Definitions of inclusion and exclusion of PwCD in primary studies.

account.t.d. The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution (see the definition for "financial institution account") or report personally using a mobile money service in the past year (see the definition for "mobile money account"). The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

Students with Disabilities reports the number of enrolled PreK students with disabilities, by age.

account.t.d. The percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution (see the definition for "financial institution account") or report personally using a mobile money service in the past year (see the definition for "mobile money account"). The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

Abstract

The PFR activities to be evaluated at end-line consists mainly of demarcation and registration of land parcels (under customary tenure) as Titre Foncier or an Attestation de Droit Coutumière. The impact evaluation aims to quantify and analyse impact of these interventions on productivity and food security disaggregated by target groups and gender.

The research questions to be answered after the endline data collection are:

1) Do PFRs (or ADCs) contribute to a perception of greater land tenure security? 2) Does improved tenure security lean to a growth in agricultural investment and/or changes to management of land? 3) Do PFRs improve access to land and rights over land among marginalised groups (women, youth and migrants)? 4) Do PFRs lead to an increased number of land transactions? 5) Does increased land security address existing constraints on land markets and lead to more efficient allocation of land resources and thereby an increase in productivity? 6) Do property rights and improved user rights result in better access to credit, possibly allowing for income diversification and thus increasing household welfare? 7) Do the new arrangements put in place during the implementation of the PFRs facilitate the resolution of land conflicts, or even prevent the emergence of these land conflicts?

Geographic coverage

The clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.

Analysis unit

• Villages
• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The impact evaluation consists of gender and youth disaggregated data collection at base line, before the start of the intervention, in both the treatment and control villages. End line data will be collected at least 2 growing seasons after issuing of documentation to farmers.

The sample consisted of 2968 households, which were taken from 26 villages selected for the implementation of a Plan Foncier Rural (PFR), or rural landholding plans, these were the treatment villages and 27 control villages that did not benefit from a PFR.

The treatment villages were assigned by the ProPFR team in geographic clusters. The assignment of control villages followed this geographic clustering, also using further village level data with the aim of finding similar villages to maximize comparability. These clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.

Villages were selected from 11 geographical clusters of villages facing similar issues, allowing easier logistical planning for the rollout of the PFRs.

Villages selected to be part of the programme had the following characteristics: • Bordering/near to a classified national forest • At high risk of land grabbing, • The presence of another GIZ supported SEWOH project1 • Agropastoral areas (in particular the presence of transhumance –cattle driving - corridors)

But should not have the following: • Villages bordering Nigeria, within the band of increased security • MCA intervention with a PFR • Suffered serious conflict which could block the realisation of a PFR, or where a PFR may reignite past conflicts.

These characteristics alongside the desire of the implementing team to select villages in clusters, for practical reasons presented the first challenge in selecting suitable comparison villages to measure the impact of the ProPFR programme. Clustering meant that villages selected for comparison should be near the clusters to be comparable, but given the typical geography of villages in northern Benin, in that most people live in the village centre rather than spread evenly with sufficient density at the village boundary, and the lack of clearly defined village boundaries, a geographic discontinuity could not be exploited.

The second challenge in selecting comparison villages arose due to a change in the village definitions in 2013, when Benin changed from 3758 to 5290 villages which is often referred to as the “nouveau découpage”. Some old villages were split but there are no clearly defined village boundaries for the new set of villages. ProPFR selected from among the new villages, so the control villages also needed to be selected from this list. Given that the last census was collected prior to this new definition of villages, no data about the villages existed that could easily be used in matching villages to those selected for the ProPFR.

Due to this lack of data on the characteristics of the people residing in the villages, Geographical Information Systems (GIS) data were used to match each of the treatment PFR villages to a control village. Villages which were previously included in the MCA’s wave of PFRs were excluded from our study due to the difficulty in separating the effects of the two programs (MCA vs ProPFR). For each PFR village, a buffer of 20km was drawn and the union constructed for each cluster. Within this area, other villages were considered as a potential control village. Of the selection criteria, the only one applicable from GIS data is the proximity to a national forest. Where villages were close to a national forest, we attempted to match it with a control village also close to a national forest. The additional criteria on which villages were matched were the proximity to a main road (as classified by the Open Street Map shapefiles for roads) and the number of buildings in the central agglomeration of a village. Main roads are used as a proxy for access to markets and thereby potentially income levels.

