variables
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id ▲ | name | unit | description | createdAt | updatedAt | code | coverage | timespan | datasetId | sourceId | shortUnit | display | columnOrder | originalMetadata | grapherConfigAdmin | shortName | catalogPath | dimensions | schemaVersion | processingLevel | processingLog | titlePublic | titleVariant | attributionShort | attribution | descriptionShort | descriptionFromProducer | descriptionKey | descriptionProcessing | licenses | license | grapherConfigETL | type | sort | dataChecksum | metadataChecksum |
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959852 | Military expenditure per capita (constant US$) | constant 2021 US$ | 2024-07-30 12:02:35 | 2024-07-30 12:02:35 | 1816-2022 | Global Military Spending Dataset 6647 | $ | { "name": "Military expenditure per capita", "unit": "constant 2021 US$", "shortUnit": "$", "tolerance": 5, "numDecimalPlaces": 0 } |
0 | milex_estimate_per_capita | grapher/harvard/2024-07-22/global_military_spending_dataset/global_military_spending_dataset#milex_estimate_per_capita | 2 | major | Military expenditure per capita | This data is expressed in US dollars. It is adjusted for inflation but does not account for differences in the cost of living between countries. | "_Latent variable model_ In [the main manuscript](https://journals.sagepub.com/doi/10.1177/00220027241232964), we present, estimate, and describe a latent variable model that links together observed dataset values from across many sources of military expenditure data. We are interested in estimating is country-year military spending. Using military ex- penditure data presents several challenges because the datasets are incomplete, cover short periods of time, and are presented in many different monetary units-of-measurement. To overcome these challenges, we specify a dynamic latent variable measurement model that links all of the available information across different contemporary and historical sources of arms spending data. We essentially want to estimate the country-year distribution or simply the average of military spending across all the available observed dataset values so that we generate the best estimate of military spending for each of the country-year units. The observed dataset values are linked together through the estimation of a country- year parameter or latent trait. However, the latent trait parameter itself is not directly of interest for inference because it does not have a direct monetary interpretation. This is because it is scaled by the item-specific intercept parameter which transforms the latent trait into the unit-of-measurement of any one of the originally observed military expenditure variables. The measurement model provides predictive intervals for each of the original observed variables on the original scales of these variables. Notationally, we represent the observed country-year dataset values as yitj where i indexes countries, t indexes years of time, and j indexes the dataset. The model then produces posterior predictive distributions of yitj, which we denote as y ̃itj. These are normally distributed values (on the natural log scale). We can therefore take the average of y ̃itj as E(y ̃itj) or the standard deviation of y ̃itj as sd(y ̃itj). For the applications in the main… | [ "This data is calculated by using nine different military expenditure data sources and combining them using a latent variable measurement model. The model links the country-year data together and estimates a mean with a prediction interval for each observation. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027241232964)." ] |
We calculated this indicator by dividing the total military expenditure by a population estimate for each year. The population estimates come from a long-run dataset [mantained by Our World in Data](https://ourworldindata.org/population-sources). | [ { "url": "https://docs.google.com/document/d/1-RmthhS2EPMK_HIpnPctcXpB0n7ADSWnXa5Hb3PxNq4/edit?usp=sharing", "name": "Creative Commons BY 4.0" } ] |
float | [] |
1bc651b727a55f51ed14ce23bdfd6dbb | 047b6fad8dccb56fcf305a5aa966efd5 | |||||||||||||
959851 | Military expenditure (% of SDP) - Subsistence level: $2 per day | % of SDP | 2024-07-30 12:02:34 | 2024-07-30 12:02:35 | 1816-2019 | Global Military Spending Dataset 6647 | % | { "name": "Military expenditure (% of SDP)", "unit": "% of SDP", "shortUnit": "%", "tolerance": 5, "numDecimalPlaces": 1 } |
