spb/countryatlas

1# Canonical CountryAtlas indicator registry.2#3# Defaults (applied by the loader when a key is absent):4#   frequency: A · precision: 1 · aggregation: none · higher_is_better: null · ranking_eligible: true · featured: false5#   scale: raw · bounds: [null, null] · jump_threshold: 4 (× MAD) · stale_after_days: by frequency (A 800, Q 200, M 75)6# `format`: currency | number | percent | years | index | per_1000 | per_100k | per_million | ratio | tonnes | kwh | ha | km7# `sources`: ordered by `priority` (1 = preferred). connector ∈ worldbank | imf | oecd | eurostat | who | fred | owid | bis | ilo8#   worldbank: {dataset: WDI, code}            imf: {dataset: WEO, code}          oecd: {dataset: <dataflow>, code/filter: …}9#   eurostat: {dataset, params: {…}}           who: {dataset: GHO, code, params}  fred: {code, countries: [USA]}10#   owid: {dataset: co2|energy|grapher, code: <column or grapher slug>}          bis: {dataset: WS_SPP|WS_CBPOL, filter}11#   `transform`: optional expression on x (e.g. "x/1e9"), `countries`: restrict a source to listed ISO3.12# Connector agents append additional sources; keep slugs stable.1314indicators:1516# =========================================================== ECONOMY ===========================================================17- slug: gdp18  name: GDP (current US$)19  short_name: GDP20  topic: economy21  subtopic: Output22  unit: current US$23  unit_short: US$24  format: currency25  precision: 026  aggregation: sum27  featured: true28  bounds: [0, null]29  description: Gross domestic product at purchaser's prices, converted to US dollars at official exchange rates.30  sources:31  - {connector: worldbank, dataset: WDI, code: NY.GDP.MKTP.CD, priority: 1}32  - {connector: imf, dataset: WEO, code: NGDPD, priority: 2, notes: "SDMX CSV OBS_VALUE is already in US$ (SCALE=9 is display-only) — no transform"}33- slug: gdp-ppp34  name: GDP, PPP (current international $)35  short_name: GDP (PPP)36  topic: economy37  subtopic: Output38  unit: current international $39  unit_short: intl $40  format: currency41  precision: 042  aggregation: sum43  bounds: [0, null]44  description: GDP converted to international dollars using purchasing power parity rates.45  sources:46  - {connector: worldbank, dataset: WDI, code: NY.GDP.MKTP.PP.CD, priority: 1}47  - {connector: imf, dataset: WEO, code: PPPGDP, priority: 2, notes: "SDMX CSV OBS_VALUE is already in international $ (SCALE=9 is display-only) — no transform"}48- slug: gdp-per-capita49  name: GDP per capita (current US$)50  short_name: GDP per capita51  topic: economy52  subtopic: Output53  unit: current US$54  unit_short: US$55  format: currency56  precision: 057  aggregation: weighted_mean58  higher_is_better: true59  featured: true60  bounds: [0, null]61  description: Gross domestic product divided by midyear population, in current US dollars.62  sources:63  - {connector: worldbank, dataset: WDI, code: NY.GDP.PCAP.CD, priority: 1}64  - {connector: imf, dataset: WEO, code: NGDPDPC, priority: 2}65- slug: gdp-per-capita-ppp66  name: GDP per capita, PPP (current international $)67  short_name: GDP per capita (PPP)68  topic: economy69  subtopic: Output70  unit: current international $71  unit_short: intl $72  format: currency73  precision: 074  higher_is_better: true75  featured: true76  bounds: [0, null]77  description: GDP per capita based on purchasing power parity.78  sources:79  - {connector: worldbank, dataset: WDI, code: NY.GDP.PCAP.PP.CD, priority: 1}80  - {connector: imf, dataset: WEO, code: PPPPC, priority: 2}81- slug: gdp-growth82  name: Real GDP growth83  short_name: GDP growth84  topic: economy85  subtopic: Growth86  unit: annual %87  unit_short: "%"88  format: percent89  higher_is_better: true90  featured: true91  bounds: [-70, 150]92  change_floor: 393  description: Annual percentage growth rate of GDP at market prices based on constant local currency.94  sources:95  - {connector: worldbank, dataset: WDI, code: NY.GDP.MKTP.KD.ZG, priority: 1}96  - {connector: imf, dataset: WEO, code: NGDP_RPCH, priority: 2}97- slug: gdp-per-capita-growth98  name: GDP per capita growth99  topic: economy100  subtopic: Growth101  unit: annual %102  unit_short: "%"103  format: percent104  higher_is_better: true105  bounds: [-70, 150]106  change_floor: 3107  description: Annual percentage growth rate of GDP per capita based on constant local currency.108  sources:109  - {connector: worldbank, dataset: WDI, code: NY.GDP.PCAP.KD.ZG, priority: 1}110- slug: gdp-constant111  name: GDP (constant 2015 US$)112  short_name: Real GDP113  topic: economy114  subtopic: Output115  unit: constant 2015 US$116  unit_short: US$ (2015)117  format: currency118  precision: 0119  aggregation: sum120  bounds: [0, null]121  description: GDP in constant 2015 US dollars — the volume measure used for real growth.122  sources:123  - {connector: worldbank, dataset: WDI, code: NY.GDP.MKTP.KD, priority: 1}124- slug: inflation125  name: Inflation, consumer prices126  short_name: Inflation127  topic: economy128  subtopic: Prices129  unit: annual %130  unit_short: "%"131  format: percent132  higher_is_better: false133  featured: true134  bounds: [-30, 100000]135  change_floor: 2136  description: Annual percentage change in the cost to the average consumer of acquiring a basket of goods and services.137  sources:138  - {connector: worldbank, dataset: WDI, code: FP.CPI.TOTL.ZG, priority: 1}139  - {connector: imf, dataset: WEO, code: PCPIPCH, priority: 2}140- slug: inflation-gdp-deflator141  name: Inflation, GDP deflator142  topic: economy143  subtopic: Prices144  unit: annual %145  unit_short: "%"146  format: percent147  bounds: [-50, 100000]148  ranking_eligible: false149  description: Annual growth rate of the GDP implicit deflator.150  sources:151  - {connector: worldbank, dataset: WDI, code: NY.GDP.DEFL.KD.ZG, priority: 1}152- slug: policy-rate153  name: Central bank policy rate154  short_name: Policy rate155  topic: economy156  subtopic: Money & rates157  unit: "% per annum"158  unit_short: "%"159  format: percent160  precision: 2161  frequency: M162  ranking_eligible: false163  bounds: [-2, 5000]164  description: Official policy interest rate set by the central bank (end of period).165  sources: []          # bis WS_CBPOL (all), fred FEDFUNDS (USA) — added by connector agents166- slug: lending-rate167  name: Lending interest rate168  topic: economy169  subtopic: Money & rates170  unit: "%"171  unit_short: "%"172  format: percent173  ranking_eligible: false174  bounds: [0, 500]175  description: Bank rate that usually meets the short- and medium-term financing needs of the private sector.176  sources:177  - {connector: worldbank, dataset: WDI, code: FR.INR.LEND, priority: 1}178- slug: exchange-rate179  name: Official exchange rate (LCU per US$)180  short_name: Exchange rate181  topic: economy182  subtopic: Money & rates183  unit: local currency units per US$184  unit_short: LCU/US$185  format: number186  precision: 3187  ranking_eligible: false188  bounds: [0, null]189  description: Period-average official exchange rate, local currency units per US dollar.190  sources:191  - {connector: worldbank, dataset: WDI, code: PA.NUS.FCRF, priority: 1}192- slug: real-effective-exchange-rate193  name: Real effective exchange rate index194  short_name: REER195  topic: economy196  subtopic: Money & rates197  unit: index (2010 = 100)198  unit_short: index199  format: index200  ranking_eligible: false201  description: Nominal effective exchange rate divided by a price deflator or index of costs (2010 = 100).202  sources:203  - {connector: worldbank, dataset: WDI, code: PX.REX.REER, priority: 1}204- slug: current-account-balance-pct-gdp205  name: Current account balance (% of GDP)206  short_name: Current account207  topic: economy208  subtopic: External209  unit: "% of GDP"210  unit_short: "% GDP"211  format: percent212  bounds: [-150, 100]213  change_floor: 3214  description: Sum of net exports of goods and services, net primary income and net secondary income, as a share of GDP.215  sources:216  - {connector: worldbank, dataset: WDI, code: BN.CAB.XOKA.GD.ZS, priority: 1}217  - {connector: imf, dataset: WEO, code: BCA_NGDPD, priority: 2}218- slug: current-account-balance219  name: Current account balance (US$)220  topic: trade221  subtopic: Balance222  unit: current US$223  unit_short: US$224  format: currency225  precision: 0226  aggregation: sum227  description: Current account balance in current US dollars.228  sources:229  - {connector: worldbank, dataset: WDI, code: BN.CAB.XOKA.CD, priority: 1}230- slug: gross-capital-formation-pct-gdp231  name: Gross capital formation (% of GDP)232  short_name: Investment233  topic: economy234  subtopic: Demand235  unit: "% of GDP"236  unit_short: "% GDP"237  format: percent238  bounds: [-20, 120]239  description: Outlays on additions to fixed assets plus net changes in inventories, as a share of GDP.240  sources:241  - {connector: worldbank, dataset: WDI, code: NE.GDI.TOTL.ZS, priority: 1}242- slug: household-consumption-pct-gdp243  name: Household consumption (% of GDP)244  topic: economy245  subtopic: Demand246  unit: "% of GDP"247  unit_short: "% GDP"248  format: percent249  bounds: [0, 200]250  description: Final consumption expenditure of households and NPISHs as a share of GDP.251  sources:252  - {connector: worldbank, dataset: WDI, code: NE.CON.PRVT.ZS, priority: 1}253- slug: fdi-inflows-pct-gdp254  name: Foreign direct investment, net inflows (% of GDP)255  short_name: FDI inflows256  topic: economy257  subtopic: External258  unit: "% of GDP"259  unit_short: "% GDP"260  format: percent261  bounds: [-500, 500]262  description: Net inflows of investment to acquire a lasting management interest in an enterprise, as a share of GDP.263  sources:264  - {connector: worldbank, dataset: WDI, code: BX.KLT.DINV.WD.GD.ZS, priority: 1}265- slug: fdi-outflows-pct-gdp266  name: Foreign direct investment, net outflows (% of GDP)267  short_name: FDI outflows268  topic: trade269  subtopic: Investment270  unit: "% of GDP"271  unit_short: "% GDP"272  format: percent273  bounds: [-500, 500]274  sources:275  - {connector: worldbank, dataset: WDI, code: BM.KLT.DINV.WD.GD.ZS, priority: 1}276- slug: broad-money-pct-gdp277  name: Broad money (% of GDP)278  topic: economy279  subtopic: Money & rates280  unit: "% of GDP"281  unit_short: "% GDP"282  format: percent283  bounds: [0, 1000]284  sources:285  - {connector: worldbank, dataset: WDI, code: FM.LBL.BMNY.GD.ZS, priority: 1}286- slug: industrial-production-index287  name: Industrial production index288  topic: economy289  subtopic: Output290  unit: index (2015 = 100)291  unit_short: index292  format: index293  frequency: M294  ranking_eligible: false295  description: Volume index of industrial production.296  sources: []          # oecd/fred — added by connector agents297- slug: gdp-per-hour-worked298  name: GDP per hour worked299  short_name: Productivity300  topic: economy301  subtopic: Productivity302  unit: US$ (PPP, current prices)303  unit_short: US$/h304  format: currency305  precision: 1306  higher_is_better: true307  bounds: [0, 500]308  description: Labour productivity — GDP per hour worked, current prices, PPP.309  sources: []          # oecd DSD_PDB — added by connector agents310- slug: gdp-per-person-employed311  name: GDP per person employed (constant 2021 PPP $)312  topic: labor313  subtopic: Productivity314  unit: constant 2021 PPP $315  unit_short: PPP $316  format: currency317  precision: 0318  higher_is_better: true319  bounds: [0, null]320  sources:321  - {connector: worldbank, dataset: WDI, code: SL.GDP.PCAP.EM.KD, priority: 1}322- slug: agriculture-value-added-pct-gdp323  name: Agriculture, forestry and fishing, value added (% of GDP)324  short_name: Agriculture share325  topic: economy326  subtopic: Structure327  unit: "% of GDP"328  unit_short: "% GDP"329  format: percent330  bounds: [0, 100]331  sources:332  - {connector: worldbank, dataset: WDI, code: NV.AGR.TOTL.ZS, priority: 1}333- slug: industry-value-added-pct-gdp334  name: Industry (incl. construction), value added (% of GDP)335  short_name: Industry share336  topic: economy337  subtopic: Structure338  unit: "% of GDP"339  unit_short: "% GDP"340  format: percent341  bounds: [0, 100]342  sources:343  - {connector: worldbank, dataset: WDI, code: NV.IND.TOTL.ZS, priority: 1}344- slug: manufacturing-value-added-pct-gdp345  name: Manufacturing, value added (% of GDP)346  short_name: Manufacturing share347  