Social Capital Inequality and Subjective Wellbeing of Older Chinese

Using longitudinal data from the China Family Panel Studies, this study provides insights on comparative wellbeing outcomes for older people who are institutionally segregated into clusters that produce uneven social capital. We present the first study that examines how institutionalized social capital inequality, measured by the social capital gap generated by hukou (household registration) status in China, affects the wellbeing of older people. Our results show that high levels of social capital inequality are associated with lower subjective wellbeing, measured by life satisfaction. This general conclusion is robust to a number of sensitivity checks including alternative ways of measuring subjective wellbeing and inequality. We also find that the negative relationship between social capital inequality and subjective wellbeing is strongest for people with a non-urban hukou living in urban areas. Our findings highlight the need for policies aimed at narrowing the social capital gap and the dismantling of institutional structures that hinder upward social capital mobility.

     

No-betting Pareto under ambiguity

It has been argued that Pareto-improving trade is not as compelling under uncertainty as it is under certainty. The former may involve agents with different beliefs, who might wish to execute trades that are no more than betting. In response, the concept of no-betting Pareto dominance was introduced, requiring that putative Pareto improvements must be rationalizable by some common probabilities, even though the participants’ beliefs may differ. In this paper, we argue that this definition might be too narrow for use when agents are not Bayesian. Agents who face ambiguity might wish to trade in ways that can be justified by common ambiguity, though not necessarily by common probabilities. We accordingly extend the notion of no-betting Pareto dominance to characterize trades than are “no-betting Pareto” ranked according to the maxmin expected utility model.

     

How Best to Flip-Flop if You Must: Integer Dynamic Stochastic Programming for Either-Or

Standard portfolio analysis presumes one can blend different securities continuously. When one must choose all of one portfolio or all of another, we are in stochastic digital programming: either-or, zero-or-one choice. The algorithm for doing this optimally is shown to be simpler than in real variable maximizing, a switch from the usual extra complexities of digital programming. The Bellman multi-period dynamic programming is shown, paradoxically, to make it possible for a risk-averse investor to want sometimes to embrace an unfair gamble. The superiority of within-time diversification over across-time diversification carries over to this flip-flop case.     

How a certain internal consistency entails the expected utility dogma

If I am coherent, in the sense that I can always replace any subset of outcomes by their certainty equivalent (occurring with the sum of their probabilities), then I must act according to the dogma of maximizing an Exp {U}, ruling out Machina [1982], Allais [1952], and Ysidro [1950] functionals.     

Common Learning

Consider two agents who learn the value of an unknown parameter by observing a sequence of private signals. The signals are independent and identically distributed across time but not necessarily across agents. We show that when each agent's signal space is finite, the agents will commonly learn the value of the parameter, that is, that the true value of the parameter will become approximate common knowledge. The essential step in this argument is to express the expectation of one agent's signals, conditional on those of the other agent, in terms of a Markov chain. This allows us to invoke a contraction mapping principle ensuring that if one agent's signals are close to those expected under a particular value of the parameter, then that agent expects the other agent's signals to be even closer to those expected under the parameter value. In contrast, if the agents' observations come from a countably infinite signal space, then this contraction mapping property fails. We show by example that common learning can fail in this case.     

Riparian woody plant diversity and forest structure along an urban-rural gradient

Changes in riparian woody plant assemblages are anticipated in the southeastern United States due to increases in urbanization rates. Because riparian forests serve important roles in maintaining water quality and biodiversity, understanding how they respond to urbanization is crucial. The objective of this study was to examine forest structure and woody vegetation diversity indices of riparian communities in response to an urbanization gradient in West Georgia, USA. Measures of forest structure and diversity were compared to measures of urbanization and land cover. Although Liquidambar styracifluaand Quercus nigrawere dominant species in the forest stand and regeneration layer for all riparian communities, the invasive, non-native shrub Ligustrum sinense was the most dominant species observed in the regeneration layer for urban, developing, and agriculture communities. The proportion of non-native species in the forest stand and regeneration layer decreased and Shannon diversity of the regeneration layer increased with increasing distance from the urban center. Shifts in diversity indicate that anthropogenic disturbance may subdue the ability of diverse communities to resist non-native plant invasions.     

The predictive role of counterfactuals

We suggest a model that describes how counterfactuals are constructed and justified. The model can describe how counterfactual beliefs are updated given the unfolding of actual history. It also allows us to examine the use of counterfactuals in prediction, and to show that a logically omniscient reasoner gains nothing from using counterfactuals for prediction.