0 | milexsurplus730 | grapher/harvard/2024-07-22/global_military_spending_dataset/global_military_spending_dataset#milexsurplus730 | 2 | minor | Military expenditure (% of SDP) | "_Latent variable model_ In [the main manuscript](https://journals.sagepub.com/doi/10.1177/00220027241232964), we present, estimate, and describe a latent variable model that links together observed dataset values from across many sources of military expenditure data. We are interested in estimating is country-year military spending. Using military ex- penditure data presents several challenges because the datasets are incomplete, cover short periods of time, and are presented in many different monetary units-of-measurement. To overcome these challenges, we specify a dynamic latent variable measurement model that links all of the available information across different contemporary and historical sources of arms spending data. We essentially want to estimate the country-year distribution or simply the average of military spending across all the available observed dataset values so that we generate the best estimate of military spending for each of the country-year units. The observed dataset values are linked together through the estimation of a country- year parameter or latent trait. However, the latent trait parameter itself is not directly of interest for inference because it does not have a direct monetary interpretation. This is because it is scaled by the item-specific intercept parameter which transforms the latent trait into the unit-of-measurement of any one of the originally observed military expenditure variables. The measurement model provides predictive intervals for each of the original observed variables on the original scales of these variables. Notationally, we represent the observed country-year dataset values as yitj where i indexes countries, t indexes years of time, and j indexes the dataset. The model then produces posterior predictive distributions of yitj, which we denote as y ̃itj. These are normally distributed values (on the natural log scale). We can therefore take the average of y ̃itj as E(y ̃itj) or the standard deviation of y ̃itj as sd(y ̃itj). For the applications in the main… | [ "This data is calculated by using nine different military expenditure data sources and combining them using a latent variable measurement model. The model links the country-year data together and estimates a mean with a prediction interval for each observation. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027241232964).", "The military expenditure data is divided by surplus domestic product (SDP), defined as the difference between gross domestic product (GDP) and the economic resources that the population consumes to survive, such that for each state i in year t, SDP(it) = GDP(it) \u2212 ((365 \u2217 \u03c4) \u2217 Population(it)). \u03c4 is the subsistence threshold, set as $2 per day per person for this indicator.", "The GDP estimates used to calculate SDP are obtained from a latent variable model that is similar to the one employed to obtain military expenditure data. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027211054432)." ] |
float | [] |
f890ba4de8d2edae5f70b333347909e1 | c3c5a86add08a47b2ccbcc58eebfb5ce | ||||||||||||||||
959850 | Military expenditure (% of SDP) - Subsistence level: $1 per day | % of SDP | 2024-07-30 12:02:34 | 2024-07-30 12:02:36 | 1816-2019 | Global Military Spending Dataset 6647 | % | { "name": "Military expenditure (% of SDP)", "unit": "% of SDP", "shortUnit": "%", "tolerance": 5, "numDecimalPlaces": 1 } |
0 | milexsurplus365 | grapher/harvard/2024-07-22/global_military_spending_dataset/global_military_spending_dataset#milexsurplus365 | 2 | minor | Military expenditure (% of SDP) | "_Latent variable model_ In [the main manuscript](https://journals.sagepub.com/doi/10.1177/00220027241232964), we present, estimate, and describe a latent variable model that links together observed dataset values from across many sources of military expenditure data. We are interested in estimating is country-year military spending. Using military ex- penditure data presents several challenges because the datasets are incomplete, cover short periods of time, and are presented in many different monetary units-of-measurement. To overcome these challenges, we specify a dynamic latent variable measurement model that links all of the available information across different contemporary and historical sources of arms spending data. We essentially want to estimate the country-year distribution or simply the average of military spending across all the available observed dataset values so that we generate the best estimate of military spending for each of the country-year units. The observed dataset values are linked together through the estimation of a country- year