From Fiduciary Duty to Impact Fidelity: Managerial Compensation in Impact Investing

Journal of Business Ethics - Investors with standard monetary preferences will give a fund manager incentives to increase firm profits, which can be achieved through a share in profits via carried...     

Co‐integration Rank Determination in Partial Systems Using Information Criteria

We investigate the asymptotic and finite sample properties of the most widely used information criteria for co‐integration rank determination in ‘partial’ systems, i.e. in co‐integrated vector autoregressive (VAR) models where a sub‐set of variables of interest is modelled conditional on another sub‐set of variables. The asymptotic properties of the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the Hannan–Quinn information criterion (HQC) are established, and consistency of BIC and HQC is proved. Notably, the consistency of BIC and HQC is robust to violations of weak exogeneity of the conditioning variables with respect to the co‐integration parameters. More precisely, BIC and HQC recover the true co‐integration rank from the partial system analysis also when the conditional model does not convey all information about the co‐integration parameters. This result opens up interesting possibilities for practitioners who can now determine the co‐integration rank in partial systems without being concerned about the weak exogeneity of the conditioning variables. A Monte Carlo experiment based on a large dimensional data generating process shows that BIC and HQC applied in partial systems perform reasonably well in small samples and comparatively better than ‘traditional’ methods for co‐integration rank determination. We further show the usefulness of our approach and the benefits of the conditional system analysis in two empirical illustrations, both based on the estimation of VAR systems on US quarterly data. Overall, our analysis shows the gains of combining information criteria with partial system analysis.     

Three decades of research on corporate governance and R&D investments: a systematic review and research agenda

This paper reviews and systematizes the empirical research on the nexus between corporate governance (CG) and investments in research and development (R&D) published in leading business, management, economics and finance journals over the past 30 years. We find that CG is key in shaping R&D investments. Moreover, the effects of both firm- and country-level CG are important for both internal and external R&D investments. Drawing on our review, we welcome future studies to examine the effect of the interplay between various CG mechanisms and different types of R&D investments, and possibly identify mediating variables besides the moderating ones. Moreover, we highlight the need for future interdisciplinary studies, as well as investigations of private companies and across developing countries. Whenever causal interpretations are attempted, both sample selection and endogeneity problems should be addressed, along with testing the CG-R&D investment nexus for nonlinear dynamics. The implications of the study for both theory and practice are also discussed.     

Sensitive Survey Questions: Measuring Attitudes Regarding Female Genital Cutting Through a List Experiment

Potential bias in survey responses is higher if sensitive outcomes are measured. This study analyses attitudes towards female genital cutting (FGC) in Ethiopia. A list experiment is designed to elicit truthful answers about FGC support and compares these outcomes with the answers given to a direct question. Our results confirm that the average bias is substantial as answers to direct questions underestimate the FGC support by about 10 percentage points. Moreover, our results provide suggestive but not statistically significant evidence that this bias is more pronounced among uneducated women and women targeted by an NGO intervention (not randomly assigned).     

Investment opportunities and R&D investments in family and nonfamily firms

The firm’s investment opportunity set (IOS) reflects the prospective growth opportunities related to physical and human capital investments. IOSs are largely firm specific, embedded in assets-in-place, or generated by experience curves, learning-by-doing, and other similar phenomena. However, the value of an IOS can be destroyed if a firm does not exercise the option to invest. In this study, we theorize that a firm’s ability to invest in R&D is conditional on the availability of a favorable IOS. We test our theoretical propositions in the European business environment using a sample of large publicly traded firms with concentrated ownership. Our findings support the notion that the IOS is a significant determinant of corporate R&D investments, but the magnitude of this effect depends on the identity of the ultimate owner. Specifically, the sensitivity of R&D investments of family- and state-owned corporations is higher to favorable IOS than that of widely held corporations, suggesting these firms are more responsive to favorable IOS than others. By introducing the IOS dimension, our results have interesting implications for both theory and practice.     

WHAT HAPPENED TO THE WILLINGNESS OF COMPANIES TO INVEST AFTER THE FINANCIAL CRISIS? EVIDENCE FROM LATIN AMERICAN COUNTRIES

In this article we investigate the changes in corporate investment dynamics in the aftermath of the global financial crisis. Using firm-level data from six Latin American countries from 2002 to 2015, we show that firms are less constrained and have greater ability to invest after the crisis. However, the willingness of firms to invest optimally is reduced. This is supported by strong evidence that during the postcrisis period investment–cash flow sensitivity disappears, investment-q sensitivity increases, and the estimated speeds of adjustment for target investment decrease. Moreover, after the crisis, firms notably increase their efforts to attain optimal cash and leverage levels. Our analysis implies that firms may not always be willing to invest optimally. The willingness to invest optimally appears to be time variant and moves together with the dynamics of cash and leverage policies, albeit in opposite directions.     

Bayesian selection of technology assumptions for the transformation from supply-use to input–output tables

ABSTRACT

In the construction of input–output models from supply-use tables, technology assumptions disambiguate how an industry uses inputs in the production recipe of multiple outputs. This paper uses Bayes' theorem to select technology assumptions, taking into account empirical observations. The paper presents a formulation to explore hybrids between product and industry technology assumptions in product-by-product tables. We then present Markov chain Monte-Carlo techniques to implement the Bayesian method for selecting technology assumptions. We apply the method in a case study using Eurostat supply-use tables of 2004 and 2005, exhibiting a volume of secondary products of less than 13%, and 59 products and industries per country. The results show that the choice of technology is not important, given that there is no strong evidence in favour of any of them.     

Inflation Risk?

Intereconomics - Inflation is on the rise again in the industrialised world. This has led to fears of a sustained surge in inflation. This article argues that while such fears may make sense in the...     

Short-Run and Long-Run Effects of Copper Mining on Peru’s Recent Economic Growth

Peru is the second-largest producer and exporter of copper in the world. This paper proposes a novel approach to assess short-run and long-run effects of copper on Peru’s recent economic growth. Annual data over the 2014–2018 period were used to calculate a Mining Contribution Index (MCI). An institutional quality indicator of the World Competitiveness Index of the World Economic Forum measured the dependence of Peruvian economic growth on mining and the quality of its institutions, respectively. Then, monthly data during the period 2005–2018 were used to run vector autoregressive (VAR) and vector error correction (VEC) models to measure copper’s effects on the country’s economy over time. VAR-VEC models included copper production, exports, international price, investment, taxes paid by producing companies, and Peru’s gross domestic product (GDP). Stationarity and causality of variables were verified with the Augmented Dickey-Fuller and Granger tests, respectively. Due to the presence of non-stationary variables, a VEC model was implemented to forecast short- and long-run effects. The main results show that real GDP responds to copper output and other related explanatory variables differently, depending upon the instrument applied. Peruvian GDP has increased dependence on copper mining. The quality of its institutions could explain the presence of Dutch Disease or resource curse theory. Short- and long-run effects of copper output on GDP were generally statistically non-significant. GDP was statistically significant in relation to other mining variables, such as copper exports and the international price of copper.