Asset pricing in created markets.

Although recent experience with the sulfur dioxide trading program in the United States has changed many perceptions, there are still questions about how well tradable permit systems for environmental pollution, greenhouse gases, agricultural production, and natural resources can work in practice. Such skepticism is in part warranted by the limited number of ex post assessments on the performance of created markets. Because building the necessary institutions can require significant political and economic costs, it is imperative to develop an empirical record of the performance of created markets in practice.

One area where market-based systems are subject to a significant degree of skepticism is in the management of ocean fisheries. One such system is individual fishing quotas, in which the total catch is capped and shares of the catch are allocated. An individual transferable quota (ITQ) system results when transfer of the shares is permitted. Over time, the least efficient fishermen should find it more profitable to sell their quota rather than fish it, both reducing excess capacity and increasing the efficiency of vessels operating in the fishery.

For ITQs to address the common pool problem in practice, it is important that quota markets are competitive and convey appropriate price signals. Price signals sent through the quota market are an essential source of information on the expected profitability of fishing and an important criterion for decisions to enter, exit, expand, or contract individual fishing activity. Quota prices also send signals to policymakers about the economic and biological health of a fishery. Arnason (1990) showed that under the assumption of competitive markets, monitoring the effect of changing the total allowable catch (TAC) on quota prices could be used to determine the optimal TAC.

In a previous study, Newell, Sanchirico, and Kerr (2005) (hereafter NSK) investigate the performance of ITQ markets using the most comprehensive dataset gathered to date for the largest system of its kind in the world. The panel dataset from New Zealand covers 15 years of transactions across the 33 species that were in the program as of 1998 and includes price and quantity data on transactions in more than 150 fishing quota markets. Markets exist in New Zealand both for selling the perpetual right to a share of a stock's TAC, as well as for leases of that right to catch a given tonnage in a particular year. NSK found that market activity appears sufficiently high to support a reasonably competitive market for most of the major quota species and that price dispersion has decreased over time. Investigating the asset and lease markets separately, they find evidence of economically rational behavior in each of the quota markets and their results show an increase in quota asset prices, consistent with increased profitability.

We extend the analysis of NSK by econometrically examining the relationship between the annual lease and sale prices in the perpetual quota asset markets. A notable exception to the virtually nonexistent literature examining quota prices in fisheries is the paper by Batstone and Sharp (2003), which investigates the relationship between fishing quota sale and lease prices and changes in the total allowable catch for the New Zealand red snapper fishery (region 1). Batstone and Sharp (2003) find support for the relationship proposed by Arnason (1990). Other related research in fisheries includes Karpoff (1984a, 1984b, 1995) and Huppert, Ellis, and Noble (1996), who look at the relationship between license prices and fishery rents in Alaska salmon fisheries.

With competitive markets, rational asset pricing theory suggests that the price of an income-producing asset in period t, [p.sub.t], should be determined by the real per-period profits from the asset, [[pi].sub.t], and the real discount rate, [r.sub.t]:

(1) [p.sub.t] = [[infinity].summation over (j=0)] [E.sub.t([[pi].sub.t] + j)]/ [[PI].sup.j.sub.k=0] (1 + [E.sub.t]([r.sub.t] + k)])

where E(*) is the expectations operator. In our setting, equation (1) states that the current quota asset price should be equal to the present discounted value of all future expected earnings, where the lease prices represent the annual flow of profits from holding quota. The price of the quota asset, therefore, will vary across fish stocks and over time based on changes in expected future lease prices or changes in the expected discount rate over time.

Under the simplifying assumption that expected lease prices and discount rates remain constant in the future, the price of the asset would simply equal the lease price divided by the discount rate, or [p.sub.t] = [[pi].sub.t]/[r.sub.t]. The expected rate of return from holding fishing quota (or dividend-price ratio) would be equal to [[pi].sub.t]/[p.sub.t]. Figure 1 supports the basic structure of such a relationship in New Zealand fishing quota, with the dividend-price ratio tracking both the level and the trend in New Zealand short-term interest rates over the sample period. For example, at the same time the dividend-price ratio fell by about half from 13% to 7%, the interest rate as measured by New Zealand Treasury bills fell from 10% to about 4% in real terms. Overall, the quota dividend-price ratio is about 2%-3% higher than the risk-free rate on average. Figure 2 likewise suggests a close, relatively linear association between asset and lease prices (in logs). The level of the average asset price is also approximately 10 times the lease price over the sample period, roughly equal to the present value of a perpetuity discounted at 10%.

[FIGURES 1-2 OMITTED]

Figure 1 also shows that there is considerable cross-sectional variation in the dividend-price ratio across fish stocks markets, where the upper and lower plus signs represent the 25th and 75th percentiles. Why might such variation exist? One reason could be that if fishers are risk averse they will prefer fish stocks with lower variance, other things equal. This effect is consistent with a higher discount rate, or higher required rate of return for riskier stocks. Such volatility could be associated with natural variation in stock abundance and economic variability in costs and fish prices. Another explanation could be differences in the expected growth rate of profits over time (Melichar 1979), possibly due to differences in output price growth, changes in fish populations, or other factors affecting costs such as cost rationalization due to quota trading.

Using panel data econometric techniques on an updated NSK dataset, we estimate models that relate the asset price of quota to their annual lease (or rental) price and observed determinants of the growth rate and volatility of rents. Within this framework, we explore the relationship between asset and lease prices, as well as whether differences in asset prices are due to differential risks associated with holding quota across fish stocks and/or different expected growth rates in fishery rents in those stocks. These data are uniquely qualified to address these questions, because of the relatively long time series, breadth of markets, and cross-sectional heterogeneity, as the market characteristics are diverse across both economic and ecological dimensions (see table 1 for a list of species included). For example, in 2000 the export value of these species ranges from about NZ$700 per ton for jack mackerel to about NZ$40,000 per ton for rock lobster. (1)

Consistent with asset pricing theory, we find a statistically (and economically) significant relationship between asset prices and contemporaneous lease prices. Stocks with a higher degree of biological volatility tend to have lower asset prices, and stocks that have rising returns or falling costs from fishing are found to have higher asset prices, ceteris paribus. Taken together, these results suggest that the price signals generated by the ITQ system are a good indication of the future profitability of individual fishing quota stocks. (2) The magnitude of some interrelationships is muted relative to what the theory suggests, possibly due to measurement error.

Our analysis also contributes to the extensive literature investigating asset prices by utilizing micro-level trading data across multiple (related) markets to measure the relationships embedded in equation (1), and the relative importance of the different factors behind the heterogeneity in figure 1. Nonfishery studies relevant to ours that investigate agricultural land prices and farming rents (e.g., Melichar 1979; Alston 1986; Falk 1991; Clark, Fulton, and Scott 1993; Just and Mirinowski 1993) or agricultural production quota (e.g., Barichello 1996; Wilson and Sumner 2004) typically focus on aggregate data and/or concentrate on a single market. For example, Falk (1991) models farmland prices in Iowa using aggregate price and rent data, and Wilson and Summer (2004) analyze the market for diary quota in California. The same holds for Batstone and Sharp (2003), who investigate a single quota market. Clark, Fulton, and Scott (1993) argue that a cross-sectional comparison of land markets can help illuminate the factors important in understanding the empirical relationship in equation (1).