member states may decide to disaggregate the IFF indicator, where relevant, by:</p> <p>• payment method (cash / trade flows / crypto currencies)</p> <p>• resulting assets (offshore wealth / real estate etc.)</p> <p>• actors (characters of individuals / types of businesses etc.)</p> <p>• industries, commodities or service categories.</p, each should be disaggregated to reflect specific IFFs categories (following the ones identified in the Conceptual Framework<sup><a href="#footnote-16" id="footnote-ref-16">[15]</a></sup>), for example: </p> <p>• IFFs from illicit tax and commercial practices (additionally, e.g., trade misinvoicing, tax evasion, aggressive tax avoidance by MNEs), </p> <p>• IFFs from illegal markets (additionally, e.g., drug trafficking, smuggling of migrants, wildlife trafficking), </p> <p>• IFFs from corruption, and</p> <p>• IFFs from exploitation-type and financing of crime and terrorism (additionally, e.g., trafficking in persons).</p> <p>Moreover, where possible and relevant, further disaggregation of IFF indicator is to be made in reference to:</p> <p>• Sector (e.g., as defined by economic sector or activity within the International Standard Industrial Classification of All Economic Activities)</p> <p>• Regions/Countries of origin/destination of the flows (to construct a country-flow matrix).</p> <p>Other possible disaggregation might be considered by countries regarding:• type of payment method (cash / trade flows / crypto currencies)</p> <p>• resulting assets (offshore wealth / real estate etc.)</p> <p>• actors (characters of individuals / types of businesses etc.)</p> <p>• industries, commodities or service categories.</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-16">15</sup><p> See page 13 at <a href="https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf">https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf</a> <a href="#footnote-ref-16">↑</a></p></div></div> a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <ul> <li>Statistics received from Member States will go through a validation process. </li> <li>The data for the indicator are externally validated by comparing to other available sources. </li> <li>Once the information has been validated and information from additional sources incorporated, any questions for clarification or proposals are shared with Member States for their review. </li> <li>In case any adjustment is needed, after Member States have reviewed the values, indicators are ready to be published and sub-regional, regional and global totals, where appropriate, can be estimated.</li> </ul> English 20 characters edited Current translation Read only <ul> <li>Statistics received from Member States will go through a validation process. </li> <li>The data for the indicator are externally validated by comparing to other available sources. </li> <li>Once the information has been validated and information from additional sources incorporated, any questions for clarification or proposals are shared with Member States for their review. </li> <li>In case any adjustment is needed, after Member States have reviewed the values, indicators are ready to be published and sub-regional, regional and global totals, where appropriate, can be estimated.</li> </ul> a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <ul> <li>UNCTAD and UNODC published a <a href="https://unctad.org/publication/conceptual-framework-statistical-measurement-illicit-financial-flows">Conceptual Framework for the Statistical Measurement of Illicit Financial Flows</a> as a joint publication in October 2020. It details the concepts, definitions and types of IFFs, and discusses the challenges of statistical production. The Conceptual Framework has been endorsed by Member States at 53<sup>rd</sup> session of the United Nations Statistical Commission in March 2022.<sup><a href="#footnote-14" id="footnote-ref-14">[13]</a></sup></li> <li>At the national level, data sources need to be identified separately for the major IFFs originating from tax and commercial practices, corruption, exploitation-type and terrorism activities, and illegal markets. These sources should cover the major flows relevant to the country and provide information for estimating total inward and outward flows separately. The <a href="https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html">ICCS</a> provides a useful listing of behaviours, events and activities that may generate IFFs, and an extended classification of IFFs from aggressive tax avoidance is being discussed. </li> <li>UNCTAD/UNODC Task Force is finalising methodological guidelines on the measurement of selected types of IFFs. To date, methodologies to measure IFFs have been tested by 22 countries on three continents in efforts coordinated by UN regional commissions (ESCAP, ECA) and UNODC field Offices (on crime related IFFs), alongside UNCTAD and UNODC statistics. This includes 12 African countries, 4 Latin American and 6 Asian countries that have produced first estimates of commercial or crime-related IFFs. Custodian agencies are now refining methodological guidelines and materials prepared and made publicly available<sup><a href="#footnote-15" id="footnote-ref-15">[14]</a></sup>. UNCTAD and UNODC are working towards a comprehensive Statistical Framework for the Measurement of Illicit Financial Flows, providing practical guidance