topic: economy348  subtopic: Structure349  unit: "% of GDP"350  unit_short: "% GDP"351  format: percent352  bounds: [0, 100]353  sources:354  - {connector: worldbank, dataset: WDI, code: NV.IND.MANF.ZS, priority: 1}355- slug: services-value-added-pct-gdp356  name: Services, value added (% of GDP)357  short_name: Services share358  topic: economy359  subtopic: Structure360  unit: "% of GDP"361  unit_short: "% GDP"362  format: percent363  bounds: [0, 100]364  sources:365  - {connector: worldbank, dataset: WDI, code: NV.SRV.TOTL.ZS, priority: 1}366- slug: gross-savings-pct-gdp367  name: Gross savings (% of GDP)368  topic: economy369  subtopic: Demand370  unit: "% of GDP"371  unit_short: "% GDP"372  format: percent373  bounds: [-100, 100]374  sources:375  - {connector: worldbank, dataset: WDI, code: NY.GNS.ICTR.ZS, priority: 1}376- slug: natural-resources-rents-pct-gdp377  name: Total natural resources rents (% of GDP)378  short_name: Resource rents379  topic: economy380  subtopic: Structure381  unit: "% of GDP"382  unit_short: "% GDP"383  format: percent384  bounds: [0, 100]385  sources:386  - {connector: worldbank, dataset: WDI, code: NY.GDP.TOTL.RT.ZS, priority: 1}387- slug: remittances-received-pct-gdp388  name: Personal remittances received (% of GDP)389  short_name: Remittances390  topic: economy391  subtopic: External392  unit: "% of GDP"393  unit_short: "% GDP"394  format: percent395  bounds: [0, 100]396  sources:397  - {connector: worldbank, dataset: WDI, code: BX.TRF.PWKR.DT.GD.ZS, priority: 1}398399# ========================================================= GOVERNMENT ==========================================================400- slug: government-debt-pct-gdp401  name: Central government debt (% of GDP)402  short_name: Debt / GDP403  topic: government404  subtopic: Debt405  unit: "% of GDP"406  unit_short: "% GDP"407  format: percent408  higher_is_better: false409  featured: true410  bounds: [0, 600]411  change_floor: 5412  description: Entire stock of direct government fixed-term contractual obligations to others outstanding, as a share of GDP.413  sources:414  - {connector: worldbank, dataset: WDI, code: GC.DOD.TOTL.GD.ZS, priority: 1}415- slug: general-government-gross-debt-pct-gdp416  name: General government gross debt (% of GDP)417  short_name: Gross debt / GDP418  topic: government419  subtopic: Debt420  unit: "% of GDP"421  unit_short: "% GDP"422  format: percent423  higher_is_better: false424  featured: true425  bounds: [0, 600]426  change_floor: 5427  description: Gross debt of the general government sector (IMF WEO), including projections.428  sources:429  - {connector: imf, dataset: WEO, code: GGXWDG_NGDP, priority: 1}430- slug: fiscal-balance-pct-gdp431  name: General government net lending/borrowing (% of GDP)432  short_name: Fiscal balance433  topic: government434  subtopic: Balance435  unit: "% of GDP"436  unit_short: "% GDP"437  format: percent438  higher_is_better: true439  bounds: [-150, 100]440  change_floor: 3441  description: Overall fiscal balance of the general government (IMF WEO), surplus positive.442  sources:443  - {connector: imf, dataset: WEO, code: GGXCNL_NGDP, priority: 1}444- slug: government-revenue-pct-gdp445  name: Central government revenue, excluding grants (% of GDP)446  short_name: Central gov. revenue447  topic: government448  subtopic: Revenue & spending449  unit: "% of GDP"450  unit_short: "% GDP"451  format: percent452  bounds: [0, 150]453  sources:454  - {connector: worldbank, dataset: WDI, code: GC.REV.XGRT.GD.ZS, priority: 1}455- slug: general-government-revenue-pct-gdp456  name: General government revenue (% of GDP)457  short_name: Gov. revenue458  topic: government459  subtopic: Revenue & spending460  unit: "% of GDP"461  unit_short: "% GDP"462  format: percent463  bounds: [0, 150]464  description: Revenue of the general government sector (all levels of government), IMF WEO, including projections.465  sources:466  - {connector: imf, dataset: WEO, code: GGR_NGDP, priority: 1}467- slug: government-expenditure-pct-gdp468  name: Central government expense (% of GDP)469  short_name: Central gov. expense470  topic: government471  subtopic: Revenue & spending472  unit: "% of GDP"473  unit_short: "% GDP"474  format: percent475  bounds: [0, 200]476  sources:477  - {connector: worldbank, dataset: WDI, code: GC.XPN.TOTL.GD.ZS, priority: 1}478- slug: general-government-expenditure-pct-gdp479  name: General government total expenditure (% of GDP)480  short_name: Gov. expenditure481  topic: government482  subtopic: Revenue & spending483  unit: "% of GDP"484  unit_short: "% GDP"485  format: percent486  featured: true487  bounds: [0, 200]488  change_floor: 3489  description: Total expenditure of the general government sector (all levels of government), IMF WEO, including projections.490  sources:491  - {connector: imf, dataset: WEO, code: GGX_NGDP, priority: 1}492- slug: tax-revenue-pct-gdp493  name: Tax revenue (% of GDP)494  short_name: Tax revenue495  topic: government496  subtopic: Revenue & spending497  unit: "% of GDP"498  unit_short: "% GDP"499  format: percent500  bounds: [0, 100]501  sources:502  - {connector: worldbank, dataset: WDI, code: GC.TAX.TOTL.GD.ZS, priority: 2, notes: "central government, cash basis — OECD Revenue Statistics (general government, accrual) is priority 1 where available"}503- slug: social-expenditure-pct-gdp504  name: Public social expenditure (% of GDP)505  short_name: Social spending506  topic: government507  subtopic: Revenue & spending508  unit: "% of GDP"509  unit_short: "% GDP"510  format: percent511  bounds: [0, 60]512  sources: []          # oecd SOCX — added by connector agents513- slug: military-expenditure-pct-gdp514  name: Military expenditure (% of GDP)515  short_name: Military spending516  topic: government517  subtopic: Revenue & spending518  unit: "% of GDP"519  unit_short: "% GDP"520  format: percent521  bounds: [0, 120]522  sources:523  - {connector: worldbank, dataset: WDI, code: MS.MIL.XPND.GD.ZS, priority: 1}524- slug: military-expenditure525  name: Military expenditure (current US$)526  topic: security527  subtopic: Defence528  unit: current US$529  unit_short: US$530  format: currency531  precision: 0532  aggregation: sum533  bounds: [0, null]534  sources:535  - {connector: worldbank, dataset: WDI, code: MS.MIL.XPND.CD, priority: 1}536- slug: health-expenditure-pct-gdp537  name: Current health expenditure (% of GDP)538  short_name: Health spending539  topic: health540  subtopic: Spending541  unit: "% of GDP"542  unit_short: "% GDP"543  format: percent544  bounds: [0, 40]545  sources:546  - {connector: worldbank, dataset: WDI, code: SH.XPD.CHEX.GD.ZS, priority: 1}547- slug: education-expenditure-pct-gdp548  name: Government expenditure on education (% of GDP)549  short_name: Education spending550  topic: education551  subtopic: Spending552  unit: "% of GDP"553  unit_short: "% GDP"554  format: percent555  bounds: [0, 30]556  sources:557  - {connector: worldbank, dataset: WDI, code: SE.XPD.TOTL.GD.ZS, priority: 1}558- slug: interest-payments-pct-revenue559  name: Interest payments (% of revenue)560  topic: government561  subtopic: Debt562  unit: "% of revenue"563  unit_short: "%"564  format: percent565  higher_is_better: false566  bounds: [0, 200]567  sources:568  - {connector: worldbank, dataset: WDI, code: GC.XPN.INTP.RV.ZS, priority: 1}569- slug: external-debt-pct-gni570  name: External debt stocks (% of GNI)571  topic: government572  subtopic: Debt573  unit: "% of GNI"574  unit_short: "% GNI"575  format: percent576  bounds: [0, 2000]577  sources:578  - {connector: worldbank, dataset: WDI, code: DT.DOD.DECT.GN.ZS, priority: 1}579- slug: government-effectiveness580  name: Government effectiveness (WGI estimate)581  topic: government582  subtopic: Institutions583  unit: estimate (−2.5 to 2.5)584  unit_short: score585  format: number586  precision: 2587  higher_is_better: true588  bounds: [-3, 3]589  description: Worldwide Governance Indicators — perceptions of the quality of public services and policy implementation.590  sources:591  - {connector: worldbank, dataset: WGI, code: GOV_WGI_GE.EST, priority: 1, params: {source: 3}}592- slug: control-of-corruption593  name: Control of corruption (WGI estimate)594  topic: government595  subtopic: Institutions596  unit: estimate (−2.5 to 2.5)597  unit_short: score598  format: number599  precision: 2600  higher_is_better: true601  bounds: [-3, 3]602  sources:603  - {connector: worldbank, dataset: WGI, code: GOV_WGI_CC.EST, priority: 1, params: {source: 3}}604- slug: rule-of-law605  name: Rule of law (WGI estimate)606  topic: government607  subtopic: Institutions608  unit: estimate (−2.5 to 2.5)609  unit_short: score610  format: number611  precision: 2612  higher_is_better: true613  bounds: [-3, 3]614  sources:615  - {connector: worldbank, dataset: WGI, code: GOV_WGI_RL.EST, priority: 1, params: {source: 3}}616- slug: political-stability617  name: Political stability and absence of violence (WGI estimate)618  short_name: Political stability619  topic: security620  subtopic: Institutions621  unit: estimate (−2.5 to 2.5)622  unit_short: score623  format: number624  precision: 2625  higher_is_better: true626  bounds: [-3, 3]627  sources:628  - {connector: worldbank, dataset: WGI, code: GOV_WGI_PV.EST, priority: 1, params: {source: 3}}629- slug: voice-and-accountability630  name: Voice and accountability (WGI estimate)631  topic: security632  subtopic: Institutions633  unit: estimate (−2.5 to 2.5)634  unit_short: score635  format: number636  precision: 2637  higher_is_better: true638  bounds: [-3, 3]639  sources:640  - {connector: worldbank, dataset: WGI, code: GOV_WGI_VA.EST, priority: 1, params: {source: 3}}641- slug: regulatory-quality642  name: Regulatory quality (WGI estimate)643  topic: security644  subtopic: Institutions645  unit: estimate (−2.5 to 2.5)646  unit_short: score647  format: number648  precision: 2649  higher_is_better: true650  bounds: [-3, 3]651  sources:652  - {connector: worldbank, dataset: WGI, code: GOV_WGI_RQ.EST, priority: 1, params: {source: 3}}653654# ========================================================= POPULATION ==========================================================655- slug: population656  name: Population, total657  short_name: Population658  topic: population659  subtopic: Size & growth660  unit: people661  unit_short: people662  format: number663  precision: 0664  aggregation: sum665  featured: true666  bounds: [0, null]667  description: Midyear estimate of all residents regardless of legal status or citizenship.668  sources:669  - {connector: worldbank, dataset: WDI, code: SP.POP.TOTL, priority: 1}670- slug: population-growth671  name: Population growth672  topic: population673  subtopic: Size & growth674  unit: annual %675  unit_short: "%"676  format: percent677  precision: 2678  featured: true679  bounds: [-30, 30]680  change_floor: 0.5681  description: Exponential rate of growth of midyear population from year t−1 to t.682  sources:683  - {connector: worldbank, dataset: WDI, code: SP.POP.GROW, priority: 1}684- slug: population-density685  name: Population density686  topic: population687  subtopic: Size & growth688  unit: people per km² of land area689  unit_short: /km²690  format: number691  precision: 1692  bounds: [0, 100000]693  sources:694  - {connector: worldbank, dataset: WDI, code: EN.POP.DNST, priority: 1}695- slug: urban-population-share696  name: Urban population (% of total)697  short_name: Urbanisation698  topic: population699  subtopic: Urbanisation700  unit: "% of population"701  unit_short: "%"702  format: percent703  bounds: [0, 100]704  sources:705  - {connector: worldbank, dataset: WDI, code: SP.URB.TOTL.IN.ZS, priority: 1}706- slug: urban-population707  name: Urban population708  topic: population709  subtopic: Urbanisation710  unit: people711  unit_short: people712  format: number713  precision: 0714  aggregation: sum715  bounds: [0, null]716  sources:717  - {connector: worldbank, dataset: WDI, code: SP.URB.TOTL, priority: 1}718- slug: rural-population719  name: Rural population720  topic: population721  subtopic: Urbanisation722  unit: people723  unit_short: people724  format: number725  precision: 0726  aggregation: sum727  bounds: [0, null]728  sources:729  - {connector: worldbank, dataset: WDI, code: SP.RUR.TOTL, priority: 1}730- slug: fertility-rate731  name: Fertility rate, total732  short_name: Fertility733  topic: population734  subtopic: Births & deaths735  unit: births