Biodiversity Case Studies

Why biodiversity is hard to value

The two missing pieces

What: Non-use values (existence, bequest) leave no market trace, so they need stated-preference surveys — which suffer hypothetical bias. Use values are often invisible until the service collapses (pest control, sanitation, pollination, carbon sequestration).

Why: You can't read "I care about blue whales" off anyone's shopping basket. Revealed-preference methods literally cannot reach non-use value, so we need clever quasi-experiments for use values and structured surveys for non-use values.

Remember: The whole point of biodiversity valuation: find a species that provides an ecosystem service, find a quasi-random shock to it, and measure the outcomes the mechanism predicts.

Recap of the valuation toolkit (from Ch. 3)

What: Four families — statistical (real-world data, but omitted-variable-bias risk), experiments (clean causal inference but narrow and hard to scale), structural models (can answer counterfactual questions data can't, but only "models"), structured surveys / contingent valuation (the only tool for non-use values).

Remember: Economists prefer revealed preference (what people do) over stated preference (what they say) because intentions and actions diverge — Barrage & Lee (China study): ~80% said they would donate hypothetically, only ~half paid when it actually counted. So surveys are a last resort, used only when nothing else reaches the value.

Contingent Valuation (CV) and liability

What: CV is a highly structured survey designed to elicit Willingness To Pay (WTP) for specific non-market goods in specific scenarios. Best practice: face-to-face (not online), give people information, then ask structured yes/no WTP questions ("Would you pay 10 extra francs/year for this scenario? 20 francs?") with internal consistency checks.

Why: It's the legally-recognized way to put a price tag on dead wildlife and damaged ecosystems in court.

Remember: Origin = 1989 Exxon Valdez spill in Alaska (birds, otters, seals, eagles, orcas killed); Alaska commissioned a CV study. 2010 BP Deepwater Horizon (largest accidental marine oil release in history at the time, also killed 11 workers): federal agencies commissioned a CV study estimating ~$17B in lost non-use value, used in court against BP. Online-survey warning: in one standard-protocol online survey, ~12% of respondents claimed to be licensed to operate a nuclear submarine.

The five types of economic value (typology)

Use vs. non-use

• Direct use value: humans directly engage/consume — whale watching, whale meat, historically whale oil as an energy source.
• Indirect use value: concrete ecosystem service without direct contact — whales eat plankton, then sink to the deep ocean, providing carbon sequestration and nutrient cycling.
• Option value (sits between use and non-use): preserving the possibility of future use — you might go whale-watching someday; an unknown rainforest species might have undiscovered medicinal value.
• Existence value (non-use): joy/utility simply from knowing a species exists; can include empathy and altruism (caring about the species for its own sake). Economists are mostly anthropocentric, but empathy/altruism slot in here.
• Bequest value (non-use): leaving future generations a world that still contains the species/ecosystem services we inherited.

Remember: Direct + indirect + option are reachable by revealed preference; existence + bequest are non-use and need surveys. (Side nugget: extinct species like dinosaurs may still give existence value from the fact they once existed — you could split existence value into "existed at some point" vs. "currently exists.")

Bats and White-Nose Syndrome (Frank)

The setup

What: Bats are voracious insect predators — they eat ~40% of their own body weight in insects every day — so they provide natural pest control. White-Nose Syndrome (WNS) is a fungal disease first detected in 2006, thought to have arrived in the US on the backpacks/shoes of European hikers (no native defenses), epicenter in the Northeast, then spread across counties in different years.

Why: WNS is a quasi-random shock to bat populations: it hit different counties at different times for reasons unrelated to crop yields, US technology, or US policy — so it's a natural experiment.

Remember: WNS killed ~70–73% of infected bats. The spread map (darker green = earlier detection) gives the staggered timing used in the event study.

Field-experiment evidence (the small-scale version)

What: A Proceedings of the National Academy of Sciences (PNAS) study put nets over some crop plots to exclude bats, leaving control plots open. "Exposure" = bats kept out; "control" = bats had access.

Remember: Excluding bats raised damaged corn kernels per ear and the proportion of ears with fungus, both significantly. Limitation: field experiments don't scale (need land, workers, grant funding) and can't capture how real farmers respond (switch inputs, switch crops). That's why you also need the quasi-experiment.

Event-study results (the real-world version)

What: Compare counties before/after WNS arrival, differencing out county averages and year effects. The coefficient plot: x-axis = years relative to WNS detection; circle = mean estimate; lines = 95% confidence interval (above 0 = significant).

Why: Reassuringly, pre-treatment coefficients are flat and insignificant — counties about to get WNS looked like those that weren't ("parallel hikers"), supporting the causal design. Effects only appear after WNS arrives.

Remember (key numbers):

• Insecticide use +25% after 5 years.
• Crop revenue −~28.9% (over 20%, ~one quarter).
• Profits (revenue − expenditure) ~−25%.
• Total chemical expenditure roughly unchanged — because farmers substituted from herbicides to insecticides.
• Infant mortality: suggestive increase (noisier signal; could be insecticide exposure or an income shock to poor farmers — harder to call causal).

