Women's relative wage rate in The Netherlands, 1950–1983: A test of alternative discrimination theories

Summary This article presents the results of an inquiry into the relationship that is to be expected on theoretical grounds between women's wage rate compared to men's on the one hand and some macroeconomic variables on the other. Becker's discrimination theory is confronted with alternative theories. On the base of data for the Netherlands the authors investigate which theory is most suited to explain the development of women's relative wage rate for the period 1950–1983. They conclude that the results of the empirical analysis predominantly support Becker's theory.The authors wish to thank Mr. R. Haagsma and Professor C.K.F. Nieuwenburg for their comments on an earlier draft of this article. Furthermore, they are grateful to Miss M.J.R. Key for computational assistance.     

Making sense of corporate social responsibility in international business: experiences from Shell

International business organizations are regularly addressed on their corporate social responsibility (CSR). As illustrated in this paper, it is not yet clear exactly what CSR means to organizations and how to deal with it. In this paper, the authors explore how a sensemaking approach helps to understand the business challenges of CSR within an organizational context. The theories of Karl Weick are applied to the experiences of CSR in Royal Dutch Shell. The authors argue that the key to CSR in international business organizations is to engage stakeholders and start a process of joint sensemaking. Three main competencies are crucial in this: the competency to engage stakeholders through listening and understanding; the creation of an organizational language so that CSR makes sense to members of the organization; and recognizing the momentum of taking action.     

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.     

Municipal welfare functions

In this paper the concept of a municipal welfare function is defined. It reflects the evaluation by local authorities of several levels of local expenditures. On the basis of an extensive survey among all Dutch municipal authorities these functions are estimated for about 550 Dutch municipalities with respect to total expenditures and differentiated with respect to several portfolios, like public works, education, etc. The variation of the estimated municipal welfare parameters is explained by objectively measurable municipal characteristics like the number of the inhabitants, age distribution of inhabitants and houses, number of unemployed, regional situation.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Forecasting interest rates with shifting endpoints

We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time‐varying mean or ‘shifting endpoint’. The shifting endpoints are captured using either (i) time series methods (exponential smoothing) or (ii) long‐range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. Allowing for shifting endpoints in yield curve factors provides substantial and significant gains in out‐of‐sample predictive accuracy, relative to stationary and random walk benchmarks. Forecast improvements are largest for long‐maturity interest rates and for long‐horizon forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

We extend the class of dynamic factor yield curve models in order to include macroeconomic factors. Our work benefits from recent developments in the dynamic factor literature related to the extraction of the common factors from a large panel of macroeconomic series and the estimation of the parameters in the model. We include these factors in a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study, we use a monthly time series panel of unsmoothed Fama–Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relationship between the macroeconomic factors and the yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.     

How to investigate the information processing strategies of students in competence-based pre-vocational secondary education: selection of the right instrument

In the Netherlands, many Pre-Vocational Secondary Education schools are implementing elements of competence-based education. These learning environments are expected to elicit the use of deep information processing strategies and to positively influence learning outcomes. While questionnaires are often used to investigate the preferences of students for particular types of information processing strategies in other educational contexts, these instruments cannot simply be adopted unaltered for use in Pre-Vocational Secondary Education where several characteristics of the students must be taken into account. This study explores the psychometric properties of three instruments for the measurement of student preferences for deep or surface information processing strategies in competence-based Pre-Vocational Secondary Education. The utility of a semi-structured interview, a questionnaire, and the think-aloud method was investigated. The questionnaire appeared to be the most accurate instrument and allowed easy classification of students in terms of their information processing preferences. The think-aloud method provided profound insight into the information processing strategies that the students preferred for a learning task and the frequencies with which the strategies were used. The interview results largely corresponded to the results produced by the other measurement instruments, but the interview data lacked the expected richness and depth.     

Assumptions on Fertility in Stochastic Population Forecasts*

In recent years Statistics Netherlands has published several stochastic population forecasts. The degree of uncertainty of the future population is assessed on the basis of assumptions about the probability distribution of future fertility, mortality and migration. The assumptions on fertility are based on an analysis of historic forecasts of the total fertility rate (TFR), on time‐series models of observations of the TFR, and on expert knowledge. This latter argument‐based approach refers to the TFR distinguished by birth order. In the most recent Dutch forecast the 95% forecast interval of the total fertility rate in 2050 is assumed to range from 1.2 to 2.3 children per woman.