Measurement, model testing, and legislative influence in the European Union

Within the last several years, new data have become available to test the various theoretical models of EU decision-making, and, in doing so, to assess actor influence. This article examines the extent to which the recent DEU and DEUII datasets provide sufficient information to distinguish between competing theoretical models of legislative decision-making, and accurately assess the power of the different branches of EU government. It argues that insufficient attention has been paid to measurement error in these data. Once measurement error is accounted for, it becomes clear that these data do not provide sufficient information to distinguish between most models of legislative politics. Moreover, empirical models that fail to account for measurement error are likely to lead researchers to erroneous conclusions about actors’ legislative influence. </jats:p

Robust Estimation for Linear Panel Data Models

In different fields of applications including, but not limited to, behavioral, environmental, medical sciences and econometrics, the use of panel data regression models has become increasingly popular as a general framework for making meaningful statistical inferences. However, when the ordinary least squares (OLS) method is used to estimate the model parameters, presence of outliers may significantly alter the adequacy of such models by producing biased and inefficient estimates. In this work we propose a new, weighted likelihood based robust estimation procedure for linear panel data models with fixed and random effects. The finite sample performances of the proposed estimators have been illustrated through an extensive simulation study as well as with an application to blood pressure data set. Our thorough study demonstrates that the proposed estimators show significantly better performances over the traditional methods in the presence of outliers and produce competitive results to the OLS based estimates when no outliers are present in the data set

Representative bureaucracy: does female police leadership affect gender-based violence arrests?

Representative bureaucracy theory postulates that passive representation leads to active representation of minority groups. This article investigates the passive representation of female police officers at leadership levels and the active representation of women vis-a-vis gender-based violence arrest rates in the UK. Much of the extant research on representative bureaucracy is located at street level, with evidence showing that discretionary power of minority bureaucrats can lead to active representation. This article is focused on leadership levels of a public bureaucracy. The empirical research is based upon a panel dataset of female police officers as an independent variable and gender-based violence arrest rates as a dependent variable. The analysis reveals that there is little evidence of active representation of women by female police leadership

Danish Aid Policy:Theory and Empirical Evidence

This paper is a study of Danish aid policy from the early 1960s to 1995. It includes (i) a review of officially stated aims and criteria, (ii) a descriptive analysis of actual behaviour in international comparative perspective, (iii) a review of the theoretical and empirical aid allocation literature, and (iv) a series of panel data regressions to further explore how Danish bilateral aid was, in actual fact, distributed country-by-country. A theoretical model explaining how the allocation process took place is also formulated. It underpins the empirical analysis from which it transpires that a two step model is a useful way of analysing Danish aid allocations. The first step is whether to select a country or not, and the second involves the decision of how much aid to commit. The empirical analysis demonstrates that Danish aid has been guided in both steps by officially stated aims and criteria in an expected and statistically significant manner although a clear Eastern and Southern Africa bias was found. Another general result is that the relative weights of the explanatory variables have varied both from year-to-year and between sub-periods

Addressing the sample size problem in behavioural operational research: simulating the newsvendor problem

Laboratory-based experimental studies with human participants are beneficial for testing hypotheses in behavioural operational research. However, such experiments are not without their problems. One specific problem is obtaining a sufficient sample size, not only in terms of the number of participants but also the time they are willing to devote to an experiment. In this paper, we explore how agent-based simulation (ABS) can be used to address the sample size problem and demonstrate the approach in the newsvendor setting. The decision-making strategies of a small sample of individual decision-makers are determined through laboratory experiments. The interactions of these suppliers and retailers are then simulated using an ABS to generate a large sample set of decisions. With only a small number of participants, we demonstrate that it is possible to produce similar results to previous experimental studies that involved much larger sample sizes. We conclude that ABS provides the potential to extend the scope of experimental research in behavioural operational research

Measuring persistent and transient energy efficiency in the US

The promotion of US energy efficiency policy is seen as a very important activity. Generally, the level of energy efficiency of a country or state is approximated by energy intensity, commonly calculated as the ratio of energy use to GDP. However, energy intensity is not an accurate proxy for energy efficiency given that changes in energy intensity are a function of changes in several factors including the structure of the economy, climate, efficiency in the use of resources, behaviour and technical change. The aim of this paper is to measure persistent and transient energy efficiency for the whole economy of 49 states in the US using a stochastic frontier energy demand approach. A total US energy demand frontier function is estimated using panel data for 49 states over the period 1995 to 2009 using two panel data models: the Mundlak version of the random effects model (which estimates the persistent part of the energy efficiency) and the true random effects model (which estimates the transient part of the energy efficiency). The analysis confirms that energy intensity is not a good indicator of energy efficiency, whereas, by controlling for a range of economic and other factors, the measures of energy efficiency obtained via the approach adopted here are. Moreover, the estimates show that although for some states energy intensity might give a reasonable indication of a state’s relative energy efficiency, this is not the case for all states.ISSN:1570-646XISSN:1570-647