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
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Last commit in Weblate Merge pull request #553 from weblate/weblate-sdg-metadata-1-1-1a d3a3ca3e2bb
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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
Translation file Download translations-metadata/es/17-1-2.yml
Last change Aug. 19, 2023, 5:01 p.m.
Last change made by None
Language Spanish
Language code es
Text direction Left to right
Number of speakers 506,540,366
Number of plurals 3
Plural type One/many/other
Plurals One 1
Many 1000000, 2000000
Other 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, …
Plural formula (n == 1) ? 0 : ((n != 0 && n % 1000000 == 0) ? 1 : 2)
5 hours ago

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 31 2,075 16,456
Approved 0% 0 0% 0 0% 0
Waiting for review 9% 3 2% 48 2% 392
Translated 9% 3 2% 48 2% 392
Needs editing 48% 15 93% 1,930 93% 15,392
Read-only 0% 0 0% 0 0% 0
Failing checks 54% 17 93% 1,948 94% 15,506
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 41% 13 4% 97 4% 672
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The “translations-metadata/en/17-1-2.yml” file was changed. a year ago
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<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>Disponibilidad de datos</h1>
<h2>Disponibilidad actual de datos / nivel del indicador:</h2>
<p>Clasificación del indicador en uno de los tres niveles siguientes:</p>
<p>Recomendamos que el indicador 17.1.2 permanezca clasificado en el nivel 1: el indicador es conceptualmente claro y los estándares están disponibles. Los datos subyacentes son producidos regularmente por los países y hay datos actuales disponibles. Según la descripción de la clasificación por niveles de IAEG-ODS en <a href="https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/">https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/</a>, un criterio clave es que &#x201C;los países produzcan regularmente los datos de al menos el 50% de los países. La base de datos de EFP del FMI, con más de 110 países que presentan informes anuales con el mismo formato, cumple sin duda este criterio clave. Todos los países miembros del FMI producen datos de ingresos (y gastos) con fines de supervisión. En la ronda actual (2017) de solicitud de series anuales de las EFP a los países, hemos alentado específicamente a los países que no informaron en los últimos años a que (como mínimo) proporcionen las series clave de ingresos y gastos necesarias para la supervisión del indicador 17.1..</p>
<h2>Desagregación:</h2>
<p> Las administraciones públicas tienen cuatro tipos de ingresos: (i) las exacciones obligatorias en forma de impuestos y ciertos tipos de contribuciones sociales; (ii) las rentas de la propiedad derivadas de la posesión de activos; (iii) las ventas de bienes y servicios; y (iv) otras transferencias a cobrar de otras unidades. De estos, los gravámenes obligatorios y las transferencias se consideran las principales fuentes de ingresos de la mayoría de las unidades de las administraciones públicas (MEPF 2014, párrafo 5.1). Estos cuatro tipos de ingresos están representados por los siguientes agregados: Impuestos, Contribuciones Sociales, Subvenciones, Otros ingresos. Del mismo modo, la clasificación económica de los gastos identifica ocho tipos de gastos incurridos según el proceso económico de que se trate. Por ejemplo, la remuneración de los trabajadores, el uso de bienes y servicios y el consumo de capital fijo están relacionados con los costes de producción de bienes y servicios que no son de mercado (y, en algunos casos, de mercado) por parte de la administración. Las subvenciones, las ayudas, las prestaciones sociales y las transferencias distintas de las subvenciones se refieren a las transferencias en metálico o en especie, y tienen por objeto redistribuir la renta y la riqueza. La clasificación funcional del gasto proporciona información sobre la finalidad para la que se ha realizado un gasto. Ejemplos de funciones son la educación, la sanidad y la protección del medio ambiente. La estructura de clasificación detallada de las EFP utilizada en el cuestionario anual que los países utilizan para informar de los datos permite un desglose suficiente para la compilación del indicador 17.1.2..</p>
a year ago
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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
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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>Estimaciones regionales y globales &amp;recopilación de datos para el seguimiento global:</h2>
