Research
Research
Causal inference with observational data: A tutorial on propensity score analysis (with J.D. Tena and Claudio Ditotto) in The Leadership Quarterly, 2023
Abstract
When treatment cannot be manipulated, propensity score analysis provides a useful way to making causal claims under the assumption of no unobserved confounders. However, it is still rarely utilised in leadership and applied psychology research. The purpose of this paper is threefold. First, it explains and discusses the application and key assumptions of the method with a particular focus on propensity score weighting. This approach is readily implementable since a weighted regression is available in most statistical software. Moreover, the approach can offer a “double robust” protection against misspecification of either the propensity score or the outcome model by including confounding variables in both models. A second aim is to discuss how propensity score analysis (and propensity score weighting, specifically) has been conducted in recent management studies and examine future challenges. Finally, we present an advanced application of the approach to illustrate how it can be employed to estimate the causal impact of leadership succession on performance using data from Italian football. The case also exemplifies how to extend the standard single treatment analysis to estimate the separate impact of different managerial characteristic changes between the old and the new manager.
Managerial Contribution to Firm Success: Evidence from Professional Football Leagues (with Benjamin Holmes and Ian McHale)
Abstract
Previous research in economics and management finds significant heterogeneity across senior managers in their contribution to organisational success. It remains, however, challenging to disentangle the impact of managers from that of other inputs, such as labour, due to limited data availability in many general organisations. In contrast, individual workers (players)’ performance and their characteristics are publicly available in professional football leagues. Therefore, this study employs data from the industry to estimate individual managers’ contributions to field performance, given the resources at hand. To measure a club’s output, we adopt expected goals, which are less influenced by randomness than conventional measures. Controlling for players’ quality based on their historical performance as well as a club’s financial strengths, we yet find significant impacts of managerial inputs. In addition, we compare our estimated manager coefficients with a more naive measure of managerial performance, such as winning percentage. This highlights the importance of taking into account the differences in resources that a manager has at his disposal as well as the randomness of the outcome when evaluating a manager’s performance.
Causes and Consequences of Multiple Dismissals: Evidence from Italian Football League (with Babatunde Buraimo and J.D. Tena)
Abstract
Previous research in leadership succession focuses on establishing whether such an event has a positive impact on the subsequent performance of an organisation. However, factors that can affect the effectiveness of leadership change are not well understood. The aim of this study is to identify the causes of first and second within-season head coach dismissals and estimate the impact of the two types of dismissal on field performance using data from the Italian professional football league (Serie A). We employ inverse propensity score weighting together with machine learning techniques in order to mitigate selection bias. Our analysis shows that the determinants of the two decisions are not identical in that the second replacement is likely to be taken with greater caution. Despite this, we find some positive effects of first dismissals on subsequent performance, whilst the second dismissals do not appear to make any difference. These findings suggest that frequent changes in leadership are not favourable options even when a recent replacement has not improved the situation. This could be because the potential benefit of leadership replacement may be counteracted by disruptive effects, or a source of underperformance may lie elsewhere rather than a manager.