Cointegration and control: Assessing the impact of events using time series data

Control groups can provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. We argue that fitting a multivariate time series model offers potential gains over a direct comparison between the target and a weighted average of controls. More importantly, it highlights the assumptions underlying methods such as difference in differences and synthetic control, suggesting ways to test these assumptions. Gains from simple and transparent time series models are analysed using examples from the literature, including the California smoking law of 1989 and German reunification. We argue that selecting controls using a time series strategy is preferable to existing data‐driven regression methods.     

Brexit: How Will UK Agriculture Fare?

There is little doubt that Brexit would have significant implications for UK agriculture, a sector with strong trade links to the EU and strong reliance on CAP income support. This article reports preliminary results from employing a Computable General Equilibrium Model, a Partial Equilibrium Model and Farm Level Models to explore selected trade and domestic policy scenarios post‐Brexit. These allow for the estimation of changes in producer prices, production and farm incomes against a baseline scenario of continued EU membership. Under a Free Trade Agreement with the EU, agricultural impacts are relatively modest. By contrast, unilateral removal of import tariffs has significant negative impacts on prices, production and incomes. Adoption of the EU's WTO tariff schedule for all imports favours net importers (e.g. dairy) and harms net exporters (e.g. sheep). Given the strong dependence of most UK farms on direct payments, their removal worsens negative impacts of new trade arrangements and offsets positive impacts. Impacts vary across different types and sizes of farm, but also regionally. However, the period of adjustment to new trade and domestic policy conditions may prove very challenging for a large number of farm businesses.     

An Analysis of the Factor Structure of Jones’ Moral Intensity Construct

In 1991, Jones developed an issue-contingent model of ethical decision making in which moral intensity is posited to affect the four stages of Rest’s 1986 model (awareness, judgment, intention, and behavior). Jones claimed that moral intensity, which is “the extent of issue-related moral imperative in a situation” (p. 372), consists of six characteristics: magnitude of consequences (MC), social consensus (SC), probability of effect (PE), temporal immediacy (TI), proximity (PX), and concentration of effect (CE). This article reports the findings of two studies that analyzed the factor structure of moral intensity, operationalized by a 12-item Perceived Moral Intensity Scale (PMIS) adapted from the work of Singhapakdi et al. [1996, Journal of Business Research, 36, 245–255] and Frey [2000, Journal of Business Ethics, 26, 181–195]. The two items that were purported to measure CE were dropped due to their inability to effectively tap into the characteristic proposed by Jones. Factor analyses of the remaining 10 items supported a 3-factor structure, with the MC, PE, and TI items loading on the first factor, the PX items loading on the second factor, and the SC items loading on the third factor. These factors were labeled: Probable Magnitude of Consequences, Proximity, and Social Consensus. The authors conclude that moral intensity consists of three characteristics, rather than the six posited by Jones.     

The Effect of Moral Intensity on Ethical Judgment

Following an extensive review of the moral intensity literature, this article reports the findings of two studies (one between-subjects, the other within-subject) that examined the effect of manipulated and perceived moral intensity on ethical judgment. In the between-subjects study participants judged actions taken in manipulated high moral intensity scenarios to be more unethical than the same actions taken in manipulated low moral intensity scenarios. Findings were mixed for the effect of perceived moral intensity. Both probable magnitude of consequences (a factor consisting of magnitude of consequences, probability of effect, and temporal immediacy) and social consensus had a significant effect; proximity did not. In the within-subject study manipulated moral intensity had a significant effect on ethical judgment, but perceived moral intensity did not. Regression of ethical judgment on age, gender, major, and the three perceived moral intensity factors was significant between-subjects, but not within-subject. Ethical judgment was found to be a more robust predictor of intention than perceived moral intensity using a within-subject design. Joan M. McMahon is an Assistant Professor of Management in the Luter School of Business at Christopher Newport University, teaching courses in Organizational Behavior, Leadership, and Human Resources. She has a B.A. in Speech from the State University of New York, College at Oneonta; an M. Ed. in Early Childhood Education from James Madison University; and an M.S. and Ph.D. in Industrial/Organizational Psychology from Virginia Polytechnic Institute and State University. Robert J. Harvey is an Associate Professor of Psychology at Virginia Polytechnic Institute and State University. He has a B.A. in Psychology and an M.A. in Experimental Psychology from the University of Missouri at Kansas City, and a Ph.D. in Industrial/Organizational Psychology from Ohio State University. Dr. Harvey has authored a number of articles in the Journal of Applied Psychology, the Journal of Personality Assessment, Personnel Psychology, and others. He is the author of the chapter on job analysis in the Handbook of Industrial and Organizational Psychology.     