The size of a village and the amount of land which can be used around it will be influenced by the size of the population as well as the presence of national forests. This strategy is similar to a Coarsened Exact Matching (CEM) strategy (see Blackwell et al, 2009), in which key characteristics are reduced (perhaps from continuous variables) to a small number of categories and matched with one another exactly. In our selection of villages, one control village was selected for each treatment village based on the key characteristics, defined as proximity to national forests (5km) and main roads (1km), and having a similar number of buildings (within 1km of the central point).

For a small number of villages, we faced an issue of common support, meaning there were no exact matches on the key characteristics. In this case other nearby villages were selected which fulfilled as many of these characteristics as possible. Data were collected on a wide range of variables following the theory of change, which states that the improvements in institutions and the PFRs may lead to improved perceived land tenure security and improved access to land for women and young men through the activities carried out by the ProPFR team. This perceived land tenure security is often seen as key to agricultural investments and thereby food security in the long term, as it allows long-term planning. The issuing of official documentation provides collateral for a loan should households wish to borrow and invest in productive activities or smooth consumption.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Survey comprised two questionnaires namely:

1. Household Questionnaire: Which comprised 14 modules with 7 rosters. Modules include household members, employment and enterprises, durable goods, housing, census of non-agricultural plots, agricultural plots, land donations, land sales, land losses, perceptions on land tenure, participation in PFR, loans, food security, young men and women.

2. Community (village) questionnaire: The community survey was administrated to each village in the form of small group interviews to collect information on the socio-economic characteristics of these villages, local land tenure structures and practices, and local prices on agricultural inputs and production. The questionnaire was organized in 9 modules: characteristics of the survey participants, land tenure, land use, land market, land conflicts, other village structures and interventions, agriculture, PFR, and village chief. The characteristics of the participants were recorded in a separate roster.

The extensive household survey was first asked to the household head with additional modules to be answered by the wife of the household head (or the female household head) as well as a young male (defined as an unmarried man, aged 18-35).

Cleaning operations

Various consistency checks were performed to ensure data quality, including systematic reports of contradictory answers and of extreme values. Throughout the data collection process, two main issues were reported. The first pertains to the sampling methodology of buildings, that led to the necessary replacement of pre-selected non-housing buildings. However, just short of 500 households required replacement. The majority of the buildings replaced were not residential buildings and were therefore not eligible for inclusion in the survey. These were replaced by the next building in the random order of buildings. The number of buildings for which nobody could be found for surveying was very low (23), thanks to the robust replacement protocol.

The second issue concerns the refusal of the village Sombouan 2 to participate in the survey. Despite several attempts, this village had to be excluded from the survey. The data were also examined for missing information for required variables, and sections. Any problems found were then reported back to the supervisors where the correction was then made.

Response rate

The response rate for

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data AttributesWard Data Overview: These municipal wards were created by grouping Census 2010 population collection blocks into municipal wards. This project started with the release of Census 2010 geography and population totals to all 72 Wisconsin counties on March 21, 2011, and were made available via the Legislative Technology Services Bureau (LTSB) GIS website and the WISE-LR web application. The 180 day statutory timeline for local redistricting ended on September 19, 2011. Wisconsin Legislative and Congressional redistricting plans were enacted in 2011 by Wisconsin Act 43 and Act 44. These new districts were created using Census 2010 block geography. Some municipal wards, created before the passing of Act 43 and 44, were required to be split between assembly, senate and congressional district boundaries. 2011 Wisconsin Act 39 allowed communities to divide wards, along census block boundaries, if they were divided by newly enacted boundaries. A number of wards created under Wisconsin Act 39 were named using alpha-numeric labels. An example would be where ward 1 divided by an assembly district would become ward 1A and ward 1B, and in other municipalities the next sequential ward number was used: ward 1 and ward 2. The process of dividing wards under Act 39 ended on April 10, 2012. On April 11, 2012, the United States Eastern District Federal Court ordered Assembly Districts 8 and 9 (both in the City of Milwaukee) be changed to follow the court’s description. On September 19, 2012, LTSB divided the few remaining municipal wards that were split by a 2011 Wisconsin Act 43 or 44 district line.Election Data Overview: Election data that is included in this file was collected by LTSB from the Government Accountability Board (GAB)/Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. The ward data that is collected after each decennial census is made up of collections of whole and split census blocks. (Note: Split census blocks occur during local redistricting when municipalities include recently annexed property in their ward submissions to the legislature).Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the GAB/WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the GAB at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2011) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election Results: The process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from GAB/WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards spring 2017 (Census 2010 totals used for disaggregation)Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (Occurred with spring 2017) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2016Blocks 2011 [with all election data] -> Wards 2011 (then MCD 2011, and County 2011) All election data (including later elections such as 2016) is aggregated to the Wards 2011 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though municipal and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same. Therefore, data totals within a county should be the same between 2011 wards and 2018 wards.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2018. This is due to (a) boundary corrections in the data from 2011 to 2018, and (b) annexations, where a block may have been reassigned.

