Asset pricing in created markets.

In the next section, we provide a selected review of the literature modeling asset prices and dividends. This is followed by a description of the design of the ITQ system in New Zealand, paying particular attention to market characteristics. We then develop an empirical model that is appropriate to a multiple-asset setting like the New Zealand fishing quota market. We discuss the empirical specification, data sources, time-series properties of the data, estimation approach, and results, before we conclude by summarizing our findings.

Modeling Asset Prices and Dividends

The literature exploring the relationship between asset prices, dividends, and other relevant factors (e.g., firm size) is extensive. A thorough literature review is therefore beyond the scope of this article, and interested readers should consult Cochrane (2001) and Campbell, Lo, and MacKinley (1997) or the review articles by LeRoy (1989), Fama (1991, 1998), and Campbell (2000). (3)

Simplifying equation (1) under the assumption that the expected discount rate follows a martingale process yields (4)

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

Equation (2) illustrates how the asset price is dependent on the expected future stream of earnings, so that information available at time t along with type of expectation process is important in modeling the relationship between asset prices and dividends. For example, if one assumes that expected future earnings are constant, then [E.sub.t]([[pi].sub.t+s]) = [E.sub.t]([pi]). Huppert, Ellis, and Noble (1996) model and find support for an adaptive expectations process where [E.sub.t]([pi]) = [beta][[pi].sub.t-1] + (1 - [beta]) [E.sub.t-1]([pi]) with [beta] [member of [0, 1], and Karpoff (1984b) models a myopic process where [beta] = 1. Wilson and Sumner (2004) find support for a second-order adaptive expectation process in California dairy quota prices. Just and Miranowski (1993) test myopic, adaptive, and rational expectation regimes and find that farmland price data support myopic expectations. Falk (1991) finds a similar result. Orazem and Miranowski (1986) provide an empirical strategy for testing competing hypotheses of expectations regimes when direct measures of expectations are unavailable. Applied to farm acreage allocation decisions as a function of expected commodity prices, it yielded little evidence for favoring any of the three regimes.

If future profits (lease prices) grow at a constant rate g, then [[pi].sub.t] = (1 + g) [[pi].sub.t-1] + [[epsilon].sub.t], where [[epsilon].sub.t] is a white noise error term. Taking expectations and solving equation (2) forward in time with g < r, the asset price follows

(3) [p.sub.t] = [[pi].sub.t]/[r.sub.t] - g.

Equation (3) is the dynamic "Gordon growth model" (Campbell, Lo, and MacKinley 1997) that forms the basis of the majority of studies on the relationship between asset prices and dividends.

Due to a divergence between simple present-value relationships and empirical observations on agricultural land prices and rents during the 1970s and 1980s, a number of authors have extended this basic structure to include other factors, such as taxes (e.g., Robison, Lins, and VenKataraman 1985; Alston 1986), changes in risks (Barry 1980), and credit market constraints (Shalit and Schmitz 1982). Instead of investigating these many factors separately, Just and Miranowski (1993) develop a detailed structural model of the determinants of asset prices, which is a function of inflation, taxes, credit market imperfections, transaction costs, and risk aversion.

Others have focused on estimating a reduced form that is consistent with equation (2). For example, Burt (1986) argues that movements in asset prices may occur because of continued adjustment to past changes in returns, implying that the price does not adjust instantaneously to changes in expected future returns. In addition, expectations of future rents may be based on past, as well as current, values of [[pi].sub.t]. He approximated the effect of both sources of dynamic behavior by using a multiplicative distributed lag specification for [[pi].sub.t], with a restriction that the lag coefficients sum to unity.

Background on NZ ITQ System

We include a brief review of the New Zealand ITQ system with special attention to the elements that are most relevant for our analysis. For further history and institutional detail, see Batstone and Sharp (1999), Yandle (2001), NSK, and the references cited therein.

The New Zealand government passed the Fisheries Amendment Act in 1986, creating a national ITQ system. The system initially covered seventeen inshore species and nine offshore species, which together expanded to a total of forty-five species by 2000. Under the system, the New Zealand Exclusive Economic Zone (EEZ) is geographically delineated into quota management regions for each species based on the location of major fish populations. Rights for catching fish are defined in terms of fish stocks that correspond to a specific species taken from a particular quota management region. In 2000, the total number of fishing-quota markets stood at 275, ranging from 1 for the species hoki to 11 for abalone. As of the mid-1990s, the species managed under the ITQ system accounted for more than 85% of the total commercial catch taken from New Zealand's EEZ and from our calculations had an estimated market capitalization of about NZ$3 billion.

The New Zealand Ministry of Fisheries sets a TAC for each fish stock based on an intertemporal biological assessment (including the prior year's catch level) and other relevant environmental, social, and economic factors. The TACs are legislated to maintain the fish population at a level (or move it to a level) that will support the largest possible annual catch (i.e., maximum sustainable yield), after an allowance for recreational and other noncommercial fishing. Not all species have their TACs adjusted for noncommercial uses, especially those in the offshore sector where there is little if any recreational fishing (see table 1).5 Most TACs remain constant from year to year and for many fish stocks (especially those of low value) there are no formal stock assessments (Annala 1996). When a TAC needs to be adjusted there is no automatic process, and the appropriate level of the adjustment is discussed with the quota owners (Sanchirico et al. 2006).

Individual quota were initially allocated to fishermen free of charge as fixed annual tonnages in perpetuity based on their average catch level over two of the years spanning 1982-1984. Beginning with the 1990 fishing year, however, the government switched from quota rights based on fixed tonnages to quota denominated as a share of the TAC. Compliance and enforcement is undertaken through a detailed set of reporting procedures that track the flow of fish from a vessel to a licensed fish receiver (on land) to export records, along with an at-sea surveillance program including onboard observers.

Given the uncertainty around the quantity and composition of catch, a fisherman's quota holdings represent a mix of ex ante and ex post leases, as well as asset purchases and sales to cover actual catch. Although there are no official statistics, the general belief is that brokers handle a majority of the transactions between small and medium-sized quota (with a fee between 1% and 3% of the total value of the trade paid by the seller) and larger companies typically have quota managers on staff and engage in bilateral trades with other large companies. Whether ex ante or ex post transactions, fishing quota are generally tradable only within the same fish stock, and not across regions or species or years, although there have been some minor exceptions. (6) The quota rights can be broken up and sold in smaller quantities and any amount may be leased or subleased any number of times. Virtually all leases are for one year or less. There are also legislative limits on aggregation for particular stocks and regions, and limitations on foreign quota holdings.

NSK find that the quota markets are active, with about 140,000 leases and 23,000 quota asset sales occurring between economically distinct private entities between 1986 and 2000--an annual average of about 9,300 leases and 1,500 asset sales. Market participation has also increased over time with around 70% of quota owners taking part in a market transaction in 2000. Although some individual quota markets are thin, these tend to be of low economic importance in the size and value of the catch. The annual number of leases has risen ten-fold between 1986 and 2000, and the median percentage of total quota that are leased in these markets has risen consistently, from 9% in 1987 to 44% in 2000. At the same time, the total number of quota asset sales declined from a high of about 3,200 sales in 1986 (when initial quota allocations for most species took place), leveling off to around 1,000 sales in the late 1990s. The median shows a similar decline, with the percentage of total outstanding quota sold per year being as high as 23% at the start of the program, gradually decreasing in subsequent years to around 5% in the late 1990s. This pattern of asset sales is consistent with a period of rationalization and reallocation proximate to the initial allocation of quota, with sales activity decreasing after the less profitable producers have exited.

Empirical Analysis of Fishing Quota Asset Prices

Empirical Model