This paper examines the implications of adverse selection in the private annuity market for the pricing of private annuities and the consequent effects on constrption and bequest behavior. With privately known heterogeneous mortality probabilities, adverse selection causes the rate of return on private annuities to be less than the actuarially fair rate based on population average mortality. However, a fully funded social security system with compulsory participation can offer an implied rate of return equal to the actuarially fair rate based on population average mortality. Thus, since social security offers a higher rate of return than private annuities, consumers cannot completely offset the effects of social security by transacting in the private annuity market. Using an overlapping generations model with uncertain lifetimes, we demonstrate that the introduction of actuarially fair social security reduces the steady state rate of return on annuities and raises the steady state levels of average bequests and average consumption of the young. The steady state national capital stock rises or falls according to the strength of the bequest motive.

Existence and uniqueness of equilibrium points for concave n-person games - dynamic model for nonequilibrium situations

Recent work demonstrates that dynastic assumptions guarantee the irrelevance of all redistributional policies, distortionary taxes, and prices-the neutrality of fiscal policy (Ricardian equivalence) is only the "tip of the iceberg." In this paper, we investigate the possibility of reinstating approximate Ricardian equivalence by introducing a small amount of friction in intergenerational links. If Ricardian equivalence depends upon significantly shorter chains of links than do these stronger neutrality results, then friction may dissipate the effects that generate strong neutrality, without significantly affecting the Ricardian result. Although this intuition turns out to be essentially correct, we show that models with small amounts of friction have other untenable implications. We conclude that the theoretical case for Ricardian equivalence remains tenuous.

We consider the problem of constructing confidence intervals (CIs) for a linear functional of a regression function, such as its value at a point, the regression discontinuity parameter, or a regression coefficient in a linear or partly linear regression. Our main assumption is that the regression function is known to lie in a convex function class, which covers most smoothness and/or shape assumptions used in econometrics. We derive finite‐sample optimal CIs and sharp efficiency bounds under normal errors with known variance. We show that these results translate to uniform (over the function class) asymptotic results when the error distribution is not known. When the function class is centrosymmetric, these efficiency bounds imply that minimax CIs are close to efficient at smooth regression functions. This implies, in particular, that it is impossible to form CIs that are substantively tighter using data‐dependent tuning parameters, and maintain coverage over the whole function class. We specialize our results to inference on the regression discontinuity parameter, and illustrate them in simulations and an empirical application.

Competing Vertical Structures: Preconmitment and Renegotiation

In recent years increasing use has been made of systems of social accounts, and a variety of new types and new forms of presentation have been suggested. The general features of such systems are: (a) the division of an economy into a number of groups or economic entities, mostly themselves a complex of elementary units, and (b) a summing up in the form of accounts of the transactions of various kinds between these economic entities. As a rule, the economy of one country is investigated, implying that all other countries are regarded as one single group. The economy considered may be split up into a number of separate groups. It is customary to distinguish between consumers' households, business enterprises, and the government sector. If special attention is to be given to banking problems for example, banks will constitute a separate group.

In many markets, transaction prices are determined in auctions. In the most common form, prospective buyers compete by submitting bids to a seller. Each bid is an offer to buy that states a quantity and a maximum price. The seller then allocates the available supply among those offering the highest prices exceeding the seller's asking price. The actual price paid by a successful bidder depends on a pricing rule, usually selected by the seller: two common pricing rules are that each successful bidder pays the price bid; or they all pay the same price, usually the highest rejected bid or the lowest accepted bid. Auctions have been used for millennia, and remain the simplest and most famil-iar means of price determination for multilateral trading without intermediary `market makers ' such as brokers and specialists. Their trading procedures, which simply process bids and offers, are direct extensions of the usual forms of bilateral bargaining. Auctions also implement directly the demand submission procedures used in Walrasian models of markets. They therefore have prominent roles in the theory of exchange and in studies of the effects of economic institutions on the volume and terms of trade. Their allocative ef-®ciency in many contexts ensures their continued prominence in economic theory. They are also favored in experimental designs investigating the predictive power of economic theories. Auctions are apt subjects for applications of game theory because they present ex-plicit trading rules that largely ®x the `rules of the game'. Moreover, they present sub-stantive problems of strategic behavior of practical importance. They are particularly valuable as illustrations of games of incomplete information because bidders ' private information is the main factor affecting strategic behavior. The simpler forms of auctions induce normal-form games that are essentially `solved ' by applying directly the basic

Self–selected migration presents one potential explanation for why observed returns to a college education in local labor markets vary widely even though U.S. workers are highly mobile. To assess the impact of self–selection on estimated returns, this paper first develops a Roy model of mobility and earnings where workers choose in which of the 50 states (plus the District of Columbia) to live and work. Available estimation methods are either infeasible for a selection model with so many alternatives or place potentially severe restrictions on earnings and the selection process. This paper develops an alternative econometric methodology that combines Lee's (1983) parametric maximum order statistic approach to reduce the dimensionality of the error terms with more recent work on semiparametric estimation of selection models (e.g., Ahn and Powell (1993)). The resulting semiparametric correction is easy to implement and can be adapted to a variety of other polychotomous choice problems. The empirical work, which uses 1990 U.S. Census data, confirms the role of comparative advantage in mobility decisions. The results suggest that self–selection of higher educated individuals to states with higher returns to education generally leads to upward biases in OLS estimates of the returns to education in state–specific labor markets. While the estimated returns to a college education are significantly biased, correcting for the bias does not narrow the range of returns across states. Consistent with the finding that the corrected return to a college education differs across the U.S., the relative state–to–state migration flows of college– versus high school–educated individuals respond strongly to differences in the return to education and amenities across states.

Suppose an observed time series is generated by a stochastic volatility model-i.e., there is an unobservable state variable controlling the volatility of the innovations in the series. As shown by Nelson (1992), and Nelson and Foster (1994), a misspecified ARCH model will often be able to consistently (as a continuous time limit is approached) estimate the unobserved volatility process, using information in the lagged residuals. This paper shows how to more efficiently estimate such a volatility process using information in both lagged and led residuals. In particular, this paper expands the optimal filtering results of Nelson and Foster (1994) and Nelson (1994) to smoothing.

The paper deals with infinite horizon optimization problems. The existence of optimal solutions is obtained as a consequence of an asymptotic growth condition. We also exhibit finite horizon approximates that yield upper and lower bounds for the optimal values and whose optimal solutions converge to the long-term optimal trajectories.