per woman736  unit_short: births/woman737  format: number738  precision: 2739  featured: true740  bounds: [0, 10]741  change_floor: 0.2742  sources:743  - {connector: worldbank, dataset: WDI, code: SP.DYN.TFRT.IN, priority: 1}744- slug: birth-rate745  name: Birth rate, crude746  topic: population747  subtopic: Births & deaths748  unit: per 1,000 people749  unit_short: /1,000750  format: per_1000751  bounds: [0, 70]752  sources:753  - {connector: worldbank, dataset: WDI, code: SP.DYN.CBRT.IN, priority: 1}754- slug: death-rate755  name: Death rate, crude756  topic: population757  subtopic: Births & deaths758  unit: per 1,000 people759  unit_short: /1,000760  format: per_1000761  bounds: [0, 100]762  sources:763  - {connector: worldbank, dataset: WDI, code: SP.DYN.CDRT.IN, priority: 1}764- slug: median-age765  name: Median age766  topic: population767  subtopic: Age structure768  unit: years769  unit_short: yrs770  format: years771  featured: true772  bounds: [10, 70]773  description: Age that divides the population into two numerically equal groups (UN World Population Prospects).774  sources:775  - {connector: owid, dataset: grapher, code: median-age, priority: 1}776- slug: population-0-14-share777  name: Population ages 0–14 (% of total)778  short_name: Children (0–14)779  topic: population780  subtopic: Age structure781  unit: "% of population"782  unit_short: "%"783  format: percent784  bounds: [0, 70]785  sources:786  - {connector: worldbank, dataset: WDI, code: SP.POP.0014.TO.ZS, priority: 1}787- slug: population-15-64-share788  name: Population ages 15–64 (% of total)789  short_name: Working age (15–64)790  topic: population791  subtopic: Age structure792  unit: "% of population"793  unit_short: "%"794  format: percent795  bounds: [0, 100]796  sources:797  - {connector: worldbank, dataset: WDI, code: SP.POP.1564.TO.ZS, priority: 1}798- slug: population-65-plus-share799  name: Population ages 65 and above (% of total)800  short_name: Seniors (65+)801  topic: population802  subtopic: Age structure803  unit: "% of population"804  unit_short: "%"805  format: percent806  bounds: [0, 60]807  sources:808  - {connector: worldbank, dataset: WDI, code: SP.POP.65UP.TO.ZS, priority: 1}809- slug: dependency-ratio810  name: Age dependency ratio811  topic: population812  subtopic: Age structure813  unit: "% of working-age population"814  unit_short: "%"815  format: percent816  bounds: [0, 200]817  sources:818  - {connector: worldbank, dataset: WDI, code: SP.POP.DPND, priority: 1}819- slug: old-age-dependency-ratio820  name: Old-age dependency ratio821  topic: population822  subtopic: Age structure823  unit: "% of working-age population"824  unit_short: "%"825  format: percent826  bounds: [0, 150]827  sources:828  - {connector: worldbank, dataset: WDI, code: SP.POP.DPND.OL, priority: 1}829- slug: net-migration830  name: Net migration831  topic: population832  subtopic: Migration833  unit: people834  unit_short: people835  format: number836  precision: 0837  aggregation: sum838  description: Total number of immigrants less the annual number of emigrants, including citizens and noncitizens.839  sources:840  - {connector: worldbank, dataset: WDI, code: SM.POP.NETM, priority: 1}841- slug: international-migrant-stock-share842  name: International migrant stock (% of population)843  short_name: Migrant stock844  topic: population845  subtopic: Migration846  unit: "% of population"847  unit_short: "%"848  format: percent849  bounds: [0, 100]850  sources:851  - {connector: worldbank, dataset: WDI, code: SM.POP.TOTL.ZS, priority: 1}852- slug: refugee-population853  name: Refugee population by country of asylum854  short_name: Refugees hosted855  topic: security856  subtopic: Displacement857  unit: people858  unit_short: people859  format: number860  precision: 0861  aggregation: sum862  bounds: [0, null]863  sources: []          # WB SM.POP.REFG archived (2026) → UNHCR via owid grapher, added by connector agents864- slug: refugees-by-origin865  name: Refugee population by country of origin866  topic: security867  subtopic: Displacement868  unit: people869  unit_short: people870  format: number871  precision: 0872  aggregation: sum873  bounds: [0, null]874  sources: []          # WB SM.POP.REFG.OR archived → owid grapher (registry/sources/owid.yaml)875- slug: internally-displaced-persons876  name: Internally displaced persons (conflict and violence)877  topic: security878  subtopic: Displacement879  unit: people880  unit_short: people881  format: number882  precision: 0883  aggregation: sum884  bounds: [0, null]885  sources: []          # WB VC.IDP.TOCV archived (no successor)886- slug: life-expectancy887  name: Life expectancy at birth888  short_name: Life expectancy889  topic: health890  subtopic: Longevity891  unit: years892  unit_short: yrs893  format: years894  higher_is_better: true895  featured: true896  bounds: [20, 100]897  change_floor: 1898  description: Number of years a newborn infant would live if prevailing patterns of mortality were to stay the same throughout its life.899  sources:900  - {connector: worldbank, dataset: WDI, code: SP.DYN.LE00.IN, priority: 1}901  - {connector: who, dataset: GHO, code: WHOSIS_000001, params: {Dim1: SEX_BTSX}, priority: 2}902- slug: life-expectancy-female903  name: Life expectancy at birth, female904  topic: health905  subtopic: Longevity906  unit: years907  unit_short: yrs908  format: years909  higher_is_better: true910  bounds: [20, 100]911  sources:912  - {connector: worldbank, dataset: WDI, code: SP.DYN.LE00.FE.IN, priority: 1}913- slug: life-expectancy-male914  name: Life expectancy at birth, male915  topic: health916  subtopic: Longevity917  unit: years918  unit_short: yrs919  format: years920  higher_is_better: true921  bounds: [20, 100]922  sources:923  - {connector: worldbank, dataset: WDI, code: SP.DYN.LE00.MA.IN, priority: 1}924- slug: adolescent-fertility-rate925  name: Adolescent fertility rate926  topic: population927  subtopic: Births & deaths928  unit: births per 1,000 women ages 15–19929  unit_short: /1,000930  format: per_1000931  higher_is_better: false932  bounds: [0, 300]933  sources:934  - {connector: worldbank, dataset: WDI, code: SP.ADO.TFRT, priority: 1}935- slug: population-female-share936  name: Population, female (% of total)937  topic: population938  subtopic: Size & growth939  unit: "% of population"940  unit_short: "%"941  format: percent942  bounds: [20, 70]943  ranking_eligible: false944  sources:945  - {connector: worldbank, dataset: WDI, code: SP.POP.TOTL.FE.ZS, priority: 1}946947# =========================================================== LABOR =============================================================948- slug: unemployment-rate949  name: Unemployment rate950  short_name: Unemployment951  topic: labor952  subtopic: Unemployment953  unit: "% of labour force"954  unit_short: "%"955  format: percent956  higher_is_better: false957  featured: true958  bounds: [0, 60]959  change_floor: 1960  description: Share of the labour force that is without work but available for and seeking employment (ILO modelled estimate).961  sources:962  - {connector: worldbank, dataset: WDI, code: SL.UEM.TOTL.ZS, priority: 1}963  - {connector: imf, dataset: WEO, code: LUR, priority: 2}964- slug: youth-unemployment-rate965  name: Youth unemployment rate (ages 15–24)966  short_name: Youth unemployment967  topic: labor968  subtopic: Unemployment969  unit: "% of labour force ages 15–24"970  unit_short: "%"971  format: percent972  higher_is_better: false973  bounds: [0, 100]974  change_floor: 2975  sources:976  - {connector: worldbank, dataset: WDI, code: SL.UEM.1524.ZS, priority: 1}977- slug: labor-force-participation-rate978  name: Labour force participation rate (ages 15+)979  short_name: Participation980  topic: labor981  subtopic: Participation982  unit: "% of population ages 15+"983  unit_short: "%"984  format: percent985  bounds: [0, 100]986  sources:987  - {connector: worldbank, dataset: WDI, code: SL.TLF.CACT.ZS, priority: 1}988- slug: labor-force-participation-female989  name: Labour force participation rate, female (ages 15+)990  topic: labor991  subtopic: Participation992  unit: "% of female population ages 15+"993  unit_short: "%"994  format: percent995  bounds: [0, 100]996  sources:997  - {connector: worldbank, dataset: WDI, code: SL.TLF.CACT.FE.ZS, priority: 1}998- slug: employment-to-population-ratio999  name: Employment to population ratio (ages 15+)1000  topic: labor1001  subtopic: Employment1002  unit: "% of population ages 15+"1003  unit_short: "%"1004  format: percent1005  bounds: [0, 100]1006  sources:1007  - {connector: worldbank, dataset: WDI, code: SL.EMP.TOTL.SP.ZS, priority: 1}1008- slug: employment-rate1009  name: Employment rate (ages 15–64)1010  topic: labor1011  subtopic: Employment1012  unit: "% of population ages 15–64"1013  unit_short: "%"1014  format: percent1015  higher_is_better: true1016  bounds: [0, 100]1017  sources: []          # oecd / eurostat lfsi_emp_a — added by connector agents1018- slug: labor-force1019  name: Labour force, total1020  topic: labor1021  subtopic: Employment1022  unit: people1023  unit_short: people1024  format: number1025  precision: 01026  aggregation: sum1027  bounds: [0, null]1028  sources:1029  - {connector: worldbank, dataset: WDI, code: SL.TLF.TOTL.IN, priority: 1}1030- slug: average-annual-wages1031  name: Average annual wages1032  short_name: Average wage1033  topic: labor1034  subtopic: Wages1035  unit: US$ PPP, constant prices1036  unit_short: US$ PPP1037  format: currency1038  precision: 01039  higher_is_better: true1040  bounds: [0, 500000]1041  sources: []          # oecd AV_AN_WAGE — added by connector agents1042- slug: hours-worked1043  name: Average annual hours worked per worker1044  short_name: Hours worked1045  topic: labor1046  subtopic: Wages1047  unit: hours per year1048  unit_short: h/yr1049  format: number1050  precision: 01051  bounds: [800, 3000]1052  sources: []          # oecd — added by connector agents1053- slug: minimum-wage-relative1054  name: Minimum wage relative to median wage1055  topic: labor1056  subtopic: Wages1057  unit: ratio1058  unit_short: ratio1059  format: ratio1060  precision: 21061  bounds: [0, 1.5]1062  sources: []          # oecd — optional1063- slug: self-employed-share1064  name: Self-employed (% of total employment)1065  topic: labor1066  subtopic: Employment1067  unit: "% of employment"1068  unit_short: "%"1069  format: percent1070  bounds: [0, 100]1071  sources:1072  - {connector: worldbank, dataset: WDI, code: SL.EMP.SELF.ZS, priority: 1}1073- slug: vulnerable-employment-share1074  name: Vulnerable employment (% of total employment)1075  topic: labor1076  subtopic: Employment1077  unit: "% of employment"1078  unit_short: "%"1079  format: percent1080  higher_is_better: false1081  bounds: [0, 100]1082  sources:1083  - {connector: worldbank, dataset: WDI, code: SL.EMP.VULN.ZS, priority: 1}1084- slug: employment-agriculture-share1085  name: Employment in agriculture (% of total employment)1086  topic: labor1087  subtopic: Structure1088  unit: "% of employment"1089  unit_short: "%"1090  format: percent1091  bounds: [0, 100]1092  sources:1093  - {connector: worldbank, dataset: WDI, code: SL.AGR.EMPL.ZS, priority: 1}1094- slug: employment-industry-share1095  name: Employment in industry (% of total employment)1096  topic: labor1097  subtopic: Structure1098  unit: "% of employment"1099  unit_short: "%"1100  format: percent1101  bounds: [0, 100]1102  sources:1103  - {connector: worldbank, dataset: WDI, code: SL.IND.EMPL.ZS, priority: 1}1104- slug: employment-services-share1105  name: Employment in services (% of total employment)1106  topic: labor1107  subtopic: Structure1108  unit: "% of employment"1109  unit_short: "%"1110  format: percent1111  bounds: [0, 100]1112  sources:1113  - {connector: worldbank, dataset: WDI, code: SL.SRV.EMPL.ZS, priority: 1}1114- slug: part-time-employment-share1115  name: Part-time employment (% of employment)1116  topic: labor1117  subtopic: Employment1118  unit: "% of employment"1119  unit_short: "%"1120  format: percent1121  bounds: [0, 100]1122  sources: []          # oecd — optional1123- slug: long-term-unemployment-share1124  name: Long-term unemployment (% of unemployed)1125  topic: labor1126  subtopic: Unemployment1127  unit: "% of unemployed"1128  unit_short: "%"1129  format: percent1130  higher_is_better: false1131  bounds: [0, 100]1132  sources: []          # oecd / eurostat — optional11331134# ============================================================ INCOME ===========================================================1135- slug: gni-per-capita1136  name: GNI per capita, Atlas method (current US$)1137  short_name: GNI per capita1138  topic: income1139  subtopic: Income1140  unit: current US$1141  unit_short: US$1142  format: currency1143  precision: 01144  higher_is_better: true1145  bounds: [0, null]1146  sources:1147  - {connector: worldbank, dataset: WDI, code: NY.GNP.PCAP.CD, priority: 1}1148- slug: gni-per-capita-ppp1149  name: GNI per capita, PPP (current international $)1150  topic: income1151  subtopic: Income1152  unit: current international $1153  unit_short: intl $1154  format: currency1155  precision: 01156  higher_is_better: true1157  bounds: [0, null]1158  sources:1159  - {connector: worldbank, dataset: WDI, code: NY.GNP.PCAP.PP.CD, priority: 1}1160- slug: gini-index1161  name: Gini index1162  topic: income1163  subtopic: Inequality1164  unit: index (0 = perfect equality, 100 = perfect inequality)1165  unit_short: Gini1166  format: number1167  precision: 11168  higher_is_better: false1169  featured: true1170  bounds: [15, 80]1171  description: Extent to which the distribution of income among individuals deviates from a perfectly equal distribution.1172  sources:1173  - {connector: worldbank, dataset: WDI, code: SI.POV.GINI, priority: 1}1174- slug: income-share-top-101175  name: Income share held by highest 10%1176  topic: income1177  subtopic: Inequality1178  unit: "% of income"1179  unit_short: "%"1180  format: percent1181  higher_is_better: false1182  bounds: [10, 80]1183  sources:1184  - {connector: worldbank, dataset: WDI, code: SI.DST.10TH.10, priority: 1}1185- slug: income-share-bottom-201186  name: Income share held by lowest 20%1187  topic: income1188  subtopic: Inequality1189  unit: "% of income"1190  unit_short: "%"1191  format: percent1192  higher_is_better: true1193  bounds: [0, 20]1194  sources:1195  - {connector: worldbank, dataset: WDI, code: SI.DST.FRST.20, priority: 1}1196- slug: poverty-headcount-2151197  name: Poverty headcount ratio at the international poverty line1198  short_name: Extreme poverty1199  topic: income1200  subtopic: Poverty1201  unit: "% of population"1202  unit_short: "%"1203  format: percent1204  higher_is_better: false1205  bounds: [0, 100]1206  description: Share of the population living below the World Bank international poverty line (US$3.00 a day, 2021 PPP).1207  sources:1208  - {connector: worldbank, dataset: WDI, code: SI.POV.DDAY, priority: 1}1209- slug: poverty-headcount-national1210  name: Poverty headcount ratio at national poverty lines1211  topic: income1212  subtopic: Poverty1213  unit: "% of population"1214  unit_short: "%"1215  format: percent1216  higher_is_better: false1217  ranking_eligible: false1218  bounds: [0, 100]1219  sources:1220  - {connector: worldbank, dataset: WDI, code: SI.POV.NAHC, priority: 1}1221- slug: median-household-income1222  name: Median equivalised net household income1223  topic: income1224  subtopic: Income1225  unit: euro (PPS)1226  unit_short: PPS1227  format: currency1228  precision: 01229  higher_is_better: true1230  sources: []          # eurostat ilc_di03 — added by connector agents1231- slug: at-risk-of-poverty-rate1232  name: At-risk-of-poverty rate1233  topic: income1234  subtopic: Poverty1235  unit: "% of population"1236  unit_short: "%"1237  format: percent1238  higher_is_better: false1239  bounds: [0, 100]1240  sources: []          # eurostat ilc_li02 — added by connector agents1241- slug: household-consumption-per-capita1242  name: Household consumption per capita (constant 2015 US$)1243  topic: income1244  subtopic: Income1245  unit: constant 2015 US$1246  unit_short: US$1247  format: currency1248  precision: 01249  higher_is_better: true1250  bounds: [0, null]1251  sources:1252  - {connector: worldbank, dataset: WDI, code: NE.CON.PRVT.PC.KD, priority: 1}12531254# =========================================================== HOUSING ===========================================================1255- slug: real-house-price-index1256  name: Real house price index1257  short_name: Real house prices1258  topic: housing1259  subtopic: Prices1260  unit: index (2015 = 100)1261  unit_short: index1262  format: index1263  frequency: Q1264  ranking_eligible: false1265  featured: true1266  bounds: [0, 2000]1267  description: Residential property prices deflated by the consumer price index.1268  sources: []          # oecd DF_HOUSE_PRICES / bis WS_SPP / fred Q??R628BIS — added by connector agents1269- slug: nominal-house-price-index1270  name: Nominal house price index1271  topic: housing1272  subtopic: Prices1273  unit: index (2015 = 100)1274  unit_short: index1275  format: index1276  frequency: Q1277  ranking_eligible: false1278  bounds: [0, 5000]1279  sources: []1280- slug: house-price-growth1281  name: Real house price growth (year on year)1282  topic: housing1283  subtopic: Prices1284  unit: annual %1285  unit_short: "%"1286  format: percent1287  frequency: Q1288  bounds: [-60, 100]1289  change_floor: 51290  sources: []          # derived by connector agents from real-house-price-index, or bis1291- slug: rent-price-index1292  name: Rent price index1293  topic: housing1294  subtopic: Prices1295  unit: index (2015 = 100)1296  unit_short: index1297  format: index1298  frequency: Q1299  ranking_eligible: false1300  sources: []1301- slug: price-to-income-ratio1302  name: House price-to-income ratio1303  topic: housing1304  subtopic: Affordability1305  unit: index (2015 = 100)1306  unit_short: index1307  format: index1308  frequency: Q1309  higher_is_better: false1310  sources: []1311- slug: price-to-rent-ratio1312  name: House price-to-rent ratio1313  topic: housing1314  subtopic: Affordability1315  unit: index (2015 = 100)1316  unit_short: index1317  format: index1318  frequency: Q1319  sources: []1320- slug: mortgage-rate1321  name: Mortgage interest rate1322  topic: housing1323  subtopic: Financing1324  unit: "%"1325  unit_short: "%"1326  format: percent1327  precision: 21328  frequency: M1329  ranking_eligible: false1330  bounds: [0, 50]1331  sources: []          # fred MORTGAGE30US (USA) — added by connector agents1332- slug: housing-starts1333  name: Housing starts1334  topic: housing1335  subtopic: Construction1336  unit: thousands of units (annual rate)1337  unit_short: k units1338  format: number1339  precision: 01340  frequency: M1341  ranking_eligible: false1342  sources: []          # fred HOUST (USA)1343- slug: building-permits1344  name: Building permits1345  topic: housing1346  subtopic: Construction1347  unit: thousands of units (annual rate)1348  unit_short: k units1349  format: number1350  precision: 01351  frequency: M1352  ranking_eligible: false1353  sources: []          # fred PERMIT (USA)1354- slug: homeownership-rate1355  name: Homeownership rate1356  topic: housing1357  subtopic: Tenure1358  unit: "% of households"1359  unit_short: "%"1360  format: percent1361  bounds: [0, 100]1362  sources: []          # eurostat ilc_lvho02 / fred RHORUSQ156N (USA)1363- slug: housing-cost-overburden-rate1364  name: Housing cost overburden rate1365  topic: housing1366  subtopic: Affordability1367  unit: "% of population"1368  unit_short: "%"1369  format: percent1370  higher_is_better: false1371  bounds: [0, 100]1372  description: Share of the population living in households where total housing costs exceed 40 % of disposable income.1373  sources: []          # eurostat ilc_lvho07a1374- slug: house-price-to-income-growth1375  name: Price-to-income ratio change (5 years)1376  topic: housing1377  subtopic: Affordability1378  unit: "%"1379  unit_short: "%"1380  format: percent1381  frequency: Q1382  sources: []          # optional derived13831384# ============================================================ HEALTH ===========================================================1385- slug: healthy-life-expectancy1386  name: Healthy life expectancy at birth1387  topic: health1388  subtopic: Longevity1389  unit: years1390  unit_short: yrs1391  format: years1392  higher_is_better: true1393  bounds: [20, 90]1394  sources:1395  - {connector: who, dataset: GHO, code: WHOSIS_000002, params: {Dim1: SEX_BTSX}, priority: 1}1396- slug: infant-mortality-rate1397  name: Infant mortality rate1398  short_name: Infant mortality1399  topic: health1400  subtopic: Mortality1401  unit: per 1,000 live births1402  unit_short: /1,0001403  format: per_10001404  higher_is_better: false1405  featured: true1406  bounds: [0, 600]1407  sources:1408  - {connector: worldbank, dataset: WDI, code: SP.DYN.IMRT.IN, priority: 1}1409- slug: under-5-mortality-rate1410  name: Under-5 mortality rate1411  topic: health1412  subtopic: Mortality1413  unit: per 1,000 live births1414  unit_short: /1,0001415  format: per_10001416  higher_is_better: false1417  bounds: [0, 800]1418  sources:1419  - {connector: worldbank, dataset: WDI, code: SH.DYN.MORT, priority: 1}1420- slug: maternal-mortality-ratio1421  name: Maternal mortality ratio1422  topic: health1423  subtopic: Mortality1424  unit: per 100,000 live births1425  unit_short: /100k1426  format: per_100k1427  higher_is_better: false1428  bounds: [0, 10000]1429  sources:1430  - {connector: worldbank, dataset: WDI, code: SH.STA.MMRT, priority: 1}1431- slug: health-expenditure-per-capita1432  name: Current health expenditure per capita (current US$)1433  topic: health1434  subtopic: Spending1435  unit: current US$1436  unit_short: US$1437  format: currency1438  precision: 01439  bounds: [0, 50000]1440  sources:1441  - {connector: worldbank, dataset: WDI, code: SH.XPD.CHEX.PC.CD, priority: 1}1442- slug: out-of-pocket-health-expenditure-share1443  name: Out-of-pocket expenditure (% of current health expenditure)1444  topic: health1445  subtopic: Spending1446  unit: "% of health expenditure"1447  unit_short: "%"1448  format: percent1449  higher_is_better: false1450  bounds: [0, 100]1451  sources:1452  - {connector: worldbank, dataset: WDI, code: SH.XPD.OOPC.CH.ZS, priority: 1}1453- slug: physicians-per-10001454  name: Physicians1455  topic: health1456  subtopic: Capacity1457  unit: per 1,000 people1458  unit_short: /1,0001459  format: per_10001460  precision: 21461  higher_is_better: true1462  bounds: [0, 30]1463  sources:1464  - {connector: worldbank, dataset: WDI, code: SH.MED.PHYS.ZS, priority: 1}1465  - {connector: who, dataset: GHO, code: HWF_0001, priority: 3, transform: "x/10"}1466- slug: nurses-per-10001467  name: Nurses and midwives1468  topic: health1469  subtopic: Capacity1470  unit: per 1,000 people1471  unit_short: /1,0001472  format: per_10001473  precision: 21474  higher_is_better: true1475  bounds: [0, 60]1476  sources:1477  - {connector: worldbank, dataset: WDI, code: SH.MED.NUMW.P3, priority: 1}1478- slug: hospital-beds-per-10001479  name: Hospital beds1480  topic: health1481  subtopic: Capacity1482  unit: per 1,000 people1483  unit_short: /1,0001484  format: per_10001485  precision: 21486  bounds: [0, 30]1487  sources:1488  - {connector: worldbank, dataset: WDI, code: SH.MED.BEDS.ZS, priority: 1}1489- slug: ncd-mortality-30-701490  name: Probability of dying between ages 30 and 70 from NCDs1491  short_name: NCD mortality1492  topic: health1493  subtopic: Mortality1494  unit: "%"1495  unit_short: "%"1496  format: percent1497  higher_is_better: false1498  bounds: [0, 60]1499  description: Cardiovascular disease, cancer, diabetes or chronic respiratory disease.1500  sources:1501  - {connector: worldbank, dataset: WDI, code: SH.DYN.NCOM.ZS, priority: 1}1502- slug: suicide-rate1503  name: Suicide mortality rate1504  topic: health1505  subtopic: Mortality1506  unit: per 100,000 people1507  unit_short: /100k1508  format: per_100k1509  higher_is_better: false1510  bounds: [0, 100]1511  sources:1512  - {connector: worldbank, dataset: WDI, code: SH.STA.SUIC.P5, priority: 1}1513- slug: smoking-prevalence1514  name: Prevalence of current tobacco use (ages 15+)1515  short_name: Smoking1516  topic: health1517  subtopic: Risk factors1518  unit: "% of adults"1519  unit_short: "%"1520  format: percent1521  higher_is_better: false1522  bounds: [0, 80]1523  sources:1524  - {connector: worldbank, dataset: WDI, code: SH.PRV.SMOK, priority: 1}1525- slug: obesity-prevalence1526  name: Prevalence of obesity among adults (BMI ≥ 30)1527  short_name: Obesity1528  topic: health1529  subtopic: Risk factors1530  unit: "% of adults"1531  unit_short: "%"1532  format: percent1533  