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data Attributes Ward Data Overview:July 2020 municipal wards were collected by LTSB through the WISE-Decade system. Current statutes require each county clerk, or board of election commissioners, no later than January 15 and July 15 of each year, to transmit to the LTSB, in an electronic format (approved by LTSB), a report confirming the boundaries of each municipality, ward and supervisory district within the county as of the preceding “snapshot” date of January 1 or July 1 respectively. Population totals for 2011 wards are carried over to the 2018 dataset for existing wards. New wards created since 2011 due to annexations, detachments, and incorporation are allocated population from Census 2010 collection blocks. LTSB has topologically integrated the data, but there may still be errors.Election Data Overview:The 1990-2000 Wisconsin election data that is included in this file was collected by LTSB from the *Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. Disaggregation of Election Data:Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward. The data then is distributed down to the block level, again based on total population. When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the WEC at ward level may not match the ward totals in the disaggregated election data file. Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note…We use a static, official ward layer (in this case created in 2020) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if Cityville has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward 5 was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards 1 and 4 according to population percentage. Outline Ward-by-Ward Election ResultsThe process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2018: Wards 2018Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (occurred with spring 2020) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2018Blocks 2011 [with all election data] -> Wards 2020 (then MCD 2020, and County 2020) All election data (including later elections) is aggregated to the Wards 2020 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though MCD and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same, so data should total within a county the same between wards 2011 and wards 2020.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2020. This is due to boundary corrections in the data from 2011 to 2020, and annexations, where a block may have been reassigned.*WEC replaced the previous Government Accountability Board (GAB) in 2016, which replaced the previous State Elections Board in 2008.

Parameters and their definitions by model type (OM = operating model, EM = estimation model).

This data set contains the primary forcing data "File A" for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is WMO GRIB-1.

Details about the generation of the NLDAS-2 forcing data sets can be found in Xia et al. (2012).

The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency. Additionally, the fields of surface pressure, surface downward longwave radiation, near-surface air temperature, and near-surface specific humidity are adjusted vertically to account for the vertical difference between the NARR and NLDAS fields of terrain height. This vertical adjustment applies the traditional vertical lapse rate of 6.5 K/km for air temperature. The details of the spatial interpolation, temporal disaggregation, and vertical adjustment are those employed in NLDAS-1, as presented by Cosgrove et al. (2003).

The surface downward shortwave radiation field in "File A" is a bias-corrected field wherein a bias-correction algorithm was applied to the NARR surface downward shortwave radiation. This bias correction utilizes five years (1996-2000) of the hourly 1/8th-degree GOES-based surface downward shortwave radiation fields derived by Pinker et al. (2003). The potential evaporation field in "File A" is that computed in NARR using the modified Penman scheme of Mahrt and Ek (1984).

The precipitation field in "File A" is not the NARR precipitation forcing, but is rather a product of a temporal disaggregation of a gauge-only CPC analysis of daily precipitation, performed directly on the NLDAS grid and including an orographic adjustment based on the widely-applied PRISM climatology. The precipitation is temporally disaggregated into hourly fields by deriving hourly disaggregation weights from either WSR-88D Doppler radar-based precipitation estimates, 8-km CMORPH hourly precipitation analyses, or NARR-simulated precipitation (based on availability, in order). The latter fields from radar, CMORPH, and NARR are used only to derive disaggregation weights and do not change the daily total precipitation. The field in "File A" that gives the fraction of total precipitation that is convective is an estimate derived from the following two NARR precipitation fields (which are provided in "File B"): NARR total precipitation and NARR convective precipitation (the latter is less than or equal to the NARR total precipitation and can be zero). The Convective Available Potential Energy (CAPE) is the final variable in the forcing data set, also interpolated from NARR.

The hourly land surface forcing fields for NLDAS-2 are grouped into two GRIB files, "File A" and "File B". "File A" is the primary (default) forcing file and contains eleven fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_NLDAS_FORA_hourly.002.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.

For more information, please see the README Document at ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/README.NLDAS2.pdf.

