Language Approved Translated Unfinished Unfinished words Unfinished characters Untranslated Checks Suggestions Comments
English MIT 0 0 0 0 20 0 0
Arabic MIT 0% 0% 31 2,075 16,456 31 6 0 0
French MIT 0% 9% 28 2,027 16,064 26 8 0 0
Portuguese MIT 0% 0% 31 2,075 16,456 31 0 0 0
Russian MIT 0% 9% 28 2,027 16,064 13 17 0 0
Spanish MIT 0% 9% 28 2,027 16,064 13 17 0 0
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Overview

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 User avatar brockfanning
Translation license MIT License
Translation process
  • Translations can be made directly.
  • Translation suggestions are turned off.
  • Translations are reviewed by dedicated reviewers.
  • Any authenticated user can contribute.
  • The translation uses monolingual files.
  • The translation base language can not be edited.
Source code repository https://github.com/worldbank/sdg-metadata
Repository branch master
Last remote commit Merge pull request #553 from weblate/weblate-sdg-metadata-1-1-1a d3a3ca3e2bb
User avatar brockfanning authored a month ago
Last commit in Weblate Merge pull request #553 from weblate/weblate-sdg-metadata-1-1-1a d3a3ca3e2bb
User avatar brockfanning authored a month ago
Weblate repository https://hosted.weblate.org/git/sdg-metadata/1-1-1a/
File mask translations-metadata/*/17-1-2.yml
Monolingual base language file translations-metadata/en/17-1-2.yml
6 hours ago

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 186 12,450 98,736
Source 31 2,075 16,456
Approved 0% 0 0% 0 0% 0
Waiting for review 4% 9 1% 144 1% 1,176
Translated 21% 40 17% 2,219 17% 17,632
Needs editing 17% 32 31% 3,865 31% 30,819
Read-only 16% 31 16% 2,075 16% 16,456
Failing checks 36% 68 47% 5,967 48% 47,521
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 61% 114 51% 6,366 50% 50,285
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Quick numbers

12,450
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186
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21%
Translated
0
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Trends of last 30 days

