2.1. SO 2-1 – Trends in population living below the relative poverty line and/or income inequality in affected areas
Indicator SO 2-1 estimates the well-being of populations in monetary terms.
Two metrics are used for this purpose and Parties should specify which metrics they would like to use:
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Proportion of the population below the international poverty line, or
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Income inequality.
These metrics can be used interchangeably according to country-specific conditions.
The proportion of the population below the international poverty line is generally considered relevant to less developed countries, where extreme poverty and destitution are core development challenges. The international poverty line is currently set at USD 1.90 a day, based on 2011 purchasing power parity. Therefore, the proportion of the population below the international poverty line is defined as the percentage of the population living on less than USD 1.90 a day at 2011 international prices.
Income inequality is a useful metric for both low-income and middle-income countries as it estimates the extent of wealth distribution in a region. It is estimated through the Gini index. The Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.
National reporting is facilitated though the provision of default data. As the proportion of population below the international poverty line by sex, age, employment status and geographical location (urban/rural) is also a Sustainable Development Goal (SDG) indicator (SDG indicator 1.1.1), default data is pre-filled from the SDG database. For income inequality (i.e., the Gini index), default data is pre-filled from the World Bank database1.
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An in-depth reading of SDG indicator 1.1.1 metadata and Gini index metadata (see section 2.1.7).
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Data complying with the specifications listed in table 15.
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A pool of national experts officially nominated by the national authorities to verify the suitability and consistency of the default data against the situation in their country, or to identify and compile data using national sources for the three metrics. Key institutions might include a country’s national statistical office and the ministry of finance, as well as universities and research centres.
The step-by-step procedure for reporting is described in the following.
Parties are invited to choose the most suitable metric to represent the well-being of the population in their countries.
The proportion of population below the international poverty line data is pre-filled from the SDG database, while income inequality (Gini index) data is pre-filled from the World Bank database.
Parties may also use national data, provided it complies with the data specifications listed in table 15.
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* - {rspan}`1` Item
- {cspan}`1` Specifications
* - __Default data__
(Sustainable Development Goal indicator 1.1.1 data and Gini index World Bank data)
- __National data__
* - Data type
- Annual data on one of the two metrics for the period 2000–2019.
- Annual data on one of the two metrics for the period from 2000 to the latest available year for the reporting period.
* - __Spatial resolution__
- Country level
- Country or sub-national levels
* - __Quality__
- Specified in the datasets’ metadata.
- To be indicated in the dataset metadata.
* - __Metadata__
- Metadata information is provided with default data.
- Minimum metadata content as per the mandatory fields listed in Annex II.
Related areas in the PRAIS 4 platform: tables SO2-1.T1, SO2-1.T2 and SO2-1.T3
Parties opting to use an alternative source of national data may enter the relevant national annual values in tables SO2-1.T1 or SO2-1.T2, according to the chosen metric.
To assist in the data interpretation, countries are encouraged to visualize their respective metrics by means of a graph (graphs for each country are available on the World Bank website). While it may be difficult to attribute specific causal factors to changes in the metrics, countries may indicate which direct and/or indirect drivers are presumably behind the observed changes and report this information in the Qualitative Assessment table (i.e., Table SO2-1.T3).
The reliability of the estimates from global data sources requires inputs from national experts to detect and highlight situations where the confidence level of the obtained results might be low. This input would contribute to a qualitative assessment of the reliability of the estimates.
Once verified by the Parties, the estimates of the proportion of population below the international poverty line, or income inequality should be officially submitted to the United Nations Convention to Combat Desertification (UNCCD). Observed changes and their interpretation may be described in the “Qualitative Assessment” table of the PRAIS 4 platform.
Optionally, Parties may include additional information in the General Comment field to describe specific country situations. Sub-national disaggregated data (e.g., per administrative division, urban vs rural, affected areas or other socio-economic strata, e.g., sex-disaggregated data) may be useful to identify where the most significant poverty/income inequality hotspots/brightspots are located.