These wards were produced by the Legislative Technology Services Bureau for the 2011 Legislative Redistricting Project. Election data from the current decade is included.Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data AttributesWard Data Overview: These municipal wards were created by grouping Census 2010 population collection blocks into municipal wards. This project started with the release of Census 2010 geography and population totals to all 72 Wisconsin counties on March 21, 2011, and were made available via the Legislative Technology Services Bureau (LTSB) GIS website and the WISE-LR web application. The 180 day statutory timeline for local redistricting ended on September 19, 2011. Wisconsin Legislative and Congressional redistricting plans were enacted in 2011 by Wisconsin Act 43 and Act 44. These new districts were created using Census 2010 block geography. Some municipal wards, created before the passing of Act 43 and 44, were required to be split between assembly, senate and congressional district boundaries. 2011 Wisconsin Act 39 allowed communities to divide wards, along census block boundaries, if they were divided by newly enacted boundaries. A number of wards created under Wisconsin Act 39 were named using alpha-numeric labels. An example would be where ward 1 divided by an assembly district would become ward 1A and ward 1B, and in other municipalities the next sequential ward number was used: ward 1 and ward 2. The process of dividing wards under Act 39 ended on April 10, 2012. On April 11, 2012, the United States Eastern District Federal Court ordered Assembly Districts 8 and 9 (both in the City of Milwaukee) be changed to follow the court’s description. On September 19, 2012, LTSB divided the few remaining municipal wards that were split by a 2011 Wisconsin Act 43 or 44 district line.Election Data Overview: Election data that is included in this file was collected by LTSB from the Government Accountability Board (GAB)/Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. The ward data that is collected after each decennial census is made up of collections of whole and split census blocks. (Note: Split census blocks occur during local redistricting when municipalities include recently annexed property in their ward submissions to the legislature).Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the GAB/WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the GAB at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2011) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election Results: The process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from GAB/WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards spring 2017 (Census 2010 totals used for disaggregation)Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (Occurred with spring 2017) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2016Blocks 2011 [with all election data] -> Wards 2011 (then MCD 2011, and County 2011) All election data (including later elections such as 2016) is aggregated to the Wards 2011 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though municipal and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same. Therefore, data totals within a county should be the same between 2011 wards and 2018 wards.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2018. This is due to (a) boundary corrections in the data from 2011 to 2018, and (b) annexations, where a block may have been reassigned.

Data These data relate to jobseekers registered on average during the quarter at Pôle emploi in categories A, B, C by sex, by age group and by municipality (based on the Official Geographic Code, as of 1 January 2022), for the 4th quarters over a rolling 10-year period. They are raw and rounded to a multiple of 5. There may therefore be slight differences between the sum of the disaggregated data and the aggregated series. Annual developments should be taken with caution: on small municipalities, the effect of rounding can be significant and the annual evolution is then very impacted. For example, a municipality that sees an increase in the number of jobseekers from 17 to 18 (an increase of 6 %) will have staff numbers rounded up to 15 and 20, i.e. an increase of 33 %. The ages used for the different series are the ages at the end of the month (age that the job seeker will have at the end of the month in question). For each of the communes, the region and the department to which they belong are specified. ### Definition Full documentation on data on registered jobseekers and vacancies collected by France Travail can be found on the Dares website (see document Methodological documentation - Jobseekers). Information on the Official Geographic Code is available on the INSEE website. ### Field * the geographical grouping ‘**** Metropolitan France’ includes all the French territories on the European continent (96 departments); * The geographical grouping ‘France’ includes metropolitan France and the overseas departments/regions (DROM), with the exception of Mayotte. ### Source The data are taken from the files of the Monthly Labour Market Statistics (STMT) of Dares and France Travail. ### Warnings In addition to labour market developments, data on jobseekers can be affected by a number of factors: changes to the rules on compensation or support for jobseekers, procedural changes, incidents. A document presents the main procedural changes and incidents affecting the statistics of jobseekers since 2011. The municipalities of Sannerville (14666) and Troarn (14712) were merged into the new municipality of Saline (14712) from 2017 to 2019, and were then re-established on 1 January 2020. The information for these two municipalities over this period should therefore be considered with caution.