higher_is_better: false1534  bounds: [0, 80]1535  sources:1536  - {connector: who, dataset: GHO, code: NCD_BMI_30A, params: {Dim1: SEX_BTSX}, priority: 1}1537- slug: alcohol-consumption1538  name: Total alcohol consumption per capita (ages 15+)1539  short_name: Alcohol1540  topic: health1541  subtopic: Risk factors1542  unit: litres of pure alcohol per year1543  unit_short: L1544  format: number1545  precision: 11546  bounds: [0, 30]1547  sources:1548  - {connector: worldbank, dataset: WDI, code: SH.ALC.PCAP.LI, priority: 1}1549- slug: measles-immunization1550  name: Immunization, measles (% of children ages 12–23 months)1551  topic: health1552  subtopic: Prevention1553  unit: "% of children"1554  unit_short: "%"1555  format: percent1556  higher_is_better: true1557  bounds: [0, 100]1558  sources:1559  - {connector: worldbank, dataset: WDI, code: SH.IMM.MEAS, priority: 1}1560- slug: dtp3-immunization1561  name: Immunization, DPT (% of children ages 12–23 months)1562  topic: health1563  subtopic: Prevention1564  unit: "% of children"1565  unit_short: "%"1566  format: percent1567  higher_is_better: true1568  bounds: [0, 100]1569  sources:1570  - {connector: worldbank, dataset: WDI, code: SH.IMM.IDPT, priority: 1}1571- slug: hiv-prevalence1572  name: Prevalence of HIV (ages 15–49)1573  topic: health1574  subtopic: Disease1575  unit: "% of population ages 15–49"1576  unit_short: "%"1577  format: percent1578  higher_is_better: false1579  bounds: [0, 40]1580  sources:1581  - {connector: worldbank, dataset: WDI, code: SH.DYN.AIDS.ZS, priority: 1}1582- slug: tuberculosis-incidence1583  name: Incidence of tuberculosis1584  topic: health1585  subtopic: Disease1586  unit: per 100,000 people1587  unit_short: /100k1588  format: per_100k1589  higher_is_better: false1590  bounds: [0, 2000]1591  sources:1592  - {connector: worldbank, dataset: WDI, code: SH.TBS.INCD, priority: 1}1593- slug: safely-managed-water1594  name: People using safely managed drinking water services1595  short_name: Safe drinking water1596  topic: health1597  subtopic: Access1598  unit: "% of population"1599  unit_short: "%"1600  format: percent1601  higher_is_better: true1602  bounds: [0, 100]1603  sources:1604  - {connector: worldbank, dataset: WDI, code: SH.H2O.SMDW.ZS, priority: 1}1605- slug: safely-managed-sanitation1606  name: People using safely managed sanitation services1607  short_name: Safe sanitation1608  topic: health1609  subtopic: Access1610  unit: "% of population"1611  unit_short: "%"1612  format: percent1613  higher_is_better: true1614  bounds: [0, 100]1615  sources:1616  - {connector: worldbank, dataset: WDI, code: SH.STA.SMSS.ZS, priority: 1}1617- slug: road-traffic-deaths1618  name: Mortality caused by road traffic injury1619  topic: health1620  subtopic: Mortality1621  unit: per 100,000 people1622  unit_short: /100k1623  format: per_100k1624  higher_is_better: false1625  bounds: [0, 100]1626  sources:1627  - {connector: worldbank, dataset: WDI, code: SH.STA.TRAF.P5, priority: 1}16281629# =========================================================== EDUCATION =========================================================1630- slug: literacy-rate-adult1631  name: Literacy rate, adult total (ages 15+)1632  short_name: Adult literacy1633  topic: education1634  subtopic: Literacy1635  unit: "% of people ages 15+"1636  unit_short: "%"1637  format: percent1638  higher_is_better: true1639  bounds: [0, 100]1640  sources:1641  - {connector: worldbank, dataset: WDI, code: SE.ADT.LITR.ZS, priority: 1}1642- slug: literacy-rate-youth1643  name: Literacy rate, youth total (ages 15–24)1644  topic: education1645  subtopic: Literacy1646  unit: "% of people ages 15–24"1647  unit_short: "%"1648  format: percent1649  higher_is_better: true1650  bounds: [0, 100]1651  sources:1652  - {connector: worldbank, dataset: WDI, code: SE.ADT.1524.LT.ZS, priority: 1}1653- slug: primary-enrollment1654  name: School enrolment, primary (% gross)1655  topic: education1656  subtopic: Enrolment1657  unit: "% gross"1658  unit_short: "%"1659  format: percent1660  bounds: [0, 200]1661  ranking_eligible: false1662  sources:1663  - {connector: worldbank, dataset: WDI, code: SE.PRM.ENRR, priority: 1}1664- slug: secondary-enrollment1665  name: School enrolment, secondary (% gross)1666  topic: education1667  subtopic: Enrolment1668  unit: "% gross"1669  unit_short: "%"1670  format: percent1671  higher_is_better: true1672  bounds: [0, 200]1673  sources:1674  - {connector: worldbank, dataset: WDI, code: SE.SEC.ENRR, priority: 1}1675- slug: tertiary-enrollment1676  name: School enrolment, tertiary (% gross)1677  short_name: Tertiary enrolment1678  topic: education1679  subtopic: Enrolment1680  unit: "% gross"1681  unit_short: "%"1682  format: percent1683  higher_is_better: true1684  featured: true1685  bounds: [0, 200]1686  sources:1687  - {connector: worldbank, dataset: WDI, code: SE.TER.ENRR, priority: 1}1688- slug: primary-completion-rate1689  name: Primary completion rate1690  topic: education1691  subtopic: Attainment1692  unit: "% of relevant age group"1693  unit_short: "%"1694  format: percent1695  higher_is_better: true1696  bounds: [0, 200]1697  sources:1698  - {connector: worldbank, dataset: WDI, code: SE.PRM.CMPT.ZS, priority: 1}1699- slug: tertiary-attainment-25-641700  name: Tertiary educational attainment (ages 25–64)1701  short_name: Tertiary attainment1702  topic: education1703  subtopic: Attainment1704  unit: "% of population ages 25–64"1705  unit_short: "%"1706  format: percent1707  higher_is_better: true1708  bounds: [0, 100]1709  sources:1710  - {connector: worldbank, dataset: WDI, code: SE.TER.CUAT.BA.ZS, priority: 3}   # Bachelor's or higher, 25+ (fallback)1711- slug: tertiary-attainment-25-341712  name: Tertiary educational attainment (ages 25–34)1713  topic: education1714  subtopic: Attainment1715  unit: "% of population ages 25–34"1716  unit_short: "%"1717  format: percent1718  higher_is_better: true1719  bounds: [0, 100]1720  sources: []          # oecd / eurostat edat_lfse_03 — added by connector agents1721- slug: upper-secondary-attainment1722  name: At least upper secondary attainment (ages 25–64)1723  topic: education1724  subtopic: Attainment1725  unit: "% of population ages 25–64"1726  unit_short: "%"1727  format: percent1728  higher_is_better: true1729  bounds: [0, 100]1730  sources:1731  - {connector: worldbank, dataset: WDI, code: SE.SEC.CUAT.UP.ZS, priority: 2}1732- slug: education-expenditure-pct-government1733  name: Government expenditure on education (% of government expenditure)1734  topic: education1735  subtopic: Spending1736  unit: "% of government expenditure"1737  unit_short: "%"1738  format: percent1739  bounds: [0, 60]1740  sources:1741  - {connector: worldbank, dataset: WDI, code: SE.XPD.TOTL.GB.ZS, priority: 1}1742- slug: expected-years-of-schooling1743  name: Expected years of schooling1744  topic: education1745  subtopic: Attainment1746  unit: years1747  unit_short: yrs1748  format: years1749  higher_is_better: true1750  bounds: [0, 25]1751  sources:1752  - {connector: owid, dataset: grapher, code: expected-years-of-schooling, priority: 1}1753- slug: mean-years-of-schooling1754  name: Mean years of schooling (ages 25+)1755  topic: education1756  subtopic: Attainment1757  unit: years1758  unit_short: yrs1759  format: years1760  higher_is_better: true1761  bounds: [0, 20]1762  sources:1763  - {connector: owid, dataset: grapher, code: mean-years-of-schooling-long-run, priority: 1}1764- slug: pupil-teacher-ratio-primary1765  name: Pupil-teacher ratio, primary1766  topic: education1767  subtopic: Capacity1768  unit: pupils per teacher1769  unit_short: pupils/teacher1770  format: number1771  precision: 11772  higher_is_better: false1773  bounds: [1, 150]1774  sources:1775  - {connector: worldbank, dataset: WDI, code: SE.PRM.ENRL.TC.ZS, priority: 1}1776- slug: out-of-school-children1777  name: Children out of school, primary1778  topic: education1779  subtopic: Enrolment1780  unit: children1781  unit_short: children1782  format: number1783  precision: 01784  aggregation: sum1785  higher_is_better: false1786  bounds: [0, null]1787  sources:1788  - {connector: worldbank, dataset: WDI, code: SE.PRM.UNER, priority: 1}1789- slug: learning-poverty1790  name: Learning poverty1791  topic: education1792  subtopic: Attainment1793  unit: "% of children at end of primary age"1794  unit_short: "%"1795  format: percent1796  higher_is_better: false1797  bounds: [0, 100]1798  description: Share of children unable to read and understand a simple text by age 10.1799  sources:1800  - {connector: worldbank, dataset: WDI, code: SE.LPV.PRIM, priority: 1}18011802# ============================================================ TRADE ============================================================1803- slug: exports-goods-services1804  name: Exports of goods and services (current US$)1805  short_name: Exports1806  topic: trade1807  subtopic: Flows1808  unit: current US$1809  unit_short: US$1810  format: currency1811  precision: 01812  aggregation: sum1813  featured: true1814  bounds: [0, null]1815  sources:1816  - {connector: worldbank, dataset: WDI, code: NE.EXP.GNFS.CD, priority: 1}1817- slug: imports-goods-services1818  name: Imports of goods and services (current US$)1819  short_name: Imports1820  topic: trade1821  subtopic: Flows1822  unit: current US$1823  unit_short: US$1824  format: currency1825  precision: 01826  aggregation: sum1827  bounds: [0, null]1828  sources:1829  - {connector: worldbank, dataset: WDI, code: NE.IMP.GNFS.CD, priority: 1}1830- slug: trade-balance1831  name: External balance on goods and services (current US$)1832  short_name: Trade balance1833  topic: trade1834  subtopic: Balance1835  unit: current US$1836  unit_short: US$1837  format: currency1838  precision: 01839  aggregation: sum1840  sources:1841  - {connector: worldbank, dataset: WDI, code: NE.RSB.GNFS.CD, priority: 1}1842- slug: exports-pct-gdp1843  name: Exports of goods and services (% of GDP)1844  topic: trade1845  subtopic: Openness1846  unit: "% of GDP"1847  unit_short: "% GDP"1848  format: percent1849  bounds: [0, 400]1850  sources:1851  - {connector: worldbank, dataset: WDI, code: NE.EXP.GNFS.ZS, priority: 1}1852- slug: imports-pct-gdp1853  name: Imports of goods and services (% of GDP)1854  topic: trade1855  subtopic: Openness1856  unit: "% of GDP"1857  unit_short: "% GDP"1858  format: percent1859  bounds: [0, 400]1860  sources:1861  - {connector: worldbank, dataset: WDI, code: NE.IMP.GNFS.ZS, priority: 1}1862- slug: trade-pct-gdp1863  name: Trade (% of GDP)1864  short_name: Trade openness1865  topic: trade1866  subtopic: Openness1867  unit: "% of GDP"1868  unit_short: "% GDP"1869  format: percent1870  bounds: [0, 800]1871  description: Sum of exports and imports of goods and services as a share of GDP.1872  sources:1873  - {connector: worldbank, dataset: WDI, code: NE.TRD.GNFS.ZS, priority: 1}1874- slug: merchandise-exports1875  name: Merchandise exports (current US$)1876  topic: trade1877  subtopic: Flows1878  unit: current US$1879  unit_short: US$1880  format: currency1881  precision: 01882  aggregation: sum1883  bounds: [0, null]1884  sources:1885  - {connector: worldbank, dataset: WDI, code: TX.VAL.MRCH.CD.WT, priority: 1}1886- slug: merchandise-imports1887  name: Merchandise imports (current US$)1888  topic: trade1889  subtopic: Flows1890  unit: current US$1891  unit_short: US$1892  format: currency1893  precision: 01894  aggregation: sum1895  bounds: [0, null]1896  sources:1897  - {connector: worldbank, dataset: WDI, code: TM.VAL.MRCH.CD.WT, priority: 1}1898- slug: high-tech-exports-share1899  name: High-technology exports (% of manufactured exports)1900  short_name: High-tech exports1901  topic: innovation1902  subtopic: Technology1903  unit: "% of manufactured exports"1904  unit_short: "%"1905  format: percent1906  bounds: [0, 100]1907  sources:1908  - {connector: worldbank, dataset: WDI, code: TX.VAL.TECH.MF.ZS, priority: 1}1909- slug: high-tech-exports1910  name: High-technology exports (current US$)1911  topic: innovation1912  subtopic: Technology1913  unit: current US$1914  unit_short: US$1915  format: currency1916  precision: 01917  aggregation: sum1918  bounds: [0, null]1919  sources:1920  - {connector: worldbank, dataset: WDI, code: TX.VAL.TECH.CD, priority: 1}1921- slug: fuel-exports-share1922  name: Fuel exports (% of merchandise exports)1923  topic: trade1924  subtopic: Composition1925  unit: "% of merchandise exports"1926  unit_short: "%"1927  format: percent1928  bounds: [0, 100]1929  sources:1930  - {connector: worldbank, dataset: WDI, code: TX.VAL.FUEL.ZS.UN, priority: 1}1931- slug: food-exports-share1932  name: Food exports (% of merchandise exports)1933  topic: trade1934  subtopic: Composition1935  unit: "% of merchandise exports"1936  unit_short: "%"1937  format: percent1938  bounds: [0, 100]1939  sources:1940  - {connector: worldbank, dataset: WDI, code: TX.VAL.FOOD.ZS.UN, priority: 1}1941- slug: manufactures-exports-share1942  name: Manufactures exports (% of merchandise exports)1943  topic: trade1944  subtopic: Composition1945  unit: "% of merchandise exports"1946  unit_short: "%"1947  format: percent1948  bounds: [0, 100]1949  sources:1950  - {connector: worldbank, dataset: WDI, code: TX.VAL.MANF.ZS.UN, priority: 1}1951- slug: ores-metals-exports-share1952  name: Ores and metals exports (% of merchandise exports)1953  topic: trade1954  subtopic: Composition1955  unit: "% of merchandise exports"1956  unit_short: "%"1957  format: percent1958  bounds: [0, 100]1959  sources:1960  - {connector: worldbank, dataset: WDI, code: TX.VAL.MMTL.ZS.UN, priority: 1}1961- slug: agricultural-exports-share1962  name: Agricultural raw materials exports (% of merchandise exports)1963  topic: agriculture1964  subtopic: Trade1965  unit: "% of merchandise exports"1966  unit_short: "%"1967  format: percent1968  bounds: [0, 100]1969  sources:1970  - {connector: worldbank, dataset: WDI, code: TX.VAL.AGRI.ZS.UN, priority: 1}1971- slug: services-exports-pct-gdp1972  name: Service exports (BoP, current US$)1973  short_name: Services exports1974  topic: trade1975  subtopic: Flows1976  unit: current US$1977  unit_short: US$1978  format: currency1979  precision: 01980  aggregation: sum1981  bounds: [0, null]1982  sources:1983  - {connector: worldbank, dataset: WDI, code: BX.GSR.NFSV.CD, priority: 1}1984- slug: terms-of-trade-index1985  name: Net barter terms of trade index1986  topic: trade1987  subtopic: Prices1988  unit: index (2015 = 100)1989  unit_short: index1990  format: index1991  ranking_eligible: false1992  sources:1993  - {connector: worldbank, dataset: WDI, code: TT.PRI.MRCH.XD.WD, priority: 1}1994- slug: tariff-rate-applied-mean1995  name: Tariff rate, applied, weighted mean, all products1996  short_name: Tariffs1997  topic: trade1998  subtopic: Policy1999  unit: "%"2000  unit_short: "%"2001  format: percent2002  bounds: [0, 100]2003  sources:2004  - {connector: worldbank, dataset: WDI, code: TM.TAX.MRCH.WM.AR.ZS, priority: 1}2005- slug: logistics-performance-index2006  name: Logistics performance index, overall2007  topic: infrastructure2008  subtopic: Logistics2009  unit: score (1 = low, 5 = high)2010  unit_short: score2011  format: number2012  precision: 22013  higher_is_better: true2014  bounds: [1, 5]2015  sources:2016  - {connector: worldbank, dataset: WDI, code: LP.LPI.OVRL.XQ, priority: 1}20172018# ============================================================ ENERGY ===========================================================2019- slug: electricity-generation2020  name: Electricity generation2021  topic: energy2022  subtopic: Electricity2023  unit: terawatt-hours2024  unit_short: TWh2025  format: number2026  precision: 12027  aggregation: sum2028  bounds: [0, null]2029  sources:2030  - {connector: owid, dataset: energy, code: electricity_generation, priority: 1}2031- slug: electricity-consumption-per-capita2032  name: Electricity consumption per capita2033  topic: energy2034  subtopic: Electricity2035  unit: kilowatt-hours per person2036  unit_short: kWh2037  format: kwh2038  precision: 02039  bounds: [0, 100000]2040  sources:2041  - {connector: owid, dataset: energy, code: per_capita_electricity, priority: 1}2042  - {connector: worldbank, dataset: WDI, code: EG.USE.ELEC.KH.PC, priority: 2}2043- slug: primary-energy-consumption2044  name: Primary energy consumption2045  topic: energy2046  subtopic: Consumption2047  unit: terawatt-hours2048  unit_short: TWh2049  format: number2050  precision: 02051  aggregation: sum2052  bounds: [0, null]2053  sources:2054  - {connector: owid, dataset: energy, code: primary_energy_consumption, priority: 1}2055- slug: energy-use-per-capita2056  name: Energy use per capita2057  topic: energy2058  subtopic: Consumption2059  unit: kilowatt-hours per person2060  unit_short: kWh2061  format: kwh2062  precision: 02063  bounds: [0, 500000]2064  sources:2065  - {connector: owid, dataset: energy, code: energy_per_capita, priority: 1}2066- slug: renewable-electricity-share2067  name: Renewable electricity share2068  short_name: Renewable electricity2069  topic: energy2070  subtopic: Electricity mix2071  unit: "% of electricity generation"2072  unit_short: "%"2073  format: percent2074  higher_is_better: true2075  featured: true2076  bounds: [0, 100]2077  change_floor: 32078  description: Share of electricity generated from renewables (hydro, wind, solar, bioenergy, other).2079  sources:2080  - {connector: owid, dataset: energy, code: renewables_share_elec, priority: 1}2081  - {connector: worldbank, dataset: WDI, code: EG.ELC.RNEW.ZS, priority: 2}2082- slug: fossil-electricity-share2083  name: Fossil fuel electricity share2084  topic: energy2085  subtopic: Electricity mix2086  unit: "% of electricity generation"2087  unit_short: "%"2088  format: percent2089  higher_is_better: false2090  bounds: [0, 100]2091  sources:2092  - {connector: owid, dataset: energy, code: fossil_share_elec, priority: 1}2093  - {connector: worldbank, dataset: WDI, code: EG.ELC.FOSL.ZS, priority: 2}2094- slug: low-carbon-electricity-share2095  name: Low-carbon electricity share2096  topic: energy2097  subtopic: Electricity mix2098  unit: "% of electricity generation"2099  unit_short: "%"2100  format: percent2101  higher_is_better: true2102  bounds: [0, 100]2103  sources:2104  - {connector: owid, dataset: energy, code: low_carbon_share_elec, priority: 1}2105- slug: hydro-electricity-share2106  name: Hydropower electricity share2107  topic: energy2108  subtopic: Electricity mix2109  unit: "% of electricity generation"2110  unit_short: "%"2111  format: percent2112  bounds: [0, 100]2113  sources:2114  - {connector: owid, dataset: energy, code: hydro_share_elec, priority: 1}2115  - {connector: worldbank, dataset: WDI, code: EG.ELC.HYRO.ZS, priority: 2}2116- slug: nuclear-electricity-share2117  name: Nuclear electricity share2118  topic: energy2119  subtopic: Electricity mix2120  unit: "% of electricity generation"2121  unit_short: "%"2122  format: percent2123  bounds: [0, 100]2124  sources:2125  - {connector: owid, dataset: energy, code: nuclear_share_elec, priority: 1}2126  - {connector: worldbank, dataset: WDI, code: EG.ELC.NUCL.ZS, priority: 2}2127- slug: solar-electricity-share2128  name: Solar electricity share2129  topic: energy2130  subtopic: Electricity mix2131  unit: "% of electricity generation"2132  unit_short: "%"2133  format: percent2134  bounds: [0, 100]2135  sources:2136  - {connector: owid, dataset: energy, code: solar_share_elec, priority: 1}2137- slug: wind-electricity-share2138  name: Wind electricity share2139  topic: energy2140  subtopic: Electricity mix2141  unit: "% of electricity generation"2142  unit_short: "%"2143  format: percent2144  bounds: [0, 100]2145  sources:2146  - {connector: owid, dataset: energy, code: wind_share_elec, priority: 1}2147- slug: coal-electricity-share2148  name: Coal electricity share2149  topic: energy2150  subtopic: Electricity mix2151  unit: "% of electricity generation"2152  unit_short: "%"2153  format: percent2154  higher_is_better: false2155  bounds: [0, 100]2156  sources:2157  - {connector: owid, dataset: energy, code: coal_share_elec, priority: 1}2158- slug: gas-electricity-share2159  name: Gas electricity share2160  topic: energy2161  subtopic: Electricity mix2162  unit: "% of electricity generation"2163  unit_short: "%"2164  format: percent2165  bounds: [0, 100]2166  sources:2167  - {connector: owid, dataset: energy, code: gas_share_elec, priority: 1}2168- slug: solar-generation2169  name: Solar electricity generation2170  topic: energy2171  subtopic: Electricity2172  unit: terawatt-hours2173  unit_short: TWh2174  format: number2175  precision: 22176  aggregation: sum2177  bounds: [0, null]2178  sources:2179  - {connector: owid, dataset: energy, code: solar_electricity, priority: 1}2180- slug: wind-generation2181  name: Wind electricity generation2182  topic: energy2183  subtopic: Electricity2184  unit: terawatt-hours2185  unit_short: TWh2186  format: number2187  precision: 22188  aggregation: sum2189  bounds: [0, null]2190  sources:2191  - {connector: owid, dataset: energy, code: wind_electricity, priority: 1}2192- slug: hydro-generation2193  name: Hydropower electricity generation2194  topic: energy2195  subtopic: Electricity2196  unit: terawatt-hours2197  unit_short: TWh2198  format: number2199  precision: 22200  aggregation: sum2201  bounds: [0, null]2202  sources:2203  - {connector: owid, dataset: energy, code: hydro_electricity, priority: 1}2204- slug: nuclear-generation2205  name: Nuclear electricity generation2206  topic: energy2207  subtopic: Electricity2208  unit: terawatt-hours2209  unit_short: TWh2210  format: number2211  precision: 22212  aggregation: sum2213  bounds: [0, null]2214  sources:2215  - {connector: owid, dataset: energy, code: nuclear_electricity, priority: 1}2216- slug: renewable-energy-consumption-share2217  name: Renewable energy consumption (% of total final energy consumption)2218  short_name: Renewable energy2219  topic: energy2220  subtopic: Consumption2221  unit: "% of final energy consumption"2222  unit_short: "%"2223  format: percent2224  higher_is_better: true2225  bounds: [0, 100]2226  sources:2227  - {connector: worldbank, dataset: WDI, code: EG.FEC.RNEW.ZS, priority: 1}2228- slug: energy-imports-share2229  name: Energy imports, net (% of energy use)2230  short_name: Energy imports2231  topic: energy2232  subtopic: Dependence2233  unit: "% of energy use"2234  unit_short: "%"2235  format: percent2236  bounds: [-2000, 100]2237  sources:2238  - {connector: worldbank, dataset: WDI, code: EG.IMP.CONS.ZS, priority: 1}2239- slug: net-electricity-imports-share2240  name: Net electricity imports (% of demand)2241  topic: energy2242  subtopic: Dependence2243  unit: "% of electricity demand"2244  unit_short: "%"2245  format: percent2246  bounds: [-500, 100]2247  sources:2248  - {connector: owid, dataset: energy, code: net_elec_imports_share_demand, priority: 1}2249- slug: energy-intensity2250  name: Energy intensity of GDP2251  topic: energy2252  subtopic: Intensity2253  unit: kilowatt-hours per US$ (2011 PPP)2254  unit_short: kWh/$2255  format: number2256  precision: 22257  higher_is_better: false2258  bounds: [0, 50]2259  sources:2260  - {connector: owid, dataset: energy, code: energy_per_gdp, priority: 1}2261- slug: access-to-electricity2262  name: Access to electricity (% of population)2263  topic: energy2264  subtopic: Access2265  unit: "% of population"2266  unit_short: "%"2267  format: percent2268  higher_is_better: true2269  bounds: [0, 100]2270  sources:2271  - {connector: worldbank, dataset: WDI, code: EG.ELC.ACCS.ZS, priority: 1}2272- slug: carbon-intensity-electricity2273  name: Carbon intensity of electricity2274  topic: energy2275  subtopic: Intensity2276  unit: grams of CO₂ per kWh2277  unit_short: gCO₂/kWh2278  format: number2279  precision: 02280  higher_is_better: false2281  bounds: [0, 1500]2282  sources:2283  - {connector: owid, dataset: energy, code: carbon_intensity_elec, priority: 1}2284- slug: oil-production2285  name: Oil production2286  topic: energy2287  subtopic: Production2288  unit: terawatt-hours2289  unit_short: TWh2290  format: number2291  precision: 02292  aggregation: sum2293  bounds: [0, null]2294  sources:2295  - {connector: owid, dataset: energy, code: oil_production, priority: 1}2296- slug: gas-production2297  name: Gas production2298  topic: energy2299  subtopic: Production2300  unit: terawatt-hours2301  unit_short: TWh2302  format: number2303  precision: 02304  aggregation: sum2305  bounds: [0, null]2306  sources:2307  - {connector: owid, dataset: energy, code: gas_production, priority: 1}2308- slug: coal-production2309  name: Coal production2310  topic: energy2311  subtopic: Production2312  unit: terawatt-hours2313  unit_short: TWh2314  format: number2315  precision: 02316  aggregation: sum2317  bounds: [0, null]2318  sources:2319  - {connector: owid, dataset: energy, code: coal_production, priority: 