Interpretation — glass half full / half empty

Remember: Half full = farmers can substitute (insecticide ↑), so man-made inputs partly replace the bat service. Half empty = substitution is imperfect — revenue still fell ~29%, profits ~25%, infant mortality rose. Imperfect substitutability is a strong-sustainability signature. (Trap: a question may list all four bat findings — insecticide +25%, revenue −29%, profits −25%, infant mortality up — and all four are correct.)

Vultures and diclofenac (Frank & Sudarshan)

The setup

What: Vultures are not charismatic megafauna (the cute, big, easy-to-fundraise-for animals like elephants), but they are nature's sanitation service: they eat carrion (dead livestock), which reduces water contamination, disease, and feral-dog populations. In India, vulture populations crashed sharply in the 1990s, especially ~1995–1997.

Why: No vultures ⇒ rotting carcasses ⇒ worse water quality + more feral dogs (which carry rabies; ~65,000–80,000 rabies deaths globally per year). Vultures compete with dogs for carcasses.

Remember: The trigger was veterinary diclofenac, a painkiller for cattle. When the patent expired and a cheap generic was approved (~1996), usage expanded — and diclofenac residue in carcasses causes kidney failure in vultures that eat them. The generic-approval price drop is the quasi-exogenous shock (people approving the generic weren't responding to urban health conditions).

Identification (why before/after isn't enough)

What: You can't just compare urban health before/after 1996 — many things change over time in a rapidly developing country. Instead, exploit pre-treatment heterogeneity: the diclofenac shock binds hardest where vulture ecological suitability AND livestock agriculture intensity were both high.

Why: Compare the differential change in urban health between high-vulture-suitability/high-livestock areas and low-suitability areas — not levels, not simple before/after. Requires the "parallel hikers" assumption: the areas were on similar development paths until the shock.

Remember: The plot has vulture suitability on the x-axis and livestock on the y-axis; the shock requires both. High- and low-suitability areas tracked each other in all-cause mortality until the generic approval, then diverged.

Results

Remember (key numbers):

• All-cause human mortality +~4.7–5% ≈ ~100,000 additional deaths per year.
• Corroborating mechanisms: water quality down (dissolved O₂ −7 to −12%, fecal coliforms +200%) in vulture-suitable areas post-approval; rabies-vaccine sales up (post-exposure prophylaxis).
• Professor's own confidence: ~7–8 out of 10 — observational data always leaves questions, but it's real-world evidence and one of several signals.

Imagining use values for other species (the recipe)

Recipe

Remember: (1) species providing an ecosystem service + (2) a quasi-random shock to it + (3) outcomes the mechanism predicts. Worked examples: bees → pollination → crop yields; wolves → fewer deer-vehicle collisions; mangroves → fisheries + flood protection; sea otters → kelp forests; whales → nutrient cycling/carbon. Insects-and-pollination is the canonical intuition the lecture used to motivate the methods.

Why we can't just regress yields on biodiversity

Remember: Cross-country/over-time correlation between biodiversity and crop yields is confounded. Growth brings both lower biodiversity (building, pollution, deforestation) and better farming tech/fertilizer — so the data may show "less biodiversity but higher yields," underestimating biodiversity's value. Environmental policy swings can simultaneously raise biodiversity and cut yields (via pesticide bans), again confounding. Bias can run either direction — pure observational data is tricky.

Implications for weak vs. strong sustainability

The throughline

Remember: Everything reduces to substitutability: how well man-made inputs replace the lost ecosystem service. Bats: substitution exists (insecticide) but is imperfect (revenue −29%, profits −25%, infant mortality up) ⇒ strong-sustainability signature. Vultures: in low-infrastructure settings there is essentially no man-made substitute (no public carcass-collection service) ⇒ an even cleaner strong-sustainability argument.

Beware the law of unintended consequences

Remember: Disrupting an ecosystem triggers effects we don't anticipate (insecticide → further ecosystem harm + human health risk; vulture loss → feral dogs + rabies). Also stay balanced: bats carry rabies; elephants trample crops — biodiversity questions are genuinely two-sided, but careful research design can still recover real economic value.

One-line takeaways

• Non-use values (existence, bequest) need surveys/CV; use values need quasi-experiments — revealed preference can't reach non-use value.
• CV originated with Exxon Valdez (1989) and underpinned the BP Deepwater Horizon ~$17B non-use damage estimate.
• Bats + WNS (Frank): insecticide +25%, crop revenue −~29%, profits −~25%, infant mortality suggestively up; chemical spend flat (herbicide→insecticide substitution).
• Vultures + diclofenac (Frank & Sudarshan): all-cause mortality +~5% ≈ ~100,000 deaths/yr; identified via vulture suitability × livestock intensity, not before/after.
• Imperfect substitution (bats) and zero substitution (vultures) are both strong-sustainability evidence.
• Always ask "what is driving the variation?" and "is there a third factor?" — and beware the law of unintended consequences.