This dataset is part of the ESCALA (Study of Urban Health and Climate Change in Informal Settlements in Latin America) project that was funded by the Lacuna Fund of the Meridian Institute https://lacunafund.org/. This dataset contains aggregated counts of mental health services by age, sex, year, service type, and diagnosis for Bogota, Colombia, 2019-2023. Data were provided by the RIPS (Spanish acronym for "Individual Records of Service Provision") and consolidated from SISPRO (Spanish acronym for "Comprehensive Social Protection Information System") - Ministry of Health and Social Protection. The data were organized and published on the portal saludata.saludcapital.gov.co and openly published on datosabiertos.bogota.gov.co. Each row in the database represents the count of care services, not the count of unique individuals served. Therefore, it is not possible to calculate the total number of individuals served by summing the partial values obtained at different levels of disaggregation. This is because the same person may be included in different groups within the same period if any of their attributes change over time. Data cleaning included: (1) Initially, two databases are obtained: one covering the period from 2019 to 2021 and another from 2022 to 2024 (up to August). First, both databases are unified, retaining only the columns they have in common. (2) Since the data are not disaggregated by month, the 2024 records are removed as they provide an unofficial count for all months, which could lead to errors during use. (3) Empty rows and reports outside Bogotá are removed. (4) Finally, each variable is adjusted by assigning the names established in the data dictionary and categorizing them according to the defined domains.

Abstract

Land is an economic asset that serves multiple important purposes: residential, agricultural, and communal (grazing lands, forests, water bodies, public infrastructure). Tenure security is crucial in ensuring poverty reduction, food security and equity. Farmers who lack secure land rights are less likely to carry out essential yield-improving investments in their land as the insecurity prevents them from committing to long-term plans.\

The Promotion d’une Politique Foncière Responsable (ProPFR), is a GIZ funded programme to improve the land tenure security of households on customary land in the Borgou department of northern Benin.

The main objectives of ProPFR are: a) Improvement of institutional conditions and procedures to provide secure land rights (PFR, ADC, formalization of user agreements, group rights) and reducing land conflicts by establishing local conflict mediation institutions. b) Participation of civil society in the formulation and implementation of the legal framework for land c) Inclusion of private agricultural investors and raising their awareness for responsible land policies.

Study Objectives: The PFR activities to be evaluated at end-line consists mainly of demarcation and registration of land parcels (under customary tenure) as Titre Foncier or an Attestation de Droit Coutumière. The impact evaluation aims to quantify and analyse impact of these interventions on productivity and food security disaggregated by target groups and gender.

The research questions to be answered after the endline data collection are: 1. Do PFRs (or ADCs) contribute to a perception of greater land tenure security? 2. Does improved tenure security lead to a growth in agricultural investment and/or changes to management of land? 3. Do PFRs improve access to land and rights over land among marginalised groups (women, youth and migrants)? 4. Do PFRs lead to an increased number of land transactions? 5. Does increased land security address existing constraints on land markets and lead to more efficient allocation of land resources and thereby an increase in productivity? 6. Do property rights and improved user rights result in better access to credit, possibly allowing for income diversification and thus increasing household welfare? 7. Do the new arrangements put in place during the implementation of the PFRs facilitate the resolution of land conflicts, or even prevent the emergence of these land conflicts?

Geographic coverage

These clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.

Analysis unit

• Villages
• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The impact evaluation consists of gender and youth disaggregated data collection at baseline, before the start of the intervention, in both the treatment and control villages. Endline data was collected at least 2 growing seasons after issuing of documentation to farmers.

The sample consisted of 2,626 households, which were taken from 52 villages of the four municipalities selected for the implementation of a Plan Foncier Rural (PFR), or rural landholding plans, these were the treatment villages and 27 control villages that did not benefit from a PFR.

Selection of Sample Areas The treatment villages were assigned by the ProPFR team in geographic clusters. The assignment of control villages followed this geographic clustering, also using further village level data with the aim of finding similar villages to maximize comparability.

These clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.

Villages were selected from 11 geographical clusters of villages facing similar issues, allowing easier logistical planning for the rollout of the PFRs.

Villages selected to be part of the programme had the following characteristics; • Bordering/near to a classified national forest • At high risk of land grabbing, • The presence of another GIZ supported SEWOH project1 • Agropastoral areas (particularly the presence of transhumance –cattle driving - corridors)

But should not have the following: • Villages bordering Nigeria, within the band of increased security; • MCA intervention with a PFR; and • Suffered serious conflict which could block the realisation of a PFR, or where a PFR may reignite past conflicts. These characteristics alongside the logistical requirement to select villages in clusters presented the first challenge in selecting suitable comparison villages to measure the impact of the ProPFR programme. Clustering meant that villages selected for comparison should be near the clusters to be comparable but given the typical geography of villages in northern Benin, in that most people live in the village centre rather than spread evenly with sufficient density at the village boundary, and the lack of clearly defined village boundaries, a geographic discontinuity could not be exploited.

