The cylindrical structure of political involvement

This research is devoted to a methodological study of interrelations among varieties of involvement in political issues, utilizing data designed and gathered in the context of the Israel election campaign for the Seventh Knesset in 1969. The observations on political involvement were defined by means of a mapping sentence. The interrelations were studied by calculating a weak monotonicity coefficient between each pair of variables. This correlation matrix was submitted to a smallest space analysis (SSA-I) in order to help ascertain the correspondence between the structure of the matrix and the facets of political issues in the mapping sentence. It is hypothesized that the regression of one form of involvement on another should be monotone and of positive sign. Such a ‘first law’ of involvement does hold empirically here. Furthermore, the structure of the intercorrelation matrix can be represented geometrically by a cylindrical interpretation of the SSA space which corresponds to the facet design of the varieties of involvement. The axis of the cylinder distinguishes between instrumental and cognitive involvement, while the circularity within strata of the cylinder corresponds to the facet of political issues which are the objects of the involvement.     

How Responsive Is Investment in Schooling to Changes in Redistributive Policies and in Returns?

This paper uses an unusual pay reform to test the responsiveness of investment in schooling to changes in redistribution schemes that increase the rate of return to education. We exploit an episode where different Israeli kibbutzim shifted from equal sharing to productivity‐based wages in different years and find that students in kibbutzim that reformed earlier invested more in high school education and, in the long run, also in post‐secondary schooling. We further show that the effect is mainly driven by students in kibbutzim that reformed to a larger degree. Our findings support the prediction that education is highly responsive to changes in the redistribution policy.     

Arrow-Pratt Risk Aversion, Risk Premium and Decision Weights

This paper analyzes two issues: (a) the effect of decision-weights on risk premium, and (b) whether risk-aversion characterizes most investors. We theoretically show that cumulative prospect theory decision-weights systematically increase Arrow's risk premium, and may induce a positive risk premium even in the absence of risk-aversion. However, decision-weights may either increase or reduce Pratt's risk premium. We present three experiments revealing a striking result: a large proportion of the subjects' choices contradicts risk-aversion. This may be due to non-concave preferences, or to decision-weights. This result may have a dramatic impact on equilibrium models in economics and finance.     

Diversity Training and Intergroup Contact: A Call to Action Research

Diversity training is a type of prejudice reduction and social inclusion intervention in need of "action research"—an integration of research and theory with practice ( Lewin, 1946 ). Hundreds of workplaces and schools use some form of diversity training, but most interventions are not grounded in theory and there is little evidence of program impact. A recent study of a school diversity training program illustrates how action research can address theoretical issues using experimental methods and unobtrusive outcome measures. For future research, the literature on intergroup contact ( Pettigrew, 1998 ) can provide theoretical guidance while testing and refining its principles in the application and investigation of diversity training. Action research will benefit diversity training and the broader theoretical and applied project of prejudice reduction and the promotion of social inclusion.     

Approximate reference priors for Gaussian random fields

Reference priors are theoretically attractive for the analysis of geostatistical data since they enable automatic Bayesian analysis and have desirable Bayesian and frequentist properties. But their use is hindered by computational hurdles that make their application in practice challenging. In this work, we derive a new class of default priors that approximate reference priors for the parameters of some Gaussian random fields. It is based on an approximation to the integrated likelihood of the covariance parameters derived from the spectral approximation of stationary random fields. This prior depends on the structure of the mean function and the spectral density of the model evaluated at a set of spectral points associated with an auxiliary regular grid. In addition to preserving the desirable Bayesian and frequentist properties, these approximate reference priors are more stable, and their computations are much less onerous than those of exact reference priors. Unlike exact reference priors, the marginal approximate reference prior of correlation parameter is always proper, regardless of the mean function or the smoothness of the correlation function. This property has important consequences for covariance model selection. An illustration comparing default Bayesian analyses is provided with a dataset of lead pollution in Galicia, Spain.     

Average and Quantile Effects in Nonseparable Panel Models

Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under time‐homogeneity conditions that are like “time is randomly assigned” or “time is an instrument.” Partial‐identification results for average and quantile effects are given for discrete regressors, under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the usual, linear, fixed‐effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The dynamic bounds provide a partial‐identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity. The impact of T, the number of time periods, is shown by deriving shrinkage rates for the identified set as T grows. We also consider semiparametric, discrete‐choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds. Computationally convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. We give empirical illustrations.     

Local Identification of Nonparametric and Semiparametric Models

In parametric, nonlinear structural models, a classical sufficient condition for local identification, like Fisher (1966) and Rothenberg (1971), is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We derive an analogous result for the nonparametric, nonlinear structural models, establishing conditions under which an infinite dimensional analog of the full rank condition is sufficient for local identification. Importantly, we show that additional conditions are often needed in nonlinear, nonparametric models to avoid nonlinearities overwhelming linear effects. We give restrictions on a neighborhood of the true value that are sufficient for local identification. We apply these results to obtain new, primitive identification conditions in several important models, including nonseparable quantile instrumental variable (IV) models and semiparametric consumption‐based asset pricing models.     

Risk heatmaps as visual displays: Opening movie studios after the COVID-19 shutdown

Upon shutting down operations in early 2020 due to the COVID-19 pandemic, the movie industry assembled teams of experts to help develop guidelines for returning to operation. It resulted in a joint report, The Safe Way Forward, which was created in consultation with union members and provided the basis for negotiations with the studios. A centerpiece of the report was a set of heatmaps displaying SARS-CoV-2 risks for a shoot, as a function of testing rate, community infection prevalence, community transmission rate (R0), and risk measure (either expected number of cases or probability of at least one case). We develop and demonstrate a methodology for evaluating such complex displays, in terms of how well they inform potential users, in this case, workers deciding whether the risks of a shoot are acceptable. We ask whether individuals making hypothetical return-to-work decisions can (a) read display entries, (b) compare display entries, and (c) make inferences based on display entries. Generally speaking, respondents recruited through the Amazon MTurk platform could interpret the display information accurately and make coherent decisions, suggesting that heatmaps can communicate complex risks to lay audiences. Although these heatmaps were created for practical, rather than theoretical, purposes, these results provide partial support for theoretical accounts of visual information processing and identify challenges in applying them to complex settings.