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In the United States, there is little difference in annual income inequality and income mobility between the rural and urban sectors of the economy. This forms a sharp contrast with China where income inequality is greater and income mobility lower among rural households than among urban households. When incomes are averaged over three years and when adjustments are made for the size and composition of households, income inequality among all households differs little between China and the United States in the 1990s. Moreover when pooling rural households and urban households and when measuring annual income inequality and income mobility of the pooled households, the mobility of incomes of households in the United States differs little from that in China. Social welfare functions are posited that allow for a trade-off between increases in income and increases in income inequality. These suggest strong increases in well-being for urban households in China. The corresponding changes in rural China and in the United States are smaller. Four sets of data on households are drawn on to document these findings.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

Financial contagion refers to the propagation of shocks that can generate widespread failures. The authors apply a financial contagion model proposed by Elliott, Golub, and Jackson (2014) to a cross-shareholding network of firms in Ecuador. The authors use a novel dataset to study the potential channels for contagion. Although diversification is not high, results reveal enough conditions for a contagion event to occur. However, the low level of integration attenuates the effects of shocks. The authors run simulations affecting a particular firm at the time, and find that two firms coming from the finance and trade industry cause the highest contagion. In addition, when an entire industry receives a shock, trade and manufacturing industries contagion more companies than the rest. Finally, the model can assist policymakers to monitor the market and evaluate the fragility of the network in different scenarios.

In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

Using data from the Health and Retirement Study, we examine behavioral responses to a new generation of retirement policies that on average are actuarially neutral. Although many conventional models predict that actuarially neutral policies will not affect retirement behavior, our model allows those with high-time preference rates to find that the promise of an actuarially fair increase in future rewards does not balance the loss from foregone current benefits. Thus together with liquidity constraints facing those with high-time preference, we find that actuarially neutral policies do affect retirement behavior. One such policy follows on the elimination of the Social Security earnings test for those over normal retirement age, and would eliminate the earnings test between early and normal retirement age. Another of these policies would increase the ages of benefit entitlement. Still another such policy emerges from a central focus of the past few years on the adoption of personal accounts. Although Social Security benefits are currently paid in the form of an annuity, benefits from either defined benefit plans or from personal accounts may be made available as an annuity or as a lump sum of equivalent actuarial value. A related policy choice between actuarially equivalent benefits emerges on the pension side. There has been discussion of relaxing the current IRS prohibition against paying a pension benefit when a person remains at work, instead allowing partial pension benefits to be paid to those who partially retire on a job.

This paper attempts to interpret the Bartholomew (1973) index of mobility in terms of a directional mobility index based on the one-step expected states of movement corresponding to a specific state of transition matrix. A partial ordering of directional mobility of a general state of transition matrices, referred to as “upward mobility favoring sequential averaging (UMFSA),” is proposed using the algebraic equivalent of the generalized Lorenz ordering of expected states. When the underlying mobility depends on the initial distribution of the states, using a Bayesian approach, the indices are reexamined for a general class of matrices. This enables us to interpret the Prais (1955) and Bibby (1975) mobility index in this framework.