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Alert triggered
Component seems unused. 4 months ago
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Resource updated
The “translations-metadata/en/17-1-2.yml” file was changed. a year ago
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String updated in the repository
<p><strong>Data availability:</strong></p>
<p>Classification of the indicator into one of the following three tiers:</p>
<p>We recommend that 17.1.2 remain classified as Tier 1: The indicator is conceptually clear and standards are available. The underlying data are regularly produced by countries, and there is current data available. From the IAEG-SDGs Tier Classification description at https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/, a key criterion is that &#x201C;data are regularly produced by countries for at least 50 per cent of countries&#x201D;. The IMF GFS database, with 130+ regular annual reporting countries using the same reporting format certainly meets this key criterion. All IMF member countries produce revenue (and expenditure) data for surveillance purposes. In recent rounds of soliciting annual GFS series from countries, we have specifically encouraged those countries that were non-reporters over the past few years to (at a minimum) provide the key revenue and expenditure series needed to monitor 17.1.</p>
<p><strong>Disaggregation:</strong></p>
<p>General government units have four types of revenue: (i) compulsory levies in the form of taxes and certain types of social contributions; (ii) property income derived from the ownership of assets; (iii) sales of goods and services; and (iv) other transfers receivable from other units. Of these, compulsory levies and transfers are considered the main sources of revenue for most general government units (GFSM 2014 paragraph 5.1). These four types of revenue are represented by the following aggregates: Taxes, Social contributions, Grants, Other revenue. Similarly, the economic classification of expense identifies eight types of expense incurred according to the economic process involved. For example, compensation of employees, use of goods and services, and consumption of fixed capital all relate to the costs of producing non-market (and, in certain instances, market) goods and services by government. Subsidies, grants, social benefits, and transfers other than grants relate to transfers in cash or in kind, and are aimed at redistributing income and wealth. The functional classification of expense provides information on the purpose for which an expense was incurred. Examples of functions are education, health, and environmental protection. The detailed GFS classification structure used in the annual questionnaire that is used by countries to report data allows for sufficient disaggregation for compiling 17.1.2.</p>
<h1>Доступность данных</h1>
<h2>Текущая доступность данных / уровень показателя:</h2>
<p>Классификация показателя по одному из трех уровней: </p>
<p> Мы рекомендуем оставить показатель 17.1.2 на Уровне 1. Показатель концептуально ясен, стандарты доступны. Базисные данные регулярно формируются странами и текущие данные имеются в наличии. Из описания классификации уровней МЭГ-ЦУР на <a href="https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/"> https://unstats.un.org/sdgs/iaeg-sdgs/ tier-Classification/</a>следует, что ключевым критерием является тот факт, что «данные регулярно формируются не менее чем в 50 процентах стран». База данных СГФ МВФ, включающая более чем 110 стран, представляющих регулярную годовую отчетность и использующих тот же формат отчетности, безусловно, подтверждает соответствие этому ключевому критерию. Все страны-члены МВФ предоставляют данные по доходам (и расходам) для целей контроля. В текущем (2017 года) раунде проведения сбора от стран ежегодных серий данных по СГФ мы специально призвали те страны, которые не направляли отчеты в последние несколько лет, предоставить (как минимум) ключевые ряды данных по доходам и расходам, необходимые для мониторинга показателя 17.1.</p>
<h2>дезагрегирование:</h2>
<p>Единицы сектора государственного управления имеют четыре типа доходов: (i) обязательные сборы в виде налогов и определенных видов социальных отчислений; (ii) имущественный доход, полученный от владения активами; (iii) продажа товаров и услуг; и (iv) прочие переводы, получаемые от других единиц. Из них обязательные сборы и трансферты считаются основными источниками доходов для большинства единиц сектора государственного управления (РСГФ 2014 год, параграф 5.1). Эти четыре типа доходов представлены следующими агрегатами: налоги, социальные отчисления, гранты, прочие доходы. Точно так же экономическая классификация расходов определяет восемь типов расходов, понесенных в соответствии с вовлеченным экономическим процессом. Например, оплата труда сотрудников, использование товаров и услуг и потребление основного капитала - все это связано с затратами на производство нерыночных (и, в некоторых случаях, рыночных) товаров и услуг государством. Субсидии, гранты, социальные пособия и трансферты, помимо грантов, относятся к трансфертам в денежной или натуральной форме и направлены на перераспределение доходов и богатства. Функциональная классификация расходов предоставляет информацию о цели, для которой были понесены расходы. Примеры функций: образование, здравоохранение и охрана окружающей среды. Подробная структура классификации СГФ, используемая в ежегодном вопроснике, который используется странами для представления данных, позволяет провести достаточную дезагрегацию для составления показателя 17.1.2.</p>
a year ago
User avatar None

Source string changed
<p><strong>Data availability:</strong></p>
<p>Classification of the indicator into one of the following three tiers:</p>
<p>We recommend that 17.1.2 remain classified as Tier 1: The indicator is conceptually clear and standards are available. The underlying data are regularly produced by countries, and there is current data available. From the IAEG-SDGs Tier Classification description at https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/, a key criteriaon is that &#x201C;data are regularly produced by countries for at least 50 per cent of countries&#x201D;. The IMF GFS database, with 130+ regular annual reporting countries using the same reporting format certainly meets this key criteriaon. All IMF member countries produce revenue (and expenditure) data for surveillance purposes. In recent rounds of soliciting annual GFS series from countries, we have specifically encouraged those countries that were non-reporters over the past few years to (at a minimum) provide the key revenue and expenditure series needed to monitor 17.1.</p>
<p><strong>Disaggregation:</strong></p>
<p>General government units have four types of revenue: (i) compulsory levies in the form of taxes and certain types of social contributions; (ii) property income derived from the ownership of assets; (iii) sales of goods and services; and (iv) other transfers receivable from other units. Of these, compulsory levies and transfers are considered the main sources of revenue for most general government units (GFSM 2014 paragraph 5.1). These four types of revenue are represented by the following aggregates: Taxes, Social contributions, Grants, Other revenue. Similarly, the economic classification of expense identifies eight types of expense incurred according to the economic process involved. For example, compensation of employees, use of goods and services, and consumption of fixed capital all relate to the costs of producing non-market (and, in certain instances, market) goods and services by government. Subsidies, grants, social benefits, and transfers other than grants relate to transfers in cash or in kind, and are aimed at redistributing income and wealth. The functional classification of expense provides information on the purpose for which an expense was incurred. Examples of functions are education, health, and environmental protection. The detailed GFS classification structure used in the annual questionnaire that is used by countries to report data allows for sufficient disaggregation for compiling 17.1.2.</p>
a year ago
User avatar None