Parties are also encouraged to submit narratives on the methodology, data sources and data accuracy in the event that the estimates are derived from national data. It would also be beneficial to report on special cases and issues, describing any deviation from the default method and providing the rationale to adopt a different methodology.
Indicator SO 2-1 has no dependencies from other SO, however it could be used in the calculation of the Drought Vulnerability Index (DVI) for indicator SO 3-3.
- International global data only generically describes the well-being of the population in a country and might not capture specific situations in need of consideration. More detailed sub-national data might be needed to represent the economic situation at the local level.
Key actions for reporting on indicator SO 2-1 are as follows:
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Choose the most suitable metric: Parties are encouraged to choose the most suitable metric to represent the well-being of the population in their countries.
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Identify the relevant dataset: Parties may decide to use the default data or alternative national sources.
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Report national annual values of the chosen metric and interpret the data: Parties are invited to report, visualize and interpret the national annual data.
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Verify the results: the reliability of the estimates from global data sources requires inputs from national experts to qualitatively assess the reliability of the estimates based on expert knowledge.
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Generate reports: once verified by the Parties, the data and supporting narrative should be officially submitted to the UNCCD.
- SDG indicator 1.1.1 metadata (https://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf)
- Gini index metadata (https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SI.POV.GINI)
Having access to water is a key determinant of child survival, maternal and child health, family well-being and economic productivity. Accordingly, an increasing trend in access to safe drinking water would help improve the living conditions of affected populations.
In order to quantify safely managed drinking water, the proportion of population using improved drinking water services is determined. This is currently being measured by the proportion of population using an improved basic drinking water source. ‘Improved’ drinking water sources are defined as piped (into dwellings, yards or plots; public taps or standpipes) and non-piped (boreholes or tube wells; protected dug wells; protected springs; rainwater; packaged or delivered water) sources which are located on the premises, available when needed, and free from fecal and priority chemical contamination.
National reporting is facilitated through the provision of default data derived from the SDG database. The proportion of population using safely managed drinking water services is SDG indicator 6.1.1. The indicator is disaggregated by urban and rural populations, and expressed as a percentage. Custodian agencies for this indicator are the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF) which, through the Joint Monitoring Programme (JMP) for Water, Sanitation and Hygiene (WASH), have produced regular estimates of national, regional and global progress on drinking water, sanitation and hygiene since 1990.
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An in-depth reading of the SDG indicator 6.1.1 metadata (see section 2.2.7).
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Data complying with the specifications listed in table 16.
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A pool of national experts officially nominated by the national authorities to verify the suitability and consistency of the default data against the situation in their country, or to identify and compile data using national sources for the three metrics. Key institutions might include a country’s national statistical office, ministry of health and ministry of water resources, as well as universities and research centres.
The step-by-step procedure for reporting is described in the following.
Default data for this indicator is pre-filled from the SDG database (SDG indicator 6.1.1); estimates of the proportion of population using improved drinking water services are regularly produced by the WHO/UNICEF JMP.
Parties may also use national data, provided it complies with the data specifications listed in table 16.
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* - {rspan}`1` Item
- {cspan}`1` Specifications
* - __Default data__
(Sustainable Development Goal indicator 6.1.1 / World Health Organization / United Nations Children’s Fund Joint Monitoring Programme)
- __National data__
* - __Type of data__
- Annual data on the total, urban and rural population using safely managed drinking water services (% of population) for the period 2000–2020.
- Annual data on the total, urban and rural population using safely managed drinking water services (% of population) for the period from 2000 to the latest available year for the reporting period.
* - __Spatial resolution__
- Country level
- Country or sub-national levels
* - __Quality__
- Specified in the datasets’ metadata.
- To be indicated in the dataset metadata.
* - __Metadata__
- Metadata information is provided with default data.
- Minimum metadata content as per the mandatory fields listed in Annex II.