parameter or latent trait. However, the latent trait parameter itself is not directly of interest for inference because it does not have a direct monetary interpretation. This is because it is scaled by the item-specific intercept parameter which transforms the latent trait into the unit-of-measurement of any one of the originally observed military expenditure variables. The measurement model provides predictive intervals for each of the original observed variables on the original scales of these variables. Notationally, we represent the observed country-year dataset values as yitj where i indexes countries, t indexes years of time, and j indexes the dataset. The model then produces posterior predictive distributions of yitj, which we denote as y ̃itj. These are normally distributed values (on the natural log scale). We can therefore take the average of y ̃itj as E(y ̃itj) or the standard deviation of y ̃itj as sd(y ̃itj). For the applications in the main… | [ "This data is calculated by using nine different military expenditure data sources and combining them using a latent variable measurement model. The model links the country-year data together and estimates a mean with a prediction interval for each observation. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027241232964).", "The military expenditure data is divided by surplus domestic product (SDP), defined as the difference between gross domestic product (GDP) and the economic resources that the population consumes to survive, such that for each state i in year t, SDP(it) = GDP(it) \u2212 ((365 \u2217 \u03c4) \u2217 Population(it)). \u03c4 is the subsistence threshold, set as $1 per day per person for this indicator.", "The GDP estimates used to calculate SDP are obtained from a latent variable model that is similar to the one employed to obtain military expenditure data. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027211054432)." ] |
float | [] |
7185ff5cfff123bf110a66a3a51ec428 | 19aae4ebf301ca765b7b9b1c3e8f4260 | ||||||||||||||||
959849 | Military expenditure (% of GDP) | % of GDP | 2024-07-30 12:02:34 | 2024-07-30 12:02:35 | 1816-2019 | Global Military Spending Dataset 6647 | % | { "name": "Military expenditure (% of GDP)", "unit": "% of GDP", "shortUnit": "%", "tolerance": 5, "numDecimalPlaces": 1 } |
0 | milexgdp | grapher/harvard/2024-07-22/global_military_spending_dataset/global_military_spending_dataset#milexgdp | 2 | minor | Military expenditure (% of GDP) | "_Latent variable model_ In [the main manuscript](https://journals.sagepub.com/doi/10.1177/00220027241232964), we present, estimate, and describe a latent variable model that links together observed dataset values from across many sources of military expenditure data. We are interested in estimating is country-year military spending. Using military ex- penditure data presents several challenges because the datasets are incomplete, cover short periods of time, and are presented in many different monetary units-of-measurement. To overcome these challenges, we specify a dynamic latent variable measurement model that links all of the available information across different contemporary and historical sources of arms spending data. We essentially want to estimate the country-year distribution or simply the average of military spending across all the available observed dataset values so that we generate the best estimate of military spending for each of the country-year units. The observed dataset values are linked together through the estimation of a country- year parameter or latent trait. However, the latent trait parameter itself is not directly of interest for inference because it does not have a direct monetary interpretation. This is because it is scaled by the item-specific intercept parameter which transforms the latent trait into the unit-of-measurement of any one of the originally observed military expenditure variables. The measurement model provides predictive intervals for each of the original observed variables on the original scales of these variables. Notationally, we represent the observed country-year dataset values as yitj where i indexes countries, t indexes years of time, and j indexes the dataset. The model then produces posterior predictive distributions of yitj, which we denote as y ̃itj. These are normally distributed values (on the natural log scale). We can therefore take the average of y ̃itj as E(y ̃itj) or the standard deviation of y ̃itj as sd(y ̃itj). For the applications in the main… | [ "This data is calculated by using nine different military expenditure data sources and combining them using a latent variable measurement model. The model links the country-year data together and estimates a mean with a prediction interval for each observation. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027241232964).", "The military expenditure data is divided by gross domestic product (GDP) estimates obtained from a similar latent variable model, explained by the same authors in [a different article](https://journals.sagepub.com/doi/10.1177/00220027211054432)." ] |