String updated in the repository
<p>The IMF Statistics Department will leverage the existing GFS database to provide cross-country comparable series in a standardized presentation format. </p>
<h2>Сбор региональных и глобальных оценочных данных для глобального мониторинга:</h2>
<p>Статистическое управление МВФ будет использовать существующую базу данных СГФ для формирования сопоставимых рядов данных по странам в стандартизированном формате представления. Мы были бы признательны за дальнейшее обсуждение с Межучрежденческой и экспертной группой по показателям достижения целей в области устойчивого развития (МЭГ-ЦУР) достоинств получения региональных или глобальных агрегированных показателей на основе представленных странами значений этого показателя.</p>
<p>Описание механизма сбора данных от стран, включая: (i) официального партнера на страновом уровне; (ii) описание любого процесса валидации и консультаций;</p>
<p>(iii) описание любых корректировок, касающихся использования стандартных классификаций и гармонизации разбивки по возрастным группам и другим параметрам, или корректировок, сделанных для приведение в соответствие с конкретными международными или национальными определениями.</p>
a year ago
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Source string changed
<p>The IMF Statistics Department will leverage the existing GFS database to provide cross-country comparable series in a standardized presentation format. We would appreciate further discussion with the IAEG SDGs on the merits of deriving regional or global aggregates from the country reported values for this indicator. </p>
a year ago
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String updated in the repository
<p>Not applicable</p>
 
a year ago
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String updated in the repository
<p>GFS budgetary central government revenue series - collected in Table 1 of the annual <a href="https://www.imf.org/external/pubs/ft/gfs/manual/gfs-qtca.htm">GFS Questionnaire</a> provided to all countries - will be combined with series on budgetary central government expenditure (actual execution of the main budget) on &#x201C;expense&#x201D; plus the &#x201C;net acquisition of non-financial assets&#x201D;, as defined in <em>GFSM 2014</em>). GFS Expenditure series are reported by the economic classification in Tables 2, and 3 (items under code 31). Alternatively, for those countries that report total expenditure according to the functional classification (COFOG) in GFS Table 7, a similar calculation can be made. The <em>Proportion of domestic budgetary central government expenditure funded by taxes</em> will be calculated as (Taxes / Expenditure expressed as a %) using the following data series:</p>
<p>An Example: Calculation of SDG Indicator 17.1.2<br></p>
<table>
<tbody>
<tr>
<td>
<p><strong>Total Revenue</strong></p>
</td>
<td>
<p><strong>963</strong></p>
</td>
<td>
<p><strong> </strong></p>
</td>
<td>
<p><strong>Expenditure</strong></p>
</td>
<td>
<p><strong>1200</strong></p>
</td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
</tr>
<tr>
<td>
<p>Taxes</p>
</td>
<td>
<p>800</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Expense</p>
</td>
<td>
<p>950</p>
</td>
</tr>
<tr>
<td>
<p>Social contributions</p>
</td>
<td>
<p>105</p>
</td>
<td>
<p> </p>
</td>
<td rowspan="2">
<p>Net acquisition of nonfinancial assets</p>
</td>
<td>
<p>250</p>
</td>
</tr>
<tr>
<td>
<p>Grants</p>
</td>
<td>
<p>25</p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
</tr>
<tr>
<td>
<p>Other revenue</p>
</td>
<td>
<p>33</p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p><strong>SDG Indicator 17.1.2</strong></p>
</td>
<td>
<p><strong>67%</strong></p>
</td>
</tr>
</tbody>
</table>
<p>Consistency across countries will be ensured through the underlying structure of the IMF GFS database and application of one simple mathematical formula to make computations on the country reported source data used to produce the indicator (no adjustments and/or weighting techniques will be applied). Mixed sources are not being used nor will the calculation change over time (i.e., there are no discontinuities in the underlying series as these are key aggregates/components in all country reported GFS series).</p>
<h1>Методология</h1>
<h2>Метод расчета: </h2>
<p>Ряды данных по доходам бюджетных учреждений центрального правительства СГФ, собранные в Таблице 1 годового вопросника, рассылаемого всем странам, будут объединены с рядами данных по расходам бюджетных учреждений центрального правительства (фактическое исполнение основного бюджета) по затратам плюс чистое приобретение нефинансовых активов, как определено в <em> РСГФ 2014 года </em>). Ряды данных по расходам СГФ представлены по экономической классификации в таблицах 2 и 3 (статьи под кодом 31). В качестве альтернативы, для тех стран, которые представляют общие расходы в соответствии с классификацией функций (Классификация функций органов управления - КФОУ) в таблице 7 СГФ, можно провести аналогичный расчет. <em> Доля внутренних расходов бюджетных учреждений центрального правительства, финансируемых за счет налогов </em>, будет рассчитана как (Налоги / расходы, выраженные в процентах) с использованием следующих рядов данных:<br></p>
<p><img 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"></p>
<p>Согласованность между странами будет обеспечиваться за счет базисной структуры базы данных СГФ МВФ и применения одной простой математической формулы для расчета на исходных данных, представленных странами, которые используются для формирования показателя (никакие корректировки и / или методы взвешивания применяться не будут). Смешанные источники не используются, и метод расчета не будет меняться с течением времени (т. е. в базисных рядах не будет разрывов, поскольку они являются ключевыми агрегатами / компонентами во всех рядах СГФ, представленных странами).</p>
a year ago
User avatar None

