Spatial crime patterns and the introduction of the UK minimum wage

In this paper we consider the connection between crime and the labour market in a different way to existing work. We focus on a situation where the introduction of a minimum wage floor to a labour market previously unregulated by minimum wage legislation provided substantial pay increases for low paid workers. From a theoretical perspective we argue that this wage boost has the potential to alter peoples’ incentives to participate in crime. We formulate empirical tests, based upon area-level data in England and Wales, which look at what happened to crime rates before and after the introduction of the national minimum wage to the UK labour market in April 1999. Comparing police force area-level crime rates before and after the minimum wage introduction produces evidence in line with the notion that changing economic incentives for low wage workers can influence crime

Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care

We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of either linear (logistic regression) or non-linear (random forests) classifiers. Experiments with data available from 2013 to 2017 for every citizen in Copenhagen receiving home care (27,775 citizens) show that prediction can achieve state of the art performance as reported in similar health related domains (AUC=0.715). We further find that competitive results can be obtained by using limited information for training, which is very useful when full records are not accessible or available. Smart city analytics does not necessarily require full city records. To our knowledge this preliminary study is the first to predict large increases in home care for smart city analytics

The benefits of limited feedback in organizations

In most firms, managers periodically assess workers' performance. Evidence suggests that managers withhold information during these reviews, and some observers argue that this necessarily reduces surplus. This paper assesses the validity of this argument when workers have career concerns. Disclosure has two effects: it exposes the worker to uncertainty about future effort levels, but allows him to use current effort to influence his employer's beliefs about future effort. The surplus-maximizing disclosure policy reveals output realizations in the center of the distribution, but not in the tails. Thus, it is efficient for firms to reveal some but not all performance information.Performance Appraisal, Career Concerns, Incentives, Risk.

Estimating bayesian decision problems with heterogeneous priors

In many areas of economics there is a growing interest in how expertise and preferences drive individual and group decision making under uncertainty. Increasingly, we wish to estimate such models to quantify which of these drive decision making. In this paper we propose a new channel through which we can empirically identify expertise and preference parameters by using variation in decisions over heterogeneous priors. Relative to existing estimation approaches, our \PriorBased Identi cation" extends the possible environments which can be estimated, and also substantially improves the accuracy and precision of estimates in those environments which can be estimated using existing methods

How Experts Decide : Identifying Preferences versus Signals from Policy Decisions

A large theoretical literature assumes that experts differ in terms of preferences and the distribution of their private signals, but the empirical literature to date has not separately identified them. This paper proposes a novel way of doing so by relating the probability a member chooses a particular policy decision to the prior belief that it is correct. We then apply this methodology to study differences between internal and external members on the Bank of England's Monetary Policy Committee. Using a variety of proxies for the prior, we provide evidence that they differ significantly on both dimensions. Key words: Bayesian decision making ; Committees ; Monetary policy JEL classification: D81 ; D82 ; E52

First Impressions Matter: Signalling as a Source of Policy Dynamics

We first establish that policymakers on the Bank of England's Monetary Policy Committee choose lower interest rates with experience. We then reject increasing confidence in private information or learning about the structure of the macroeconomy as explanations for this shift. Instead, a model in which voters signal their hawkishness to observers better fits the data. The motivation for signalling is consistent with wanting to control inflation expectations, but not career concerns or pleasing colleagues. There is also no evidence of capture by industry. The paper suggests that policy-motivated reputation building may be important for explaining dynamics in experts' policy choices.Signalling, learning, monetary policy

How Experts Decide: Identifying Preferences versus Signals from Policy Decisions

A large theoretical literature assumes that experts di ffer in terms of preferences and the distribution of their private signals, but the empirical literature to date has not separately identi ed them. This paper proposes a novel way of doing so by relating the probability a member chooses a particular policy decision to the prior belief that it is correct. We then apply this methodology to study diff erences between internal and external members on the Bank of England's Monetary Policy Committee. Using a variety of proxies for the prior, we provide evidence that they di ffer significantly on both dimensions.Bayesian decision making, committees, monetary policy

Sequence Modelling For Analysing Student Interaction with Educational Systems

The analysis of log data generated by online educational systems is an important task for improving the systems, and furthering our knowledge of how students learn. This paper uses previously unseen log data from Edulab, the largest provider of digital learning for mathematics in Denmark, to analyse the sessions of its users, where 1.08 million student sessions are extracted from a subset of their data. We propose to model students as a distribution of different underlying student behaviours, where the sequence of actions from each session belongs to an underlying student behaviour. We model student behaviour as Markov chains, such that a student is modelled as a distribution of Markov chains, which are estimated using a modified k-means clustering algorithm. The resulting Markov chains are readily interpretable, and in a qualitative analysis around 125,000 student sessions are identified as exhibiting unproductive student behaviour. Based on our results this student representation is promising, especially for educational systems offering many different learning usages, and offers an alternative to common approaches like modelling student behaviour as a single Markov chain often done in the literature.Comment: The 10th International Conference on Educational Data Mining 201

What Do Outside Experts Bring To A Committee? Evidence From The Bank of England

We test whether outside experts have information not available to insiders by using the voting record of the Bank of England's Monetary Policy Committee. Members with more private information should vote more often against conventional wisdom, which we measure as the average belief of market economists about future interest rates. We nd evidence that external members indeed have information not available to internals, but also use a quasi-natural experiment to show they may exaggerate their expertise to obtain reappointment. This implies that an optimal committee, even outside monetary policy, should potentially include outsiders, but needs to manage career concerns.Expert Behavior ; Committees ; Monetary Policy JEL Classification: D70 ; E52