The second challenge in selecting comparison villages arose due to a change in the village definitions in 2013, when Benin changed from 3,758 to 5,290 villages which is often referred to as the “nouveau découpage”. Some old villages were split but there are no clearly defined village boundaries for the new set of villages. ProPFR selected from among the new villages, so the control villages also needed to be selected from this list. Given that the last census was collected prior to this new definition of villages, no data about the villages existed that could easily be used in matching villages to those selected for the ProPFR.

Due to this lack of data on the characteristics of the people residing in the villages, Geographical Information Systems (GIS) data were used to match each of the treatment PFR villages to a control village. Villages which were previously included in the MCA’s wave of PFRs were excluded from our study due to the difficulty in separating the effects of the two programs (MCA vs ProPFR).

For each PFR village, a buffer of 20km was drawn and the union constructed for each cluster. Within this area, other villages were considered as a potential control village. Of the selection criteria, the only one applicable from GIS data is the proximity to a national forest. Where villages were close to a national forest, we attempted to match it with a control village also close to a national forest.

The additional criteria on which villages were matched were the proximity to a main road (as classified by the Open Street Map shapefiles for roads) and the number of buildings in the central agglomeration of a village. Main roads are used as a proxy for access to markets and thereby potentially income levels.

The size of a village and the amount of land which can be used around it will be influenced by the size of the population as well as the presence of national forests.

This strategy is similar to a Coarsened Exact Matching (CEM) strategy (see Blackwell et al, 2009), in which key characteristics are reduced (perhaps from continuous variables) to a small number of categories and matched with one another exactly.

In our selection of villages, one control village was selected for each treatment village based on the key characteristics, defined as proximity to national forests (5km) and main roads (1km), and having a similar number of buildings (within 1km of the central point).

For a small number of villages, we faced an issue of common support, meaning there were no exact matches on the key characteristics. In this case other nearby villages were selected which fulfilled as many of these characteristics as possible.

Data were collected on a wide range of variables following the theory of change, which states that the improvements in institutions and the PFRs may lead to improved perceived land tenure security and improved access to land for women and young men through the activities carried out by the ProPFR team.

This perceived land tenure security is often seen as key to agricultural investments and thereby food security in the long term, as it allows long-term planning. The issuing of official documentation provides collateral for a loan should households wish to borrow and invest in productive activities or smooth consumption.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Survey comprised two questionnaires, namely; 1. Household Questionnaire: Which comprised 17 modules with 19 rosters. Modules include household members, employment and enterprises, durable goods, housing, census of non-agricultural plots, agricultural plots, land donations, land sales, land losses, perceptions on land tenure, participation in PFR, loans, food security, young men and women.

1. Community (village) questionnaire: The community survey was administrated to each village in the form of small group interviews to collect information on the socio-economic characteristics of these villages, local land tenure structures and practices, and local prices on agricultural inputs and production. The questionnaire was organized in 9 modules: characteristics of the survey participants, land tenure, land use, land market, land conflicts, other village structures and interventions, agriculture, PFR, and village chief. The characteristics of the participants were recorded in a separate roster.

Cleaning operations

Various consistency checks were performed to ensure data quality, including systematic reports of contradictory answers and of extreme values. The data were also examined for missing information for required variables, and

This dataset contains information about the total numbers of hours and minutes per day spent on unpaid work, disaggregated by sex in 1997 and 2006. (a) Person aged 15 years and over. (b) Data based …Show full descriptionThis dataset contains information about the total numbers of hours and minutes per day spent on unpaid work, disaggregated by sex in 1997 and 2006. (a) Person aged 15 years and over. (b) Data based on person's primary activity. For more information on definition of primary activity see the ABS Work and Family Balance glossary. (c) Some differences between 1997 and 2006 may partially be due to coding changes in 2006 rather than actual changes. For further information see Explanatory Notes in How Australians Use Their Time, 2006 (cat. no. 4153.0). (d) Aggregated time for primary activity averaged across all persons. (e) For definition of unpaid work see Work and Family Balance glossary. Source: ABS How Australians Use Their Time, (cat. no. 4153.0); ABS data available on request, Time Use Survey.

With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database. The employment-to-population ratio is the number of persons who are employed as a percent of the total of working-age population. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED. The term disability, as defined in the International Classification of Functioning, Disability and Health (ICF), is used as an umbrella term, covering impairments, activity limitations, and participation restrictions. For measurement purposes, a person with disability is defined as a person who is limited in the kind or amount of activities that he or she can do because of ongoing difficulties due to a long-term physical condition, mental condition or health problem. For more information, refer to the Disability Labour Market Indicators (DLMI) database description.