to national statistical authorities including suggested methodologies to measure different types of IFFs, to be submitted to the United Nations Statistical Commission for its review once finalised. </li> </ul><div class="footnotes"><div><sup class="footnote-number" id="footnote-14">13</sup><p> https://unstats.un.org/unsd/statcom/53rd-session/documents/2022-14-CrimeStats-E.pdf <a href="#footnote-ref-14">↑</a></p></div><div><sup class="footnote-number" id="footnote-15">14</sup><p> For methodological guidelines to measure tax and commercial IFFs, see: <a href="https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot">https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot</a>. UNODC has developed guidance on measuring IFFs from trafficking in persons, drug trafficking, smuggling of migrants and wildlife trafficking. <a href="#footnote-ref-15">↑</a></p></div></div> English 1567 characters edited Current translation Read only <ul> <li>UNCTAD and UNODC published a <a href="https://~~www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_C~~unctad.org/publication/conceptual_F-framework~~_for_publication_15Oct.pdf~~-statistical-measurement-illicit-financial-flows">Conceptual Framework for the Statistical Measurement of Illicit Financial Flows</a> as a joint publication in October 2020. It details the concepts, definitions and types of IFFs, and discusses the challenges of statistical production. The Conceptual Framework has been endorsed by Member States at 53<sup>rd</sup> session of the United Nations Statistical Commission in March 2022.<sup><a href="#footnote-14" id="footnote-ref-14">[13]</a></sup></li> <li>At the national level, data sources need to be identified separately for the major IFFs originating from tax and commercial practices, corruption, exploitation-type and terrorism activities, and illegal markets. These sources should cover the major flows relevant to the country and provide information for estimating total inward and outward flows separately. The <a href="https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html">ICCS</a> provides a useful listing of behaviours, events and activities that may generate IFFs, and an extended classification of IFFs from aggressive tax avoidance is being discussed. </li> <li>The methodology follows the approach suggested in the Eurostat Handbook on the compilation of statistics on illegal economic activities in national accounts and balance of payments<sup><sup><a href="#footnote-14" id="footnote-ref-14">[13]</a></sup></sup> with the exception mentioned in footnote 1. </li> <li>UNCTAD/UNODC Task Force is finalising methodological guidelines on the measurement of selected types of IFFs. These are pilot testing in 2021 to be refined and included in a comprehensive Statistical Framework for the Measurement of Illicit Financial Flows, to be submitted to the United Nations Statistical Commission (UNSC) for its review once finalised. The custodians have published or will publish methodological materials for the pilot testing of different types of IFFs and their measurement in 2021<sup><a href="#footnote-15" id="footnote-ref-15">[14]</a></sup>. The guidelines will describe the functioning of selected illegal markets or criminal activities, the possible IFF types that can emerge from these activities and provide practical guidance on statistical sources and estimation methods. </li> </ul><div class="footnotes"><div><sup class="footnote-number" id="footnote-14">13</sup><p>Available here: <a href="about:blank">https://ec.europa.eu/eurostat/documents/3859598/8714610/KS-05-17-202-EN-N.pdf/eaf638df-17dc-47a1-9ab7-fe68476100ec</a>UNCTAD/UNODC Task Force is finalising methodological guidelines on the measurement of selected types of IFFs. To date, methodologies to measure IFFs have been tested by 22 countries on three continents in efforts coordinated by UN regional commissions (ESCAP, ECA) and UNODC field Offices (on crime related IFFs), alongside UNCTAD and UNODC statistics. This includes 12 African countries, 4 Latin American and 6 Asian countries that have produced first estimates of commercial or crime-related IFFs. Custodian agencies are now refining methodological guidelines and materials prepared and made publicly available<sup><a href="#footnote-15" id="footnote-ref-15">[14]</a></sup>. UNCTAD and UNODC are working towards a comprehensive Statistical Framework for the Measurement of Illicit Financial Flows, providing practical guidance to national statistical authorities including suggested methodologies to measure different types of IFFs, to be submitted to the United Nations Statistical Commission for its review once finalised. </li> </ul><div class="footnotes"><div><sup class="footnote-number" id="footnote-14">13</sup><p> https://unstats.un.org/unsd/statcom/53rd-session/documents/2022-14-CrimeStats-E.pdf <a href="#footnote-ref-14">↑</a></p></div><div><sup class="footnote-number" id="footnote-15">14</sup><p> For methodological guidelines to measure tax and commercial IFFs, see: <a href="https://unctad.org/~~system/files/non-off~~webflyer/methodologicial-~~document/20210917_IFFsGuidelinesForPilots_en_0.pdf">https://unctad.org/system/files/non-official-document/20210917_IFFsGuidelinesForPilots_en_0.pdf</a>~~guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot">https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot</a>. UNODC