War involving a government
Sri Lanka (Ceylon): Eelam
Nine criteria, each scored from 0 to 10 using a published guide, each with the source it was scored from. The scores are turned into a share for each industry, and five scenarios filter those shares in five different ways.
This page shows how a war is linked to industries, and it shows no death toll per industry. The shares below are settings of the model, each with sources behind it; multiplying a share by a number of deaths gives a result, and no result is published until it comes from a recorded calculation with a range and a confidence grade. Read the scores, follow the links, and question the method while there is still nothing to quote out of context.
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What is on this page, and what is not
A share is not a toll. Everything below is either a count published by the Uppsala Conflict Data Program — shown here with the credit its licence asks for — or a setting of the model: a 0-to-10 score against a guide anyone can read, the source it came from, and the share those scores add up to.
Nothing on this page multiplies one by the other. That multiplication is the result the project exists to produce, and it is not published until a calculation produces it with a range and a confidence grade. A project can only publish a wrong figure for how many people died for a barrel of oil once.
What the Uppsala Conflict Data Program recorded
The publisher’s own figures for this war, from the version of the data listed on the sources page. Nothing here has been linked to anything yet.
60,816
Deaths recorded
4,011
Events
1989–2009
Years
2
Countries
87.0%
Precisely located
The last figure is the share of recorded deaths whose location is known more precisely than a province. It matters because it limits one of the nine criteria: a war whose events are known only to the level of a country cannot show it was fought close to a mine or a field, so if fewer than half its deaths are precisely located, that score cannot go above the middle of the scale.
Scenario
One set of scores, five filters. A scenario never stretches a share and never deletes one — what it declines to pass on moves to Other and stays in the total. Switching below changes what you are looking at, not what was scored.
Routes the entire attributable toll through the economic system, including the share no specific sector can be evidenced for: that share follows the production basket of the country where each death was counted, an assumption graded low. Quantifies the economic activity associated with conflict rather than the motivation of anyone fighting it.
1989–2009 The Eelam wars
One period, ending with the military defeat of the LTTE in 2009. A separatist war over territory and recognition, in a country with a licit export economy that had little to do with it — the second of the two cases in this dataset that the model must be able to report as economically thin.
Share by industry
| Industry | Share | Confidence |
|---|---|---|
| Other and unattributed economic activity OTHER |
47.1% | MEDIUM |
| Crop and animal production, hunting and related service activities 01 |
22.1% | MEDIUM |
| Fishing and aquaculture 03 |
15.4% | MEDIUM |
| Defence activities 8422 |
8.7% | MEDIUM |
| Manufacture of weapons and ammunition 252 |
6.7% | MEDIUM |
Shares add up to exactly 100%, Other included. That is checked every time the model is built, and a build that loses even a fraction of a percent between two steps stops with an error rather than rounding it away.
47.1% of this war goes to Other — economic activity that none of the named industries above accounts for.
Reviewed: truly outside the named industries. Close to half of this conflict sits in the sink and that is the correct reading. It is a separatist war over citizenship and territory, in a country whose export economy — garments, tourism, remittances — had almost nothing to do with it. Together with Rwanda it is the pair this dataset needs in order for the strong cases to mean anything.
The scores, and what each one is based on
Each cell links to the source it was scored from; hover over it for the reference and the reasoning. The letter beside a score says how strong that kind of evidence is: H for official statistics or a UN expert panel, M for research checked by other experts or a report by a major institution, L for news reports. Deaths recorded in this period: 60,816.
| Industry | Territorial dependence | Export dependence | Government-revenue dependence | Conflict-location proximity | Actor statements | Resource control | Trade dependency | Arms relationship | Historical evidence | Weighted score |
|---|---|---|---|---|---|---|---|---|---|---|
| Other and unattributed economic activity OTHER |
6 M | 7 H | 6 H | 7 M | 9 M | 5 M | – | – | 9 M | 49 |
| Crop and animal production, hunting and related service activities 01 |
5 H | 6 H | 3 H | 3 M | 2 M | – | – | – | 4 M | 23 |
| Fishing and aquaculture 03 |
4 H | – | – | 4 M | – | 5 M | – | – | 3 M | 16 |
| Defence activities 8422 |
– | – | 4 M | – | – | – | – | – | 5 M | 9 |
| Manufacture of weapons and ammunition 252 |
– | – | – | – | – | – | – | 7 M | – | 7 |
An empty cell is a criterion nobody has scored yet, not a zero. Nothing was assumed about it, and the weight it would have carried stays in Other rather than being spread over the criteria that were scored. A 0 written in says something different: somebody looked and found no link.