This data set contains the forcing data for Phase 1 of the North American Land Data Assimilation System (NLDAS-1). The data are in 1/8th degree grid spacing and range from 29 Sep 1996 to 31 Dec 2007. The temporal resolution is hourly. The file format is WMO GRIB-1.

The chief source of NLDAS-1 forcing is NCEP's Eta model-based Data Assimilation System (EDAS) [Rogers et al., 1995], a continuously cycled North American 4DDA system. It utilizes 3-hourly analysis-forecast cycles to derive atmospheric states by assimilating many types of observations, including station observations of surface pressure and screen-level atmospheric temperature, humidity, and U and V wind components. EDAS 3-hourly fields of the latter five variables plus surface downward shortwave and longwave radiation and total and convective precipitation are provided on a 40-km grid, and then interpolated spatially to the NLDAS grid and temporally to one hour. Last, to account for NLDAS versus EDAS surface-elevation differences, a terrain-height adjustment is applied to the air temperature and surface pressure using a standard lapse rate (6.5 K/km), then to specific humidity (keeping original relative humidity) and downward longwave radiation (for new air temperature, specific humidity). The details of the spatial interpolation, temporal disaggregation, and vertical adjustment are presented by Cosgrove et al. (2003).

GOES-based solar insolation (Pinker et al., 2003) provides the primary insolation forcing (shorwave down at the surface) for NLDAS-1. GOES insolation is not retrieved for zenith angles below 75 degrees and so is supplemented with EDAS insolation near the day/night terminator. Last from the GOES-based product suite, Photosynthetically Active Radiation (PAR) and surface brightness temperature fields are included in the NLDAS-1 forcing files.

NLDAS-1 precipitation forcing over CONUS is anchored to NCEP's 1/4th degree gauge-only daily precipitation analyses of Higgins et al. [2000]. In NLDAS-1, this daily analysis is interpolated to 1/8th degree, then temporally disaggregated to hourly values by applying hourly weights derived from hourly, 4-km, radar-based (WSR-88D) precipitation fields. The latter radar-based fields are used only to derive disaggregation weights and do not change the daily total precipitation. Last, convective precipitation is estimated by multiplying NLDAS-1 total precipitation by the ratio of EDAS convective to EDAS total precipitation. The Convective Available Potential Energy (CAPE) is the final variable in the forcing data set, also interpolated from EDAS.

The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_NLDAS_FORA_hourly.001.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.

For more information, please see the README Document at ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/README.NLDAS1.pdf.

Abstract

The 2016 Integrated Household Panel Survey (IHPS) was launched in April 2016 as part of the Malawi Fourth Integrated Household Survey fieldwork operation. The IHPS 2016 targeted 1,989 households that were interviewed in the IHPS 2013 and that could be traced back to half of the 204 enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11. The 2019 IHPS was launched in April 2019 as part of the Malawi Fifth Integrated Household Survey fieldwork operations targeting the 2,508 households that were interviewed in 2016. The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. Available as part of this project is the IHPS 2019 data, the IHPS 2016 data as well as the rereleased IHPS 2010 & 2013 data including only the subsample of 102 EAs with updated panel weights. Additionally, the IHPS 2016 was the first survey that received complementary financial and technical support from the Living Standards Measurement Study – Plus (LSMS+) initiative, which has been established with grants from the Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust Fund for Statistical Capacity Building, and the International Fund for Agricultural Development, and is implemented by the World Bank Living Standards Measurement Study (LSMS) team, in collaboration with the World Bank Gender Group and partner national statistical offices. The LSMS+ aims to improve the availability and quality of individual-disaggregated household survey data, and is, at start, a direct response to the World Bank IDA18 commitment to support 6 IDA countries in collecting intra-household, sex-disaggregated household survey data on 1) ownership of and rights to selected physical and financial assets, 2) work and employment, and 3) entrepreneurship – following international best practices in questionnaire design and minimizing the use of proxy respondents while collecting personal information. This dataset is included here.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Children under 5 years
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop

Universe

The IHPS 2016 and 2019 attempted to track all IHPS 2013 households stemming from 102 of the original 204 baseline panel enumeration areas as well as individuals that moved away from the 2013 dwellings between 2013 and 2016 as long as they were neither servants nor guests at the time of the IHPS 2013; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sub-sample of IHS3 2010 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS 2013) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban. The IHPS 2013 main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