has developed guidance on measuring IFFs from trafficking in persons, drug trafficking, smuggling of migrants and wildlife trafficking. <a href="#footnote-ref-15">↑</a></p></div></div> a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <p>Once values of country indicators have been released, missing indicators estimated, any sub-regional, regional and global estimates will be obtained by aggregating the country indicators within a specific sub-region and region. The global value would be calculated by aggregating the regional values in a similar manner. National differences in the comprehensiveness of IFF coverage will influence the quality of regional aggregates. Regional aggregations will be further methodologically developed once sufficient country-level statistics on IFFs become fully available. </p> English 138 characters edited Current translation Read only <p>Once values of country indicators have been released, missing indicators estimated, any sub-regional, regional and global estimates will be obtained by aggregating the country indicators within a specific sub-region and region. The global value would be calculated by aggregating the regional values in a similar manner. National differences in the comprehensiveness of IFF coverage will influence the quality of regional aggregates. Regional aggregations will be further methodologically developed once sufficient country-level statistics on IFFs become fully available. </p> a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <ul> <li><strong>At country level</strong></li> </ul> <p>When national data are missing, transnational data sources or alternative data sources can be examined. It is important to provide comprehensive metadata explaining current issues related to missing data and exhaustiveness of the indicator. Although national data may only partially cover IFFs, they are still valuable for assessing the significance of IFFs globally and regionally. UNCTAD and UNODC may support countries to assess alternative sources for obtaining the missing information. </p> <ul> <li><strong>At regional and global levels</strong></li> </ul> <p>In order to calculate regional and global aggregates, missing data may be estimated using information from international sources. As historical data for countries become available with time, it will be possible to impute using the same country’s data as well. Estimated indicators are not to be released at the country level, but only in aggregated form at regional and global levels. There will be certain thresholds to be met for the regional and global estimates to be acceptable. If these thresholds are not met, the regional and global estimates will not be published.</p> English 51 characters edited Current translation Read only <pul> <li><strong>~~(i)~~ At country level</strong></pli> </ul> <p>When national data are missing, transnational data sources or alternative data sources can be examined. It is important to provide comprehensive metadata explaining current issues related to missing data and exhaustiveness of the indicator. Although national data may only partially cover IFFs, they are still valuable for assessing the significance of IFFs globally and regionally. UNCTAD and UNODC may support countries to assess alternative sources for obtaining the missing information. </p> <pul> <li><strong>~~(ii)~~ At regional and global levels</strong></pli> </ul> <p>In order to calculate regional and global aggregates, missing data may be estimated using information from international sources. As historical data for countries becomes available with time, it will be possible to impute using the same country’s data as well. Estimated indicators are not to be released at the country level, but only in aggregated form at regional and global levels. There will be certain thresholds to be met for the regional and global estimates to be acceptable. If these thresholds are not met, the regional and global estimates will not be published.</p> a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <p>Given the compilation process outlined above, national circumstances will come at play when measuring the IFFs. The need for adjustment can be assessed based on information on the breakdowns included in the reported IFFs estimates (in the accompanying metadata). The goal is to base the indicator on nationally compiled and reported data. Ongoing work on classification and aggregation of IFFs will result in further guidance on how to adjust for potential duplication and to harmonise breakdowns. </p> English 1 character edited Current translation Read only <p>Given the compilation process outlined above, national circumstances will come at play when measuring the IFFs. The need for adjustment can be assessed based on information on the breakdowns included in the reported IFFs estimates (in the accompanying metadata). The goal is to base the Iindicator on nationally compiled and reported data. Ongoing work on classification and aggregation of IFFs will result in further guidance on how to adjust for potential duplication and to harmonise breakdowns. </p> a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <p>UNODC and UNCTAD request Member States to provide sufficient metadata accompanying their compiled IFFs estimates. UNODC annually reviews methods used to compile crime-related IFFs estimates and to make sure they are compatible with the definition and concepts presented in the <em>Conceptual Framework for the Statistical Measurement of IFFs</em>.