The Performance Effects of European Demergers

This paper examines security price reactions of European demergers. For a period ranging from one and a half years prior to the demerger announcement through to three years after the execution date, the relative performance of the parent, spin-off and the combined effect is analysed relative to the overall market performance. Significant announcement effects were established for a sample of 48 European demergers. In addition, significant positive long-term value creation, in particular in year 2 after the demerger, was found for the spin-off but not for the parent firm. While size has, on average, a decisive but inverse impact on performance for both parent and spin-off, takeover activity does not.Corporate restructuring, demerger, spin-off

Corporate Ownership Structure and Performance in Europe

In this paper, we show that ownership structures vary considerably across Europe, and that the dominant form ofownership is not necessarily the most efficient one. These findings are in contradiction to similar research basedon US samples. The results also demonstrate that firms without a dominant shareholder tend to outperform theircountry peer groups. We base our analysis on a new and unique dataset of uniform ownership data of the largest100 firms in the five major European economies. We quantify the differences in ownership by comparing threedistinct ownership structures of firms and relating them to performance. For the first time we employ aHodrick-Prescott Filter, a methodology widely used in macroeconomics to isolate the trend growth componentsfrom cyclical fluctuations, to estimate the share price trend of each firm. We take this trend as a good indirectindicator of the quality of governance.Corporate governance, ownership structures, performance, Europe

Corporate Restructuring and Firm Performance of British and German Non-Financial Firms

This paper examines the ongoing changes in strategy, structure, and performance of the largest 250 non-financial firms in both Britain and Germany. To this end, publicly available firm-level data is presented at first and supplemented by the results of a questionnaire survey that was sent to the chief executives of those companies. What came through from the survey was that many firms in both countries are driven by the desire to specialise and internationalise and are primarily achieving this via ¿horizontal¿ mergers and acquisitions. While seeing a definite convergence in certain areas, clear and distinctive differences remain between the two countries.

Home is not a safe place for everyone: domestic abuse between partners increased during lockdown

What has happened to the prevalence and nature of domestic abuse during lockdown? Crime economists Ria Ivandic and Tom Kirchmaier collaborated with the Strategic Insights Unit (SIU), from the Metropolitan Police, to answer this question by analysing data from calls to the police and recorded crime in London

Shareholders’ votes on CEO pay focus mostly on top-line figures

They disregard a wealth of information, write Carsten Gerner-Beuerle and Tom Kirchmaie

Women on boards in finance and STEM industries

We document that women are less represented on corporate boards in Finance and more traditional STEM industry sectors. Even after controlling for differences in firm and country characteristics, average diversity in these sectors is 24% lower than the mean. Our findings suggest that well-documented gender differences in STEM university enrolments and occupations have long-term consequences for female business leadership. The leadership gap in Finance and STEM may be difficult to eliminate using blanket boardroom diversity policies. Diversity policies are also likely to have a different impact on firms in these sectors than in non-STEM sectors

Why bike thefts, anti-social behaviour and drug offences have gone up during the pandemic

How has crime evolved in England and Wales during the pandemic? Tom Kirchmaier and Carmen Villa-Llera (Centre for Economic Performance, LSE) look at the effects of lockdown and job loss on the kinds of crimes being committed, and the ability of police to detect them

Changing patterns of domestic abuse during Covid-19 lockdown

The effects of preventing a COVID-19 health crisis have had unintended consequences on domestic abuse (DA) victimization. We contribute to the literature on domestic abuse in lockdown by providing insight on how changing patterns of domestic abuse can explain differences in magnitudes reported across studies. We examine the patterns of domestic abuse during the COVID-19 lockdown in Greater London and find that the lockdown changed the nature of reporting and the type of relationship the abuse occurs within. While abuse by current partners as well as family members increased on average by 8.1% and 17.1% respectively over the lockdown period, abuse by ex-partners declined by 11.4%. These findings show that reporting the average change in domestic abuse during lockdown can be misleading when designing a policy response. Moreover, we show that all the increase in domestic abuse calls is driven by third party reporting, particularly evident in areas with high density. This suggests that under reporting is present in the lockdown, particularly in households where the abuse cannot be reported by an outsider. Although these findings pertain to the COVID-19 lockdown, they also highlight the role that victim exposure and proximity has in affecting domestic abuse

Commuting for crime

People care about crime, with the spatial distribution of both actual and perceived crime affecting the amenities from living in different areas and residential decisions. The literature finds that crime tends to happen close to the offender’s residence but does not clearly establish whether this is because the location of likely offenders and crime opportunities are close to each other or whether there is a high commuting cost for criminals. We use a rich administrative dataset from one of the biggest UK police forces to disentangle these two hypotheses, providing an estimate of the cost of distance and how local socio-economic characteristics affect both crimes that are committed and the offenders’ location. We find that the cost of distance is very high and has a great deterrence effect. We also propose a procedure for controlling for the selection bias induced by the fact that offenders’ location is only known when they are caught

Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases

We compare predictions from a conventional protocol-based approach to risk assessment with those based on a machine-learning approach. We first show that the conventional predictions are less accurate than, and have similar rates of negative prediction error as, a simple Bayes classifier that makes use only of the base failure rate. A random forest based on the underlying risk assessment questionnaire does better under the assumption that negative prediction errors are more costly than positive prediction errors. A random forest based on two-year criminal histories does better still. Indeed, adding the protocol-based features to the criminal histories adds almost nothing to the predictive adequacy of the model. We suggest using the predictions based on criminal histories to prioritize incoming calls for service, and devising a more sensitive instrument to distinguish true from false positives that result from this initial screening