Furthermore, currency options’ implicit volatilities do appear to forecast future exchange rate volatility reasonably well in general — in contrast to those from stock or stock index options.6 Consequently, setting implicit forward variances
equal to the probability-weighted average of the zero value expected conditional upon currency union, and a higher estimated value conditional upon EMU not occurring, would appear a natural and direct way of assessing the probability of EMU transpiring fully.
The major difficulty with this approach is data availability. Interbank options on pound/DM, DM/FF, and DM/lira are actively traded, but primarily only for maturities of up to one year. International convertible bonds such as those studied by Jennergren and Naslund (1990) often contain longer-maturity intra-European cross-rate options, but extracting implicit option prices is severely complicated by the plethora of bond-specific features. Consequently, the effective absence until 1998 of data on actively traded options maturing after January 1, 1999 has precluded the construction of EMU calculators based upon intra-European currency options. Some studies have, however, used such options to assess developments in the run-up to the currency union; notably Campa, Chang, and Reider (1997), Butler and Cooper (1997), and Adao, Cassola, and Luiz (1998).
On May 2, 1998, the leaders of the countries of the European Union formally selected those countries deemed eligible to participate in a common currency union. This union, commonly referred to as the European Monetary Union (EMU)1, was the third and final stage of a process established under the Maastricht treaty of December, 1991. While various criteria were established in that treaty as pre-conditions for membership, the most severe constraints were with respect to the “convergence” criteria regarding acceptable levels of inflation rates, interest rates, budget deficits and national debt. An additional criterion of exchange rate stability became obsolete after the European currency crises of 1992 and 1993. 11 countries were selected as eligible; an additional 3 (Denmark, Sweden, and the U.K.) chose not to participate, while Greece failed to meet the convergence criteria.
Although the EMU now appears virtually certain to begin on January 1, 1999 with broad participation, that outcome was not expected over most of the 1992-98 period. Academics discussed a two-tiered Europe consisting of a few core countries that would meet the convergence criteria, and a broader group that would not qualify until later. Paralleling this uncertainty, various academic researchers and banks developed “EMU probability calculators” to infer from financial data the probability of various countries qualifying for admission. The best-known is probably the one developed by J.P. Morgan, the results of which were regularly reported in the Financial Times. Four such probability calculations for Italy are illustrated below in Figure 1.
We apply our ideas to a Medicaid data set to illustrate how to calculate distortion incentives, and we conduct policy analyses of risk adjustment and carve out options add comment.
Our paper is related to other recent research in applied industrial organization that begins with an explicit characterization of conditions for profit maximization and information constraints in the market. That literature has explained phenomena such as the inefficient choice of the number of product lines (Klemperer and Padilla, 1997) and entry and exit in hub and spoke networks (Hendricks, Piccione and Tan, 1997). These papers use the profit maximization conditions to explain observed equilibria that appear to deviate from simple market models.
contract. For example, mental health and substance abuse benefits are often carved out, meaning that the regular health plan chosen by the enrollee is not responsible for providing mental health and substance abuse care. A separate managed care company specializing in this area of care receives a contract from the employer or other payer to provide this benefit. Carve outs can be done for reasons of controlling moral hazard or moderating selection incentives (Frank and McGuire, 1998).
Our analysis shows how this might happen. A profit-maximizing plan might distort the shadow price for service s’ to affect its enrollee profile. If any service is carved out, this strategy will change. In particular, if service s’ is carved out, rationing strategies for all other services will generally be affected. Carving out any one service will affect the efficiency of that service provision as well as the nature of insurance market equilibrium overall.
As table 4 shows, the calculations for shadow prices are sensitive to how much information individuals have in making their predictions. In Figure 3 we graph the profit-maximizing q’s for two services, mental health/substance abuse and musculoskeletal care as individuals know more and more of the information contained in prior use in the absence of risk adjustment. Distortions go up exponentially as information improves.
In the case of the q for mental health and substance abuse, the damage from selection incentives accelerates rapidly around the 30% range. For the muskuloskeletal category the graph is smoother than in the mental health and substance abuse care. When individuals know as much as 50% of prior use, profit-maximizing q’s go off the charts, signaling that incentives to over and underprovide are very strong.