Visualising Africa at diaspora expense? How and why humanitarian organisations ignore diaspora audiences in their ‘ethical’ communications

Critical commentaries on visual constructions of distant suffering within humanitarian communication research have critiqued and problematised how International non-governmental organisations (INGOs) frame Black and Brown distant ‘Others.’ However, much focus has been on the implications of these mediated imageries for overseas communities, while African diaspora have received much less attention. African diaspora is critical in current debates around representation, especially given increased criticisms around the ethicality of INGO fundraising communications for UK-situated Black racialised publics. This article thus complicates existing debates by repositioning the empirical preoccupation with distant Others ‘over there,’ towards UK-situated African diaspora ‘over here.’ Using interview evidence with UK-based INGO professionals involved in the production and dissemination of imageries of humanitarian issues, it explores how INGOs construe African diaspora populations in considerations around, and implications of, the ethicality of their communications. Revealing that African diaspora are largely absent or ‘afterthoughts’ in INGO consciousness and practices for three central reasons. (1) They are not considered distinct and differentiated donor audiences worthy of strategic prioritisation, (2) Implicit professional/organisational ‘whiteness’ limits reflexive foresight of potential implications (negative or positive) of communications for diaspora and, (3) INGOs temporise over the (im)practicalities of ‘diaspora inclusive’ agendas for ethical communication.     

Dodd–Frank's impact on community-bank investment models: A Bayesian structural time series analysis

We use Bayesian structural time series (BSTS) methodology to test whether the Wall Street Reform and Consumer Protection Act of 2010 (DF) caused changes in community bank business models. The BSTS methodology uses the pre-DF period to create synthetic counterfactuals for community-bank dependent variables of interest. In the post-DF period, the counterfactuals become predictions of the dependent variables had DF not been enacted. Comparing post-DF predicted versus actual dependent variables allows us to estimate the causal impact of DF on these variables of interest. We find that relative to assets, community banks significantly reduce their lending activities and significantly increase investment in securities and excess reserves.     

Economic imperialism and resource nationalism: Comment on Kemp and Ohyama

Kemp and Ohyama show that a self-seeking resource-poor imperial economy can use tariffs to completely exploit its resource-rich raw-material producing colonial trading partner, but that when capital is immobile, the colonial economy loses all power to exploit by means of ad valorem trade taxes. We use geometry to explain why, and demonstrate that there are other instruments (export quotas, import quotas, an export tariff specified in units of the import per unit of the export, and destruction of the raw material by the colonial economy's government) which may restore the colonial economy's ability to exploit the imperial one.     

Enchantment in Business Ethics Research

Journal of Business Ethics - This article draws attention to the importance of enchantment in business ethics research. Starting from a Weberian understanding of disenchantment, as a force that...     

Correction: Managerial incentives and stock price dynamics: an experimental approach

Experimental Economics - Typo error noticed by author in acknowledgement.     

Sentiment,Loss Firms,and Investor Expectations of Future Earnings*

This study investigates the mispricing of market‐wide investor sentiment by exploring the relation between sentiment and investor expectations of future earnings. Prior research argues that sentiment‐driven mispricing should be most pronounced for hard‐to‐value firms, such as those reporting losses (Baker and Wurgler 2006). Using investor expectations of future earnings, we provide empirical results consistent with this behavioral finance theory. We predict and find that investors perceive losses to be more (less) persistent during periods of low (high) sentiment; that (in contrast) investors perceive profit persistence to be lower (higher) during periods of low (high) sentiment; and that the effects appear stronger for loss firms relative to profit firms. We also document predictable cross‐sectional variation within losses (with the mispricing mitigated for losses associated with activities expected to generate future benefits), R&D, growth, large negative special items, and severe financial distress. Overall, our results document a new and important channel—investor expectations of future earnings—to explain sentiment‐driven mispricing.     

The U.S. small business bankruptcy amendments: A global model for reform?

On February 19, 2020, the Small Business Reorganization Act of 2019 went into effect in the United States. This statute was intended to make the rescue regime of Chapter 11 of the United States Bankruptcy Code more effective for smaller businesses that would not otherwise have the financial wherewithal to complete a traditional Chapter 11 reorganization. This article describes the central innovations of the new statute, and considers whether they might be adaptable by other countries.     

The crucial role of financial intermediaries for facilitating trade among strangers

The Review of Austrian Economics - How are markets possible under conditions of anonymity and lack of repeat dealing? Many scholars consider the problem of fraud as one that must be dealt with by...     

Cohort versus patient level simulation for the economic evaluation of single versus combination immuno-oncology therapies in metastatic melanoma

Abstract

Background: Model structure, despite being a key source of uncertainty in economic evaluations, is often not treated as a priority for model development. In oncology, partitioned survival models (PSMs) and Markov models, both types of cohort model, are commonly used, but patient responses to newer immuno-oncology (I-O) agents suggest that more innovative model frameworks should be explored.

Objective: A discussion of the theoretical pros and cons of cohort level vs patient level simulation (PLS) models provides the background for an illustrative comparison of I-O therapies, namely nivolumab/ipilimumab combination and ipilimumab alone using patient level data from the CheckMate 067 trial in metastatic melanoma. PSM, Markov, and PLS models were compared on the basis of coherence with short-term clinical trial endpoints and long-term cost per QALY outcomes reported.

Methods: The PSM was based on Kaplan-Meier curves from CheckMate 067 with 3-year data on progression free survival (PFS) and overall survival (OS). The Markov model used time independent transition probabilities based on the average trajectory of PFS and OS over the trial period. The PLS model was developed based on baseline characteristics hypothesized to be associated with disease as well as significant mortality and disease progression risk factors identified through a proportional hazards model.

Results: The short-term Markov model outputs matched the 1–3?year clinical trial results approximately as well as the PSMs for OS but not PFS. The fixed (average) cohort PLS results corresponded as well as the PSMs for OS in the combination therapy arm and PFS in the monotherapy arm. Over the lifetime horizon, the PLS produced an additional 5.95 quality adjusted life years (QALYs) associated with combination therapy relative to ipilimumab alone, resulting in an incremental cost-effectiveness ratio (ICER) of £6,474 per QALY, compared with £14,194 for the PSMs which gave an incremental benefit of between 2.2 and 2.4 QALYs. The Markov model was an outlier (～ £49,000 per QALY in the base case).

Conclusions: The 4- and 5-state versions of the PSM cohort model estimated in this study deviate from the standard 3-state approach to better capture I-O response patterns. Markov and PLS approaches, by modeling state transitions explicitly, could be more informative in understanding I-O immune response, the PLS particularly so by reflecting heterogeneity in treatment response. However, both require a number of assumptions to capture the immune response effectively. Better I-O representation with surrogate endpoints in future clinical trials could yield greater model validity across all models.