<sup><a href="#footnote-13" id="footnote-ref-13">[12]</a></sup> In addition, in Q1 2023 UNODC started to include estimates on SDG indicator 16.4.1 in the annual SDG Pre-Publication, a process that allows countries to comment or review the data of each indicator UNODC is custodian of, before such data are submitted to UNSD. Deviations to account for national circumstances will clearly need to be identified, justified and their impact on international comparability and methodological comprehensiveness be estimated. </p><div class="footnotes"><div><sup class="footnote-number" id="footnote-13">12</sup><p>https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf <a href="#footnote-ref-13">↑</a></p></div></div> English 716 characters edited Current translation Read only <p>UNODC and UNCTAD ~~will~~ request mMember sStates to provide sufficient metadata accompanying their compiled IFFs estimates. Currently still work in progress, however, once finalised, the methodological guidelines will provide practical guidance to national statistical authorities, including suggested methodologies to measure IFFs. Deviations to account for national circumstances will clearly need to be identified, justified and their impact on international comparability and methodological comprehensiveness be estimated. </pUNODC annually reviews methods used to compile crime-related IFFs estimates and to make sure they are compatible with the definition and concepts presented in the <em>Conceptual Framework for the Statistical Measurement of IFFs</em>.<sup><a href="#footnote-13" id="footnote-ref-13">[12]</a></sup> In addition, in Q1 2023 UNODC started to include estimates on SDG indicator 16.4.1 in the annual SDG Pre-Publication, a process that allows countries to comment or review the data of each indicator UNODC is custodian of, before such data are submitted to UNSD. Deviations to account for national circumstances will clearly need to be identified, justified and their impact on international comparability and methodological comprehensiveness be estimated. </p><div class="footnotes"><div><sup class="footnote-number" id="footnote-13">12</sup><p>https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf <a href="#footnote-ref-13">↑</a></p></div></div> a year ago
None String updated in the repository SDG Metadata 16-4-1 English	English <p>A bottom-up and direct measurement approach is preferred for constructing the indicator. Bottom-up methods estimate illicit financial flows (IFFs) directly in relation to the four main activities and build them up departing from the overall economic income that illicit activities generate. Direct refers to the fact that data referring to the various stages of the economic processes generating IFFs are individually measured (via surveys, administrative data or other transparent methods) and are not the exclusive result of model-based procedures. The measurement approach is in line with the “Eurostat Handbook on the compilation of statistics on illegal economic activities in national accounts and balance of payments”<sup><a href="#footnote-8" id="footnote-ref-8">[7]</a></sup> for the estimation of the contribution of illegal activities to the GDP.<sup><sup><a href="#footnote-9" id="footnote-ref-9">[8]</a></sup></sup> The proposed compilation methods follow the principles developed in economic measurement frameworks such as the System of National Accounts and the Balance of Payments.</p> <p>In 2021, UNCTAD released a draft <a href="https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot">Methodological guidelines to measure tax and commercial illicit financial flows</a>. They identify a suite of methods for the measurement of the main types of tax and commercial IFFs, specifically two methods for each of the three main types of tax and commercial IFFs:</p> <ol> <li>Trade misinvoicing by entities <ul> <li>Method #1 - Partner Country Method Plus</li> <li>Method #2 - Price Filter Method Plus</li> </ul> </li> <li>Aggressive tax avoidance or profit shifting by multinational enterprise groups (MNEs)<ul> <li>Method #3 – Global distribution of MNEs’ profits and corporate taxes</li> <li>Method #4 – MNE vs comparable non-MNE profit shifting</li> </ul> </li> <li>Transfer of wealth to evade taxes by individuals <ul> <li>Method #5 – Flows of undeclared offshore assets indicator</li> <li>Method #6 – Flows of offshore financial wealth by country</li> </ul> </li> </ol> <p>UNODC has developed and continues to enhance methods to address IFFs from criminal activities, such as smuggling of migrants, drugs trafficking, illegal mining, wildlife trafficking, trafficking in persons, and corruption, providing guidance and expert support to national authorities undertaking measurement.</p> <p>The methodology foresees:</p> <ol> <li>A risk assessment that identifies the major and most relevant sources of IFFs in a country. This risk assessment can follow and build on existing risk assessments, e.g., the ones mandated by the Financial Action Task Force (FATF).