1}23202321# =========================================================== CLIMATE ===========================================================2322- slug: co2-emissions2323  name: CO₂ emissions (fossil fuels and industry)2324  short_name: CO₂ emissions2325  topic: climate2326  subtopic: Emissions2327  unit: million tonnes2328  unit_short: Mt2329  format: tonnes2330  precision: 12331  aggregation: sum2332  featured: true2333  bounds: [0, null]2334  sources:2335  - {connector: owid, dataset: co2, code: co2, priority: 1}2336  - {connector: worldbank, dataset: WDI, code: EN.GHG.CO2.MT.CE.AR5, priority: 2}2337- slug: co2-per-capita2338  name: CO₂ emissions per capita2339  short_name: CO₂ per capita2340  topic: climate2341  subtopic: Emissions2342  unit: tonnes per person2343  unit_short: t2344  format: tonnes2345  precision: 22346  higher_is_better: false2347  featured: true2348  bounds: [0, 120]2349  change_floor: 0.52350  sources:2351  - {connector: owid, dataset: co2, code: co2_per_capita, priority: 1}2352  - {connector: worldbank, dataset: WDI, code: EN.GHG.CO2.PC.CE.AR5, priority: 2}2353- slug: co2-per-gdp2354  name: CO₂ emissions per unit of GDP2355  short_name: CO₂ intensity2356  topic: climate2357  subtopic: Intensity2358  unit: kg per US$ (2011 PPP)2359  unit_short: kg/$2360  format: number2361  precision: 32362  higher_is_better: false2363  bounds: [0, 10]2364  sources:2365  - {connector: owid, dataset: co2, code: co2_per_gdp, priority: 1}2366- slug: consumption-co2-per-capita2367  name: Consumption-based CO₂ emissions per capita2368  topic: climate2369  subtopic: Emissions2370  unit: tonnes per person2371  unit_short: t2372  format: tonnes2373  precision: 22374  higher_is_better: false2375  bounds: [0, 120]2376  sources:2377  - {connector: owid, dataset: co2, code: consumption_co2_per_capita, priority: 1}2378- slug: cumulative-co22379  name: Cumulative CO₂ emissions2380  topic: climate2381  subtopic: Emissions2382  unit: million tonnes2383  unit_short: Mt2384  format: tonnes2385  precision: 02386  aggregation: sum2387  bounds: [0, null]2388  tags: [cumulative]   # monotone stock: change/event detectors are skipped (nothing is newsworthy)2389  sources:2390  - {connector: owid, dataset: co2, code: cumulative_co2, priority: 1}2391- slug: share-global-co22392  name: Share of global CO₂ emissions2393  topic: climate2394  subtopic: Emissions2395  unit: "% of global emissions"2396  unit_short: "%"2397  format: percent2398  precision: 22399  bounds: [0, 100]2400  sources:2401  - {connector: owid, dataset: co2, code: share_global_co2, priority: 1}2402- slug: methane-emissions2403  name: Methane emissions2404  topic: climate2405  subtopic: Other gases2406  unit: million tonnes CO₂-equivalent2407  unit_short: Mt CO₂e2408  format: tonnes2409  precision: 12410  aggregation: sum2411  bounds: [0, null]2412  sources:2413  - {connector: owid, dataset: co2, code: methane, priority: 1}2414  - {connector: worldbank, dataset: WDI, code: EN.GHG.CH4.MT.CE.AR5, priority: 2}2415- slug: nitrous-oxide-emissions2416  name: Nitrous oxide emissions2417  topic: climate2418  subtopic: Other gases2419  unit: million tonnes CO₂-equivalent2420  unit_short: Mt CO₂e2421  format: tonnes2422  precision: 12423  aggregation: sum2424  bounds: [0, null]2425  sources:2426  - {connector: owid, dataset: co2, code: nitrous_oxide, priority: 1}2427  - {connector: worldbank, dataset: WDI, code: EN.GHG.N2O.MT.CE.AR5, priority: 2}2428- slug: total-ghg-emissions2429  name: Total greenhouse gas emissions2430  topic: climate2431  subtopic: Emissions2432  unit: million tonnes CO₂-equivalent2433  unit_short: Mt CO₂e2434  format: tonnes2435  precision: 12436  aggregation: sum2437  bounds: [0, null]2438  sources:2439  - {connector: owid, dataset: co2, code: total_ghg, priority: 1}2440  - {connector: worldbank, dataset: WDI, code: EN.GHG.ALL.MT.CE.AR5, priority: 2}2441- slug: ghg-per-capita2442  name: Greenhouse gas emissions per capita2443  topic: climate2444  subtopic: Emissions2445  unit: tonnes CO₂-equivalent per person2446  unit_short: t CO₂e2447  format: tonnes2448  precision: 22449  higher_is_better: false2450  bounds: [0, 200]2451  sources:2452  - {connector: owid, dataset: co2, code: ghg_per_capita, priority: 1}2453  - {connector: worldbank, dataset: WDI, code: EN.GHG.ALL.PC.CE.AR5, priority: 2}2454- slug: coal-co22455  name: CO₂ emissions from coal2456  topic: climate2457  subtopic: By fuel2458  unit: million tonnes2459  unit_short: Mt2460  format: tonnes2461  precision: 12462  aggregation: sum2463  bounds: [0, null]2464  sources:2465  - {connector: owid, dataset: co2, code: coal_co2, priority: 1}2466- slug: oil-co22467  name: CO₂ emissions from oil2468  topic: climate2469  subtopic: By fuel2470  unit: million tonnes2471  unit_short: Mt2472  format: tonnes2473  precision: 12474  aggregation: sum2475  bounds: [0, null]2476  sources:2477  - {connector: owid, dataset: co2, code: oil_co2, priority: 1}2478- slug: gas-co22479  name: CO₂ emissions from gas2480  topic: climate2481  subtopic: By fuel2482  unit: million tonnes2483  unit_short: Mt2484  format: tonnes2485  precision: 12486  aggregation: sum2487  bounds: [0, null]2488  sources:2489  - {connector: owid, dataset: co2, code: gas_co2, priority: 1}2490- slug: land-use-change-co22491  name: CO₂ emissions from land-use change2492  topic: climate2493  subtopic: By fuel2494  unit: million tonnes2495  unit_short: Mt2496  format: tonnes2497  precision: 12498  aggregation: sum2499  sources:2500  - {connector: owid, dataset: co2, code: land_use_change_co2, priority: 1}2501- slug: temperature-change-from-ghg2502  name: Contribution to global warming from greenhouse gases2503  topic: climate2504  subtopic: Temperature2505  unit: °C2506  unit_short: °C2507  format: celsius2508  precision: 42509  aggregation: sum2510  bounds: [0, 2]2511  description: Change in global mean surface temperature caused by the country's cumulative CO₂, methane and N₂O emissions.2512  sources:2513  - {connector: owid, dataset: co2, code: temperature_change_from_ghg, priority: 1}25142515# ========================================================= ENVIRONMENT =========================================================2516- slug: forest-area-share2517  name: Forest area (% of land area)2518  short_name: Forest cover2519  topic: environment2520  subtopic: Land2521  unit: "% of land area"2522  unit_short: "%"2523  format: percent2524  bounds: [0, 100]2525  sources:2526  - {connector: worldbank, dataset: WDI, code: AG.LND.FRST.ZS, priority: 1}2527- slug: forest-area2528  name: Forest area2529  topic: environment2530  subtopic: Land2531  unit: km²2532  unit_short: km²2533  format: km2534  precision: 02535  aggregation: sum2536  bounds: [0, null]2537  sources:2538  - {connector: worldbank, dataset: WDI, code: AG.LND.FRST.K2, priority: 1}2539- slug: pm25-exposure2540  name: PM2.5 air pollution, mean annual exposure2541  short_name: Air pollution (PM2.5)2542  topic: environment2543  subtopic: Air2544  unit: micrograms per m³2545  unit_short: µg/m³2546  format: number2547  precision: 12548  higher_is_better: false2549  featured: true2550  bounds: [0, 200]2551  sources:2552  - {connector: worldbank, dataset: WDI, code: EN.ATM.PM25.MC.M3, priority: 1}2553- slug: protected-areas-share2554  name: Terrestrial protected areas (% of total land area)2555  topic: environment2556  subtopic: Land2557  unit: "% of land area"2558  unit_short: "%"2559  format: percent2560  higher_is_better: true2561  bounds: [0, 100]2562  sources:2563  - {connector: worldbank, dataset: WDI, code: ER.LND.PTLD.ZS, priority: 1}2564- slug: agricultural-land-share2565  name: Agricultural land (% of land area)2566  topic: agriculture2567  subtopic: Land2568  unit: "% of land area"2569  unit_short: "%"2570  format: percent2571  bounds: [0, 100]2572  sources:2573  - {connector: worldbank, dataset: WDI, code: AG.LND.AGRI.ZS, priority: 1}2574- slug: arable-land-share2575  name: Arable land (% of land area)2576  topic: agriculture2577  subtopic: Land2578  unit: "% of land area"2579  unit_short: "%"2580  format: percent2581  bounds: [0, 100]2582  sources:2583  - {connector: worldbank, dataset: WDI, code: AG.LND.ARBL.ZS, priority: 1}2584- slug: freshwater-withdrawal-share2585  name: Annual freshwater withdrawals (% of internal resources)2586  short_name: Water stress2587  topic: environment2588  subtopic: Water2589  unit: "% of internal resources"2590  unit_short: "%"2591  format: percent2592  higher_is_better: false2593  bounds: [0, 10000]2594  sources:2595  - {connector: worldbank, dataset: WDI, code: ER.H2O.FWTL.ZS, priority: 1}2596- slug: renewable-freshwater-per-capita2597  name: Renewable internal freshwater resources per capita2598  topic: environment2599  subtopic: Water2600  unit: cubic metres per person2601  unit_short: m³2602  format: number2603  precision: 02604  bounds: [0, null]2605  sources:2606  - {connector: worldbank, dataset: WDI, code: ER.H2O.INTR.PC, priority: 1}26072608# ======================================================= INFRASTRUCTURE ========================================================2609- slug: air-passengers2610  name: Air transport, passengers carried2611  topic: infrastructure2612  subtopic: Air2613  unit: passengers2614  unit_short: passengers2615  format: number2616  precision: 02617  aggregation: sum2618  bounds: [0, null]2619  sources:2620  - {connector: worldbank, dataset: WDI, code: IS.AIR.PSGR, priority: 1}2621- slug: air-freight2622  name: Air transport, freight2623  topic: infrastructure2624  subtopic: Air2625  unit: million tonne-km2626  unit_short: Mt-km2627  format: number2628  precision: 02629  aggregation: sum2630  bounds: [0, null]2631  sources:2632  - {connector: worldbank, dataset: WDI, code: IS.AIR.GOOD.MT.K1, priority: 1}2633- slug: rail-lines2634  name: Rail lines (total route-km)2635  topic: infrastructure2636  subtopic: Rail2637  unit: km2638  unit_short: km2639  format: km2640  precision: 02641  aggregation: sum2642  bounds: [0, null]2643  sources:2644  - {connector: worldbank, dataset: WDI, code: IS.RRS.TOTL.KM, priority: 1}2645- slug: rail-passengers2646  name: Railways, passengers carried2647  topic: infrastructure2648  subtopic: Rail2649  unit: million passenger-km2650  unit_short: M pkm2651  format: number2652  precision: 02653  aggregation: sum2654  bounds: [0, null]2655  sources:2656  - {connector: worldbank, dataset: WDI, code: IS.RRS.PASG.KM, priority: 1}2657- slug: rail-freight2658  name: Railways, goods transported2659  topic: infrastructure2660  subtopic: Rail2661  unit: million tonne-km2662  unit_short: Mt-km2663  format: number2664  precision: 02665  aggregation: sum2666  bounds: [0, null]2667  sources:2668  - {connector: worldbank, dataset: WDI, code: IS.RRS.GOOD.MT.K6, priority: 1}2669- slug: container-port-traffic2670  name: Container port traffic2671  topic: infrastructure2672  subtopic: Shipping2673  unit: TEU (20-foot equivalent units)2674  unit_short: TEU2675  format: number2676  precision: 02677  aggregation: sum2678  bounds: [0, null]2679  sources:2680  - {connector: worldbank, dataset: WDI, code: IS.SHP.GOOD.TU, priority: 1}2681- slug: liner-shipping-connectivity2682  name: Liner shipping connectivity index2683  topic: infrastructure2684  subtopic: Shipping2685  unit: index (maximum value in 2004 = 100)2686  unit_short: index2687  format: index2688  higher_is_better: true2689  sources:2690  - {connector: worldbank, dataset: WDI, code: IS.SHP.GCNW.XQ, priority: 1}26912692# =========================================================== DIGITAL ===========================================================2693- slug: internet-users2694  name: Individuals using the Internet (% of population)2695  short_name: Internet users2696  topic: digital2697  subtopic: Adoption2698  unit: "% of population"2699  unit_short: "%"2700  format: percent2701  higher_is_better: true2702  featured: true2703  bounds: [0, 100]2704  change_floor: 52705  sources:2706  - {connector: worldbank, dataset: WDI, code: IT.NET.USER.ZS, priority: 1}2707- slug: fixed-broadband-subscriptions2708  name: Fixed broadband subscriptions (per 100 people)2709  short_name: Fixed broadband2710  topic: digital2711  subtopic: Infrastructure2712  unit: per 100 people2713  unit_short: /1002714  format: number2715  precision: 12716  higher_is_better: true2717  bounds: [0, 100]2718  sources:2719  - {connector: worldbank, dataset: WDI, code: IT.NET.BBND.P2, priority: 1}2720- slug: mobile-subscriptions2721  name: Mobile cellular subscriptions (per 100 people)2722  short_name: Mobile subscriptions2723  topic: digital2724  subtopic: Infrastructure2725  unit: per 100 people2726  unit_short: /1002727  format: number2728  precision: 12729  bounds: [0, 500]2730  sources:2731  - {connector: worldbank, dataset: WDI, code: IT.CEL.SETS.P2, priority: 1}2732- slug: secure-internet-servers2733  name: Secure Internet servers (per 1 million people)2734  topic: digital2735  subtopic: Infrastructure2736  unit: per million people2737  unit_short: /M2738  format: per_million2739  precision: 02740  higher_is_better: true2741  bounds: [0, null]2742  sources:2743  - {connector: worldbank, dataset: WDI, code: IT.NET.SECR.P6, priority: 1}2744- slug: ict-goods-exports-share2745  name: ICT goods exports (% of total goods exports)2746  topic: digital2747  subtopic: Economy2748  unit: "% of goods exports"2749  unit_short: "%"2750  format: percent2751  bounds: [0, 100]2752  sources:2753  - {connector: worldbank, dataset: WDI, code: TX.VAL.ICTG.ZS.UN, priority: 1}2754- slug: ict-service-exports-share2755  name: ICT service exports (% of service exports)2756  topic: digital2757  subtopic: Economy2758  unit: "% of service exports"2759  unit_short: "%"2760  format: percent2761  bounds: [0, 100]2762  sources:2763  - {connector: worldbank, dataset: WDI, code: BX.GSR.CCIS.ZS, priority: 1}27642765# ========================================================== INNOVATION =========================================================2766- slug: rd-expenditure-pct-gdp2767  name: Research and development expenditure (% of GDP)2768  short_name: R&D spending2769  topic: innovation2770  subtopic: Research2771  unit: "% of GDP"2772  unit_short: "% GDP"2773  format: percent2774  precision: 22775  higher_is_better: true2776  featured: true2777  bounds: [0, 10]2778  sources:2779  - {connector: worldbank, dataset: WDI, code: GB.XPD.RSDV.GD.ZS, priority: 1}2780- slug: researchers-per-million2781  name: Researchers in R&D (per million people)2782  topic: innovation2783  subtopic: Research2784  unit: per million people2785  unit_short: /M2786  format: per_million2787  precision: 02788  higher_is_better: true2789  bounds: [0, 20000]2790  sources:2791  - {connector: worldbank, dataset: WDI, code: SP.POP.SCIE.RD.P6, priority: 1}2792- slug: patent-applications-residents2793  name: Patent applications, residents2794  topic: innovation2795  subtopic: Patents2796  unit: applications2797  unit_short: applications2798  format: number2799  precision: 02800  aggregation: sum2801  bounds: [0, null]2802  sources:2803  - {connector: worldbank, dataset: WDI, code: IP.PAT.RESD, priority: 1}2804- slug: patent-applications-nonresidents2805  name: Patent applications, nonresidents2806  topic: innovation2807  subtopic: Patents2808  unit: applications2809  unit_short: applications2810  format: number2811  precision: 02812  aggregation: sum2813  bounds: [0, null]2814  sources:2815  - {connector: worldbank, dataset: WDI, code: IP.PAT.NRES, priority: 1}2816- slug: scientific-articles2817  name: Scientific and technical journal articles2818  topic: innovation2819  subtopic: Research2820  unit: articles2821  unit_short: articles2822  format: number2823  precision: 02824  aggregation: sum2825  bounds: [0, null]2826  sources:2827  - {connector: worldbank, dataset: WDI, code: IP.JRN.ARTC.SC, priority: 1}2828- slug: trademark-applications2829  name: Trademark applications, total2830  topic: innovation2831  subtopic: Patents2832  unit: applications2833  unit_short: applications2834  format: number2835  precision: 02836  aggregation: sum2837  bounds: [0, null]2838  sources:2839  - {connector: worldbank, dataset: WDI, code: IP.TMK.RSCT, priority: 1, notes: 'resident applications by count (IP.TMK.TOTL archived)'}28402841# ========================================================== AGRICULTURE ========================================================2842- slug: arable-land-per-capita2843  name: Arable land (hectares per person)2844  topic: agriculture2845  subtopic: Land2846  unit: hectares per person2847  unit_short: ha2848  format: ha2849  precision: 22850  bounds: [0, 10]2851  sources:2852  - {connector: worldbank, dataset: WDI, code: AG.LND.ARBL.HA.PC, priority: 1}2853- slug: cereal-yield2854  name: Cereal yield2855  topic: agriculture2856  subtopic: Production2857  unit: kg per hectare2858  unit_short: kg/ha2859  format: number2860  precision: 02861  higher_is_better: true2862  bounds: [0, 50000]2863  sources:2864  - {connector: worldbank, dataset: WDI, code: AG.YLD.CREL.KG, priority: 1}2865- slug: food-production-index2866  name: Food production index2867  topic: agriculture2868  subtopic: Production2869  unit: index (2014–2016 = 100)2870  unit_short: index2871  format: index2872  ranking_eligible: false2873  sources:2874  - {connector: worldbank, dataset: WDI, code: AG.PRD.FOOD.XD, priority: 1}2875- slug: crop-production-index2876  name: Crop production index2877  topic: agriculture2878  subtopic: Production2879  unit: index (2014–2016 = 100)2880  unit_short: index2881  format: index2882  ranking_eligible: false2883  sources:2884  - {connector: worldbank, dataset: WDI, code: AG.PRD.CROP.XD, priority: 1}2885- slug: livestock-production-index2886  name: Livestock production index2887  topic: agriculture2888  subtopic: Production2889  unit: index (2014–2016 = 100)2890  unit_short: index2891  format: index2892  ranking_eligible: false2893  sources:2894  - {connector: worldbank, dataset: WDI, code: AG.PRD.LVSK.XD, priority: 1}2895- slug: fertilizer-consumption2896  name: Fertilizer consumption2897  topic: agriculture2898  subtopic: Inputs2899  unit: kg per hectare of arable land2900  unit_short: kg/ha2901  format: number2902  precision: 02903  bounds: [0, 50000]2904  sources:2905  - {connector: worldbank, dataset: WDI, code: AG.CON.FERT.ZS, priority: 1}2906- slug: oil-rents-pct-gdp2907  name: Oil rents (% of GDP)2908  topic: agriculture2909  subtopic: Resources2910  unit: "% of GDP"2911  unit_short: "% GDP"2912  format: percent2913  bounds: [0, 100]2914  sources:2915  - {connector: worldbank, dataset: WDI, code: NY.GDP.PETR.RT.ZS, priority: 1}2916- slug: mineral-rents-pct-gdp2917  name: Mineral rents (% of GDP)2918  topic: agriculture2919  subtopic: Resources2920  unit: "% of GDP"2921  unit_short: "% GDP"2922  format: percent2923  bounds: [0, 100]2924  sources:2925  - {connector: worldbank, dataset: WDI, code: NY.GDP.MINR.RT.ZS, priority: 1}2926- slug: forest-rents-pct-gdp2927  name: Forest rents (% of GDP)2928  topic: agriculture2929  subtopic: Resources2930  unit: "% of GDP"2931  unit_short: "% GDP"2932  format: percent2933  bounds: [0, 100]2934  sources:2935  - {connector: worldbank, dataset: WDI, code: NY.GDP.FRST.RT.ZS, priority: 1}2936- slug: food-insecurity-prevalence2937  name: Prevalence of moderate or severe food insecurity2938  topic: agriculture2939  subtopic: Food security2940  unit: "% of population"2941  unit_short: "%"2942  format: percent2943  higher_is_better: false2944  bounds: [0, 100]2945  sources:2946  - {connector: worldbank, dataset: WDI, code: SN.ITK.MSFI.ZS, priority: 1}2947- slug: undernourishment-prevalence2948  name: Prevalence of undernourishment2949  topic: agriculture2950  subtopic: Food security2951  unit: "% of population"2952  unit_short: "%"2953  format: percent2954  higher_is_better: false2955  bounds: [0, 100]2956  sources:2957  - {connector: worldbank, dataset: WDI, code: SN.ITK.DEFC.ZS, priority: 1}29582959# =========================================================== TOURISM ===========================================================2960- slug: tourist-arrivals2961  name: International tourism, number of arrivals2962  short_name: Tourist arrivals2963  topic: tourism2964  subtopic: Flows2965  unit: arrivals2966  unit_short: arrivals2967  format: number2968  precision: 02969  aggregation: sum2970  featured: true2971  bounds: [0, null]2972  sources:2973  - {connector: worldbank, dataset: WDI, code: ST.INT.ARVL, priority: 1}2974- slug: tourist-departures2975  name: International tourism, number of departures2976  topic: tourism2977  subtopic: Flows2978  unit: departures2979  unit_short: departures2980  format: number2981  precision: 02982  aggregation: sum2983  bounds: [0, null]2984  sources:2985  - {connector: worldbank, dataset: WDI, code: ST.INT.DPRT, priority: 1}2986- slug: tourism-receipts2987  name: International tourism, receipts (current US$)2988  short_name: Tourism receipts2989  topic: tourism2990  subtopic: Money2991  unit: current US$2992  unit_short: US$2993  format: currency2994  precision: 02995  aggregation: sum2996  bounds: [0, null]2997  sources:2998  - {connector: worldbank, dataset: WDI, code: ST.INT.RCPT.CD, priority: 1}2999- slug: tourism-receipts-pct-exports3000  name: International tourism receipts (% of total exports)3001  topic: tourism3002  subtopic: Money3003  unit: "% of total exports"3004  unit_short: "%"3005  format: percent3006  bounds: [0, 100]3007  sources:3008  - {connector: worldbank, dataset: WDI, code: ST.INT.RCPT.XP.ZS, priority: 1}3009- slug: tourism-expenditures3010  name: International tourism, expenditures (current US$)3011  topic: tourism3012  subtopic: Money3013  unit: current US$3014  unit_short: US$3015  format: currency3016  precision: 03017  aggregation: sum3018  bounds: [0, null]3019  sources:3020  - {connector: worldbank, dataset: WDI, code: ST.INT.XPND.CD, priority: 1}30213022# =========================================================== SECURITY ==========================================================3023- slug: homicide-rate3024  name: Intentional homicides3025  short_name: Homicide rate3026  topic: security3027  subtopic: Crime3028  unit: per 100,000 people3029  unit_short: /100k3030  format: per_100k3031  precision: 13032  higher_is_better: false3033  featured: true3034  bounds: [0, 150]3035  sources:3036  - {connector: worldbank, dataset: WDI, code: VC.IHR.PSRC.P5, priority: 1}3037- slug: armed-forces-personnel3038  name: Armed forces personnel, total3039  topic: security3040  subtopic: Defence3041  unit: people3042  unit_short: people3043  format: number3044  precision: 03045  aggregation: sum3046  bounds: [0, null]3047  sources:3048  - {connector: worldbank, dataset: WDI, code: MS.MIL.TOTL.P1, priority: 1}3049- slug: armed-forces-personnel-share3050  name: Armed forces personnel (% of total labour force)3051  topic: security3052  subtopic: Defence3053  unit: "% of labour force"3054  unit_short: "%"3055  format: percent3056  bounds: [0, 50]3057  sources:3058  - {connector: worldbank, dataset: WDI, code: MS.MIL.TOTL.TF.ZS, priority: 1}3059- slug: battle-related-deaths3060  name: Battle-related deaths3061  topic: security3062  subtopic: Conflict3063  unit: deaths3064  unit_short: deaths3065  format: number3066  precision: 03067  aggregation: sum3068  higher_is_better: false3069  bounds: [0, null]3070  sources:3071  - {connector: worldbank, dataset: WDI, code: VC.BTL.DETH, priority: 1}30723073# ======================================================= QUALITY OF LIFE =======================================================3074- slug: human-development-index3075  name: Human Development Index3076  short_name: HDI3077  topic: quality-of-life3078  subtopic: Composite3079  unit: index (0–1)3080  unit_short: HDI3081  format: number3082  precision: 33083  higher_is_better: true3084  featured: true3085  bounds: [0, 1]3086  description: UNDP composite of life expectancy, education and income per capita.3087  sources:3088  - {connector: owid, dataset: grapher, code: human-development-index, priority: 1}3089- slug: life-satisfaction3090  name: Self-reported life satisfaction (Cantril ladder)3091  short_name: Life satisfaction3092  topic: quality-of-life3093  subtopic: Wellbeing3094  unit: score (0–10)3095  unit_short: /103096  format: number3097  precision: 23098  higher_is_better: true3099  bounds: [0, 10]3100  description: Average answer to the Cantril ladder question in the Gallup World Poll, as compiled by the World Happiness Report.3101  sources:3102  - {connector: owid, dataset: grapher, code: happiness-cantril-ladder, priority: 1}3103- slug: women-in-parliament-share3104  name: Proportion of seats held by women in national parliaments3105  short_name: Women in parliament3106  topic: quality-of-life3107  subtopic: Gender3108  unit: "% of seats"3109  unit_short: "%"3110  format: percent3111  higher_is_better: true3112  bounds: [0, 100]3113  sources:3114  - {connector: worldbank, dataset: WDI, code: SG.GEN.PARL.ZS, priority: 1}3115