Example: Did the Exxon Valdez oil spill (in Prince William Sound, Alaska, in March of 1989) have a discernible negative effect on the incomes of commercial salmon fishers in Alaska? If so, how much revenue was lost due to the oil spill?

1. Even a non-econometrician could gather data on the revenues earned by commercial salmon fishers in Alaska, over time, and plot these data to show what happen to the revenues for different salmon species over a time period that includes the oil spill event in early 1989:
   
   



2. Just looking at these time series for revenues from each salmon species makes it tempting to conclude that fishers' revenues dropped substantially between the end of 1988 and the end of 1989, especially for red salmon and coho salmon, and perhaps for silver salmon. It looks like king salmon and pink salmon revenues did not fall too sharply in that particular window of time.



3. Is an economist happy with this level of analysis? Probably not.



4. What we need to know is "What would have been commercial salmon revenues for each species under non-oil-spill conditions." It is the difference between that number and the observed number that interests us...



5. How do we know what revenues should have been in 1989 and later, without the oil spill? We need to know what factors have historically determined commercial salmon-fishing revenues. We need a "model" (equation) that explains "price times quantity" for each salmon species fairly accurately in the time period 1964-1988, based on the levels of variables that an economist would expect would influence these revenues.



6. First, need to think about how demand and supply look in this market.  Bonus: In the demand/supply diagram in this market, the supply curve can be viewed as essentially a vertical line. Supply is almost completely price inelastic because the number of fish caught is determined by fisheries regulations and depends upon the estimated stock of fish in each year. Overall catch quotas are established for a season, and fishers readily catch the full amount of the quota and then must quit. Fisheries managers do not respond to the price of salmon in deciding what quota to set. The amount is determined by the biology of each species. This supply curve means that if the demand curve stayed stationary over this time period, then the variations in revenue are solely due to different quantities of fish available. As the vertical supply curve shifts left and right over time, we would be tracing out this demand curve.



7. It is reasonable to suppose, however, that the demand curve has not remained stable over time. Instead, it shifts inwards or outwards with things (other than the price of salmon) that affect quantity demanded: incomes of US consumers and consumers in the countries to which Alaska salmon is exported; prices of substitutes, like tuna; inventories of canned salmon of each type; exchange rates between the US dollar and foreign currencies like the Japanese yen (since much premium salmon was exported fresh for sushi and sashimi); prices of substitutes such as farmed salmon from aquaculture enterprises, where output has been growing almost exponentially in the last few decades, etc. As these factors vary over time, one would expect Alaska wild salmon prices to fluctuate.



8. Before attributing lost commercial salmon fishing revenues to the oil spill (and thinking that the guilty party should be expected to compensate fishers for their losses), we need to see if anything else, coincidentally, might have impacted salmon revenues in 1989 and subsequent years.



9. It is possible that the "taint" perceived by consumers of Alaskan salmon caused demand to shift in a way that could be attributed to the oil spill (even though the fisheries were closed so that no tainted fish reached the market...there were plenty of unaffected salmon elsewhere in Alaska with which fishers could fill the annual quota).



10. However, it is also possible that the oil spill had no discernible effect on salmon prices, after controlling for all of the other factors that typically contribute to price variations.



11. More remotely, but still possible, it could even be the case that the oil spill increased demand for Alaskan salmon--perhaps because people became more aware of Alaska and its resources as a result of publicity surrounding the oil spill. This effect could be overwhelmed in the data, perhaps, by something like the growth in farmed salmon output that acts as a viable substitute, driving down wild salmon prices. The observed effect might be a smaller revenue drop than would have otherwise occurred.  Just looking at the time series above does not give us enough information to discriminate between all these possibilities.



12. From Econ 1, you know that quantity demanded = f(own price, income, other prices, tastes). In Economic 143, we will seek to quantify this relationship by appealing to data.



13. For this example, quantity sold is determined by catch quotas, so we solve the demand function for price: price = g(quantity available, income, other quantities, tastes). For the generic variables on the right side of this "inverse demand" function, we will use all of the factors measured above.



14. We will "fit" this inverse demand function to the pre-spill data, then use it along with post-spill values for the right-hand-side variables, to "predict" what would have been the prices of each salmon species under pre-spill market conditions. We can multiply hese prices by the quota quantity permitted each year to arrive at a "counterfactual" revenue stream after the spill that can be argued to be the revenues that fishers should have enjoyed, in the absence of the spill. The difference between these revenues and the actual revenues is a much more valid measure of the financial losses suffered by these fishers than just to take the difference between actual 1988 revenues and 1989 revenues.



15. We have the data needed for such an exercise. The data are in a file called alaska.dat, and can be read by the SHAZAM program fragment called alaska0.sha. We will explore this estimation and counterfactual forecasting problem later in the course.



16. If the plight of salmon fishers in Alaska doesn't interest you, there will be dozens of other applications...something for everyone.