<sup><a href="#footnote-10" id="footnote-ref-10">[9]</a></sup> </li> <li>Once the activities that generate the most important flows are identified, the flows are estimated in a disaggregated manner and then summed up for the indicator. </li> </ol> <p>Given the broad scope of activities generating IFFs, each type of flow needs to be treated in a separate manner. </p> <p>A two-step process was developed that aids Member States in calculating Indicator 16.4.1.</p> <p>As a first step in constructing the IFFs Indicator is to focus, for each IFF type, on IFFs generated during the <em><u>illicit income generation</u></em>: this refers to the set of transactions – such as those related to international trade of illicit goods - that either directly generate illicit income for an actor during a productive or non-productive illicit activity, or that are performed in the context of the illicit production of goods and services. </p> <table> <tbody> <tr> <td> <p><strong><u>Examples of income generation IFFs related to selected illegal activities</u></strong></p> <p><strong><u>IFFs from drug trafficking</u></strong></p> <p>In a drug producing country, the method to estimate IFFs derived from drug trafficking can be broadly described as follows:</p> <p>All drug produced in the country (P) is either consumed domestically (C), seized by law enforcement (S), exported (E) or lost (L).</p> <p>With that <math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>P</mi> <mo>=</mo> <mi>C</mi> <mo>+</mo> <mi>S</mi> <mo>+</mo> <mi>E</mi> <mo>+</mo> <mi>L</mi> </math>.</p> <p>Countries with extended illicit drug cultivation, normally collect data on P, C, and S (losses cannot be estimated and are excluded from the calculations) and annual exports of drugs can be estimated. </p> <p>The value of exports can be measured by the wholesale value of the relevant drug in countries of destination of the drug produced in the country. These data can be retrieved from international data on seizures reported by other Member States (which provide information on the country of origin) and price data, which is as well reported annually through the mandated Annual Report Questionnaire (ARQ) submitted to UNODC (see <a href="about:blank">https://dataunodc.un.org/</a>)</p> <p>This methodology has been applied in Peru, Mexico and Afghanistan<sup><a href="#footnote-11" id="footnote-ref-11">[10]</a></sup> where certain portions of the income generated from drug production and trafficking are accounted for in the national accounts.</p> <p><strong><u>IFFs from smuggling of migrants<sup><a href="#footnote-12" id="footnote-ref-12">[11]</a></sup></u></strong></p> <p>Following the Eurostat manual “Handbook on the compilation of statistics on illegal economic activities in national accounts and balance of payments” four types of smuggling transactions can be distinguished, two of which create IFF:</p> <p><strong>Type I: Resident smugglers and resident migrants </strong>does not cover transnationality and illegal entry and does not create IFFs </p> <p> </p> <p><strong>Type II: Resident smugglers and non-resident migrants</strong></p> <p>Constitutes an export of services and does incur an inward IFF:</p> <p>Export of transportation services = number of non-resident migrants smuggled by resident smugglers * prices</p> <p><strong>Type III: Non-resident smugglers and resident migrants</strong></p> <p>Estimations recorded as import of illegal services and constitute and outward IFF:</p> <p>Import of illegal transportation services = number of residents smuggled by non-resident smugglers * </p> <p>prices</p> <p><strong>Type IV: Non-resident smugglers and non-resident migrants</strong></p> <p>No estimations recorded</p> <p>The pilot studies found the methodology to be feasible, however, limitations on data exist, in particular on pricing.</p> </td> </tr> </tbody> </table> <p>At a second stage, IFFs in relation to <em><u>illicit income management</u></em> are estimated. These refer e.g., to IFFs generated when income generated from illegal activities is invested abroad (e.g., into property). To assess these flows, quantitative and qualitative information held by financial authorities, central banks and other entities concerned with money laundering and financial crimes can be used. Further methodological deliberations on income generation / income management are being undertaken by the custodian agencies to be refined, finalized and included in a comprehensive Statistical Framework for the measurement of IFFs. </p> <p>The methodological work of custodians on aggregation to measure IFFs as a single SDG indicator proposes a matrix approach, allowing activities identified to be analysed with respect to an aggregated income generation (IG) and income management (IM) approach as well as according to methods used to measure IFFs from these activities (see Figure 1). Using such a matrix, areas of (potential) overlap between different methods and types of IFFs can be identified – in the figure, by observing which areas are covered by a specific method (marked in green; light green indicates merely partial coverage by a particular method). Further practical studies in countries will be needed to design suitable and robust aggregation methods in the future. </p> <p>Figure 1. Activity-method matrix for aggregated IG-IM representation of IFF measurement</p> <p><img 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"></p> <p>Source: Deliberations by Task Force on the Statistical Measurement</p> <p>It is advised that the estimates of IFFs are reported as the (best) estimate, accompanied by a lower and an upper bound estimate to account for uncertainties in the data sources and methods. Custodian agencies are currently developing further guidance to Member States to be included in the Statistical Framework for measurement of IFFs. </p><div class="footnotes"><div><sup class="footnote-number" id="footnote-8">7</sup><p> Available here: <a href="about:blank">https://ec.europa.eu/eurostat/documents/3859598/8714610/KS-05-17-202-EN-N.pdf/eaf638df-17dc-47a1-9ab7-fe68476100ec</a> <a href="#footnote-ref-8">↑</a></p></div><div><sup class="footnote-number" id="footnote-9">8</sup><p> With one principle difference. The mere transfer of funds (exploitation-type activities and terrorism financing) are not considered in the GDP estimates, as they are not productive transactions and may not be carried out with the mutual agreement of both parties. Such activities can, however, generate noteworthy amounts of illicit income and subsequent IFFs. The present framework includes activities that are not considered as being productive in the framework of the System of National Accounts. <a href="#footnote-ref-9">↑</a></p></div><div><sup class="footnote-number" id="footnote-10">9</sup><p> https://www.fatf-gafi.org/ <a href="#footnote-ref-10">↑</a></p></div><div><sup class="footnote-number" id="footnote-11">10</sup><p> See e.g., National Statistics and Information Authority, Afghanistan and UNODC, “Afghanistan Opium Survey 2018 – Challenges to sustainable development, peace and security”, July 2019. <a href="#footnote-ref-11">↑</a></p></div><div><sup class="footnote-number" id="footnote-12">11</sup><p> The Protocol against the Smuggling of Migrants, supplementing the United Nations Convention against Transnational Organized Crime (the Migrant Smuggling Protocol) defines migrant smuggling as: ”in order to obtain, directly or indirectly, a financial or other material benefits, of the illegal entry of a person into a State Party of which the person is not a national or a permanent resident”. See as well ICCS. <a href="#footnote-ref-12">↑</a></p></div></div> English 28774 characters edited Current translation Read only <p>A bottom-up and direct measurement approach is preferred for constructing the Iindicator. Bottom-up methods estimate illicit financial flows (IFFs) directly in relation to the four main activities and build them up departing from the overall economic income that illicit activities generate. Direct refers to the fact that data referring to the various stages of the economic processes generating IFFs are individually measured (via surveys, administrative data or other transparent methods) and are not the exclusive result of model-based procedures. The measurement approach is in line with the “Eurostat Handbook on the compilation of statistics on illegal economic activities in national accounts and balance of payments”<sup><a href="#footnote-8" id="footnote-ref-8">[7]</a></sup> for the estimation of the contribution of illegal activities to the GDP.<sup><sup><a href="#footnote-9" id="footnote-ref-9">[8]</a></sup></sup></p> <p> The proposed compilation methods follow the principles developed in economic measurement frameworks such as the System of National Accounts and the Balance of Payments.</p> <p>A two-step process was developed that aids Member States in calculating Indicator 16.4.1. The methodology has been tested in four countries. In 2021, twelve pilots have been launched in Africa and six in Asia and the Pacific. These will lead to the refinement and finalisation of practical guidelines for the measurement of IFFs. In 2021, UNCTAD released a draft Methodological guidelines to measure tax and commercial illicit financial flows - Methods for pilot testing to support measurement exercises in these countries.<sup><a href="#footnote-10" id="footnote-ref-10">[9]</a></sup> In 2021, UNCTAD released a draft <a href="https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot">Methodological guidelines to measure tax and commercial illicit financial flows</a>. They identify a suite of methods for the measurement of the main types of tax and commercial IFFs, specifically two methods for each of the three main types of tax and commercial IFFs:</p> <ol> <li>Trade misinvoicing by entities <ul> <li>Method #1 - Partner Country Method Plus</li> <li>Method #2 - Price Filter Method Plus</li> </ul> </li> <li>Aggressive tax avoidance or profit shifting by multinational enterprise groups (MNEs)<ul> <li>Method #3 – Global distribution of MNEs’ profits and corporate taxes</li> <li>Method #4 – MNE vs comparable non-MNE profit shifting</li> </ul> </li> <li>Transfer of wealth to evade taxes by individuals <ul> <li>Method #5 – Flows of undeclared offshore assets indicator</li> <li>Method #6 – Flows of offshore financial wealth by country</li> </ul> </li> </ol> <p>UNODC has developed and continues to enhance methods to address IFFs from criminal activities, such as smuggling of migrants, drugs trafficking, illegal mining, wildlife trafficking, trafficking in persons, and corruption, providing guidance and expert support to national authorities undertaking measurement.</p> <p>The methodology foresees:</p> <ol> <li>A risk assessment that identifies the major and most relevant sources of IFFs in a country. This risk assessment can follow and build on existing risk assessments, e.g., the ones mandated by the Financial Action Task Force (FATF).