Confidence sets for the date of a break in level and trend when the order of integration is unknown

We propose methods for constructing confidence sets for the timing of a break in level and/or trend that have asymptotically correct coverage for both I(0) and I(1) processes. These are based on inverting a sequence of tests for the break location, evaluated across all possible break dates. We separately derive locally best invariant tests for the I(0) and I(1) cases; under their respective assumptions, the resulting confidence sets provide correct asymptotic coverage regardless of the magnitude of the break. We suggest use of a pre-test procedure to select between the I(0)- and I(1)-based confidence sets, and Monte Carlo evidence demonstrates that our recommended procedure achieves good finite sample properties in terms of coverage and length across both I(0) and I(1) environments. An application using US macroeconomic data is provided which further evinces the value of these procedures.     

Learning and development expertise: an Australian analysis

Learning and development (L&D) practitioners draw on a distinctive range of knowledge, skills and techniques in their work. Over the years, there have been attempts to capture this range and identify typical L&D roles. The research presented here was undertaken to identify characteristic areas of expertise (AOEs) of L&D practice in Australia, and to collect data about roles and organizational settings in which these AOEs are deployed. The research was commissioned by the Australian Institute of Training and Development. Literature relating to L&D was consulted, and Australian L&D experts were interviewed to draft a list of L&D AOEs. Responses to a survey by 589 Australian L&D practitioners were used to appraise the model. A principal components analysis of perceptions of the importance of the different types of expertise revealed that practitioners tended to rate one of three ‘clusters’ as important to their work: a strategy and analysis cluster, a learning facilitation cluster and a design and systems cluster. This analysis raises the question of contemporary L&D roles with implications for strategic HRD policy and professional associations. The typology may be utilized to target professional L&D activities, and provides impetus for further research to investigate the internal relationships between cluster components.     

Schadenfreude: The (not so) Secret Joy of Another’s Misfortune

Despite growing interest in emotions, organizational scholars have largely ignored the moral emotion of schadenfreude, which refers to pleasure felt in response to another’s misfortune. As a socially undesirable emotion, it might be assumed that individuals would be hesitant to share their schadenfreude. In two experimental studies involving emotional responses to unethical behaviors, we find evidence to the contrary. Study 1 revealed that subjects experiencing schadenfreude were willing to share their feelings, especially if the misfortune was perceived to be deserved (i.e., resulting from unethical behaviors). Study 2 extends this work by incorporating schadenfreude targets of different status (CEO versus employee). Consistent with the “tall poppy syndrome,” subjects were more willing to share schadenfreude concerning high status targets than low status targets when the perceived severity of the target’s misconduct was low. This status effect disappeared at higher levels of perceived deservingness, however. Reported willingness to share schadenfreude was strongest at these levels but did not differ significantly between high and low status targets. These findings build on the social functional account of emotions, suggesting that sharing schadenfreude may signal normative cues to others regarding workplace behaviors that are deemed to be unethical.     

Asymptotic behaviour of tests for a unit root against an explosive alternative

We compare the asymptotic local power of upper-tail unit root tests against an explosive alternative based on ordinary least squares (OLS) and quasi-differenced (QD) demeaning/detrending. We find that under an asymptotically negligible initialisation, the QD-based tests are near asymptotically efficient and generally offer superior power to OLS-based approaches; however, the power gains are much more modest than in the lower-tail testing context. We also find that asymptotically non-negligible initial conditions do not affect the power ranking in the same way as they do for lower-tail tests, with the QD-based tests retaining a power advantage in such cases.     

Unit Root Testing under a Local Break in Trend using Partial Information on the Break Date*

We consider unit root testing allowing for a break in trend when partial information is available regarding the location of the break date. This takes the form of knowledge of a relatively narrow window of data within which the break takes place, should it occur at all. For such circumstances, we suggest employing a union of rejections strategy, which combines a unit root test that allows for a trend break somewhere within the window with a unit root test that makes no allowance for a trend break. Asymptotic and finite sample evidence shows that our suggested strategy works well, provided that, when a break does occur, the partial information is correct. An empirical application to UK interest rate data containing the 1973 ‘oil shock’ is also considered.