Do parents,teachers and children use the SDQ in a similar fashion?

Few studies have investigated whether parents’, teachers’ and children’s responses to the strengths and difficulties questionnaire (SDQ) have equivalence. In this study, data from 854 matched questionnaires collected in Malaysia were subjected to tau equivalence confirmatory factor analysis, to assess if all three groups responded to the same target (the child) similarly. We first fitted Goodman’s (Journal of Child Psychology and Psychiatry, 38, 581–586, (1997a); Journal of Child Psychology and Psychiatry, 40, 791–801, 1997b) five factor model, but found that this did not fit the model despite attempts to improve and rectify model fit. We thereafter attempted to fit Dickey and Blumberg (Journal of the American Academy of Child Adolescent Psychiatry, 43, 1159–1167, 2004) three factor model, but similarly found a lack of fit. We then undertook an exploratory model with a random half of the data, obtaining a three factor solution, and tested this in a confirmatory tau equivalence model. The Unconstrained Model provided a fit to the data, revealing a similar structure across the three informant groups. As this fit was for the Unconstrained Model, it reveals that groups differ in the value they place on each of the variables but overall that held a similar underlying factor structure. The findings are discussed in relation to the possible cultural issues involved and the use of the SDQ.     

Which Elasticity? Estimating the Responsiveness of Taxpayer Reporting Decisions

In this paper we present estimates of the responses of individuals to marginal tax rates in their reporting of income, using data from individual tax returns for the year 1995. One estimation method is ordinary least squares regression. A second method uses quantile regression, which provides evidence on behavioral responses at different points (or quantiles) in the distribution of income and so is relevant to the question of whether the responses of, say, the rich differ from those at other points in the income distribution. Our results clearly indicate that marginal tax rates affect the reporting decisions of individuals. However, there are significant differences in the marginal tax rate reporting responses for the various types of reported income, there are major differences across income classes, and there are notable differences in the estimated responses across estimation methods.

How Monetary Policy can have Permanent Real Effects with Only Temporary Nominal Rigidity

A macroeconomic model is developed in which the psychological concept of loss aversion is incorporated into workers' preferences. The impact of monetary policy in the presence of loss aversion depends on the specification of the reference wage. The plausible specification that a worker's reference wage is the real wage she was paid in the previous period is considered in detail. Specifying the reference wage in this way, we show that an unanticipated change in monetary policy has a permanent, real effect when short term labour contracts are written in nominal wages.     

Retroactive Liability or the Public Purse?

The article addresses the question of whether responsibility for pollution created in the past should be retroactively applied to firms, or if the costs of cleaning up existing pollution should be financed by the public. We show that making firms liable for retrospective environmental costs can weaken the incentive to take precautions against future environmental costs. This follows since public financing of these costs can lead to greater prospective risk deterrence by allowing firms to more fully internalize the costs of future environmental risks. However, an analysis of existing public financing approaches highlights a set of dangers associated with their practical use.     

Bayesian exploratory factor analysis

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.     

Disclosure‐protected Inference Using Generalised Linear Models

Vast amounts of data that could be used in the development and evaluation of policy for the benefit of society are collected by statistical agencies. It is therefore no surprise that there is very strong demand from analysts, within business, government, universities and other organisations, to access such data. When allowing access to micro‐data, a statistical agency is obliged, often legally, to ensure that it is unlikely to result in the disclosure of information about a particular person or organisation. Managing the risk of disclosure is referred to as statistical disclosure control (SDC). This paper describes an approach to SDC for output from analysis using generalised linear models, including estimates of regression parameters and their variances, diagnostic statistics and plots. The Australian Bureau of Statistics has implemented the approach in a remote analysis system, which returns analysis output from remotely submitted queries. A framework for measuring disclosure risk associated with a remote server is proposed. The disclosure risk and utility of approach are measured in two real‐life case studies and in simulation.