<sup><a href="#footnote-110" id="footnote-ref-1~~1">[10~~0">[9]</a></sup> </li> <li>Once the activities that generate the most important flows are identified, the flows are estimated in a disaggregated manner and then summed up for the indicator. </li> </ol> <p>Given the broad scope of activities generating IFFs, each type of flow needs to be treated in a separate manner. </p> <p>A two-step process was developed that aids Member States in calculating Indicator 16.4.1.</p> <p>As a first step in constructing the IFFs Indicator is to focus, for each IFF type, on IFFs generated during the <em><u>illicit income generation</u></em>: this refers to the set of transactions – such as those related to international trade of illicit goods - that either directly generate illicit income for an actor during a productive or non-productive illicit activity, or that are performed in the context of the illicit production of goods and services. </p> <table> <tbody> <tr> <td> <p><strong><u>Examples of income generation IFFs related to selected illegal activities</u></strong></p> <p><strong><u>IFFs from drug trafficking</u></strong></p> <p>In a drug producing country, the method to estimate IFFs derived from drug trafficking can be broadly described as follows:</p> <p>All drug produced in the country (P) is either consumed domestically (C), seized by law enforcement (S), exported (E) or lost (L).</p> <p>With that ~~P=C+S+E+L~~<math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>P</mi> <mo>=</mo> <mi>C</mi> <mo>+</mo> <mi>S</mi> <mo>+</mo> <mi>E</mi> <mo>+</mo> <mi>L</mi> </math>.</p> <p>Countries with extended illicit drug cultivation, normally collect data on P, C, and S (losses cannot be estimated and are excluded from the calculations) and annual exports of drugs can be estimated. </p> <p>The value of exports can be measured by the wholesale value of the relevant drug in countries of destination of the drug produced in the country. These data can be retrieved from international data on seizures reported by other Member States (which provide information on the country of origin) and price data, which is as well reported annually through the mandated Annual Report Questionnaire (ARQ) submitted to UNODC (see <a href="about:blank">https://dataunodc.un.org/</a>)</p> <p>This methodology has been applied in Peru, Mexico and Afghanistan<sup><a href="#footnote-121" id="footnote-ref-121">[110]</a></sup> where certain portions of the income generated from drug production and trafficking are accounted for in the national accounts.</p> <p><strong><u>IFFs from smuggling of migrants<sup><a href="#footnote-132" id="footnote-ref-132">[121]</a></sup></u></strong></p> <p>Following the Eurostat manual “Handbook on the compilation of statistics on illegal economic activities in national accounts and balance of payments” four types of smuggling transactions can be distinguished, two of which create IFF:</p> <p><strong>Type I: Resident smugglers and resident migrants </strong>does not cover transnationality and illegal entry and does not create IFFs </p> <p> </p> <p><strong>Type II: Resident smugglers and non-resident migrants</strong></p> <p>Constitutes an export of services and does incur an inward IFF:</p> <p>Export of transportation services = number of non-resident migrants smuggled by resident smugglers * prices</p> <p><strong>Type III: Non-resident smugglers and resident migrants</strong></p> <p>Estimations recorded as import of illegal services and constitute and outward IFF:</p> <p>Import of illegal transportation services = number of residents smuggled by non-resident smugglers * </p> <p>prices</p> <p><strong>Type IV: Non-resident smugglers and non-resident migrants</strong></p> <p>No estimations recorded</p> <p>The pilot studies found the methodology to be feasible, however, limitations on data exist, in particular on pricing.</p> </td> </tr> </tbody> </table> <p>At a second stage, IFFs in relation to <em><u>illicit income management</u></em> are estimated. These refer e.g., to IFFs generated when income generated from illegal activities is invested abroad (e.g., into property). To assess these flows, quantitative and qualitative information held by financial authorities, central banks and other entities concerned with money laundering and financial crimes can be used.</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-8">7</sup><p> Available here: <a href="about:blank">https://ec.europa.eu/eurostat/documents/3859598/8714610/KS-05-17-202-EN-N.pdf/eaf638df-17dc-47a1-9ab7-fe68476100ec</a> <a href="#footnote-ref-8">↑</a></p></div><div><sup class="footnote-number" id="footnote-9">8</sup><p> With one principle difference. The mere transfer of funds (exploitation-type activities and terrorism financing) are not considered in the GDP estimates, as they are not productive transactions and may not be carried out with the mutual agreement of both parties. Such activities can, however, generate noteworthy amounts of illicit income and subsequent IFFs. The present framework includes activities that are not considered as being productive in the framework of the System of National Accounts. <a href="#footnote-ref-9">↑</a></p></div><div><sup class="footnote-number" id="footnote-10">9</sup><p> Available here: <a