float | [] |
8a2f3e0890c790aa527f7228d0be6d89 | 6a51a679d567d8740b087ba6fe41c168 | ||||||||||||||||
959848 | Military expenditure (% of SDP) - Subsistence level: $3 per day | % of SDP | 2024-07-30 12:02:34 | 2024-07-30 12:02:35 | 1816-2019 | Global Military Spending Dataset 6647 | % | { "name": "Military expenditure (% of SDP)", "unit": "% of SDP", "shortUnit": "%", "tolerance": 5, "numDecimalPlaces": 1 } |
0 | milexsurplus1095 | grapher/harvard/2024-07-22/global_military_spending_dataset/global_military_spending_dataset#milexsurplus1095 | 2 | minor | Military expenditure (% of SDP) | "_Latent variable model_ In [the main manuscript](https://journals.sagepub.com/doi/10.1177/00220027241232964), we present, estimate, and describe a latent variable model that links together observed dataset values from across many sources of military expenditure data. We are interested in estimating is country-year military spending. Using military ex- penditure data presents several challenges because the datasets are incomplete, cover short periods of time, and are presented in many different monetary units-of-measurement. To overcome these challenges, we specify a dynamic latent variable measurement model that links all of the available information across different contemporary and historical sources of arms spending data. We essentially want to estimate the country-year distribution or simply the average of military spending across all the available observed dataset values so that we generate the best estimate of military spending for each of the country-year units. The observed dataset values are linked together through the estimation of a country- year parameter or latent trait. However, the latent trait parameter itself is not directly of interest for inference because it does not have a direct monetary interpretation. This is because it is scaled by the item-specific intercept parameter which transforms the latent trait into the unit-of-measurement of any one of the originally observed military expenditure variables. The measurement model provides predictive intervals for each of the original observed variables on the original scales of these variables. Notationally, we represent the observed country-year dataset values as yitj where i indexes countries, t indexes years of time, and j indexes the dataset. The model then produces posterior predictive distributions of yitj, which we denote as y ̃itj. These are normally distributed values (on the natural log scale). We can therefore take the average of y ̃itj as E(y ̃itj) or the standard deviation of y ̃itj as sd(y ̃itj). For the applications in the main… | [ "This data is calculated by using nine different military expenditure data sources and combining them using a latent variable measurement model. The model links the country-year data together and estimates a mean with a prediction interval for each observation. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027241232964).", "The military expenditure data is divided by surplus domestic product (SDP), defined as the difference between gross domestic product (GDP) and the economic resources that the population consumes to survive, such that for each state i in year t, SDP(it) = GDP(it) \u2212 ((365 \u2217 \u03c4) \u2217 Population(it)). \u03c4 is the subsistence threshold, set as $3 per day per person for this indicator.", "The GDP estimates used to calculate SDP are obtained from a latent variable model that is similar to the one employed to obtain military expenditure data. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027211054432)." ] |
float | [] |
6d54bd092bad7fc8aac1bf9ee3d72a1a | 93f0d5b9a3cca67b04d520fdf640f5a5 | ||||||||||||||||
959847 | Military expenditure (constant US$) | constant 2021 US$ | 2024-07-30 12:02:34 | 2024-07-30 12:02:35 | 1816-2022 | Global Military Spending Dataset 6647 | $ | { "name": "Military expenditure", "unit": "constant 2021 US$", "shortUnit": "$", "tolerance": 5, "numDecimalPlaces": 0 } |
