Household debt and financial assets: evidence from Germany, Great Britain and the USA

Summary.  We explore the determinants of debt, financial assets and net worth at the household level by using survey data for Germany, Great Britain and the USA. To identify which households are potentially vulnerable to adverse changes in the economic environment, we also explore the determinants of a range of measures of financial pressure: the probability that a household has negative net worth; the debt-to-income ratio; mortgage income gearing; the saving-to-income ratio. Our empirical findings suggest that the poorest and the youngest households are the most vulnerable to adverse changes in their financial circumstances.     

Economics, sociobiology and behavioral psychology on preferences

Economists have become increasingly interested in hypotheses from sociobiology as a source of inspiration for filling gaps in the economic model of behavior. To avoid borrowing eclectically and arbitrarily from neighboring disciplines, this paper attempts to outline in a systematic way the similarities and differences between the approaches taken in economics and sociobiology. In doing so, special attention is given to an empirical theory of preferences that is lacking in economics. Here, inspiration from sociobiology would seem to be particularly useful. The considerations in the paper suggest that sociobiological arguments may indeed be helpful, albeit at a very elementary level only. A more comprehensive theory cannot ignore the influences of innate learning mechanisms in higher living beings. An elaborated theory of preferences in economics will have to acknowledge and incorporate insights from behavioral psychology.     

De minimis and equity in risk

Indices and orderings are developed for evaluating alternative strategies in the management of risk. They reflect the goals of reducing individual and collective risks, of increasing equity, and of assigning priority to the reduction and to the equity of high risks. Individual risk is defined as the (random or non-random) level of exposure to a danger. In particular the role of a lower negligibility level is investigated. A class of indices is proposed which involves two parameters, a negligibility level and a parameter of inequality aversion, and several interpretations of the indices are discussed. We provide a set of eight axioms which are necessary and sufficient for this class of indices, and we present an approach to deal with partial information on the parameters.     

The efficacy of using judgmental versus quantitative forecasting methods in practice

In an era where forecasts drive entire supply chains forecasting is seen as an increasingly critical organizational capability. However, business forecasting continues to rely on judgmental methods despite large advancements in information technology and quantitative method capability, prompting calls for research to help understand the reasons behind this practice. Our study is designed to contribute to this knowledge by profiling differences between firms identified as primary users of either judgmental or quantitative forecasting methods. Relying on survey data from 240 firms we statistically analyzed differences between these categories of users based on a range of organizational and forecasting issues. Our study finds large differences in forecast error rates between the two groups, with users of quantitative methods significantly outperforming users of judgmental methods. The former are found to be equally prevalent regardless of industry, firm size, and product positioning strategy, documenting the benefits of quantitative method use in a variety of settings. By contrast, the latter are found to have significantly lower access to quantifiable data and to use information and technology to a lesser degree.     

An Extended Single‐index Model with Missing Response at Random

An extended single‐index model is considered when responses are missing at random. A three‐step estimation procedure is developed to define an estimator for the single‐index parameter vector by a joint estimating equation. The proposed estimator is shown to be asymptotically normal. An algorithm for computing this estimator is proposed. This algorithm only involves one‐dimensional nonparametric smoothers, thereby avoiding the data sparsity problem caused by high model dimensionality. Some simulation studies are conducted to investigate the finite sample performances of the proposed estimators.     

Phonological Specificity in 12‐ and 17‐Month‐Old French‐Speaking Infants

The literature reports some contradictory results on the degree of phonological specificity of infants’ early lexical representations in the Romance language, French, and Germanic languages. It is not clear whether these discrepancies are because of differences in method, in language characteristics, or in participants’ age. In this study, we examined whether 12‐ and 17‐month‐old French‐speaking infants are able to distinguish well‐pronounced from mispronounced words (one or two features of their initial consonant). To this end, 46 infants participated in a preferential looking experiment in which they were presented with pairs of pictures together with a spoken word well pronounced or mispronounced. The results show that both 12‐ and 17‐month‐old infants look longer at the pictures corresponding to well‐pronounced words than to mispronounced words, but show no difference between the two mispronunciation types. These results suggest that, as early as 12 months, French‐speaking infants, like those exposed to Germanic languages, already possess detailed phonological representations of familiar words.     

A hierarchical model for ordinal matrix factorization

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based on Gibbs sampling and one based on variational Bayes. Importantly, these algorithms may be implemented in the factorization of very large matrices with missing entries.