Source string changed
<p>GFS budgetary central government revenue series - collected in Table 1 of the annual data q<a href="https://www.imf.org/external/pubs/ft/gfs/manual/gfs-qtca.htm">GFS Questionnaire</a> provided to all countries - will be combined with series on budgetary central government expenditure (actual execution of the main budget) on &#x201C;expense&#x201D; plus the &#x201C;net acquisition of non-financial assets&#x201D;, as defined in <em>GFSM 2014</em>). GFS Expenditure series are reported by the economic classification in Tables 2, and 3 (items under code 31). Alternatively, for those countries that report total expenditure according to the functional classification (COFOG) in GFS Table 7, a similar calculation can be made. The <em>Proportion of domestic budgetary central government expenditure funded by taxes</em> will be calculated as (Taxes / Expenditure expressed as a %) using the following data series:</p>
<p>An Example: Calculation of SDG Indicator 17.1.2<br></p>
<table>
<tbody>
<tr>
<td>
<p><strong>Total Revenue</strong></p>
</td>
<td>
<p><strong>963</strong></p>
</td>
<td>
<p><strong> </strong></p>
</td>
<td>
<p><strong>Expenditure</strong></p>
</td>
<td>
<p><strong>1200</strong></p>
</td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
</tr>
<tr>
<td>
<p>Taxes</p>
</td>
<td>
<p>800</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Expense</p>
</td>
<td>
<p>950</p>
</td>
</tr>
<tr>
<td>
<p>Social contributions</p>
</td>
<td>
<p>105</p>
</td>
<td>
<p> </p>
</td>
<td rowspan="2">
<p>Net acquisition of nonfinancial assets</p>
</td>
<td>
<p>250</p>
</td>
</tr>
<tr>
<td>
<p>Grants</p>
</td>
<td>
<p>25</p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
</tr>
<tr>
<td>
<p>Other revenue</p>
</td>
<td>
<p>33</p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p><strong>SDG Indicator 17.1.2</strong></p>
</td>
<td>
<p><strong>67%</strong></p>
</td>
</tr>
</tbody>
</table>
<p>Consistency across countries will be ensured through the underlying structure of the IMF GFS database and application of one simple mathematical formulas to make computations on the country reported source data used to produce the indicator (no adjustments and/or weighting techniques will be applied). Mixed sources are not being used nor will the calculation change over time (i.e., there are no discontinuities in the underlying series as these are key aggregates/ components in all country reported GFS series).</p>
a year ago
User avatar None