Related areas in the PRAIS 4 platform: tables SO2-2.T1 and SO2-2.T2
Parties opting to use an alternative source of national data may enter the relevant data in table SO2-2.T1. Parties may also provide information on the dominant change in the metric using the “Qualitative Assessment” table SO2-2.T2.
To assist in the data interpretation, countries are encouraged to visualize their respective SDG Indicator 6.1.1 by means of a graph (graphs for each country, representing each disaggregation, i.e., % rural population, % urban population, % total population, are available to view and download from the JMP and World Bank websites)2. While it may be difficult to attribute specific causal factors to changes in the metrics, countries may indicate which direct and/or indirect drivers are presumably behind the observed changes and report this information in the Qualitative Assessment table.
The reliability of the estimates from global data sources requires inputs from national experts to detect and highlight situations where the confidence level of the obtained results might be low. This input would contribute to a qualitative assessment of the reliability of the estimates.
Once verified by the Parties, the estimates of the proportion of population using safely managed drinking water services should be officially submitted to the UNCCD.
Disaggregated data for this metric (e.g., per administrative division, urban vs rural, affected areas or other socio-economic strata, e.g., sex-disaggregated data) may be useful to identify where the most significant hotspots/brightspots are located. Optionally, Parties may include additional information to describe specific country situations and provide more details on data interpretation.
Parties are also encouraged to submit narratives on the methodology, data sources and data accuracy in the event that the estimates are derived from national data. It would also be beneficial to report on special cases and issues, describing any deviation from the default method and providing the rationale to adopt a different methodology. A General Comment field is provided in the PRAIS 4 platform for this purpose.
Indicator SO 2-2 has no dependencies from other SOs. However, it could be used in the calculation of the Drought Vulnerability Index (DVI) for indicator SO 3-3.
- International global data only generically describes the well-being of the population in a country and might not capture specific situations in need of consideration. More detailed sub-national data might be needed to represent the economic situation at the local level.
Key actions for reporting on indicator SO 2-2 are as follows:
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Identify the relevant dataset: Parties may decide to use the recommended default international data or alternative national sources.
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Report national annual values and interpret the data: Parties are invited to report, visualize and interpret the national annual data.
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Verify the results: the reliability of the estimates from global data sources requires inputs from national experts to qualitatively assess the reliability of the estimates based on expert knowledge.
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Generate reports: once verified by the Parties, the data and supporting narrative should be officially submitted to the UNCCD.
- SDG indicator 6.1.1 metadata (https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf)
Indicator SO 3-2 was developed in response to decision 11/COP.14 to align the reporting process for SO 1 to 5 with gender-responsive indicators and guidelines and ensure that the gender dimensions of land degradation are captured.
The indicator estimates the proportion of populations exposed to land degradation, disaggregated by sex, as a first step towards addressing the gender data gap on land degradation within the UNCCD reporting framework. The methodology uses the spatial distribution of the population or sub-population group (i.e., by sex) to establish its exposure to land degradation, as determined by indicator SO 1-4 (i.e., SDG Indicator 15.3.1).
The indicator trends in the proportion of population exposed to land degradation, disaggregated by sex, uses the following metrics:
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Percentage of the female population exposed to land degradation
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Percentage of the male population exposed to land degradation
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Percentage of the total (female and male) population exposed to land degradation
National reporting is facilitated though the provision of default data derived from the Worldpop global dataset on population distributions, demographics and dynamics and the default indicator SO 1-4 estimates.
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An in-depth reading of the methodological note for indicator SO 3-2 (see section 2.3.7).
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Population data complying with the specifications listed in table 17.
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A pool of national experts officially nominated by the national authorities to verify the suitability and consistency of the default data against the situation in their country, or to identify and compile data using national sources for the three metrics. Key institutions might include a country’s national statistical office, ministry of environment and ministry of agriculture, as well as universities and research centres.
The step-by-step procedure for reporting is described in the following. If Parties decide to use the default data, steps 2 and 3 are unnecessary.