Evidence
- Uppsala Conflict Data Program, Conflict Encyclopedia, conflict 352 (Sri Lanka (Ceylon): Eelam): background, actors and incompatibility. report by a major institution · MEDIUM · 11 scores
- SIPRI Military Expenditure Database: military spending by country, compiled from official budget documents. report by a major institution · MEDIUM · 2 scores
- World Bank World Integrated Trade Solution, LKA country profile: merchandise exports by product group. official statistic · HIGH · 2 scores
- World Bank, World Development Indicators, total natural resource rents as a share of GDP. official statistic · HIGH · 2 scores
- FAO, FAOSTAT, crops and livestock production by country and year. official statistic · HIGH · 1 scores
- FAO, FAOSTAT, land use: cropland, arable land and permanent pasture by country and year. official statistic · HIGH · 1 scores
- SIPRI Arms Transfers Database: recorded transfers of major conventional weapons by supplier and recipient. report by a major institution · MEDIUM · 1 scores
The nine criteria, and how each is scored
Without worked examples, two people would score the same war differently, and the model would measure the person rather than the war. So every criterion spells out what a 0, a 5 and a 10 look like, and four of the nine are tied to published statistics rather than to a form of words.
Territorial dependence — Does control of the contested territory amount to control of this sector?
- 0
- No known deposit, field, plantation, facility or right-of-way of this sector lies inside the contested area.
- 5
- The sector is present in the contested area but not concentrated there: the area holds roughly the share of national capacity its size would suggest.
- 10
- The contested area contains the great majority — three quarters or more — of the country's known capacity for this sector, so holding the ground IS holding the sector.
Check it against: National geological or agricultural surveys; USGS Minerals Yearbook country chapters; the operator's own concession maps.
Export dependence — How much of what the country sells abroad is this sector?
- 0
- Under 2% of merchandise exports over the period, or not exported at all.
- 5
- Between 20% and 35% of merchandise exports — a major earner among several.
- 10
- 70% or more of merchandise exports: the country sells this and comparatively little else.
Check it against: World Bank WITS country profile, merchandise exports by product group, averaged over the period; UN Comtrade mirrored data where WITS is thin.
Government-revenue dependence — How much of what the state spends comes from this sector?
- 0
- Under 2% of general government revenue, or no fiscal link at all.
- 5
- Between 20% and 35% of general government revenue.
- 10
- 60% or more of general government revenue: the state is substantially this sector's fiscal agent.
Check it against: IMF Article IV staff reports, fiscal tables; EITI country reports; World Bank natural resource rents as a share of GDP (NY.GDP.TOTL.RT.ZS and its components).
Conflict-location proximity — Did the recorded violence actually happen where this sector operates?
- 0
- No spatial relationship between the recorded events and the sector's sites — or the events are too coarsely located to support any claim about where they were.
- 5
- A substantial minority of recorded deaths fall in administrative areas containing the sector's principal sites.
- 10
- The majority of recorded deaths fall in administrative areas containing the sector's principal sites.
Check it against: UCDP GED event coordinates and adm_1, filtered on where_prec; the sector's site locations from a survey or operator map.
Actor statements — Do the armed parties themselves name this sector as an aim or a means?
- 0
- No party names it, in any founding document, communiqué, demand or negotiation position.
- 5
- A party names it among several aims, or the claim reaches us through a third party rather than from the actor.
- 10
- A party's own founding document, communiqué or negotiating demand names control of this sector, or of its revenue, as a principal aim.
Check it against: UCDP Conflict Encyclopedia actor pages; peace-agreement texts in the UN Peacemaker database; the actor's own published statements.
Resource control — Did armed parties hold, tax or sell this sector's output?
- 0
- No evidence that any party controlled, taxed or traded the sector's output.
- 5
- A party taxed, extorted or licensed production it did not itself control.
- 10
- A party held production sites and sold the output, and an official investigation documents it.
Check it against: UN Security Council panels and groups of experts; sanctions-committee reports; Kimberley Process and OECD due-diligence findings.
Trade dependency — Does an identifiable market outside the country take this output?
- 0
- Output is consumed domestically, or is not traded in any traceable form.
- 5
- Exported, but into diversified markets with no dominant buyer and no traceability regime.
- 10
- Exported and traceable to a specific downstream industry or buyer country — typically because a due-diligence regime exists for it (3TG conflict minerals, the Kimberley Process, EUDR).
Check it against: World Bank WITS bilateral flows; OECD due-diligence guidance and its covered commodities; the EU conflict-minerals and deforestation regulations.
Arms relationship — Did this sector pay for weapons, or is it weapons?
- 0
- No documented link between the sector and the procurement of arms.
- 5
- The sector contributes to general state revenue, which funds a defence budget — an indirect, non-earmarked link.
- 10
- Output was directly exchanged for weapons, or the sector IS arms manufacture or military procurement.
Check it against: SIPRI Arms Transfers Database and Military Expenditure Database; UN Register of Conventional Arms; sanctions-committee findings on barter arrangements.
Historical evidence — Does the scholarly and official record already make this link?
- 0
- No peer-reviewed study or official inquiry makes the link.
- 5
- Contested: some peer-reviewed work makes the link and other work of comparable standing rejects it.
- 10
- The link is the mainstream reading in the peer-reviewed literature and in official inquiries, and its critics argue about magnitude rather than existence.
Check it against: Peer-reviewed political economy of the specific conflict; truth commissions and official inquiries; the UCDP Conflict Encyclopedia's own background text.