Capital Allocation: Evidence,Analytical Methods,and Assessment Guidance

Capital allocation is one of top management's primary responsibilities. Although always important, it is critical today because corporate operating returns on invested capital are at an all‐time high, while recent growth and investment have been modest, and corporate balance sheets in the U.S. have substantial cash. Yet few senior executives are sufficiently well‐versed in finance theory and methods to allocate capital as effectively as possible. Further, incentive programs that focus on meeting earnings per share often encourage behavior that is not in the best interests of long‐term shareholders. In this report, the authors begin with the premise that the goal of corporate capital allocation is to build long‐term value per share; and with that view in mind, they examine the main sources and uses of capital by the largest 1,500 U.S. companies during the last 30 years. More specifically, the authors identify the amounts of capital allocated to each of seven important alternatives, including major uses of capital such as M&Amp;A, capital expenditures, R&D, and distributions of capital to investors such as dividends and stock repurchases. And after reviewing the past allocations of capital to each of these alternatives, the authors summarize the academic research on the effects on corporate values of each of these uses of capital. The authors report that U.S. corporations fund most of their investments internally, and that M&Amp;A and capital expenditures have long been, and continue to be, the largest operating uses of capital, though both capital expenditures and growth in assets have fallen in recent years. At the same time, both corporate cash holdings and distributions to shareholders in the form of dividends and stock buybacks are at record levels. But even with such high payouts, R&D spending as a percentage of revenue by U.S. companies has remained high, and actually increased during the past decade. Finally, the authors provide a framework that can be used either internally or by outsiders to evaluate the capital allocation practices and effectiveness of a management team. This framework asks management to assess its past performance, provide realistic projections of future returns on invested capital, and evaluate their own incentive programs—all while renewing their commitment to the five principles of thoughtful capital allocation: (1) zero‐based capital allocation; (2) funding of strategies, not projects; (3) no capital rationing; (4) zero tolerance for bad growth; and (5) continuous monitoring of the value of all assets and business, and willingness to take action if and when such values are larger outside than inside the firm.     

An N-dimensional generalization of the Amiti–Weinstein estimator

Amiti and Weinstein proposed an estimation framework to disentangle credit demand and supply shocks using matched bank-firm loan data. Here, we show that their estimator can be generalized to capture shocks arising in an arbitrary number of dimensions. Our algorithm permits empirical researchers to analyse multi-dimensional data sets using the Amiti–Weinstein framework. This may be beneficial both for studies on micro-level outcomes as well as for the literature on assessing the macroeconomic impact of idiosyncratic shocks. In an empirical application to a firm-product-country export data set, we highlight the usefulness of the generalized Amiti–Weinstein estimator, and we demonstrate the importance of considering additional dimensions when gauging the effect of granular shocks on aggregate fluctuations.     

COVID-19 impact on fruit and vegetable markets

Canadian fruit and vegetable markets were significantly impacted by the spread of the novel coronavirus SARS-CoV-2 (and COVID-19 disease), beginning in March 2020. Due to the closure of restaurants, bars, and schools, produce growers and distributors were forced to shift supplies almost entirely from the foodservice to the retail channel. Shippers reported labor and logistical constraints in making the change, but the fresh produce supply chain remained robust. In the long term, we expect lasting changes in consumers’ online food-purchasing habits, heightened constraints on immigrant labor markets, and tighter concentration in fresh produce distribution and perhaps retailing.     

Connecting strategic orientation,innovation strategy,and corporate sustainability: A model for sustainable development through stakeholder engagement

This paper aims to enhance our understanding of the relationship between organizations' strategic orientation (i.e., instrumental, equidistant, and stewardship) and their innovational strategy (i.e., organizational ambidexterity and open innovation), and this relationship's influence on their sustainability performance. We expected organizations' strategic orientation to influence their innovational strategy and exhibit better corporate sustainability. We focused on 12 different multinational organizations in the Eurozone and found that inbound open innovation acts as a driver for corporate sustainability while simultaneously positively enabling organizations to pursue both financial and social initiatives. After suggesting open innovation as a new corporate sustainability antecedent, we only found empirical evidence of inbound open innovation. However, we would suggest that if organizations incorporated outbound open innovation in their strategy, they could likely improve their corporate sustainability. Also, we found that achieving an equidistant or stewardship orientation paves the way for sustainability to become an integral part of an organization's innovational culture and actualizes the organization's strategic behavior. Our findings further contribute to a deeper understanding of organizations' characteristics as their sustainability commitment grows and as they move from an instrumental to a stewardship orientation and implement an open innovational strategy.     

Reluctant Generalised Additive Modelling

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.     

The effect of entrepreneurship education in schools on entrepreneurial outcomes: a systematic review

Management Review Quarterly - This study systematically reviewed 21 quantitative and qualitative articles on the effect of entrepreneurship education in schools. We reviewed short-term effects,...     

When is escalation of commitment unstoppable in group settings? An iterative economic modeling approach to unveil the dark side of group decision-making

Escalation of commitment (EOC) has been usually studied from a psychological lens, and only recently have scholars approached EOC from an economic perspective. We contribute to this by focusing on iterative decision-making in group settings with a game theory approach. We study how the group members' strategic interaction may result in continuing failing courses of action. Drawing on the Byzantine generals' problem, our model considers an iterative decision-making process where committee participants vote based on private information to escalate or not. Our article demonstrates that if decision-makers reset their beliefs based on the committee's previous decision, then EOC becomes perpetual.     

Ensuring survey research data integrity in the era of internet bots

Quality & Quantity - We used an internet-based survey platform to conduct a cross-sectional survey regarding the impact of COVID-19 on the LGBTQ?+?population in the United States....     

Investment behaviour and “bull & bear” dynamics: modelling real and stock market interactions

In this article, we develop a simple behavioural macrodynamic model in continuous-time with the purpose of investigating the interaction of the real economy and the financial markets. Building on Westerhoff (Discret Dyn Nat Soc, 2012), we improve the specification of aggregate demand by distinguishing between consumption and investment expenditure and assuming that the latter is determined by the flexible accelerator principle. We remove the ad hoc nonlinearity in the fundamentalist behavioural rule and allow the composition of the population between chartists and fundamentalists to be endogenously determined. The resulting nonlinear dynamic systems are shown to generate various dynamic regimes, among which the coexistence of periodic attractors with interesting economic implications. Endogenous investment and stock market dynamics emerge, procyclical to each other, reflecting the interaction of induced investment with alternating waves in speculators’ sentiments. We show that a strong investment accelerator might be a crucial force generating fluctuations that, on the one hand, are transmitted and amplified by chartists and, on the other hand, are contained by fundamentalists.