String updated in the repository
<p>At this time, the IMF recommends no regional and global aggregates be established. While we see no issues in terms of the feasibility and suitability of 17.1.2 for cross-country comparisons, we question the relevance of one single global indicator that combines data for advanced economies with those of emerging market and low-income countries. </p>
<p>For reporting this indicator, budgetary central government is considered the most appropriate level of institutional coverage as it will encompass all countries. In principle, GFS should cover all entities that materially affect fiscal policies. However, for most developing and many emerging market economies compiling data for the consolidated general government and its subsectors is problematic owing to limitations in the availability and/or timeliness of source data. A country may have one central government; several state, provincial, or regional governments; and many local governments, and the GFSM 2014 recommends that statistics should be compiled for all such general government units. This reporting structure is illustrated below:</p>
<p>Structure of the general government sector and its subsectors</p>
<table>
<tbody>
<tr>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td colspan="2">
<p>General Government</p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td>
<p> </p>
</td>
<td rowspan="3">
<p>Memorandum: Central Govt. (incl. SSF of central level)</p>
</td>
</tr>
<tr>
<td colspan="4">
<p>Central Government (excluding social security funds)</p>
</td>
<td rowspan="2">
<p>Social Security Funds</p>
</td>
<td rowspan="2">
<p>State Governments</p>
</td>
<td rowspan="2">
<p>Local Governments</p>
</td>
<td rowspan="2">
<p>Consolidation Column</p>
</td>
<td rowspan="2">
<p>General Government</p>
</td>
</tr>
<tr>
<td>
<p>Budgetary</p>
</td>
<td>
<p>Extrabudgetary</p>
</td>
<td>
<p>Consolidation Column</p>
</td>
<td>
<p>Central Government</p>
</td>
</tr>
<tr>
<td>
<p>BA = GL1</p>
</td>
<td>
<p>EA</p>
</td>
<td>
<p>CC</p>
</td>
<td>
<p>CG</p>
</td>
<td>
<p>SSF</p>
</td>
<td>
<p>SG</p>
</td>
<td>
<p>LG</p>
</td>
<td>
<p>CT</p>
</td>
<td>
<p>GG = GL3</p>
</td>
<td>
<p>GL2</p>
</td>
</tr>
</tbody>
</table>
<p>There are some countries that report &#x201C;consolidated central government&#x201D; without necessarily providing the budgetary central government sub-sector separately. The IMF intends to provide data for the budgetary central government and will work to address this issue, where needed, as outlined under section 5, above.</p>
<h2>Комментарии и ограничения:</h2>
<p>В настоящее время МВФ не рекомендует формировать региональные и глобальные агрегированные показатели. Хотя мы не видим проблем с точки зрения осуществимости и пригодности показателя 17.1.2 для межстрановых сравнений, мы сомневаемся в актуальности единого глобального показателя, который объединит данные по странам с развитой экономикой, странам с формирующимся рынком и странам с низким уровнем дохода. </p>
<p>Для отчетности по этому показателю бюджетные учреждения центрального правительства считаются наиболее подходящим уровнем институционального охвата, поскольку показатель будет охватывать все страны. В принципе, СГФ должна охватывать все организации, которые существенно влияют на налогово-бюджетную политику. Однако для большинства развивающихся стран и многих стран с формирующейся рыночной экономикой составление данных для консолидированного сектора государственного управления и его подсекторов проблематично из-за ограничений в доступности и / или своевременности исходных данных. В стране может быть одно центральное правительство; правительства нескольких штатов, провинций или регионов; и много местных органов власти, а РСГФ 2014 года рекомендует составлять статистику по всем таким единицам сектора государственного управления. Эта структура отчетности проиллюстрирована ниже:</p>
<p><img 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"></p>
<p>Есть некоторые страны, которые представляют отчет о консолидированном центральном правительстве без обязательного предоставления отдельно подсектора бюджетных учреждений центрального правительства. МВФ намерен предоставлять данные по бюджетным учреждениям центрального правительства и будет работать над решением этой проблемы, где это необходимо, как сказано в разделе 5 выше.</p>
a year ago
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