href="https://unctad.org/system/files/non-official-document/20210917_IFFsGuidelinesForPilots_en_0.pdf">https://unctad.org/system/files/non-official-document/20210917_IFFsGuidelinesForPilots_en_0.pdf</a> <a href="#footnote-ref-10">↑</a></p></div><div><sup class="footnote-number" id="footnote-11">10</sup><p> https://www.fatf-gafi.org/ <a href="#footnote-ref-11">↑</a></p></div><div><sup class="footnote-number" id="footnote-12">11</sup><p> See e.g., National Statistics and Information Authority, Afghanistan and UNODC, “Afghanistan Opium Survey 2018 – Challenges to sustainable development, peace and security”, July 2019. <a href="#footnote-ref-12">↑</a></p></div><div><sup class="footnote-number" id="footnote-13">12</sup><p> The Protocol against the Smuggling of Migrants, supplementing the United Nations Convention against Transnational Organized Crime (the Migrant Smuggling Protocol) defines migrant smuggling as: ”in order to obtain, directly or indirectly, a financial or other material benefits, of the illegal entry of a person into a State Party of which the person is not a national or a permanent resident”. See as well ICCS. <a href="#footnote-ref-13 Further methodological deliberations on income generation / income management are being undertaken by the custodian agencies to be refined, finalized and included in a comprehensive Statistical Framework for the measurement of IFFs. </p> <p>The methodological work of custodians on aggregation to measure IFFs as a single SDG indicator proposes a matrix approach, allowing activities identified to be analysed with respect to an aggregated income generation (IG) and income management (IM) approach as well as according to methods used to measure IFFs from these activities (see Figure 1). Using such a matrix, areas of (potential) overlap between different methods and types of IFFs can be identified – in the figure, by observing which areas are covered by a specific method (marked in green; light green indicates merely partial coverage by a particular method). Further practical studies in countries will be needed to design suitable and robust aggregation methods in the future. </p> <p>Figure 1. Activity-method matrix for aggregated IG-IM representation of IFF measurement</p> <p><img 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"></p> <p>Source: Deliberations by Task Force on the Statistical Measurement</p> <p>It is advised that the estimates of IFFs are reported as the (best) estimate, accompanied by a lower and an upper bound estimate to account for uncertainties in the data sources and methods. Custodian agencies are currently developing further guidance to Member States to be included in the Statistical Framework for measurement of IFFs. </p><div class="footnotes"><div><sup class="footnote-number" id="footnote-8">7</sup><p> Available here: <a href="about:blank">https://ec.europa.eu/eurostat/documents/3859598/8714610/KS-05-17-202-EN-N.pdf/eaf638df-17dc-47a1-9ab7-fe68476100ec</a> <a href="#footnote-ref-8">↑</a></p></div><div><sup class="footnote-number" id="footnote-9">8</sup><p> With one principle difference. The mere transfer of funds (exploitation-type activities and terrorism financing) are not considered in the GDP estimates, as they are not productive transactions and may not be carried out with the mutual agreement of both parties. Such activities can, however, generate noteworthy amounts of illicit income and subsequent IFFs. The present framework includes activities that are not considered as being productive in the framework of the System of National Accounts. <a href="#footnote-ref-9">↑</a></p></div><div><sup class="footnote-number" id="footnote-10">9</sup><p> https://www.fatf-gafi.org/ <a href="#footnote-ref-10">↑</a></p></div><div><sup class="footnote-number" id="footnote-11">10</sup><p> See e.g., National Statistics and Information Authority, Afghanistan and UNODC, “Afghanistan Opium Survey 2018 – Challenges to sustainable development, peace and security”, July 2019. <a href="#footnote-ref-11">↑</a></p></div><div><sup class="footnote-number" id="footnote-12">11</sup><p> The Protocol against the Smuggling of Migrants, supplementing the United Nations Convention against Transnational Organized Crime (the Migrant Smuggling Protocol) defines migrant smuggling as: ”in order to obtain, directly or indirectly, a financial or other material benefits, of the illegal entry of a person into a State Party of which the person is not a national or a permanent resident”. See as well ICCS. <a href="#footnote-ref-12">↑</a></p></div></div> a year ago

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32	File in original format as translated in the repository	YAML file
32	All strings, converted files enriched with comments; suitable for offline translation	Android String Resource	CSV	JSON	JSON nested structure file	gettext PO	iOS strings	TBX	TMX	XLIFF 1.1 with gettext extensions	XLIFF 1.1	XLSX

Review strings changed by other users	`changed:>="1 month ago" AND NOT changed_by:anonymous`	Add
Translated strings	`state:>=translated`	Add
Strings with comments	`has:comment`	Add
Strings with any failing checks	`has:check`	Add
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Approved strings with suggestions	`state:approved AND has:suggestion`	Add
All untranslated strings added the past month	`added:>="1 month ago" AND state:<=needs-editing`	Add
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