Suitable data for the calculation of indicator SO 2-3 is a sex-disaggregated gridded count of the human population, or a georeferenced set of sub-national data that covers the full extent of the country. It must represent the number of male and female individuals per grid cell, ideally annually, in the time period in question (i.e., the date timestamp should be at least one of the years within the baseline and reporting period).
Among the publicly available population datasets at the global scale, the WorldPop dataset is used by default by the UNCCD for calculating indicator SO2-3 and provided to Parties in Trends.Earth.
An alternative dataset is the Gridded Population of the World, version 4 (GPWv4).
Parties may also use national data, provided it complies with the data specifications listed in table 17.
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* - {rspan}`1` Item
- {cspan}`1` Specifications
* - Default data
- National data
* - __Input data__
(Data needed to estimate the population exposed to land degradation)
- WorldPop data disaggregated by sex for the baseline year (2015) and the latest available year of the reporting period (2019).
Gridded data on land degradation as determined by indicator SO 1-4 for the baseline and reporting periods.
- Gridded population products derived from national official statistics, disaggregated by sex for the baseline year (ideally the year 2015) and the latest available year of the reporting period (e.g., 2019).
Gridded data on land degradation as determined by indicator SO 1-4 for the baseline and reporting periods.
* - __Output data__
(Gridded products resulting from the analysis of the three metrics)
- Gridded products of the female, male and total population exposed to land degradation in the baseline and reporting periods.
- Gridded products of the female, male and total population exposed to land degradation in the baseline and reporting periods.
* - __Spatial resolution__
- WorldPop data: 3-arc seconds (~100 m)
- Assessed by national authorities based on available data.
* - __Quality__
- Specified in the datasets’ metadata.
- To be indicated in the dataset metadata.
* - __Metadata__
- Metadata information is provided with default data.
- Minimum metadata content as per the mandatory fields listed in Annex II.
The population and the land degradation datasets must be harmonized to the same grid cell size. For example, the WorldPop dataset and the SO 1-4 land degradation default dataset have resolutions of 100 and 300 metres, respectively and should be resampled to a common grid cell size. For the default data, the grid cell size for the analysis is fixed at the 300 metre resolution of the land degradation dataset to which the population data is resampled. Countries using national datasets should assess them in terms of projection and resolution and standardize them through a resampling process in order to be able to combine them in the analysis of population exposure to land degradation.
The resampling should take into consideration that, for datasets representing population counts, changes in cell size implies changes in the number of people in each cell; a resampling method that ensures the integrity of the continuous data should be used, such as bilinear interpolation (avoid nearest neighbour techniques).
Step 3: Estimate the female, male and total population count and percentage exposed to land degradation
Related areas in the PRAIS 4 platform: tables SO2-3.T1 and SO2-3.T2
The female and male population grids for the baseline and reporting periods are intersected with the respective land degradation grids. The values of the cells falling on degraded land are then combined to derive the female and male population exposed to land degradation. The total population exposed to land degradation is obtained by combining the obtained female and male population values.
This analysis should be carried out over two time periods (i.e., the baseline and reporting period) in order to measure changes over time and report the observed change in table SO2-3.T2. However, it should be noted that the land degradation spatial dataset (i.e., the SO1-4 output) captures temporal trends in the three subindicators (land cover, land productivity and soil organic carbon (SOC)) over a certain number of years, whereas population data reflects the populations in specific years (e.g., 2015 and 2019). To increase accuracy in capturing the number of people exposed to land degradation in the two reference years (i.e., 2015 for the baseline and 2019 for reporting period), it is recommended that the population gird closest to the above-mentioned years be used.
To calculate the percentage of female, male and total population exposed to land degradation, the respective populations exposed to land degradation are divided by the total populations of the corresponding sex types, multiplied by 100.
Related areas in the PRAIS 4 platform: table SO2-3.T3
Observed changes in the indicator and their interpretation may be described in the “Qualitative Assessment” table of the PRAIS 4 platform (table SO2-3.T3).
