Can cities be held responsible for early school leaving? Evidence from the Netherlands

This paper examines if ‘naming and shaming’ is an effective tool to increase accountability in school dropout for cities with disadvantaged student populations. It argues that a comparison with other cities might be unfair if regional and population characteristics differ. It discusses the example of two Dutch new towns. The new town policy deliberately attracted low- and medium-income households in the past, such that today the population of those cities differs from other cities. We use a matching analysis to account for observed differences in population and regional characteristics. The results point out that ‘naming and shaming’ may be a dangerous policy to increase accountability: early school leaving differences are driven, to a large extent, by observed differences in population and regional characteristics.     

Variance bounds for estimators in autoregressive models with constraints

We consider nonlinear and heteroscedastic autoregressive models whose residuals are martingale increments with conditional distributions that fulfil certain constraints. We treat two classes of constraints: residuals depending on the past through some function of the past observations only, and residuals that are invariant under some finite group of transformations. We determine the efficient influence function for estimators of the autoregressive parameter in such models, calculate variance bounds, discuss information gains, and suggest how to construct efficient estimators. Without constraints, efficient estimators can be given by weighted least squares estimators. With the constraints considered here, efficient estimators are obtained differently, as one-step improvements of some initial estimator, similarly as in autoregressive models with independent increments.     

The Science of Decision-making: Behaviours Related to Gambling

Abstract

The behaviour analysis of decision-making has studied three realms of behaviour that play a role in gambling: risk taking, behavioural persistence and self-control. Research from our laboratory and others suggests that in all three areas, the manner in which choices are presented to subjects is of central importance. The paper argues that a behavioural perspective may be useful in understanding the factors controlling gambling and compares this perspective with one based on a cognitive orientation.     

Root n consistent and optimal density estimators for moving average processes

Abstract.  The marginal density of a first order moving average process can be written as a convolution of two innovation densities. Saavedra & Cao [Can. J. Statist. (2000), 28, 799] propose to estimate the marginal density by plugging in kernel density estimators for the innovation densities, based on estimated innovations. They obtain that for an appropriate choice of bandwidth the variance of their estimator decreases at the rate 1/ n . Their estimator can be interpreted as a specific U -statistic. We suggest a slightly simplified U -statistic as estimator of the marginal density, prove that it is asymptotically normal at the same rate, and describe the asymptotic variance explicitly. We show that the estimator is asymptotically efficient if no structural assumptions are made on the innovation density. For innovation densities known to have mean zero or to be symmetric, we describe improvements of our estimator which are again asymptotically efficient.     

Survey nonresponse and the distribution of income

The paper examines the distributional implications of selective compliance in sample surveys, whereby households with different incomes are not equally likely to participate. Poverty and inequality measurement implications are discussed for monotonically decreasing and inverted-U compliance-income relationships. We demonstrate that the latent income effect on the probability of compliance can be estimated from information on response rates across geographic areas. On implementing the method on the Current Population Survey for the U.S. we find that the compliance probability falls monotonically as income rises. Correcting for nonresponse appreciably increases mean income and inequality, but has only a small impact on poverty incidence up to poverty lines common in the U.S. Martin Ravallion: Corresponding author.     

Will oil markets tighten again? A survey of policies to manage possible oil supply disruptions

The effects of an oil supply disruption on the U.S. economy are analyzed along with the various policy options. Part of the analysis is based on a computerized model of the U.S. economy and the world oil market. Micro as well as macroeconomic issues are considered. The paper argues against the use of price controls and allocations and also against a disruption tariff used as a stand-alone measure. It advocates the use of some stabilizing macroeconomic measures and a temporary producer tax similar to the windfall profits tax.     

Analysis of the relationship between air pollution and asthma

In recent years, considerable attention has been devoted to methods for analyzing longitudinal data. A review of some of the issues surrounding the use of regression analysis is undertaken as they relate to a study of the health effects of air pollution in a group of asthmatics. Daily diary recordings of hours with asthma symptoms are found to be positively related to the daily mean concentration of total reduced sulphur as well as to the daily intake of nonsteroidal drug medication. This finding is corroborated using three different statistical models.     

The use of canonical analysis

The first part of this non-technical review of canonical analysis is concerned with the principle, the data requirements, the interpretation, the evaluation and the application in consumer research of canonical analysis. In this context, concepts as canonical correlation, weights, loadings and scores are explained. Their interrelationships are discussed. Then the characteristics of specific forms of canonical analysis: canonical correlation, canonical regression, redundancy analysis and partial canonical analysis are discussed. Their different application properties are emphasized. The last part consists of an application of canonical correlation analysis for brand positioning. Here the relationship with discriminant analysis is illustrated. Furthermore the usage of canonical analysis for optimal scaling purposes is illustrated for the same example.     

Leveraging Open Innovation Using Intermediary Networks

Open innovation, fuelled by the rise of the Internet, has made it feasible and cheaper for firms to open themselves up to a wide range of external sources of innovative ideas. The explosive growth of open innovation intermediary networks, such as InnoCentive or Linked‐in, enables the rapid pairing of firms seeking knowledge to address a wide range of business challenges (seekers) with other firms or individuals who already have relevant knowledge (solvers or knowledge brokers). These intermediary networks allow procurement departments to source codified and un‐codified knowledge from firms or individuals outside their traditional supplier networks using one‐off transactional relationships. Although sourcing ideas in this way theoretically poses problems for knowledge search and transfer, we have found that companies can draw on processes and integration mechanisms developed by procurement and design engineering to develop effective organizational learning routines. These routines are strategically vital to source new ideas through open innovation using intermediary networks and create competitive advantage.     

Abstractions on steroids: A critical realist approach to computer simulations

Computer simulations are increasingly used within various fields of the social sciences, including analytical sociology and computational social science. However, the method has yet to fully find its place within critical realism, generally regarded as reductionist and methodological individualist and thus inadequate to the task of grasping the full and enigmatic complexity of social life. This article contributes to the discussion by suggesting an alternative approach to computer simulations in the social realm. Instead of representing reality, simulations are seen as a way of boosting abstraction by enabling us to isolate and study the effects of emergent mechanisms. The need for such tools is growing more urgent in an age characterized by rapid change and global networks of causal interrelation. Hence there is a need not only for new tools to deal with causal complexity within critical realism, but also for a critical realist perspective to fill the meta‐theoretical vacuum on which the simulation approach is largely based. The approach developed here provides a challenge to the predominant ways in which simulations are utilized today, with increasingly sophisticated models aiming towards realistic representation on the basis of empirical data.