Changes in Employment Security in Asia

Much attention has been focused on the decline of traditional employment structures in the advanced industrial countries. Lesser attention has focused on this issue in Asia. In this comparative essay, the authors examine the changes in employment security in China, India, Japan, and South Korea. They focus on the historical development of the employment security social contract in these countries, noting the institutional features that gave rise to it in each country. They then examine the resilience of employment security norms under recent economic pressures. They find there has been substantial erosion in employment security during the 1990s in all four countries due to both increased competition and economic liberalization, although there is some variation in both the rate of erosion as well as the prospects for revival of the social contract. They assess the possibilities of a revival in this particular social contract, and the impact of the erosion on unorganized workers

The iterated auxiliary particle filter

We present an offline, iterated particle filter to facilitate statistical inference in general state space hidden Markov models. Given a model and a sequence of observations, the associated marginal likelihood L is central to likelihood-based inference for unknown statistical parameters. We define a class of "twisted" models: each member is specified by a sequence of positive functions psi and has an associated psi-auxiliary particle filter that provides unbiased estimates of L. We identify a sequence psi* that is optimal in the sense that the psi*-auxiliary particle filter's estimate of L has zero variance. In practical applications, psi* is unknown so the psi*-auxiliary particle filter cannot straightforwardly be implemented. We use an iterative scheme to approximate psi*, and demonstrate empirically that the resulting iterated auxiliary particle filter significantly outperforms the bootstrap particle filter in challenging settings. Applications include parameter estimation using a particle Markov chain Monte Carlo algorithm

Contagion and state dependent mutations

Early results of evolutionary game theory showed that the risk dominant equilibrium is uniquely selected on the long run by the best response dynamics with mutation. Bergin and Lipman (1996) qualified this result by showing that for a given population size the evolutionary process can select any strict Nash equilibrium if the probability of choosing a nonbest reply is state-dependent. This paper shows that the unique selection of the risk dominant equilibrium is robust with respect to state dependent mutation in local interaction games. More precisely, for a given mutation structure there exists a minimum population size beyond which the risk dominant equilibrium is uniquely selected. Our result is driven by contagion and cohesion among players, which exists only in local interaction settings and favors the play of the risk dominant strategy. Our result strengthens the equilibrium selection result of evolutionary game theor

Intersection Bounds: Estimation and Inference

We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set. We show that many bounds characterizations in econometrics, for instance bounds on parameters under conditional moment inequalities, can be formulated as intersection bounds. Our approach is especially convenient for models comprised of a continuum of inequalities that are separable in parameters, and also applies to models with inequalities that are non-separable in parameters. Since analog estimators for intersection bounds can be severely biased in finite samples, routinely underestimating the size of the identified set, we also offer a median-bias-corrected estimator of such bounds as a by-product of our inferential procedures. We develop theory for large sample inference based on the strong approximation of a sequence of series or kernel-based empirical processes by a sequence of "penultimate" Gaussian processes. These penultimate processes are generally not weakly convergent, and thus non-Donsker. Our theoretical results establish that we can nonetheless perform asymptotically valid inference based on these processes. Our construction also provides new adaptive inequality/moment selection methods. We provide conditions for the use of nonparametric kernel and series estimators, including a novel result that establishes strong approximation for any general series estimator admitting linearization, which may be of independent interest