<p>El Departamento de Estadística del FMI aprovechará la base de datos existente de EFP para proporcionar series comparables entre países en un formato de presentación estandarizado. Apreciaríamos que se discutiera más a fondo con el IAEG-SDGs (según su sigla en inglés) sobre las ventajas de derivar agregados regionales o globales a partir de los valores reportados por los países para este indicador. </p>
<p> Descripción del mecanismo de recopilación de datos de los países, incluyendo (i) la contraparte oficial a nivel de país; (ii) descripción de cualquier proceso de validación y consulta;</p>
<p>(iii) descripción de cualquier ajuste con respecto al uso de clasificaciones estándar y la armonización de los desgloses por grupo de edad y otras dimensiones, o los ajustes realizados para cumplir con definiciones internacionales o nacionales específicas.</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>Metodología</h1>
<h2>Método de cálculo: </h2>
<p>Las series de ingresos de la administración central presupuestaria de las EFP -recolectadas en el cuadro 1 del cuestionario de datos anual que se facilita a todos los países- se combinarán con las series de gastos de la administración central presupuestaria (ejecución real del presupuesto principal) en &#x201C;gasto&#x201D; más la &#x201C;adquisición neta de activos no financieros&#x201D;, según la definición del <em>MEFP 2014</em>). Las series de gasto de las EFP se reportan por la clasificación económica en las Tablas 2, y 3 (partidas bajo el código 31). Alternativamente, para aquellos países que reportan el gasto total según la clasificación de las funciones del gobierno (CFG) en la Tabla 7 de las EFP, se puede hacer un cálculo similar. La <em>Proporción del gasto del gobierno central presupuestario nacional financiado por impuestos</em> se calculará como (Impuestos / Gasto expresado en %) utilizando las siguientes series de datos:<br></p>
<p><img 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"></p>
<p>La coherencia entre países se garantizará a través de la estructura subyacente de la base de datos EFP del FMI y de la aplicación de una fórmula matemática simple para realizar los cálculos sobre los datos fuente declarados por el país y utilizados para producir el indicador (no se aplicarán ajustes y/o técnicas de ponderación). No se utilizarán fuentes mixtas ni el cálculo cambiará a lo largo del tiempo (es decir, no hay discontinuidades en las series subyacentes, ya que se trata de agregados/componentes clave en todas las series de las EFP comunicadas por los países).</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>Comentarios y limitaciones:</h2>
<p>En este momento, el FMI recomienda que no se establezcan agregados regionales y globales. Aunque no vemos problemas en cuanto a la viabilidad y la idoneidad del indicador 17.1.2 para las comparaciones entre países, cuestionamos la pertinencia de un único indicador mundial que combine los datos de las economías avanzadas con los de los países de mercados emergentes y de renta baja.</p>
<p>Para informar sobre este indicador, el gobierno central presupuestario se considera el nivel más apropiado de cobertura institucional, ya que abarcará todos los países. En principio, las EFP deberían cubrir todas las entidades que afectan materialmente a las políticas fiscales. Sin embargo, para la mayoría de las economías en desarrollo y muchas economías de mercado emergentes, la compilación de datos para el gobierno general consolidado y sus sub-sectores es problemática debido a las limitaciones en la disponibilidad y/o puntualidad de los datos fuente. Un país puede tener un gobierno central, varios gobiernos estatales, provinciales o regionales, y muchos gobiernos locales, y el MEFP 2014 recomienda que se compilen estadísticas para todas esas unidades del gobierno general. Esta estructura de información se ilustra a continuación:</p>
<p><img 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"></p>
<p>Hay algunos países que informan sobre el gobierno central consolidado sin proporcionar necesariamente el sub-sector del gobierno central presupuestario por separado. El FMI tiene la intención de proporcionar datos para el gobierno central presupuestario y trabajará para resolver este problema, cuando sea necesario, como se indica en la sección 5, más arriba.</p>
a year ago
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Source string changed
<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>
a year ago
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