Four essays on UK takeovers : evidence from matching analysis

In four empirical chapters, matching analysis is employed to estimate the effects of specific contractual and regulatory arrangements on particular deal outcomes in the UK takeover market. The first chapter highlights the positive effect of earnout financing on the acquiring firms' returns in private target acquisitions. Furthermore, this chapter offers a detailed example of how the non-parametric Propensity Score Matching, despite its growing popularity in financial research, can lead to inaccurate inferences when relevant private-target-specific factors are omitted from the analysis. The second chapter provides the first empirical examination of the effect of the earnout's terms on the premium offered to the target firm's shareholders, and how information asymmetry concerns influence this premium. Additionally, the findings indicate that increases in the premia are negatively interpreted by the market in non-earnout financed deals. However, this negative effect is neutralised in comparable earnout financed deals. The third chapter provides the first empirical contribution that highlights the deal- and firm-related factors that contribute to the growing reliance on the Scheme of Arrangement, as a substitute for the Contractual Offer, in conducting UK public target deals. Despite the concerns raised in the legal literature about the limited bargaining power of the target shareholders under the Scheme, the robust conclusions indicate that such shareholders manage to receive premia that are at least as high as the premia received by shareholders in comparable Offer deals. The fourth chapter employs a hand-collected dataset that covers the incidences of termination fee use in the UK takeover market. The main result is that, in the period preceding the ban that The Panel on Takeovers and Mergers had imposed on termination fees, the inclusion of these fees had a beneficial, or at worst neutral, effect on target shareholders' wealth. Consequently, it is recommended that the Panel ends its ban

The earnout structure matters : takeover premia and acquirer gains in earnout financed M&As

In this article, based on both parametric and non-parametric methods, we provide a robust solution to the long-standing issue on how earnouts in corporate takeovers are structured and how their structure influences the takeover premia and the abnormal returns earned by acquirers. First, we quantify the effect of the terms of earnout contract (relative size and length) on the takeover premia. Second, we demonstrate how adverse selection considerations lead the merging firms to set the initial payment in an earnout financed deal at a level that is lower than, or equal to, the full deal payment in a comparable non-earnout financed deal. Lastly, we show that while acquirers in non-earnout financed deals experience negative abnormal returns from an increase in the takeover premia, this effect is neutralised in earnout financed deals

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Distraction by the Release of Economic Indicators and the Wealth Effects of Takeovers

We show that acquirers exploit the market’s distraction by newly released economic indicators to announce value-destroying acquisitions. This strategy is effective in private target acquisitions due to the difficulty of valuing private companies. Acquirers in value-destroying private target acquisitions are more likely than public target acquirers to time their announcements during days when key economic indicators are released. After correcting for selection bias, private target acquirers during the time of the release of key economic indicators experience negative abnormal returns. Because market distraction reduces the initial negative reaction, the magnitude of these losses becomes more noticeable during the post-acquisition period