0 | milex_estimate | grapher/harvard/2024-07-22/global_military_spending_dataset/global_military_spending_dataset#milex_estimate | 2 | minor | Military expenditure | This data is expressed in US dollars. It is adjusted for inflation but does not account for differences in the cost of living between countries. | "_Latent variable model_ In [the main manuscript](https://journals.sagepub.com/doi/10.1177/00220027241232964), we present, estimate, and describe a latent variable model that links together observed dataset values from across many sources of military expenditure data. We are interested in estimating is country-year military spending. Using military ex- penditure data presents several challenges because the datasets are incomplete, cover short periods of time, and are presented in many different monetary units-of-measurement. To overcome these challenges, we specify a dynamic latent variable measurement model that links all of the available information across different contemporary and historical sources of arms spending data. We essentially want to estimate the country-year distribution or simply the average of military spending across all the available observed dataset values so that we generate the best estimate of military spending for each of the country-year units. The observed dataset values are linked together through the estimation of a country- year parameter or latent trait. However, the latent trait parameter itself is not directly of interest for inference because it does not have a direct monetary interpretation. This is because it is scaled by the item-specific intercept parameter which transforms the latent trait into the unit-of-measurement of any one of the originally observed military expenditure variables. The measurement model provides predictive intervals for each of the original observed variables on the original scales of these variables. Notationally, we represent the observed country-year dataset values as yitj where i indexes countries, t indexes years of time, and j indexes the dataset. The model then produces posterior predictive distributions of yitj, which we denote as y ̃itj. These are normally distributed values (on the natural log scale). We can therefore take the average of y ̃itj as E(y ̃itj) or the standard deviation of y ̃itj as sd(y ̃itj). For the applications in the main… | [ "This data is calculated by using nine different military expenditure data sources and combining them using a latent variable measurement model. The model links the country-year data together and estimates a mean with a prediction interval for each observation. For more information about the methodology, see [the original article](https://journals.sagepub.com/doi/10.1177/00220027241232964)." ] |
float | [] |
92b4de16be585b7ff784332c89248600 | 8ac7552611f3c1a8a6e57a6c861d5f03 |
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CREATE TABLE "variables" ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "name" VARCHAR(750) NULL , "unit" VARCHAR(255) NOT NULL , "description" TEXT NULL , "createdAt" DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP , "updatedAt" DATETIME NULL , "code" VARCHAR(255) NULL , "coverage" VARCHAR(255) NOT NULL , "timespan" VARCHAR(255) NOT NULL , "datasetId" INTEGER NOT NULL , "sourceId" INTEGER NULL , "shortUnit" VARCHAR(255) NULL , "display" TEXT NOT NULL , "columnOrder" INTEGER NOT NULL DEFAULT '0' , "originalMetadata" TEXT NULL , "grapherConfigAdmin" TEXT NULL , "shortName" VARCHAR(255) NULL , "catalogPath" VARCHAR(767) NULL , "dimensions" TEXT NULL , "schemaVersion" INTEGER NOT NULL DEFAULT '1' , "processingLevel" VARCHAR(30) NULL , "processingLog" TEXT NULL , "titlePublic" VARCHAR(512) NULL , "titleVariant" VARCHAR(255) NULL , "attributionShort" VARCHAR(512) NULL , "attribution" TEXT NULL , "descriptionShort" TEXT NULL , "descriptionFromProducer" TEXT NULL , "descriptionKey" TEXT NULL , "descriptionProcessing" TEXT NULL , "licenses" TEXT NULL , "license" TEXT NULL , "grapherConfigETL" TEXT NULL , "type" TEXT NULL , "sort" TEXT NULL , "dataChecksum" VARCHAR(64) NULL , "metadataChecksum" VARCHAR(64) NULL, FOREIGN KEY("datasetId") REFERENCES "datasets" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT, FOREIGN KEY("sourceId") REFERENCES "sources" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT ); CREATE UNIQUE INDEX "idx_catalogPath" ON "variables" ("catalogPath"); CREATE UNIQUE INDEX "unique_short_name_per_dataset" ON "variables" ("shortName", "datasetId"); CREATE UNIQUE INDEX "variables_code_fk_dst_id_7bde8c2a_uniq" ON "variables" ("code", "datasetId"); CREATE INDEX "variables_datasetId_50a98bfd_fk_datasets_id" ON "variables" ("datasetId"); CREATE UNIQUE INDEX "variables_name_fk_dst_id_f7453c33_uniq" ON "variables" ("name", "datasetId"); CREATE INDEX "variables_sourceId_31fce80a_fk_sources_id" ON "variables" ("sourceId");