It is important to note that changes in the proportion of population exposure to land degradation may not only be due to the expansion of land degradation but also to population growth, among other factors.
The reliability of the estimates from global data sources requires inputs from national experts to identify and highlight situations where the confidence level of the obtained results might be low.
Once verified by the Parties, the estimates of the female, male and total population exposed to land degradation should be officially submitted to the UNCCD.
Default maps or maps generated in Trends.Earth using national data representing population exposure to land degradation by sex are made available in the PRAIS 4 platform. More specifically, the following maps will be available online:
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Total population exposed to land degradation
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Female population exposed to land degradation
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Male population exposed to land degradation
Parties are also encouraged to submit narratives on the methodology, data sources and data accuracy in the event that the estimates are derived from national data. It would also be beneficial to report on special cases and issues, describing any deviation from the default method and providing the rationale to adopt a different methodology. A “General comment” field is provided in the PRAIS 4 platform for this purpose.
Indicator SO 2-3 relies on the SO 4 indicator spatial datasets, both for the baseline and reporting periods, as a basis to identify degraded areas.
- Spatial resolution of international data might not always be suitable to produce a sufficiently detailed representation of the population exposed to land degradation and its changes. More detailed sub-national data might be needed to represent local situations with a higher degree of accuracy. However, this will require downscaling of existing gridded population datasets to a finer resolution which might incur further errors. Capacity in performing downscaling processes is therefore required.
- The WorldPop sex-disaggregated national datasets are presented as several individual rasters, each representing an age/sex class per year. This amounts to a large volume of spatial data in Geotiff format. Capacity in raster data processing and access to appropriate computing power, e.g., a cloud service, is required to store and process the data, especially for large countries. The UNCCD is developing a procedure for the bulk preprocessing of raster data, which will eventually make sex-disaggregated data available on the PRAIS 4 platform as default data. Parties will be notified when the challenge is solved and the forms pre-filled with the default data.
- Sex-disaggregated data alone might not be sufficient to represent the gender dynamics and related issues in a specific region. Further socio-economic and demographic indicators are required to conduct gender analysis in order to better understand how and why specific populations are affected by land degradation.
- On-site exposed populations to land degradation may produce lower-bound estimates of the exposure of populations to land degradation. In fact, land degradation in a specific area affects not only populations residing on degraded land, but also – through environmental, economic and social linkages – populations elsewhere. In addition, further disaggregation of data in urban and rural populations could be useful to improve the indicator.
- There are two challenges related to the temporality of the analysis: i) the land degradation spatial dataset (i.e., the SO1-4 output) captures temporal trends over a certain number of years, whereas population data reflects the populations in specific years; ii) changes in the proportion of population exposure to land degradation over time may not only be due to the expansion of land degradation but also to population growth, among other factors.
Key actions for reporting on population exposure to land degradation are as follows:
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Select the population dataset: Parties may decide to use the default data or alternative national sources, provided they comply with the data specifications listed in table 17.
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Standardize the selected datasets: the land degradation datasets must be harmonized to the same grid cell size as the population gridded data (assuming it is the finer resolution) in order to combine them in the analysis of population exposure to land degradation.
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Estimate the number and percentage of the female, male and total population exposed to land degradation: the male and female population grids are intersected with the land degradation grid to derive the total, male and female population exposed to land degradation and the percentage of the total population. Data should be entered in tables SO2-3.T1.
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Qualitatively assess the results: changes in the proportion of populations exposed to land degradation as well as their direct or indirect drivers should be described in table SO2-3.T3.
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Verify the results: the reliability of the estimates from global data sources should be assessed in consultation with national experts.
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Generate reports: once verified by the Parties, the data and supporting narrative should be officially submitted to the UNCCD.
- Methodological note on trends in population exposure to land degradation (https://www.unccd.int/sites/default/files/inline-files/MethodologicalNote_PopExposureToLD.pdf)