Given budget and resource constraints, for the IHPS 2016 the number of sample EAs in the panel was reduced to 102 out of the 204 EAs. As a result, the domains of analysis are limited to the national, urban and rural areas. Although the results of the IHPS 2016 cannot be tabulated by region, the stratification of the IHPS by region, urban and rural strata was maintained. The IHPS 2019 tracked all individuals 12 years or older from the 2016 households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data Entry Platform To ensure data quality and timely availability of data, the IHPS 2019 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHPS 2019, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer that the NSO provided. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

Data Management The IHPS 2019 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHPS 2019 Interviews were mainly collected in “sample” mode (assignments generated from headquarters) and a few in “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample. This hybrid approach was necessary to aid the tracking operations whereby an enumerator could quickly create a tracking assignment considering that they were mostly working in areas with poor network connection and hence could not quickly receive tracking cases from Headquarters.

The range and consistency checks built into the application was informed by the LSMS-ISA experience with the IHS3 2010/11, IHPS 2013 and IHPS 2016. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (the NSO management) assigned work to the supervisors based on their regions of coverage. The supervisors then made assignments to the enumerators linked to their supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHPS 2019 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to Stata for other consistency checks, data cleaning, and analysis.

Data Cleaning The data cleaning process was done in several stages over the course of fieldwork and through preliminary analysis. The first stage of data cleaning was conducted in the field by the field-based field teams utilizing error messages generated by the Survey Solutions application when a response did not fit the rules for a particular question. For questions that flagged an error, the enumerators were expected to record a comment within the questionnaire to explain to their supervisor the reason for the error and confirming that they double checked the response with the respondent. The supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some supervisors preferred to review completed interviews on the tablets so they would review prior to syncing but still record the notes in the supervisor account and reject questionnaires accordingly. The second stage of data cleaning was also done in the field, and this resulted from the additional error reports generated in Stata, which were in turn sent to the field teams via email or DropBox. The field supervisors collected reports for their assignments and in coordination with the enumerators reviewed, investigated, and collected errors. Due to the quick turn-around in error reporting, it was possible to conduct call-backs while the team was still operating in the EA when required. Corrections to the data were entered in the rejected questionnaires and sent back to headquarters.

The data cleaning process was done in several stages over the course of the fieldwork and through preliminary analyses. The first stage was during the interview itself. Because CAPI software was used, as enumerators asked the questions and recorded information, error messages were provided immediately when the information recorded did not match previously defined rules for that variable. For example, if the education level for a 12 year old respondent was given as post graduate. The second stage occurred during the review of the questionnaire by the Field Supervisor. The Survey Solutions software allows errors to remain in the data if the enumerator does not make a correction. The enumerator can write a comment to explain why the data appears to be incorrect. For example, if the previously mentioned 12 year old was, in fact, a genius who had completed graduate studies. The next stage occurred when the data were transferred to headquarters where the NSO staff would again review the data for errors and verify the comments from the

The infra-annual dataflow on employed population by economic activity is a subset of the infra-annual labour statistics database, which contains predominantly monthly and quarterly statistics on the working age population by economic activity (Agriculture, Manufacturing, Industry including and excluding construction, Construction and Services) and associated statistical methodological information, for the OECD member countries and for selected other economies.

The employed population commonly comprises employed persons “at work”(i.e. who worked in a job for at least one hour) and employed persons “not at work” due to temporary absence from a job, or to working-time arrangements (such as shift work, flexitime and compensatory leave for overtime).

The infra-annual labour statistics compiled for all OECD member countries, are drawn from Labour Force Surveys based on definition provided by the 19th Conference of Labour Statisticians in 2013. The uniform application of these definitions across all OECD member countries results in estimates that are internationally comparable. Data disaggregated by economic activity are provided according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC), namely Revision 4.

London Borough level tourism trip estimates (thousands). The ‘top-down’ nature of the Local Area Tourism Impact (LATI) model (starting with London data) means it is best suited to disaggregate expenditure. However, tourism trips were also disaggregated for comparative purposes using the estimated proportions of spending by overseas, domestic and day visitors in the boroughs. Since the trip estimates are derived from data on trips to London they do not account for trips to different boroughs by visitors whilst in London. Indicative borough level day visitor/tourist estimates for 2007 were derived from the LDA’s own experimental London level day visitor estimates. As such the borough level day visitor estimates should be treated with caution and the 2007 day visitor estimates are not comparable with those from previous years. They are intended only to give a best estimate of the scale of day visitor tourism in each borough from the currently available data. Further tourism data for UK regions covering trends in visits, nights, and spend to London by visitors from overseas is available on the Visit Britain website. Analyse data by age, purpose, duration, and quarter. This dataset is no longer updated.